From 2719bd3802ae6a338b28eca9e3e6c83a23356e72 Mon Sep 17 00:00:00 2001 From: yuzhouw313 Date: Tue, 17 Oct 2023 15:08:42 +0000 Subject: [PATCH 01/51] Finished week 3 MN EDA work --- notebooks/MN_dataset_analysis.ipynb | 50571 ++++++++++++++++++++++++++ 1 file changed, 50571 insertions(+) create mode 100644 notebooks/MN_dataset_analysis.ipynb diff --git a/notebooks/MN_dataset_analysis.ipynb b/notebooks/MN_dataset_analysis.ipynb new file mode 100644 index 0000000..a0672b4 --- /dev/null +++ b/notebooks/MN_dataset_analysis.ipynb @@ -0,0 +1,50571 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MN Campaign Finance Report Dataset EDA" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 0. Dataset Preparation" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import plotly.express as px" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 0.1 Read in all contribution csv files, each file corresponds to one race" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2292/974020143.py:8: DtypeWarning: Columns (11) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df_gc = pd.read_csv('/project/data/CandReceipts/CandReceiptsGC.csv')\n", + "/tmp/ipykernel_2292/974020143.py:10: DtypeWarning: Columns (12) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df_house = pd.read_csv('/project/data/CandReceipts/CandReceiptsHouse.csv')\n", + "/tmp/ipykernel_2292/974020143.py:16: DtypeWarning: Columns (12) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df_senate = pd.read_csv('/project/data/CandReceipts/CandReceiptsSenate.csv')\n" + ] + } + ], + "source": [ + "# Attorney General\n", + "df_ag = pd.read_csv('/project/data/CandReceipts/CandReceiptsAG.csv')\n", + "# State Appeals Court Judge\n", + "df_ap = pd.read_csv('/project/data/CandReceipts/CandReceiptsAP.csv')\n", + "# State District Court Judge\n", + "df_dc = pd.read_csv('/project/data/CandReceipts/CandReceiptsDC.csv')\n", + "# Governor\n", + "df_gc = pd.read_csv('/project/data/CandReceipts/CandReceiptsGC.csv')\n", + "# House\n", + "df_house = pd.read_csv('/project/data/CandReceipts/CandReceiptsHouse.csv')\n", + "# State Auditor\n", + "df_sa = pd.read_csv('/project/data/CandReceipts/CandReceiptsSA.csv')\n", + "# State Supreme Court Justice\n", + "df_sc = pd.read_csv('/project/data/CandReceipts/CandReceiptsSC.csv')\n", + "# State Senator\n", + "df_senate = pd.read_csv('/project/data/CandReceipts/CandReceiptsSenate.csv')\n", + "# Secretary of State\n", + "df_ss = pd.read_csv('/project/data/CandReceipts/CandReceiptsSS.csv')\n", + "# State Treasurer (this office was abolished in 2003 and no longer exists)\n", + "df_st = pd.read_csv('/project/data/CandReceipts/CandReceiptsST.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 0.2 Check if all files have consistent features\n", + "- These files are separate but not relational as they do not relate to each other" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['OfficeSought', 'Party', 'District', 'CandRegNumb', 'CandFirstName',\n", + " 'CandLastName', 'CommitteeName', 'DonationDate', 'DonorType',\n", + " 'DonorName', 'DonationAmount', 'InKindDonAmount',\n", + " 'InKindDescriptionText'],\n", + " dtype='object')\n", + "All dfs have consistent columns\n" + ] + } + ], + "source": [ + "df_lst = [df_ag, df_ap, df_dc, df_gc, df_house, df_sa, df_sc, df_ss, df_st, df_senate]\n", + "previous_columns = df_ag.columns\n", + "print(previous_columns)\n", + "consistent_col_count = 1\n", + "\n", + "for df in df_lst[1:]:\n", + " if not (df.columns == previous_columns).all():\n", + " print('Columns not consistent across races')\n", + " else:\n", + " consistent_col_count += 1\n", + "if consistent_col_count == len(df_lst):\n", + " print('All dfs have consistent columns')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 0.3 Conbine all individual contribution files into one" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "merged_df = pd.concat(df_lst, ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3065479" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "row_count_sum = 0\n", + "for df in df_lst:\n", + " row_count_sum += len(df)\n", + "row_count_sum" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Column Analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.1 Check for missing values" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "OfficeSought 467\n", + "Party 39402\n", + "District 1299280\n", + "CandRegNumb 467\n", + "CandFirstName 467\n", + "CandLastName 467\n", + "CommitteeName 467\n", + "DonationDate 536\n", + "DonorType 512\n", + "DonorName 467\n", + "DonationAmount 2195\n", + "InKindDonAmount 439376\n", + "InKindDescriptionText 3033884\n", + "dtype: int64" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "missing_count = merged_df.isna().sum()\n", + "missing_count" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Observation: CandRegNumb, CandFirstName, CandLastName, CommitteeName, and DonorName all have missing value counts 467\n", + "- So let's figure out what are these 467 NaN rows" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " OfficeSought Party District CandRegNumb CandFirstName CandLastName \\\n", + "148772 NaN NaN NaN NaN NaN NaN \n", + "148773 NaN NaN NaN NaN NaN NaN \n", + "148774 NaN NaN NaN NaN NaN NaN \n", + "148775 NaN NaN NaN NaN NaN NaN \n", + "148776 NaN NaN NaN NaN NaN NaN \n", + "\n", + " CommitteeName DonationDate DonorType DonorName DonationAmount \\\n", + "148772 NaN NaN NaN NaN NaN \n", + "148773 NaN NaN NaN NaN NaN \n", + "148774 NaN NaN NaN NaN NaN \n", + "148775 NaN NaN NaN NaN NaN \n", + "148776 NaN NaN NaN NaN NaN \n", + "\n", + " InKindDonAmount InKindDescriptionText \n", + "148772 NaN Registration Fee for Netroots Event \n", + "148773 NaN Registration Fee for Netroots Event \n", + "148774 NaN Registration Fee for Netroots Event \n", + "148775 NaN Registration Fee for Netroots Event \n", + "148776 NaN Registration Fee for Netroots Event \n" + ] + } + ], + "source": [ + "missing_467 = merged_df[merged_df['CandRegNumb'].isna()]\n", + "print(missing_467.head(5))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Observation: Such donations seem to all belong to \"Registration Fee for Netroots Event\". Netroots Nation is a non-profit organization that provides trainings, resources and connection opportunities to help progressive activists. Based on their column values, these contributions have no monetary amount, no donor, and no recipient." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2 Column Content" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OfficeSought: AG\n", + "Party: RPM\n", + "District: nan\n", + "CandRegNumb: 18731.0\n", + "CandFirstName: James\n", + "CandLastName: Schultz\n", + "CommitteeName: Jim Schultz For Minnesota Attorney General\n", + "DonationDate: 2023/5/10\n", + "DonorType: I\n", + "DonorName: Lockhart, Theodore C\n", + "DonationAmount: 500.00\n", + "InKindDonAmount: 0.00\n", + "InKindDescriptionText: nan\n" + ] + } + ], + "source": [ + "# Snapshot of what are the contents of each column/feature\n", + "for column_name, column_value in merged_df.iloc[1].items():\n", + " print(f\"{column_name}: {column_value}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "OfficeSought object\n", + "Party object\n", + "District object\n", + "CandRegNumb float64\n", + "CandFirstName object\n", + "CandLastName object\n", + "CommitteeName object\n", + "DonationDate object\n", + "DonorType object\n", + "DonorName object\n", + "DonationAmount object\n", + "InKindDonAmount object\n", + "InKindDescriptionText object\n", + "dtype: object" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "col_types = merged_df.dtypes\n", + "col_types" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Check unique races, parties, districts, donor types, and contributor types." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['AG', nan, 'AP', 'DC', 'GC', 'House', 'SA', 'SC', 'SS', 'ST',\n", + " 'Senate'], dtype=object)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "OfficeSought_unique = merged_df['OfficeSought'].unique()\n", + "OfficeSought_unique" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Races: \n", + "- AG = Attorney General\n", + "- AP = State Appeals Court Judge\n", + "- DC = State District Court Judge\n", + "- GC = Governor\n", + "- House = State Representative\n", + "- SA = State Auditor\n", + "- SC = State Supreme Court Justice\n", + "- SS = Secretary of State\n", + "- ST = State Treasurer (this office was abolished in 2003 and no longer exists)\n", + "- Senate = State Senator\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['RPM', 'DFL', 'GPM', 'IPMN', 'Other', 'LIB', nan, 'GRP', 'LMP',\n", + " 'LPM', 'RP', 'CPM'], dtype=object)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Party_unique = merged_df['Party'].unique()\n", + "Party_unique" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([nan, 2.0, 16.0, 12.0, 14.0, 4.0, 13.0, 15.0, 11.0, 3.0, '1-11',\n", + " '2-17', '4-17', '9- 4', '9-19', '2-13', '2-8', '2-0', '1-32',\n", + " '4-61', '1-19', '2-20', '2-11', '2-14', '2-2', '1-2', '2-28',\n", + " '1-31', '3-17', '3-16', '4-45', '3-21', '4-43', '4-37', '3-9',\n", + " '3- 9', '10-3', '10-14', '10-1', '4-16', '4-53', '7-11', '5-5',\n", + " '7-27', '7-15', '4-44', '1-20', '9-7', '4-22', '1-10', '2-6',\n", + " '4-18', '4-54', '4-13', '2-21', '9-16', '2-16', '10-39', '10-27',\n", + " '1-8', '2-27', '6-11', '8-1', '9-17', '8-7', '6-8', '4-41', '3-1',\n", + " '4-9', '4-58', '6-9', '4-46', '3-0', '3-22', '1-26', '4-48',\n", + " '6-10', '2-7', '2-29', '1- 6', '1-6', '4-27', '4-47', '4-1',\n", + " '4-26', '10-8', '1-33', '6-6', '10-31', '2-18', '4-0', '4-12',\n", + " '9-4', '4-56', '1-5', '1-28', '4-60', '7-17', '1-1', '1-3', '9-12',\n", + " '1-0', '4-20', '1-12', '6-14', '2-25', '5-16', '7-21', '10-16',\n", + " '2', '50B', '39A', '53B', '52A', '11B', '8B', '12B', '17A', '31A',\n", + " '27B', '55B', '14B', '66B', '56A', '55A', '45A', '41A', '57B',\n", + " '28A', '26A', '67B', '40B', '38B', '63A', '53A', '36B', '62B',\n", + " '62A', '50A', '51A', '41B', '39B', '30A', '24B', '7B', '8A', '56B',\n", + " '3B', '51B', '54A', '66A', '15B', '27A', '47B', '34B', '64B',\n", + " '48B', '49B', '25A', '34A', '37B', '58A', '5A', '11A', '54B',\n", + " '36A', '35A', '63B', '48A', '49A', '52B', '5B', '65B', '6B', '57A',\n", + " '21B', '19A', '18A', '64A', '59B', '46A', '2B', '12A', '33A',\n", + " '37A', '2A', '58B', '3A', '65A', '13A', '9B', '21A', '22A', '38A',\n", + " '32B', '67A', '20A', '19B', '29A', '29B', '14A', '31B', '28B',\n", + " '26B', '10A', '44A', '42B', '61B', '33B', '4B', '42A', '44B',\n", + " '45B', '32A', '23A', '16A', '15A', '35B', '47A', '25B', '9A',\n", + " '10B', '6A', '18B', '17B', '16B', '24A', '60B', '7A', '23B', '20B',\n", + " '43A', '22B', '4A', '30B', '1B', '40A', '13B', '43B', '1A', '46B',\n", + " '60A', '59A', '61A', '6', '4', '3', '5', '1', 'Chief', 43, 44, 65,\n", + " 60, 61, 49, 56, 55, 63, 30, 41, 25, 42, 26, 24, 34, 20, 22, 32, 53,\n", + " 39, 62, 36, 45, 66, 67, 23, 17, 50, 51, 10, 47, 38, 52, 57, 19, 18,\n", + " 64, 54, 7, 8, 48, 35, 58, 37, 1, 9, 31, 28, 40, 33, 59, 5, 6, 27,\n", + " 46, 29, 21], dtype=object)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "District_unique = merged_df['District'].unique()\n", + "District_unique" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['I', 'F', 'L', 'P', 'O', 'C', 'S', nan, 's', 'H', 'c', 'i'],\n", + " dtype=object)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "DonorType_unique = merged_df['DonorType'].unique()\n", + "DonorType_unique" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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OfficeSoughtPartyDistrictCandRegNumbCandFirstNameCandLastNameCommitteeNameDonationDateDonorTypeDonorNameDonationAmountInKindDonAmountInKindDescriptionText
2177675HouseDFL43A15214.0SherylFriemanFriends of Sheryl Frieman2002/11/4iHerman, Randall J200.000.00NaN
2177676HouseDFL43A15214.0SherylFriemanFriends of Sheryl Frieman2002/11/4iHerman, Randall J200.000.00NaN
2177677HouseDFL43A15214.0SherylFriemanFriends of Sheryl Frieman2002/11/4iHerman, Randall J200.000.00NaN
2177678HouseDFL43A15214.0SherylFriemanFriends of Sheryl Frieman2002/11/4iHerman, Randall J200.000.00NaN
2177679HouseDFL43A15214.0SherylFriemanFriends of Sheryl Frieman2002/11/4iHerman, Randall J200.000.00NaN
2177680HouseDFL43A15214.0SherylFriemanFriends of Sheryl Frieman2002/11/4iHerman, Randall J200.000.00NaN
2177681HouseDFL43A15214.0SherylFriemanFriends of Sheryl Frieman2002/11/4iHerman, Randall J200.000.00NaN
2177682HouseDFL43A15214.0SherylFriemanFriends of Sheryl Frieman2002/11/4iHerman, Randall J200.000.00NaN
2177683HouseDFL43A15214.0SherylFriemanFriends of Sheryl Frieman2002/11/4iHerman, Randall J200.000.00NaN
2177684HouseDFL43A15214.0SherylFriemanFriends of Sheryl Frieman2002/11/4iHerman, Randall J200.000.00NaN
2177685HouseDFL43A15214.0SherylFriemanFriends of Sheryl Frieman2002/11/4iHerman, Randall J200.000.00NaN
2177686HouseDFL43A15214.0SherylFriemanFriends of Sheryl Frieman2002/11/4iHerman, Randall J200.000.00NaN
2177687HouseDFL43A15214.0SherylFriemanFriends of Sheryl Frieman2002/11/4iHerman, Randall J200.000.00NaN
\n", + "
" + ], + "text/plain": [ + " OfficeSought Party District CandRegNumb CandFirstName CandLastName \\\n", + "2177675 House DFL 43A 15214.0 Sheryl Frieman \n", + "2177676 House DFL 43A 15214.0 Sheryl Frieman \n", + "2177677 House DFL 43A 15214.0 Sheryl Frieman \n", + "2177678 House DFL 43A 15214.0 Sheryl Frieman \n", + "2177679 House DFL 43A 15214.0 Sheryl Frieman \n", + "2177680 House DFL 43A 15214.0 Sheryl Frieman \n", + "2177681 House DFL 43A 15214.0 Sheryl Frieman \n", + "2177682 House DFL 43A 15214.0 Sheryl Frieman \n", + "2177683 House DFL 43A 15214.0 Sheryl Frieman \n", + "2177684 House DFL 43A 15214.0 Sheryl Frieman \n", + "2177685 House DFL 43A 15214.0 Sheryl Frieman \n", + "2177686 House DFL 43A 15214.0 Sheryl Frieman \n", + "2177687 House DFL 43A 15214.0 Sheryl Frieman \n", + "\n", + " CommitteeName DonationDate DonorType DonorName \\\n", + "2177675 Friends of Sheryl Frieman 2002/11/4 i Herman, Randall J \n", + "2177676 Friends of Sheryl Frieman 2002/11/4 i Herman, Randall J \n", + "2177677 Friends of Sheryl Frieman 2002/11/4 i Herman, Randall J \n", + "2177678 Friends of Sheryl Frieman 2002/11/4 i Herman, Randall J \n", + "2177679 Friends of Sheryl Frieman 2002/11/4 i Herman, Randall J \n", + "2177680 Friends of Sheryl Frieman 2002/11/4 i Herman, Randall J \n", + "2177681 Friends of Sheryl Frieman 2002/11/4 i Herman, Randall J \n", + "2177682 Friends of Sheryl Frieman 2002/11/4 i Herman, Randall J \n", + "2177683 Friends of Sheryl Frieman 2002/11/4 i Herman, Randall J \n", + "2177684 Friends of Sheryl Frieman 2002/11/4 i Herman, Randall J \n", + "2177685 Friends of Sheryl Frieman 2002/11/4 i Herman, Randall J \n", + "2177686 Friends of Sheryl Frieman 2002/11/4 i Herman, Randall J \n", + "2177687 Friends of Sheryl Frieman 2002/11/4 i Herman, Randall J \n", + "\n", + " DonationAmount InKindDonAmount InKindDescriptionText \n", + "2177675 200.00 0.00 NaN \n", + "2177676 200.00 0.00 NaN \n", + "2177677 200.00 0.00 NaN \n", + "2177678 200.00 0.00 NaN \n", + "2177679 200.00 0.00 NaN \n", + "2177680 200.00 0.00 NaN \n", + "2177681 200.00 0.00 NaN \n", + "2177682 200.00 0.00 NaN \n", + "2177683 200.00 0.00 NaN \n", + "2177684 200.00 0.00 NaN \n", + "2177685 200.00 0.00 NaN \n", + "2177686 200.00 0.00 NaN \n", + "2177687 200.00 0.00 NaN " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "i_values = merged_df[merged_df['DonorType'] == 'i']\n", + "i_values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Based on the above filtered result comparing with old datasets, realize contributor Sheryl Frieman who is originally labeled as Individual was labeled here as i instead of I" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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OfficeSoughtPartyDistrictCandRegNumbCandFirstNameCandLastNameCommitteeNameDonationDateDonorTypeDonorNameDonationAmountInKindDonAmountInKindDescriptionText
1945886HouseRPM32B15969.0KurtZellersVolunteers for Zellers (Kurt)2008/8/21c(Christopher) DeLaForest Volunteer Committee300.000.00NaN
1945887HouseRPM32B15969.0KurtZellersVolunteers for Zellers (Kurt)2008/8/21c(Christopher) DeLaForest Volunteer Committee300.000.00NaN
1945888HouseRPM32B15969.0KurtZellersVolunteers for Zellers (Kurt)2008/8/21c(Christopher) DeLaForest Volunteer Committee300.000.00NaN
1945889HouseRPM32B15969.0KurtZellersVolunteers for Zellers (Kurt)2008/8/21c(Christopher) DeLaForest Volunteer Committee300.000.00NaN
1945890HouseRPM32B15969.0KurtZellersVolunteers for Zellers (Kurt)2008/8/21c(Christopher) DeLaForest Volunteer Committee300.000.00NaN
1945891HouseRPM32B15969.0KurtZellersVolunteers for Zellers (Kurt)2008/8/21c(Christopher) DeLaForest Volunteer Committee300.000.00NaN
1945892HouseRPM34B15969.0KurtZellersVolunteers for Zellers (Kurt)2008/8/21c(Christopher) DeLaForest Volunteer Committee300.000.00NaN
1945893HouseRPM34B15969.0KurtZellersVolunteers for Zellers (Kurt)2008/8/21c(Christopher) DeLaForest Volunteer Committee300.000.00NaN
1945894HouseRPM34B15969.0KurtZellersVolunteers for Zellers (Kurt)2008/8/21c(Christopher) DeLaForest Volunteer Committee300.000.00NaN
1945895HouseRPM32B15969.0KurtZellersVolunteers for Zellers (Kurt)2008/8/21c(Christopher) DeLaForest Volunteer Committee300.000.00NaN
1945896HouseRPM32B15969.0KurtZellersVolunteers for Zellers (Kurt)2008/8/21c(Christopher) DeLaForest Volunteer Committee300.000.00NaN
1945897HouseRPM32B15969.0KurtZellersVolunteers for Zellers (Kurt)2008/8/21c(Christopher) DeLaForest Volunteer Committee300.000.00NaN
1945898HouseRPM32B15969.0KurtZellersVolunteers for Zellers (Kurt)2008/8/21c(Christopher) DeLaForest Volunteer Committee300.000.00NaN
1945899HouseRPM34B15969.0KurtZellersVolunteers for Zellers (Kurt)2008/8/21c(Christopher) DeLaForest Volunteer Committee300.000.00NaN
1945900HouseRPM34B15969.0KurtZellersVolunteers for Zellers (Kurt)2008/8/21c(Christopher) DeLaForest Volunteer Committee300.000.00NaN
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" + ], + "text/plain": [ + " OfficeSought Party District CandRegNumb CandFirstName CandLastName \\\n", + "1945886 House RPM 32B 15969.0 Kurt Zellers \n", + "1945887 House RPM 32B 15969.0 Kurt Zellers \n", + "1945888 House RPM 32B 15969.0 Kurt Zellers \n", + "1945889 House RPM 32B 15969.0 Kurt Zellers \n", + "1945890 House RPM 32B 15969.0 Kurt Zellers \n", + "1945891 House RPM 32B 15969.0 Kurt Zellers \n", + "1945892 House RPM 34B 15969.0 Kurt Zellers \n", + "1945893 House RPM 34B 15969.0 Kurt Zellers \n", + "1945894 House RPM 34B 15969.0 Kurt Zellers \n", + "1945895 House RPM 32B 15969.0 Kurt Zellers \n", + "1945896 House RPM 32B 15969.0 Kurt Zellers \n", + "1945897 House RPM 32B 15969.0 Kurt Zellers \n", + "1945898 House RPM 32B 15969.0 Kurt Zellers \n", + "1945899 House RPM 34B 15969.0 Kurt Zellers \n", + "1945900 House RPM 34B 15969.0 Kurt Zellers \n", + "\n", + " CommitteeName DonationDate DonorType \\\n", + "1945886 Volunteers for Zellers (Kurt) 2008/8/21 c \n", + "1945887 Volunteers for Zellers (Kurt) 2008/8/21 c \n", + "1945888 Volunteers for Zellers (Kurt) 2008/8/21 c \n", + "1945889 Volunteers for Zellers (Kurt) 2008/8/21 c \n", + "1945890 Volunteers for Zellers (Kurt) 2008/8/21 c \n", + "1945891 Volunteers for Zellers (Kurt) 2008/8/21 c \n", + "1945892 Volunteers for Zellers (Kurt) 2008/8/21 c \n", + "1945893 Volunteers for Zellers (Kurt) 2008/8/21 c \n", + "1945894 Volunteers for Zellers (Kurt) 2008/8/21 c \n", + "1945895 Volunteers for Zellers (Kurt) 2008/8/21 c \n", + "1945896 Volunteers for Zellers (Kurt) 2008/8/21 c \n", + "1945897 Volunteers for Zellers (Kurt) 2008/8/21 c \n", + "1945898 Volunteers for Zellers (Kurt) 2008/8/21 c \n", + "1945899 Volunteers for Zellers (Kurt) 2008/8/21 c \n", + "1945900 Volunteers for Zellers (Kurt) 2008/8/21 c \n", + "\n", + " DonorName DonationAmount \\\n", + "1945886 (Christopher) DeLaForest Volunteer Committee 300.00 \n", + "1945887 (Christopher) DeLaForest Volunteer Committee 300.00 \n", + "1945888 (Christopher) DeLaForest Volunteer Committee 300.00 \n", + "1945889 (Christopher) DeLaForest Volunteer Committee 300.00 \n", + "1945890 (Christopher) DeLaForest Volunteer Committee 300.00 \n", + "1945891 (Christopher) DeLaForest Volunteer Committee 300.00 \n", + "1945892 (Christopher) DeLaForest Volunteer Committee 300.00 \n", + "1945893 (Christopher) DeLaForest Volunteer Committee 300.00 \n", + "1945894 (Christopher) DeLaForest Volunteer Committee 300.00 \n", + "1945895 (Christopher) DeLaForest Volunteer Committee 300.00 \n", + "1945896 (Christopher) DeLaForest Volunteer Committee 300.00 \n", + "1945897 (Christopher) DeLaForest Volunteer Committee 300.00 \n", + "1945898 (Christopher) DeLaForest Volunteer Committee 300.00 \n", + "1945899 (Christopher) DeLaForest Volunteer Committee 300.00 \n", + "1945900 (Christopher) DeLaForest Volunteer Committee 300.00 \n", + "\n", + " InKindDonAmount InKindDescriptionText \n", + "1945886 0.00 NaN \n", + "1945887 0.00 NaN \n", + "1945888 0.00 NaN \n", + "1945889 0.00 NaN \n", + "1945890 0.00 NaN \n", + "1945891 0.00 NaN \n", + "1945892 0.00 NaN \n", + "1945893 0.00 NaN \n", + "1945894 0.00 NaN \n", + "1945895 0.00 NaN \n", + "1945896 0.00 NaN \n", + "1945897 0.00 NaN \n", + "1945898 0.00 NaN \n", + "1945899 0.00 NaN \n", + "1945900 0.00 NaN " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c_values = merged_df[merged_df['DonorType'] == 'c']\n", + "c_values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After filtered observation, (Christopher) DeLaForest Volunteer Committee is a committee which should have been labeled as C instead of c" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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OfficeSoughtPartyDistrictCandRegNumbCandFirstNameCandLastNameCommitteeNameDonationDateDonorTypeDonorNameDonationAmountInKindDonAmountInKindDescriptionText
160243DCNaN4-5316778.0DavidPiperDavid L Piper Campaign2008/10/30sPiper, David L40,000.000.00NaN
160244DCNaN4-5316778.0DavidPiperDavid L Piper Campaign2008/10/30sPiper, David L40,000.000.00NaN
160481DCNaN4-5316778.0DavidPiperDavid L Piper Campaign2008/10/20sPiper, David L40,000.000.00NaN
160482DCNaN4-5316778.0DavidPiperDavid L Piper Campaign2008/10/20sPiper, David L40,000.000.00NaN
745268GCDFLNaN16920.0ThomasRukavinaRukavina (Thomas) for Governor Campaign Committee2009/12/10sAnzelc, Thomas E100.000.00NaN
..........................................
2880903SenateDFL1116419.0AnthonyLoureyTony Lourey for Senate2008/10/10sLourey, Anthony L5,000.000.00NaN
2880904SenateDFL1116419.0AnthonyLoureyTony Lourey for Senate2008/10/10sLourey, Anthony L5,000.000.00NaN
2880905SenateDFL1116419.0AnthonyLoureyTony Lourey for Senate2008/10/10sLourey, Anthony L5,000.000.00NaN
2880906SenateDFL1116419.0AnthonyLoureyTony Lourey for Senate2008/10/10sLourey, Anthony L5,000.000.00NaN
2880907SenateDFL1116419.0AnthonyLoureyTony Lourey for Senate2008/10/10sLourey, Anthony L5,000.000.00NaN
\n", + "

122 rows × 13 columns

\n", + "
" + ], + "text/plain": [ + " OfficeSought Party District CandRegNumb CandFirstName CandLastName \\\n", + "160243 DC NaN 4-53 16778.0 David Piper \n", + "160244 DC NaN 4-53 16778.0 David Piper \n", + "160481 DC NaN 4-53 16778.0 David Piper \n", + "160482 DC NaN 4-53 16778.0 David Piper \n", + "745268 GC DFL NaN 16920.0 Thomas Rukavina \n", + "... ... ... ... ... ... ... \n", + "2880903 Senate DFL 11 16419.0 Anthony Lourey \n", + "2880904 Senate DFL 11 16419.0 Anthony Lourey \n", + "2880905 Senate DFL 11 16419.0 Anthony Lourey \n", + "2880906 Senate DFL 11 16419.0 Anthony Lourey \n", + "2880907 Senate DFL 11 16419.0 Anthony Lourey \n", + "\n", + " CommitteeName DonationDate \\\n", + "160243 David L Piper Campaign 2008/10/30 \n", + "160244 David L Piper Campaign 2008/10/30 \n", + "160481 David L Piper Campaign 2008/10/20 \n", + "160482 David L Piper Campaign 2008/10/20 \n", + "745268 Rukavina (Thomas) for Governor Campaign Committee 2009/12/10 \n", + "... ... ... \n", + "2880903 Tony Lourey for Senate 2008/10/10 \n", + "2880904 Tony Lourey for Senate 2008/10/10 \n", + "2880905 Tony Lourey for Senate 2008/10/10 \n", + "2880906 Tony Lourey for Senate 2008/10/10 \n", + "2880907 Tony Lourey for Senate 2008/10/10 \n", + "\n", + " DonorType DonorName DonationAmount InKindDonAmount \\\n", + "160243 s Piper, David L 40,000.00 0.00 \n", + "160244 s Piper, David L 40,000.00 0.00 \n", + "160481 s Piper, David L 40,000.00 0.00 \n", + "160482 s Piper, David L 40,000.00 0.00 \n", + "745268 s Anzelc, Thomas E 100.00 0.00 \n", + "... ... ... ... ... \n", + "2880903 s Lourey, Anthony L 5,000.00 0.00 \n", + "2880904 s Lourey, Anthony L 5,000.00 0.00 \n", + "2880905 s Lourey, Anthony L 5,000.00 0.00 \n", + "2880906 s Lourey, Anthony L 5,000.00 0.00 \n", + "2880907 s Lourey, Anthony L 5,000.00 0.00 \n", + "\n", + " InKindDescriptionText \n", + "160243 NaN \n", + "160244 NaN \n", + "160481 NaN \n", + "160482 NaN \n", + "745268 NaN \n", + "... ... \n", + "2880903 NaN \n", + "2880904 NaN \n", + "2880905 NaN \n", + "2880906 NaN \n", + "2880907 NaN \n", + "\n", + "[122 rows x 13 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s_values = merged_df[merged_df['DonorType'] == 's']\n", + "s_values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After filtered observation, these contributions are made by the candidates to the candidates themselves, so they belong to \"S\" type. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- C – Candidate Committee \n", + "- I – Individual \n", + "- L – Lobbyist \n", + "- F – Political Committee/Fund \n", + "- S – Self \n", + "- P – Party Unit \n", + "- H – Registered with Hennepin County \n", + "- O – Other \n", + "\n", + "- i – Individual = I\n", + "- c – Candidate Committee = C\n", + "- s - Self = S" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "merged_df['DonorType'] = merged_df['DonorType'].replace('c', 'C')\n", + "merged_df['DonorType'] = merged_df['DonorType'].replace('i', 'I')\n", + "merged_df['DonorType'] = merged_df['DonorType'].replace('s', 'S')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.3 Preprocess Donation Date into Year" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "merged_df['DonationDate'] = pd.to_datetime(merged_df['DonationDate'])\n", + "merged_df['DonationYear'] = merged_df['DonationDate'].dt.year" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.4 Compare this merged dataset to the outdated one" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "merged_df1 = merged_df[(merged_df['DonationYear'] >= 2015) & (merged_df['DonationYear'] <= 2023)]\n", + "merged_df2 = merged_df1[~merged_df1['OfficeSought'].isin(['AP'])]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "df_con = pd.read_csv('/project/data/All - Itemized Contributions Received Of Over $200 - Campaign Finance.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3065479\n", + "1225430\n", + "134697\n" + ] + } + ], + "source": [ + "print(len(merged_df))\n", + "print(len(merged_df2))\n", + "print(len(df_con))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "New dataset has more rows than old dataset, i.e., 1225430 > 134697" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Top 10 \n", + "### 2.1 Top 10 contributors" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: Since we are interested in the total amount of donation, regardless of in kind, we combine the DonationAmount and InKindDonAmount to obtain a TotalAmount." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "merged_df['DonationAmount'] = pd.to_numeric(merged_df['DonationAmount'], errors='coerce') # Convert to numeric\n", + "merged_df['InKindDonAmount'] = pd.to_numeric(merged_df['InKindDonAmount'], errors='coerce') # Convert to numeric\n", + "merged_df['TotalAmount'] = merged_df['DonationAmount'] + merged_df['InKindDonAmount']" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DonationYear\n", + "1998.0 20251764.56\n", + "1999.0 1439713.47\n", + "2000.0 12272618.88\n", + "2001.0 4142509.70\n", + "2002.0 46267738.68\n", + "2003.0 2649053.87\n", + "2004.0 24198184.82\n", + "2005.0 14312546.15\n", + "2006.0 73790391.88\n", + "2007.0 6630279.06\n", + "2008.0 30198069.43\n", + "2009.0 15864715.89\n", + "2010.0 63473871.58\n", + "2011.0 3665640.65\n", + "2012.0 52332226.23\n", + "2013.0 14818332.12\n", + "2014.0 36623686.97\n", + "2015.0 18216539.31\n", + "2016.0 35300464.24\n", + "2017.0 14262418.11\n", + "2018.0 33070660.45\n", + "2019.0 20705451.67\n", + "2020.0 29656928.92\n", + "2021.0 17658710.08\n", + "2022.0 34059087.02\n", + "2023.0 40405.00\n", + "Name: TotalAmount, dtype: float64" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "donation_by_year = merged_df.groupby('DonationYear') \n", + "donation_by_year['TotalAmount'].sum() # check the total contribution across years" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DonationYearDonorNameTotalAmount
01998.0Faegre & Benson Prof Ltd Liability Partnership25050.00
11998.0Committee of Nine PAC24700.00
21998.0Minneapolis Police Relief Assoc23650.00
31998.0Hoffman, Robert23350.42
41998.0Weaver, Pamela21490.00
............
2552023.0Collins, Jane1500.00
2562023.0Doherty, John1500.00
2572023.0Dworsky, David1500.00
2582023.0Husom, Constance L1500.00
2592023.0Jerde, Judy1500.00
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260 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " DonationYear DonorName TotalAmount\n", + "0 1998.0 Faegre & Benson Prof Ltd Liability Partnership 25050.00\n", + "1 1998.0 Committee of Nine PAC 24700.00\n", + "2 1998.0 Minneapolis Police Relief Assoc 23650.00\n", + "3 1998.0 Hoffman, Robert 23350.42\n", + "4 1998.0 Weaver, Pamela 21490.00\n", + ".. ... ... ...\n", + "255 2023.0 Collins, Jane 1500.00\n", + "256 2023.0 Doherty, John 1500.00\n", + "257 2023.0 Dworsky, David 1500.00\n", + "258 2023.0 Husom, Constance L 1500.00\n", + "259 2023.0 Jerde, Judy 1500.00\n", + "\n", + "[260 rows x 3 columns]" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Group by 'Year' and 'Contributor' to calculate the total contribution for each contributor in each year\n", + "don_by_year_contributor = merged_df.groupby(['DonationYear', 'DonorName'])['TotalAmount'].sum().reset_index()\n", + "\n", + "# Find the top 10 contributors\n", + "top_10_contributors = don_by_year_contributor.groupby('DonationYear').apply(lambda group: group.nlargest(10, 'TotalAmount')).reset_index(drop=True)\n", + "top_10_contributors" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DonationYearCandRegNumbCandFirstNameCandLastNameTotalAmount
01998.013682.0HubertHumphrey4223008.08
11998.014913.0NormanColeman3049350.25
21998.012955.0MichaelFreeman2826653.06
31998.013681.0CharlesWeaver2354878.56
41998.013679.0TedMondale1706177.16
..................
