Python_Project(London housing Dataset Analysis).ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "# Project (Big data analysis using London_housing_data)\n",
    "***project questions : -\n",
    "   Q. 1) Convert the Datatype of 'Date' column to Date-Time format.\n",
    "Q. 2) Add a new column ''year'' in the dataframe, which contains years only.(B.2) Add a new column ''month'' as 2nd column in the dataframe, which contains month only.\n",
    "Q. 3) Remove the columns 'year' and 'month' from the dataframe.\n",
    "Q. 4) Show all the records where 'No. of Crimes' is 0. And, how many such records are there ?\n",
    "Q. 5) What is the maximum & minimum 'average_price' per year in england ?\n",
    "Q. 6) What is the Maximum & Minimum No. of Crimes recorded per area ?\n",
    "Q. 7) Show the total count of records of each area, where average price is less than 100000.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "eef67510",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "06b2576b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df =pd.read_csv('London_housing_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7b7b7239",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>area</th>\n",
       "      <th>average_price</th>\n",
       "      <th>code</th>\n",
       "      <th>houses_sold</th>\n",
       "      <th>no_of_crimes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/1995</td>\n",
       "      <td>city of london</td>\n",
       "      <td>91449</td>\n",
       "      <td>E09000001</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2/1/1995</td>\n",
       "      <td>city of london</td>\n",
       "      <td>82203</td>\n",
       "      <td>E09000001</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3/1/1995</td>\n",
       "      <td>city of london</td>\n",
       "      <td>79121</td>\n",
       "      <td>E09000001</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4/1/1995</td>\n",
       "      <td>city of london</td>\n",
       "      <td>77101</td>\n",
       "      <td>E09000001</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5/1/1995</td>\n",
       "      <td>city of london</td>\n",
       "      <td>84409</td>\n",
       "      <td>E09000001</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13544</th>\n",
       "      <td>9/1/2019</td>\n",
       "      <td>england</td>\n",
       "      <td>249942</td>\n",
       "      <td>E92000001</td>\n",
       "      <td>64605.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13545</th>\n",
       "      <td>10/1/2019</td>\n",
       "      <td>england</td>\n",
       "      <td>249376</td>\n",
       "      <td>E92000001</td>\n",
       "      <td>68677.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13546</th>\n",
       "      <td>11/1/2019</td>\n",
       "      <td>england</td>\n",
       "      <td>248515</td>\n",
       "      <td>E92000001</td>\n",
       "      <td>67814.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13547</th>\n",
       "      <td>12/1/2019</td>\n",
       "      <td>england</td>\n",
       "      <td>250410</td>\n",
       "      <td>E92000001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13548</th>\n",
       "      <td>1/1/2020</td>\n",
       "      <td>england</td>\n",
       "      <td>247355</td>\n",
       "      <td>E92000001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13549 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date            area  average_price       code  houses_sold  \\\n",
       "0       1/1/1995  city of london          91449  E09000001         17.0   \n",
       "1       2/1/1995  city of london          82203  E09000001          7.0   \n",
       "2       3/1/1995  city of london          79121  E09000001         14.0   \n",
       "3       4/1/1995  city of london          77101  E09000001          7.0   \n",
       "4       5/1/1995  city of london          84409  E09000001         10.0   \n",
       "...          ...             ...            ...        ...          ...   \n",
       "13544   9/1/2019         england         249942  E92000001      64605.0   \n",
       "13545  10/1/2019         england         249376  E92000001      68677.0   \n",
       "13546  11/1/2019         england         248515  E92000001      67814.0   \n",
       "13547  12/1/2019         england         250410  E92000001          NaN   \n",
       "13548   1/1/2020         england         247355  E92000001          NaN   \n",
       "\n",
       "       no_of_crimes  \n",
       "0               NaN  \n",
       "1               NaN  \n",
       "2               NaN  \n",
       "3               NaN  \n",
       "4               NaN  \n",
       "...             ...  \n",
       "13544           NaN  \n",
       "13545           NaN  \n",
       "13546           NaN  \n",
       "13547           NaN  \n",
       "13548           NaN  \n",
       "\n",
       "[13549 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
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    "df"
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   "cell_type": "code",
   "execution_count": 6,
   "id": "5278179e",
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    {
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      "text/plain": [
       "       date            area  average_price       code  houses_sold  \\\n",
       "0  1/1/1995  city of london          91449  E09000001         17.0   \n",
       "1  2/1/1995  city of london          82203  E09000001          7.0   \n",
