"
- ],
- "text/plain": [
- " transaction_id donor_id \\\n",
- "0 7773a71e-9f67-438e-8313-80b1b75deeb4 4544b60d-da6b-4dd5-9efe-334152ccf1f1 \n",
- "1 95f74915-a945-491f-8751-8c970a76fc24 946d7561-42a3-4a4b-b410-3a10271c9f18 \n",
- "\n",
- " year amount recipient_id office_sought \\\n",
- "0 2018 1000.0 981a0414-b738-4e20-91b8-a29ee2cc7edf none \n",
- "1 2018 1000.0 981a0414-b738-4e20-91b8-a29ee2cc7edf none \n",
- "\n",
- " purpose transaction_type \\\n",
- "0 bob worsley for state senate contribute to a candidate committee \n",
- "1 drew john for state house contribute to a candidate committee \n",
- "\n",
- " donor_type recipient_type donor_office \n",
- "0 NaN NaN NaN \n",
- "1 NaN NaN NaN "
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "transactions.head(2)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array(['neutral', 'f'], dtype=object)"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "inds_df.classification.unique()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(9926, 9919, 10000, 10000)"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "inds_ids = set(inds_df.id.tolist())\n",
- "orgs_ids = set(orgs_df.id.tolist())\n",
- "trans_donorids = set(transactions.donor_id.tolist())\n",
- "trans_recepids = set(transactions.recipient_id.tolist())\n",
- "ind_id_there, org_id_there = [], []\n",
- "for ind_id in inds_ids:\n",
- " if ind_id in trans_donorids:\n",
- " ind_id_there.append(ind_id)\n",
- " elif ind_id in trans_recepids:\n",
- " ind_id_there.append(ind_id)\n",
- "\n",
- "for org_id in orgs_ids:\n",
- " if org_id in trans_donorids:\n",
- " org_id_there.append(org_id)\n",
- " elif org_id in trans_recepids:\n",
- " org_id_there.append(org_id)\n",
- "\n",
- "len(inds_ids), len(ind_id_there), len(orgs_ids), len(org_id_there)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['242d019c-e0ab-405e-8e77-abae7418b87f',\n",
- " '8b2ad550-64a1-4975-8b77-5eb1f24a8871',\n",
- " 'aee69307-194f-4c40-af3d-a55a34e1068e',\n",
- " '55e5e946-6261-4f19-9752-fb58219b2e99',\n",
- " '4faf251a-73d9-46ef-9e17-d3cf0a3052ae',\n",
- " '3b5c0a9e-c6f2-44e9-ad05-fde071447564',\n",
- " '3936bdf5-9a7a-462c-9e8c-9124f2bd7f57',\n",
- " '13882059-3c74-4d9e-825d-a03a72b43b08',\n",
- " '50c78f1a-3e9b-4996-a319-eef4fe01ccfb',\n",
- " 'ae96f38f-68c8-47e3-95b3-c6f096d3c22e',\n",
- " '74ba8a8a-7256-4eb3-b0f8-995f7a6319fb',\n",
- " '12823a76-78e2-4b09-b606-859efaa5c8ef',\n",
- " '9de9bf03-8c4a-4d2f-9a95-283b230ddfad',\n",
- " '588593b9-9bba-4597-94d9-1b3a7fd5b402',\n",
- " '5277b642-6bf0-4423-9350-3602ae51c6ac',\n",
- " 'd98985b4-f55d-4ada-b279-0497e3176512',\n",
- " 'c8586d36-f188-4684-aa99-193407d4d068',\n",
- " '3798fda1-83cd-4e48-974a-e1a390060198',\n",
- " 'a536b509-f052-4984-a35d-10397308daec',\n",
- " '80996477-ce99-4f34-b5fc-bab4d676fc77',\n",
- " 'cd1a740c-b1d7-4334-b335-925bd5708753',\n",
- " '46af8908-f4e4-4041-9d1e-5b442d051921',\n",
- " '2969075a-86d2-4b04-a991-a81832e096a0',\n",
- " 'd0337f72-b701-4524-891b-c48ef6f771ec',\n",
- " '591aa72b-511b-4dbb-a161-80458f257471']"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a = []\n",
- "for ind_id in inds_ids:\n",
- " if ((ind_id in trans_donorids) and (ind_id in trans_recepids)):\n",
- " a.append(ind_id)\n",
- "a"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "data = {'id':['50c7d9a1-b448-46a5-8e2d-cd15b3097360','50c7d9a1-b448-46a5-8e2d-cd15b3097360','50c7d9a1-b448-46a5-8e2d-cd15b3097360',\n",
- " '62ea1e9c-ac12-400c-b3dc-519389c0f7d3','62ea1e9c-ac12-400c-b3dc-519389c0f7d3','62ea1e9c-ac12-400c-b3dc-519389c0f7d3',\n",
- " 'd31df1ca-714e-4a82-9e88-1892c0451a71','d31df1ca-714e-4a82-9e88-1892c0451a71','62ea1e9c-ac12-400c-b3dc-519389c0f7d3',\n",
- " '4db76e6e-f0d5-40eb-82de-6dbcdb562dd7','f71341d7-d27e-47eb-9b66-903af39d6cb5','c875d7de-94be-42f1-b994-dd89b114d51e',\n",
- " '910c4d36-b036-469e-aa2a-ea4ff8855a6c','60d454d1-3773-4d88-80e9-132c161da0f0','1d2b5bc0-9385-4cd7-ac48-df43b3eca6fd',\n",
- " '1d2b5bc0-9385-4cd7-ac48-df43b3eca6fd','1d2b5bc0-9385-4cd7-ac48-df43b3eca6fe','1d2b5bc0-9385-4cd7-ac48-df43b3eca6ff',\n",
- " '1d2b5bc0-9385-4cd7-ac48-df43b3eca6fd'],\n",
- " 'name':['REPUBLICAN STATE LEADERSHIP COMMITTEE MICHIGAN PAC','REPUBLICAN STATE LEADERSHIP COMMITTEE MICHIGAN PAC',\n",