2492022.017373.0TorreyWestrom358200.00
2502023.018690.0ScottJensen32505.00
2512023.018708.0LeslieLienemann5400.00
2522023.018731.0JamesSchultz1500.00
2532023.018973.0BethBeebe1000.00
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254 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " DonationYear CandRegNumb CandFirstName CandLastName TotalAmount\n", + "0 1998.0 13682.0 Hubert Humphrey 4223008.08\n", + "1 1998.0 14913.0 Norman Coleman 3049350.25\n", + "2 1998.0 12955.0 Michael Freeman 2826653.06\n", + "3 1998.0 13681.0 Charles Weaver 2354878.56\n", + "4 1998.0 13679.0 Ted Mondale 1706177.16\n", + ".. ... ... ... ... ...\n", + "249 2022.0 17373.0 Torrey Westrom 358200.00\n", + "250 2023.0 18690.0 Scott Jensen 32505.00\n", + "251 2023.0 18708.0 Leslie Lienemann 5400.00\n", + "252 2023.0 18731.0 James Schultz 1500.00\n", + "253 2023.0 18973.0 Beth Beebe 1000.00\n", + "\n", + "[254 rows x 5 columns]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Find the top 10 recipients\n", + "don_by_year_recipients = merged_df.groupby(['DonationYear', 'CandRegNumb', 'CandFirstName', 'CandLastName'])['TotalAmount'].sum().reset_index()\n", + "top_10_recipients = don_by_year_recipients.groupby('DonationYear').apply(lambda group: group.nlargest(10, 'TotalAmount')).reset_index(drop=True)\n", + "top_10_recipients" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Compare donation by donor type and by recipients office type" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1 Compare donation by donor type" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "year_lst = [year for year in range(1998, 2024)]" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " DonationYear DonorType TotalAmount\n", + "0 1998.0 C 95915.33\n", + "1 1998.0 F 871552.40\n", + "2 1998.0 I 18343177.40\n", + "3 1998.0 L 773990.85\n", + "4 1998.0 O 9500.00\n", + "5 1998.0 P 143113.46\n", + "6 1998.0 S 14515.12\n", + " DonationYear DonorType TotalAmount\n", + "7 1999.0 C 15935.16\n", + "8 1999.0 F 338260.80\n", + "9 1999.0 I 848938.06\n", + "10 1999.0 L 82622.00\n", + "11 1999.0 P 146258.29\n", + "12 1999.0 S 7699.16\n", + " DonationYear DonorType TotalAmount\n", + "13 2000.0 C 123121.23\n", + "14 2000.0 F 5751238.93\n", + "15 2000.0 H 17500.00\n", + "16 2000.0 I 5407234.41\n", + "17 2000.0 L 448501.25\n", + "18 2000.0 O 8850.00\n", + "19 2000.0 P 466021.75\n", + "20 2000.0 S 48151.31\n", + " DonationYear DonorType TotalAmount\n", + "21 2001.0 C 32505.86\n", + "22 2001.0 F 152000.00\n", + "23 2001.0 I 3490746.52\n", + "24 2001.0 L 81050.00\n", + "25 2001.0 P 359310.50\n", + "26 2001.0 S 26896.82\n", + " DonationYear DonorType TotalAmount\n", + "27 2002.0 C 868270.72\n", + "28 2002.0 F 15884103.87\n", + "29 2002.0 H 10000.00\n", + "30 2002.0 I 26177709.02\n", + "31 2002.0 L 1405335.64\n", + "32 2002.0 O 28836.00\n", + "33 2002.0 P 1797337.29\n", + "34 2002.0 S 96146.14\n", + " DonationYear DonorType TotalAmount\n", + "35 2003.0 C 138243.70\n", + "36 2003.0 F 382676.72\n", + "37 2003.0 I 1529568.24\n", + "38 2003.0 L 97003.90\n", + "39 2003.0 O 10300.00\n", + "40 2003.0 P 463325.60\n", + "41 2003.0 S 27935.71\n", + " DonationYear DonorType TotalAmount\n", + "42 2004.0 C 195488.01\n", + "43 2004.0 F 8842537.79\n", + "44 2004.0 H 10750.00\n", + "45 2004.0 I 12958597.34\n", + "46 2004.0 L 483078.32\n", + "47 2004.0 O 20400.00\n", + "48 2004.0 P 1611215.67\n", + "49 2004.0 S 68767.69\n", + " DonationYear DonorType TotalAmount\n", + "50 2005.0 C 108953.70\n", + "51 2005.0 F 534279.52\n", + "52 2005.0 H 5500.00\n", + "53 2005.0 I 12746809.45\n", + "54 2005.0 L 301900.00\n", + "55 2005.0 O 1500.00\n", + "56 2005.0 P 572219.76\n", + "57 2005.0 S 41383.72\n", + " DonationYear DonorType TotalAmount\n", + "58 2006.0 C 665800.69\n", + "59 2006.0 F 17531956.40\n", + "60 2006.0 H 4700.00\n", + "61 2006.0 I 50586770.91\n", + "62 2006.0 L 2453405.96\n", + "63 2006.0 O 17500.00\n", + "64 2006.0 P 2336897.96\n", + "65 2006.0 S 193359.96\n", + " DonationYear DonorType TotalAmount\n", + "66 2007.0 C 132098.63\n", + "67 2007.0 F 277127.54\n", + "68 2007.0 H 1050.00\n", + "69 2007.0 I 5515373.07\n", + "70 2007.0 L 72975.00\n", + "71 2007.0 P 589239.35\n", + "72 2007.0 S 42415.47\n", + " DonationYear DonorType TotalAmount\n", + "73 2008.0 C 247484.81\n", + "74 2008.0 F 9774055.78\n", + "75 2008.0 I 17335825.54\n", + "76 2008.0 L 1019581.64\n", + "77 2008.0 O 7120.00\n", + "78 2008.0 P 1761542.28\n", + "79 2008.0 S 52459.38\n", + " DonationYear DonorType TotalAmount\n", + "80 2009.0 C 60246.89\n", + "81 2009.0 F 1071225.64\n", + "82 2009.0 I 13155850.36\n", + "83 2009.0 L 917451.09\n", + "84 2009.0 O 30858.52\n", + "85 2009.0 P 589295.98\n", + "86 2009.0 S 39787.41\n", + " DonationYear DonorType TotalAmount\n", + "87 2010.0 C 575226.07\n", + "88 2010.0 F 16590592.53\n", + "89 2010.0 I 40571084.28\n", + "90 2010.0 L 2712476.22\n", + "91 2010.0 O 9962.72\n", + "92 2010.0 P 2800850.78\n", + "93 2010.0 S 213678.98\n", + " DonationYear DonorType TotalAmount\n", + "94 2011.0 C 83408.72\n", + "95 2011.0 F 706860.00\n", + "96 2011.0 I 2137701.99\n", + "97 2011.0 L 288691.65\n", + "98 2011.0 P 404124.80\n", + "99 2011.0 S 44853.49\n", + " DonationYear DonorType TotalAmount\n", + "100 2012.0 C 829614.06\n", + "101 2012.0 F 17219708.10\n", + "102 2012.0 I 28867408.49\n", + "103 2012.0 L 1616676.85\n", + "104 2012.0 O 26850.00\n", + "105 2012.0 P 3499123.03\n", + "106 2012.0 S 272845.70\n", + " DonationYear DonorType TotalAmount\n", + "107 2013.0 C 61016.66\n", + "108 2013.0 F 3877007.20\n", + "109 2013.0 I 9610537.15\n", + "110 2013.0 L 847558.00\n", + "111 2013.0 O 2250.00\n", + "112 2013.0 P 375373.59\n", + "113 2013.0 S 44589.52\n", + " DonationYear DonorType TotalAmount\n", + "114 2014.0 C 275031.03\n", + "115 2014.0 F 8090318.44\n", + "116 2014.0 I 24730972.63\n", + "117 2014.0 L 1004741.12\n", + "118 2014.0 O 12918.08\n", + "119 2014.0 P 2212720.97\n", + "120 2014.0 S 296984.70\n", + " DonationYear DonorType TotalAmount\n", + "121 2015.0 C 75461.94\n", + "122 2015.0 F 6868706.81\n", + "123 2015.0 I 9419134.26\n", + "124 2015.0 L 1460626.83\n", + "125 2015.0 O 0.00\n", + "126 2015.0 P 241696.44\n", + "127 2015.0 S 150913.03\n", + " DonationYear DonorType TotalAmount\n", + "128 2016.0 C 392292.98\n", + "129 2016.0 F 9463042.60\n", + "130 2016.0 I 20360716.23\n", + "131 2016.0 L 1428607.03\n", + "132 2016.0 O 0.00\n", + "133 2016.0 P 3354018.01\n", + "134 2016.0 S 301787.39\n", + " DonationYear DonorType TotalAmount\n", + "135 2017.0 C 120886.18\n", + "136 2017.0 F 4481777.52\n", + "137 2017.0 I 8326874.29\n", + "138 2017.0 L 1014375.39\n", + "139 2017.0 O 2227.05\n", + "140 2017.0 P 146035.87\n", + "141 2017.0 S 170241.81\n", + " DonationYear DonorType TotalAmount\n", + "142 2018.0 C 204173.91\n", + "143 2018.0 F 6869529.91\n", + "144 2018.0 I 22099479.16\n", + "145 2018.0 L 1112664.69\n", + "146 2018.0 O 10920.00\n", + "147 2018.0 P 2442400.23\n", + "148 2018.0 S 331492.55\n", + " DonationYear DonorType TotalAmount\n", + "149 2019.0 C 89059.42\n", + "150 2019.0 F 8203254.17\n", + "151 2019.0 I 9642214.75\n", + "152 2019.0 L 2262743.67\n", + "153 2019.0 O 35867.68\n", + "154 2019.0 P 279534.20\n", + "155 2019.0 S 192777.78\n", + " DonationYear DonorType TotalAmount\n", + "156 2020.0 C 454526.20\n", + "157 2020.0 F 6514796.96\n", + "158 2020.0 I 19345583.62\n", + "159 2020.0 L 1121973.00\n", + "160 2020.0 O 16750.00\n", + "161 2020.0 P 1877387.16\n", + "162 2020.0 S 325911.98\n", + " DonationYear DonorType TotalAmount\n", + "163 2021.0 C 38533.24\n", + "164 2021.0 F 5605625.00\n", + "165 2021.0 I 9425601.45\n", + "166 2021.0 L 2328111.86\n", + "167 2021.0 O 0.00\n", + "168 2021.0 P 190325.92\n", + "169 2021.0 S 70512.61\n", + " DonationYear DonorType TotalAmount\n", + "170 2022.0 C 544248.14\n", + "171 2022.0 F 5854415.05\n", + "172 2022.0 H 750.00\n", + "173 2022.0 I 24608738.99\n", + "174 2022.0 L 1359579.69\n", + "175 2022.0 O 3500.00\n", + "176 2022.0 P 1431415.20\n", + "177 2022.0 S 255894.95\n", + " DonationYear DonorType TotalAmount\n", + "178 2023.0 I 39405.0\n", + "179 2023.0 S 1000.0\n" + ] + } + ], + "source": [ + "group_by_donor_type = merged_df.groupby(['DonationYear', 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"zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Donations by donor type for year 2023" + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "title": { + "text": "DonorType" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "TotalAmount" + } + } + } + }, + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for year in year_lst:\n", + " donor_bar_chart(year)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2 Analyze yearly trend of donations by donor type" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. From 1998 to 2023, individual donors consistently contribute the majority of the total donation amount, surpassing all other donor types in nearly every year except for year 2000.\n", + "2. Across the years, donor types \"S\", \"C\" and \"O\"continue to remain low, i.e., donations made by candidates themselves and by candidate committees are comparatively very small in amount.\n", + "3. Donations made by \"Fund\" exhibited notably higher contributions in 2000, 2002, 2004, 2015, and 2019 when compared to other years, nearly rivaling the largest donor type, \"Individual.\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.3 Compare donation by recipient type" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " DonationYear OfficeSought TotalAmount\n", + "0 1998.0 AG 5124359.38\n", + "1 1998.0 DC 78246.72\n", + "2 1998.0 GC 13896258.03\n", + "3 1998.0 SA 454494.25\n", + "4 1998.0 SC 41847.32\n", + "5 1998.0 SS 502730.35\n", + "6 1998.0 ST 129142.31\n", + "7 1998.0 Senate 24686.20\n", + " DonationYear OfficeSought TotalAmount\n", + "8 1999.0 AG 154813.00\n", + "9 1999.0 DC 0.00\n", + "10 1999.0 GC 619423.40\n", + "11 1999.0 SA 1930.75\n", + "12 1999.0 SC 0.00\n", + "13 1999.0 SS 15175.35\n", + "14 1999.0 ST 1930.75\n", + "15 1999.0 Senate 646440.22\n", + " DonationYear OfficeSought TotalAmount\n", + "16 2000.0 AG 105675.00\n", + "17 2000.0 AP 9300.00\n", + "18 2000.0 DC 173677.29\n", + "19 2000.0 GC 605720.75\n", + "20 2000.0 SC 754310.92\n", + "21 2000.0 SS 0.00\n", + "22 2000.0 Senate 10623934.92\n", + " DonationYear OfficeSought TotalAmount\n", + "23 2001.0 AG 365084.18\n", + "24 2001.0 DC 1350.00\n", + "25 2001.0 GC 3287971.52\n", + "26 2001.0 House 323093.23\n", + "27 2001.0 SA 3750.00\n", + "28 2001.0 SC 3600.00\n", + "29 2001.0 SS 0.00\n", + "30 2001.0 Senate 157660.77\n", + " DonationYear OfficeSought TotalAmount\n", + "31 2002.0 AG 1774431.00\n", + "32 2002.0 AP 11562.98\n", + "33 2002.0 DC 449117.40\n", + "34 2002.0 GC 10143972.36\n", + "35 2002.0 House 15549229.25\n", + "36 2002.0 SA 1373262.28\n", + "37 2002.0 SC 350150.00\n", + "38 2002.0 SS 1168041.00\n", + "39 2002.0 Senate 15447972.41\n", + " DonationYear OfficeSought TotalAmount\n", + "40 2003.0 AG 148775.00\n", + "41 2003.0 DC 12176.43\n", + "42 2003.0 GC 1058562.27\n", + "43 2003.0 House 1123215.59\n", + "44 2003.0 SA 4500.00\n", + "45 2003.0 SC 46507.50\n", + "46 2003.0 SS 9100.00\n", + "47 2003.0 Senate 246217.08\n", + " DonationYear OfficeSought TotalAmount\n", + "48 2004.0 AG 133375.00\n", + "49 2004.0 AP 256500.34\n", + "50 2004.0 DC 413003.43\n", + "51 2004.0 GC 4266349.50\n", + "52 2004.0 House 18701069.93\n", + "53 2004.0 SA 7250.00\n", + "54 2004.0 SC 188309.72\n", + "55 2004.0 Senate 232326.90\n", + " DonationYear OfficeSought TotalAmount\n", + "56 2005.0 AG 341469.72\n", + "57 2005.0 AP 1081.20\n", + "58 2005.0 DC 4514.06\n", + "59 2005.0 GC 12374181.14\n", + "60 2005.0 House 511216.55\n", + "61 2005.0 SA 29774.00\n", + "62 2005.0 SS 6951.90\n", + "63 2005.0 Senate 1043357.58\n", + " DonationYear OfficeSought TotalAmount\n", + "64 2006.0 AG 4124556.17\n", + "65 2006.0 AP 72750.00\n", + "66 2006.0 DC 289587.97\n", + "67 2006.0 GC 27396566.32\n", + "68 2006.0 House 19386787.46\n", + "69 2006.0 SA 1990952.88\n", + "70 2006.0 SS 2329778.26\n", + "71 2006.0 Senate 18199412.82\n", + " DonationYear OfficeSought TotalAmount\n", + "72 2007.0 AG 194142.00\n", + "73 2007.0 DC 33414.16\n", + "74 2007.0 GC 5204294.54\n", + "75 2007.0 House 653670.91\n", + "76 2007.0 SC 122340.00\n", + "77 2007.0 SS 13504.32\n", + "78 2007.0 Senate 408913.13\n", + " DonationYear OfficeSought TotalAmount\n", + "79 2008.0 AG 177100.00\n", + "80 2008.0 AP 57757.48\n", + "81 2008.0 DC 568829.83\n", + "82 2008.0 GC 6971683.85\n", + "83 2008.0 House 21647610.86\n", + "84 2008.0 SC 386408.98\n", + "85 2008.0 SS 0.00\n", + "86 2008.0 Senate 388678.43\n", + " DonationYear OfficeSought TotalAmount\n", + "87 2009.0 AG 209452.04\n", + "88 2009.0 AP 9750.00\n", + "89 2009.0 DC 3000.00\n", + "90 2009.0 GC 14782461.32\n", + "91 2009.0 House 544150.62\n", + "92 2009.0 SA 640.00\n", + "93 2009.0 SC 28250.00\n", + "94 2009.0 SS 1803.04\n", + "95 2009.0 Senate 285208.87\n", + " DonationYear OfficeSought TotalAmount\n", + "96 2010.0 AG 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for year in year_lst:\n", + " recipient_bar_chart(year)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.4 Analyze yearly trend of donations by recipient type" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Attorney General: Donations were moderately high in 1998 and 1999 but consistently remained low in subsequent years.\n", + "\n", + "- District Court: This recipient type consistently received very low contributions, likely due to an overall lower contribution count.\n", + "\n", + "- State Appeals Court Judge: In several years, this recipient type did not receive any contributions, and in other years, the amounts remained extremely low.\n", + "\n", + "- Governor: Donation amounts were significantly high in 1998, 1999, 2001, 2005, 2006, 2007, 2009, 2010, and 2011. They were moderately high in 2004 and 2008, while the rest of the years saw relatively low contributions.\n", + "\n", + "- Years with very large donations overall:\n", + " - In 2010, the Governor, House, and Senate received very high donations.\n", + " - In 2011, the Governor and Senate received very high donations.\n", + " - In 2012, the House and Senate received very high donations.\n", + " - In 2015, 2016, 2019, 2020, 2021, and 2022, the House and Senate received high donation amounts." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From d8e72ed2b43d46fc8a3246e6864f87013f297938 Mon Sep 17 00:00:00 2001 From: yuzhouw313 Date: Sun, 22 Oct 2023 22:02:17 +0000 Subject: [PATCH 02/51] EDA on new candidate&noncandidate dataset with clean notebook --- notebooks/MN_EDA.ipynb | 5764 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 5764 insertions(+) create mode 100644 notebooks/MN_EDA.ipynb diff --git a/notebooks/MN_EDA.ipynb b/notebooks/MN_EDA.ipynb new file mode 100644 index 0000000..4b4f000 --- /dev/null +++ b/notebooks/MN_EDA.ipynb @@ -0,0 +1,5764 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import plotly.express as px\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Read in and Preprocess Datasets\n", + "\n", + "### 1.1 Read in datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Read in candidate-recipient contribution data\n", + "df_ag = pd.read_csv('/project/data/cand_con.csv/AG.csv')\n", + "df_ap = pd.read_csv('/project/data/cand_con.csv/AP.csv')\n", + "df_dc = pd.read_csv('/project/data/cand_con.csv/DC.csv')\n", + "df_gc = pd.read_csv('/project/data/cand_con.csv/GC.csv')\n", + "df_house = pd.read_csv('/project/data/cand_con.csv/House.csv')\n", + "df_sa = pd.read_csv('/project/data/cand_con.csv/SA.csv')\n", + "df_sc = pd.read_csv('/project/data/cand_con.csv/SC.csv')\n", + "df_senate = pd.read_csv('/project/data/cand_con.csv/Senate.csv')\n", + "df_ss = pd.read_csv('/project/data/cand_con.csv/SS.csv')\n", + "df_st = pd.read_csv('/project/data/cand_con.csv/ST.csv')\n", + "\n", + "# Read in non-candidate-recipient contribution data\n", + "df_non_cand = pd.read_csv('/project/data/non_candidate_con.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "10 datasets on candidate-recipient contributions and 1 dataset on non-candidate-recipient contributions. They are seperate and not relational" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2 Check for DataFrames' column consistency" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13\n", + "13\n", + "13\n", + "13\n", + "13\n", + "13\n", + "13\n", + "13\n", + "13\n", + "13\n", + "11\n" + ] + } + ], + "source": [ + "# First check for DataFrames' column numbers\n", + "df_lst = [df_ag, df_ap, df_dc, df_gc, df_house, df_sa, df_sc, df_ss, df_st, df_senate, df_non_cand]\n", + "for df in df_lst:\n", + " print(df.shape[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All dfs have consistent columns\n" + ] + } + ], + "source": [ + "from utils.MN_util import datasets_col_consistent\n", + "\n", + "datasets_col_consistent(df_lst[:-1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.3 Adjust for DataFrames' column consistency" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(Index(['OfficeSought', 'Party', 'District', 'CandRegNumb', 'CandFirstName',\n", + " 'CandLastName', 'CommitteeName', 'DonationDate', 'DonorType',\n", + " 'DonorName', 'DonationAmount', 'InKindDonAmount',\n", + " 'InKindDescriptionText'],\n", + " dtype='object'),\n", + " Index(['PCFRegNumb', 'Committee', 'ETType', 'ETSubType', 'DonationDate',\n", + " 'DonorType', 'DonorRegNumb', 'DonorName', 'DonationAmount',\n", + " 'InKindDonAmount', 'InKindDescriptionText'],\n", + " dtype='object'))" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_ag.columns, df_non_cand.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Based on the project need and dataset consistency, use these columns: RegNumb, RecipientType, OfficeSought, CandFirstName, CandLastName, Committee, DonationDate, DonorType, DonorName, DonationAmount, InKindDonAmount, InKindDescriptionText" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from utils.MN_util import standardize_cand_df\n", + "\n", + "df_ag = standardize_cand_df(df_ag)\n", + "df_ap = standardize_cand_df(df_ap)\n", + "df_dc = standardize_cand_df(df_dc)\n", + "df_gc = standardize_cand_df(df_gc)\n", + "df_house = standardize_cand_df(df_house)\n", + "df_sa = standardize_cand_df(df_sa)\n", + "df_sc = standardize_cand_df(df_sc)\n", + "df_ss = standardize_cand_df(df_ss)\n", + "df_st = standardize_cand_df(df_st)\n", + "df_senate = standardize_cand_df(df_senate)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from utils.MN_util import standardize_noncand_df\n", + "df_non_cand = standardize_noncand_df(df_non_cand)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from utils.MN_util import preprocess_contribution_df\n", + "\n", + "new_df_lst = [df_ag, df_ap, df_dc, df_gc, df_house, df_sa, df_sc, df_ss, df_st, \n", + " df_senate, df_non_cand]\n", + "\n", + "contribution_df = preprocess_contribution_df(new_df_lst)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['I', 'F', 'C', 'O', 'L', 'P', 'H', 'U', 'S', nan, 'B'],\n", + " dtype=object)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "contribution_df['DonorType'].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Donor Types:\n", + "- C: Candidate Committee (limited to state-level candidates who had a principal campaign committee registered with the Board from which the contribution was made)\n", + "- I: Non-lobbyist individual \n", + "- L: Lobbyist \n", + "- F: Political Committee/Fund \n", + "- S: Supporting association of a political fund registered with the Board that donates to its own political fund\n", + "- P: Political party unit\n", + "- H: Local candidate committee (limited to candidates within Hennepin County who satisfy the definition of local candidate, did not exist until 2022)\n", + "- O: Other (catch-all category that in some cases includes businesses, supporting associations of political funds registered with the Board that donate to their own political fund, associations that are not registered with the Board, and any entity that does not fall within one of the other categories)\n", + "- U: Association not registered with the Board (may include a committee registered with the FEC or a regulatory committee in another state, a 501(c)(4), 501(c)(6), or 527 nonprofit organization, the campaign committee of a candidate for local office (excluding certain Hennepin County candidates from 2022 onward), etc.)\n", + "- B: Business (company & corporation)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['AG', nan, 'GC', 'House', 'Senate', 'SA', 'SS', 'SC', 'DC', 'AP',\n", + " 'ST'], dtype=object)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "contribution_df['OfficeSought'].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Recipient Types:\n", + "- Candidate\n", + "- PCF: Political committee or fund\n", + "- PTU: Political party unit\n", + "\n", + "#### Office Types (within candidate recipient):\n", + "- AG = Attorney General\n", + "- AP = State Appeals Court Judge\n", + "- DC = State District Court Judge\n", + "- GC = Governor\n", + "- House = State Representative\n", + "- SA = State Auditor\n", + "- SC = State Supreme Court Justice\n", + "- SS = Secretary of State\n", + "- ST = State Treasurer (this office was abolished in 2003 and no longer exists)\n", + "- Senate = State Senator" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.4 Check column types" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "OfficeSought object\n", + "RegNumb float64\n", + "CandFirstName object\n", + "CandLastName object\n", + "Committee object\n", + "DonationDate datetime64[ns]\n", + "DonorType object\n", + "DonorName object\n", + "DonationAmount float64\n", + "InKindDonAmount float64\n", + "InKindDescriptionText object\n", + "RecipientType object\n", + "DonationYear float64\n", + "TotalAmount float64\n", + "dtype: object" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "contribution_df.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.4 Check Missing Values" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "OfficeSought 483861\n", + "RegNumb 467\n", + "CandFirstName 483861\n", + "CandLastName 483861\n", + "Committee 467\n", + "DonationDate 536\n", + "DonorType 580\n", + "DonorName 477\n", + "DonationAmount 333217\n", + "InKindDonAmount 674347\n", + "InKindDescriptionText 3508947\n", + "RecipientType 0\n", + "DonationYear 536\n", + "TotalAmount 922715\n", + "dtype: int64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "contribution_df.isna().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. 467 of the 'nan' contributions belong to \"Registration Fee for Netroots Event\", which is a non-profit organization that provides trainings, resources and connection opportunities to help progressive activists. Based on their column values, these contributions have no monetary amount, no donor, and no recipient.\n", + "2. 483861 nan values are mostly those recipients are non-candidates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Top 10\n", + "### 2.1 Top 10 Donors" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DonationYear\n", + "1998.0 20678745.70\n", + "1999.0 1545675.89\n", + "2000.0 12322727.60\n", + "2001.0 4163753.21\n", + "2002.0 46600539.55\n", + "2003.0 2926595.94\n", + "2004.0 24702549.81\n", + "2005.0 14655970.48\n", + "2006.0 74537059.95\n", + "2007.0 7312400.10\n", + "2008.0 30794346.71\n", + "2009.0 16390499.70\n", + "2010.0 64319478.61\n", + "2011.0 4859395.88\n", + "2012.0 55252164.36\n", + "2013.0 16482255.60\n", + "2014.0 38667352.15\n", + "2015.0 19816206.40\n", + "2016.0 37338776.71\n", + "2017.0 15943601.05\n", + "2018.0 35105013.46\n", + "2019.0 22417142.37\n", + "2020.0 31573129.27\n", + "2021.0 19326521.95\n", + "2022.0 36476756.74\n", + "2023.0 160433.71\n", + "Name: TotalAmount, dtype: float64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "donation_by_year = contribution_df.groupby('DonationYear') \n", + "donation_by_year['TotalAmount'].sum() # check the total contribution across years" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# Group by 'Year' and 'Contributor' to calculate the total contribution for each contributor in each year\n", + "don_by_year_contributor = contribution_df.groupby(\n", + " ['DonationYear', 'DonorName'])['TotalAmount'].sum().reset_index()\n", + "\n", + "# Find the top 10 contributors\n", + "top_10_contributors = don_by_year_contributor.groupby('DonationYear').apply(\n", + " lambda group: group.nlargest(10, 'TotalAmount')).reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DonationYearDonorNameTotalAmount
2502023.0IBEW Local 29211400.11
2512023.0SEIU local 265194.45
2522023.0Zarth, John2700.00
2532023.0Zarth, Kelly2700.00
2542023.0Krech, Kathy1800.00
2552023.0Restemayer, Douglas1585.93
2562023.0Carlson, Jessica1500.00
2572023.0Collins, Greg1500.00
2582023.0Collins, Jane1500.00
2592023.0Doherty, John1500.00
\n", + "
" + ], + "text/plain": [ + " DonationYear DonorName TotalAmount\n", + "250 2023.0 IBEW Local 292 11400.11\n", + "251 2023.0 SEIU local 26 5194.45\n", + "252 2023.0 Zarth, John 2700.00\n", + "253 2023.0 Zarth, Kelly 2700.00\n", + "254 2023.0 Krech, Kathy 1800.00\n", + "255 2023.0 Restemayer, Douglas 1585.93\n", + "256 2023.0 Carlson, Jessica 1500.00\n", + "257 2023.0 Collins, Greg 1500.00\n", + "258 2023.0 Collins, Jane 1500.00\n", + "259 2023.0 Doherty, John 1500.00" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "top_10_contributors[-10:] # Top 10 contributors in 2023" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Top 10 Recipients" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "don_by_year_recipients = contribution_df.groupby(['DonationYear', 'RegNumb', \n", + " 'CandFirstName', 'CandLastName', 'Committee', \n", + " 'RecipientType'])['TotalAmount'].sum().reset_index()\n", + "top_10_recipients = don_by_year_recipients.groupby('DonationYear').apply(\n", + " lambda group: group.nlargest(10, 'TotalAmount')).reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DonationYearRegNumbCandFirstNameCandLastNameCommitteeRecipientTypeTotalAmount
2442022.013262.0WarrenLimmerLimmer (Warren) for Senate CommitteeCandidate454275.00
2452022.010601.0MaryMurphyMary Murphy Volunteer CommitteeCandidate452742.21
2462022.012604.0GregoryDavidsPeople for (Gregory) Davids CommitteeCandidate402975.00
2472022.017105.0CarlaNelsonNelson (Carla) for SenateCandidate400540.00
2482022.018732.0KimCrockettKim Crockett for Secretary of State CommitteeCandidate362551.26
2492022.017373.0TorreyWestromWestrom (Torrey) for Senate CommitteeCandidate358200.00
2502023.018690.0ScottJensenDr. Scott Jensen for GovernorCandidate32505.00
2512023.018708.0LeslieLienemannLeslie for MinnesotaCandidate5400.00
2522023.018731.0JamesSchultzJim Schultz For Minnesota Attorney GeneralCandidate1500.00
2532023.018973.0BethBeebeBeth Beebe for State RepCandidate1000.00
\n", + "
" + ], + "text/plain": [ + " DonationYear RegNumb CandFirstName CandLastName \\\n", + "244 2022.0 13262.0 Warren Limmer \n", + "245 2022.0 10601.0 Mary Murphy \n", + "246 2022.0 12604.0 Gregory Davids \n", + "247 2022.0 17105.0 Carla Nelson \n", + "248 2022.0 18732.0 Kim Crockett \n", + "249 2022.0 17373.0 Torrey Westrom \n", + "250 2023.0 18690.0 Scott Jensen \n", + "251 2023.0 18708.0 Leslie Lienemann \n", + "252 2023.0 18731.0 James Schultz \n", + "253 2023.0 18973.0 Beth Beebe \n", + "\n", + " Committee RecipientType TotalAmount \n", + "244 Limmer (Warren) for Senate Committee Candidate 454275.00 \n", + "245 Mary Murphy Volunteer Committee Candidate 452742.21 \n", + "246 People for (Gregory) Davids Committee Candidate 402975.00 \n", + "247 Nelson (Carla) for Senate Candidate 400540.00 \n", + "248 Kim Crockett for Secretary of State Committee Candidate 362551.26 \n", + "249 Westrom (Torrey) for Senate Committee Candidate 358200.00 \n", + "250 Dr. Scott Jensen for Governor Candidate 32505.00 \n", + "251 Leslie for Minnesota Candidate 5400.00 \n", + "252 Jim Schultz For Minnesota Attorney General Candidate 1500.00 \n", + "253 Beth Beebe for State Rep Candidate 1000.00 " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "top_10_recipients[-10:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Compare donation by donor and recipient types\n", + "\n", + "### 3.1 Compare donation by donor types" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "alignmentgroup": "True", + "hovertemplate": "DonorType=C
