{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Reports from Court Records"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
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"<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from __future__ import division, print_function\n",
"import pandas as pd\n",
"import sys\n",
"import calendar\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import plotly.plotly as py\n",
"from plotly.offline import init_notebook_mode, iplot\n",
"import plotly.graph_objs as go\n",
"import numpy as np\n",
"init_notebook_mode(connected=True)\n",
"\n",
"\n",
"def get_calendar_df():\n",
" '''Read the parsed court records from a file'''\n",
" df = pd.read_csv('calendars_parsed.csv')\n",
" df['Date'] = pd.to_datetime(df['Date'], errors='coerce')\n",
" df['Title'] = df['Title'].astype(str)\n",
" df['day_of_week'] = df['Date'].dt.day_name()\n",
" df['month'] = df['Date'].dt.month_name()\n",
" df['day_of_month'] = df['Date'].dt.day\n",
" df['year'] = df['Date'].dt.year\n",
" return df[df.Status == 'Confirmed']\n",
"\n",
"\n",
"def filter_df(df, date_min=None, date_max=None, day_of_week=None,\n",
" day_of_month=None, month=None, year=None,\n",
" case_number=None, case_title=None, event=None, court=None):\n",
" '''Filter a dataframe based on specified criteria'''\n",
" if date_min is not None:\n",
" df = df.loc[df['Date'] >= date_min]\n",
" if date_max is not None:\n",
" df = df.loc[df['Date'] <= date_max]\n",
" if day_of_week is not None:\n",
" df = df[df['day_of_week'].str.lower().str.contains(day_of_week.lower())]\n",
" if day_of_month is not None:\n",
" df = df[df['day_of_month'] == day_of_month]\n",
" if month is not None:\n",
" df = df[df['month'].str.lower().str.contains(month.lower())]\n",
" if year is not None:\n",
" df = df[df['year'] == year]\n",
" if case_number is not None:\n",
" df = df[df['Case Number'].str.lower().str.contains(case_number.lower())]\n",
" if case_title is not None:\n",
" df = df[df['Title'].str.lower().str.contains(case_title.lower())]\n",
" if event is not None:\n",
" df = df[df['Event'].str.lower().str.contains(event.lower())]\n",
" if court is not None:\n",
" df = df[df['Court'].str.lower().str.contains(court.lower())]\n",
" return df\n",
"\n",
"\n",
"def number_by_month(df):\n",
" '''Return a count of the number of rows of the dataframe by month'''\n",
" df_by_month = pd.DataFrame(columns=['Month', 'Count'])\n",
" min_year = min(df.year)\n",
" max_year = max(df.year)\n",
" for year in range(min_year, max_year+1):\n",
" for month_number in range(1, 13):\n",
" month = calendar.month_name[month_number]\n",
" count = rows(filter_df(df, month=month, year=year))\n",
" df_by_month = df_by_month.append(\n",
" {'Month': month + ' ' + str(year), 'Count': count},\n",
" ignore_index=True)\n",
" first_nonzero_index = df_by_month.Count.replace(0, np.nan).first_valid_index()\n",
" last_nonzero_index = df_by_month.Count.replace(0, np.nan).last_valid_index()\n",
" return df_by_month.iloc[first_nonzero_index:last_nonzero_index+1]\n",
"\n",
"\n",
"def number_by_day_of_week(df):\n",
" '''Return a count of the number of rows of the dataframe by day of week'''\n",
" df_by_day_of_week = pd.DataFrame(columns=['Day of Week', 'Count'])\n",
" for day_number in range(1, 8):\n",
" day_name = calendar.day_name[day_number]\n",
" count = rows(filter_df(df, day_of_week=day_name))\n",
" df_by_day_of_week = df_by_day_of_week.append({'Day of Week': day_name, 'Count': count}, ignore_index=True)\n",
" return df_by_day_of_week\n",
"\n",
"\n",
"def number_by_day_of_month(df):\n",
" '''Return a count of the number of rows of the dataframe by day of month'''\n",
" df_by_day_of_month = pd.DataFrame(columns=['Day of Month', 'Count'])\n",
" for day_number in range(1, 32):\n",
" count = rows(filter_df(df, day_of_month=day_number))\n",
" df_by_day_of_month = df_by_day_of_month.append({'Day of Month': day_number, 'Count': count}, ignore_index=True)\n",
" return df_by_day_of_month\n",
"\n",
"\n",
"def rows(df):\n",
" '''Convenience function to return the number of rows of a dataframe'''\n",
" return len(df.index)\n",
"\n",
"\n",
"def bar_chart(labels, counts, xlabel=None, ylabel=None):\n",
" '''Make a bar chart'''\n",
" percent = [str(round(100*count/sum(counts),1)) + '%' for count in counts]\n",
" data = [go.Bar(\n",
" x=labels,\n",
" y=counts,\n",
" text=percent,\n",
" textposition = 'auto',\n",
" opacity=0.6\n",
" )]\n",
" layout = go.Layout(xaxis=dict(title=xlabel), yaxis=dict(title=ylabel))\n",