- " 'REPUBLICAN STATE LEADERSHIP COMMITTEE MICHIGAN PAC','UNITED FOOD AND COMMERCIAL WORKERS ACTIVE BALLOT CLUB',\n",
- " 'UNITED FOOD AND COMMERCIAL WORKERS ACTIVE BALLOT CLUB','UNITED FOOD AND COMMERCIAL WORKERS ACTIVE BALLOT CLUB',\n",
- " 'COMMITTEE TO ELECT DR PATRICIA BERNARD','COMMITTEE TO ELECT DR PATRICIA BERNARD','UNITED FOOD AND COMMERCIAL WORKERS ACTIVE BALLOT CLUB',\n",
- " 'Ugi Utilities Inc/Ugi Energy Services Llc Pac','Pabar Pac (Pa Bar Assn)','Pa Fraternal Order Of Police Pac','Citizens For Kail',\n",
- " 'Paa Pac','MICHIGAN ASSOCIATION OF NURSE ANESTHETISTS PAC','MICHIGAN ASSOCIATION OF NURSE ANESTHETISTS PAC',\n",
- " 'MICHIGAN ASSOCIATION OF NURSE ANESTHETISTS PAC','MICHIGAN ASSOCIATION OF NURSE ANESTHETISTS PAC','Paa Pac'],\n",
- " 'state':['MI','MI','MI','MI','MI','MI','MI','MI','MI','PA','PA','PA','PA','PA','MI','MI','MI','MI','PA'],\n",
- " 'entity_type':['committee','committee','committee','committee','committee','committee','committee','committee','committee',\n",
- " 'Organization','Organization','Organization','Organization','Organization','committee','committee','committee','committee','Organization']}\n",
- "\n",
- "sample_df = pd.DataFrame(data)\n",
- "sample_df['donations'] = np.random.randint(100, 6000, sample_df.shape[0])\n",
- "sample_df['donations_to'] = np.random.choice(sample_df.name.tolist(), size=len(sample_df))\n",
- "sample_df['received'] = np.random.randint(0, 6000, sample_df.shape[0])\n",
- "sample_df['donations_from'] = np.random.choice(sample_df.name.tolist(), size=len(sample_df))\n",
- "sample_df.head(5)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Some Considerations to Remember Moving Forward:\n",
- "1. The 'get_likely_name' function takes in 3 string inputs. The data is not clean and when there are NaN entries, the function is somehow inputing null values as strings, so a column that has \"Tim\", \"Walz\" and Nan in the first, last, and full name columns, is being combined as \"Tim Walz Nan\". When calling this function account for this possibility"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Playing Around with Graphs\n",
- "\n",
- "**Some considerations**\n",
- "1. What attributes do we want each Node to Have?\n",
- "- UUID, Name, Entity Type, Address, {from transactions table: money_donated and money_given}, affilition?\n",
- "- Should transaction info also be included? If so, how would we show transaction info to multiple recipients / from multiple donors?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Notes for Graphs\n",
- "**Generating Graphs**\n",
- "* nx.Graph() → the most simple undirected graph (edges going both ways)\n",
- "* nx.DiGraph() → a graph with directed edges\n",
- "* nx.MultiGraph() → multiple edges between nodes\n",
- "* nx.MultiDiGraph() → the MultiGraph equivalent for directed graphs\n",
- "\n",
- "**Finding Centrality**\n",
- "There are 4 main ways to find the centrality of a node (how important or frequent is a node / how influential are some donors potentially)\n",
- "* nx.degree_centrality : based on the assumption that important nodes have many connections\n",
- "* nx.closeness_centrality : based on the assumption that important nodes are close to other nodes. It is calculated as the sum of the path lengths from the given node to all other nodes. \n",
- "* nx.eigenvector_centrality : assumes that important nodes connect other nodes. Considers the number of shortest paths between 2 nodes .For Graphs with a large number of nodes, the value of betweenness centrality is very high\n",
- "* nx.betweeness_centrality : a measure of centrality in a graph based on shortest paths. For every pair of vertices in a connected graph, there exists at least one shortest path between the vertices such that either the number of edges that the path passes through (for unweighted graphs) or the sum of the weights of the edges (for weighted graphs) is minimized. The betweenness centrality for each vertex is the number of these shortest paths that pass through the vertex\n",
- "* nx.pagerank : Page Rank Algorithm (developed by Google founders to measure the importance of webpages) assigns a score of importance to each node. Important nodes are those with many inlinks from important pages. It mainly works for Directed Networks\n",
