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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploratory Data Analysis on Evictions in L.A."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Category</th>\n",
+       "      <th>Notice Date</th>\n",
+       "      <th>Received</th>\n",
+       "      <th>Address</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Zip Code</th>\n",
+       "      <th>CD#</th>\n",
+       "      <th>Rent Owed</th>\n",
+       "      <th>Bedroom Count</th>\n",
+       "      <th>Notice Type</th>\n",
+       "      <th>Just Cause</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>At-Fault</td>\n",
+       "      <td>2/3/2023</td>\n",
+       "      <td>2/3/2023</td>\n",
+       "      <td>7367 Hollywood Blvd</td>\n",
+       "      <td>Los Angeles</td>\n",
+       "      <td>90046</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>$1,850.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3 Day</td>\n",
+       "      <td>Non-payment of rent</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>At-Fault</td>\n",
+       "      <td>2/3/2023</td>\n",
+       "      <td>2/3/2023</td>\n",
+       "      <td>7367 Hollywood Blvd</td>\n",
+       "      <td>Los Angeles</td>\n",
+       "      <td>90046</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>$1,800.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3 Day</td>\n",
+       "      <td>Non-payment of rent</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>At-Fault</td>\n",
+       "      <td>2/3/2023</td>\n",
+       "      <td>2/3/2023</td>\n",
+       "      <td>7367 Hollywood Blvd</td>\n",
+       "      <td>Los Angeles</td>\n",
+       "      <td>90046</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>$1,500.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3 Day</td>\n",
+       "      <td>Non-payment of rent</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>At-Fault</td>\n",
+       "      <td>2/3/2023</td>\n",
+       "      <td>2/3/2023</td>\n",
+       "      <td>7367 Hollywood Blvd</td>\n",
+       "      <td>Los Angeles</td>\n",
+       "      <td>90046</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>$1,866.78</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3 Day</td>\n",
+       "      <td>Non-payment of rent</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>At-Fault</td>\n",
+       "      <td>2/3/2023</td>\n",
+       "      <td>2/3/2023</td>\n",
+       "      <td>7367 Hollywood Blvd</td>\n",
+       "      <td>Los Angeles</td>\n",
+       "      <td>90046</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>$1,742.97</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3 Day</td>\n",
+       "      <td>Non-payment of rent</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Category Notice Date  Received              Address         City Zip Code  \\\n",
+       "0  At-Fault    2/3/2023  2/3/2023  7367 Hollywood Blvd  Los Angeles    90046   \n",
+       "1  At-Fault    2/3/2023  2/3/2023  7367 Hollywood Blvd  Los Angeles    90046   \n",
+       "2  At-Fault    2/3/2023  2/3/2023  7367 Hollywood Blvd  Los Angeles    90046   \n",
+       "3  At-Fault    2/3/2023  2/3/2023  7367 Hollywood Blvd  Los Angeles    90046   \n",
+       "4  At-Fault    2/3/2023  2/3/2023  7367 Hollywood Blvd  Los Angeles    90046   \n",
+       "\n",
+       "   CD#   Rent Owed  Bedroom Count Notice Type           Just Cause  \n",
+       "0  4.0  $1,850.00             NaN       3 Day  Non-payment of rent  \n",
+       "1  4.0  $1,800.00             NaN       3 Day  Non-payment of rent  \n",
+       "2  4.0  $1,500.00             NaN       3 Day  Non-payment of rent  \n",
+       "3  4.0  $1,866.78             NaN       3 Day  Non-payment of rent  \n",
+       "4  4.0  $1,742.97             NaN       3 Day  Non-payment of rent  "
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "pd.set_option('display.max_columns', None)\n",
+    "pd.set_option('display.max_rows', None)\n",
+    "\n",
+    "# Load the CSV file into a DataFrame\n",
+    "# file_path = '/drive/Shareddrives/data_science_colabs/la_evictions_2023_07.csv'\n",
+    "\n",
+    "file_path = '../data/Eviction Data Feb - July 2023.csv'\n",
+    "df = pd.read_csv(file_path)\n",
+    "\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(39677, 11)"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Category            0\n",
+       "Notice Date         0\n",
+       "Received            0\n",
+       "Address             0\n",
+       "City                0\n",
+       "Zip Code            2\n",
+       "CD#               598\n",
+       "Rent Owed           0\n",
+       "Bedroom Count    6507\n",
+       "Notice Type         0\n",