" fig = go.Figure(data=data, layout=layout)\n",
" iplot(fig)\n",
"\n",
"\n",
"def line_plot(labels, counts, xlabel=None, ylabel=None):\n",
" '''Make a bar chart'''\n",
" data = [go.Scatter(\n",
" x=labels,\n",
" y=counts\n",
" )]\n",
" layout = go.Layout(xaxis=dict(title=xlabel),\n",
" yaxis=dict(title=ylabel, range=[0, 1.1*max(counts)]))\n",
" fig = go.Figure(data=data, layout=layout)\n",
" iplot(fig)\n",
"\n",
"\n",
"df = get_calendar_df()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cases broken down by type"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"data": [
{
"opacity": 0.6,
"text": [
"25.0%",
"9.1%",
"18.4%",
"29.3%",
"10.8%",
"7.3%"
],
"textposition": "auto",
"type": "bar",
"uid": "416afa2e-c85c-11e9-beab-6c4008929994",
"x": [
"Small Claims",
"Civil Collection",
"Misdemeanors",
"Felonies",
"Family",
"Other"
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"y": [
5244,
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3868,
6155,
2264,
1539
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"layout": {
"yaxis": {
"title": "Number of hearings"
}
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"<div id=\"3e132d8b-3cf4-4be1-8ab6-96db737d4bcd\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"3e132d8b-3cf4-4be1-8ab6-96db737d4bcd\", [{\"opacity\": 0.6, \"type\": \"bar\", \"y\": [5244, 1913, 3868, 6155, 2264, 1539], \"text\": [\"25.0%\", \"9.1%\", \"18.4%\", \"29.3%\", \"10.8%\", \"7.3%\"], \"x\": [\"Small Claims\", \"Civil Collection\", \"Misdemeanors\", \"Felonies\", \"Family\", \"Other\"], \"textposition\": \"auto\", \"uid\": \"416afa2e-c85c-11e9-beab-6c4008929994\"}], {\"yaxis\": {\"title\": \"Number of hearings\"}}, {\"linkText\": \"Export to plot.ly\", \"showLink\": true})});</script>"
]
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"metadata": {},
"output_type": "display_data"
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],
"source": [
"labels = ['Small Claims', 'Civil Collection', 'Misdemeanors', 'Felonies',\n",
" 'Family', 'Other']\n",
"counts = [0]*6\n",
"counts[0] = rows(filter_df(df, case_number='SC'))\n",
"counts[1] = rows(filter_df(df, case_number='CC'))\n",
"counts[2] = rows(filter_df(df, case_number='CM'))\n",
"counts[3] = rows(filter_df(df, case_number='-F1-|-F2-|-F3-|-F4-|-F5-|-F6-'))\n",
"counts[4] = rows(filter_df(df, case_number='-AD-|-RS-|-JP-|-DC-|-DR-|-GU-'))\n",
"counts[5] = rows(df) - sum(counts[0:5])\n",
"bar_chart(labels, counts, ylabel='Number of hearings')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Criminal cases by type"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"data": [
{
"opacity": 0.6,
"text": [
"0.8%",
"1.6%",
"2.4%",
"2.7%",
"8.5%",
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"37.1%",
"3.8%"
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"textposition": "auto",
"type": "bar",
"uid": "44d45ec8-c85c-11e9-83f3-6c4008929994",
"x": [
"Felony 1",
"Felony 2",
"Felony 3",
"Felony 4",
"Felony 5",
"Felony 6",
"Misdemeanor",
"Infraction"
],
"y": [
81,
165,
247,
283,
884,
4495,
3868,
394
]
}
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"layout": {
"yaxis": {
"title": "Number of hearings"
}
}
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"text/html": [
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"metadata": {},
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"source": [
"labels = ['Felony 1', 'Felony 2', 'Felony 3', 'Felony 4', 'Felony 5', 'Felony 6', 'Misdemeanor', 'Infraction']\n",
"counts = [0]*8\n",
"df_criminal = filter_df(df, case_number='-CM-|-IF-|-F1-|-F2-|-F3-|-F4-|-F5-|-F6-')\n",
"counts[0] = rows(filter_df(df_criminal, case_number='-F1-'))\n",
"counts[1] = rows(filter_df(df_criminal, case_number='-F2-'))\n",
"counts[2] = rows(filter_df(df_criminal, case_number='-F3-'))\n",
"counts[3] = rows(filter_df(df_criminal, case_number='-F4-'))\n",
"counts[4] = rows(filter_df(df_criminal, case_number='-F5-'))\n",
"counts[5] = rows(filter_df(df_criminal, case_number='-F6-'))\n",
"counts[6] = rows(filter_df(df_criminal, case_number='-CM-'))\n",
"counts[7] = rows(filter_df(df_criminal, case_number='-IF-'))\n",
"bar_chart(labels, counts, ylabel='Number of hearings')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Small claims cases by type"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
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"type": "bar",
"uid": "482ce1da-c85c-11e9-af01-6c4008929994",
"x": [
