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Merge branch 'dev-f23' into MN_abstract_class
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@averyschoen
averyschoen authored Dec 5, 2023
2 parents ac48cb2 + 261fa60 commit 40f92a1
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1 change: 1 addition & 0 deletions .gitignore
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# data files
*.avro
data/*.txt
1 change: 0 additions & 1 deletion data/README.md
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This directory contains information for use in this project.

Please make sure to document each source file here.
#### Arizona Campaign Finance Data

##### Summary
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97 changes: 34 additions & 63 deletions notebooks/AZ_EDA.ipynb
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"import plotly.express as px\n",
"import plotly.graph_objects as go\n",
"import warnings\n",
"\n",
"warnings.filterwarnings(\"ignore\")\n",
"warnings.filterwarnings('ignore')\n",
"from utils.helper_fns import pre_process_az\n",
"\n",
"ind_bla, pac_bla, org_bla, bla_cands = pre_process_az()"
]
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}
],
"source": [
"# looking at just proper contributors, ignoring 'multiple contributors' in first place\n",
"#looking at just proper contributors, ignoring 'multiple contributors' in first place\n",
"ind_bla[1:11]"
]
},
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}
],
"source": [
"# looking at all expenses by individual contributors, PACs and organizations\n",
"top_expenses = (\n",
" ind_bla.append(org_bla)\n",
" .append(pac_bla)\n",
" .sort_values(by=\"total_spending\", ascending=False)\n",
")\n",
"#looking at all expenses by individual contributors, PACs and organizations\n",
"top_expenses = ind_bla.append(org_bla).append(pac_bla).sort_values(by=\"total_spending\", ascending=False)\n",
"\n",
"top_expenses.head(11)"
]
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}
],
"source": [
"# means\n",
"#means\n",
"\n",
"grp_cands = cands_14_22.groupby(by=\"Office Summary\").mean()\n",
"\n",
"fig = px.bar(\n",
" x=list(grp_cands.index),\n",
" y=grp_cands[\"Expense\"].values,\n",
" title=\"Mean Candidate Income by Office, 2014-22\",\n",
" labels={\"x\": \"Year\", \"y\": \"US Dollars\"},\n",
")\n",
"fig = px.bar(x=list(grp_cands.index), y=grp_cands[\"Expense\"].values, title=\"Mean Candidate Income by Office, 2014-22\", labels = {\"x\":\"Year\", \"y\":\"US Dollars\"})\n",
"\n",
"fig.show()"
]
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"source": [
"grp_cands = cands_14_22.groupby(by=\"Office Summary\").sum()\n",
"\n",
"fig = px.bar(\n",
" x=list(grp_cands.index),\n",
" y=grp_cands[\"Expense\"].values,\n",
" title=\"Gross Candidate Income by Office, 2014-22\",\n",
" labels={\"x\": \"Year\", \"y\": \"US Dollars\"},\n",
")\n",
"fig = px.bar(x=list(grp_cands.index), y=grp_cands[\"Expense\"].values, title=\"Gross Candidate Income by Office, 2014-22\", labels = {\"x\":\"Year\", \"y\":\"US Dollars\"})\n",
"\n",
"fig.show()"
]
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}
],
"source": [
"# recipient income by year\n",
"#recipient income by year\n",
"\n",
"by_year_cands_sum = cands_14_22.groupby(by=\"Year\").sum()\n",
"\n",
"fig = px.bar(\n",
" x=list(by_year_cands_sum.index),\n",
" y=by_year_cands_sum[\"Income\"].values,\n",