       "2  3/1/1995  city of london          79121  E09000001         14.0   \n",
       "\n",
       "   no_of_crimes  \n",
       "0           NaN  \n",
       "1           NaN  \n",
       "2           NaN  "
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    "df.head(3)"
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   "execution_count": 7,
   "id": "47101c4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date             13549\n",
       "area             13549\n",
       "average_price    13549\n",
       "code             13549\n",
       "houses_sold      13455\n",
       "no_of_crimes      7439\n",
       "dtype: int64"
      ]
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     "execution_count": 7,
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   "source": [
    "df.count()"
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   "execution_count": 9,
   "id": "5d8fa917",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                0\n",
       "area                0\n",
       "average_price       0\n",
       "code                0\n",
       "houses_sold        94\n",
       "no_of_crimes     6110\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
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   "cell_type": "code",
   "execution_count": 12,
   "id": "20711ad7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13549 entries, 0 to 13548\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   date           13549 non-null  object \n",
      " 1   area           13549 non-null  object \n",
      " 2   average_price  13549 non-null  int64  \n",
      " 3   code           13549 non-null  object \n",
      " 4   houses_sold    13455 non-null  float64\n",
      " 5   no_of_crimes   7439 non-null   float64\n",
      "dtypes: float64(2), int64(1), object(3)\n",
      "memory usage: 635.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "68d4b3a5",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>count</th>\n",
       "      <td>1.354900e+04</td>\n",
       "      <td>13455.000000</td>\n",
       "      <td>7439.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.635197e+05</td>\n",
       "      <td>3893.994129</td>\n",
       "      <td>2158.352063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.876175e+05</td>\n",
       "      <td>12114.402476</td>\n",
       "      <td>902.087742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.072200e+04</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.323800e+05</td>\n",
       "      <td>247.000000</td>\n",
       "      <td>1623.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.229190e+05</td>\n",
       "      <td>371.000000</td>\n",
       "      <td>2132.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.368430e+05</td>\n",
       "      <td>3146.000000</td>\n",
       "      <td>2582.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.463378e+06</td>\n",
       "      <td>132163.000000</td>\n",
       "      <td>7461.000000</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "       average_price    houses_sold  no_of_crimes\n",
       "count   1.354900e+04   13455.000000   7439.000000\n",
       "mean    2.635197e+05    3893.994129   2158.352063\n",
       "std     1.876175e+05   12114.402476    902.087742\n",
       "min     4.072200e+04       2.000000      0.000000\n",
       "25%     1.323800e+05     247.000000   1623.000000\n",
       "50%     2.229190e+05     371.000000   2132.000000\n",
       "75%     3.368430e+05    3146.000000   2582.000000\n",
       "max     1.463378e+06  132163.000000   7461.000000"
      ]
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     "execution_count": 13,
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   "source": [
    "df.describe()"
   ]
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   "cell_type": "code",
   "execution_count": 14,
   "id": "02c1ff38",
   "metadata": {},
   "outputs": [
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       "      <td>1/1/1995</td>\n",
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       "      <td>2/1/1995</td>\n",
       "      <td>city of london</td>\n",
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      "text/plain": [
       "       date            area  average_price       code  houses_sold  \\\n",
       "0  1/1/1995  city of london          91449  E09000001         17.0   \n",
       "1  2/1/1995  city of london          82203  E09000001          7.0   \n",
       "\n",
       "   no_of_crimes  \n",
       "0           NaN  \n",
       "1           NaN  "
      ]
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     "metadata": {},
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    "df.head(2)"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "date             datetime64[ns]\n",
       "area                     object\n",
       "average_price             int64\n",
       "code                     object\n",
       "houses_sold             float64\n",
       "no_of_crimes            float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
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    "df.dtypes"
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  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "0904b1e4",
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   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f71fb003",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['date']=pd.to_datetime(df.date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0dafc45f",