"Eviction Hearing",
"Damages Hearing",
"Finance System of Richmond",
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"Other"
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"y": [
1026,
833,
2455,
172,
4486
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"<div id=\"71101bed-f251-4a66-8252-1d147921c446\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"71101bed-f251-4a66-8252-1d147921c446\", [{\"opacity\": 0.6, \"type\": \"bar\", \"y\": [1026, 833, 2455, 172, 4486], \"text\": [\"11.4%\", \"9.3%\", \"27.4%\", \"1.9%\", \"50.0%\"], \"x\": [\"Eviction Hearing\", \"Damages Hearing\", \"Finance System of Richmond\", \"Natco Credit Union\", \"Other\"], \"textposition\": \"auto\", \"uid\": \"482ce1da-c85c-11e9-af01-6c4008929994\"}], {\"yaxis\": {\"title\": \"Number of hearings\"}}, {\"linkText\": \"Export to plot.ly\", \"showLink\": true})});</script>"
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"<div id=\"71101bed-f251-4a66-8252-1d147921c446\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"71101bed-f251-4a66-8252-1d147921c446\", [{\"opacity\": 0.6, \"type\": \"bar\", \"y\": [1026, 833, 2455, 172, 4486], \"text\": [\"11.4%\", \"9.3%\", \"27.4%\", \"1.9%\", \"50.0%\"], \"x\": [\"Eviction Hearing\", \"Damages Hearing\", \"Finance System of Richmond\", \"Natco Credit Union\", \"Other\"], \"textposition\": \"auto\", \"uid\": \"482ce1da-c85c-11e9-af01-6c4008929994\"}], {\"yaxis\": {\"title\": \"Number of hearings\"}}, {\"linkText\": \"Export to plot.ly\", \"showLink\": true})});</script>"
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"metadata": {},
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"source": [
"labels = ['Eviction Hearing', 'Damages Hearing', 'Finance System of Richmond', 'Natco Credit Union', 'Other']\n",
"df_sc = filter_df(df, case_number='-SC-')\n",
"counts = [0]*5\n",
"\n",
"counts[0] = rows(filter_df(df_sc, event='eviction'))\n",
"counts[1] = rows(filter_df(df_sc, event='damage'))\n",
"counts[2] = rows(filter_df(df_sc, case_title='Fs V|Finance System'))\n",
"counts[3] = rows(filter_df(df_sc, case_title='Natco'))\n",
"counts[4] = sum(counts[0:4])\n",
"\n",
"bar_chart(labels, counts, ylabel='Number of hearings')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Civil collection cases by type"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
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"application/vnd.plotly.v1+json": {
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"x": [
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"Damages Hearing",
"Finance System of Richmond",
"Natco Credit Union",
"Creditmax",
"Capital One",
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426,
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54,
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146,
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"<div id=\"1987559c-12e3-4a99-b0f9-7388c8c52958\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"1987559c-12e3-4a99-b0f9-7388c8c52958\", [{\"opacity\": 0.6, \"type\": \"bar\", \"y\": [271, 426, 284, 54, 39, 146, 1220], \"text\": [\"11.1%\", \"17.5%\", \"11.6%\", \"2.2%\", \"1.6%\", \"6.0%\", \"50.0%\"], \"x\": [\"Reid Hospital\", \"Damages Hearing\", \"Finance System of Richmond\", \"Natco Credit Union\", \"Creditmax\", \"Capital One\", \"Other\"], \"textposition\": \"auto\", \"uid\": \"4beafd70-c85c-11e9-9877-6c4008929994\"}], {\"yaxis\": {\"title\": \"Number of hearings\"}}, {\"linkText\": \"Export to plot.ly\", \"showLink\": true})});</script>"
]
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"metadata": {},
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],
"source": [
"labels = ['Reid Hospital', 'Damages Hearing', 'Finance System of Richmond',\n",
" 'Natco Credit Union', 'Creditmax', 'Capital One', 'Other']\n",
"df_cc = filter_df(df, case_number='-CC-')\n",
"counts = [0]*7\n",
"\n",
"counts[0] = rows(filter_df(df_cc, case_title='Reid Hospital'))\n",
"counts[1] = rows(filter_df(df_cc, event='damage'))\n",
"counts[2] = rows(filter_df(df_cc, case_title='Fs V|Finance System'))\n",
"counts[3] = rows(filter_df(df_cc, case_title='Natco'))\n",
"counts[4] = rows(filter_df(df_cc, case_title='Creditmax'))\n",
"counts[5] = rows(filter_df(df_cc, case_title='Capital One'))\n",
"counts[6] = sum(counts[0:6])\n",
"\n",
"bar_chart(labels, counts, ylabel='Number of hearings')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Eviction hearings as a function of time"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"data": [