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"showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "filtered_df1 = contribution_df[(contribution_df['DonationYear'] >= 2018) & (contribution_df['DonationYear'] <= 2022)]\n", + "grouped4 = filtered_df1.groupby(['DonationYear', 'RecipientType'])['TotalAmount'].sum().reset_index()\n", + "fig = px.bar(\n", + " grouped4,\n", + " x='DonationYear',\n", + " y='TotalAmount',\n", + " color='RecipientType',\n", + " title='Donations by Recipient Type for the Last 5 Years',\n", + " labels={\"DonationYear\": \"Year\", \"TotalAmount\": \"Total Contributions\"},\n", + ")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Observations and Interpretations\n", + "1. Candidates, as the recipients, make up the overwhelming majority of contributions.\n", + "2. Examining the period from 1998 to 2023, a distinct cyclical pattern emerges, characterized by alternating years of increased and decreased contributions, which may correspond to congressional elections or MN state house representatives elections which take place every two years.\n", + "3. Starting in 2012, recipient types \"Political Committee or Fund\" and \"Political Party Unit\" began receiving a larger share of contributions compared to prior years." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From d4c263fe5ee0d3eef5c6c6ede7ff8da99ad36f94 Mon Sep 17 00:00:00 2001 From: yuzhouw313 Date: Sun, 22 Oct 2023 22:18:37 +0000 Subject: [PATCH 03/51] added MN util file and updated notebook README, added clean MN jupyter with new data --- notebooks/MN_EDA.ipynb | 1213 +++++++++++++++++++++++++++++++++++++++- notebooks/README.md | 21 +- utils/MN_util.py | 77 +++ 3 files changed, 1305 insertions(+), 6 deletions(-) create mode 100644 utils/MN_util.py diff --git a/notebooks/MN_EDA.ipynb b/notebooks/MN_EDA.ipynb index 4b4f000..a836c2d 100644 --- a/notebooks/MN_EDA.ipynb +++ b/notebooks/MN_EDA.ipynb @@ -4722,7 +4722,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -5682,9 +5682,9 @@ } }, "text/html": [ - "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "grouped5 = filtered_df.groupby(['DonationYear', 'OfficeSought'])['TotalAmount'].sum().reset_index()\n", + "fig = px.bar(\n", + " grouped5,\n", + " x='DonationYear',\n", + " y='TotalAmount',\n", + " color='OfficeSought',\n", + " title='Donations by Candidate Recipient Race for the Last 5 Years',\n", + " labels={\"DonationYear\": \"Year\", \"TotalAmount\": \"Total Contributions\"},\n", + ")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Clearly, state senators and house representatives recieve the most contributions" + ] } ], "metadata": { diff --git a/notebooks/README.md b/notebooks/README.md index 0751ad0..3d3aeed 100644 --- a/notebooks/README.md +++ b/notebooks/README.md @@ -2,4 +2,23 @@ This should contain information about what is done in each notebook -* `Test.ipynb` : This is a test notebook to demonstrate how to use this repository. \ No newline at end of file +* `Test.ipynb` : This is a test notebook to demonstrate how to use this repository. + +MN EDA +File Name: +- MN_EDA: The clear jupyter notebook for MN EDA +- MN_util: the util file that stores necessary preprocessing functions in MN_EDA + +1. 10 datasets on candidate-recipient contributions and 1 dataset on non-candidate-recipient contributions. They are seperate and not relational. +2. Clean dataset column: OfficeSought (object), RegNumb (float64), CandFirstName (object), CandLastName (object), Committee (object), DonationDate (datetime64), DonorType (object), DonorName (object), DonationAmount (float64), InKindDonAmount, InKindDescriptionText (object), RecipientType (object), DonationYear (float64), TotalAmount (float64) +3. Top 10 contributors in 2023: IBEW Local 292, SEIU local 26, Zarth John, Zarth Kelly, Krech Kathy, Restemayer Douglas, Carlson Jessica, Collins Greg, Collins Jane, and Doherty John. Top 10 recipients in 2023: Warren Limmer, Mary Murphy, Gregory Davids, Carla Nelson, Kim Crockett, Torrey Westrom, Scott Jensen, Leslie Lienemann, James Schultz, and Beth Beebe. +4. Yearly Trends: +- Individuals, excluding lobbyists, constitute the largest share of contributions in the MN dataset. +- The second most substantial contributor category is General Purpose Political Committee or Fund, followed by lobbyists. +- Contributions from other donor types are notably lower throughout the years. +- Analyzing a sample from 2018 to 2022, we observe a cyclical pattern with a major increase in contributions, followed by three years of reduced contribution totals. This cycle aligns with the four-year election cycle. +- From 1998 to 2023, there are several years with significantly lower contribution amount: 1999, 2001, 2003, 2007, 2011. + +- Candidates, as the recipients, make up the overwhelming majority of contributions. +- Examining the period from 1998 to 2023, a distinct cyclical pattern emerges, characterized by alternating years of increased and decreased contributions, which may correspond to congressional elections or MN state house representatives elections which take place every two years. +- Starting in 2012, recipient types "Political Committee or Fund" and "Political Party Unit" began receiving a larger share of contributions compared to prior years. diff --git a/utils/MN_util.py b/utils/MN_util.py new file mode 100644 index 0000000..360e8a5 --- /dev/null +++ b/utils/MN_util.py @@ -0,0 +1,77 @@ +from pathlib import Path +import numpy as np +import pandas as pd + + +def datasets_col_consistent(df_lst): + ''' + Check if a list of DataFrames have the same columns for future merge + Return: nothing, just print out the result for checking column consistency + ''' + + previous_columns = df_lst[0].columns + consistent_col_count = 1 + + for df in df_lst[1:]: + if not (df.columns == previous_columns).all(): + print('Columns not consistent across races') + else: + consistent_col_count += 1 + if consistent_col_count == len(df_lst): + print('All dfs have consistent columns') + + +def standardize_cand_df(df): + ''' + To preprocess all the candidate-recipient contribution datasets such that + they are consistent with non-candidate recipient dataset + ''' + df_copy = df.copy(deep=True) + columns_to_keep = ['OfficeSought', 'CandRegNumb', 'CandFirstName', + 'CandLastName', 'CommitteeName', 'DonationDate', + 'DonorType', 'DonorName', 'DonationAmount', + 'InKindDonAmount', 'InKindDescriptionText'] + df_copy = df_copy[columns_to_keep] + column_mapping = {'CandRegNumb': 'RegNumb', 'CommitteeName': 'Committee'} + df_copy.rename(columns=column_mapping, inplace=True) + df_copy['RecipientType'] = 'Candidate' + return df_copy + + +def standardize_noncand_df(df): + ''' + To preprocess non-candidate-recipient contribution dataset such that we + can merge all candidate-recipient and non-candidate-recipient datasets + ''' + df_copy = df.copy(deep=True) + columns_to_keep = ['PCFRegNumb', 'Committee', 'ETType', 'DonationDate', + 'DonorType', 'DonorName', 'DonationAmount', + 'InKindDonAmount', 'InKindDescriptionText'] + df_copy = df_copy[columns_to_keep] + column_mapping = {'PCFRegNumb': 'RegNumb', 'ETType': 'RecipientType'} + df_copy.rename(columns=column_mapping, inplace=True) + return df_copy + +def preprocess_contribution_df(df_lst): + contribution_df = pd.concat(df_lst, ignore_index=True) + + contribution_df['DonationDate'] = pd.to_datetime(contribution_df['DonationDate']) + contribution_df['DonationYear'] = contribution_df['DonationDate'].dt.year + contribution_df = contribution_df.sort_values(by='DonationYear', ascending=False) + + contribution_df['DonorType'] = contribution_df['DonorType'].replace('b', 'B') + contribution_df['DonorType'] = contribution_df['DonorType'].replace('u', 'U') + contribution_df['DonorType'] = contribution_df['DonorType'].replace('s', 'S') + contribution_df['DonorType'] = contribution_df['DonorType'].replace('c', 'C') + contribution_df['DonorType'] = contribution_df['DonorType'].replace('l', 'L') + contribution_df['DonorType'] = contribution_df['DonorType'].replace('i', 'I') + contribution_df['DonorType'] = contribution_df['DonorType'].replace('h', 'H') + + contribution_df['DonationAmount'] = pd.to_numeric( + contribution_df['DonationAmount'], errors='coerce') + contribution_df['InKindDonAmount'] = pd.to_numeric( + contribution_df['InKindDonAmount'], errors='coerce') + contribution_df['TotalAmount'] = \ + contribution_df['DonationAmount'] + contribution_df['InKindDonAmount'] + + return contribution_df \ No newline at end of file From bab4e545047bcac3ffc0ecd5f9a818efefc7ea58 Mon Sep 17 00:00:00 2001 From: yuzhouw313 Date: Tue, 24 Oct 2023 19:44:18 +0000 Subject: [PATCH 04/51] updated util and notebook README, revised EDA code based on TA input --- notebooks/MN_EDA.ipynb | 1699 +- notebooks/MN_dataset_analysis.ipynb | 50571 -------------------------- notebooks/README.md | 22 +- utils/MN_util.py | 66 +- utils/README.md | 13 + 5 files changed, 960 insertions(+), 51411 deletions(-) delete mode 100644 notebooks/MN_dataset_analysis.ipynb create mode 100644 utils/README.md diff --git a/notebooks/MN_EDA.ipynb b/notebooks/MN_EDA.ipynb index a836c2d..bd94f36 100644 --- a/notebooks/MN_EDA.ipynb +++ b/notebooks/MN_EDA.ipynb @@ -218,22 +218,42 @@ "metadata": {}, "source": [ "#### Donor Types:\n", - "- C: Candidate Committee (limited to state-level candidates who had a principal campaign committee registered with the Board from which the contribution was made)\n", - "- I: Non-lobbyist individual \n", - "- L: Lobbyist \n", - "- F: Political Committee/Fund \n", - "- S: Supporting association of a political fund registered with the Board that donates to its own political fund\n", - "- P: Political party unit\n", - "- H: Local candidate committee (limited to candidates within Hennepin County who satisfy the definition of local candidate, did not exist until 2022)\n", - "- O: Other (catch-all category that in some cases includes businesses, supporting associations of political funds registered with the Board that donate to their own political fund, associations that are not registered with the Board, and any entity that does not fall within one of the other categories)\n", - "- U: Association not registered with the Board (may include a committee registered with the FEC or a regulatory committee in another state, a 501(c)(4), 501(c)(6), or 527 nonprofit organization, the campaign committee of a candidate for local office (excluding certain Hennepin County candidates from 2022 onward), etc.)\n", - "- B: Business (company & corporation)" + "1. C: Candidate Committee (limited to state-level candidates who had a principal campaign committee registered with the Board from which the contribution was made)\n", + "2. I: Non-lobbyist individual \n", + "3. L: Lobbyist \n", + "4. F: Political Committee/Fund \n", + "5. S: Supporting association of a political fund registered with the Board that donates to its own political fund\n", + "6. P: Political party unit\n", + "7. H: Local candidate committee (limited to candidates within Hennepin County who satisfy the definition of local candidate, did not exist until 2022)\n", + "8. O: Other (catch-all category that in some cases includes businesses, supporting associations of political funds registered with the Board that donate to their own political fund, associations that are not registered with the Board, and any entity that does not fall within one of the other categories)\n", + "9. U: Association not registered with the Board (may include a committee registered with the FEC or a regulatory committee in another state, a 501(c)(4), 501(c)(6), or 527 nonprofit organization, the campaign committee of a candidate for local office (excluding certain Hennepin County candidates from 2022 onward), etc.)\n", + "10. B: Business (company & corporation)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Candidate', 'PCF', 'PTU'], dtype=object)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "contribution_df['RecipientType'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, "outputs": [ { "data": { @@ -242,7 +262,7 @@ " 'ST'], dtype=object)" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -282,7 +302,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -305,7 +325,7 @@ "dtype: object" ] }, - "execution_count": 13, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -323,7 +343,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -337,16 +357,16 @@ "DonationDate 536\n", "DonorType 580\n", "DonorName 477\n", - "DonationAmount 333217\n", - "InKindDonAmount 674347\n", + "DonationAmount 0\n", + "InKindDonAmount 0\n", "InKindDescriptionText 3508947\n", "RecipientType 0\n", "DonationYear 536\n", - "TotalAmount 922715\n", + "TotalAmount 0\n", "dtype: int64" ] }, - "execution_count": 14, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -355,12 +375,73 @@ "contribution_df.isna().sum()" ] }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of contribution entries = 3548873\n", + "Total number of nonclassifiable contribution amount = 336051\n", + "Total number of nonclassifiable recipients = 467\n", + "Total number of nonclassifiable donors = 477\n" + ] + } + ], + "source": [ + "print('Total number of contribution entries = ', len(contribution_df))\n", + "print('Total number of nonclassifiable contribution amount = ', len(contribution_df[contribution_df['TotalAmount'] == 0]))\n", + "print('Total number of nonclassifiable recipients = ', contribution_df['RegNumb'].isna().sum())\n", + "print('Total number of nonclassifiable donors = ', contribution_df['DonorName'].isna().sum())" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "1. 467 of the 'nan' contributions belong to \"Registration Fee for Netroots Event\", which is a non-profit organization that provides trainings, resources and connection opportunities to help progressive activists. Based on their column values, these contributions have no monetary amount, no donor, and no recipient.\n", - "2. 483861 nan values are mostly those recipients are non-candidates" + "1. 467 of the 'nan' contributions belong to \"Registration Fee for Netroots Event\", which is a non-profit organization that help progressive activists. Based on their column values, these contributions have no monetary amount, no donor, and no recipient.\n", + "2. 483861 nan OfficeSought are mostly those recipients are non-candidates\n", + "3. Contribution entries with no contribution amount, recipient information, or donor information should be dropped" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.5 Drop Non-classifiable Contribution Data" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "contribution_df = contribution_df[contribution_df['TotalAmount'] != 0]\n", + "contribution_df = contribution_df.dropna(subset=['RegNumb', 'DonorName'], how='any')\n", + "contribution_df = contribution_df.reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Contribution entries after dropping non-classifiable data = 3212822\n", + "Number of non-classifiable contrinutions = 336051\n" + ] + } + ], + "source": [ + "print('Contribution entries after dropping non-classifiable data = ', len(contribution_df))\n", + "print('Number of non-classifiable contrinutions = ', 3548873-len(contribution_df))" ] }, { @@ -373,43 +454,43 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DonationYear\n", - "1998.0 20678745.70\n", - "1999.0 1545675.89\n", - "2000.0 12322727.60\n", - "2001.0 4163753.21\n", - "2002.0 46600539.55\n", - "2003.0 2926595.94\n", - "2004.0 24702549.81\n", - "2005.0 14655970.48\n", - "2006.0 74537059.95\n", - "2007.0 7312400.10\n", - "2008.0 30794346.71\n", - "2009.0 16390499.70\n", - "2010.0 64319478.61\n", - "2011.0 4859395.88\n", - "2012.0 55252164.36\n", - "2013.0 16482255.60\n", - "2014.0 38667352.15\n", - "2015.0 19816206.40\n", - "2016.0 37338776.71\n", - "2017.0 15943601.05\n", - "2018.0 35105013.46\n", - "2019.0 22417142.37\n", - "2020.0 31573129.27\n", - "2021.0 19326521.95\n", - "2022.0 36476756.74\n", - "2023.0 160433.71\n", + "1998.0 21973071.61\n", + "1999.0 2636233.26\n", + "2000.0 14373458.56\n", + "2001.0 5223945.02\n", + "2002.0 48261465.33\n", + "2003.0 4363080.36\n", + "2004.0 26942236.14\n", + "2005.0 16161501.54\n", + "2006.0 76698607.22\n", + "2007.0 8423719.54\n", + "2008.0 32462700.99\n", + "2009.0 17605765.69\n", + "2010.0 66311709.21\n", + "2011.0 6057089.02\n", + "2012.0 61005414.73\n", + "2013.0 20285780.15\n", + "2014.0 44521142.83\n", + "2015.0 21097676.32\n", + "2016.0 39298419.13\n", + "2017.0 20058310.89\n", + "2018.0 47781848.87\n", + "2019.0 25764905.60\n", + "2020.0 34614419.07\n", + "2021.0 26898778.21\n", + "2022.0 57692246.12\n", + "2023.0 264961.92\n", "Name: TotalAmount, dtype: float64" ] }, - "execution_count": 12, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -421,7 +502,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -436,7 +517,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -481,50 +562,50 @@ " \n", " 252\n", " 2023.0\n", - " Zarth, John\n", - " 2700.00\n", + " Teamsters Local 346\n", + " 3776.37\n", " \n", " \n", " 253\n", " 2023.0\n", - " Zarth, Kelly\n", - " 2700.00\n", + " Johnson, Erin\n", + " 2899.00\n", " \n", " \n", " 254\n", " 2023.0\n", - " Krech, Kathy\n", - " 1800.00\n", + " Teamsters Local 792\n", + " 2897.77\n", " \n", " \n", " 255\n", " 2023.0\n", - " Restemayer, Douglas\n", - " 1585.93\n", + " Zarth, John\n", + " 2700.00\n", " \n", " \n", " 256\n", " 2023.0\n", - " Carlson, Jessica\n", - " 1500.00\n", + " Zarth, Kelly\n", + " 2700.00\n", " \n", " \n", " 257\n", " 2023.0\n", - " Collins, Greg\n", - " 1500.00\n", + " Teamsters Local 970\n", + " 2401.89\n", " \n", " \n", " 258\n", " 2023.0\n", - " Collins, Jane\n", - " 1500.00\n", + " Teamsters Local 974\n", + " 2390.62\n", " \n", " \n", " 259\n", " 2023.0\n", - " Doherty, John\n", - " 1500.00\n", + " Wilbert, Michelle\n", + " 2241.00\n", " \n", " \n", "\n", @@ -534,17 +615,17 @@ " DonationYear DonorName TotalAmount\n", "250 2023.0 IBEW Local 292 11400.11\n", "251 2023.0 SEIU local 26 5194.45\n", - "252 2023.0 Zarth, John 2700.00\n", - "253 2023.0 Zarth, Kelly 2700.00\n", - "254 2023.0 Krech, Kathy 1800.00\n", - "255 2023.0 Restemayer, Douglas 1585.93\n", - "256 2023.0 Carlson, Jessica 1500.00\n", - "257 2023.0 Collins, Greg 1500.00\n", - "258 2023.0 Collins, Jane 1500.00\n", - "259 2023.0 Doherty, John 1500.00" + "252 2023.0 Teamsters Local 346 3776.37\n", + "253 2023.0 Johnson, Erin 2899.00\n", + "254 2023.0 Teamsters Local 792 2897.77\n", + "255 2023.0 Zarth, John 2700.00\n", + "256 2023.0 Zarth, Kelly 2700.00\n", + "257 2023.0 Teamsters Local 970 2401.89\n", + "258 2023.0 Teamsters Local 974 2390.62\n", + "259 2023.0 Wilbert, Michelle 2241.00" ] }, - "execution_count": 20, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -562,20 +643,44 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ - "don_by_year_recipients = contribution_df.groupby(['DonationYear', 'RegNumb', \n", - " 'CandFirstName', 'CandLastName', 'Committee', \n", - " 'RecipientType'])['TotalAmount'].sum().reset_index()\n", + "import numpy as np # Import numpy for handling NaN values\n", + "\n", + "don_by_year_recipients = contribution_df.groupby(['DonationYear', 'RegNumb', 'CandLastName', 'CandFirstName', 'Committee', 'RecipientType'])['TotalAmount'].sum().reset_index()\n", + "\n", + "don_by_year_recipients['CandLastName'] = np.nan\n", + "don_by_year_recipients['CandFirstName'] = np.nan\n", + "\n", + "don_by_year_recipients.loc[don_by_year_recipients['RecipientType'] == 'Candidate', 'CandLastName'] = don_by_year_recipients['CandLastName']\n", + "don_by_year_recipients.loc[don_by_year_recipients['RecipientType'] == 'Candidate', 'CandFirstName'] = don_by_year_recipients['CandFirstName']\n", + "\n", "top_10_recipients = don_by_year_recipients.groupby('DonationYear').apply(\n", - " lambda group: group.nlargest(10, 'TotalAmount')).reset_index(drop=True)" + " lambda group: group.nlargest(10, 'TotalAmount')).reset_index(drop=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "contribution_df1 = contribution_df.copy(deep=True)\n", + "contribution_df1['CandLastName'].fillna('NA', inplace=True)\n", + "contribution_df1['CandFirstName'].fillna('NA', inplace=True)\n", + "\n", + "don_by_year_recipients = contribution_df1.groupby(\n", + " ['DonationYear', 'RegNumb', 'RecipientType', 'CandLastName', 'CandFirstName'])['TotalAmount'].sum().reset_index()\n", + "\n", + "top_10_recipient = don_by_year_recipients.groupby('DonationYear').apply(\n", + " lambda group: group.nlargest(10, 'TotalAmount')).reset_index(drop=True)" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -601,151 +706,140 @@ " \n", " DonationYear\n", " RegNumb\n", - " CandFirstName\n", - " CandLastName\n", - " Committee\n", " RecipientType\n", + " CandLastName\n", + " CandFirstName\n", " TotalAmount\n", " \n", " \n", " \n", " \n", - " 244\n", - " 2022.0\n", - " 13262.0\n", - " Warren\n", - " Limmer\n", - " Limmer (Warren) for Senate Committee\n", - " Candidate\n", - " 454275.00\n", + " 250\n", + " 2023.0\n", + " 41291.0\n", + " PCF\n", + " NA\n", + " NA\n", + " 32650.00\n", " \n", " \n", - " 245\n", - " 2022.0\n", - " 10601.0\n", - " Mary\n", - " Murphy\n", - " Mary Murphy Volunteer Committee\n", + " 251\n", + " 2023.0\n", + " 18690.0\n", " Candidate\n", - " 452742.21\n", + " Jensen\n", + " Scott\n", + " 32505.00\n", " \n", " \n", - " 246\n", - " 2022.0\n", - " 12604.0\n", - " Gregory\n", - " Davids\n", - " People for (Gregory) Davids Committee\n", - " Candidate\n", - " 402975.00\n", + " 252\n", + " 2023.0\n", + " 30689.0\n", + " PCF\n", + " NA\n", + " NA\n", + " 29900.00\n", " \n", " \n", - " 247\n", - " 2022.0\n", - " 17105.0\n", - " Carla\n", - " Nelson\n", - " Nelson (Carla) for Senate\n", - " Candidate\n", - " 400540.00\n", + " 253\n", + " 2023.0\n", + " 41345.0\n", + " PCF\n", + " NA\n", + " NA\n", + " 28371.96\n", " \n", " \n", - " 248\n", - " 2022.0\n", - " 18732.0\n", - " Kim\n", - " Crockett\n", - " Kim Crockett for Secretary of State Committee\n", - " Candidate\n", - " 362551.26\n", + " 254\n", + " 2023.0\n", + " 30726.0\n", + " PCF\n", + " NA\n", + " NA\n", + " 28000.00\n", " \n", " \n", - " 249\n", - " 2022.0\n", - " 17373.0\n", - " Torrey\n", - " Westrom\n", - " Westrom (Torrey) for Senate Committee\n", - " Candidate\n", - " 358200.00\n", + " 255\n", + " 2023.0\n", + " 30013.0\n", + " PCF\n", + " NA\n", + " NA\n", + " 18072.03\n", " \n", " \n", - " 250\n", + " 256\n", " 2023.0\n", - " 18690.0\n", - " Scott\n", - " Jensen\n", - " Dr. Scott Jensen for Governor\n", - " Candidate\n", - " 32505.00\n", + " 30345.0\n", + " PCF\n", + " NA\n", + " NA\n", + " 12000.00\n", " \n", " \n", - " 251\n", + " 257\n", " 2023.0\n", - " 18708.0\n", - " Leslie\n", - " Lienemann\n", - " Leslie for Minnesota\n", - " Candidate\n", - " 5400.00\n", + " 41281.0\n", + " PCF\n", + " NA\n", + " NA\n", + " 11832.00\n", " \n", " \n", - " 252\n", + " 258\n", " 2023.0\n", - " 18731.0\n", - " James\n", - " Schultz\n", - " Jim Schultz For Minnesota Attorney General\n", - " Candidate\n", - " 1500.00\n", + " 41262.0\n", + " PCF\n", + " NA\n", + " NA\n", + " 11805.00\n", " \n", " \n", - " 253\n", + " 259\n", " 2023.0\n", - " 18973.0\n", - " Beth\n", - " Beebe\n", - " Beth Beebe for State Rep\n", - " Candidate\n", - " 1000.00\n", + " 30119.0\n", + " PCF\n", + " NA\n", + " NA\n", + " 11400.11\n", " \n", " \n", "\n", "" ], "text/plain": [ - " DonationYear RegNumb CandFirstName CandLastName \\\n", - "244 2022.0 13262.0 Warren Limmer \n", - "245 2022.0 10601.0 Mary Murphy \n", - "246 2022.0 12604.0 Gregory Davids \n", - "247 2022.0 17105.0 Carla Nelson \n", - "248 2022.0 18732.0 Kim Crockett \n", - "249 2022.0 17373.0 Torrey Westrom \n", - "250 2023.0 18690.0 Scott Jensen \n", - "251 2023.0 18708.0 Leslie Lienemann \n", - "252 2023.0 18731.0 James Schultz \n", - "253 2023.0 18973.0 Beth Beebe \n", + " DonationYear RegNumb RecipientType CandLastName CandFirstName \\\n", + "250 2023.0 41291.0 PCF NA NA \n", + "251 2023.0 18690.0 Candidate Jensen Scott \n", + "252 2023.0 30689.0 PCF NA NA \n", + "253 2023.0 41345.0 PCF NA NA \n", + "254 2023.0 30726.0 PCF NA NA \n", + "255 2023.0 30013.0 PCF NA NA \n", + "256 2023.0 30345.0 PCF NA NA \n", + "257 2023.0 41281.0 PCF NA NA \n", + "258 2023.0 41262.0 PCF NA NA \n", + "259 2023.0 30119.0 PCF NA NA \n", "\n", - " Committee RecipientType TotalAmount \n", - "244 Limmer (Warren) for Senate Committee Candidate 454275.00 \n", - "245 Mary Murphy Volunteer Committee Candidate 452742.21 \n", - "246 People for (Gregory) Davids Committee Candidate 402975.00 \n", - "247 Nelson (Carla) for Senate Candidate 400540.00 \n", - "248 Kim Crockett for Secretary of State Committee Candidate 362551.26 \n", - "249 Westrom (Torrey) for Senate Committee Candidate 358200.00 \n", - "250 Dr. Scott Jensen for Governor Candidate 32505.00 \n", - "251 Leslie for Minnesota Candidate 5400.00 \n", - "252 Jim Schultz For Minnesota Attorney General Candidate 1500.00 \n", - "253 Beth Beebe for State Rep Candidate 1000.00 " + " TotalAmount \n", + "250 32650.00 \n", + "251 32505.00 \n", + "252 29900.00 \n", + "253 28371.96 \n", + "254 28000.00 \n", + "255 18072.03 \n", + "256 12000.00 \n", + "257 11832.00 \n", + "258 11805.00 \n", + "259 11400.11 " ] }, - "execution_count": 23, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "top_10_recipients[-10:]" + "top_10_recipient[-10:] # Top 10 recipients in 2023" ] }, { @@ -761,6 +855,26 @@ "cell_type": "code", "execution_count": 29, "metadata": {}, + "outputs": [], + "source": [ + "donor_type_mapping = {\n", + " 'B': 'Business',\n", + " 'C': 'Candidate committee',\n", + " 'F': 'Political committee or fund',\n", + " 'H': 'Local candidate committee registered with Hennepin County',\n", + " 'I': 'Non-lobbyist individual',\n", + " 'L': 'Lobbyist',\n", + " 'O': 'Other',\n", + " 'P': 'Political party unit',\n", + " 'S': 'Self',\n", + " 'U': 'Association not registered with the Board'\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, "outputs": [ { "data": { @@ -771,166 +885,82 @@ "data": [ { "alignmentgroup": "True", - "hovertemplate": "DonorType=C
Year=%{x}
Total Contributions=%{y}", - "legendgroup": "C", + "hovertemplate": "Donor Type=Association not registered with the Board
Year=%{x}
Total Contributions=%{y}", + "legendgroup": "Association not registered with the Board", "marker": { "color": "#636efa", "pattern": { "shape": "" } }, - "name": "C", - "offsetgroup": "C", + "name": "Association not registered with the Board", + "offsetgroup": "Association not registered with the Board", "orientation": "v", "showlegend": true, "textposition": "auto", "type": "bar", "x": [ - 1998, - 1999, - 2000, - 2001, - 2002, - 2003, - 2004, - 2005, - 2006, - 2007, - 2008, - 2009, 2010, 2011, 2012, - 2013, - 2014, - 2015, 2016, - 2017, - 2018, - 2019, - 2020, - 2021, - 2022, - 2023 + 2022 ], "xaxis": "x", "y": [ - 101165.07, - 23161.48, - 123585.23, - 34683.92, - 875369.27, - 144841.46, - 201628.59, - 111884.62, - 677127.06, - 149827.61, - 261344.91, - 66096, - 595557.84, - 93032.06, - 883385.7, - 101935.83, - 313720.37, - 96548.44, - 438298.21, - 151570.56, - 244330.1, - 130207.08, - 499791.93, - 59729.81, - 579718.88, - 1310 + 3706.25, + 6989.52, + 107.8, + 818.9, + 1067.47 ], "yaxis": "y" }, { "alignmentgroup": "True", - "hovertemplate": "DonorType=F
Year=%{x}
Total Contributions=%{y}", - "legendgroup": "F", + "hovertemplate": "Donor Type=Business
Year=%{x}
Total Contributions=%{y}", + "legendgroup": "Business", "marker": { "color": "#EF553B", "pattern": { "shape": "" } }, - "name": "F", - "offsetgroup": "F", + "name": "Business", + "offsetgroup": "Business", "orientation": "v", "showlegend": true, "textposition": "auto", "type": "bar", "x": [ - 1998, - 1999, - 2000, - 2001, - 2002, - 2003, - 2004, - 2005, - 2006, - 2007, - 2008, - 2009, 2010, 2011, 2012, - 2013, - 2014, - 2015, - 2016, 2017, - 2018, - 2019, - 2020, - 2021, - 2022, - 2023 + 2019 ], "xaxis": "x", "y": [ - 874038.4, - 353960.8, - 5752634.26, - 153000, - 15896701.44, - 388826.72, - 8869075.79, - 546387.08, - 17585369.81, - 322875.56, - 9827145.33, - 1105737.06, - 16669462.41, - 751576.11, - 17429604.89, - 3998262.37, - 8290657.34, - 6999965.04, - 9714716.51, - 4599634.52, - 7093338.43, - 8325174.12, - 6655345.51, - 5662421.12, - 5981805.37, - 0 + 5700, + 4892.66, + 400, + 500, + 250 ], "yaxis": "y" }, { "alignmentgroup": "True", - "hovertemplate": "DonorType=H
Year=%{x}
Total Contributions=%{y}", - "legendgroup": "H", + "hovertemplate": "Donor Type=Candidate committee
Year=%{x}
Total Contributions=%{y}", + "legendgroup": "Candidate committee", "marker": { "color": "#00cc96", "pattern": { "shape": "" } }, - "name": "H", - "offsetgroup": "H", + "name": "Candidate committee", + "offsetgroup": "Candidate committee", "orientation": "v", "showlegend": true, "textposition": "auto", @@ -952,48 +982,60 @@ 2011, 2012, 2013, + 2014, + 2015, + 2016, 2017, 2018, + 2019, + 2020, + 2021, 2022, 2023 ], "xaxis": "x", "y": [ - 0, - 0, - 17500, - 0, - 10000, - 0, - 12564.24, - 5500, - 4700, - 1050, - 0, - 0, - 0, - 523.76, - 1450, - 350, - 0, - 0, - 1750, - 290 + 191422.86, + 51438.77, + 190885.96, + 64955.08, + 947533.25, + 188787.5, + 229301.8, + 132074.39, + 722714.8, + 190084.9, + 303212.64, + 113900.6, + 659743.72, + 127841.39, + 933861.06, + 137224.96, + 340672.7, + 112969.44, + 469611.57, + 171160.74, + 294413.35, + 151774.38, + 520202, + 84590.11, + 641245.11, + 1810 ], "yaxis": "y" }, { "alignmentgroup": "True", - "hovertemplate": "DonorType=I
Year=%{x}
Total Contributions=%{y}", - "legendgroup": "I", + "hovertemplate": "Donor Type=Lobbyist
Year=%{x}
Total Contributions=%{y}", + "legendgroup": "Lobbyist", "marker": { "color": "#ab63fa", "pattern": { "shape": "" } }, - "name": "I", - "offsetgroup": "I", + "name": "Lobbyist", + "offsetgroup": "Lobbyist", "orientation": "v", "showlegend": true, "textposition": "auto", @@ -1028,47 +1070,47 @@ ], "xaxis": "x", "y": [ - 18748873.89, - 921745.66, - 5452707.41, - 3505630.62, - 26466335.08, - 1765413.11, - 13379361.23, - 13049482.36, - 51094838.36, - 6063446.73, - 17807916.21, - 13597212.48, - 41229418.45, - 3176583.87, - 31025897.99, - 10788950.52, - 26178068.25, - 10575649.9, - 21735816.57, - 9566865.55, - 23473515.99, - 10916317.58, - 20736221.94, - 10726359.58, - 26453708.4, - 79788.53 + 831806.69, + 136725.59, + 532388.64, + 138253, + 1496998.96, + 170506.87, + 602606.98, + 377230.73, + 2625401.72, + 185081.66, + 1139728.76, + 1021329.85, + 2919676.27, + 454099.02, + 2035057.01, + 1243087.49, + 1539565.21, + 1716511.33, + 1821964.77, + 1480548.64, + 1901804.56, + 2715933.95, + 1400427.52, + 2804633.25, + 1953755.2, + 2950 ], "yaxis": "y" }, { "alignmentgroup": "True", - "hovertemplate": "DonorType=L
Year=%{x}
Total Contributions=%{y}", - "legendgroup": "L", + "hovertemplate": "Donor Type=Local candidate committee registered with Hennepin County
Year=%{x}