+       "Just Cause          9\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Category\n",
+       "At-Fault    39631\n",
+       "No-Fault       46\n",
+       "Name: Category, dtype: int64"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby(df['Category']).Category.count()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Analyzing At-Fault category first, since No-Fault is very small compared to At-Fault category."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Notice Type\n",
+       "10 Day      459\n",
+       "15 Day        1\n",
+       "3 Day     37080\n",
+       "30 Day     2014\n",
+       "60 Day       62\n",
+       "90 Day       15\n",
+       "Name: Notice Type, dtype: int64"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df['Category']=='At-Fault'].groupby(df['Just Cause'])['Just Cause'].count()\n",
+    "\n",
+    "df[df['Category']=='At-Fault'].groupby(df['Notice Type'])['Notice Type'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Criminal activity\n",
+      "Criminal activity, Disorderly behavior/disturbing the peace (Nuisance), Violation of the rental agreement or lease\n",
+      "Damage to the rental unit, Disorderly behavior/disturbing the peace (Nuisance), Refuse access to the apartment when requested in accordance with law, Unapproved subtenant at the end of the lease/tenancy\n",
+      "Non-payment of rent, Damage to the rental unit, Disorderly behavior/disturbing the peace (Nuisance), Refusal to renew a lease or rental agreement of like terms and conditions, Unapproved subtenant at the end of the lease/tenancy\n",
+      "Non-payment of rent, Violation of the rental agreement or lease, Damage to the rental unit, Disorderly behavior/disturbing the peace (Nuisance), Refusal to renew a lease or rental agreement of like terms and conditions, Refuse access to the apartment when requested in accordance with law, Criminal activity\n",
+      "Non-payment of rent, Violation of the rental agreement or lease, Damage to the rental unit, Disorderly behavior/disturbing the peace (Nuisance), Refusal to renew a lease or rental agreement of like terms and conditions, Refuse access to the apartment when requested in accordance with law, Unapproved subtenant at the end of the lease/tenancy, Criminal activity\n",
+      "8\n",
+      "[' Criminal activity', ' Damage to the rental unit', ' Disorderly behavior/disturbing the peace (Nuisance)', ' Refusal to renew a lease or rental agreement of like terms and conditions', ' Refuse access to the apartment when requested in accordance with law', ' Unapproved subtenant at the end of the lease/tenancy', ' Violation of the rental agreement or lease', 'Criminal activity', 'Damage to the rental unit', 'Non-payment of rent']\n"
+     ]
+    }
+   ],
+   "source": [
+    "comb_cat = df[df['Category']=='At-Fault'].groupby(df['Just Cause'])['Just Cause'].count()\n",
+    "\n",
+    "all_cat = set()\n",
+    "ml = -1\n",
+    "for e in comb_cat.index:\n",
+    "    if ml<len(e.split(',')):\n",
+    "        ml = len(e.split(','))\n",
+    "        print(e)\n",
+    "        all_cat.update(e.split(','))\n",
+    "print(ml)\n",
+    "all_cat = sorted(all_cat)\n",
+    "print(all_cat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Category</th>\n",
+       "      <th>Notice Date</th>\n",
+       "      <th>Received</th>\n",
+       "      <th>Address</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Zip Code</th>\n",
+       "      <th>CD#</th>\n",
+       "      <th>Rent Owed</th>\n",
+       "      <th>Bedroom Count</th>\n",
+       "      <th>Notice Type</th>\n",
+       "      <th>Just Cause</th>\n",
+       "      <th>cat0</th>\n",
+       "      <th>cat1</th>\n",
+       "      <th>cat2</th>\n",
+       "      <th>cat3</th>\n",
+       "      <th>cat4</th>\n",
+       "      <th>cat5</th>\n",
+       "      <th>cat6</th>\n",
+       "      <th>cat7</th>\n",
+       "      <th>cat8</th>\n",
+       "      <th>cat9</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>At-Fault</td>\n",
+       "      <td>2/3/2023</td>\n",
+       "      <td>2/3/2023</td>\n",
+       "      <td>7367 Hollywood Blvd</td>\n",
+       "      <td>Los Angeles</td>\n",
+       "      <td>90046</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>$1,850.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3 Day</td>\n",
+       "      <td>Non-payment of rent</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Non-payment of rent</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>At-Fault</td>\n",
+       "      <td>2/3/2023</td>\n",
+       "      <td>2/3/2023</td>\n",
+       "      <td>7367 Hollywood Blvd</td>\n",
+       "      <td>Los Angeles</td>\n",