{
"type": "scatter",
"uid": "4df353ba-c85c-11e9-9b5b-6c4008929994",
"x": [
"July 2018",
"August 2018",
"September 2018",
"October 2018",
"November 2018",
"December 2018",
"January 2019",
"February 2019",
"March 2019",
"April 2019",
"May 2019",
"June 2019",
"July 2019",
"August 2019",
"September 2019"
],
"y": [
44,
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41,
63,
70,
55,
94,
58,
63,
81,
85,
74,
113,
58,
45
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}
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]
},
"metadata": {},
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}
],
"source": [
"df_evictions = filter_df(df, event='eviction')\n",
"df_evictions_by_month = number_by_month(df_evictions)\n",
"line_plot(df_evictions_by_month.Month,\n",
" df_evictions_by_month.Count,\n",
" xlabel=None,\n",
" ylabel='Number of eviction hearings')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Eviction-related damages hearings as a function of time for all landlords"
]
},
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],
"source": [
"df_damages = filter_df(df, event='damages hearing')\n",
"df_damages = df_damages[df_damages['Case Number'].isin(df_evictions['Case Number'])]\n",
"df_damages_by_month = number_by_month(df_damages)\n",
"line_plot(df_damages_by_month.Month,\n",
" df_damages_by_month.Count,\n",
" xlabel=None,\n",
" ylabel='Number of eviction-related damages hearings')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that damages hearings from evictions typically occur 30-45 days after a property is vacated."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fraction of overall eviction-related damages hearings over time initiated by K&R Hoosier Investments (Anderson Rentals) and KT Property Group"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
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0.5
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"title": "Fraction of total due to Anderson Rentals and KT Property Group"
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"<div id=\"55bc5489-29a1-442f-b4dc-b88517e5ad48\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"55bc5489-29a1-442f-b4dc-b88517e5ad48\", [{\"y\": [0.7, 0.2558139534883721, 0.4482758620689655, 0.41304347826086957, 0.38461538461538464, 0.5737704918032787, 0.45901639344262296, 0.38235294117647056, 0.4117647058823529, 0.3142857142857143, 0.40625, 0.5875, 0.4375, 0.5396825396825397, 0.5], \"x\": [\"August 2018\", \"September 2018\", \"October 2018\", \"November 2018\", \"December 2018\", \"January 2019\", \"February 2019\", \"March 2019\", \"April 2019\", \"May 2019\", \"June 2019\", \"July 2019\", \"August 2019\", \"September 2019\", \"October 2019\"], \"type\": \"scatter\", \"uid\": \"2cde911a-c860-11e9-ae04-6c4008929994\"}], {\"yaxis\": {\"range\": [0, 0.77], \"title\": \"Fraction of total due to Anderson Rentals and KT Property Group\"}}, {\"linkText\": \"Export to plot.ly\", \"showLink\": true})});</script>"
]
},
"metadata": {},
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],
"source": [
"df_damages_filtered = filter_df(df, event='damages hearing', case_title='KT Property Group LLC|Hoosier Investment LLC')\n",
"df_damages_filtered = df_damages_filtered[df_damages_filtered['Case Number'].isin(df_evictions['Case Number'])]\n",
"df_damages_by_month_filtered = number_by_month(df_damages_filtered)\n",
"line_plot(df_damages_by_month.Month,\n",
" df_damages_by_month_filtered.Count/df_damages_by_month.Count,\n",
" xlabel=None,\n",
" ylabel='Fraction of total due to Anderson Rentals and KT Property Group')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Felonies over time"
]
},
{
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"execution_count": 8,
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{
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],
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"title": "Number of felony hearings"
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},
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df_felony = filter_df(df, case_number='-F1-|-F2-|-F3-|-F4-|-F5-|-F6-')\n",
"#print(df_felony.sort_values('Date'))\n",
"df_felonies_by_month = number_by_month(df_felony)\n",
"line_plot(df_felonies_by_month.Month,\n",
" df_felonies_by_month.Count,\n",
" xlabel=None,\n",
" ylabel='Number of felony hearings')"
]
}
],
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