- "\n",
- "**Finding Connections**\n",
- "* nx.find_cliques (undirected graphs): finds the maximum subgraphs based on the number of interconnected nodes\n",
- "* nx.k_core : A k-core is a maximal subgraph that contains nodes of degree k or more. Groups clusters meeting the threshold k (can be used as a toggle)\n",
- "\n",
- "**Sources**\n",
- "* https://www.youtube.com/watch?v=VetBkjcm9Go\n",
- "* https://www.activestate.com/blog/graph-theory-using-python-introduction-and-implementation/ \n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Things to think about\n",
- "* Apply the deduplicated_uuids.csv info to the transactions table\n",
- "* After doing a left join on the inds/orgs dataset with the transactions data, the recipient_id column needs to have a recipient_name column so that a new node can be created\n",
- "* for ppl who have multiple donations {and so have various attributes like office_sought, purpose, transaction_type}, should this information be saved?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "
\n",
- "\n",
- "
\n",
- " \n",
- "
\n",
- "
\n",
- "
transaction_id
\n",
- "
donor_id
\n",
- "
year
\n",
- "
amount
\n",
- "
recipient_id
\n",
- "
office_sought
\n",
- "
purpose
\n",
- "
transaction_type
\n",
- "
donor_type
\n",
- "
recipient_type
\n",
- "
donor_office
\n",
- "
recipient_name
\n",
- "
\n",
- " \n",
- " \n",
- "
\n",
- "
0
\n",
- "
7773a71e-9f67-438e-8313-80b1b75deeb4
\n",
- "
4544b60d-da6b-4dd5-9efe-334152ccf1f1
\n",
- "
2018
\n",
- "
1000.0
\n",
- "
981a0414-b738-4e20-91b8-a29ee2cc7edf
\n",
- "
none
\n",
- "
bob worsley for state senate
\n",
- "
contribute to a candidate committee
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
#1022 arizona committee of automotive retailers
\n",
- "
\n",
- "
\n",
- "
1
\n",
- "
95f74915-a945-491f-8751-8c970a76fc24
\n",
- "
946d7561-42a3-4a4b-b410-3a10271c9f18
\n",
- "
2018
\n",
- "
1000.0
\n",
- "
981a0414-b738-4e20-91b8-a29ee2cc7edf
\n",
- "
none
\n",
- "
drew john for state house
\n",
- "
contribute to a candidate committee
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
#1022 arizona committee of automotive retailers
\n",
- "
\n",
- "
\n",
- "
2
\n",
- "
d05f1763-132d-4717-addc-8ff6239ad4d9
\n",
- "
c8f98436-9562-48ed-b51f-45b2b217aad1
\n",
- "
2018
\n",
- "
1000.0
\n",
- "
981a0414-b738-4e20-91b8-a29ee2cc7edf
\n",
- "
none
\n",
- "
elect karen fann ld1
\n",
- "
contribute to a candidate committee
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
#1022 arizona committee of automotive retailers
\n",
- "
\n",
- "
\n",
- "
3
\n",
- "
3dc3da30-6562-4755-bfad-6a26f1baec15
\n",
- "
b9965bc2-c94d-4f69-98d1-bc4f5ad701c5
\n",
- "
2018
\n",
- "
1000.0
\n",
- "
981a0414-b738-4e20-91b8-a29ee2cc7edf
\n",
- "
none
\n",
- "
elect noel campbell for house
\n",
- "
contribute to a candidate committee
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
#1022 arizona committee of automotive retailers
\n",
- "
\n",
- "
\n",
- "
4
\n",
- "
a4340a2c-7b8a-4eeb-8290-746f0f436c83
\n",
- "
946d7561-42a3-4a4b-b410-3a10271c9f18
\n",
- "
2018
\n",
- "
1000.0
\n",
- "
981a0414-b738-4e20-91b8-a29ee2cc7edf
\n",
- "
none
\n",
- "
closed to new donations
\n",
- "
refund from contrib to a cand committee
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
#1022 arizona committee of automotive retailers
\n",
- "
\n",
- " \n",
- "
\n",
- "
"
- ],
- "text/plain": [
- " transaction_id donor_id \\\n",
- "0 7773a71e-9f67-438e-8313-80b1b75deeb4 4544b60d-da6b-4dd5-9efe-334152ccf1f1 \n",
- "1 95f74915-a945-491f-8751-8c970a76fc24 946d7561-42a3-4a4b-b410-3a10271c9f18 \n",
- "2 d05f1763-132d-4717-addc-8ff6239ad4d9 c8f98436-9562-48ed-b51f-45b2b217aad1 \n",
- "3 3dc3da30-6562-4755-bfad-6a26f1baec15 b9965bc2-c94d-4f69-98d1-bc4f5ad701c5 \n",
- "4 a4340a2c-7b8a-4eeb-8290-746f0f436c83 946d7561-42a3-4a4b-b410-3a10271c9f18 \n",
- "\n",
- " year amount recipient_id office_sought \\\n",
- "0 2018 1000.0 981a0414-b738-4e20-91b8-a29ee2cc7edf none \n",
- "1 2018 1000.0 981a0414-b738-4e20-91b8-a29ee2cc7edf none \n",
- "2 2018 1000.0 981a0414-b738-4e20-91b8-a29ee2cc7edf none \n",
- "3 2018 1000.0 981a0414-b738-4e20-91b8-a29ee2cc7edf none \n",
- "4 2018 1000.0 981a0414-b738-4e20-91b8-a29ee2cc7edf none \n",
- "\n",
- " purpose transaction_type \\\n",