   "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>date</th>\n",
       "      <th>area</th>\n",
       "      <th>average_price</th>\n",
       "      <th>code</th>\n",
       "      <th>houses_sold</th>\n",
       "      <th>no_of_crimes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>city of london</td>\n",
       "      <td>91449</td>\n",
       "      <td>E09000001</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1995-02-01</td>\n",
       "      <td>city of london</td>\n",
       "      <td>82203</td>\n",
       "      <td>E09000001</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1995-03-01</td>\n",
       "      <td>city of london</td>\n",
       "      <td>79121</td>\n",
       "      <td>E09000001</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1995-04-01</td>\n",
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      "text/plain": [
       "        date            area  average_price       code  houses_sold  \\\n",
       "0 1995-01-01  city of london          91449  E09000001         17.0   \n",
       "1 1995-02-01  city of london          82203  E09000001          7.0   \n",
       "2 1995-03-01  city of london          79121  E09000001         14.0   \n",
       "3 1995-04-01  city of london          77101  E09000001          7.0   \n",
       "4 1995-05-01  city of london          84409  E09000001         10.0   \n",
       "\n",
       "   no_of_crimes  \n",
       "0           NaN  \n",
       "1           NaN  \n",
       "2           NaN  \n",
       "3           NaN  \n",
       "4           NaN  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
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   "source": [
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   "execution_count": 18,
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   "outputs": [
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       "date             datetime64[ns]\n",
       "area                     object\n",
       "average_price             int64\n",
       "code                     object\n",
       "houses_sold             float64\n",
       "no_of_crimes            float64\n",
       "dtype: object"
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     "execution_count": 18,
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       "            date            area  average_price       code  houses_sold  \\\n",
       "0     1995-01-01  city of london          91449  E09000001         17.0   \n",
       "1     1995-02-01  city of london          82203  E09000001          7.0   \n",
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       "4     1995-05-01  city of london          84409  E09000001         10.0   \n",
       "...          ...             ...            ...        ...          ...   \n",
       "13544 2019-09-01         england         249942  E92000001      64605.0   \n",
       "13545 2019-10-01         england         249376  E92000001      68677.0   \n",
       "13546 2019-11-01         england         248515  E92000001      67814.0   \n",
       "13547 2019-12-01         england         250410  E92000001          NaN   \n",
       "13548 2020-01-01         england         247355  E92000001          NaN   \n",
       "\n",
       "       no_of_crimes  year  \n",
       "0               NaN  1995  \n",
       "1               NaN  1995  \n",
       "2               NaN  1995  \n",
       "3               NaN  1995  \n",
       "4               NaN  1995  \n",
       "...             ...   ...  \n",
       "13544           NaN  2019  \n",
       "13545           NaN  2019  \n",
       "13546           NaN  2019  \n",
       "13547           NaN  2019  \n",
       "13548           NaN  2020  \n",
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    "df.insert(1,'month',df.date.dt.month)"
   ]
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   "id": "77491d90",
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       "            date  month            area  average_price       code  \\\n",
       "0     1995-01-01      1  city of london          91449  E09000001   \n",
       "1     1995-02-01      2  city of london          82203  E09000001   \n",
       "2     1995-03-01      3  city of london          79121  E09000001   \n",
       "3     1995-04-01      4  city of london          77101  E09000001   \n",
       "4     1995-05-01      5  city of london          84409  E09000001   \n",
       "...          ...    ...             ...            ...        ...   \n",
       "13544 2019-09-01      9         england         249942  E92000001   \n",
       "13545 2019-10-01     10         england         249376  E92000001   \n",
       "13546 2019-11-01     11         england         248515  E92000001   \n",
       "13547 2019-12-01     12         england         250410  E92000001   \n",
       "13548 2020-01-01      1         england         247355  E92000001   \n",
       "\n",
       "       houses_sold  no_of_crimes  year  \n",
       "0             17.0           NaN  1995  \n",
       "1              7.0           NaN  1995  \n",
       "2             14.0           NaN  1995  \n",
       "3              7.0           NaN  1995  \n",
       "4             10.0           NaN  1995  \n",
       "...            ...           ...   ...  \n",
       "13544      64605.0           NaN  2019  \n",
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       "13546      67814.0           NaN  2019  \n",
