TDI_milestone_project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('vec_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "439\n"
     ]
    }
   ],
   "source": [
    "#How many different companies are represented in the data set?\n",
    "no_of_companies=df['Company Name'].nunique()\n",
    "print(no_of_companies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "102.0"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#What is the total number of jobs created for businesses in Queens?\n",
    "df.groupby('Borough').sum()['Job created']['Queens']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "361"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#How many different unique email domains names are there in the data set?\n",
    "df['company email'].dropna().apply(lambda x: x.split('@')[-1]).nunique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Considering only NTAs with at least 5 listed businesses, what is the average total savings and the total jobs created for each NTA?\n",
    "df3=pd.DataFrame(df['NTA'].value_counts())\n",
    "df2=df.set_index('NTA')\n",
    "df2['lb']=df3\n",
    "out1=pd.DataFrame(df2[df2['lb']>=5].groupby('NTA').sum()['Job created'])\n",
    "out2=pd.DataFrame(df2[df2['lb']>=5].groupby('NTA').mean()['Total Savings']) #average total savings\n",
    "out2.rename(columns={'Total Savings': 'Average Total Savings'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "out3=pd.concat([out1,out2],axis=1)\n",
    "out1.to_csv('out1')\n",
    "out2.to_csv('out2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Log of average total savings')]"
      ]
     },
     "execution_count": 141,
     "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": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "logdata=np.log(out2)\n",
    "logdata.rename(columns={'Average Total Savings': 'Log of average total savings'},inplace=True)\n",
    "import seaborn as sns\n",
    "sns.histplot(logdata,y='Log of average total savings',bins=30).set(ylabel=\"Log of average total savings\")\n",
    "#sns.countplot(out2).set(ylabel=\"Log of average total savings\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>Log of average total savings</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NTA</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Battery Park City-Lower Manhattan</th>\n",
       "      <td>13.060256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bushwick South</th>\n",
       "      <td>10.171239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carroll Gardens-Columbia Street-Red Hook</th>\n",
       "      <td>10.099769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>College Point</th>\n",
       "      <td>9.750300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crown Heights North</th>\n",
       "      <td>8.303083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill</th>\n",
       "      <td>11.001272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East New York</th>\n",
       "      <td>10.787939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East Williamsburg</th>\n",
       "      <td>9.546143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greenpoint</th>\n",
       "      <td>9.185397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hunters Point-Sunnyside-West Maspeth</th>\n",
       "      <td>10.082947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hunts Point</th>\n",
       "      <td>10.577605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manhattanville</th>\n",
       "      <td>7.923308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maspeth</th>\n",
       "      <td>8.803617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mott Haven-Port Morris</th>\n",
       "      <td>9.789419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Queensbridge-Ravenswood-Long Island City</th>\n",
       "      <td>11.032156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SoHo-TriBeCa-Civic Center-Little Italy</th>\n",
       "      <td>10.524441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Springfield Gardens South-Brookville</th>\n",
       "      <td>9.784572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steinway</th>\n",
       "      <td>9.326091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunset Park West</th>\n",
       "      <td>9.569201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>park-cemetery-etc-Brooklyn</th>\n",
       "      <td>9.569930</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  Log of average total savings\n",
       "NTA                                                                           \n",
       "Battery Park City-Lower Manhattan                                    13.060256\n",
       "Bushwick South                                                       10.171239\n",
       "Carroll Gardens-Columbia Street-Red Hook                             10.099769\n",
       "College Point                                                         9.750300\n",
       "Crown Heights North                                                   8.303083\n",
       "DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill                     11.001272\n",
       "East New York                                                        10.787939\n",
       "East Williamsburg                                                     9.546143\n",
       "Greenpoint                                                            9.185397\n",
       "Hunters Point-Sunnyside-West Maspeth                                 10.082947\n",
       "Hunts Point                                                          10.577605\n",
       "Manhattanville                                                        7.923308\n",
       "Maspeth                                                               8.803617\n",
       "Mott Haven-Port Morris                                                9.789419\n",
       "Queensbridge-Ravenswood-Long Island City                             11.032156\n",
       "SoHo-TriBeCa-Civic Center-Little Italy                               10.524441\n",
       "Springfield Gardens South-Brookville                                  9.784572\n",
       "Steinway                                                              9.326091\n",
       "Sunset Park West                                                      9.569201\n",
       "park-cemetery-etc-Brooklyn                                            9.569930"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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": [
    "s3=sns.scatterplot(data=out3, y='Job created', x='Average Total Savings',legend='full').set(xscale=\"log\",title=\"Log scatter plot\",xlabel='Average Total Savings')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Average Total Savings'), Text(0.5, 1.0, 'Non-Log scatter plot')]"
      ]
     },
     "execution_count": 144,
     "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": [
    "sns.scatterplot(data=out3, y='Job created', x='Average Total Savings',legend='full').set(title='Non-Log scatter plot',xlabel='Average Total Savings')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Job created by year')"
      ]
     },
     "execution_count": 145,
     "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['Effective Date']=pd.to_datetime(df['Effective Date'],format='%m/%d/%Y')\n",
    "df.set_index(\"Effective Date\",inplace=True)\n",
    "monthly_resampled_data = df['Job created'].resample('M')\n",
    "plt.plot(monthly_resampled_data.sum())\n",
    "plt.ylabel('Jobs created')\n",
    "plt.title('Job created by year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Jobs created by month')"
      ]
     },
     "execution_count": 149,
     "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=pd.read_csv('vec_data.csv')\n",
    "df['Month']=df['Effective Date'].apply(lambda x: x.split('/')[0])\n",
    "df2=df.groupby('Month')['Job created'].sum()\n",
    "plt.plot(df2.rename(index={'01': 'Jan','02':'Feb', '03':'Mar', '04':'Apr', '05': 'May', '06':'Jun', '07': 'Jul', '08': 'Aug', '09': 'Sep', '10': 'Oct', '11': 'Nov', '12': 'Dec'}))\n",
    "plt.ylabel('Jobs created')\n",
    "plt.title('Jobs created by month')"
   ]
  },
  {
   "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
}