NYC_Business_Analysis_COVID.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\booos\\anaconda3\\envs\\learn-env\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3145: DtypeWarning: Columns (19,20) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "df_nyc = pd.read_csv('Legally_Operating_Businesses.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DCA License Number</th>\n",
       "      <th>License Type</th>\n",
       "      <th>License Expiration Date</th>\n",
       "      <th>License Status</th>\n",
       "      <th>License Creation Date</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Business Name</th>\n",
       "      <th>Business Name 2</th>\n",
       "      <th>Address Building</th>\n",
       "      <th>Address Street Name</th>\n",
       "      <th>...</th>\n",
       "      <th>Community Board</th>\n",
       "      <th>Council District</th>\n",
       "      <th>BIN</th>\n",
       "      <th>BBL</th>\n",
       "      <th>NTA</th>\n",
       "      <th>Census Tract</th>\n",
       "      <th>Detail</th>\n",
       "      <th>Longitude</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1412954-DCA</td>\n",
       "      <td>Individual</td>\n",
       "      <td>09/30/2012</td>\n",
       "      <td>Inactive</td>\n",
       "      <td>11/04/2011</td>\n",
       "      <td>General Vendor</td>\n",
       "      <td>LATELLA, SALVATORE F.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>General Vendor Type: General Vendor (White)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0967332-DCA</td>\n",
       "      <td>Business</td>\n",
       "      <td>02/28/2017</td>\n",
       "      <td>Inactive</td>\n",
       "      <td>02/04/2010</td>\n",
       "      <td>Home Improvement Contractor</td>\n",
       "      <td>BARBARINO, JOHN JR.</td>\n",
       "      <td>JOHN BARBARINO JR_HOME IMPROVEMENT</td>\n",
       "      <td>239</td>\n",
       "      <td>MEDFORD CT</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1057563-DCA</td>\n",
       "      <td>Business</td>\n",
       "      <td>02/28/2023</td>\n",
       "      <td>Active</td>\n",
       "      <td>07/27/2000</td>\n",
       "      <td>Home Improvement Contractor</td>\n",
       "      <td>HITE CONSTRUCTION, INC.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "      <td>WHITNEY RD</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1108528-DCA</td>\n",
       "      <td>Individual</td>\n",
       "      <td>03/31/2004</td>\n",
       "      <td>Inactive</td>\n",
       "      <td>05/10/2002</td>\n",
       "      <td>Sightseeing Guide</td>\n",
       "      <td>STEWART, KATHLEEN J</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1400778-DCA</td>\n",
       "      <td>Individual</td>\n",
       "      <td>04/30/2014</td>\n",
       "      <td>Inactive</td>\n",
       "      <td>05/31/2013</td>\n",
       "      <td>Pedicab Driver</td>\n",
       "      <td>DURASINOVIC, MILOS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>272244</th>\n",
       "      <td>1029195-DCA</td>\n",
       "      <td>Individual</td>\n",
       "      <td>12/31/2010</td>\n",
       "      <td>Inactive</td>\n",
       "      <td>03/21/2000</td>\n",
       "      <td>Motion Picture Projectionist</td>\n",
       "      <td>DE MORA, FRANKLIN C.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272245</th>\n",
       "      <td>1466957-DCA</td>\n",
       "      <td>Individual</td>\n",
       "      <td>04/30/2014</td>\n",
       "      <td>Inactive</td>\n",
       "      <td>06/11/2013</td>\n",
       "      <td>Pedicab Driver</td>\n",
       "      <td>RYSKELDI UULU, ERMEK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272246</th>\n",
       "      <td>1227004-DCA</td>\n",
       "      <td>Individual</td>\n",
       "      <td>09/30/2006</td>\n",
       "      <td>Inactive</td>\n",
       "      <td>05/16/2006</td>\n",
       "      <td>General Vendor</td>\n",
       "      <td>JONES, RICHARD C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>General Vendor Type: General Vendor (White)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272247</th>\n",
       "      <td>0796771-DCA</td>\n",
       "      <td>Individual</td>\n",
       "      <td>02/28/2004</td>\n",
       "      <td>Inactive</td>\n",
       "      <td>02/09/1996</td>\n",
       "      <td>Process Server Individual</td>\n",
       "      <td>KUSHEL, BERNARD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272248</th>\n",
       "      <td>1004519-DCA</td>\n",
       "      <td>Individual</td>\n",
       "      <td>10/31/2008</td>\n",
       "      <td>Inactive</td>\n",