       "13547          NaN           NaN  2019  \n",
       "13548          NaN           NaN  2020  \n",
       "\n",
       "[13549 rows x 8 columns]"
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     "execution_count": 25,
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       "      <td>city of london</td>\n",
       "      <td>79121</td>\n",
       "      <td>E09000001</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1995-04-01</td>\n",
       "      <td>4</td>\n",
       "      <td>city of london</td>\n",
       "      <td>77101</td>\n",
       "      <td>E09000001</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1995-05-01</td>\n",
       "      <td>5</td>\n",
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       "      <td>84409</td>\n",
       "      <td>E09000001</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1995</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1995-06-01</td>\n",
       "      <td>6</td>\n",
       "      <td>city of london</td>\n",
       "      <td>94901</td>\n",
       "      <td>E09000001</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1995-07-01</td>\n",
       "      <td>7</td>\n",
       "      <td>city of london</td>\n",
       "      <td>110128</td>\n",
       "      <td>E09000001</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  month            area  average_price       code  houses_sold  \\\n",
       "0 1995-01-01      1  city of london          91449  E09000001         17.0   \n",
       "1 1995-02-01      2  city of london          82203  E09000001          7.0   \n",
       "2 1995-03-01      3  city of london          79121  E09000001         14.0   \n",
       "3 1995-04-01      4  city of london          77101  E09000001          7.0   \n",
       "4 1995-05-01      5  city of london          84409  E09000001         10.0   \n",
       "5 1995-06-01      6  city of london          94901  E09000001         17.0   \n",
       "6 1995-07-01      7  city of london         110128  E09000001         13.0   \n",
       "\n",
       "   no_of_crimes  year  \n",
       "0           NaN  1995  \n",
       "1           NaN  1995  \n",
       "2           NaN  1995  \n",
       "3           NaN  1995  \n",
       "4           NaN  1995  \n",
       "5           NaN  1995  \n",
       "6           NaN  1995  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9feffabe",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['month','year'],axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0f6dcdc5",
   "metadata": {},
   "outputs": [
    {
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       "      <th>13544</th>\n",
       "      <td>2019-09-01</td>\n",
       "      <td>england</td>\n",
       "      <td>249942</td>\n",
       "      <td>E92000001</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>E92000001</td>\n",
       "      <td>68677.0</td>\n",
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       "      <td>2019-11-01</td>\n",
       "      <td>england</td>\n",
       "      <td>248515</td>\n",
       "      <td>E92000001</td>\n",
       "      <td>67814.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>13547</th>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>england</td>\n",
       "      <td>250410</td>\n",
       "      <td>E92000001</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>13548</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>england</td>\n",
       "      <td>247355</td>\n",
       "      <td>E92000001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13549 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date            area  average_price       code  houses_sold  \\\n",
       "0     1995-01-01  city of london          91449  E09000001         17.0   \n",
       "1     1995-02-01  city of london          82203  E09000001          7.0   \n",
       "2     1995-03-01  city of london          79121  E09000001         14.0   \n",
       "3     1995-04-01  city of london          77101  E09000001          7.0   \n",
       "4     1995-05-01  city of london          84409  E09000001         10.0   \n",
       "...          ...             ...            ...        ...          ...   \n",
       "13544 2019-09-01         england         249942  E92000001      64605.0   \n",
       "13545 2019-10-01         england         249376  E92000001      68677.0   \n",
       "13546 2019-11-01         england         248515  E92000001      67814.0   \n",
       "13547 2019-12-01         england         250410  E92000001          NaN   \n",
       "13548 2020-01-01         england         247355  E92000001          NaN   \n",
       "\n",
       "       no_of_crimes  \n",
       "0               NaN  \n",
       "1               NaN  \n",
       "2               NaN  \n",
       "3               NaN  \n",
       "4               NaN  \n",
       "...             ...  \n",
       "13544           NaN  \n",
       "13545           NaN  \n",
       "13546           NaN  \n",
       "13547           NaN  \n",
       "13548           NaN  \n",
       "\n",
       "[13549 rows x 6 columns]"
      ]
     },
     "execution_count": 30,
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    "df"
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  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f2e4583f",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "        date            area  average_price       code  houses_sold  \\\n",