Total Contributions=%{y}", + "legendgroup": "Local candidate committee registered with Hennepin County", "marker": { "color": "#FFA15A", "pattern": { "shape": "" } }, - "name": "L", - "offsetgroup": "L", + "name": "Local candidate committee registered with Hennepin County", + "offsetgroup": "Local candidate committee registered with Hennepin County", "orientation": "v", "showlegend": true, "textposition": "auto", @@ -1079,7 +1121,6 @@ 2000, 2001, 2002, - 2003, 2004, 2005, 2006, @@ -1090,60 +1131,45 @@ 2011, 2012, 2013, - 2014, - 2015, - 2016, - 2017, 2018, - 2019, - 2020, - 2021, 2022, 2023 ], "xaxis": "x", "y": [ - 783979.34, - 92600.5, - 450977.64, - 83050, - 1413723.64, - 115946.09, - 521522.73, - 322000.73, - 2493187.14, - 111095.75, - 1051147.23, - 947431.87, - 2762207.11, - 329047.02, - 1723744.28, - 941308.56, - 1136755.32, - 1583008.44, - 1596389.48, - 1138837.82, - 1282871.59, - 2390865.47, - 1248921.02, - 2432257.7, - 1466847.45, - 1750 + 2675, + 3387.49, + 19949, + 13120.86, + 19446.15, + 26883.76, + 17565.47, + 11354.55, + 1050, + 2760.6, + 3950, + 4300.5, + 1602.51, + 1600, + 350, + 650, + 6750, + 1490 ], "yaxis": "y" }, { "alignmentgroup": "True", - "hovertemplate": "DonorType=O
Year=%{x}
Total Contributions=%{y}", - "legendgroup": "O", + "hovertemplate": "Donor Type=Non-lobbyist individual
Year=%{x}
Total Contributions=%{y}", + "legendgroup": "Non-lobbyist individual", "marker": { "color": "#19d3f3", "pattern": { "shape": "" } }, - "name": "O", - "offsetgroup": "O", + "name": "Non-lobbyist individual", + "offsetgroup": "Non-lobbyist individual", "orientation": "v", "showlegend": true, "textposition": "auto", @@ -1178,47 +1204,47 @@ ], "xaxis": "x", "y": [ - 9893, - 0, - 9150, - 1181.35, - 29598.76, - 13516.55, - 22925, - 1500, - 138574.16, - 23519.06, - 16761.44, - 32357.2, - 22834.71, - 32751.23, - 337984.51, - 196941.45, - 206449.46, - 153039.95, - 169587.23, - 148305.29, - 206573.79, - 154864.65, - 180167.75, - 169433.73, - 228749.14, - 75417.65 + 19650402.25, + 1821021.11, + 7091200.17, + 4344698.68, + 27654673.18, + 2942139.6, + 15231963.37, + 14315307.91, + 52778825.83, + 6903261.94, + 19071724.71, + 14468593.4, + 42482854.7, + 4035126.22, + 35690989.86, + 13329157.91, + 30719469.25, + 10971190.26, + 23222674.88, + 13110087.07, + 34883943.13, + 13551729.61, + 23319309.17, + 17640251.09, + 46536784.82, + 162987.49 ], "yaxis": "y" }, { "alignmentgroup": "True", - "hovertemplate": "DonorType=P
Year=%{x}
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}, - "xaxis": { - "anchor": "y", - "domain": [ - 0, - 1 - ], - "title": { - "text": "DonorType" - } - }, - "yaxis": { - "anchor": "x", - "domain": [ - 0, - 1 - ], - "title": { - "text": "TotalAmount" - } - } - } - }, - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for year in year_lst:\n", - " donor_bar_chart(year)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.2 Analyze yearly trend of donations by donor type" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "1. From 1998 to 2023, individual donors consistently contribute the majority of the total donation amount, surpassing all other donor types in nearly every year except for year 2000.\n", - "2. Across the years, donor types \"S\", \"C\" and \"O\"continue to remain low, i.e., donations made by candidates themselves and by candidate committees are comparatively very small in amount.\n", - "3. Donations made by \"Fund\" exhibited notably higher contributions in 2000, 2002, 2004, 2015, and 2019 when compared to other years, nearly rivaling the largest donor type, \"Individual.\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.3 Compare donation by recipient type" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " DonationYear OfficeSought TotalAmount\n", - "0 1998.0 AG 5124359.38\n", - "1 1998.0 DC 78246.72\n", - "2 1998.0 GC 13896258.03\n", - "3 1998.0 SA 454494.25\n", - "4 1998.0 SC 41847.32\n", - "5 1998.0 SS 502730.35\n", - "6 1998.0 ST 129142.31\n", - "7 1998.0 Senate 24686.20\n", - " DonationYear OfficeSought TotalAmount\n", - "8 1999.0 AG 154813.00\n", - "9 1999.0 DC 0.00\n", - "10 1999.0 GC 619423.40\n", - "11 1999.0 SA 1930.75\n", - "12 1999.0 SC 0.00\n", - "13 1999.0 SS 15175.35\n", - "14 1999.0 ST 1930.75\n", - "15 1999.0 Senate 646440.22\n", - " DonationYear OfficeSought TotalAmount\n", - "16 2000.0 AG 105675.00\n", - "17 2000.0 AP 9300.00\n", - "18 2000.0 DC 173677.29\n", - "19 2000.0 GC 605720.75\n", - "20 2000.0 SC 754310.92\n", - "21 2000.0 SS 0.00\n", - "22 2000.0 Senate 10623934.92\n", - " DonationYear OfficeSought TotalAmount\n", - "23 2001.0 AG 365084.18\n", - "24 2001.0 DC 1350.00\n", - "25 2001.0 GC 3287971.52\n", - "26 2001.0 House 323093.23\n", - "27 2001.0 SA 3750.00\n", - "28 2001.0 SC 3600.00\n", - "29 2001.0 SS 0.00\n", - "30 2001.0 Senate 157660.77\n", - " DonationYear OfficeSought TotalAmount\n", - "31 2002.0 AG 1774431.00\n", - "32 2002.0 AP 11562.98\n", - "33 2002.0 DC 449117.40\n", - "34 2002.0 GC 10143972.36\n", - "35 2002.0 House 15549229.25\n", - "36 2002.0 SA 1373262.28\n", - "37 2002.0 SC 350150.00\n", - "38 2002.0 SS 1168041.00\n", - "39 2002.0 Senate 15447972.41\n", - " DonationYear OfficeSought TotalAmount\n", - "40 2003.0 AG 148775.00\n", - "41 2003.0 DC 12176.43\n", - "42 2003.0 GC 1058562.27\n", - 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"text": "Donations by recipient type for year 2023" - }, - "xaxis": { - "anchor": "y", - "domain": [ - 0, - 1 - ], - "title": { - "text": "OfficeSought" - } - }, - "yaxis": { - "anchor": "x", - "domain": [ - 0, - 1 - ], - "title": { - "text": "TotalAmount" - } - } - } - }, - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for year in year_lst:\n", - " recipient_bar_chart(year)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.4 Analyze yearly trend of donations by recipient type" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- Attorney General: Donations were moderately high in 1998 and 1999 but consistently remained low in subsequent years.\n", - "\n", - "- District Court: This recipient type consistently received very low contributions, likely due to an overall lower contribution count.\n", - "\n", - "- State Appeals Court Judge: In several years, this recipient type did not receive any contributions, and in other years, the amounts remained extremely low.\n", - "\n", - "- Governor: Donation amounts were significantly high in 1998, 1999, 2001, 2005, 2006, 2007, 2009, 2010, and 2011. They were moderately high in 2004 and 2008, while the rest of the years saw relatively low contributions.\n", - "\n", - "- Years with very large donations overall:\n", - " - In 2010, the Governor, House, and Senate received very high donations.\n", - " - In 2011, the Governor and Senate received very high donations.\n", - " - In 2012, the House and Senate received very high donations.\n", - " - In 2015, 2016, 2019, 2020, 2021, and 2022, the House and Senate received high donation amounts." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "base", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/README.md b/notebooks/README.md index 3d3aeed..747907e 100644 --- a/notebooks/README.md +++ b/notebooks/README.md @@ -4,21 +4,7 @@ This should contain information about what is done in each notebook * `Test.ipynb` : This is a test notebook to demonstrate how to use this repository. -MN EDA -File Name: -- MN_EDA: The clear jupyter notebook for MN EDA -- MN_util: the util file that stores necessary preprocessing functions in MN_EDA - -1. 10 datasets on candidate-recipient contributions and 1 dataset on non-candidate-recipient contributions. They are seperate and not relational. -2. Clean dataset column: OfficeSought (object), RegNumb (float64), CandFirstName (object), CandLastName (object), Committee (object), DonationDate (datetime64), DonorType (object), DonorName (object), DonationAmount (float64), InKindDonAmount, InKindDescriptionText (object), RecipientType (object), DonationYear (float64), TotalAmount (float64) -3. Top 10 contributors in 2023: IBEW Local 292, SEIU local 26, Zarth John, Zarth Kelly, Krech Kathy, Restemayer Douglas, Carlson Jessica, Collins Greg, Collins Jane, and Doherty John. Top 10 recipients in 2023: Warren Limmer, Mary Murphy, Gregory Davids, Carla Nelson, Kim Crockett, Torrey Westrom, Scott Jensen, Leslie Lienemann, James Schultz, and Beth Beebe. -4. Yearly Trends: -- Individuals, excluding lobbyists, constitute the largest share of contributions in the MN dataset. -- The second most substantial contributor category is General Purpose Political Committee or Fund, followed by lobbyists. -- Contributions from other donor types are notably lower throughout the years. -- Analyzing a sample from 2018 to 2022, we observe a cyclical pattern with a major increase in contributions, followed by three years of reduced contribution totals. This cycle aligns with the four-year election cycle. -- From 1998 to 2023, there are several years with significantly lower contribution amount: 1999, 2001, 2003, 2007, 2011. - -- Candidates, as the recipients, make up the overwhelming majority of contributions. -- Examining the period from 1998 to 2023, a distinct cyclical pattern emerges, characterized by alternating years of increased and decreased contributions, which may correspond to congressional elections or MN state house representatives elections which take place every two years. -- Starting in 2012, recipient types "Political Committee or Fund" and "Political Party Unit" began receiving a larger share of contributions compared to prior years. +MN_EDA.ipynb +- The clear jupyter notebook for MN data preprocessing and EDA +- Column include recipient and donor information, and contribution trasaction details +- EDA shows the yearly top 10 recipients and contributors, yearly contribution trends by donor types and by candidate recipient race types diff --git a/utils/MN_util.py b/utils/MN_util.py index 360e8a5..3982514 100644 --- a/utils/MN_util.py +++ b/utils/MN_util.py @@ -1,14 +1,15 @@ -from pathlib import Path -import numpy as np import pandas as pd def datasets_col_consistent(df_lst): ''' - Check if a list of DataFrames have the same columns for future merge - Return: nothing, just print out the result for checking column consistency + This function checks if a list of DataFrames have the same columns/features + Args: + df_lst (list): a list of DataFrames whose columns will be checked + Returns: + Nothing, print out the checking result for column consistency ''' - + previous_columns = df_lst[0].columns consistent_col_count = 1 @@ -23,26 +24,35 @@ def datasets_col_consistent(df_lst): def standardize_cand_df(df): ''' - To preprocess all the candidate-recipient contribution datasets such that - they are consistent with non-candidate recipient dataset + This function preprocesses all candidate-recipient contribution dfs + Args: + df (DataFrame): the DataFrames to preprocess + Returns: + DataFrame: Preprocessed contribution df with candidate recipients ''' + df_copy = df.copy(deep=True) columns_to_keep = ['OfficeSought', 'CandRegNumb', 'CandFirstName', - 'CandLastName', 'CommitteeName', 'DonationDate', - 'DonorType', 'DonorName', 'DonationAmount', + 'CandLastName', 'CommitteeName', 'DonationDate', + 'DonorType', 'DonorName', 'DonationAmount', 'InKindDonAmount', 'InKindDescriptionText'] df_copy = df_copy[columns_to_keep] column_mapping = {'CandRegNumb': 'RegNumb', 'CommitteeName': 'Committee'} df_copy.rename(columns=column_mapping, inplace=True) df_copy['RecipientType'] = 'Candidate' + return df_copy def standardize_noncand_df(df): - ''' - To preprocess non-candidate-recipient contribution dataset such that we - can merge all candidate-recipient and non-candidate-recipient datasets ''' + This function preprocesses the non-candidate-recipient contribution df + Args: + df (DataFrame): the DataFrames to preprocess + Returns: + DataFrame: Preprocessed contribution df with non-candidate recipients + ''' + df_copy = df.copy(deep=True) columns_to_keep = ['PCFRegNumb', 'Committee', 'ETType', 'DonationDate', 'DonorType', 'DonorName', 'DonationAmount', @@ -50,28 +60,36 @@ def standardize_noncand_df(df): df_copy = df_copy[columns_to_keep] column_mapping = {'PCFRegNumb': 'RegNumb', 'ETType': 'RecipientType'} df_copy.rename(columns=column_mapping, inplace=True) + return df_copy + def preprocess_contribution_df(df_lst): + ''' + This function preprocesses separate dfs into a complete contribution df + Args: + df_lst (list): a list of DataFrames to merge and adjust columns + Returns: + DataFrame: the merged and preprocessed contribution df + ''' + contribution_df = pd.concat(df_lst, ignore_index=True) - + contribution_df['DonationDate'] = pd.to_datetime(contribution_df['DonationDate']) contribution_df['DonationYear'] = contribution_df['DonationDate'].dt.year contribution_df = contribution_df.sort_values(by='DonationYear', ascending=False) - - contribution_df['DonorType'] = contribution_df['DonorType'].replace('b', 'B') - contribution_df['DonorType'] = contribution_df['DonorType'].replace('u', 'U') - contribution_df['DonorType'] = contribution_df['DonorType'].replace('s', 'S') - contribution_df['DonorType'] = contribution_df['DonorType'].replace('c', 'C') - contribution_df['DonorType'] = contribution_df['DonorType'].replace('l', 'L') - contribution_df['DonorType'] = contribution_df['DonorType'].replace('i', 'I') - contribution_df['DonorType'] = contribution_df['DonorType'].replace('h', 'H') - + + contribution_df['DonorType'] = contribution_df['DonorType'].str.upper() + contribution_df['DonationAmount'] = pd.to_numeric( - contribution_df['DonationAmount'], errors='coerce') + contribution_df['DonationAmount'], errors='coerce') + contribution_df['DonationAmount'] = contribution_df['DonationAmount'].fillna(0) + contribution_df['InKindDonAmount'] = pd.to_numeric( contribution_df['InKindDonAmount'], errors='coerce') + contribution_df['InKindDonAmount'] = contribution_df['InKindDonAmount'].fillna(0) + contribution_df['TotalAmount'] = \ contribution_df['DonationAmount'] + contribution_df['InKindDonAmount'] - + return contribution_df \ No newline at end of file diff --git a/utils/README.md b/utils/README.md new file mode 100644 index 0000000..dab5f3b --- /dev/null +++ b/utils/README.md @@ -0,0 +1,13 @@ +### MN Contribution EDA util + +1. datasets_col_consistent + - Check if the input list of DataFrames have the consistent columns/features for later merge + +2. standardize_cand_df + - Standardize those contribution DataFrames whose recipients are only candidates + +3. standardize_noncand_df + - Standardize those contribution DataFrames whose recipients are non-candidates + +4. preprocess_contribution_df + - Preprocess the separate contribution DataFrames by first merging them, adjust the contribution date and create the contribution year column, and calculate total contribution amount by summing up both direct monetary ones and inkind ones From e9ca7647909885633e34d7eefdbd497230b6482b Mon Sep 17 00:00:00 2001 From: yuzhouw313 Date: Sat, 28 Oct 2023 19:31:19 +0000 Subject: [PATCH 05/51] some changes based on Trevor's input on util file --- notebooks/MN_EDA.ipynb | 127 ++++++++++++++++++++++++++++------------- utils/MN_util.py | 28 ++++++--- utils/README.md | 13 +---- 3 files changed, 111 insertions(+), 57 deletions(-) diff --git a/notebooks/MN_EDA.ipynb b/notebooks/MN_EDA.ipynb index bd94f36..40e89b6 100644 --- a/notebooks/MN_EDA.ipynb +++ b/notebooks/MN_EDA.ipynb @@ -154,18 +154,18 @@ "metadata": {}, "outputs": [], "source": [ - "from utils.MN_util import standardize_cand_df\n", + "from utils.MN_util import standardize_candidate_df\n", "\n", - "df_ag = standardize_cand_df(df_ag)\n", - "df_ap = standardize_cand_df(df_ap)\n", - "df_dc = standardize_cand_df(df_dc)\n", - "df_gc = standardize_cand_df(df_gc)\n", - "df_house = standardize_cand_df(df_house)\n", - "df_sa = standardize_cand_df(df_sa)\n", - "df_sc = standardize_cand_df(df_sc)\n", - "df_ss = standardize_cand_df(df_ss)\n", - "df_st = standardize_cand_df(df_st)\n", - "df_senate = standardize_cand_df(df_senate)" + "df_ag = standardize_candidate_df(df_ag)\n", + "df_ap = standardize_candidate_df(df_ap)\n", + "df_dc = standardize_candidate_df(df_dc)\n", + "df_gc = standardize_candidate_df(df_gc)\n", + "df_house = standardize_candidate_df(df_house)\n", + "df_sa = standardize_candidate_df(df_sa)\n", + "df_sc = standardize_candidate_df(df_sc)\n", + "df_ss = standardize_candidate_df(df_ss)\n", + "df_st = standardize_candidate_df(df_st)\n", + "df_senate = standardize_candidate_df(df_senate)" ] }, { @@ -174,8 +174,8 @@ "metadata": {}, "outputs": [], "source": [ - "from utils.MN_util import standardize_noncand_df\n", - "df_non_cand = standardize_noncand_df(df_non_cand)" + "from utils.MN_util import standardize_noncandidate_df\n", + "df_non_cand = standardize_noncandidate_df(df_non_cand)" ] }, { @@ -444,6 +444,26 @@ "print('Number of non-classifiable contrinutions = ', 3548873-len(contribution_df))" ] }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.10459683107249639" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "336051/3212822" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -454,7 +474,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -490,7 +510,7 @@ "Name: TotalAmount, dtype: float64" ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -502,7 +522,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -517,7 +537,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -625,7 +645,7 @@ "259 2023.0 Wilbert, Michelle 2241.00" ] }, - "execution_count": 19, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -643,7 +663,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -663,7 +683,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -680,7 +700,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -833,7 +853,7 @@ "259 11400.11 " ] }, - "execution_count": 22, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -853,7 +873,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -873,9 +893,38 @@ }, { "cell_type": "code", - 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "filtered_df = contribution_df[(contribution_df['DonationYear'] != -1) & (contribution_df['DonationYear'] >= 2018) & (contribution_df['DonationYear'] <= 2022)]\n", + "grouped2 = filtered_df.groupby(['DonationYear', 'DonorType'])['TotalAmount'].sum().reset_index()\n", + "\n", + "grouped2['FullDonorType'] = grouped2['DonorType'].map(donor_type_mapping)\n", + "\n", + "fig = px.bar(\n", + " grouped2,\n", + " x='DonationYear',\n", + " y='TotalAmount',\n", + " color='FullDonorType',\n", + " title='Donations by Donor Type from 2018 to 2022',\n", + " labels={\"DonationYear\": \"Year\", \"TotalAmount\": \"Total Contributions\", \"FullDonorType\": \"Donor Type\"},\n", + " category_orders={\"FullDonorType\": sorted(donor_type_mapping.values())}\n", + ")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "alignmentgroup": "True", + "hovertemplate": "Donor Type=Candidate committee
Year=%{x}
Total Contributions=%{y}", + "legendgroup": "Candidate committee", + "marker": { + "color": "#636efa", + "pattern": { + "shape": "" + } + }, + "name": "Candidate committee", + "offsetgroup": "Candidate committee", + "orientation": "v", + "showlegend": true, + "textposition": "auto", + "type": "bar", + "x": [ + 2018 + ], + "xaxis": "x", + "y": [ + 294413.35 + ], + "yaxis": "y" + }, + { + "alignmentgroup": "True", + "hovertemplate": "Donor Type=Political committee or fund
Year=%{x}
Total Contributions=%{y}", + "legendgroup": "Political committee or fund", + "marker": { + "color": "#EF553B", + "pattern": { + "shape": "" + } + }, + "name": "Political committee or fund", + "offsetgroup": "Political committee or fund", + "orientation": "v", + "showlegend": true, + "textposition": "auto", + "type": "bar", + "x": [ + 2018 + ], + "xaxis": "x", + "y": [ + 7583168.95 + ], + "yaxis": "y" + }, + { + "alignmentgroup": "True", + "hovertemplate": "Donor Type=Local candidate committee registered with Hennepin County
Year=%{x}
Total Contributions=%{y}", + "legendgroup": "Local candidate committee registered with Hennepin County", + "marker": { + "color": "#00cc96", + "pattern": { + "shape": "" + } + }, + "name": "Local candidate committee registered with Hennepin County", + "offsetgroup": "Local candidate committee registered with Hennepin County", + "orientation": "v", + "showlegend": true, + "textposition": "auto", + "type": "bar", + "x": [ + 2018 + ], + "xaxis": "x", + "y": [ + 650 + ], + "yaxis": "y" + }, + { + "alignmentgroup": "True", + "hovertemplate": "Donor Type=Non-lobbyist individual
Year=%{x}
Total Contributions=%{y}", + "legendgroup": "Non-lobbyist individual", + "marker": { + "color": "#ab63fa", + "pattern": { + "shape": "" + } + }, + "name": "Non-lobbyist individual", + "offsetgroup": "Non-lobbyist individual", + "orientation": "v", + "showlegend": true, + "textposition": "auto", + "type": "bar", + "x": [ + 2018 + ], + "xaxis": "x", + "y": [ + 34883943.13 + ], + "yaxis": "y" + }, + { + "alignmentgroup": "True", + "hovertemplate": "Donor Type=Lobbyist
Year=%{x}
Total Contributions=%{y}", + "legendgroup": "Lobbyist", + "marker": { + "color": "#FFA15A", + "pattern": { + "shape": "" + } + }, + "name": "Lobbyist", + "offsetgroup": "Lobbyist", + "orientation": "v", + "showlegend": true, + "textposition": "auto", + "type": "bar", + "x": [ + 2018 + ], + "xaxis": "x", + "y": [ + 1901804.56 + ], + "yaxis": "y" + }, + { + "alignmentgroup": "True", + "hovertemplate": "Donor Type=Other
Year=%{x}
Total Contributions=%{y}", + "legendgroup": "Other", + "marker": { + "color": "#19d3f3", + "pattern": { + "shape": "" + } + }, + "name": "Other", + "offsetgroup": "Other", + "orientation": "v", + "showlegend": true, + "textposition": "auto", + "type": "bar", + "x": [ + 2018 + ], + "xaxis": "x", + "y": [ + 222136.57 + ], + "yaxis": "y" + }, + { + "alignmentgroup": "True", + "hovertemplate": "Donor Type=Political party unit
Year=%{x}
Total Contributions=%{y}", + "legendgroup": "Political party unit", + "marker": { + "color": "#FF6692", + "pattern": { + "shape": "" + } + }, + "name": "Political party unit", + "offsetgroup": "Political party unit", + "orientation": "v", + "showlegend": true, + "textposition": "auto", + "type": "bar", + "x": [ + 2018 ], "xaxis": "x", "y": [ - 211002.97, - 165681.29, - 524674.66, - 373608.35, - 1903961.25, - 495855.01, - 1675081.16, - 591051, - 2378429.11, - 623469.58, - 1824218.38, - 625034.6, - 2890677.25, - 437927.2, - 3619051.89, - 418929.47, - 2280464.95, - 269781.66, - 3405604.04, - 192348.39, - 2561953.7, - 343542.51, - 1993899.57, - 222952.33, - 1624652.29, - 877.53 + 2561953.7 ], "yaxis": "y" }, @@ -1510,7 +3814,7 @@ "hovertemplate": "Donor Type=Self
Year=%{x}
Total Contributions=%{y}", "legendgroup": "Self", "marker": { - "color": "#FECB52", + "color": "#B6E880", "pattern": { "shape": "" } @@ -1522,61 +3826,11 @@ "textposition": "auto", "type": "bar", "x": [ - 1998, - 1999, - 2000, - 2001, - 2002, - 2003, - 2004, - 2005, - 2006, - 2007, - 2008, - 2009, - 2010, - 2011, - 2012, - 2013, - 2014, - 2015, - 2016, - 2017, - 2018, - 2019, - 2020, - 2021, - 2022, - 2023 + 2018 ], "xaxis": "x", "y": [ - 14744.62, - 7699.16, - 48541.31, - 29905.82, - 108430.18000000001, - 27935.71, - 74677.57, - 52240.72, - 200581.4, - 42415.47, - 53194.38, - 40107.78, - 216570.02, - 44853.49, - 272845.7, - 44589.52, - 300589.7, - 150913.03, - 307032.75, - 174261.81, - 333696.61, - 192777.78, - 327697.4, - 96724.28, - 268027.56, - 1000 + 333696.61 ], "yaxis": "y" } @@ -2406,10 +4660,14 @@ } }, "title": { - "text": "Donations by Donor Type from 1998 to 2022" + "text": "Donations by Donor Type in 2018" }, "xaxis": { "anchor": "y", + "categoryarray": [ + 2018 + ], + "categoryorder": "array", "domain": [ 0, 1 @@ -2431,9 +4689,9 @@ } }, "text/html": [ - "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "grouped = df_expenditure.groupby(['Year', 'Spender type'])['TotalAmount'].sum().reset_index()\n", + "grouped['Full Spender Type'] = grouped['Spender type'].map(spendor_type_mapping)\n", + "\n", + "fig = px.bar(\n", + " grouped,\n", + " x='Year',\n", + " y='TotalAmount',\n", + " color='Full Spender Type',\n", + " title='Expenditure by Spender Type from 2015 to 2022',\n", + " labels={\"Year\": \"Year\", \"TotalAmount\": \"Total Expenditure Log\", \"Full Spender Type\": \"Spender type\"},\n", + " category_orders={\"Full Spender Type\": sorted(spendor_type_mapping.values())}\n", + ")\n", + "fig.update_yaxes(type='log')\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Year int64\n", + "Spender Reg Num int64\n", + "Spender Name object\n", + "Spender type object\n", + "Spender sub-type object\n", + "Vendor name object\n", + "Vendor state object\n", + "Vendor city object\n", + "Amount float64\n", + "Unpaid amount float64\n", + "In-kind? object\n", + "In-kind descr object\n", + "Type object\n", + "Purpose object\n", + "Affected committee name object\n", + "Affected committee reg num float64\n", + "TotalAmount float64\n", + "dtype: object" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_expenditure.dtypes" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From caef88282fac27635642f793e8f201c30ba677b0 Mon Sep 17 00:00:00 2001 From: yuzhouw313 Date: Tue, 7 Nov 2023 17:20:38 +0000 Subject: [PATCH 08/51] addressed Mon Avery's feedback and combined con and exp --- notebooks/MN_EDA.ipynb | 5941 +++++++++++++++++++++--------------- notebooks/MN_exp_EDA.ipynb | 2451 --------------- utils/MN_util.py | 139 +- 3 files changed, 3612 insertions(+), 4919 deletions(-) delete mode 100644 notebooks/MN_exp_EDA.ipynb diff --git a/notebooks/MN_EDA.ipynb b/notebooks/MN_EDA.ipynb index 2c847f2..92ca2b0 100644 --- a/notebooks/MN_EDA.ipynb +++ b/notebooks/MN_EDA.ipynb @@ -12,6 +12,13 @@ "warnings.filterwarnings('ignore')" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MN Contribution EDA" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -59,37 +66,29 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "13\n", - "13\n", - "13\n", - "13\n", - "13\n", - "13\n", - "13\n", - "13\n", - "13\n", - "13\n", - "11\n" + "[13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 11]\n" ] } ], "source": [ "# First check for DataFrames' column numbers\n", "df_lst = [df_ag, df_ap, df_dc, df_gc, df_house, df_sa, df_sc, df_ss, df_st, df_senate, df_non_cand]\n", + "df_lens = []\n", "for df in df_lst:\n", - " print(df.shape[1])" + " df_lens.append(df.shape[1])\n", + "print(df_lens)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -115,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -132,7 +131,7 @@ " dtype='object'))" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -150,7 +149,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -170,7 +169,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -180,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -194,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -204,7 +203,7 @@ " dtype=object)" ] }, - "execution_count": 10, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -215,30 +214,30 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "OfficeSought object\n", - "RegNumb int64\n", - "CandFirstName object\n", - "CandLastName object\n", - "Committee object\n", - "DonationDate datetime64[ns]\n", - "DonorType object\n", - "DonorName object\n", - "DonationAmount float64\n", - "InKindDonAmount float64\n", - "InKindDescriptionText object\n", - "RecipientType object\n", - "DonationYear int64\n", - "TotalAmount float64\n", + "OfficeSought object\n", + "RegNumb int64\n", + "CandFirstName object\n", + "CandLastName object\n", + "Committee object\n", + "Date datetime64[ns]\n", + "DonorType object\n", + "DonorName object\n", + "Amount float64\n", + "InKindAmount float64\n", + "InKindDescription object\n", + "RecipientType object\n", + "Year int64\n", + "TotalAmount float64\n", "dtype: object" ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -266,7 +265,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -275,7 +274,7 @@ "array(['Candidate', 'PCF', 'PTU'], dtype=object)" ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -286,7 +285,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -296,7 +295,7 @@ " 'ST'], dtype=object)" ] }, - "execution_count": 13, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -336,30 +335,30 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "OfficeSought 483861\n", - "RegNumb 0\n", - "CandFirstName 483861\n", - "CandLastName 483861\n", - "Committee 467\n", - "DonationDate 536\n", - "DonorType 580\n", - "DonorName 477\n", - "DonationAmount 0\n", - "InKindDonAmount 0\n", - "InKindDescriptionText 3508947\n", - "RecipientType 0\n", - "DonationYear 0\n", - "TotalAmount 0\n", + "OfficeSought 483861\n", + "RegNumb 0\n", + "CandFirstName 483861\n", + "CandLastName 483861\n", + "Committee 467\n", + "Date 536\n", + "DonorType 580\n", + "DonorName 477\n", + "Amount 0\n", + "InKindAmount 0\n", + "InKindDescription 3508947\n", + "RecipientType 0\n", + "Year 0\n", + "TotalAmount 0\n", "dtype: int64" ] }, - "execution_count": 14, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -370,7 +369,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -412,9 +411,18 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click here for more info. View Jupyter log for further details." + ] + } + ], "source": [ "from utils.MN_util import drop_nonclassifiable\n", "contribution_df = drop_nonclassifiable(contribution_df)" @@ -430,71 +438,51 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "DonationYear\n", - "-1 17400.00\n", - " 1998 21973071.61\n", - " 1999 2636233.26\n", - " 2000 14373458.56\n", - " 2001 5223945.02\n", - " 2002 48261465.33\n", - " 2003 4363080.36\n", - " 2004 26942236.14\n", - " 2005 16161501.54\n", - " 2006 76698607.22\n", - " 2007 8423719.54\n", - " 2008 32462700.99\n", - " 2009 17605765.69\n", - " 2010 66311709.21\n", - " 