- "0 bob worsley for state senate contribute to a candidate committee \n",
- "1 drew john for state house contribute to a candidate committee \n",
- "2 elect karen fann ld1 contribute to a candidate committee \n",
- "3 elect noel campbell for house contribute to a candidate committee \n",
- "4 closed to new donations refund from contrib to a cand committee \n",
- "\n",
- " donor_type recipient_type donor_office \\\n",
- "0 NaN NaN NaN \n",
- "1 NaN NaN NaN \n",
- "2 NaN NaN NaN \n",
- "3 NaN NaN NaN \n",
- "4 NaN NaN NaN \n",
- "\n",
- " recipient_name \n",
- "0 #1022 arizona committee of automotive retailers \n",
- "1 #1022 arizona committee of automotive retailers \n",
- "2 #1022 arizona committee of automotive retailers \n",
- "3 #1022 arizona committee of automotive retailers \n",
- "4 #1022 arizona committee of automotive retailers "
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from utils.network import name_identifier\n",
- "from utils.linkage import deduplicate_perfect_matches\n",
- "transactions = transactions.loc[(transactions.recipient_id.isin(inds_df.id)) | \n",
- " (transactions.recipient_id.isin(orgs_df.id)) |\n",
- " (transactions.donor_id.isin(inds_df.id)) |\n",
- " (transactions.donor_id.isin(inds_df.id))]\n",
- "inds = deduplicate_perfect_matches(inds_df) \n",
- "orgs = deduplicate_perfect_matches(orgs_df)\n",
- "transactions[\"recipient_name\"] = transactions[\"recipient_id\"].apply(name_identifier, args=([orgs, inds],))\n",
- "\n",
- "transactions.head(5)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "87"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "x = transactions.loc[transactions.donor_id.isin(inds_df.id)]\n",
- "len(x.recipient_name.unique())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "
\n",
- "\n",
- "
\n",
- " \n",
- "
\n",
- "
\n",
- "
id
\n",
- "
first_name
\n",
- "
last_name
\n",
- "
full_name
\n",
- "
entity_type
\n",
- "
state
\n",
- "
party
\n",
- "
company
\n",
- "
occupation
\n",
- "
address
\n",
- "
...
\n",
- "
year
\n",
- "
amount
\n",
- "
recipient_id
\n",
- "
office_sought
\n",
- "
purpose
\n",
- "
transaction_type
\n",
- "
donor_type
\n",
- "
recipient_type
\n",
- "
donor_office
\n",
- "
recipient_name
\n",
- "
\n",
- " \n",
- " \n",
- "
\n",
- "
55243
\n",
- "
0e24b503-b209-48b5-8edb-cca0cdaca78c
\n",
- "
M.
\n",
- "
TANG
\n",
- "
m. tang ...
\n",
- "
Individual
\n",
- "
MD
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
6614 23RD PLACE
\n",
- "
...
\n",
- "
2022.0
\n",
- "
2.0
\n",
- "
49a2d46f-5e75-433c-94fa-f910e66d1a1e
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
direct
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
None
\n",
- "
\n",
- "
\n",
- "
55244
\n",
- "
0e24b503-b209-48b5-8edb-cca0cdaca78c
\n",
- "
M.
\n",
- "
TANG
\n",
- "
m. tang ...
\n",
- "
Individual
\n",
- "
MD
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
6614 23RD PLACE
\n",
- "
...
\n",
- "
2022.0
\n",
- "
95.0
\n",
- "
49a2d46f-5e75-433c-94fa-f910e66d1a1e
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
direct
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
None
\n",
- "
\n",
- "
\n",
- "
55245
\n",
- "
0e24b503-b209-48b5-8edb-cca0cdaca78c
\n",
- "
M.
\n",
- "
TANG
\n",
- "
m. tang ...
\n",
- "
Individual
\n",
- "
MD
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
6614 23RD PLACE
\n",
- "
...
\n",
- "
2022.0
\n",
- "
10.0
\n",
- "
49a2d46f-5e75-433c-94fa-f910e66d1a1e
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
direct
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
None
\n",
- "
\n",
- "
\n",
- "
55246
\n",
- "
a23037f6-741c-43a5-8a6d-0f1db4371e1d
\n",
- "
OLIVIA N
\n",
- "
DALMASSO
\n",
- "
olivia n dalmasso ...
\n",
- "
Individual
\n",
- "
IL
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
PO BOX 574
\n",
- "
...
\n",
- "
2022.0
\n",
- "
12.6
\n",
- "
6b33721f-3f6a-47c0-bce2-284fc58e0d2a
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
direct
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
None
\n",
- "
\n",
- "
\n",
- "
55247
\n",
- "
a23037f6-741c-43a5-8a6d-0f1db4371e1d
\n",
- "
OLIVIA N
\n",
- "
DALMASSO
\n",
- "
olivia n dalmasso ...
\n",
- "
Individual
\n",
- "
IL
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
PO BOX 574
\n",
- "
...