       "0 1995-01-01  city of london          91449  E09000001         17.0   \n",
       "1 1995-02-01  city of london          82203  E09000001          7.0   \n",
       "\n",
       "   no_of_crimes  \n",
       "0           NaN  \n",
       "1           NaN  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
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   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b9746b9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date             104\n",
       "area             104\n",
       "average_price    104\n",
       "code             104\n",
       "houses_sold      104\n",
       "no_of_crimes     104\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['no_of_crimes'] == 0].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "05ba47d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['year']=df.date.dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "fe3c204f",
   "metadata": {},
   "outputs": [
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       "      <th>5</th>\n",
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       "      <td>1995</td>\n",
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      ],
      "text/plain": [
       "        date            area  average_price       code  houses_sold  \\\n",
       "0 1995-01-01  city of london          91449  E09000001         17.0   \n",
       "1 1995-02-01  city of london          82203  E09000001          7.0   \n",
       "2 1995-03-01  city of london          79121  E09000001         14.0   \n",
       "3 1995-04-01  city of london          77101  E09000001          7.0   \n",
       "4 1995-05-01  city of london          84409  E09000001         10.0   \n",
       "5 1995-06-01  city of london          94901  E09000001         17.0   \n",
       "\n",
       "   no_of_crimes  year  \n",
       "0           NaN  1995  \n",
       "1           NaN  1995  \n",
       "2           NaN  1995  \n",
       "3           NaN  1995  \n",
       "4           NaN  1995  \n",
       "5           NaN  1995  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "be48e747",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df[df['area']=='england']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2b3b393c",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1995</td>\n",
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      ],
      "text/plain": [
       "            date     area  average_price       code  houses_sold  \\\n",
       "13248 1995-01-01  england          53203  E92000001      47639.0   \n",
       "13249 1995-02-01  england          53096  E92000001      47880.0   \n",
       "13250 1995-03-01  england          53201  E92000001      67025.0   \n",
       "13251 1995-04-01  england          53591  E92000001      56925.0   \n",
       "13252 1995-05-01  england          53678  E92000001      64192.0   \n",
       "\n",
       "       no_of_crimes  year  \n",
       "13248           NaN  1995  \n",
       "13249           NaN  1995  \n",
       "13250           NaN  1995  \n",
       "13251           NaN  1995  \n",
       "13252           NaN  1995  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "94979290",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1995     53901\n",
       "1996     55755\n",
       "1997     61564\n",
       "1998     65743\n",
       "1999     75071\n",
       "2000     84191\n",
       "2001     95992\n",
       "2002    119982\n",
       "2003    138985\n",
       "2004    160330\n",
       "2005    167244\n",
       "2006    182031\n",
       "2007    194764\n",
       "2008    191750\n",
       "2009    174136\n",
       "2010    180807\n",
       "2011    177335\n",
       "2012    180129\n",
       "2013    188544\n",
       "2014    203639\n",
       "2015    219582\n",
       "2016    231922\n",
       "2017    242628\n",
       "2018    248620\n",
       "2019    250410\n",
       "2020    247355\n",
       "Name: average_price, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.groupby('year').average_price.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "cc889ce8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2020    247355\n",
       "2019    243281\n",
       "2018    240428\n",
       "2017    231593\n",
       "2016    220361\n",
       "Name: average_price, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.groupby('year').average_price.min().sort_values(ascending = False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "36c21db2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>10.0</td>\n",
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       "      <td>1995</td>\n",
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      ],
      "text/plain": [
       "        date            area  average_price       code  houses_sold  \\\n",
       "0 1995-01-01  city of london          91449  E09000001         17.0   \n",
       "1 1995-02-01  city of london          82203  E09000001          7.0   \n",
       "2 1995-03-01  city of london          79121  E09000001         14.0   \n",
       "3 1995-04-01  city of london          77101  E09000001          7.0   \n",
       "4 1995-05-01  city of london          84409  E09000001         10.0   \n",
       "\n",
       "   no_of_crimes  year  \n",
       "0           NaN  1995  \n",
       "1           NaN  1995  \n",
       "2           NaN  1995  \n",
       "3           NaN  1995  \n",
       "4           NaN  1995  "
      ]
     },
     "execution_count": 53,