2011 6057089.02\n", - " 2012 61005414.73\n", - " 2013 20285780.15\n", - " 2014 44521142.83\n", - " 2015 21097676.32\n", - " 2016 39298419.13\n", - " 2017 20058310.89\n", - " 2018 47781848.87\n", - " 2019 25764905.60\n", - " 2020 34614419.07\n", - " 2021 26898778.21\n", - " 2022 57692246.12\n", - " 2023 264961.92\n", + "Year\n", + "2018 47781848.87\n", + "2019 25764905.60\n", + "2020 34614419.07\n", + "2021 26898778.21\n", + "2022 57692246.12\n", + "2023 264961.92\n", "Name: TotalAmount, dtype: float64" ] }, - "execution_count": 31, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "donation_by_year = contribution_df.groupby('DonationYear') \n", - "donation_by_year['TotalAmount'].sum() # check the total contribution across years" + "filtered_df = contribution_df[(contribution_df['Year'] >= 2018) & (contribution_df['Year'] <= 2023) & (contribution_df['Year'] != -1)]\n", + "donation_by_year = filtered_df.groupby('Year')['TotalAmount'].sum()\n", + "donation_by_year" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# Group by 'Year' and 'Contributor' to calculate the total contribution for each contributor in each year\n", - "don_by_year_contributor = contribution_df.groupby(\n", - " ['DonationYear', 'DonorName'])['TotalAmount'].sum().reset_index()\n", + "# Group by 'Year' and 'Contributor' for total contribution for each contributor in each year\n", + "don_by_year_contributor = filtered_df.groupby(\n", + " ['Year', 'DonorName'])['TotalAmount'].sum().reset_index()\n", "\n", "# Find the top 10 contributors\n", - "top_10_contributors = don_by_year_contributor.groupby('DonationYear').apply(\n", + "top_10_contributors = don_by_year_contributor.groupby('Year').apply(\n", " lambda group: group.nlargest(10, 'TotalAmount')).reset_index(drop=True)" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -518,68 +506,368 @@ " \n", " \n", " \n", - " DonationYear\n", + " Year\n", " DonorName\n", " TotalAmount\n", " \n", " \n", " \n", " \n", - " 259\n", + " 0\n", + " 2018\n", + " Minn DFL State Central Committee\n", + " 519799.42\n", + " \n", + " \n", + " 1\n", + " 2018\n", + " Minn Chamber of Commerce Leadership Fd\n", + " 462600.00\n", + " \n", + " \n", + " 2\n", + " 2018\n", + " Minn Nurses Assn Pol Comm (MNA-PC)\n", + " 322750.00\n", + " \n", + " \n", + " 3\n", + " 2018\n", + " MTA PAC (fka SITCO PAC)\n", + " 301400.00\n", + " \n", + " \n", + " 4\n", + " 2018\n", + " Education Minn PAC\n", + " 291200.00\n", + " \n", + " \n", + " 5\n", + " 2018\n", + " Shakopee Mdewakanton Sioux\n", + " 290550.00\n", + " \n", + " \n", + " 6\n", + " 2018\n", + " Faegre Baker Daniels State-Reg Pol Fund\n", + " 276350.00\n", + " \n", + " \n", + " 7\n", + " 2018\n", + " IBEW Minn State Council PAC\n", + " 188500.00\n", + " \n", + " \n", + " 8\n", + " 2018\n", + " Haselow, Robert\n", + " 175025.00\n", + " \n", + " \n", + " 9\n", + " 2018\n", + " Minn Dental Political Action Committee\n", + " 167350.00\n", + " \n", + " \n", + " 10\n", + " 2019\n", + " Shakopee Mdewakanton Sioux\n", + " 683300.00\n", + " \n", + " \n", + " 11\n", + " 2019\n", + " Minn Chamber of Commerce Leadership Fd\n", + " 466000.00\n", + " \n", + " \n", + " 12\n", + " 2019\n", + " Minn Business Partnership PAC\n", + " 369750.00\n", + " \n", + " \n", + " 13\n", + " 2019\n", + " Faegre Baker Daniels State-Reg Pol Fund\n", + " 297650.00\n", + " \n", + " \n", + " 14\n", + " 2019\n", + " Beer PAC-Minn Beer Wholesalers Assoc\n", + " 279250.00\n", + " \n", + " \n", + " 15\n", + " 2019\n", + " Winthrop & Weinstine PA Political Fund\n", + " 262750.00\n", + " \n", + " \n", + " 16\n", + " 2019\n", + " Minn CPAs Public Affairs Committee\n", + " 262250.00\n", + " \n", + " \n", + " 17\n", + " 2019\n", + " IBEW Minn State Council PAC\n", + " 257000.00\n", + " \n", + " \n", + " 18\n", + " 2019\n", + " Haselow, Robert\n", + " 237800.00\n", + " \n", + " \n", + " 19\n", + " 2019\n", + " Rural Electric Political Action Comm\n", + " 236200.00\n", + " \n", + " \n", + " 20\n", + " 2020\n", + " Education Minn PAC\n", + " 367950.00\n", + " \n", + " \n", + " 21\n", + " 2020\n", + " MTA PAC (fka SITCO PAC)\n", + " 335800.00\n", + " \n", + " \n", + " 22\n", + " 2020\n", + " Minn Nurses Assn Pol Comm (MNA-PC)\n", + " 310360.00\n", + " \n", + " \n", + " 23\n", + " 2020\n", + " Laborers District Council of Minn & ND Pol Fund\n", + " 223750.00\n", + " \n", + " \n", + " 24\n", + " 2020\n", + " Haselow, Justine\n", + " 221800.00\n", + " \n", + " \n", + " 25\n", + " 2020\n", + " Mendoza, Salvador\n", + " 210500.00\n", + " \n", + " \n", + " 26\n", + " 2020\n", + " Prairie Island Indian Community PAC\n", + " 206300.00\n", + " \n", + " \n", + " 27\n", + " 2020\n", + " Haselow, Robert\n", + " 192150.00\n", + " \n", + " \n", + " 28\n", + " 2020\n", + " Faegre Drinker State Political Fund\n", + " 184150.00\n", + " \n", + " \n", + " 29\n", + " 2020\n", + " Minn Business Partnership PAC\n", + " 175000.00\n", + " \n", + " \n", + " 30\n", + " 2021\n", + " Faegre Drinker State Political Fund\n", + " 470400.00\n", + " \n", + " \n", + " 31\n", + " 2021\n", + " Minn Chamber of Commerce Leadership Fd\n", + " 349500.00\n", + " \n", + " \n", + " 32\n", + " 2021\n", + " Einess, Ward\n", + " 268200.00\n", + " \n", + " \n", + " 33\n", + " 2021\n", + " North Central States Carpenters PAC\n", + " 252550.00\n", + " \n", + " \n", + " 34\n", + " 2021\n", + " Minn CPAs Public Affairs Committee\n", + " 231000.00\n", + " \n", + " \n", + " 35\n", + " 2021\n", + " Rural Electric Political Action Comm\n", + " 221150.00\n", + " \n", + " \n", + " 36\n", + " 2021\n", + " Minn Business Partnership PAC\n", + " 201475.00\n", + " \n", + " \n", + " 37\n", + " 2021\n", + " Christopherson, Kirsten\n", + " 173200.00\n", + " \n", + " \n", + " 38\n", + " 2021\n", + " Beer PAC-Minn Beer Wholesalers Assoc\n", + " 165750.00\n", + " \n", + " \n", + " 39\n", + " 2021\n", + " Winthrop & Weinstine PA Political Fund\n", + " 159750.00\n", + " \n", + " \n", + " 40\n", + " 2022\n", + " Shakopee Mdewakanton Sioux\n", + " 340900.00\n", + " \n", + " \n", + " 41\n", + " 2022\n", + " Minn Chamber of Commerce Leadership Fd\n", + " 272750.00\n", + " \n", + " \n", + " 42\n", + " 2022\n", + " Laborers District Council of Minn & ND Pol Fund\n", + " 235050.00\n", + " \n", + " \n", + " 43\n", + " 2022\n", + " Faegre Drinker State Political Fund\n", + " 202500.00\n", + " \n", + " \n", + " 44\n", + " 2022\n", + " Education Minn PAC\n", + " 196258.16\n", + " \n", + " \n", + " 45\n", + " 2022\n", + " MTA PAC (fka SITCO PAC)\n", + " 192250.00\n", + " \n", + " \n", + " 46\n", + " 2022\n", + " Haselow, Robert\n", + " 167300.00\n", + " \n", + " \n", + " 47\n", + " 2022\n", + " Mendoza, Salvador\n", + " 167000.00\n", + " \n", + " \n", + " 48\n", + " 2022\n", + " Mendoza, Mia\n", + " 154000.00\n", + " \n", + " \n", + " 49\n", + " 2022\n", + " North Central States Carpenters PAC\n", + " 151500.00\n", + " \n", + " \n", + " 50\n", " 2023\n", " IBEW Local 292\n", " 11400.11\n", " \n", " \n", - " 260\n", + " 51\n", " 2023\n", " SEIU local 26\n", " 5194.45\n", " \n", " \n", - " 261\n", + " 52\n", " 2023\n", " Teamsters Local 346\n", " 3776.37\n", " \n", " \n", - " 262\n", + " 53\n", " 2023\n", " Johnson, Erin\n", " 2899.00\n", " \n", " \n", - " 263\n", + " 54\n", " 2023\n", " Teamsters Local 792\n", " 2897.77\n", " \n", " \n", - " 264\n", + " 55\n", " 2023\n", " Zarth, John\n", " 2700.00\n", " \n", " \n", - " 265\n", + " 56\n", " 2023\n", " Zarth, Kelly\n", " 2700.00\n", " \n", " \n", - " 266\n", + " 57\n", " 2023\n", " Teamsters Local 970\n", " 2401.89\n", " \n", " \n", - " 267\n", + " 58\n", " 2023\n", " Teamsters Local 974\n", " 2390.62\n", " \n", " \n", - " 268\n", + " 59\n", " 2023\n", " Wilbert, Michelle\n", " 2241.00\n", @@ -589,26 +877,76 @@ "
" ], "text/plain": [ - " DonationYear DonorName TotalAmount\n", - "259 2023 IBEW Local 292 11400.11\n", - "260 2023 SEIU local 26 5194.45\n", - "261 2023 Teamsters Local 346 3776.37\n", - "262 2023 Johnson, Erin 2899.00\n", - "263 2023 Teamsters Local 792 2897.77\n", - "264 2023 Zarth, John 2700.00\n", - "265 2023 Zarth, Kelly 2700.00\n", - "266 2023 Teamsters Local 970 2401.89\n", - "267 2023 Teamsters Local 974 2390.62\n", - "268 2023 Wilbert, Michelle 2241.00" + " Year DonorName TotalAmount\n", + "0 2018 Minn DFL State Central Committee 519799.42\n", + "1 2018 Minn Chamber of Commerce Leadership Fd 462600.00\n", + "2 2018 Minn Nurses Assn Pol Comm (MNA-PC) 322750.00\n", + "3 2018 MTA PAC (fka SITCO PAC) 301400.00\n", + "4 2018 Education Minn PAC 291200.00\n", + "5 2018 Shakopee Mdewakanton Sioux 290550.00\n", + "6 2018 Faegre Baker Daniels State-Reg Pol Fund 276350.00\n", + "7 2018 IBEW Minn State Council PAC 188500.00\n", + "8 2018 Haselow, Robert 175025.00\n", + "9 2018 Minn Dental Political Action Committee 167350.00\n", + "10 2019 Shakopee Mdewakanton Sioux 683300.00\n", + "11 2019 Minn Chamber of Commerce Leadership Fd 466000.00\n", + "12 2019 Minn Business Partnership PAC 369750.00\n", + "13 2019 Faegre Baker Daniels State-Reg Pol Fund 297650.00\n", + "14 2019 Beer PAC-Minn Beer Wholesalers Assoc 279250.00\n", + "15 2019 Winthrop & Weinstine PA Political Fund 262750.00\n", + "16 2019 Minn CPAs Public Affairs Committee 262250.00\n", + "17 2019 IBEW Minn State Council PAC 257000.00\n", + "18 2019 Haselow, Robert 237800.00\n", + "19 2019 Rural Electric Political Action Comm 236200.00\n", + "20 2020 Education Minn PAC 367950.00\n", + "21 2020 MTA PAC (fka SITCO PAC) 335800.00\n", + "22 2020 Minn Nurses Assn Pol Comm (MNA-PC) 310360.00\n", + "23 2020 Laborers District Council of Minn & ND Pol Fund 223750.00\n", + "24 2020 Haselow, Justine 221800.00\n", + "25 2020 Mendoza, Salvador 210500.00\n", + "26 2020 Prairie Island Indian Community PAC 206300.00\n", + "27 2020 Haselow, Robert 192150.00\n", + "28 2020 Faegre Drinker State Political Fund 184150.00\n", + "29 2020 Minn Business Partnership PAC 175000.00\n", + "30 2021 Faegre Drinker State Political Fund 470400.00\n", + "31 2021 Minn Chamber of Commerce Leadership Fd 349500.00\n", + "32 2021 Einess, Ward 268200.00\n", + "33 2021 North Central States Carpenters PAC 252550.00\n", + "34 2021 Minn CPAs Public Affairs Committee 231000.00\n", + "35 2021 Rural Electric Political Action Comm 221150.00\n", + "36 2021 Minn Business Partnership PAC 201475.00\n", + "37 2021 Christopherson, Kirsten 173200.00\n", + "38 2021 Beer PAC-Minn Beer Wholesalers Assoc 165750.00\n", + "39 2021 Winthrop & Weinstine PA Political Fund 159750.00\n", + "40 2022 Shakopee Mdewakanton Sioux 340900.00\n", + "41 2022 Minn Chamber of Commerce Leadership Fd 272750.00\n", + "42 2022 Laborers District Council of Minn & ND Pol Fund 235050.00\n", + "43 2022 Faegre Drinker State Political Fund 202500.00\n", + "44 2022 Education Minn PAC 196258.16\n", + "45 2022 MTA PAC (fka SITCO PAC) 192250.00\n", + "46 2022 Haselow, Robert 167300.00\n", + "47 2022 Mendoza, Salvador 167000.00\n", + "48 2022 Mendoza, Mia 154000.00\n", + "49 2022 North Central States Carpenters PAC 151500.00\n", + "50 2023 IBEW Local 292 11400.11\n", + "51 2023 SEIU local 26 5194.45\n", + "52 2023 Teamsters Local 346 3776.37\n", + "53 2023 Johnson, Erin 2899.00\n", + "54 2023 Teamsters Local 792 2897.77\n", + "55 2023 Zarth, John 2700.00\n", + "56 2023 Zarth, Kelly 2700.00\n", + "57 2023 Teamsters Local 970 2401.89\n", + "58 2023 Teamsters Local 974 2390.62\n", + "59 2023 Wilbert, Michelle 2241.00" ] }, - "execution_count": 33, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "top_10_contributors[-10:] # Top 10 contributors in 2023" + "top_10_contributors" ] }, { @@ -620,24 +958,24 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "contribution_df1 = contribution_df.copy(deep=True)\n", + "contribution_df1 = filtered_df.copy(deep=True)\n", "contribution_df1['CandLastName'].fillna('NA', inplace=True)\n", "contribution_df1['CandFirstName'].fillna('NA', inplace=True)\n", "contribution_df1['Committee'].fillna('NA', inplace=True)\n", "by_year_recipients = contribution_df1.groupby(\n", - " ['DonationYear', 'RegNumb', 'RecipientType', 'CandLastName', 'CandFirstName', 'Committee'])['TotalAmount'].sum().reset_index()\n", + " ['Year', 'RegNumb', 'RecipientType', 'CandLastName', 'CandFirstName', 'Committee'])['TotalAmount'].sum().reset_index()\n", "\n", - "top_10_recipient = by_year_recipients.groupby('DonationYear').apply(\n", + "top_10_recipient = by_year_recipients.groupby('Year').apply(\n", " lambda group: group.nlargest(10, 'TotalAmount')).reset_index(drop=True)" ] }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -661,7 +999,7 @@ " \n", " \n", " \n", - " DonationYear\n", + " Year\n", " RegNumb\n", " RecipientType\n", " CandLastName\n", @@ -672,163 +1010,763 @@ " \n", " \n", " \n", - " 256\n", - " 2023\n", - " 41291\n", - " PCF\n", - " NA\n", - " NA\n", - " All of Mpls\n", - " 32650.00\n", + " 0\n", + " 2018\n", + " 18135\n", + " Candidate\n", + " Walz\n", + " Tim\n", + " Tim Walz for Governor\n", + " 8095546.97\n", " \n", " \n", - " 257\n", - " 2023\n", - " 18690\n", + " 1\n", + " 2018\n", + " 17641\n", " Candidate\n", - " Jensen\n", - " Scott\n", - " Dr. Scott Jensen for Governor\n", - " 32505.00\n", + " Johnson\n", + " Jeff\n", + " Johnson (Jeff) for Governor\n", + " 4060066.88\n", " \n", " \n", - " 258\n", - " 2023\n", - " 30689\n", - " PCF\n", - " NA\n", - " NA\n", - " AGC Building Constructors\n", - " 29900.00\n", + " 2\n", + " 2018\n", + " 18336\n", + " Candidate\n", + " Ellison\n", + " Keith\n", + " Keith Ellison for Attorney General\n", + " 1708185.84\n", " \n", " \n", - " 259\n", - " 2023\n", - " 41345\n", - " PCF\n", - " NA\n", - " NA\n", - " Greater Than\n", - " 28371.96\n", + " 3\n", + " 2018\n", + " 18133\n", + " Candidate\n", + " Wardlow\n", + " Doug\n", + " Doug Wardlow for Attorney General\n", + " 1621028.92\n", " \n", " \n", - " 260\n", - " 2023\n", - " 30726\n", - " PCF\n", - " NA\n", - " NA\n", - " AGC Transportation Builders\n", - " 28000.00\n", + " 4\n", + " 2018\n", + " 18125\n", + " Candidate\n", + " Murphy\n", + " Erin\n", + " Murphy (Erin) for Minnesota\n", + " 1396061.64\n", " \n", " \n", - " 261\n", - " 2023\n", - " 30013\n", - " PCF\n", - " NA\n", - " NA\n", - " Joint Council 32 DRIVE\n", - " 18072.03\n", + " 5\n", + " 2018\n", + " 17653\n", + " Candidate\n", + " Simon\n", + " Steve\n", + " Simon (Steve) for Secretary of State\n", + " 904363.13\n", " \n", " \n", - " 262\n", - " 2023\n", - " 30345\n", - " PCF\n", - " NA\n", - " NA\n", - " Winthrop & Weinstine PA Political Fund\n", - " 12000.00\n", + " 6\n", + " 2018\n", + " 18292\n", + " Candidate\n", + " Pawlenty\n", + " Tim\n", + " Tim Pawlenty for Governor\n", + " 545366.12\n", " \n", " \n", - " 263\n", - " 2023\n", - " 41281\n", - " PCF\n", - " NA\n", - " NA\n", - " Faith in Minnesota Action\n", - " 11832.00\n", + " 7\n", + " 2018\n", + " 15677\n", + " Candidate\n", + " Hortman\n", + " Melissa\n", + " Melissa Hortman Campaign Committee\n", + " 535150.00\n", " \n", " \n", - " 264\n", - " 2023\n", - " 41262\n", - " PCF\n", - " NA\n", - " NA\n", - " Nutrien Ag Solutions Employee Citizenship Fund...\n", - " 11805.00\n", + " 8\n", + " 2018\n", + " 17902\n", + " Candidate\n", + " Edelson\n", + " Heather\n", + " Heather Edelson for House\n", + " 492906.69\n", " \n", " \n", - " 265\n", - " 2023\n", - " 30119\n", - " PCF\n", - " NA\n", - " NA\n", - " IBEW Local 292 Political Education Fund\n", - " 11400.11\n", + " 9\n", + " 2018\n", + " 12604\n", + " Candidate\n", + " Davids\n", + " Gregory\n", + " People for (Gregory) Davids Committee\n", + " 460620.00\n", " \n", - " \n", - "\n", - "
" - ], - "text/plain": [ - " DonationYear RegNumb RecipientType CandLastName CandFirstName \\\n", - "256 2023 41291 PCF NA NA \n", - "257 2023 18690 Candidate Jensen Scott \n", - "258 2023 30689 PCF NA NA \n", - "259 2023 41345 PCF NA NA \n", - "260 2023 30726 PCF NA NA \n", - "261 2023 30013 PCF NA NA \n", - "262 2023 30345 PCF NA NA \n", - "263 2023 41281 PCF NA NA \n", - "264 2023 41262 PCF NA NA \n", - "265 2023 30119 PCF NA NA \n", - "\n", - " Committee TotalAmount \n", - "256 All of Mpls 32650.00 \n", - "257 Dr. Scott Jensen for Governor 32505.00 \n", - "258 AGC Building Constructors 29900.00 \n", - "259 Greater Than 28371.96 \n", - "260 AGC Transportation Builders 28000.00 \n", - "261 Joint Council 32 DRIVE 18072.03 \n", - "262 Winthrop & Weinstine PA Political Fund 12000.00 \n", - "263 Faith in Minnesota Action 11832.00 \n", - "264 Nutrien Ag Solutions Employee Citizenship Fund... 11805.00 \n", - "265 IBEW Local 292 Political Education Fund 11400.11 " - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "top_10_recipient[-10:] # Top 10 recipients in 2023" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Compare donation by donor and recipient types\n", - "\n", - "### 3.1 Compare donation by donor types" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "donor_type_mapping = {\n", - " 'B': 'Business',\n", - " 'C': 'Candidate committee',\n", - " 'F': 'Political committee or fund',\n", + " \n", + " 10\n", + " 2019\n", + " 18135\n", + " Candidate\n", + " Walz\n", + " Tim\n", + " Tim Walz for Governor\n", + " 1956067.75\n", + " \n", + " \n", + " 11\n", + " 2019\n", + " 11829\n", + " Candidate\n", + " Cohen\n", + " Richard\n", + " Cohen (Richard) Volunteer Committee\n", + " 707400.00\n", + " \n", + " \n", + " 12\n", + " 2019\n", + " 16964\n", + " Candidate\n", + " Housley\n", + " Karin\n", + " Housley (Karin) For Senate\n", + " 700500.00\n", + " \n", + " \n", + " 13\n", + " 2019\n", + " 15677\n", + " Candidate\n", + " Hortman\n", + " Melissa\n", + " Melissa Hortman Campaign Committee\n", + " 565400.00\n", + " \n", + " \n", + " 14\n", + " 2019\n", + " 12604\n", + " Candidate\n", + " Davids\n", + " Gregory\n", + " People for (Gregory) Davids Committee\n", + " 536355.00\n", + " \n", + " \n", + " 15\n", + " 2019\n", + " 15317\n", + " Candidate\n", + " Tomassoni\n", + " David\n", + " Tomassoni (David) for State Senate\n", + " 491970.00\n", + " \n", + " \n", + " 16\n", + " 2019\n", + " 13262\n", + " Candidate\n", + " Limmer\n", + " Warren\n", + " Limmer (Warren) for Senate Committee\n", + " 398250.00\n", + " \n", + " \n", + " 17\n", + " 2019\n", + " 18336\n", + " Candidate\n", + " Ellison\n", + " Keith\n", + " Keith Ellison for Attorney General\n", + " 390704.04\n", + " \n", + " \n", + " 18\n", + " 2019\n", + " 17040\n", + " Candidate\n", + " Osmek\n", + " David\n", + " David Osmek Volunteer Committee\n", + " 379960.00\n", + " \n", + " \n", + " 19\n", + " 2019\n", + " 17653\n", + " Candidate\n", + " Simon\n", + " Steve\n", + " Simon (Steve) for Secretary of State\n", + " 372350.00\n", + " \n", + " \n", + " 20\n", + " 2020\n", + " 18135\n", + " Candidate\n", + " Walz\n", + " Tim\n", + " Tim Walz for Governor\n", + " 1426981.50\n", + " \n", + " \n", + " 21\n", + " 2020\n", + " 17105\n", + " Candidate\n", + " Nelson\n", + " Carla\n", + " Nelson (Carla) for Senate\n", + " 514803.24\n", + " \n", + " \n", + " 22\n", + " 2020\n", + " 17481\n", + " Candidate\n", + " Hawj\n", + " Foung\n", + " Foung (Hawj) for Senate 67\n", + " 451980.00\n", + " \n", + " \n", + " 23\n", + " 2020\n", + " 15501\n", + " Candidate\n", + " Rosen\n", + " Julie\n", + " Julie Rosen for State Senate\n", + " 420900.00\n", + " \n", + " \n", + " 24\n", + " 2020\n", + " 17443\n", + " Candidate\n", + " Kent\n", + " Susan\n", + " Susan Kent for Senate\n", + " 384646.92\n", + " \n", + " \n", + " 25\n", + " 2020\n", + " 15667\n", + " Candidate\n", + " Dibble\n", + " D Scott\n", + " Volunteers for (Scott) Dibble\n", + " 376200.00\n", + " \n", + " \n", + " 26\n", + " 2020\n", + " 18021\n", + " Candidate\n", + " Westlin\n", + " Bonnie\n", + " Bonnie Westlin for MN Senate\n", + " 373766.56\n", + " \n", + " \n", + " 27\n", + " 2020\n", + " 15705\n", + " Candidate\n", + " Senjem\n", + " David\n", + " Senjem (David) for Senate\n", + " 367400.00\n", + " \n", + " \n", + " 28\n", + " 2020\n", + " 17653\n", + " Candidate\n", + " Simon\n", + " Steve\n", + " Simon (Steve) for Secretary of State\n", + " 362450.00\n", + " \n", + " \n", + " 29\n", + " 2020\n", + " 16891\n", + " Candidate\n", + " Miller\n", + " Jeremy\n", + " Friends for (Jeremy) Miller\n", + " 357750.00\n", + " \n", + " \n", + " 30\n", + " 2021\n", + " 18135\n", + " Candidate\n", + " Walz\n", + " Tim\n", + " Tim Walz for Governor\n", + " 5709831.89\n", + " \n", + " \n", + " 31\n", + " 2021\n", + " 17653\n", + " Candidate\n", + " Simon\n", + " Steve\n", + " Simon (Steve) for Secretary of State\n", + " 1143340.00\n", + " \n", + " \n", + " 32\n", + " 2021\n", + " 18690\n", + " Candidate\n", + " Jensen\n", + " Scott\n", + " Dr. Scott Jensen for Governor\n", + " 1003867.95\n", + " \n", + " \n", + " 33\n", + " 2021\n", + " 18336\n", + " Candidate\n", + " Ellison\n", + " Keith\n", + " Keith Ellison for Attorney General\n", + " 481584.00\n", + " \n", + " \n", + " 34\n", + " 2021\n", + " 15317\n", + " Candidate\n", + " Tomassoni\n", + " David\n", + " Tomassoni (David) for State Senate\n", + " 453100.00\n", + " \n", + " \n", + " 35\n", + " 2021\n", + " 15677\n", + " Candidate\n", + " Hortman\n", + " Melissa\n", + " Melissa Hortman Campaign Committee\n", + " 364540.00\n", + " \n", + " \n", + " 36\n", + " 2021\n", + " 16964\n", + " Candidate\n", + " Housley\n", + " Karin\n", + " Housley (Karin) For Senate\n", + " 357900.00\n", + " \n", + " \n", + " 37\n", + " 2021\n", + " 17140\n", + " Candidate\n", + " Bakk\n", + " Thomas (Tom)\n", + " Bakk (Thomas) for Senate\n", + " 336560.00\n", + " \n", + " \n", + " 38\n", + " 2021\n", + " 12604\n", + " Candidate\n", + " Davids\n", + " Gregory\n", + " People for (Gregory) Davids Committee\n", + " 333450.00\n", + " \n", + " \n", + " 39\n", + " 2021\n", + " 18133\n", + " Candidate\n", + " Wardlow\n", + " Doug\n", + " Doug Wardlow for Attorney General\n", + " 318997.98\n", + " \n", + " \n", + " 40\n", + " 2022\n", + " 18135\n", + " Candidate\n", + " Walz\n", + " Tim\n", + " Tim Walz for Governor\n", + " 15408317.75\n", + " \n", + " \n", + " 41\n", + " 2022\n", + " 17653\n", + " Candidate\n", + " Simon\n", + " Steve\n", + " Simon (Steve) for Secretary of State\n", + " 4425042.27\n", + " \n", + " \n", + " 42\n", + " 2022\n", + " 18690\n", + " Candidate\n", + " Jensen\n", + " Scott\n", + " Dr. Scott Jensen for Governor\n", + " 3356977.05\n", + " \n", + " \n", + " 43\n", + " 2022\n", + " 18336\n", + " Candidate\n", + " Ellison\n", + " Keith\n", + " Keith Ellison for Attorney General\n", + " 2897191.44\n", + " \n", + " \n", + " 44\n", + " 2022\n", + " 15677\n", + " Candidate\n", + " Hortman\n", + " Melissa\n", + " Melissa Hortman Campaign Committee\n", + " 894300.00\n", + " \n", + " \n", + " 45\n", + " 2022\n", + " 18731\n", + " Candidate\n", + " Schultz\n", + " James\n", + " Jim Schultz For Minnesota Attorney General\n", + " 893429.04\n", + " \n", + " \n", + " 46\n", + " 2022\n", + " 18133\n", + " Candidate\n", + " Wardlow\n", + " Doug\n", + " Doug Wardlow for Attorney General\n", + " 469847.28\n", + " \n", + " \n", + " 47\n", + " 2022\n", + " 13262\n", + " Candidate\n", + " Limmer\n", + " Warren\n", + " Limmer (Warren) for Senate Committee\n", + " 454275.00\n", + " \n", + " \n", + " 48\n", + " 2022\n", + " 10601\n", + " Candidate\n", + " Murphy\n", + " Mary\n", + " Mary Murphy Volunteer Committee\n", + " 452742.21\n", + " \n", + " \n", + " 49\n", + " 2022\n", + " 18218\n", + " Candidate\n", + " Blaha\n", + " Julie\n", + " Minnesotans for Julie Blaha\n", + " 432397.00\n", + " \n", + " \n", + " 50\n", + " 2023\n", + " 41291\n", + " PCF\n", + " NA\n", + " NA\n", + " All of Mpls\n", + " 32650.00\n", + " \n", + " \n", + " 51\n", + " 2023\n", + " 18690\n", + " Candidate\n", + " Jensen\n", + " Scott\n", + " Dr. Scott Jensen for Governor\n", + " 32505.00\n", + " \n", + " \n", + " 52\n", + " 2023\n", + " 30689\n", + " PCF\n", + " NA\n", + " NA\n", + " AGC Building Constructors\n", + " 29900.00\n", + " \n", + " \n", + " 53\n", + " 2023\n", + " 41345\n", + " PCF\n", + " NA\n", + " NA\n", + " Greater Than\n", + " 28371.96\n", + " \n", + " \n", + " 54\n", + " 2023\n", + " 30726\n", + " PCF\n", + " NA\n", + " NA\n", + " AGC Transportation Builders\n", + " 28000.00\n", + " \n", + " \n", + " 55\n", + " 2023\n", + " 30013\n", + " PCF\n", + " NA\n", + " NA\n", + " Joint Council 32 DRIVE\n", + " 18072.03\n", + " \n", + " \n", + " 56\n", + " 2023\n", + " 30345\n", + " PCF\n", + " NA\n", + " NA\n", + " Winthrop & Weinstine PA Political Fund\n", + " 12000.00\n", + " \n", + " \n", + " 57\n", + " 2023\n", + " 41281\n", + " PCF\n", + " NA\n", + " NA\n", + " Faith in Minnesota Action\n", + " 11832.00\n", + " \n", + " \n", + " 58\n", + " 2023\n", + " 41262\n", + " PCF\n", + " NA\n", + " NA\n", + " Nutrien Ag Solutions Employee Citizenship Fund...\n", + " 11805.00\n", + " \n", + " \n", + " 59\n", + " 2023\n", + " 30119\n", + " PCF\n", + " NA\n", + " NA\n", + " IBEW Local 292 Political Education Fund\n", + " 11400.11\n", + " \n", + " \n", + "\n", + "" + ], + "text/plain": [ + " Year RegNumb RecipientType CandLastName CandFirstName \\\n", + "0 2018 18135 Candidate Walz Tim \n", + "1 2018 17641 Candidate Johnson Jeff \n", + "2 2018 18336 Candidate Ellison Keith \n", + "3 2018 18133 Candidate Wardlow Doug \n", + "4 2018 18125 Candidate Murphy Erin \n", + "5 2018 17653 Candidate Simon Steve \n", + "6 2018 18292 Candidate Pawlenty Tim \n", + "7 2018 15677 Candidate Hortman Melissa \n", + "8 2018 17902 Candidate Edelson Heather \n", + "9 2018 12604 Candidate Davids Gregory \n", + "10 2019 18135 Candidate Walz Tim \n", + "11 2019 11829 Candidate Cohen Richard \n", + "12 2019 16964 Candidate Housley Karin \n", + "13 2019 15677 Candidate Hortman Melissa \n", + "14 2019 12604 Candidate Davids Gregory \n", + "15 2019 15317 Candidate Tomassoni David \n", + "16 2019 13262 Candidate Limmer Warren \n", + "17 2019 18336 Candidate Ellison Keith \n", + "18 2019 17040 Candidate Osmek David \n", + "19 2019 17653 Candidate Simon Steve \n", + "20 2020 18135 Candidate Walz Tim \n", + "21 2020 17105 Candidate Nelson Carla \n", + "22 2020 17481 Candidate Hawj Foung \n", + "23 2020 15501 Candidate Rosen Julie \n", + "24 2020 17443 Candidate Kent Susan \n", + "25 2020 15667 Candidate Dibble D Scott \n", + "26 2020 18021 Candidate Westlin Bonnie \n", + "27 2020 15705 Candidate Senjem David \n", + "28 2020 17653 Candidate Simon Steve \n", + "29 2020 16891 Candidate Miller Jeremy \n", + "30 2021 18135 Candidate Walz Tim \n", + "31 2021 17653 Candidate Simon Steve \n", + "32 2021 18690 Candidate Jensen Scott \n", + "33 2021 18336 Candidate Ellison Keith \n", + "34 2021 15317 Candidate Tomassoni David \n", + "35 2021 15677 Candidate Hortman Melissa \n", + "36 2021 16964 Candidate Housley Karin \n", + "37 2021 17140 Candidate Bakk Thomas (Tom) \n", + "38 2021 12604 Candidate Davids Gregory \n", + "39 2021 18133 Candidate Wardlow Doug \n", + "40 2022 18135 Candidate Walz Tim \n", + "41 2022 17653 Candidate Simon Steve \n", + "42 2022 18690 Candidate Jensen Scott \n", + "43 2022 18336 Candidate Ellison Keith \n", + "44 2022 15677 Candidate Hortman Melissa \n", + "45 2022 18731 Candidate Schultz James \n", + "46 2022 18133 Candidate Wardlow Doug \n", + "47 2022 13262 Candidate Limmer Warren \n", + "48 2022 10601 Candidate Murphy Mary \n", + "49 2022 18218 Candidate Blaha Julie \n", + "50 2023 41291 PCF NA NA \n", + "51 2023 18690 Candidate Jensen Scott \n", + "52 2023 30689 PCF NA NA \n", + "53 2023 41345 PCF NA NA \n", + "54 2023 30726 PCF NA NA \n", + "55 2023 30013 PCF NA NA \n", + "56 2023 30345 PCF NA NA \n", + "57 2023 41281 PCF NA NA \n", + "58 2023 41262 PCF NA NA \n", + "59 2023 30119 PCF NA NA \n", + "\n", + " Committee TotalAmount \n", + "0 Tim Walz for Governor 8095546.97 \n", + "1 Johnson (Jeff) for Governor 4060066.88 \n", + "2 Keith Ellison for Attorney General 1708185.84 \n", + "3 Doug Wardlow for Attorney General 1621028.92 \n", + "4 Murphy (Erin) for Minnesota 1396061.64 \n", + "5 Simon (Steve) for Secretary of State 904363.13 \n", + "6 Tim Pawlenty for Governor 545366.12 \n", + "7 Melissa Hortman Campaign Committee 535150.00 \n", + "8 Heather Edelson for House 492906.69 \n", + "9 People for (Gregory) Davids Committee 460620.00 \n", + "10 Tim Walz for Governor 1956067.75 \n", + "11 Cohen (Richard) Volunteer Committee 707400.00 \n", + "12 Housley (Karin) For Senate 700500.00 \n", + "13 Melissa Hortman Campaign Committee 565400.00 \n", + "14 People for (Gregory) Davids Committee 536355.00 \n", + "15 Tomassoni (David) for State Senate 491970.00 \n", + "16 Limmer (Warren) for Senate Committee 398250.00 \n", + "17 Keith Ellison for Attorney General 390704.04 \n", + "18 David Osmek Volunteer Committee 379960.00 \n", + "19 Simon (Steve) for Secretary of State 372350.00 \n", + "20 Tim Walz for Governor 1426981.50 \n", + "21 Nelson (Carla) for Senate 514803.24 \n", + "22 Foung (Hawj) for Senate 67 451980.00 \n", + "23 Julie Rosen for State Senate 420900.00 \n", + "24 Susan Kent for Senate 384646.92 \n", + "25 Volunteers for (Scott) Dibble 376200.00 \n", + "26 Bonnie Westlin for MN Senate 373766.56 \n", + "27 Senjem (David) for Senate 367400.00 \n", + "28 Simon (Steve) for Secretary of State 362450.00 \n", + "29 Friends for (Jeremy) Miller 357750.00 \n", + "30 Tim Walz for Governor 5709831.89 \n", + "31 Simon (Steve) for Secretary of State 1143340.00 \n", + "32 Dr. Scott Jensen for Governor 1003867.95 \n", + "33 Keith Ellison for Attorney General 481584.00 \n", + "34 Tomassoni (David) for State Senate 453100.00 \n", + "35 Melissa Hortman Campaign Committee 364540.00 \n", + "36 Housley (Karin) For Senate 357900.00 \n", + "37 Bakk (Thomas) for Senate 336560.00 \n", + "38 People for (Gregory) Davids Committee 333450.00 \n", + "39 Doug Wardlow for Attorney General 318997.98 \n", + "40 Tim Walz for Governor 15408317.75 \n", + "41 