\n",
- "
2022.0
\n",
- "
4.2
\n",
- "
6b33721f-3f6a-47c0-bce2-284fc58e0d2a
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
direct
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
NaN
\n",
- "
None
\n",
- "
\n",
- " \n",
- "
\n",
- "
5 rows × 25 columns
\n",
- "
"
- ],
- "text/plain": [
- " id first_name \\\n",
- "55243 0e24b503-b209-48b5-8edb-cca0cdaca78c M. \n",
- "55244 0e24b503-b209-48b5-8edb-cca0cdaca78c M. \n",
- "55245 0e24b503-b209-48b5-8edb-cca0cdaca78c M. \n",
- "55246 a23037f6-741c-43a5-8a6d-0f1db4371e1d OLIVIA N \n",
- "55247 a23037f6-741c-43a5-8a6d-0f1db4371e1d OLIVIA N \n",
- "\n",
- " last_name \\\n",
- "55243 TANG \n",
- "55244 TANG \n",
- "55245 TANG \n",
- "55246 DALMASSO \n",
- "55247 DALMASSO \n",
- "\n",
- " full_name entity_type state \\\n",
- "55243 m. tang ... Individual MD \n",
- "55244 m. tang ... Individual MD \n",
- "55245 m. tang ... Individual MD \n",
- "55246 olivia n dalmasso ... Individual IL \n",
- "55247 olivia n dalmasso ... Individual IL \n",
- "\n",
- " party company occupation address ... year amount \\\n",
- "55243 NaN NaN NaN 6614 23RD PLACE ... 2022.0 2.0 \n",
- "55244 NaN NaN NaN 6614 23RD PLACE ... 2022.0 95.0 \n",
- "55245 NaN NaN NaN 6614 23RD PLACE ... 2022.0 10.0 \n",
- "55246 NaN NaN NaN PO BOX 574 ... 2022.0 12.6 \n",
- "55247 NaN NaN NaN PO BOX 574 ... 2022.0 4.2 \n",
- "\n",
- " recipient_id office_sought purpose \\\n",
- "55243 49a2d46f-5e75-433c-94fa-f910e66d1a1e NaN NaN \n",
- "55244 49a2d46f-5e75-433c-94fa-f910e66d1a1e NaN NaN \n",
- "55245 49a2d46f-5e75-433c-94fa-f910e66d1a1e NaN NaN \n",
- "55246 6b33721f-3f6a-47c0-bce2-284fc58e0d2a NaN NaN \n",
- "55247 6b33721f-3f6a-47c0-bce2-284fc58e0d2a NaN NaN \n",
- "\n",
- " transaction_type donor_type recipient_type donor_office \\\n",
- "55243 direct NaN NaN NaN \n",
- "55244 direct NaN NaN NaN \n",
- "55245 direct NaN NaN NaN \n",
- "55246 direct NaN NaN NaN \n",
- "55247 direct NaN NaN NaN \n",
- "\n",
- " recipient_name \n",
- "55243 None \n",
- "55244 None \n",
- "55245 None \n",
- "55246 None \n",
- "55247 None \n",
- "\n",
- "[5 rows x 25 columns]"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# left merge according to ind_id and transaction donor_id. This was entities that only received money will still be there, no info from ind_dataset\n",
- "# is lost\n",
- "merged_inds_sample = pd.merge(inds_df,transactions,how='left',left_on='id',right_on='donor_id')\n",
- "merged_inds_sample.dropna(subset = ['amount'], inplace=True)\n",
- "merged_inds_sample.tail(5)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Index(['id', 'first_name', 'last_name', 'full_name', 'entity_type', 'state',\n",
- " 'party', 'company', 'occupation', 'address', 'zip', 'city',\n",
- " 'classification', 'transaction_id', 'donor_id', 'year', 'amount',\n",
- " 'recipient_id', 'office_sought', 'purpose', 'transaction_type',\n",
- " 'donor_type', 'recipient_type', 'donor_office', 'recipient_name'],\n",
- " dtype='object')"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "merged_inds_sample.columns"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "
\n",
- "\n",
- "
\n",
- " \n",
- "
\n",
- "
\n",
- "
donor_id
\n",
- "
recipient_id
\n",
- "
full_name
\n",
- "
recipient_name
\n",
- "
address
\n",
- "
amount
\n",
- "
city
\n",
- "
classification
\n",
- "
company
\n",
- "
donor_office
\n",
- "
...
\n",
- "
occupation
\n",
- "
office_sought
\n",
- "
party
\n",
- "
purpose
\n",
- "
recipient_type
\n",
- "
state
\n",
- "
transaction_id
\n",
- "
transaction_type
\n",
- "
year
\n",
- "
zip
\n",
- "
\n",
- " \n",
- " \n",
- "
\n",
- "
0
\n",
- "
0007b184-4e1d-401a-ba51-99733d2e13e7
\n",
- "
d461f2bd-9074-44b3-8948-e659bead3e58
\n",
- "
graham filler ...
\n",
- "
saginaw county republican committee
\n",
- "
12705 WARM CREEK
\n",
- "
500.00
\n",
- "
DEWITT
\n",
- "
neutral
\n",
- "
None
\n",
- "
None
\n",
- "
...
\n",
- "
None
\n",
- "
None
\n",
- "
None
\n",
- "
None
\n",
- "
None
\n",
- "
MI
\n",
- "
None
\n",
- "
direct
\n",
- "
2022.0
\n",
- "
48820-0000
\n",
- "
\n",
- "
\n",
- "
1
\n",
- "
00523627-46c7-4f76-ab42-fb2c1fbac1b1
\n",
- "
6126e78b-4e80-4361-a019-9d99aa1623ed
\n",
- "
daniel millstone ...