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    "df.head()"
   ]
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   "cell_type": "code",
   "execution_count": 54,
   "id": "a97fafaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "area\n",
       "barking and dagenham      2049.0\n",
       "barnet                    2893.0\n",
       "bexley                    1914.0\n",
       "brent                     2937.0\n",
       "bromley                   2637.0\n",
       "camden                    4558.0\n",
       "city of london              10.0\n",
       "croydon                   3263.0\n",
       "ealing                    3401.0\n",
       "east midlands                NaN\n",
       "east of england              NaN\n",
       "enfield                   2798.0\n",
       "england                      NaN\n",
       "greenwich                 2853.0\n",
       "hackney                   3466.0\n",
       "hammersmith and fulham    2645.0\n",
       "haringey                  3199.0\n",
       "harrow                    1763.0\n",
       "havering                  1956.0\n",
       "hillingdon                2819.0\n",
       "hounslow                  2817.0\n",
       "inner london                 NaN\n",
       "islington                 3384.0\n",
       "kensington and chelsea    2778.0\n",
       "kingston upon thames      1379.0\n",
       "lambeth                   4701.0\n",
       "lewisham                  2813.0\n",
       "london                       NaN\n",
       "merton                    1623.0\n",
       "newham                    3668.0\n",
       "north east                   NaN\n",
       "north west                   NaN\n",
       "outer london                 NaN\n",
       "redbridge                 2560.0\n",
       "richmond upon thames      1551.0\n",
       "south east                   NaN\n",
       "south west                   NaN\n",
       "southwark                 3821.0\n",
       "sutton                    1425.0\n",
       "tower hamlets             3316.0\n",
       "waltham forest            2941.0\n",
       "wandsworth                3051.0\n",
       "west midlands                NaN\n",
       "westminster               7461.0\n",
       "yorks and the humber         NaN\n",
       "Name: no_of_crimes, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('area').no_of_crimes.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e9cc5b9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "area\n",
       "westminster               3504.0\n",
       "lambeth                   2381.0\n",
       "southwark                 2267.0\n",
       "newham                    2130.0\n",
       "camden                    2079.0\n",
       "croydon                   2031.0\n",
       "islington                 1871.0\n",
       "ealing                    1871.0\n",
       "hackney                   1870.0\n",
       "brent                     1850.0\n",
       "barnet                    1703.0\n",
       "lewisham                  1675.0\n",
       "tower hamlets             1646.0\n",
       "enfield                   1635.0\n",
       "wandsworth                1582.0\n",
       "waltham forest            1575.0\n",
       "haringey                  1536.0\n",
       "hounslow                  1529.0\n",
       "greenwich                 1513.0\n",
       "redbridge                 1487.0\n",
       "hillingdon                1445.0\n",
       "bromley                   1441.0\n",
       "kensington and chelsea    1347.0\n",
       "hammersmith and fulham    1323.0\n",
       "barking and dagenham      1217.0\n",
       "havering                  1130.0\n",
       "harrow                     937.0\n",
       "bexley                     860.0\n",
       "merton                     819.0\n",
       "sutton                     787.0\n",
       "richmond upon thames       700.0\n",
       "kingston upon thames       692.0\n",
       "city of london               0.0\n",
       "east midlands                NaN\n",
       "east of england              NaN\n",
       "england                      NaN\n",
       "inner london                 NaN\n",
       "london                       NaN\n",
       "north east                   NaN\n",
       "north west                   NaN\n",
       "outer london                 NaN\n",
       "south east                   NaN\n",
       "south west                   NaN\n",
       "west midlands                NaN\n",
       "yorks and the humber         NaN\n",
       "Name: no_of_crimes, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('area').no_of_crimes.min().sort_values(ascending =False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "22dee906",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1995</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "        date            area  average_price       code  houses_sold  \\\n",
       "0 1995-01-01  city of london          91449  E09000001         17.0   \n",
       "1 1995-02-01  city of london          82203  E09000001          7.0   \n",
       "2 1995-03-01  city of london          79121  E09000001         14.0   \n",
       "3 1995-04-01  city of london          77101  E09000001          7.0   \n",
       "4 1995-05-01  city of london          84409  E09000001         10.0   \n",
       "\n",
       "   no_of_crimes  year  \n",
       "0           NaN  1995  \n",
       "1           NaN  1995  \n",
       "2           NaN  1995  \n",