       "      <td>03/10/1999</td>\n",
       "      <td>Tow Truck Driver</td>\n",
       "      <td>CARRILLO, RICARDO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>272249 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       DCA License Number License Type License Expiration Date License Status  \\\n",
       "0             1412954-DCA   Individual              09/30/2012       Inactive   \n",
       "1             0967332-DCA     Business              02/28/2017       Inactive   \n",
       "2             1057563-DCA     Business              02/28/2023         Active   \n",
       "3             1108528-DCA   Individual              03/31/2004       Inactive   \n",
       "4             1400778-DCA   Individual              04/30/2014       Inactive   \n",
       "...                   ...          ...                     ...            ...   \n",
       "272244        1029195-DCA   Individual              12/31/2010       Inactive   \n",
       "272245        1466957-DCA   Individual              04/30/2014       Inactive   \n",
       "272246        1227004-DCA   Individual              09/30/2006       Inactive   \n",
       "272247        0796771-DCA   Individual              02/28/2004       Inactive   \n",
       "272248        1004519-DCA   Individual              10/31/2008       Inactive   \n",
       "\n",
       "       License Creation Date                      Industry  \\\n",
       "0                 11/04/2011                General Vendor   \n",
       "1                 02/04/2010   Home Improvement Contractor   \n",
       "2                 07/27/2000   Home Improvement Contractor   \n",
       "3                 05/10/2002             Sightseeing Guide   \n",
       "4                 05/31/2013                Pedicab Driver   \n",
       "...                      ...                           ...   \n",
       "272244            03/21/2000  Motion Picture Projectionist   \n",
       "272245            06/11/2013                Pedicab Driver   \n",
       "272246            05/16/2006                General Vendor   \n",
       "272247            02/09/1996     Process Server Individual   \n",
       "272248            03/10/1999              Tow Truck Driver   \n",
       "\n",
       "                  Business Name                     Business Name 2  \\\n",
       "0         LATELLA, SALVATORE F.                                 NaN   \n",
       "1           BARBARINO, JOHN JR.  JOHN BARBARINO JR_HOME IMPROVEMENT   \n",
       "2       HITE CONSTRUCTION, INC.                                 NaN   \n",
       "3           STEWART, KATHLEEN J                                 NaN   \n",
       "4            DURASINOVIC, MILOS                                 NaN   \n",
       "...                         ...                                 ...   \n",
       "272244     DE MORA, FRANKLIN C.                                 NaN   \n",
       "272245     RYSKELDI UULU, ERMEK                                 NaN   \n",
       "272246         JONES, RICHARD C                                 NaN   \n",
       "272247          KUSHEL, BERNARD                                 NaN   \n",
       "272248        CARRILLO, RICARDO                                 NaN   \n",
       "\n",
       "       Address Building Address Street Name  ... Community Board  \\\n",
       "0                   NaN                 NaN  ...             NaN   \n",
       "1                   239          MEDFORD CT  ...             NaN   \n",
       "2                    60          WHITNEY RD  ...             NaN   \n",
       "3                   NaN                 NaN  ...             NaN   \n",
       "4                   NaN                 NaN  ...             NaN   \n",
       "...                 ...                 ...  ...             ...   \n",
       "272244              NaN                 NaN  ...             NaN   \n",
       "272245              NaN                 NaN  ...             NaN   \n",
       "272246              NaN                 NaN  ...             NaN   \n",
       "272247              NaN                 NaN  ...             NaN   \n",
       "272248              NaN                 NaN  ...             NaN   \n",
       "\n",
       "       Council District  BIN  BBL  NTA Census Tract  \\\n",
       "0                   NaN  NaN  NaN  NaN          NaN   \n",
       "1                   NaN  NaN  NaN  NaN          NaN   \n",
       "2                   NaN  NaN  NaN  NaN          NaN   \n",
       "3                   NaN  NaN  NaN  NaN          NaN   \n",
       "4                   NaN  NaN  NaN  NaN          NaN   \n",