+       "      <td>90046</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>$1,800.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3 Day</td>\n",
+       "      <td>Non-payment of rent</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Non-payment of rent</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>At-Fault</td>\n",
+       "      <td>2/3/2023</td>\n",
+       "      <td>2/3/2023</td>\n",
+       "      <td>7367 Hollywood Blvd</td>\n",
+       "      <td>Los Angeles</td>\n",
+       "      <td>90046</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>$1,500.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3 Day</td>\n",
+       "      <td>Non-payment of rent</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Non-payment of rent</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>At-Fault</td>\n",
+       "      <td>2/3/2023</td>\n",
+       "      <td>2/3/2023</td>\n",
+       "      <td>7367 Hollywood Blvd</td>\n",
+       "      <td>Los Angeles</td>\n",
+       "      <td>90046</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>$1,866.78</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3 Day</td>\n",
+       "      <td>Non-payment of rent</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Non-payment of rent</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>At-Fault</td>\n",
+       "      <td>2/3/2023</td>\n",
+       "      <td>2/3/2023</td>\n",
+       "      <td>7367 Hollywood Blvd</td>\n",
+       "      <td>Los Angeles</td>\n",
+       "      <td>90046</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>$1,742.97</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3 Day</td>\n",
+       "      <td>Non-payment of rent</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Non-payment of rent</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Category Notice Date  Received              Address         City Zip Code  \\\n",
+       "0  At-Fault    2/3/2023  2/3/2023  7367 Hollywood Blvd  Los Angeles    90046   \n",
+       "1  At-Fault    2/3/2023  2/3/2023  7367 Hollywood Blvd  Los Angeles    90046   \n",
+       "2  At-Fault    2/3/2023  2/3/2023  7367 Hollywood Blvd  Los Angeles    90046   \n",
+       "3  At-Fault    2/3/2023  2/3/2023  7367 Hollywood Blvd  Los Angeles    90046   \n",
+       "4  At-Fault    2/3/2023  2/3/2023  7367 Hollywood Blvd  Los Angeles    90046   \n",
+       "\n",
+       "   CD#   Rent Owed  Bedroom Count Notice Type           Just Cause  cat0  \\\n",
+       "0  4.0  $1,850.00             NaN       3 Day  Non-payment of rent  None   \n",
+       "1  4.0  $1,800.00             NaN       3 Day  Non-payment of rent  None   \n",
+       "2  4.0  $1,500.00             NaN       3 Day  Non-payment of rent  None   \n",
+       "3  4.0  $1,866.78             NaN       3 Day  Non-payment of rent  None   \n",
+       "4  4.0  $1,742.97             NaN       3 Day  Non-payment of rent  None   \n",
+       "\n",
+       "   cat1  cat2  cat3  cat4  cat5  cat6  cat7  cat8                 cat9  \n",
+       "0  None  None  None  None  None  None  None  None  Non-payment of rent  \n",
+       "1  None  None  None  None  None  None  None  None  Non-payment of rent  \n",
+       "2  None  None  None  None  None  None  None  None  Non-payment of rent  \n",
+       "3  None  None  None  None  None  None  None  None  Non-payment of rent  \n",
+       "4  None  None  None  None  None  None  None  None  Non-payment of rent  "
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def check_categories(cause, cat):\n",
+    "    if cause and isinstance(cause, str) and cat in cause:\n",
+    "        return cat\n",
+    "    else:\n",
+    "        return None\n",
+    "\n",
+    "\n",
+    "for j,cat in enumerate(all_cat):\n",
+    "    df['cat{}'.format(j)] = df['Just Cause'].apply(lambda x: check_categories(x, cat))\n",
+    "\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Flatten the cat0 to cat9 columns into a single Series\n",
+    "cat_columns = ['cat0', 'cat1', 'cat2', 'cat3', 'cat4', 'cat5', 'cat6', 'cat7', 'cat8', 'cat9']\n",
+    "flattened_cats = df[cat_columns].values.flatten()\n",
+    "\n",
+    "# Create a DataFrame for the flattened data\n",
+    "cat_df = pd.DataFrame(flattened_cats, columns=['Category'])\n",
+    "\n",
+    "# Count the occurrences of each category\n",
+    "cat_counts = cat_df['Category'].value_counts()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Plot the histogram\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "cat_counts.plot(kind='bar')\n",
+    "plt.title('Category Counts in cat0 to cat9 Columns')\n",
+    "plt.xlabel('Category')\n",
+    "plt.ylabel('Count')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}