\n",
- "
rooted in community leadership pac
\n",
- "
10518 ROUNTREE RD
\n",
- "
0.77
\n",
- "
LOS ANGELES
\n",
- "
neutral
\n",
- "
None
\n",
- "
None
\n",
- "
...
\n",
- "
None
\n",
- "
None
\n",
- "
None
\n",
- "
None
\n",
- "
None
\n",
- "
CA
\n",
- "
None
\n",
- "
direct
\n",
- "
2022.0
\n",
- "
90064-0000
\n",
- "
\n",
- "
\n",
- "
2
\n",
- "
00934782-86e5-4941-94cf-0a700100a2c0
\n",
- "
2d1a0919-218e-4692-98ec-c4a73a126482
\n",
- "
josie petersheim ...
\n",
- "
mi greenstone pac
\n",
- "
7196 W. BRIGGS RD.
\n",
- "
25.00
\n",
- "
STANTON
\n",
- "
neutral
\n",
- "
None
\n",
- "
None
\n",
- "
...
\n",
- "
None
\n",
- "
None
\n",
- "
None
\n",
- "
None
\n",
- "
None
\n",
- "
MI
\n",
- "
None
\n",
- "
direct
\n",
- "
2022.0
\n",
- "
48888-0000
\n",
- "
\n",
- "
\n",
- "
3
\n",
- "
00f22bdd-96bf-4074-9620-4737e8444958
\n",
- "
af8417ee-5bca-49f5-91e9-d2de65d73631
\n",
- "
robert doerfler ...
\n",
- "
michigan senate democratic fund
\n",
- "
1534 NE 5TH AVE
\n",
- "
50.00
\n",
- "
FORT LAUDERDALE
\n",
- "
neutral
\n",
- "
None
\n",
- "
None
\n",
- "
...
\n",
- "
None
\n",
- "
None
\n",
- "
None
\n",
- "
None
\n",
- "
None
\n",
- "
FL
\n",
- "
None
\n",
- "
direct
\n",
- "
2022.0
\n",
- "
33304-1006
\n",
- "
\n",
- "
\n",
- "
4
\n",
- "
0138403b-b5b9-453a-a1d2-b6ed9fa5fe58
\n",
- "
6126e78b-4e80-4361-a019-9d99aa1623ed
\n",
- "
joseph martinez ...
\n",
- "
rooted in community leadership pac
\n",
- "
139 HURON AVE
\n",
- "
1.65
\n",
- "
MOUNT CLEMENS
\n",
- "
neutral
\n",
- "
None
\n",
- "
None
\n",
- "
...
\n",
- "
None
\n",
- "
None
\n",
- "
None
\n",
- "
None
\n",
- "
None
\n",
- "
MI
\n",
- "
None
\n",
- "
direct
\n",
- "
2022.0
\n",
- "
48043-0000
\n",
- "
\n",
- "
\n",
- "
...
\n",
- "
...
\n",
- "
...
\n",
- "
...
\n",
- "
...
\n",
- "
...
\n",
- "
...
\n",
- "
...
\n",
- "
...
\n",
- "
...
\n",
- "
...
\n",
- "
...
\n",
- "
...
\n",
- "
...
\n",
- "
...
\n",
- "
...
\n",
- "
...
\n",
- "
...
\n",
- "
...
\n",
- "
...
\n",
- "
...
\n",
- "
...
\n",
- "
\n",
- "
\n",
- "
1120
\n",
- "
fdccce6b-e55f-4f1d-bd95-1714f2a667ed
\n",
- "
a3fe20e2-8019-448e-9b54-bfdce4d87f2f
\n",
- "
michael olthoff ...
\n",
- "
bumstead leadership fund
\n",
- "
1499 MIDDLEBROOK DR
\n",
- "
1000.00
\n",
- "
NORTON SHORES
\n",
- "
neutral
\n",
- "
nichols
\n",
- "
None
\n",
- "
...
\n",
- "
ceo
\n",
- "
None
\n",
- "
None
\n",
- "
None
\n",
- "
None
\n",
- "
MI
\n",
- "
None
\n",
- "
direct
\n",
- "
2022.0
\n",
- "
49441-0000
\n",
- "
\n",
- "
\n",
- "
1121
\n",
- "
fe969829-b8a4-4d38-88e2-8314b340d567
\n",
- "
6126e78b-4e80-4361-a019-9d99aa1623ed
\n",
- "
joanna simon ...
\n",
- "
rooted in community leadership pac
\n",
- "
1546 POPLAR GROVE DR
\n",
- "
3.82
\n",
- "
RESTON
\n",
- "
neutral
\n",
- "
None
\n",
- "
None
\n",
- "
...
\n",
- "
None
\n",
- "
None
\n",
- "
None
\n",
- "
None
\n",
- "
None
\n",
- "
VA
\n",
- "
None
\n",
- "
direct
\n",
- "
2022.0
\n",
- "
20194-1731
\n",
- "
\n",
- "
\n",
- "
1122
\n",
- "
ff1423ba-ff5e-4bc1-b864-303a9dcc9b32
\n",
- "
6126e78b-4e80-4361-a019-9d99aa1623ed
\n",
- "
adriana p{on ce ...