       "...                 ...  ...  ...  ...          ...   \n",
       "272244              NaN  NaN  NaN  NaN          NaN   \n",
       "272245              NaN  NaN  NaN  NaN          NaN   \n",
       "272246              NaN  NaN  NaN  NaN          NaN   \n",
       "272247              NaN  NaN  NaN  NaN          NaN   \n",
       "272248              NaN  NaN  NaN  NaN          NaN   \n",
       "\n",
       "                                             Detail  Longitude  Latitude  \\\n",
       "0       General Vendor Type: General Vendor (White)        NaN       NaN   \n",
       "1                                               NaN        NaN       NaN   \n",
       "2                                               NaN        NaN       NaN   \n",
       "3                                               NaN        NaN       NaN   \n",
       "4                                               NaN        NaN       NaN   \n",
       "...                                             ...        ...       ...   \n",
       "272244                                          NaN        NaN       NaN   \n",
       "272245                                          NaN        NaN       NaN   \n",
       "272246  General Vendor Type: General Vendor (White)        NaN       NaN   \n",
       "272247                                          NaN        NaN       NaN   \n",
       "272248                                          NaN        NaN       NaN   \n",
       "\n",
       "       Location  \n",
       "0           NaN  \n",
       "1           NaN  \n",
       "2           NaN  \n",
       "3           NaN  \n",
       "4           NaN  \n",
       "...         ...  \n",
       "272244      NaN  \n",
       "272245      NaN  \n",
       "272246      NaN  \n",
       "272247      NaN  \n",
       "272248      NaN  \n",
       "\n",
       "[272249 rows x 27 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nyc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 272249 entries, 0 to 272248\n",
      "Data columns (total 27 columns):\n",
      " #   Column                         Non-Null Count   Dtype  \n",
      "---  ------                         --------------   -----  \n",
      " 0   DCA License Number             272249 non-null  object \n",
      " 1   License Type                   272249 non-null  object \n",
      " 2   License Expiration Date        251840 non-null  object \n",
      " 3   License Status                 272249 non-null  object \n",
      " 4   License Creation Date          272249 non-null  object \n",
      " 5   Industry                       272249 non-null  object \n",
      " 6   Business Name                  272190 non-null  object \n",
      " 7   Business Name 2                49639 non-null   object \n",
      " 8   Address Building               179442 non-null  object \n",
      " 9   Address Street Name            180510 non-null  object \n",
      " 10  Secondary Address Street Name  2524 non-null    object \n",
      " 11  Address City                   271441 non-null  object \n",
      " 12  Address State                  271234 non-null  object \n",
      " 13  Address ZIP                    271389 non-null  object \n",
      " 14  Contact Phone Number           173453 non-null  object \n",
      " 15  Address Borough                178977 non-null  object \n",
      " 16  Borough Code                   165222 non-null  float64\n",
      " 17  Community Board                160541 non-null  float64\n",
      " 18  Council District               161019 non-null  float64\n",
      " 19  BIN                            158969 non-null  object \n",
      " 20  BBL                            158969 non-null  object \n",
      " 21  NTA                            137306 non-null  object \n",
      " 22  Census Tract                   137306 non-null  float64\n",
      " 23  Detail                         38805 non-null   object \n",
      " 24  Longitude                      171192 non-null  float64\n",
      " 25  Latitude                       171192 non-null  float64\n",
      " 26  Location                       171192 non-null  object \n",
      "dtypes: float64(6), object(21)\n",
      "memory usage: 56.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df_nyc.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_nyc_1 = pd.read_csv('Legally_Operating_Businesses.csv', usecols=['License Creation Date','License Type', 'Industry', 'Address ZIP'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_nyc_1['License Creation Date'] = pd.to_datetime(df_nyc_1['License Creation Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas._libs.tslibs.timestamps.Timestamp"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df_nyc_1['License Creation Date'][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_nyc_1 = df_nyc_1[df_nyc_1['License Creation Date'].dt.year > 1990]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>License Type</th>\n",