" title=\"Gross Candidate Income by Year, 2014-22\",\n",
" labels={\"x\": \"Year\", \"y\": \"US Dollars\"},\n",
")\n",
"fig = px.bar(x=list(by_year_cands_sum.index), y=by_year_cands_sum[\"Income\"].values, title=\"Gross Candidate Income by Year, 2014-22\", labels = {\"x\":\"Year\", \"y\":\"US Dollars\"})\n",
"\n",
"fig.show()"
]
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}
],
"source": [
"# individual contributions by year\n",
"#individual contributions by year\n",
"\n",
"grp = inds_14_22.groupby(by=\"Year\").sum()\n",
"\n",
"grp[\"total_spending\"].values\n",
"\n",
"fig = go.Figure(\n",
" data=[\n",
" go.Bar(\n",
" name=\"Individual\",\n",
" x=list(range(2014, 2023)),\n",
" y=grp[\"total_spending\"].values,\n",
" )\n",
" ]\n",
")\n",
"fig = go.Figure(data = [\n",
" go.Bar(name = \"Individual\", x = list(range(2014, 2023)), y = grp[\"total_spending\"].values, )\n",
"])\n",
"\n",
"fig.update_layout(\n",
" title=\"Gross Individual Contributions by Year, 2014-22\",\n",
" xaxis_title=\"Year\",\n",
" yaxis_title=\"US Dollars\",\n",
" # legend_title=\"Legend Title\",\n",
" # font=dict(\n",
" # family=\"Courier New, monospace\",\n",
" # size=18,\n",
" # color=\"RebeccaPurple\"\n",
" # )\n",
"# legend_title=\"Legend Title\",\n",
"# font=dict(\n",
"# family=\"Courier New, monospace\",\n",
"# size=18,\n",
"# color=\"RebeccaPurple\"\n",
"# )\n",
")\n",
"\n",
"fig.show()"
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}
],
"source": [
"# overall expenses by type by year\n",
"#overall expenses by type by year\n",
"\n",
"donors_14_22 = (\n",
" org_14_22[[\"Name\", \"total_spending\", \"type\", \"Year\"]]\n",
" .append(pac_14_22[[\"Name\", \"total_spending\", \"type\", \"Year\"]])\n",
" .append(inds_14_22[[\"Name\", \"total_spending\", \"type\", \"Year\"]])\n",
")\n",
"donors_14_22 = org_14_22[[\"Name\", \"total_spending\", \"type\", \"Year\"]].append(pac_14_22[[\"Name\", \"total_spending\", \"type\", \"Year\"]]).append(inds_14_22[[\"Name\", \"total_spending\", \"type\", \"Year\"]])\n",
"\n",
"donors_14_22.groupby(by=[\"type\", \"Year\"]).sum()\n",
"donors_14_22.groupby(by = [\"type\", \"Year\"]).sum()\n",
"\n",
"years = [\"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", \"2021\", \"2022\"]\n",
"years = ['2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021', '2022']\n",
"\n",
"yrs = []\n",
"\n",
"df = donors_14_22.groupby(by=[\"type\", \"Year\"]).sum()\n",
"df=donors_14_22.groupby(by = [\"type\", \"Year\"]).sum()\n",
"\n",
"for i in range(26):\n",
" yrs.append(df.take([i]).values[0][0])\n",
"\n",
"fig = go.Figure(\n",
" data=[\n",
" go.Bar(name=\"Individual\", x=years, y=yrs[0:9]),\n",
" go.Bar(name=\"Organization\", x=years, y=[yrs[9]] + [0] + list(yrs[10:17])),\n",
" go.Bar(name=\"PAC\", x=years, y=yrs[17:26]),\n",
" ]\n",
")\n",
"fig = go.Figure(data = [\n",
" go.Bar(name = \"Individual\", x = years, y = yrs[0:9]),\n",
" go.Bar(name = \"Organization\", x = years, y = [yrs[9]]+[0]+list(yrs[10:17])),\n",
" go.Bar(name = \"PAC\", x = years, y = yrs[17:26])\n",
"])\n",
"\n",
"fig.update_layout(\n",
" title=\"Gross Overall Expenses by Donor Type by Year, 2014-22\",\n",
" xaxis_title=\"Year\",\n",
" yaxis_title=\"US Dollars\",\n",
"\n",
")\n",
"\n",
"fig.show()"
"fig.show()\n",
"\n"
]
},
{
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.12"
"version": "3.10.12"
}
},
"nbformat": 4,
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