Simon (Steve) for Secretary of State 4425042.27 \n", + "42 Dr. Scott Jensen for Governor 3356977.05 \n", + "43 Keith Ellison for Attorney General 2897191.44 \n", + "44 Melissa Hortman Campaign Committee 894300.00 \n", + "45 Jim Schultz For Minnesota Attorney General 893429.04 \n", + "46 Doug Wardlow for Attorney General 469847.28 \n", + "47 Limmer (Warren) for Senate Committee 454275.00 \n", + "48 Mary Murphy Volunteer Committee 452742.21 \n", + "49 Minnesotans for Julie Blaha 432397.00 \n", + "50 All of Mpls 32650.00 \n", + "51 Dr. Scott Jensen for Governor 32505.00 \n", + "52 AGC Building Constructors 29900.00 \n", + "53 Greater Than 28371.96 \n", + "54 AGC Transportation Builders 28000.00 \n", + "55 Joint Council 32 DRIVE 18072.03 \n", + "56 Winthrop & Weinstine PA Political Fund 12000.00 \n", + "57 Faith in Minnesota Action 11832.00 \n", + "58 Nutrien Ag Solutions Employee Citizenship Fund... 11805.00 \n", + "59 IBEW Local 292 Political Education Fund 11400.11 " + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "top_10_recipient" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Compare donation by donor and recipient types\n", + "\n", + "### 3.1 Compare donation by donor types" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "donor_type_mapping = {\n", + " 'B': 'Business',\n", + " 'C': 'Candidate committee',\n", + " 'F': 'Political committee or fund',\n", " 'H': 'Local candidate committee registered with Hennepin County',\n", " 'I': 'Non-lobbyist individual',\n", " 'L': 'Lobbyist',\n", @@ -841,7 +1779,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -868,18 +1806,10 @@ "textposition": "auto", "type": "bar", "x": [ - 2010, - 2011, - 2012, - 2016, 2022 ], "xaxis": "x", "y": [ - 3706.25, - 6989.52, - 107.8, - 818.9, 1067.47 ], "yaxis": "y" @@ -901,18 +1831,10 @@ "textposition": "auto", "type": "bar", "x": [ - 2010, - 2011, - 2012, - 2017, 2019 ], "xaxis": "x", "y": [ - 5700, - 4892.66, - 400, - 500, 250 ], "yaxis": "y" @@ -934,26 +1856,6 @@ "textposition": "auto", "type": "bar", "x": [ - 1998, - 1999, - 2000, - 2001, - 2002, - 2003, - 2004, - 2005, - 2006, - 2007, - 2008, - 2009, - 2010, - 2011, - 2012, - 2013, - 2014, - 2015, - 2016, - 2017, 2018, 2019, 2020, @@ -963,26 +1865,6 @@ ], "xaxis": "x", "y": [ - 191422.86, - 51438.77, - 190885.96, - 64955.08, - 947533.25, - 188787.5, - 229301.8, - 132074.39, - 722714.8, - 190084.9, - 303212.64, - 113900.6, - 659743.72, - 127841.39, - 933861.06, - 137224.96, - 340672.7, - 112969.44, - 469611.57, - 171160.74, 294413.35, 151774.38, 520202, @@ -1009,26 +1891,6 @@ "textposition": "auto", "type": "bar", "x": [ - 1998, - 1999, - 2000, - 2001, - 2002, - 2003, - 2004, - 2005, - 2006, - 2007, - 2008, - 2009, - 2010, - 2011, - 2012, - 2013, - 2014, - 2015, - 2016, - 2017, 2018, 2019, 2020, @@ -1038,26 +1900,6 @@ ], "xaxis": "x", "y": [ - 831806.69, - 136725.59, - 532388.64, - 138253, - 1496998.96, - 170506.87, - 602606.98, - 377230.73, - 2625401.72, - 185081.66, - 1139728.76, - 1021329.85, - 2919676.27, - 454099.02, - 2035057.01, - 1243087.49, - 1539565.21, - 1716511.33, - 1821964.77, - 1480548.64, 1901804.56, 2715933.95, 1400427.52, @@ -1084,42 +1926,12 @@ "textposition": "auto", "type": "bar", "x": [ - 1998, - 1999, - 2000, - 2001, - 2002, - 2004, - 2005, - 2006, - 2007, - 2008, - 2009, - 2010, - 2011, - 2012, - 2013, 2018, 2022, 2023 ], "xaxis": "x", "y": [ - 2675, - 3387.49, - 19949, - 13120.86, - 19446.15, - 26883.76, - 17565.47, - 11354.55, - 1050, - 2760.6, - 3950, - 4300.5, - 1602.51, - 1600, - 350, 650, 6750, 1490 @@ -1143,26 +1955,6 @@ "textposition": "auto", "type": "bar", "x": [ - 1998, - 1999, - 2000, - 2001, - 2002, - 2003, - 2004, - 2005, - 2006, - 2007, - 2008, - 2009, - 2010, - 2011, - 2012, - 2013, - 2014, - 2015, - 2016, - 2017, 2018, 2019, 2020, @@ -1172,26 +1964,6 @@ ], "xaxis": "x", "y": [ - 19650402.25, - 1821021.11, - 7091200.17, - 4344698.68, - 27654673.18, - 2942139.6, - 15231963.37, - 14315307.91, - 52778825.83, - 6903261.94, - 19071724.71, - 14468593.4, - 42482854.7, - 4035126.22, - 35690989.86, - 13329157.91, - 30719469.25, - 10971190.26, - 23222674.88, - 13110087.07, 34883943.13, 13551729.61, 23319309.17, @@ -1218,26 +1990,6 @@ "textposition": "auto", "type": "bar", "x": [ - 1998, - 1999, - 2000, - 2001, - 2002, - 2003, - 2004, - 2005, - 2006, - 2007, - 2008, - 2009, - 2010, - 2011, - 2012, - 2013, - 2014, - 2015, - 2016, - 2017, 2018, 2019, 2020, @@ -1247,26 +1999,6 @@ ], "xaxis": "x", "y": [ - 35347.25, - 9453.06, - 19102.74, - 4661.35, - 33502.94, - 19885.85, - 29432.72, - 7193, - 156316.9, - 32524.5, - 33262.69, - 88283.43, - 91567.76, - 42562.43, - 427291.29, - 806516.56, - 801093.86, - 762306.86, - 180601.75, - 156489.29, 222136.57, 165915.45, 194767.4, @@ -1293,26 +2025,6 @@ "textposition": "auto", "type": "bar", "x": [ - 1998, - 1999, - 2000, - 2001, - 2002, - 2003, - 2004, - 2005, - 2006, - 2007, - 2008, - 2009, - 2010, - 2011, - 2012, - 2013, - 2014, - 2015, - 2016, - 2017, 2018, 2019, 2020, @@ -1321,26 +2033,6 @@ ], "xaxis": "x", "y": [ - 1035669.97, - 440826.79, - 5944716.08, - 254491.88, - 16094119.42, - 517399.82, - 9064938.78, - 668688.32, - 17824192.91, - 445831.49, - 10034598.83, - 1244566.03, - 17036662.74, - 901194.58, - 18024210.12, - 4305924.24, - 8539287.16, - 7114003.74, - 9890110.47, - 4772914.95, 7583168.95, 8642981.92, 6856305.01, @@ -1366,26 +2058,6 @@ "textposition": "auto", "type": "bar", "x": [ - 1998, - 1999, - 2000, - 2001, - 2002, - 2003, - 2004, - 2005, - 2006, - 2007, - 2008, - 2009, - 2010, - 2011, - 2012, - 2013, - 2014, - 2015, - 2016, - 2017, 2018, 2019, 2020, @@ -1395,26 +2067,6 @@ ], "xaxis": "x", "y": [ - 211002.97, - 165681.29, - 524674.66, - 373608.35, - 1903961.25, - 495855.01, - 1675081.16, - 591051, - 2378429.11, - 623469.58, - 1824218.38, - 625034.6, - 2890677.25, - 437927.2, - 3619051.89, - 418929.47, - 2280464.95, - 269781.66, - 3405604.04, - 192348.39, 2561953.7, 343542.51, 1993899.57, @@ -1441,26 +2093,6 @@ "textposition": "auto", "type": "bar", "x": [ - 1998, - 1999, - 2000, - 2001, - 2002, - 2003, - 2004, - 2005, - 2006, - 2007, - 2008, - 2009, - 2010, - 2011, - 2012, - 2013, - 2014, - 2015, - 2016, - 2017, 2018, 2019, 2020, @@ -1470,26 +2102,6 @@ ], "xaxis": "x", "y": [ - 14744.62, - 7699.16, - 48541.31, - 29905.82, - 108430.18000000001, - 27935.71, - 74677.57, - 52240.72, - 200581.4, - 42415.47, - 53194.38, - 40107.78, - 216570.02, - 44853.49, - 272845.7, - 44589.52, - 300589.7, - 150913.03, - 307032.75, - 174261.81, 333696.61, 192777.78, 327697.4, @@ -2325,7 +2937,7 @@ } }, "title": { - "text": "Donations by Donor Type from 1998 to 2022" + "text": "Donations by Donor Type From 2018 To 2023" }, "xaxis": { "anchor": "y", @@ -2350,9 +2962,9 @@ } }, "text/html": [ - "
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YearVendorNameAmount
02018Great American Media7.092650e+06
12018Democratic Governors Association5.096131e+06
22018Clarify Agency4.289870e+06
32018Nebo Media3.538082e+06
42018Drake Bank Federal3.040606e+06
52018GMMB Inc2.512423e+06
62018Berlin Rosen LTD1.446830e+06
72018Ax Media1.370301e+06
82018Strategic Media for Placement1.326959e+06
92018Education Minnesota1.302605e+06
102019Democratic Governors Association - Mississippi2.434730e+06
112019Drake Bank Federal1.060554e+06
122019Republican Party of Minnesota - Federal Account4.960728e+05
132019Canal Partners Media3.626000e+05
142019SEIU MN State Council3.363778e+05
152019Western Bank3.161543e+05
162019Pinnacle Direct2.952088e+05
172019Seven Corners Printing2.412480e+05
182019EFTPS Fed Tax Payment1.835754e+05
192019Perkins Coie LLP1.743259e+05
202020Clarify Agency5.621665e+06
212020Drake Bank Federal2.728263e+06
222020Democratic Governors Association2.397500e+06
232020Left Hook2.380277e+06
242020WIN Minnesota Federal PAC2.220500e+06
252020FP1 Digital LLC2.092333e+06
262020Canal Partners Media1.448225e+06
272020Sage Media Planning Placement1.375934e+06
282020Gumbinner Davies1.272660e+06
292020Education Minnesota1.008506e+06
302021Drake Bank Federal1.539032e+06
312021AllofMpls1.229037e+06
322021Education Minnesota9.120825e+05
332021Robin for Minneapolis6.014500e+05
342021Cleansweep Campaigns4.226354e+05
352021Republican Party of Minnesota - Federal Account3.682714e+05
362021EFTPS Fed Tax Payment2.897723e+05
372021McCool Digital LLC2.571873e+05
382021Yes 4 Minneapolis2.190000e+05
392021Democratic Attorneys General Assocation Inc2.136878e+05
402022Clarify Agency2.647481e+08
412022Great American Media2.582326e+08
422022Canal Partners Media1.593249e+08
432022Berlin Rosen LTD1.105615e+08
442022Red Eagle Media1.082093e+08
452022Deliver Strategies1.074506e+08
462022Nebo Media8.849665e+07
472022Sage Media Planning Placement8.776352e+07
482022FP1 Digital LLC8.138634e+07
492022Schultz, Jennifer House Committee8.054502e+07
502023MN DFL State Central Committee7.828450e+05
512023GRSG Company5.000000e+05
522023The People's Canvass4.230976e+05
532023MN DFL Senate Caucus2.496439e+05
542023MN DFL House Caucus2.427772e+05
552023AGC of MN1.343460e+05
562023SMART PAL-Education1.200000e+05
572023Teamsters Joint Council 328.893821e+04
582023All of Mpls8.621453e+04
592023Apparatus7.000000e+04
\n", + "" + ], + "text/plain": [ + " Year VendorName Amount\n", + "0 2018 Great American Media 7.092650e+06\n", + "1 2018 Democratic Governors Association 5.096131e+06\n", + "2 2018 Clarify Agency 4.289870e+06\n", + "3 2018 Nebo Media 3.538082e+06\n", + "4 2018 Drake Bank Federal 3.040606e+06\n", + "5 2018 GMMB Inc 2.512423e+06\n", + "6 2018 Berlin Rosen LTD 1.446830e+06\n", + "7 2018 Ax Media 1.370301e+06\n", + "8 2018 Strategic Media for Placement 1.326959e+06\n", + "9 2018 Education Minnesota 1.302605e+06\n", + "10 2019 Democratic Governors Association - Mississippi 2.434730e+06\n", + "11 2019 Drake Bank Federal 1.060554e+06\n", + "12 2019 Republican Party of Minnesota - Federal Account 4.960728e+05\n", + "13 2019 Canal Partners Media 3.626000e+05\n", + "14 2019 SEIU MN State Council 3.363778e+05\n", + "15 2019 Western Bank 3.161543e+05\n", + "16 2019 Pinnacle Direct 2.952088e+05\n", + "17 2019 Seven Corners Printing 2.412480e+05\n", + "18 2019 EFTPS Fed Tax Payment 1.835754e+05\n", + "19 2019 Perkins Coie LLP 1.743259e+05\n", + "20 2020 Clarify Agency 5.621665e+06\n", + "21 2020 Drake Bank Federal 2.728263e+06\n", + "22 2020 Democratic Governors Association 2.397500e+06\n", + "23 2020 Left Hook 2.380277e+06\n", + "24 2020 WIN Minnesota Federal PAC 2.220500e+06\n", + "25 2020 FP1 Digital LLC 2.092333e+06\n", + "26 2020 Canal Partners Media 1.448225e+06\n", + "27 2020 Sage Media Planning Placement 1.375934e+06\n", + "28 2020 Gumbinner Davies 1.272660e+06\n", + "29 2020 Education Minnesota 1.008506e+06\n", + "30 2021 Drake Bank Federal 1.539032e+06\n", + "31 2021 AllofMpls 1.229037e+06\n", + "32 2021 Education Minnesota 9.120825e+05\n", + "33 2021 Robin for Minneapolis 6.014500e+05\n", + "34 2021 Cleansweep Campaigns 4.226354e+05\n", + "35 2021 Republican Party of Minnesota - Federal Account 3.682714e+05\n", + "36 2021 EFTPS Fed Tax Payment 2.897723e+05\n", + "37 2021 McCool Digital LLC 2.571873e+05\n", + "38 2021 Yes 4 Minneapolis 2.190000e+05\n", + "39 2021 Democratic Attorneys General Assocation Inc 2.136878e+05\n", + "40 2022 Clarify Agency 2.647481e+08\n", + "41 2022 Great American Media 2.582326e+08\n", + "42 2022 Canal Partners Media 1.593249e+08\n", + "43 2022 Berlin Rosen LTD 1.105615e+08\n", + "44 2022 Red Eagle Media 1.082093e+08\n", + "45 2022 Deliver Strategies 1.074506e+08\n", + "46 2022 Nebo Media 8.849665e+07\n", + "47 2022 Sage Media Planning Placement 8.776352e+07\n", + "48 2022 FP1 Digital LLC 8.138634e+07\n", + "49 2022 Schultz, Jennifer House Committee 8.054502e+07\n", + "50 2023 MN DFL State Central Committee 7.828450e+05\n", + "51 2023 GRSG Company 5.000000e+05\n", + "52 2023 The People's Canvass 4.230976e+05\n", + "53 2023 MN DFL Senate Caucus 2.496439e+05\n", + "54 2023 MN DFL House Caucus 2.427772e+05\n", + "55 2023 AGC of MN 1.343460e+05\n", + "56 2023 SMART PAL-Education 1.200000e+05\n", + "57 2023 Teamsters Joint Council 32 8.893821e+04\n", + "58 2023 All of Mpls 8.621453e+04\n", + "59 2023 Apparatus 7.000000e+04" ] }, + "execution_count": 17, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], - "source": [ - "filtered_df = contribution_df[(contribution_df['DonationYear'] != -1) & (contribution_df['DonationYear'] >= 2018) & (contribution_df['DonationYear'] <= 2022)]\n", - "grouped4 = filtered_df.groupby(['DonationYear', 'RecipientType'])['TotalAmount'].sum().reset_index()\n", - "\n", - "fig = px.bar(\n", - " grouped4,\n", - " x='DonationYear',\n", - " y='TotalAmount',\n", - " color='RecipientType',\n", - " title='Donations by Recipient Type for the Last 5 Years',\n", - " labels={\"DonationYear\": \"Year\", \"TotalAmount\": \"Total Contributions\", 'RecipientType': 'Recipient Type'},\n", - ")\n", - "\n", - "fig.show()" + "source": [ + "top_10_vendor" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Observations and Interpretations\n", - "1. Candidates, as the recipients, make up the overwhelming majority of contributions.\n", - "2. Examining the period from 1998 to 2023, a distinct cyclical pattern emerges, characterized by alternating years of increased and decreased contributions, which may correspond to congressional elections or MN state house representatives elections which take place every two years.\n", - "3. Starting in 2012, recipient types \"Political Committee or Fund\" and \"Political Party Unit\" began receiving a larger share of contributions compared to prior years." + "### 4. Compare Expenditure by Spender Type and Vendor Type (2018-2023)" ] }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ - "race_type_mapping = {\n", - " 'AG': 'Attorney General',\n", - " 'AP': 'Political committee or fund',\n", - " 'DC': 'Candidate committee',\n", - " 'GC': 'Other',\n", - " 'House': 'Lobbyist',\n", - " 'SA': 'State Auditor',\n", - " 'SC': 'Local candidate committee registered with Hennepin County',\n", - " 'SS': 'Secretary of State',\n", - " 'Senate': 'Senate',\n", - "}" + "spendor_type_mapping = {\n", + " 'PCC': 'Candidate', \n", + " 'PCF': 'Political Committee and Fund', \n", + " 'PTU': 'Political Party Unit'\n", + "}\n", + "\n", + "grouped = filtered_df.groupby(['Year', 'SpenderType'])['Amount'].sum().reset_index()\n", + "grouped['FullSpenderType'] = grouped['SpenderType'].map(spendor_type_mapping)" ] }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -8059,16 +9293,16 @@ "data": [ { "alignmentgroup": "True", - "hovertemplate": "Full Race Type=Attorney General
Year=%{x}
Total Contributions=%{y}", - "legendgroup": "Attorney General", + "hovertemplate": "Spender Type=Candidate
Year=%{x}
Amount=%{y}", + "legendgroup": "Candidate", "marker": { "color": "#636efa", "pattern": { "shape": "" } }, - "name": "Attorney General", - "offsetgroup": "Attorney General", + "name": "Candidate", + "offsetgroup": "Candidate", "orientation": "v", "showlegend": true, "textposition": "auto", @@ -8082,212 +9316,26 @@ ], "xaxis": "x", "y": [ - 4003902.58, - 392454.04, - 293103, - 895901.69, - 4278017.76 + 26548504.63, + 3229041.3, + 17033964.74, + 4899697.12, + 56693359.6 ], "yaxis": "y" }, { "alignmentgroup": "True", - "hovertemplate": "Full Race Type=Candidate committee
Year=%{x}
Total Contributions=%{y}", - "legendgroup": "Candidate committee", + "hovertemplate": "Spender Type=Political Committee and Fund
Year=%{x}
Amount=%{y}", + "legendgroup": "Political Committee and Fund", "marker": { "color": "#EF553B", "pattern": { "shape": "" } }, - "name": "Candidate committee", - "offsetgroup": "Candidate committee", - "orientation": "v", - "showlegend": true, - "textposition": "auto", - "type": "bar", - "x": [ - 2018, - 2019, - 2020, - 2022 - ], - "xaxis": "x", - "y": [ - 611312.41, - 2750, - 98175.46, - 32351.04 - ], - "yaxis": "y" - }, - { - "alignmentgroup": "True", - "hovertemplate": "Full Race Type=Lobbyist
Year=%{x}
Total Contributions=%{y}", - "legendgroup": "Lobbyist", - "marker": { - "color": "#00cc96", - "pattern": { - "shape": "" - } - }, - "name": "Lobbyist", - "offsetgroup": "Lobbyist", - "orientation": "v", - "showlegend": true, - "textposition": "auto", - "type": "bar", - "x": [ - 2018, - 2019, - 2020, - 2021, - 2022 - ], - "xaxis": "x", - "y": [ - 20544735.5, - 9615308.49, - 14631841.16, - 7864215.63, - 14370826.86 - ], - "yaxis": "y" - }, - { - "alignmentgroup": "True", - "hovertemplate": "Full Race Type=Local candidate committee registered with Hennepin County
Year=%{x}
Total Contributions=%{y}", - "legendgroup": "Local candidate committee registered with Hennepin County", - "marker": { - "color": "#ab63fa", - "pattern": { - "shape": "" - } - }, - "name": "Local candidate committee registered with Hennepin County", - "offsetgroup": "Local candidate committee registered with Hennepin County", - "orientation": "v", - "showlegend": true, - "textposition": "auto", - "type": "bar", - "x": [ - 2018, - 2019, - 2020, - 2022 - ], - "xaxis": "x", - "y": [ - 301463.58, - 99000, - 303532.2, - 2000 - ], - "yaxis": "y" - }, - { - "alignmentgroup": "True", - "hovertemplate": "Full Race Type=Other
Year=%{x}
Total Contributions=%{y}", - "legendgroup": "Other", - "marker": { - "color": "#FFA15A", - "pattern": { - "shape": "" - } - }, - "name": "Other", - "offsetgroup": "Other", - "orientation": "v", - "showlegend": true, - "textposition": "auto", - "type": "bar", - "x": [ - 2018, - 2019, - 2020, - 2021, - 2022 - ], - "xaxis": "x", - "y": [ - 14891205.43, - 1993660.43, - 1426981.5, - 7058411.1, - 19091855.92 - ], - "yaxis": "y" - }, - { - "alignmentgroup": "True", - "hovertemplate": "Full Race Type=Political committee or fund
Year=%{x}
Total Contributions=%{y}", - "legendgroup": "Political committee or fund", - "marker": { - "color": "#19d3f3", - "pattern": { - "shape": "" - } - }, - "name": "Political committee or fund", - "offsetgroup": "Political committee or fund", - "orientation": "v", - "showlegend": true, - "textposition": "auto", - "type": "bar", - "x": [ - 2018 - ], - "xaxis": "x", - "y": [ - 139482.88 - ], - "yaxis": "y" - }, - { - "alignmentgroup": "True", - "hovertemplate": "Full Race Type=Secretary of State
Year=%{x}
Total Contributions=%{y}", - "legendgroup": "Secretary of State", - "marker": { - "color": "#FF6692", - "pattern": { - "shape": "" - } - }, - "name": "Secretary of State", - "offsetgroup": "Secretary of State", - "orientation": "v", - "showlegend": true, - "textposition": "auto", - "type": "bar", - "x": [ - 2018, - 2019, - 2020, - 2021, - 2022 - ], - "xaxis": "x", - "y": [ - 1137408.7, - 372350, - 362450, - 1167468.76, - 4795282.29 - ], - "yaxis": "y" - }, - { - "alignmentgroup": "True", - "hovertemplate": "Full Race Type=Senate
Year=%{x}
Total Contributions=%{y}", - "legendgroup": "Senate", - "marker": { - "color": "#B6E880", - "pattern": { - "shape": "" - } - }, - "name": "Senate", - "offsetgroup": "Senate", + "name": "Political Committee and Fund", + "offsetgroup": "Political Committee and Fund", "orientation": "v", "showlegend": true, "textposition": "auto", @@ -8297,30 +9345,32 @@ 2019, 2020, 2021, - 2022 + 2022, + 2023 ], "xaxis": "x", "y": [ - 3036186.27, - 11018745.95, - 14723556.76, - 7701612.07, - 11084849.84 + 79594084.34, + 14796360.9756, + 58282735.1, + 13892135.67, + 1805191463.08, + 4122720.01 ], "yaxis": "y" }, { "alignmentgroup": "True", - "hovertemplate": "Full Race Type=State Auditor
Year=%{x}
Total Contributions=%{y}", - "legendgroup": "State Auditor", + "hovertemplate": "Spender Type=Political Party Unit
Year=%{x}
Amount=%{y}", + "legendgroup": "Political Party Unit", "marker": { - "color": "#FF97FF", + "color": "#00cc96", "pattern": { "shape": "" } }, - "name": "State Auditor", - "offsetgroup": "State Auditor", + "name": "Political Party Unit", + "offsetgroup": "Political Party Unit", "orientation": "v", "showlegend": true, "textposition": "auto", @@ -8330,15 +9380,17 @@ 2019, 2020, 2021, - 2022 + 2022, + 2023 ], "xaxis": "x", "y": [ - 409451.94, - 50750, - 20335.42, - 85461.67, - 567693.37 + 31848724.06, + 8098960.0222, + 36032934.727, + 8015610.15, + 937871124.335, + 32803.71 ], "yaxis": "y" } @@ -8347,7 +9399,7 @@ "barmode": "relative", "legend": { "title": { - "text": "Full Race Type" + "text": "Spender Type" }, "tracegroupgap": 0 }, @@ -9168,7 +10220,7 @@ } }, "title": { - "text": "Donations by Candidate Recipient Race for the Last 5 Years" + "text": "Expenditure by Spender Type From 2018 To 2023" }, "xaxis": { "anchor": "y", @@ -9187,15 +10239,16 @@ 1 ], "title": { - "text": "Total Contributions" - } + "text": "Amount" + }, + "type": "log" } } }, "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "grouped = df_expenditure.groupby(['Year', 'Spender type'])['TotalAmount'].sum().reset_index()\n", - "grouped['Full Spender Type'] = grouped['Spender type'].map(spendor_type_mapping)\n", - "\n", - "fig = px.bar(\n", - " grouped,\n", - " x='Year',\n", - " y='TotalAmount',\n", - " color='Full Spender Type',\n", - " title='Expenditure by Spender Type from 2015 to 2022',\n", - " labels={\"Year\": \"Year\", \"TotalAmount\": \"Total Expenditure Log\", \"Full Spender Type\": \"Spender type\"},\n", - " category_orders={\"Full Spender Type\": sorted(spendor_type_mapping.values())}\n", - ")\n", - "fig.update_yaxes(type='log')\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Year int64\n", - "Spender Reg Num int64\n", - "Spender Name object\n", - "Spender type object\n", - "Spender sub-type object\n", - "Vendor name object\n", - "Vendor state object\n", - "Vendor city object\n", - "Amount float64\n", - "Unpaid amount float64\n", - "In-kind? object\n", - "In-kind descr object\n", - "Type object\n", - "Purpose object\n", - "Affected committee name object\n", - "Affected committee reg num float64\n", - "TotalAmount float64\n", - "dtype: object" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_expenditure.dtypes" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "base", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/utils/MN_util.py b/utils/MN_util.py index eb75f4a..96171af 100644 --- a/utils/MN_util.py +++ b/utils/MN_util.py @@ -48,7 +48,14 @@ def preprocess_candidate_df(df: pd.DataFrame) -> pd.DataFrame: "InKindDescriptionText", ] df_copy = df_copy[columns_to_keep] - column_mapping = {"CandRegNumb": "RegNumb", "CommitteeName": "Committee"} + column_mapping = { + "CandRegNumb": "RegNumb", + "CommitteeName": "Committee", + "DonationDate": "Date", + "DonationAmount": "Amount", + "InKindDonAmount": "InKindAmount", + "InKindDescriptionText": "InKindDescription", + } df_copy.rename(columns=column_mapping, inplace=True) df_copy["RecipientType"] = "Candidate" @@ -78,7 +85,14 @@ def preprocess_noncandidate_df(df: pd.DataFrame) -> pd.DataFrame: "InKindDescriptionText", ] df_copy = df_copy[columns_to_keep] - column_mapping = {"PCFRegNumb": "RegNumb", "ETType": "RecipientType"} + column_mapping = { + "PCFRegNumb": "RegNumb", + "ETType": "RecipientType", + "DonationDate": "Date", + "DonationAmount": "Amount", + "InKindDonAmount": "InKindAmount", + "InKindDescriptionText": "InKindDescription", + } df_copy.rename(columns=column_mapping, inplace=True) return df_copy @@ -95,34 +109,29 @@ def preprocess_contribution_df(df_lst: list) -> pd.DataFrame: """ contribution_df = pd.concat(df_lst, ignore_index=True) - contribution_df["DonationDate"] = pd.to_datetime( - contribution_df["DonationDate"]) - contribution_df["DonationYear"] = contribution_df["DonationDate"].dt.year - contribution_df = contribution_df.sort_values(by="DonationYear", - ascending=False) + contribution_df["Date"] = pd.to_datetime(contribution_df["Date"]) + contribution_df["Year"] = contribution_df["Date"].dt.year + contribution_df = contribution_df.sort_values(by="Year", ascending=False) contribution_df["DonorType"] = contribution_df["DonorType"].str.upper() - contribution_df["DonationAmount"] = pd.to_numeric( - contribution_df["DonationAmount"], errors="coerce" + contribution_df["Amount"] = pd.to_numeric( + contribution_df["Amount"], errors="coerce" ) - contribution_df["DonationAmount"] = \ - contribution_df["DonationAmount"].fillna(0) + contribution_df["Amount"] = contribution_df["Amount"].fillna(0) - contribution_df["InKindDonAmount"] = pd.to_numeric( - contribution_df["InKindDonAmount"], errors="coerce" + contribution_df["InKindAmount"] = pd.to_numeric( + contribution_df["InKindAmount"], errors="coerce" ) - contribution_df["InKindDonAmount"] = \ - contribution_df["InKindDonAmount"].fillna(0) + contribution_df["InKindAmount"] = contribution_df["InKindAmount"].fillna(0) contribution_df["TotalAmount"] = ( - contribution_df["DonationAmount"] + contribution_df["InKindDonAmount"] + contribution_df["Amount"] + contribution_df["InKindAmount"] ) - contribution_df["DonationYear"].fillna(-1, inplace=True) + contribution_df["Year"].fillna(-1, inplace=True) contribution_df["RegNumb"].fillna(-1, inplace=True) - contribution_df["DonationYear"] = \ - contribution_df["DonationYear"].astype(int) + contribution_df["Year"] = contribution_df["Year"].astype(int) contribution_df["RegNumb"] = contribution_df["RegNumb"].astype(int) return contribution_df @@ -144,3 +153,95 @@ def drop_nonclassifiable(df: pd.DataFrame) -> pd.DataFrame: df = df.reset_index(drop=True) return df + + +def preprocess_general_exp_df(df: pd.DataFrame) -> pd.DataFrame: + """ + Preprocesses MN general expenditure dataset into a DataFrame. + + Args: + df (DataFrame): the MN general expenditure DataFrames to preprocess + Returns: + DataFrame: Preprocessed MN general expenditure DataFrames + """ + + df_copy = df.copy(deep=True) + columns_to_keep = [ + 'Committee reg num', 'Committee name', 'Entity type', 'Vendor name', + 'Amount', 'Unpaid amount', 'Date', 'Year', 'Purpose', 'Type', + 'In-kind?', 'In-kind descr', 'Affected committee name', + 'Affected committee reg num' + ] + column_mapping = { + 'Committee reg num': 'SpenderRegNum', 'Committee name': 'SpenderName', + 'Entity type': 'SpenderType', 'Vendor name': 'VendorName', + 'Unpaid amount': 'UnpaidAmount', 'In-kind descr': 'InKindDescription', + 'Affected committee name': 'AffectedCommitteeName', + 'Affected committee reg num': 'AffectedCommitteeRegNum'} + + df_copy = df_copy[columns_to_keep] + df_copy.rename(columns=column_mapping, inplace=True) + + return df_copy + + +def preprocess_independent_exp_df(df: pd.DataFrame) -> pd.DataFrame: + """ + Preprocesses MN independent expenditure dataset into a DataFrame. + + Args: + df (DataFrame): the MN independent expenditure DataFrames to preprocess + Returns: + DataFrame: Preprocessed MN general expenditure DataFrames + """ + + df_copy = df.copy(deep=True) + columns_to_keep = [ + 'Spender Reg Num', 'Spender', 'Spender type', 'Vendor name', 'Amount', + 'Unpaid amount', 'Date', 'Year', 'Purpose', 'Type', 'In kind?', + 'In kind descr', 'Affected Comte Name', 'Affected Cmte Reg Num'] + column_mapping = { + 'Spender Reg Num': 'SpenderRegNum', 'Spender': 'SpenderName', + 'Spender type': 'SpenderType', 'Vendor name': 'VendorName', + 'Unpaid amount': 'UnpaidAmount', 'In kind?': 'In-kind?', + 'In kind descr': 'InKindDescription', + 'Affected Comte Name': 'AffectedCommitteeName', + 'Affected Cmte Reg Num': 'AffectedCommitteeRegNum'} + + df_copy = df_copy[columns_to_keep] + df_copy.rename(columns=column_mapping, inplace=True) + + return df_copy + + +def preprocess_expenditure_df(df_lst: list) -> pd.DataFrame: + """ + Preprocesses general & individual dfs into a complete MN expenditure df + + Args: + df_lst (list): a list of MN DataFrames to merge and adjust columns + Returns: + DataFrame: the merged and preprocessed expenditure df + """ + + expenditure_df = pd.concat(df_lst, ignore_index=True) + + return expenditure_df + + +def drop_nonclassifiable_exp(df: pd.DataFrame) -> pd.DataFrame: + """ + Drop contributions with zero transaction amount or no spender registration + number and name + + Args: + df (DataFrame): MN expenditure DataFrames to drop nonclassifiable data + Returns: + DataFrame: the expenditure df without non-classifiable data + """ + + df = df[df["Amount"] != 0] + df = df.dropna(subset=["SpenderRegNum", "SpenderName"], how="any") + df = df.reset_index(drop=True) + + return df From c7aeb13c1fbac864dd1f46aa9e95505359301d60 Mon Sep 17 00:00:00 2001 From: yuzhouw313 Date: Wed, 8 Nov 2023 19:10:15 +0000 Subject: [PATCH 09/51] passed black test --- utils/MN_util.py | 68 ++++++++++++++++++++++++++++++++++-------------- 1 file changed, 49 insertions(+), 19 deletions(-) diff --git a/utils/MN_util.py b/utils/MN_util.py index 96171af..fd3a00f 100644 --- a/utils/MN_util.py +++ b/utils/MN_util.py @@ -167,17 +167,31 @@ def preprocess_general_exp_df(df: pd.DataFrame) -> pd.DataFrame: df_copy = df.copy(deep=True) columns_to_keep = [ - 'Committee reg num', 'Committee name', 'Entity type', 'Vendor name', - 'Amount', 'Unpaid amount', 'Date', 'Year', 'Purpose', 'Type', - 'In-kind?', 'In-kind descr', 'Affected committee name', - 'Affected committee reg num' - ] + "Committee reg num", + "Committee name", + "Entity type", + "Vendor name", + "Amount", + "Unpaid amount", + "Date", + "Year", + "Purpose", + "Type", + "In-kind?", + "In-kind descr", + "Affected committee name", + "Affected committee reg num", + ] column_mapping = { - 'Committee reg num': 'SpenderRegNum', 'Committee name': 'SpenderName', - 'Entity type': 'SpenderType', 'Vendor name': 'VendorName', - 'Unpaid amount': 'UnpaidAmount', 'In-kind descr': 'InKindDescription', - 'Affected committee name': 'AffectedCommitteeName', - 'Affected committee reg num': 'AffectedCommitteeRegNum'} + "Committee reg num": "SpenderRegNum", + "Committee name": "SpenderName", + "Entity type": "SpenderType", + "Vendor name": "VendorName", + "Unpaid amount": "UnpaidAmount", + "In-kind descr": "InKindDescription", + "Affected committee name": "AffectedCommitteeName", + "Affected committee reg num": "AffectedCommitteeRegNum", + } df_copy = df_copy[columns_to_keep] df_copy.rename(columns=column_mapping, inplace=True) @@ -197,16 +211,32 @@ def preprocess_independent_exp_df(df: pd.DataFrame) -> pd.DataFrame: df_copy = df.copy(deep=True) columns_to_keep = [ - 'Spender Reg Num', 'Spender', 'Spender type', 'Vendor name', 'Amount', - 'Unpaid amount', 'Date', 'Year', 'Purpose', 'Type', 'In kind?', - 'In kind descr', 'Affected Comte Name', 'Affected Cmte Reg Num'] + "Spender Reg Num", + "Spender", + "Spender type", + "Vendor name", + "Amount", + "Unpaid amount", + "Date", + "Year", + "Purpose", + "Type", + "In kind?", + "In kind descr", + "Affected Comte Name", + "Affected Cmte Reg Num", + ] column_mapping = { - 'Spender Reg Num': 'SpenderRegNum', 'Spender': 'SpenderName', - 'Spender type': 'SpenderType', 'Vendor name': 'VendorName', - 'Unpaid amount': 'UnpaidAmount', 'In kind?': 'In-kind?', - 'In kind descr': 'InKindDescription', - 'Affected Comte Name': 'AffectedCommitteeName', - 'Affected Cmte Reg Num': 'AffectedCommitteeRegNum'} + "Spender Reg Num": "SpenderRegNum", + "Spender": "SpenderName", + "Spender type": "SpenderType", + "Vendor name": "VendorName", + "Unpaid amount": "UnpaidAmount", + "In kind?": "In-kind?", + "In kind descr": "InKindDescription", + "Affected Comte Name": "AffectedCommitteeName", + "Affected Cmte Reg Num": "AffectedCommitteeRegNum", + } df_copy = df_copy[columns_to_keep] df_copy.rename(columns=column_mapping, inplace=True) From 404025ad17f5c106abca36a9127f1ec9b4f429d8 Mon Sep 17 00:00:00 2001 From: yuzhouw313 Date: Tue, 14 Nov 2023 21:14:45 +0000 Subject: [PATCH 10/51] first draft of MN abstract class, entity map not done --- notebooks/MN_EDA.ipynb | 2679 +++++++------------------------------ utils/clean.py | 14 +- utils/constants.py | 81 ++ utils/mn_state_cleaner.py | 317 +++++ 4 files changed, 884 insertions(+), 2207 deletions(-) create mode 100644 utils/mn_state_cleaner.py diff --git a/notebooks/MN_EDA.ipynb b/notebooks/MN_EDA.ipynb index 92ca2b0..2a80427 100644 --- a/notebooks/MN_EDA.ipynb +++ b/notebooks/MN_EDA.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -3011,6 +3011,25 @@ "fig.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Observations and Interpretations\n", + "1. Individuals, excluding lobbyists, constitute the largest share of contributions in the MN dataset.\n", + "2. The second most substantial contributor category is General Purpose Political Committee or Fund, followed by lobbyists.\n", + "3. Contributions from other donor types are notably lower throughout the years.\n", + "4. Analyzing a sample from 2018 to 2022, we observe a cyclical pattern with a major increase in contributions, followed by three years of reduced contribution totals. This cycle aligns with the four-year election cycle.\n", + "5. From 1998 to 2023, there are several years with significantly lower contribution amount: 1999, 2001, 2003, 2007, 2011." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2 Compare donation by recipient types" + ] + }, { "cell_type": "code", "execution_count": null, @@ -3025,226 +3044,106 @@ "data": [ { "alignmentgroup": "True", - "hovertemplate": "Donor Type=Association not registered with the Board