       "      <th>License Creation Date</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Address ZIP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>97173</th>\n",
       "      <td>Business</td>\n",
       "      <td>1993-12-02</td>\n",
       "      <td>Laundry Jobber</td>\n",
       "      <td>11217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71537</th>\n",
       "      <td>Individual</td>\n",
       "      <td>1993-12-14</td>\n",
       "      <td>Tow Truck Driver</td>\n",
       "      <td>10310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50265</th>\n",
       "      <td>Individual</td>\n",
       "      <td>1993-12-20</td>\n",
       "      <td>Tow Truck Driver</td>\n",
       "      <td>10804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93637</th>\n",
       "      <td>Individual</td>\n",
       "      <td>1993-12-22</td>\n",
       "      <td>Tow Truck Driver</td>\n",
       "      <td>11373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110548</th>\n",
       "      <td>Individual</td>\n",
       "      <td>1993-12-28</td>\n",
       "      <td>Tow Truck Driver</td>\n",
       "      <td>11710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240828</th>\n",
       "      <td>Business</td>\n",
       "      <td>2022-01-13</td>\n",
       "      <td>Home Improvement Contractor</td>\n",
       "      <td>11218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240900</th>\n",
       "      <td>Business</td>\n",
       "      <td>2022-01-13</td>\n",
       "      <td>Garage</td>\n",
       "      <td>11211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240867</th>\n",
       "      <td>Business</td>\n",
       "      <td>2022-01-13</td>\n",
       "      <td>Home Improvement Contractor</td>\n",
       "      <td>11548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241206</th>\n",
       "      <td>Individual</td>\n",
       "      <td>2022-01-13</td>\n",
       "      <td>Tow Truck Driver</td>\n",
       "      <td>10306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240865</th>\n",
       "      <td>Business</td>\n",
       "      <td>2022-01-13</td>\n",
       "      <td>Home Improvement Contractor</td>\n",
       "      <td>11418</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>272238 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       License Type License Creation Date                     Industry  \\\n",
       "97173      Business            1993-12-02               Laundry Jobber   \n",
       "71537    Individual            1993-12-14             Tow Truck Driver   \n",
       "50265    Individual            1993-12-20             Tow Truck Driver   \n",
       "93637    Individual            1993-12-22             Tow Truck Driver   \n",
       "110548   Individual            1993-12-28             Tow Truck Driver   \n",
       "...             ...                   ...                          ...   \n",
       "240828     Business            2022-01-13  Home Improvement Contractor   \n",
       "240900     Business            2022-01-13                       Garage   \n",
       "240867     Business            2022-01-13  Home Improvement Contractor   \n",
       "241206   Individual            2022-01-13             Tow Truck Driver   \n",
       "240865     Business            2022-01-13  Home Improvement Contractor   \n",
       "\n",
       "       Address ZIP  \n",
       "97173        11217  \n",
       "71537        10310  \n",
       "50265        10804  \n",
       "93637        11373  \n",
       "110548       11710  \n",
       "...            ...  \n",
       "240828       11218  \n",
       "240900       11211  \n",
       "240867       11548  \n",
       "241206       10306  \n",
       "240865       11418  \n",
       "\n",
       "[272238 rows x 4 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nyc_1.sort_values(by='License Creation Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Home Improvement Salesperson      41020\n",
       "Home Improvement Contractor       39808\n",
       "Tobacco Retail Dealer             39335\n",
       "Secondhand Dealer - General       16657\n",
       "Electronics Store                 14850\n",
       "General Vendor                     9945\n",