       "3           NaN  1995  \n",
       "4           NaN  1995  "
      ]
     },
     "execution_count": 58,
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    "df.head()"
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   "cell_type": "code",
   "execution_count": 63,
   "id": "8cc58ca0",
   "metadata": {},
   "outputs": [
    {
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       "        date            area  average_price       code  houses_sold  \\\n",
       "0 1995-01-01  city of london          91449  E09000001         17.0   \n",
       "1 1995-02-01  city of london          82203  E09000001          7.0   \n",
       "\n",
       "   no_of_crimes  year  \n",
       "0           NaN  1995  \n",
       "1           NaN  1995  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "9ce65791",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df[df['average_price']<100000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "49a76eb6",
   "metadata": {},
   "outputs": [
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       "      <td>2002-01-01</td>\n",
       "      <td>england</td>\n",
       "      <td>96215</td>\n",
       "      <td>E92000001</td>\n",
       "      <td>71678.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13333</th>\n",
       "      <td>2002-02-01</td>\n",
       "      <td>england</td>\n",
       "      <td>96676</td>\n",
       "      <td>E92000001</td>\n",
       "      <td>77131.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13334</th>\n",
       "      <td>2002-03-01</td>\n",
       "      <td>england</td>\n",
       "      <td>98962</td>\n",
       "      <td>E92000001</td>\n",
       "      <td>102828.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2209 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date            area  average_price       code  houses_sold  \\\n",
       "0     1995-01-01  city of london          91449  E09000001         17.0   \n",
       "1     1995-02-01  city of london          82203  E09000001          7.0   \n",
       "2     1995-03-01  city of london          79121  E09000001         14.0   \n",
       "3     1995-04-01  city of london          77101  E09000001          7.0   \n",
       "4     1995-05-01  city of london          84409  E09000001         10.0   \n",
       "...          ...             ...            ...        ...          ...   \n",
       "13330 2001-11-01         england          95083  E92000001     109149.0   \n",
       "13331 2001-12-01         england          95992  E92000001      93329.0   \n",
       "13332 2002-01-01         england          96215  E92000001      71678.0   \n",
       "13333 2002-02-01         england          96676  E92000001      77131.0   \n",
       "13334 2002-03-01         england          98962  E92000001     102828.0   \n",
       "\n",
       "       no_of_crimes  year  \n",
       "0               NaN  1995  \n",
       "1               NaN  1995  \n",
       "2               NaN  1995  \n",
       "3               NaN  1995  \n",
       "4               NaN  1995  \n",
       "...             ...   ...  \n",
       "13330           NaN  2001  \n",
       "13331           NaN  2001  \n",
       "13332           NaN  2002  \n",
       "13333           NaN  2002  \n",
       "13334           NaN  2002  \n",
       "\n",
       "[2209 rows x 7 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "f1e681bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "area\n",
       "north west              6273436\n",
       "yorks and the humber    6205189\n",
       "england                 5957390\n",
       "north east              5946020\n",
       "west midlands           5872439\n",
       "east midlands           5616647\n",
       "barking and dagenham    5514680\n",
       "south west              5389240\n",
       "east of england         5357761\n",
       "newham                  4876523\n",
       "bexley                  4805257\n",
       "havering                4708030\n",
       "waltham forest          4597082\n",
       "croydon                 4505317\n",
       "south east              4488947\n",
       "enfield                 4425645\n",
       "greenwich               4376351\n",
       "sutton                  4357668\n",
       "lewisham                4336375\n",
       "redbridge               4300919\n",
       "hackney                 4094375\n",
       "outer london            3757544\n",
       "southwark               3666098\n",
       "tower hamlets           3658049\n",
       "hillingdon              3593176\n",
       "hounslow                3259042\n",
       "london                  3207771\n",
       "lambeth                 3204629\n",
       "brent                   3203043\n",
       "merton                  3019591\n",
       "haringey                2816588\n",
       "bromley                 2814808\n",
       "ealing                  2625621\n",
       "inner london            2607266\n",
       "kingston upon thames    2606177\n",
       "harrow                  2595339\n",
       "wandsworth              2367937\n",
       "barnet                  2328074\n",
       "islington               1806938\n",
       "city of london           992436\n",
       "Name: average_price, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby('area').average_price.sum().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c208fcd8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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