Year=%{x}
Total Contributions=%{y}", - "legendgroup": "Association not registered with the Board", + "hovertemplate": "Recipient Type=Candidate
Year=%{x}
Total Contributions=%{y}", + "legendgroup": "Candidate", "marker": { "color": "#636efa", "pattern": { "shape": "" } }, - "name": "Association not registered with the Board", - "offsetgroup": "Association not registered with the Board", - "orientation": "v", - "showlegend": true, - "textposition": "auto", - "type": "bar", - "x": [ - 2022 - ], - "xaxis": "x", - "y": [ - 1067.47 - ], - "yaxis": "y" - }, - { - "alignmentgroup": "True", - "hovertemplate": "Donor Type=Candidate committee
Year=%{x}
Total Contributions=%{y}", - "legendgroup": "Candidate committee", - "marker": { - "color": "#00cc96", - "pattern": { - "shape": "" - } - }, - "name": "Candidate committee", - "offsetgroup": "Candidate committee", - "orientation": "v", - "showlegend": true, - "textposition": "auto", - "type": "bar", - "x": [ - 2022 - ], - "xaxis": "x", - "y": [ - 641245.11 - ], - "yaxis": "y" - }, - { - "alignmentgroup": "True", - "hovertemplate": "Donor Type=Lobbyist
Year=%{x}
Total Contributions=%{y}", - "legendgroup": "Lobbyist", - "marker": { - "color": "#ab63fa", - "pattern": { - "shape": "" - } - }, - "name": "Lobbyist", - "offsetgroup": "Lobbyist", - "orientation": "v", - "showlegend": true, - "textposition": "auto", - "type": "bar", - "x": [ - 2022 - ], - "xaxis": "x", - "y": [ - 1953755.2 - ], - "yaxis": "y" - }, - { - "alignmentgroup": "True", - "hovertemplate": "Donor Type=Local candidate committee registered with Hennepin County
Year=%{x}
Total Contributions=%{y}", - "legendgroup": "Local candidate committee registered with Hennepin County", - "marker": { - "color": "#FFA15A", - "pattern": { - "shape": "" - } - }, - "name": "Local candidate committee registered with Hennepin County", - "offsetgroup": "Local candidate committee registered with Hennepin County", - "orientation": "v", - "showlegend": true, - "textposition": "auto", - "type": "bar", - "x": [ - 2022 - ], - "xaxis": "x", - "y": [ - 6750 - ], - "yaxis": "y" - }, - { - "alignmentgroup": "True", - "hovertemplate": "Donor Type=Non-lobbyist individual
Year=%{x}
Total Contributions=%{y}", - "legendgroup": "Non-lobbyist individual", - "marker": { - "color": "#19d3f3", - "pattern": { - "shape": "" - } - }, - "name": "Non-lobbyist individual", - "offsetgroup": "Non-lobbyist individual", - "orientation": "v", - "showlegend": true, - "textposition": "auto", - "type": "bar", - "x": [ - 2022 - ], - "xaxis": "x", - "y": [ - 46536784.82 - ], - "yaxis": "y" - }, - { - "alignmentgroup": "True", - "hovertemplate": "Donor Type=Other
Year=%{x}
Total Contributions=%{y}", - "legendgroup": "Other", - "marker": { - "color": "#FF6692", - "pattern": { - "shape": "" - } - }, - "name": "Other", - "offsetgroup": "Other", - "orientation": "v", - "showlegend": true, - "textposition": "auto", - "type": "bar", - "x": [ - 2022 - ], - "xaxis": "x", - "y": [ - 239708.38 - ], - "yaxis": "y" - }, - { - "alignmentgroup": "True", - "hovertemplate": "Donor Type=Political committee or fund
Year=%{x}
Total Contributions=%{y}", - "legendgroup": "Political committee or fund", - "marker": { - "color": "#B6E880", - "pattern": { - "shape": "" - } - }, - "name": "Political committee or fund", - "offsetgroup": "Political committee or fund", + "name": "Candidate", + "offsetgroup": "Candidate", "orientation": "v", "showlegend": true, "textposition": "auto", "type": "bar", "x": [ - 2022 + 2018, + 2019, + 2020, + 2021, + 2022, + 2023 ], "xaxis": "x", "y": [ - 6411623.78 + 45075149.29, + 23545018.91, + 31859975.5, + 24773070.92, + 54222877.08, + 40405 ], "yaxis": "y" }, { "alignmentgroup": "True", - "hovertemplate": "Donor Type=Political party unit
Year=%{x}
Total Contributions=%{y}", - "legendgroup": "Political party unit", + "hovertemplate": "Recipient Type=PCF
Year=%{x}
Total Contributions=%{y}", + "legendgroup": "PCF", "marker": { - "color": "#FF97FF", + "color": "#EF553B", "pattern": { "shape": "" } }, - "name": "Political party unit", - "offsetgroup": "Political party unit", + "name": "PCF", + "offsetgroup": "PCF", "orientation": "v", "showlegend": true, "textposition": "auto", "type": "bar", "x": [ - 2022 + 2018, + 2019, + 2020, + 2021, + 2022, + 2023 ], "xaxis": "x", "y": [ - 1624652.29 + 1656145.98, + 1459419.13, + 1701776.18, + 1508361.7, + 2292700.66, + 218273.57 ], "yaxis": "y" }, { "alignmentgroup": "True", - "hovertemplate": "Donor Type=Self
Year=%{x}
Total Contributions=%{y}", - "legendgroup": "Self", + "hovertemplate": "Recipient Type=PTU
Year=%{x}
Total Contributions=%{y}", + "legendgroup": "PTU", "marker": { - "color": "#FECB52", + "color": "#00cc96", "pattern": { "shape": "" } }, - "name": "Self", - "offsetgroup": "Self", + "name": "PTU", + "offsetgroup": "PTU", "orientation": "v", "showlegend": true, "textposition": "auto", "type": "bar", "x": [ - 2022 + 2018, + 2019, + 2020, + 2021, + 2022, + 2023 ], "xaxis": "x", "y": [ - 268027.56 + 1050553.6, + 760467.56, + 1052667.39, + 617345.59, + 1176668.38, + 6283.35 ], "yaxis": "y" } @@ -3253,7 +3152,7 @@ "barmode": "relative", "legend": { "title": { - "text": "Donor Type" + "text": "Recipient Type" }, "tracegroupgap": 0 }, @@ -4074,7 +3973,7 @@ } }, "title": { - "text": "Donations by Donor Type in 2022" + "text": "Donations by Recipient Type from 2018 to 2023" }, "xaxis": { "anchor": "y", @@ -4099,2030 +3998,9 @@ } }, "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "filtered_df2 = contribution_df[contribution_df['Year'] == 2022]\n", - "grouped3 = filtered_df2.groupby(['Year', 'DonorType'])['TotalAmount'].sum().reset_index()\n", - "\n", - "grouped3['FullDonorType'] = grouped3['DonorType'].map(donor_type_mapping)\n", - "\n", - "fig = px.bar(\n", - " grouped3,\n", - " x='Year',\n", - " y='TotalAmount',\n", - " color='FullDonorType',\n", - " title='Donations by Donor Type in 2022',\n", - " labels={\"DonationYear\": \"Year\", \"TotalAmount\": \"Total Contributions\", \"FullDonorType\": \"Donor Type\"},\n", - " category_orders={\"FullDonorType\": sorted(donor_type_mapping.values())}\n", - ")\n", - "\n", - "fig.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Observations and Interpretations\n", - "1. Individuals, excluding lobbyists, constitute the largest share of contributions in the MN dataset.\n", - "2. The second most substantial contributor category is General Purpose Political Committee or Fund, followed by lobbyists.\n", - "3. Contributions from other donor types are notably lower throughout the years.\n", - "4. Analyzing a sample from 2018 to 2022, we observe a cyclical pattern with a major increase in contributions, followed by three years of reduced contribution totals. This cycle aligns with the four-year election cycle.\n", - "5. From 1998 to 2023, there are several years with significantly lower contribution amount: 1999, 2001, 2003, 2007, 2011." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.2 Compare donation by recipient types" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "alignmentgroup": "True", - "hovertemplate": "Recipient Type=Candidate
Year=%{x}
Total Contributions=%{y}", - "legendgroup": "Candidate", - "marker": { - "color": "#636efa", - "pattern": { - "shape": "" - } - }, - "name": "Candidate", - "offsetgroup": "Candidate", - "orientation": "v", - "showlegend": true, - "textposition": "auto", - "type": "bar", - "x": [ - 2018, - 2019, - 2020, - 2021, - 2022, - 2023 - ], - "xaxis": "x", - "y": [ - 45075149.29, - 23545018.91, - 31859975.5, - 24773070.92, - 54222877.08, - 40405 - ], - "yaxis": "y" - }, - { - "alignmentgroup": "True", - "hovertemplate": "Recipient Type=PCF
Year=%{x}
Total Contributions=%{y}", - "legendgroup": "PCF", - "marker": { - "color": "#EF553B", - "pattern": { - "shape": "" - } - }, - "name": "PCF", - "offsetgroup": "PCF", - "orientation": "v", - "showlegend": true, - "textposition": "auto", - "type": "bar", - "x": [ - 2018, - 2019, - 2020, - 2021, - 2022, - 2023 - ], - "xaxis": "x", - "y": [ - 1656145.98, - 1459419.13, - 1701776.18, - 1508361.7, - 2292700.66, - 218273.57 - ], - "yaxis": "y" - }, - { - "alignmentgroup": "True", - "hovertemplate": "Recipient Type=PTU
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"text": "Donations by Recipient Type in 2022" - }, - "xaxis": { - "anchor": "y", - "domain": [ - 0, - 1 - ], - "title": { - "text": "Year" - } - }, - "yaxis": { - "anchor": "x", - "domain": [ - 0, - 1 - ], - "title": { - "text": "Total Contributions" - } - } - } - }, - "text/html": [ - "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "data": { "application/vnd.plotly.v1+json": { @@ -2962,9 +2996,9 @@ } }, "text/html": [ - "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "grouped4 = filtered_df2.groupby(['Year', 'RecipientType'])['TotalAmount'].sum().reset_index()\n", + "\n", + "fig = px.bar(\n", + " grouped4,\n", + " x='RecipientType', # Use 'RecipientType' as the x-axis\n", + " y='TotalAmount',\n", + " color='RecipientType',\n", + " title='Donations by Recipient Type in 2022',\n", + " labels={\"TotalAmount\": \"Total Contributions\", 'RecipientType': 'Recipient Type'},\n", + " facet_col='Year', # Use facet_col to create separate bars for each year (optional)\n", + ")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Observations and Interpretations\n", + "1. Candidates, as the recipients, make up the overwhelming majority of contributions.\n", + "2. Examining the period from 1998 to 2023, a distinct cyclical pattern emerges, characterized by alternating years of increased and decreased contributions, which may correspond to congressional elections or MN state house representatives elections which take place every two years.\n", + "3. Starting in 2012, recipient types \"Political Committee or Fund\" and \"Political Party Unit\" began receiving a larger share of contributions compared to prior years." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "race_type_mapping = {\n", + " 'AG': 'Attorney General',\n", + " 'AP': 'State Appeals Court Judge',\n", + " 'DC': 'State District Court Judge',\n", + " 'GC': 'Governor',\n", + " 'House': 'State Representative',\n", + " 'SA': 'State Auditor',\n", + " 'SC': 'State Supreme Court Justice',\n", + " 'SS': 'Secretary of State',\n", + " 'ST': 'State Treasurer',\n", + " 'Senate': 'Senate'\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
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OfficeSoughtRegNumbCandFirstNameCandLastNameCommitteeDateDonorTypeDonorNameAmountInKindAmountInKindDescriptionRecipientTypeYearTotalAmount
22NaN30119NaNNaNIBEW Local 292 Political Education Fund2023-03-05OIBEW Local 29255.760.0NaNPCF202355.76
58NaN30119NaNNaNIBEW Local 292 Political Education Fund2023-03-09OIBEW Local 292262.220.0NaNPCF2023262.22
95NaN30119NaNNaNIBEW Local 292 Political Education Fund2023-02-17OIBEW Local 292550.420.0NaNPCF2023550.42
103NaN30119NaNNaNIBEW Local 292 Political Education Fund2023-02-23OIBEW Local 292133.660.0NaNPCF2023133.66
108NaN30119NaNNaNIBEW Local 292 Political Education Fund2023-02-28OIBEW Local 29275.100.0NaNPCF202375.10
.............................................
1621101NaN30119NaNNaNIBEW Local 292 Political Education Fund2012-10-18OIBEW Local 29262.300.0NaNPCF201262.30
1624800NaN30119NaNNaNIBEW Local 292 Political Education Fund2012-10-11OIBEW Local 292196.120.0NaNPCF2012196.12
2297031NaN30119NaNNaNIBEW Local 292 Political Education Fund2008-12-30OIBEW Local 29259.310.0NaNPCF200859.31
2298939NaN30119NaNNaNIBEW Local 292 Political Education Fund2008-10-14OIBEW Local 292351.600.0NaNPCF2008351.60
2300737NaN30119NaNNaNIBEW Local 292 Political Education Fund2008-10-22OIBEW Local 292112.140.0NaNPCF2008112.14
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" ] }, - "execution_count": 29, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "contribution_df[contribution_df['RegNumb']==30119]" + "grouped5 = filtered_df.groupby(['Year', 'OfficeSought'])['TotalAmount'].sum().reset_index()\n", + "grouped5['FullRaceType'] = grouped5['OfficeSought'].map(race_type_mapping)\n", + "\n", + "fig = px.bar(\n", + " grouped5,\n", + " x='Year',\n", + " y='TotalAmount',\n", + " color='FullRaceType',\n", + " title='Donations by Candidate Recipient Race From 2018 To 2023',\n", + " labels={\"Year\": \"Year\", \"TotalAmount\": \"Total Contributions\", 'FullRaceType': 'Full Race Type'},\n", + " category_orders={\"FullRaceType\": sorted(race_type_mapping.values())}\n", + ")\n", + "\n", + "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# MN Expenditure EDA" + "Clearly, state senators and house representatives recieve the most contributions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 1. Read in and Preprocess Datasets" + "# MN Expenditure EDA" ] }, { - "cell_type": "code", - "execution_count": 13, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "df_general = pd.read_csv('/project/data/general_exp_con.csv')\n", - "df_independent = pd.read_csv('/project/data/independent_exp.csv')" + "### 1. Read in and Preprocess Datasets" ] }, { @@ -5614,44 +7529,31 @@ "metadata": {}, "outputs": [], "source": [ - "from utils.MN_util import preprocess_general_exp_df\n", - "df_general = preprocess_general_exp_df(df_general)" + "df_independent = pd.read_csv('/project/data/independent_exp.csv')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, - "outputs": [], - "source": [ - "from utils.MN_util import preprocess_independent_exp_df\n", - "df_independent = preprocess_independent_exp_df(df_independent)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array([ True, True, True, True, True, True, True, True, True,\n", - " True, True, True, True, True])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_56567/3801224637.py:2: DeprecationWarning: Call to deprecated function preprocess_expenditure.\n", + " df_expenditure = preprocess_expenditure(df_independent)\n" + ] } ], "source": [ - "df_independent.columns==df_general.columns" + "from utils.MN_util import preprocess_expenditure\n", + "df_expenditure = preprocess_expenditure(df_independent)" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -5674,29 +7576,18 @@ "dtype: object" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_independent.dtypes" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from utils.MN_util import preprocess_expenditure_df\n", - "\n", - "df_expenditure = preprocess_expenditure_df([df_general, df_independent])" + "df_expenditure.dtypes" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -5739,88 +7630,88 @@ " \n", " \n", " 0\n", - " 15863\n", - " Hooten, Carol Dist Court Committee\n", - " PCC\n", - " NaN\n", - " 102.84\n", + " 20003\n", + " MN DFL State Central Committee\n", + " PTU\n", + " 76 Words\n", + " 2500.00\n", " 0.0\n", - " 10/10/2015\n", - " 2015\n", - " Repayment of part of loan\n", - " Other Disbursement\n", + " 10/28/2022\n", + " 2022\n", + " Production, Radio\n", + " Independent Expenditure\n", " No\n", " NaN\n", - " NaN\n", - " NaN\n", + " Walz, Tim Gov Committee\n", + " 18135.0\n", " \n", " \n", " 1\n", - " 16008\n", - " Faust, Timothy D House Committee\n", - " PCC\n", - " Kanabec Publications\n", - " 429.11\n", + " 20003\n", + " MN DFL State Central Committee\n", + " PTU\n", + " 76 Words\n", + " 2500.00\n", " 0.0\n", - " 02/18/2015\n", - " 2015\n", - " Advertising - Print: $4.96 was service charge ...\n", - " Campaign Expenditure\n", + " 10/28/2022\n", + " 2022\n", + " Production, Radio\n", + " Independent Expenditure\n", " No\n", " NaN\n", - " NaN\n", - " NaN\n", + " Walz, Tim Gov Committee\n", + " 18135.0\n", " \n", " \n", " 2\n", - " 16248\n", - " Swails, Marsha G House Committee\n", - " PCC\n", - " NaN\n", - " 1293.77\n", + " 20003\n", + " MN DFL State Central Committee\n", + " PTU\n", + " 76 Words\n", + " 2500.00\n", " 0.0\n", - " 03/25/2015\n", - " 2015\n", - " NaN\n", - " Contribution\n", + " 10/28/2022\n", + " 2022\n", + " Production, Radio\n", + " Independent Expenditure\n", " No\n", " NaN\n", - " womenwinning State PAC\n", - " 40268.0\n", + " Walz, Tim Gov Committee\n", + " 18135.0\n", " \n", " \n", " 3\n", - " 16248\n", - " Swails, Marsha G House Committee\n", - " PCC\n", - " Women Candidate Development Coalition\n", - " 1293.76\n", + " 20003\n", + " MN DFL State Central Committee\n", + " PTU\n", + " 76 Words\n", + " 2500.00\n", " 0.0\n", - " 03/25/2015\n", - " 2015\n", - " contribution to non-profit\n", - " Other Disbursement\n", + " 10/28/2022\n", + " 2022\n", + " Production, Radio\n", + " Independent Expenditure\n", " No\n", " NaN\n", - " NaN\n", - " NaN\n", + " Walz, Tim Gov Committee\n", + " 18135.0\n", " \n", " \n", " 4\n", - " 16276\n", - " Sawatzke, Pat Senate Committee\n", - " PCC\n", - " UND Foundation\n", - " 500.00\n", + " 20003\n", + " MN DFL State Central Committee\n", + " PTU\n", + " 76 Words\n", + " 2500.00\n", " 0.0\n", - " 07/30/2015\n", - " 2015\n", - " charitable contribution\n", - " Other Disbursement\n", + " 10/28/2022\n", + " 2022\n", + " Production, Radio\n", + " Independent Expenditure\n", " No\n", " NaN\n", - " NaN\n", - " NaN\n", + " Walz, Tim Gov Committee\n", + " 18135.0\n", " \n", " \n", " ...\n", @@ -5840,24 +7731,24 @@ " ...\n", " \n", " \n", - " 664185\n", + " 441858\n", " 80026\n", " MN Assoc of Professional Employees Political Fund\n", " PCF\n", " MN Association of Professional Employees\n", - " 304.28\n", + " 163.34\n", " 0.0\n", - " 10/21/2020\n", + " 10/28/2020\n", " 2020\n", " Employee Expense: Staff costs\n", " Independent Expenditure\n", " No\n", " NaN\n", - " Isaacson, Jason (Ike) Senate Committee\n", - " 17929.0\n", + " Kent, Susan Senate Committee\n", + " 17443.0\n", " \n", " \n", - " 664186\n", + " 441859\n", " 80026\n", " MN Assoc of Professional Employees Political Fund\n", " PCF\n", @@ -5870,16 +7761,16 @@ " Independent Expenditure\n", " No\n", " NaN\n", - " Newton, Jerry Senate Committee\n", - " 18004.0\n", + " Bayley, Lisa Pritchard House Committee\n", + " 17933.0\n", " \n", " \n", - " 664187\n", + " 441860\n", " 80026\n", " MN Assoc of Professional Employees Political Fund\n", " PCF\n", " No Coast Workshop\n", - " 217.40\n", + " 217.39\n", " 0.0\n", " 10/13/2016\n", " 2016\n", @@ -5887,11 +7778,11 @@ " Independent Expenditure\n", " No\n", " NaN\n", - " Calvert, Deborah (Deb) Senate Committee\n", - " 18032.0\n", + " Ward, JoAnn House Committee\n", + " 17438.0\n", " \n", " \n", - " 664188\n", + " 441861\n", " 80026\n", " MN Assoc of Professional Employees Political Fund\n", " PCF\n", @@ -5904,11 +7795,11 @@ " Independent Expenditure\n", " No\n", " NaN\n", - " Maye Quade, Erin House Committee\n", - " 17885.0\n", + " Koenen, Lyle J Senate Committee\n", + " 17407.0\n", " \n", " \n", - " 664189\n", + " 441862\n", " 80026\n", " MN Assoc of Professional Employees Political Fund\n", " PCF\n", @@ -5921,97 +7812,97 @@ " Independent Expenditure\n", " No\n", " NaN\n", - " Witt, Susan D House Committee\n", - " 17723.0\n", + " Pryor, Laurie House Committee\n", + " 18031.0\n", " \n", " \n", "\n", - "

664190 rows × 14 columns

\n", + "

441863 rows × 14 columns

\n", "
" ], "text/plain": [ " SpenderRegNum SpenderName \\\n", - "0 15863 Hooten, Carol Dist Court Committee \n", - "1 16008 Faust, Timothy D House Committee \n", - "2 16248 Swails, Marsha G House Committee \n", - "3 16248 Swails, Marsha G House Committee \n", - "4 16276 Sawatzke, Pat Senate Committee \n", + "0 20003 MN DFL State Central Committee \n", + "1 20003 MN DFL State Central Committee \n", + "2 20003 MN DFL State Central Committee \n", + "3 20003 MN DFL State Central Committee \n", + "4 20003 MN DFL State Central Committee \n", "... ... ... \n", - "664185 80026 MN Assoc of Professional Employees Political Fund \n", - "664186 80026 MN Assoc of Professional Employees Political Fund \n", - "664187 80026 MN Assoc of Professional Employees Political Fund \n", - "664188 80026 MN Assoc of Professional Employees Political Fund \n", - "664189 80026 MN Assoc of Professional Employees Political Fund \n", + "441858 80026 MN Assoc of Professional Employees Political Fund \n", + "441859 80026 MN Assoc of Professional Employees Political Fund \n", + "441860 80026 MN Assoc of Professional Employees Political Fund \n", + "441861 80026 MN Assoc of Professional Employees Political Fund \n", + "441862 80026 MN Assoc of Professional Employees Political Fund \n", "\n", " SpenderType VendorName Amount \\\n", - "0 PCC NaN 102.84 \n", - "1 PCC Kanabec Publications 429.11 \n", - "2 PCC NaN 1293.77 \n", - "3 PCC Women Candidate Development Coalition 1293.76 \n", - "4 PCC UND Foundation 500.00 \n", + "0 PTU 76 Words 2500.00 \n", + "1 PTU 76 Words 2500.00 \n", + "2 PTU 76 Words 2500.00 \n", + "3 PTU 76 Words 2500.00 \n", + "4 PTU 76 Words 2500.00 \n", "... ... ... ... \n", - "664185 PCF MN Association of Professional Employees 304.28 \n", - "664186 PCF No Coast Workshop 217.39 \n", - "664187 PCF No Coast Workshop 217.40 \n", - "664188 PCF No Coast Workshop 144.32 \n", - "664189 PCF No Coast Workshop 144.33 \n", + "441858 PCF MN Association of Professional Employees 163.34 \n", + "441859 PCF No Coast Workshop 217.39 \n", + "441860 PCF No Coast Workshop 217.39 \n", + "441861 PCF No Coast Workshop 144.32 \n", + "441862 PCF No Coast Workshop 144.33 \n", "\n", " UnpaidAmount Date Year \\\n", - "0 0.0 10/10/2015 2015 \n", - "1 0.0 02/18/2015 2015 \n", - "2 0.0 03/25/2015 2015 \n", - "3 0.0 03/25/2015 2015 \n", - "4 0.0 07/30/2015 2015 \n", + "0 0.0 10/28/2022 2022 \n", + "1 0.0 10/28/2022 2022 \n", + "2 0.0 10/28/2022 2022 \n", + "3 0.0 10/28/2022 2022 \n", + "4 0.0 10/28/2022 2022 \n", "... ... ... ... \n", - "664185 0.0 10/21/2020 2020 \n", - "664186 0.0 10/13/2016 2016 \n", - "664187 0.0 10/13/2016 2016 \n", - "664188 0.0 11/03/2016 2016 \n", - "664189 0.0 11/03/2016 2016 \n", + "441858 0.0 10/28/2020 2020 \n", + "441859 0.0 10/13/2016 2016 \n", + "441860 0.0 10/13/2016 2016 \n", + "441861 0.0 11/03/2016 2016 \n", + "441862 0.0 11/03/2016 2016 \n", "\n", " Purpose \\\n", - "0 Repayment of part of loan \n", - "1 Advertising - Print: $4.96 was service charge ... \n", - "2 NaN \n", - "3 contribution to non-profit \n", - "4 charitable contribution \n", + "0 Production, Radio \n", + "1 Production, Radio \n", + "2 Production, Radio \n", + "3 Production, Radio \n", + "4 Production, Radio \n", "... ... \n", - "664185 Employee Expense: Staff costs \n", - "664186 Printing and Photocopying: Independent Expendi... \n", - "664187 Printing and Photocopying: Independent Expendi... \n", - "664188 Postage/ Delivery: Independent Expenditure Mai... \n", - "664189 Postage/ Delivery: Independent Expenditure Mai... \n", + "441858 Employee Expense: Staff costs \n", + "441859 Printing and Photocopying: Independent Expendi... \n", + "441860 Printing and Photocopying: Independent Expendi... \n", + "441861 Postage/ Delivery: Independent Expenditure Mai... \n", + "441862 Postage/ Delivery: Independent Expenditure Mai... \n", "\n", " Type In-kind? InKindDescription \\\n", - "0 Other Disbursement No NaN \n", - "1 Campaign Expenditure No NaN \n", - "2 Contribution No NaN \n", - "3 Other Disbursement No NaN \n", - "4 Other Disbursement No NaN \n", + "0 Independent Expenditure No NaN \n", + "1 Independent Expenditure No NaN \n", + "2 Independent Expenditure No NaN \n", + "3 Independent Expenditure No NaN \n", + "4 Independent Expenditure No NaN \n", "... ... ... ... \n", - "664185 Independent Expenditure No NaN \n", - "664186 Independent Expenditure No NaN \n", - "664187 Independent Expenditure No NaN \n", - "664188 Independent Expenditure No NaN \n", - "664189 Independent Expenditure No NaN \n", + "441858 Independent Expenditure No NaN \n", + "441859 Independent Expenditure No NaN \n", + "441860 Independent Expenditure No NaN \n", + "441861 Independent Expenditure No NaN \n", + "441862 Independent Expenditure No NaN \n", "\n", - " AffectedCommitteeName AffectedCommitteeRegNum \n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 womenwinning State PAC 40268.0 \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "664185 Isaacson, Jason (Ike) Senate Committee 17929.0 \n", - "664186 Newton, Jerry Senate Committee 18004.0 \n", - "664187 Calvert, Deborah (Deb) Senate Committee 18032.0 \n", - "664188 Maye Quade, Erin House Committee 17885.0 \n", - "664189 Witt, Susan D House Committee 17723.0 \n", + " AffectedCommitteeName AffectedCommitteeRegNum \n", + "0 Walz, Tim Gov Committee 18135.0 \n", + "1 Walz, Tim Gov Committee 18135.0 \n", + "2 Walz, Tim Gov Committee 18135.0 \n", + "3 Walz, Tim Gov Committee 18135.0 \n", + "4 Walz, Tim Gov Committee 18135.0 \n", + "... ... ... \n", + "441858 Kent, Susan Senate Committee 17443.0 \n", + "441859 Bayley, Lisa Pritchard House Committee 17933.0 \n", + "441860 Ward, JoAnn House Committee 17438.0 \n", + "441861 Koenen, Lyle J Senate Committee 17407.0 \n", + "441862 Pryor, Laurie House Committee 18031.0 \n", "\n", - "[664190 rows x 14 columns]" + "[441863 rows x 14 columns]" ] }, - "execution_count": 8, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -6022,24 +7913,19 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "['PCC' 'PCF' 'PTU']\n", - "['Other Disbursement' 'Campaign Expenditure' 'Contribution'\n", - " 'Non-Campaign Disbursement' 'General Expenditure'\n", - " 'Ballot Question Expenditure' 'Party Unit' 'Candidate Committee'\n", - " 'Political Committee/Fund' 'Independent Expenditure']\n" + "['PTU' 'PCF']\n" ] } ], "source": [ - "print(df_expenditure['SpenderType'].unique())\n", - "print(df_expenditure['Type'].unique())" + "print(df_expenditure['SpenderType'].unique())" ] }, { @@ -6051,30 +7937,30 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "SpenderRegNum 0\n", - "SpenderName 81\n", + "SpenderName 0\n", "SpenderType 0\n", - "VendorName 38386\n", + "VendorName 605\n", "Amount 0\n", "UnpaidAmount 0\n", "Date 0\n", "Year 0\n", - "Purpose 66290\n", + "Purpose 22801\n", "Type 0\n", "In-kind? 0\n", - "InKindDescription 639990\n", - "AffectedCommitteeName 236995\n", - "AffectedCommitteeRegNum 236693\n", + "InKindDescription 437803\n", + "AffectedCommitteeName 11151\n", + "AffectedCommitteeRegNum 11146\n", "dtype: int64" ] }, - "execution_count": 10, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -6085,17 +7971,17 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Total number of expenditure entries = 664190\n", - "Total number of nonclassifiable expenditure amount = 355\n", - "Total number of nonclassifiable spenders = 81\n", - "Proportion of nonclassifiable entries = 0.0656%\n" + "Total number of expenditure entries = 441863\n", + "Total number of nonclassifiable expenditure amount = 306\n", + "Total number of nonclassifiable spenders = 0\n", + "Proportion of nonclassifiable entries = 0.0693%\n" ] } ], @@ -6112,33 +7998,42 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_56567/3446921023.py:2: DeprecationWarning: Call to deprecated function drop_nonclassifiable_expenditure.