       "Tow Truck Driver                   9726\n",
       "Stoop Line Stand                   9509\n",
       "Sightseeing Guide                  7309\n",
       "Electronic & Appliance Service     7001\n",
       "Laundry                            6883\n",
       "Pedicab Driver                     6281\n",
       "Process Server Individual          6059\n",
       "Locksmith                          5113\n",
       "Debt Collection Agency             5100\n",
       "Laundries                          4947\n",
       "Laundry Jobber                     4368\n",
       "Sidewalk Cafe                      3830\n",
       "Ticket Seller                      3522\n",
       "Garage                             3425\n",
       "Electronic Cigarette Dealer        3398\n",
       "Secondhand Dealer - Auto           2865\n",
       "Dealer In Products                 2748\n",
       "Parking Lot                        1827\n",
       "Amusement Device Portable          1779\n",
       "Tow Truck Company                  1575\n",
       "Employment Agency                  1427\n",
       "Amusement Device Temporary         1326\n",
       "Pawnbroker                         1093\n",
       "Special Sale                       1048\n",
       "Auctioneer                         1010\n",
       "Motion Picture Projectionist        989\n",
       "Horse Drawn Driver                  668\n",
       "Pedicab Business                    665\n",
       "Newsstand                           661\n",
       "Cabaret                             615\n",
       "Process Serving Agency              475\n",
       "Amusement Device Permanent          436\n",
       "Games of Chance                     306\n",
       "Garage and Parking Lot              303\n",
       "Bingo Game Operator                 299\n",
       "Pool or Billiard Room               214\n",
       "Catering Establishment              213\n",
       "Car Wash                            208\n",
       "Scrap Metal Processor               187\n",
       "Tow Truck Exemption                 167\n",
       "Horse Drawn Cab Owner               165\n",
       "Storage Warehouse                   164\n",
       "Gaming Cafe                         157\n",
       "Auction House Premises              117\n",
       "Scale Dealer Repairer               103\n",
       "Amusement Arcade                     99\n",
       "Locksmith Apprentice                 93\n",
       "Sightseeing Bus                      63\n",
       "General Vendor Distributor           29\n",
       "Booting Company                      20\n",
       "Secondhand Dealer - Firearms         12\n",
       "Commercial Lessor                    12\n",
       "Ticket Seller Business               11\n",
       "Third Party Food Delivery             3\n",
       "Name: Industry, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nyc_1['Industry'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11385    3479\n",
       "11220    3260\n",
       "11214    3252\n",
       "11368    3060\n",
       "11235    2869\n",
       "         ... \n",
       "34601       1\n",
       "76086       1\n",
       "13492       1\n",
       "94949       1\n",
       "30907       1\n",
       "Name: Address ZIP, Length: 3998, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nyc_1['Address ZIP'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1993        8\n",
       "1994     1207\n",
       "1995     1807\n",
       "1996     2252\n",
       "1997     2981\n",
       "1998     2625\n",
       "1999     2946\n",
       "2000     8504\n",
       "2001     6867\n",
       "2002    10057\n",
       "2003    14141\n",
       "2004    11202\n",
       "2005    11569\n",
       "2006    13980\n",
       "2007    13459\n",
       "2008    12698\n",
       "2009    13751\n",
       "2010    13656\n",
       "2011    14206\n",
       "2012    14848\n",
       "2013    14536\n",
       "2014    12251\n",
       "2015    12531\n",
       "2016    12270\n",
       "2017    14757\n",
       "2018    14234\n",
       "2019     9913\n",
       "2020     3880\n",
       "2021     4927\n",
       "2022      175\n",
       "Name: License Creation Date, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nyc_1['License Creation Date'].dt.year.value_counts(sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'NYC BUSINESS LICENSE CREATION PER YEAR'}>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_nyc_1['License Creation Date'].dt.year.value_counts(sort=False).plot(kind='bar', title='NYC BUSINESS LICENSE CREATION PER YEAR')"
   ]
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}