\n",
- "
rooted in community leadership pac
\n",
- "
9 BIRCH CT
\n",
- "
3.82
\n",
- "
NORMAL
\n",
- "
neutral
\n",
- "
None
\n",
- "
None
\n",
- "
...
\n",
- "
None
\n",
- "
None
\n",
- "
None
\n",
- "
None
\n",
- "
None
\n",
- "
IL
\n",
- "
None
\n",
- "
direct
\n",
- "
2022.0
\n",
- "
61761-3900
\n",
- "
\n",
- "
\n",
- "
1123
\n",
- "
ff24644e-d64a-4a8a-a87f-cdb53b86dd63
\n",
- "
6126e78b-4e80-4361-a019-9d99aa1623ed
\n",
- "
david friedman ...
\n",
- "
rooted in community leadership pac
\n",
- "
8823 MOUNTAIN PATH CIR
\n",
- "
0.15
\n",
- "
AUSTIN
\n",
- "
neutral
\n",
- "
None
\n",
- "
None
\n",
- "
...
\n",
- "
None
\n",
- "
None
\n",
- "
None
\n",
- "
None
\n",
- "
None
\n",
- "
TX
\n",
- "
None
\n",
- "
direct
\n",
- "
2022.0
\n",
- "
78759-0000
\n",
- "
\n",
- "
\n",
- "
1124
\n",
- "
ffb25947-c03f-43b2-abb4-23531cdb7324
\n",
- "
7f272fe4-d592-453c-9ca1-315ea3fdcff1
\n",
- "
dennis starner ...
\n",
- "
bill g schuette for state representative
\n",
- "
4612 CONGRESS DRIVE
\n",
- "
525.00
\n",
- "
MIDLAND
\n",
- "
neutral
\n",
- "
retired
\n",
- "
None
\n",
- "
...
\n",
- "
retired
\n",
- "
None
\n",
- "
None
\n",
- "
None
\n",
- "
None
\n",
- "
MI
\n",
- "
None
\n",
- "
direct/fund raiser
\n",
- "
2022.0
\n",
- "
48642-0000
\n",
- "
\n",
- " \n",
- "
\n",
- "
1125 rows × 25 columns
\n",
- "
"
- ],
- "text/plain": [
- " donor_id \\\n",
- "0 0007b184-4e1d-401a-ba51-99733d2e13e7 \n",
- "1 00523627-46c7-4f76-ab42-fb2c1fbac1b1 \n",
- "2 00934782-86e5-4941-94cf-0a700100a2c0 \n",
- "3 00f22bdd-96bf-4074-9620-4737e8444958 \n",
- "4 0138403b-b5b9-453a-a1d2-b6ed9fa5fe58 \n",
- "... ... \n",
- "1120 fdccce6b-e55f-4f1d-bd95-1714f2a667ed \n",
- "1121 fe969829-b8a4-4d38-88e2-8314b340d567 \n",
- "1122 ff1423ba-ff5e-4bc1-b864-303a9dcc9b32 \n",
- "1123 ff24644e-d64a-4a8a-a87f-cdb53b86dd63 \n",
- "1124 ffb25947-c03f-43b2-abb4-23531cdb7324 \n",
- "\n",
- " recipient_id \\\n",
- "0 d461f2bd-9074-44b3-8948-e659bead3e58 \n",
- "1 6126e78b-4e80-4361-a019-9d99aa1623ed \n",
- "2 2d1a0919-218e-4692-98ec-c4a73a126482 \n",
- "3 af8417ee-5bca-49f5-91e9-d2de65d73631 \n",
- "4 6126e78b-4e80-4361-a019-9d99aa1623ed \n",
- "... ... \n",
- "1120 a3fe20e2-8019-448e-9b54-bfdce4d87f2f \n",
- "1121 6126e78b-4e80-4361-a019-9d99aa1623ed \n",
- "1122 6126e78b-4e80-4361-a019-9d99aa1623ed \n",
- "1123 6126e78b-4e80-4361-a019-9d99aa1623ed \n",
- "1124 7f272fe4-d592-453c-9ca1-315ea3fdcff1 \n",
- "\n",
- " full_name \\\n",
- "0 graham filler ... \n",
- "1 daniel millstone ... \n",
- "2 josie petersheim ... \n",
- "3 robert doerfler ... \n",
- "4 joseph martinez ... \n",
- "... ... \n",
- "1120 michael olthoff ... \n",
- "1121 joanna simon ... \n",
- "1122 adriana p{on ce ... \n",
- "1123 david friedman ... \n",
- "1124 dennis starner ... \n",
- "\n",
- " recipient_name address \\\n",
- "0 saginaw county republican committee 12705 WARM CREEK \n",
- "1 rooted in community leadership pac 10518 ROUNTREE RD \n",
- "2 mi greenstone pac 7196 W. BRIGGS RD. \n",
- "3 michigan senate democratic fund 1534 NE 5TH AVE \n",
- "4 rooted in community leadership pac 139 HURON AVE \n",
- "... ... ... \n",
- "1120 bumstead leadership fund 1499 MIDDLEBROOK DR \n",
- "1121 rooted in community leadership pac 1546 POPLAR GROVE DR \n",
- "1122 rooted in community leadership pac 9 BIRCH CT \n",
- "1123 rooted in community leadership pac 8823 MOUNTAIN PATH CIR \n",
- "1124 bill g schuette for state representative 4612 CONGRESS DRIVE \n",
- "\n",
- " amount city classification company donor_office ... \\\n",
- "0 500.00 DEWITT neutral None None ... \n",
- "1 0.77 LOS ANGELES neutral None None ... \n",
- "2 25.00 STANTON neutral None None ... \n",
- "3 50.00 FORT LAUDERDALE neutral None None ... \n",
- "4 1.65 MOUNT CLEMENS neutral None None ... \n",
- "... ... ... ... ... ... ... \n",
- "1120 1000.00 NORTON SHORES neutral nichols None ... \n",
- "1121 3.82 RESTON neutral None None ... \n",
- "1122 3.82 NORMAL neutral None None ... \n",
- "1123 0.15 AUSTIN neutral None None ... \n",
- "1124 525.00 MIDLAND neutral retired None ... \n",
- "\n",
- " occupation office_sought party purpose recipient_type state \\\n",
- "0 None None None None None MI \n",