\n", + " df_expenditure = drop_nonclassifiable_expenditure(df_expenditure)\n" + ] + } + ], "source": [ - "from utils.MN_util import drop_nonclassifiable_exp\n", - "df_expenditure = drop_nonclassifiable_exp(df_expenditure)" + "from utils.MN_util import drop_nonclassifiable_expenditure\n", + "df_expenditure = drop_nonclassifiable_expenditure(df_expenditure)" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Year\n", - "2018 1.379913e+08\n", - "2019 2.612436e+07\n", - "2020 1.113496e+08\n", - "2021 2.680744e+07\n", - "2022 2.799756e+09\n", - "2023 4.155524e+06\n", + "2018 3.313543e+07\n", + "2019 8.444989e+05\n", + "2020 1.088950e+08\n", + "2021 3.822523e+05\n", + "2022 2.761982e+09\n", + "2023 2.746372e+06\n", "Name: Amount, dtype: float64" ] }, - "execution_count": 13, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -6165,7 +8060,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -6178,7 +8073,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -6211,362 +8106,314 @@ " \n", " 0\n", " 2018\n", - " MN DFL State Central Committee\n", - " 1.634667e+07\n", + " Alliance for a Better Minnesota Action Fund\n", + " 8.425909e+06\n", " \n", " \n", " 1\n", " 2018\n", - " Alliance for a Better Minnesota Action Fund\n", - " 9.388784e+06\n", + " MN DFL State Central Committee\n", + " 4.856272e+06\n", " \n", " \n", " 2\n", " 2018\n", - " DGA Victory Fund\n", - " 7.639284e+06\n", + " MN Victory PAC\n", + " 3.331147e+06\n", " \n", " \n", " 3\n", " 2018\n", - " 2024 Fund\n", - " 5.702886e+06\n", + " HRCC\n", + " 2.016482e+06\n", " \n", " \n", " 4\n", " 2018\n", - " DFL House Caucus\n", - " 5.409447e+06\n", + " Freedom Club State PAC\n", + " 1.852156e+06\n", " \n", " \n", " 5\n", " 2018\n", - " Education Minn PAC\n", - " 5.224434e+06\n", + " DFL House Caucus\n", + " 1.513452e+06\n", " \n", " \n", " 6\n", " 2018\n", - " Walz, Tim Gov Committee\n", - " 4.608298e+06\n", + " Pro Jobs Majority\n", + " 1.395251e+06\n", " \n", " \n", " 7\n", " 2018\n", - " MN Victory PAC\n", - " 3.785053e+06\n", + " MN Jobs Coalition Legislative Fund\n", + " 1.372963e+06\n", " \n", " \n", " 8\n", " 2018\n", - " WIN Minnesota Political Action Fund\n", - " 3.263076e+06\n", + " Coalition of Minnesota Businesses IEPC\n", + " 8.758362e+05\n", " \n", " \n", " 9\n", " 2018\n", - " HRCC\n", - " 3.079447e+06\n", + " Housing First Fund\n", + " 8.530283e+05\n", " \n", " \n", " 10\n", " 2019\n", " MN DFL State Central Committee\n", - " 3.582806e+06\n", + " 4.473529e+05\n", " \n", " \n", " 11\n", " 2019\n", - " DGA Victory Fund\n", - " 2.554925e+06\n", + " Advance Minnesota Independent Expenditure Comm...\n", + " 1.026880e+05\n", " \n", " \n", " 12\n", " 2019\n", - " Deloitte Political Action Committee\n", - " 2.316457e+06\n", + " Coalition of Minnesota Businesses IEPC\n", + " 8.046022e+04\n", " \n", " \n", " 13\n", " 2019\n", - " Education Minn PAC\n", - " 1.066196e+06\n", + " Senate Victory Fund (SVF)\n", + " 4.077900e+04\n", " \n", " \n", " 14\n", " 2019\n", - " DFL Senate Caucus\n", - " 8.271695e+05\n", + " Right Now Minnesota\n", + " 3.890146e+04\n", " \n", " \n", " 15\n", " 2019\n", - " Republican Party of Minn\n", - " 7.829564e+05\n", + " Alliance for a Better Minnesota Action Fund\n", + " 3.358908e+04\n", " \n", " \n", " 16\n", " 2019\n", - " DFL House Caucus\n", - " 6.504046e+05\n", + " Republican Party of Minn\n", + " 3.298655e+04\n", " \n", " \n", " 17\n", " 2019\n", - " HRCC\n", - " 6.390662e+05\n", + " Win at the Door PAC\n", + " 2.585051e+04\n", " \n", " \n", " 18\n", " 2019\n", - " Senate Victory Fund (SVF)\n", - " 6.190183e+05\n", + " Planned Parenthood of Minn Pol Action Fund\n", + " 1.400514e+04\n", " \n", " \n", " 19\n", " 2019\n", - " DLCC Victory Fund\n", - " 4.963511e+05\n", + " Housing First Fund\n", + " 9.000000e+03\n", " \n", " \n", " 20\n", " 2020\n", " MN DFL State Central Committee\n", - " 1.186727e+07\n", + " 2.875138e+07\n", " \n", " \n", " 21\n", " 2020\n", - " Alliance for a Better Minnesota Action Fund\n", - " 7.422896e+06\n", + " DFL House Caucus\n", + " 1.914924e+07\n", " \n", " \n", " 22\n", " 2020\n", - " DFL Senate Caucus\n", - " 7.156607e+06\n", + " Alliance for a Better Minnesota Action Fund\n", + " 1.165052e+07\n", " \n", " \n", " 23\n", " 2020\n", - " DFL House Caucus\n", - " 6.292336e+06\n", + " HRCC\n", + " 8.943520e+06\n", " \n", " \n", " 24\n", " 2020\n", - " WIN Minnesota Political Action Fund\n", - " 5.211757e+06\n", + " Senate Victory Fund (SVF)\n", + " 8.658417e+06\n", " \n", " \n", " 25\n", " 2020\n", - " 2024 Fund\n", - " 4.862000e+06\n", + " DFL Senate Caucus\n", + " 6.625248e+06\n", " \n", " \n", " 26\n", " 2020\n", - " Senate Victory Fund (SVF)\n", - " 4.144662e+06\n", + " Advance Minnesota Independent Expenditure Comm...\n", + " 3.480636e+06\n", " \n", " \n", " 27\n", " 2020\n", - " Education Minn PAC\n", - " 3.919966e+06\n", + " Freedom Club State PAC\n", + " 1.869632e+06\n", " \n", " \n", " 28\n", " 2020\n", - " SEIU Minn State Council Political Fund\n", - " 2.728316e+06\n", + " Planned Parenthood of Minn Pol Action Fund\n", + " 1.788499e+06\n", " \n", " \n", " 29\n", " 2020\n", - " DGA Victory Fund\n", - " 2.432946e+06\n", + " Everytown for Gun Safety Action Fund\n", + " 1.729842e+06\n", " \n", " \n", " 30\n", " 2021\n", - " MN DFL State Central Committee\n", - " 4.348914e+06\n", + " Plan for Progress\n", + " 2.306310e+05\n", " \n", " \n", " 31\n", " 2021\n", - " Plan for Progress\n", - " 1.382791e+06\n", + " MN Realtors Political Action Committee\n", + " 1.329537e+05\n", " \n", " \n", " 32\n", " 2021\n", - " Education Minn PAC\n", - " 1.360038e+06\n", + " Planned Parenthood of Minn Pol Action Fund\n", + " 1.024660e+04\n", " \n", " \n", " 33\n", " 2021\n", - " Walz, Tim Gov Committee\n", - " 1.324421e+06\n", + " MN Homeowners Alliance Independent Expenditure...\n", + " 7.671000e+03\n", " \n", " \n", " 34\n", " 2021\n", - " Deloitte Political Action Committee\n", - " 1.284125e+06\n", + " Carpenters Local 322\n", + " 7.500000e+02\n", " \n", " \n", " 35\n", - " 2021\n", - " DFL Senate Caucus\n", - " 6.201821e+05\n", - " \n", - " \n", - " 36\n", - " 2021\n", - " MN State Council of UNITE HERE Unions\n", - " 6.034500e+05\n", - " \n", - " \n", - " 37\n", - " 2021\n", - " Republican Party of Minn\n", - " 5.811059e+05\n", - " \n", - " \n", - " 38\n", - " 2021\n", - " DFL House Caucus\n", - " 5.769969e+05\n", - " \n", - " \n", - " 39\n", - " 2021\n", - " DGA Victory Fund\n", - " 5.041542e+05\n", - " \n", - " \n", - " 40\n", " 2022\n", " MN DFL State Central Committee\n", - " 5.299254e+08\n", + " 5.595062e+08\n", " \n", " \n", - " 41\n", + " 36\n", " 2022\n", " Alliance for a Better Minnesota Action Fund\n", - " 4.077091e+08\n", + " 4.210824e+08\n", " \n", " \n", - " 42\n", + " 37\n", " 2022\n", " DFL House Caucus\n", - " 2.187665e+08\n", + " 2.336432e+08\n", " \n", " \n", - " 43\n", + " 38\n", " 2022\n", " MN for Freedom\n", - " 1.541938e+08\n", + " 1.594200e+08\n", " \n", " \n", - " 44\n", + " 39\n", " 2022\n", " Safe Accessible Fair Elections Minnesota\n", - " 1.314013e+08\n", + " 1.346198e+08\n", " \n", " \n", - " 45\n", + " 40\n", " 2022\n", " DAGA MN People's Lawyer Project\n", - " 1.109658e+08\n", + " 1.128677e+08\n", " \n", " \n", - " 46\n", + " 41\n", " 2022\n", - " Advance Minnesota Independent Expenditure Comm...\n", - " 9.911244e+07\n", + " HRCC\n", + " 1.014986e+08\n", " \n", " \n", - " 47\n", + " 42\n", " 2022\n", - " HRCC\n", - " 9.192176e+07\n", + " Advance Minnesota Independent Expenditure Comm...\n", + " 1.011120e+08\n", " \n", " \n", - " 48\n", + " 43\n", " 2022\n", " MN Jobs Coalition Legislative Fund\n", - " 9.078141e+07\n", + " 9.271635e+07\n", " \n", " \n", - " 49\n", + " 44\n", " 2022\n", " iVote Fund MN\n", - " 7.980807e+07\n", + " 8.180324e+07\n", " \n", " \n", - " 50\n", + " 45\n", " 2023\n", " All of Mpls\n", - " 6.872487e+05\n", - " \n", - " \n", - " 51\n", - " 2023\n", - " Laborers District Council of Minn & ND Pol Fund\n", - " 4.739393e+05\n", - " \n", - " \n", - " 52\n", - " 2023\n", - " North Central States Carpenters PAC\n", - " 4.647250e+05\n", + " 1.410424e+06\n", " \n", " \n", - " 53\n", + " 46\n", " 2023\n", " TakeAction Political Fund\n", - " 4.630456e+05\n", - " \n", - " \n", - " 54\n", - " 2023\n", - " AFSCME Council 5 PEOPLE Fund\n", - " 2.176542e+05\n", + " 7.931047e+05\n", " \n", " \n", - " 55\n", + " 47\n", " 2023\n", - " Sheet Metal Workers PAC 10\n", - " 1.817000e+05\n", + " Minneapolis for the Many\n", + " 2.365442e+05\n", " \n", " \n", - " 56\n", + " 48\n", " 2023\n", - " Joint Council 32 DRIVE\n", - " 1.603323e+05\n", + " Laborers District Council of Minn & ND Pol Fund\n", + " 1.541600e+05\n", " \n", " \n", - " 57\n", + " 49\n", " 2023\n", - " IBEW Local 292 Political Education Fund\n", - " 1.378147e+05\n", + " Minneapolis Regional Labor Federation\n", + " 1.200286e+05\n", " \n", " \n", - " 58\n", + " 50\n", " 2023\n", - " MN State Bldg & Construction Trades Cncl Pol Fund\n", - " 1.251500e+05\n", + " Faith in Minnesota Fund\n", + " 3.046350e+04\n", " \n", " \n", - " 59\n", + " 51\n", " 2023\n", - " International Union of Operating Engineers\n", - " 1.212000e+05\n", + " Move Minnesota Action Fund\n", + " 1.647280e+03\n", " \n", " \n", "\n", @@ -6574,69 +8421,61 @@ ], "text/plain": [ " Year SpenderName Amount\n", - "0 2018 MN DFL State Central Committee 1.634667e+07\n", - "1 2018 Alliance for a Better Minnesota Action Fund 9.388784e+06\n", - "2 2018 DGA Victory Fund 7.639284e+06\n", - "3 2018 2024 Fund 5.702886e+06\n", - "4 2018 DFL House Caucus 5.409447e+06\n", - "5 2018 Education Minn PAC 5.224434e+06\n", - "6 2018 Walz, Tim Gov Committee 4.608298e+06\n", - "7 2018 MN Victory PAC 3.785053e+06\n", - "8 2018 WIN Minnesota Political Action Fund 3.263076e+06\n", - "9 2018 HRCC 3.079447e+06\n", - "10 2019 MN DFL State Central Committee 3.582806e+06\n", - "11 2019 DGA Victory Fund 2.554925e+06\n", - "12 2019 Deloitte Political Action Committee 2.316457e+06\n", - "13 2019 Education Minn PAC 1.066196e+06\n", - "14 2019 DFL Senate Caucus 8.271695e+05\n", - "15 2019 Republican Party of Minn 7.829564e+05\n", - "16 2019 DFL House Caucus 6.504046e+05\n", - "17 2019 HRCC 6.390662e+05\n", - "18 2019 Senate Victory Fund (SVF) 6.190183e+05\n", - "19 2019 DLCC Victory Fund 4.963511e+05\n", - "20 2020 MN DFL State Central Committee 1.186727e+07\n", - "21 2020 Alliance for a Better Minnesota Action Fund 7.422896e+06\n", - "22 2020 DFL Senate Caucus 7.156607e+06\n", - "23 2020 DFL House Caucus 6.292336e+06\n", - "24 2020 WIN Minnesota Political Action Fund 5.211757e+06\n", - "25 2020 2024 Fund 4.862000e+06\n", - "26 2020 Senate Victory Fund (SVF) 4.144662e+06\n", - "27 2020 Education Minn PAC 3.919966e+06\n", - "28 2020 SEIU Minn State Council Political Fund 2.728316e+06\n", - "29 2020 DGA Victory Fund 2.432946e+06\n", - "30 2021 MN DFL State Central Committee 4.348914e+06\n", - "31 2021 Plan for Progress 1.382791e+06\n", - "32 2021 Education Minn PAC 1.360038e+06\n", - "33 2021 Walz, Tim Gov Committee 1.324421e+06\n", - "34 2021 Deloitte Political Action Committee 1.284125e+06\n", - "35 2021 DFL Senate Caucus 6.201821e+05\n", - "36 2021 MN State Council of UNITE HERE Unions 6.034500e+05\n", - "37 2021 Republican Party of Minn 5.811059e+05\n", - "38 2021 DFL House Caucus 5.769969e+05\n", - "39 2021 DGA Victory Fund 5.041542e+05\n", - "40 2022 MN DFL State Central Committee 5.299254e+08\n", - "41 2022 Alliance for a Better Minnesota Action Fund 4.077091e+08\n", - "42 2022 DFL House Caucus 2.187665e+08\n", - "43 2022 MN for Freedom 1.541938e+08\n", - "44 2022 Safe Accessible Fair Elections Minnesota 1.314013e+08\n", - "45 2022 DAGA MN People's Lawyer Project 1.109658e+08\n", - "46 2022 Advance Minnesota Independent Expenditure Comm... 9.911244e+07\n", - "47 2022 HRCC 9.192176e+07\n", - "48 2022 MN Jobs Coalition Legislative Fund 9.078141e+07\n", - "49 2022 iVote Fund MN 7.980807e+07\n", - "50 2023 All of Mpls 6.872487e+05\n", - "51 2023 Laborers District Council of Minn & ND Pol Fund 4.739393e+05\n", - "52 2023 North Central States Carpenters PAC 4.647250e+05\n", - "53 2023 TakeAction Political Fund 4.630456e+05\n", - "54 2023 AFSCME Council 5 PEOPLE Fund 2.176542e+05\n", - "55 2023 Sheet Metal Workers PAC 10 1.817000e+05\n", - "56 2023 Joint Council 32 DRIVE 1.603323e+05\n", - "57 2023 IBEW Local 292 Political Education Fund 1.378147e+05\n", - "58 2023 MN State Bldg & Construction Trades Cncl Pol Fund 1.251500e+05\n", - "59 2023 International Union of Operating Engineers 1.212000e+05" + "0 2018 Alliance for a Better Minnesota Action Fund 8.425909e+06\n", + "1 2018 MN DFL State Central Committee 4.856272e+06\n", + "2 2018 MN Victory PAC 3.331147e+06\n", + "3 2018 HRCC 2.016482e+06\n", + "4 2018 Freedom Club State PAC 1.852156e+06\n", + "5 2018 DFL House Caucus 1.513452e+06\n", + "6 2018 Pro Jobs Majority 1.395251e+06\n", + "7 2018 MN Jobs Coalition Legislative Fund 1.372963e+06\n", + "8 2018 Coalition of Minnesota Businesses IEPC 8.758362e+05\n", + "9 2018 Housing First Fund 8.530283e+05\n", + "10 2019 MN DFL State Central Committee 4.473529e+05\n", + "11 2019 Advance Minnesota Independent Expenditure Comm... 1.026880e+05\n", + "12 2019 Coalition of Minnesota Businesses IEPC 8.046022e+04\n", + "13 2019 Senate Victory Fund (SVF) 4.077900e+04\n", + "14 2019 Right Now Minnesota 3.890146e+04\n", + "15 2019 Alliance for a Better Minnesota Action Fund 3.358908e+04\n", + "16 2019 Republican Party of Minn 3.298655e+04\n", + "17 2019 Win at the Door PAC 2.585051e+04\n", + "18 2019 Planned Parenthood of Minn Pol Action Fund 1.400514e+04\n", + "19 2019 Housing First Fund 9.000000e+03\n", + "20 2020 MN DFL State Central Committee 2.875138e+07\n", + "21 2020 DFL House Caucus 1.914924e+07\n", + "22 2020 Alliance for a Better Minnesota Action Fund 1.165052e+07\n", + "23 2020 HRCC 8.943520e+06\n", + "24 2020 Senate Victory Fund (SVF) 8.658417e+06\n", + "25 2020 DFL Senate Caucus 6.625248e+06\n", + "26 2020 Advance Minnesota Independent Expenditure Comm... 3.480636e+06\n", + "27 2020 Freedom Club State PAC 1.869632e+06\n", + "28 2020 Planned Parenthood of Minn Pol Action Fund 1.788499e+06\n", + "29 2020 Everytown for Gun Safety Action Fund 1.729842e+06\n", + "30 2021 Plan for Progress 2.306310e+05\n", + "31 2021 MN Realtors Political Action Committee 1.329537e+05\n", + "32 2021 Planned Parenthood of Minn Pol Action Fund 1.024660e+04\n", + "33 2021 MN Homeowners Alliance Independent Expenditure... 7.671000e+03\n", + "34 2021 Carpenters Local 322 7.500000e+02\n", + "35 2022 MN DFL State Central Committee 5.595062e+08\n", + "36 2022 Alliance for a Better Minnesota Action Fund 4.210824e+08\n", + "37 2022 DFL House Caucus 2.336432e+08\n", + "38 2022 MN for Freedom 1.594200e+08\n", + "39 2022 Safe Accessible Fair Elections Minnesota 1.346198e+08\n", + "40 2022 DAGA MN People's Lawyer Project 1.128677e+08\n", + "41 2022 HRCC 1.014986e+08\n", + "42 2022 Advance Minnesota Independent Expenditure Comm... 1.011120e+08\n", + "43 2022 MN Jobs Coalition Legislative Fund 9.271635e+07\n", + "44 2022 iVote Fund MN 8.180324e+07\n", + "45 2023 All of Mpls 1.410424e+06\n", + "46 2023 TakeAction Political Fund 7.931047e+05\n", + "47 2023 Minneapolis for the Many 2.365442e+05\n", + "48 2023 Laborers District Council of Minn & ND Pol Fund 1.541600e+05\n", + "49 2023 Minneapolis Regional Labor Federation 1.200286e+05\n", + "50 2023 Faith in Minnesota Fund 3.046350e+04\n", + "51 2023 Move Minnesota Action Fund 1.647280e+03" ] }, - "execution_count": 15, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -6654,7 +8493,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -6667,7 +8506,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -6706,426 +8545,412 @@ " \n", " 1\n", " 2018\n", - " Democratic Governors Association\n", - " 5.096131e+06\n", + " Clarify Agency\n", + " 3.904962e+06\n", " \n", " \n", " 2\n", " 2018\n", - " Clarify Agency\n", - " 4.289870e+06\n", + " Nebo Media\n", + " 3.017132e+06\n", " \n", " \n", " 3\n", " 2018\n", - " Nebo Media\n", - " 3.538082e+06\n", + " Berlin Rosen LTD\n", + " 1.425687e+06\n", " \n", " \n", " 4\n", " 2018\n", - " Drake Bank Federal\n", - " 3.040606e+06\n", + " Ax Media\n", + " 1.370301e+06\n", " \n", " \n", " 5\n", " 2018\n", - " GMMB Inc\n", - " 2.512423e+06\n", + " Sage Media Planning Placement\n", + " 9.975067e+05\n", " \n", " \n", " 6\n", " 2018\n", - " Berlin Rosen LTD\n", - " 1.446830e+06\n", + " Larry John Wright Advertising Inc\n", + " 8.335000e+05\n", " \n", " \n", " 7\n", " 2018\n", - " Ax Media\n", - " 1.370301e+06\n", + " Gumbinner Davies\n", + " 7.910048e+05\n", " \n", " \n", " 8\n", " 2018\n", - " Strategic Media for Placement\n", - " 1.326959e+06\n", + " Targeted CreativeCommunication\n", + " 6.808424e+05\n", " \n", " \n", " 9\n", " 2018\n", - " Education Minnesota\n", - " 1.302605e+06\n", + " E Street Group LLC\n", + " 6.327749e+05\n", " \n", " \n", " 10\n", " 2019\n", - " Democratic Governors Association - Mississippi\n", - " 2.434730e+06\n", + " Canal Partners Media\n", + " 3.626000e+05\n", " \n", " \n", " 11\n", " 2019\n", - " Drake Bank Federal\n", - " 1.060554e+06\n", + " Berlin Rosen LTD\n", + " 6.373017e+04\n", " \n", " \n", " 12\n", " 2019\n", - " Republican Party of Minnesota - Federal Account\n", - " 4.960728e+05\n", + " Nebo Media\n", + " 5.850000e+04\n", " \n", " \n", " 13\n", " 2019\n", - " Canal Partners Media\n", - " 3.626000e+05\n", + " Majority Strategies\n", + " 5.400000e+04\n", " \n", " \n", " 14\n", " 2019\n", - " SEIU MN State Council\n", - " 3.363778e+05\n", + " Weber Johnson Public Affairs\n", + " 4.471800e+04\n", " \n", " \n", " 15\n", " 2019\n", - " Western Bank\n", - " 3.161543e+05\n", + " Front Runner Digital\n", + " 3.847900e+04\n", " \n", " \n", " 16\n", " 2019\n", - " Pinnacle Direct\n", - " 2.952088e+05\n", + " Clarify Agency\n", + " 3.158908e+04\n", " \n", " \n", " 17\n", " 2019\n", - " Seven Corners Printing\n", - " 2.412480e+05\n", + " Singularis\n", + " 2.251155e+04\n", " \n", " \n", " 18\n", " 2019\n", - " EFTPS Fed Tax Payment\n", - " 1.835754e+05\n", + " AppNexus Inc\n", + " 1.951000e+04\n", " \n", " \n", " 19\n", " 2019\n", - " Perkins Coie LLP\n", - " 1.743259e+05\n", + " Connection Strategy LLC\n", + " 1.796022e+04\n", " \n", " \n", " 20\n", " 2020\n", " Clarify Agency\n", - " 5.621665e+06\n", + " 1.710700e+07\n", " \n", " \n", " 21\n", " 2020\n", - " Drake Bank Federal\n", - " 2.728263e+06\n", + " Canal Partners Media\n", + " 1.013758e+07\n", " \n", " \n", " 22\n", " 2020\n", - " Democratic Governors Association\n", - " 2.397500e+06\n", + " Sage Media Planning Placement\n", + " 9.441231e+06\n", " \n", " \n", " 23\n", " 2020\n", - " Left Hook\n", - " 2.380277e+06\n", + " Gumbinner Davies\n", + " 8.908617e+06\n", " \n", " \n", " 24\n", " 2020\n", - " WIN Minnesota Federal PAC\n", - " 2.220500e+06\n", + " FP1 Digital LLC\n", + " 7.931499e+06\n", " \n", " \n", " 25\n", " 2020\n", - " FP1 Digital LLC\n", - " 2.092333e+06\n", + " Blueprint Interactive\n", + " 5.614262e+06\n", " \n", " \n", " 26\n", " 2020\n", - " Canal Partners Media\n", - " 1.448225e+06\n", + " Berlin Rosen LTD\n", + " 4.278030e+06\n", " \n", " \n", " 27\n", " 2020\n", - " Sage Media Planning Placement\n", - " 1.375934e+06\n", + " Wildfire Mail\n", + " 3.743325e+06\n", " \n", " \n", " 28\n", " 2020\n", - " Gumbinner Davies\n", - " 1.272660e+06\n", + " Targeted CreativeCommunication\n", + " 3.461442e+06\n", " \n", " \n", " 29\n", " 2020\n", - " Education Minnesota\n", - " 1.008506e+06\n", + " Left Hook\n", + " 3.343557e+06\n", " \n", " \n", " 30\n", " 2021\n", - " Drake Bank Federal\n", - " 1.539032e+06\n", + " New Publica LLC\n", + " 2.006310e+05\n", " \n", " \n", " 31\n", " 2021\n", - " AllofMpls\n", - " 1.229037e+06\n", + " Real Strategies\n", + " 1.199037e+05\n", " \n", " \n", " 32\n", " 2021\n", - " Education Minnesota\n", - " 9.120825e+05\n", + " Eye Contact Media\n", + " 3.000000e+04\n", " \n", " \n", " 33\n", " 2021\n", - " Robin for Minneapolis\n", - " 6.014500e+05\n", + " (MN) Access Marketing\n", + " 1.305000e+04\n", " \n", " \n", " 34\n", " 2021\n", - " Cleansweep Campaigns\n", - " 4.226354e+05\n", + " Planned Parenthood MN ND SD Action Fund\n", + " 9.520600e+03\n", " \n", " \n", " 35\n", " 2021\n", - " Republican Party of Minnesota - Federal Account\n", - " 3.682714e+05\n", + " CliftonLarsonAllen\n", + " 7.671000e+03\n", " \n", " \n", " 36\n", " 2021\n", - " EFTPS Fed Tax Payment\n", - " 2.897723e+05\n", + " Minnesota Campaign Finance Board\n", + " 7.500000e+02\n", " \n", " \n", " 37\n", " 2021\n", - " McCool Digital LLC\n", - " 2.571873e+05\n", + " Facebook\n", + " 7.260000e+02\n", " \n", " \n", " 38\n", - " 2021\n", - " Yes 4 Minneapolis\n", - " 2.190000e+05\n", + " 2022\n", + " Clarify Agency\n", + " 2.831990e+08\n", " \n", " \n", " 39\n", - " 2021\n", - " Democratic Attorneys General Assocation Inc\n", - " 2.136878e+05\n", + " 2022\n", + " Great American Media\n", + " 2.675292e+08\n", " \n", " \n", " 40\n", " 2022\n", - " Clarify Agency\n", - " 2.647481e+08\n", + " Canal Partners Media\n", + " 1.777085e+08\n", " \n", " \n", " 41\n", " 2022\n", - " Great American Media\n", - " 2.582326e+08\n", + " Deliver Strategies\n", + " 1.184445e+08\n", " \n", " \n", " 42\n", " 2022\n", - " Canal Partners Media\n", - " 1.593249e+08\n", + " Berlin Rosen LTD\n", + " 1.128677e+08\n", " \n", " \n", " 43\n", " 2022\n", - " Berlin Rosen LTD\n", - " 1.105615e+08\n", + " Red Eagle Media\n", + " 1.118814e+08\n", " \n", " \n", " 44\n", " 2022\n", - " Red Eagle Media\n", - " 1.082093e+08\n", + " Sage Media Planning Placement\n", + " 9.786503e+07\n", " \n", " \n", " 45\n", " 2022\n", - " Deliver Strategies\n", - " 1.074506e+08\n", + " Nebo Media\n", + " 9.038561e+07\n", " \n", " \n", " 46\n", " 2022\n", - " Nebo Media\n", - " 8.849665e+07\n", + " FP1 Digital LLC\n", + " 8.575528e+07\n", " \n", " \n", " 47\n", " 2022\n", - " Sage Media Planning Placement\n", - " 8.776352e+07\n", + " Targeted Creative Communication\n", + " 8.388532e+07\n", " \n", " \n", " 48\n", - " 2022\n", - " FP1 Digital LLC\n", - " 8.138634e+07\n", + " 2023\n", + " GRSG Company\n", + " 1.007000e+06\n", " \n", " \n", " 49\n", - " 2022\n", - " Schultz, Jennifer House Committee\n", - " 8.054502e+07\n", + " 2023\n", + " The People's Canvass\n", + " 7.261599e+05\n", " \n", " \n", " 50\n", " 2023\n", - " MN DFL State Central Committee\n", - " 7.828450e+05\n", + " Eye Contact Media\n", + " 2.510000e+05\n", " \n", " \n", " 51\n", " 2023\n", - " GRSG Company\n", - " 5.000000e+05\n", + " Nordeast Digital\n", + " 1.600000e+05\n", " \n", " \n", " 52\n", " 2023\n", - " The People's Canvass\n", - " 4.230976e+05\n", + " Pivot Mailing\n", + " 1.073689e+05\n", " \n", " \n", " 53\n", " 2023\n", - " MN DFL Senate Caucus\n", - " 2.496439e+05\n", + " Printastik\n", + " 7.429610e+04\n", " \n", " \n", " 54\n", " 2023\n", - " MN DFL House Caucus\n", - " 2.427772e+05\n", + " Seven Corners Print and Promo\n", + " 7.181544e+04\n", " \n", " \n", " 55\n", " 2023\n", - " AGC of MN\n", - " 1.343460e+05\n", + " Apparatus\n", + " 6.426000e+04\n", " \n", " \n", " 56\n", " 2023\n", - " SMART PAL-Education\n", - " 1.200000e+05\n", + " Do-GoodBiz\n", + " 5.364402e+04\n", " \n", " \n", " 57\n", " 2023\n", - " Teamsters Joint Council 32\n", - " 8.893821e+04\n", - " \n", - " \n", - " 58\n", - " 2023\n", - " All of Mpls\n", - " 8.621453e+04\n", - " \n", - " \n", - " 59\n", - " 2023\n", - " Apparatus\n", - " 7.000000e+04\n", + " Postmaster\n", + " 4.603125e+04\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Year VendorName Amount\n", - "0 2018 Great American Media 7.092650e+06\n", - "1 2018 Democratic Governors Association 5.096131e+06\n", - "2 2018 Clarify Agency 4.289870e+06\n", - "3 2018 Nebo Media 3.538082e+06\n", - "4 2018 Drake Bank Federal 3.040606e+06\n", - "5 2018 GMMB Inc 2.512423e+06\n", - "6 2018 Berlin Rosen LTD 1.446830e+06\n", - "7 2018 Ax Media 1.370301e+06\n", - "8 2018 Strategic Media for Placement 1.326959e+06\n", - "9 2018 Education Minnesota 1.302605e+06\n", - "10 2019 Democratic Governors Association - Mississippi 2.434730e+06\n", - "11 2019 Drake Bank Federal 1.060554e+06\n", - "12 2019 Republican Party of Minnesota - Federal Account 4.960728e+05\n", - "13 2019 Canal Partners Media 3.626000e+05\n", - "14 2019 SEIU MN State Council 3.363778e+05\n", - "15 2019 Western Bank 3.161543e+05\n", - "16 2019 Pinnacle Direct 2.952088e+05\n", - "17 2019 Seven Corners Printing 2.412480e+05\n", - "18 2019 EFTPS Fed Tax Payment 1.835754e+05\n", - "19 2019 Perkins Coie LLP 1.743259e+05\n", - "20 2020 Clarify Agency 5.621665e+06\n", - "21 2020 Drake Bank Federal 2.728263e+06\n", - "22 2020 Democratic Governors Association 2.397500e+06\n", - "23 2020 Left Hook 2.380277e+06\n", - "24 2020 WIN Minnesota Federal PAC 2.220500e+06\n", - "25 2020 FP1 Digital LLC 2.092333e+06\n", - "26 2020 Canal Partners Media 1.448225e+06\n", - "27 2020 Sage Media Planning Placement 1.375934e+06\n", - "28 2020 Gumbinner Davies 1.272660e+06\n", - "29 2020 Education Minnesota 1.008506e+06\n", - "30 2021 Drake Bank Federal 1.539032e+06\n", - "31 2021 AllofMpls 1.229037e+06\n", - "32 2021 Education Minnesota 9.120825e+05\n", - "33 2021 Robin for Minneapolis 6.014500e+05\n", - "34 2021 Cleansweep Campaigns 4.226354e+05\n", - "35 2021 Republican Party of Minnesota - Federal Account 3.682714e+05\n", - "36 2021 EFTPS Fed Tax Payment 2.897723e+05\n", - "37 2021 McCool Digital LLC 2.571873e+05\n", - "38 2021 Yes 4 Minneapolis 2.190000e+05\n", - "39 2021 Democratic Attorneys General Assocation Inc 2.136878e+05\n", - "40 2022 Clarify Agency 2.647481e+08\n", - "41 2022 Great American Media 2.582326e+08\n", - "42 2022 Canal Partners Media 1.593249e+08\n", - "43 2022 Berlin Rosen LTD 1.105615e+08\n", - "44 2022 Red Eagle Media 1.082093e+08\n", - "45 2022 Deliver Strategies 1.074506e+08\n", - "46 2022 Nebo Media 8.849665e+07\n", - "47 2022 Sage Media Planning Placement 8.776352e+07\n", - "48 2022 FP1 Digital LLC 8.138634e+07\n", - "49 2022 Schultz, Jennifer House Committee 8.054502e+07\n", - "50 2023 MN DFL State Central Committee 7.828450e+05\n", - "51 2023 GRSG Company 5.000000e+05\n", - "52 2023 The People's Canvass 4.230976e+05\n", - "53 2023 MN DFL Senate Caucus 2.496439e+05\n", - "54 2023 MN DFL House Caucus 2.427772e+05\n", - "55 2023 AGC of MN 1.343460e+05\n", - "56 2023 SMART PAL-Education 1.200000e+05\n", - "57 2023 Teamsters Joint Council 32 8.893821e+04\n", - "58 2023 All of Mpls 8.621453e+04\n", - "59 2023 Apparatus 7.000000e+04" + " Year VendorName Amount\n", + "0 2018 Great American Media 7.092650e+06\n", + "1 2018 Clarify Agency 3.904962e+06\n", + "2 2018 Nebo Media 3.017132e+06\n", + "3 2018 Berlin Rosen LTD 1.425687e+06\n", + "4 2018 Ax Media 1.370301e+06\n", + "5 2018 Sage Media Planning Placement 9.975067e+05\n", + "6 2018 Larry John Wright Advertising Inc 8.335000e+05\n", + "7 2018 Gumbinner Davies 7.910048e+05\n", + "8 2018 Targeted CreativeCommunication 6.808424e+05\n", + "9 2018 E Street Group LLC 6.327749e+05\n", + "10 2019 Canal Partners Media 3.626000e+05\n", + "11 2019 Berlin Rosen LTD 6.373017e+04\n", + "12 2019 Nebo Media 5.850000e+04\n", + "13 2019 Majority Strategies 5.400000e+04\n", + "14 2019 Weber Johnson Public Affairs 4.471800e+04\n", + "15 2019 Front Runner Digital 3.847900e+04\n", + "16 2019 Clarify Agency 3.158908e+04\n", + "17 2019 Singularis 2.251155e+04\n", + "18 2019 AppNexus Inc 1.951000e+04\n", + "19 2019 Connection Strategy LLC 1.796022e+04\n", + "20 2020 Clarify Agency 1.710700e+07\n", + "21 2020 Canal Partners Media 1.013758e+07\n", + "22 2020 Sage Media Planning Placement 9.441231e+06\n", + "23 2020 Gumbinner Davies 8.908617e+06\n", + "24 2020 FP1 Digital LLC 7.931499e+06\n", + "25 2020 Blueprint Interactive 5.614262e+06\n", + "26 2020 Berlin Rosen LTD 4.278030e+06\n", + "27 2020 Wildfire Mail 3.743325e+06\n", + "28 2020 Targeted CreativeCommunication 3.461442e+06\n", + "29 2020 Left Hook 3.343557e+06\n", + "30 2021 New Publica LLC 2.006310e+05\n", + "31 2021 Real Strategies 1.199037e+05\n", + "32 2021 Eye Contact Media 3.000000e+04\n", + "33 2021 (MN) Access Marketing 1.305000e+04\n", + "34 2021 Planned Parenthood MN ND SD Action Fund 9.520600e+03\n", + "35 2021 CliftonLarsonAllen 7.671000e+03\n", + "36 2021 Minnesota Campaign Finance Board 7.500000e+02\n", + "37 2021 Facebook 7.260000e+02\n", + "38 2022 Clarify Agency 2.831990e+08\n", + "39 2022 Great American Media 2.675292e+08\n", + "40 2022 Canal Partners Media 1.777085e+08\n", + "41 2022 Deliver Strategies 1.184445e+08\n", + "42 2022 Berlin Rosen LTD 1.128677e+08\n", + "43 2022 Red Eagle Media 1.118814e+08\n", + "44 2022 Sage Media Planning Placement 9.786503e+07\n", + "45 2022 Nebo Media 9.038561e+07\n", + "46 2022 FP1 Digital LLC 8.575528e+07\n", + "47 2022 Targeted Creative Communication 8.388532e+07\n", + "48 2023 GRSG Company 1.007000e+06\n", + "49 2023 The People's Canvass 7.261599e+05\n", + "50 2023 Eye Contact Media 2.510000e+05\n", + "51 2023 Nordeast Digital 1.600000e+05\n", + "52 2023 Pivot Mailing 1.073689e+05\n", + "53 2023 Printastik 7.429610e+04\n", + "54 2023 Seven Corners Print and Promo 7.181544e+04\n", + "55 2023 Apparatus 6.426000e+04\n", + "56 2023 Do-GoodBiz 5.364402e+04\n", + "57 2023 Postmaster 4.603125e+04" ] }, - 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