- "1 None None None None None CA \n",
- "2 None None None None None MI \n",
- "3 None None None None None FL \n",
- "4 None None None None None MI \n",
- "... ... ... ... ... ... ... \n",
- "1120 ceo None None None None MI \n",
- "1121 None None None None None VA \n",
- "1122 None None None None None IL \n",
- "1123 None None None None None TX \n",
- "1124 retired None None None None MI \n",
- "\n",
- " transaction_id transaction_type year zip \n",
- "0 None direct 2022.0 48820-0000 \n",
- "1 None direct 2022.0 90064-0000 \n",
- "2 None direct 2022.0 48888-0000 \n",
- "3 None direct 2022.0 33304-1006 \n",
- "4 None direct 2022.0 48043-0000 \n",
- "... ... ... ... ... \n",
- "1120 None direct 2022.0 49441-0000 \n",
- "1121 None direct 2022.0 20194-1731 \n",
- "1122 None direct 2022.0 61761-3900 \n",
- "1123 None direct 2022.0 78759-0000 \n",
- "1124 None direct/fund raiser 2022.0 48642-0000 \n",
- "\n",
- "[1125 rows x 25 columns]"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "attribute_cols = merged_inds_sample.columns.difference(['donor_id','recipient_id','full_name','recipient_name'])\n",
- "agg_functions = {col: 'sum' if col == 'amount' else 'first' for col in attribute_cols}\n",
- "grouped_sample = merged_inds_sample.groupby(['donor_id','recipient_id','full_name','recipient_name']).agg(agg_functions).reset_index()\n",
- "grouped_sample"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [],
- "source": [
- "def create_network_nodes(df: pd.DataFrame) -> nx.MultiDiGraph:\n",
- " G = nx.MultiDiGraph()\n",
- " # first check if df is individuals or organizations dataset\n",
- " if \"name\" in df.columns:\n",
- " node_name = \"name\"\n",
- " else:\n",
- " node_name = \"full_name\"\n",
- " \n",
- " transact_info = ['office_sought', 'purpose', 'transaction_type', 'year','transaction_id','donor_office','amount']\n",
- " for _, row in df.iterrows(): \n",
- " # add node attributes based on the columns relevant to the entity\n",
- " G.add_node(row[node_name])\n",
- " for column in df.columns.difference(transact_info):\n",
- " if not pd.isnull(row[column]):\n",
- " G.nodes[row[node_name]][column] = row[column]\n",
- " \n",
- " # link the donor node to the recipient node. add the attributes of the\n",
- " # edge based on relevant nodes \n",
- " edge_dictionary = {}\n",
- " for column in transact_info:\n",
- " if not pd.isnull(row[column]):\n",
- " edge_dictionary[column] = row[column]\n",
- " G.add_edge(row[node_name], row['recipient_name'], **edge_dictionary)\n",
- "\n",
- " # the added 'recipient_name' node has no attributes at this moment\n",
- " # for the final code this line won't be necessary, as each recipient\n",
- " # should ideally be referenced later on. For now, all added nodes for\n",
- " # the recipient will only have one default attribute: classification\n",
- " G.nodes[row['recipient_name']]['classification'] = 'neutral' \n",
- " \n",
- " edge_labels = {(u,v):d['amount'] for u,v,d in G.edges(data=True)}\n",
- " entity_colors = {'neutral': 'green', 'c':'blue', 'f':'red'}\n",
- " node_colors = [entity_colors[G.nodes[node]['classification']] for node in G.nodes()]\n",
- "\n",
- " nx.draw_planar(G, with_labels=False,node_color=node_colors)\n",
- " plt.figure(3,figsize=(12,12)) \n",
- " nx.draw_networkx_edge_labels(G, pos=nx.planar_layout(G),edge_labels=edge_labels, label_pos=0.5)\n",
- "\n",
- " #nx.draw_planar(G, with_labels=False)\n",
- " plt.show()\n",
- " return G"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 122,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{}"
- ]
- },
- "execution_count": 122,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#for u,v in G.nodes(data=True):\n",
- " #print(u)#['classification'])\n",
- " \n",
- "G.nodes['michigan association of health plans political action committee']#['classification'])#['nancy davis ']['classification']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 66,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array(['neutral', 'f'], dtype=object)"
- ]
- },
- "execution_count": 66,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "grouped_sample.classification.unique()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "