Project 5 London Housing Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# London Housing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('5. London Housing Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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",
       "  </tbody>\n",
       "</table>\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",
       "   no_of_crimes  \n",
       "0           NaN  \n",
       "1           NaN  \n",
       "2           NaN  \n",
       "3           NaN  \n",
       "4           NaN  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date              object\n",
       "area              object\n",
       "average_price      int64\n",
       "code              object\n",
       "houses_sold      float64\n",
       "no_of_crimes     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13549, 6)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           17.0\n",
       "1            7.0\n",
       "2           14.0\n",
       "3            7.0\n",
       "4           10.0\n",
       "          ...   \n",
       "13544    64605.0\n",
       "13545    68677.0\n",
       "13546    67814.0\n",
       "13547        NaN\n",
       "13548        NaN\n",
       "Name: houses_sold, Length: 13549, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.houses_sold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the missing values with heatmap\n",
    "sns.heatmap(data.isnull())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (A). Convert the Datatype of Date Column to Datetime Format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.date = pd.to_datetime(data.date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "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": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (B). Add a New Column to the DataFrame which Contains Years Only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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",
       "      <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>1995-05-01</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",
       "  </tbody>\n",
       "</table>\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",
       "   no_of_crimes  \n",
       "0           NaN  \n",
       "1           NaN  \n",
       "2           NaN  \n",
       "3           NaN  \n",
       "4           NaN  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['year'] = data.date.dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "<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",
       "      <th>year</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",
       "      <td>1995</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",
       "      <td>1995</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",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1995-04-01</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>city of london</td>\n",
       "      <td>84409</td>\n",
       "      <td>E09000001</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "   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": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date             540\n",
       "area             540\n",
       "average_price    540\n",
       "code             540\n",
       "houses_sold      540\n",
       "no_of_crimes     387\n",
       "year             540\n",
       "dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.year == 2010].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (B2) Add a new Column 'month' as 2nd column in the dataframe, which contains month only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.insert(1, 'month', data.date.dt.month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>month</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",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>1</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",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1995-02-01</td>\n",
       "      <td>2</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",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1995-03-01</td>\n",
       "      <td>3</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",
       "      <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",
       "      <td>city of london</td>\n",
       "      <td>84409</td>\n",
       "      <td>E09000001</td>\n",
       "      <td>10.0</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",
       "\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": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remove the Columns 'Year' and 'Month' from the Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['month', 'year'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (D) Show all records where number of crimes is 0. And, how many records are there ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "104"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data[data.no_of_crimes == 0])\n",
    "# data[data.no_of_crimes == 0].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (E) What is the Maximum and Minimum 'average_price' per year in england ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['year'] = data.date.dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = data[data.area == 'england']\n",
    "df1.groupby('year').average_price.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.area == 'england'].groupby('year').average_price.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1995     52788\n",
       "1996     52333\n",
       "1997     55789\n",
       "1998     61659\n",
       "1999     65522\n",
       "2000     75219\n",
       "2001     84245\n",
       "2002     96215\n",
       "2003    121610\n",
       "2004    139719\n",
       "2005    158572\n",
       "2006    166544\n",
       "2007    181824\n",
       "2008    165795\n",
       "2009    159340\n",
       "2010    174458\n",
       "2011    173046\n",
       "2012    174161\n",
       "2013    176816\n",
       "2014    188265\n",
       "2015    202856\n",
       "2016    220361\n",
       "2017    231593\n",
       "2018    240428\n",
       "2019    243281\n",
       "2020    247355\n",
       "Name: average_price, dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.area == 'england'].groupby('year').average_price.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1995     53322.416667\n",
       "1996     54151.500000\n",
       "1997     59160.666667\n",
       "1998     64301.666667\n",
       "1999     70070.750000\n",
       "2000     80814.333333\n",
       "2001     90306.750000\n",
       "2002    107981.500000\n",
       "2003    130218.583333\n",
       "2004    152314.416667\n",
       "2005    163570.000000\n",
       "2006    174351.500000\n",
       "2007    190025.583333\n",
       "2008    182379.916667\n",
       "2009    166558.666667\n",
       "2010    177472.666667\n",
       "2011    175230.000000\n",
       "2012    177488.000000\n",
       "2013    182581.416667\n",
       "2014    197771.083333\n",
       "2015    211174.750000\n",
       "2016    227337.166667\n",
       "2017    238161.166667\n",
       "2018    245018.333333\n",
       "2019    247101.083333\n",
       "2020    247355.000000\n",
       "Name: average_price, dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.area == 'england'].groupby('year').average_price.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (F) What is the Maximum and Minimum No. of Crimes recorded per area ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "area\n",
       "westminster               7461.0\n",
       "lambeth                   4701.0\n",
       "camden                    4558.0\n",
       "southwark                 3821.0\n",
       "newham                    3668.0\n",
       "hackney                   3466.0\n",
       "ealing                    3401.0\n",
       "islington                 3384.0\n",
       "tower hamlets             3316.0\n",
       "croydon                   3263.0\n",
       "haringey                  3199.0\n",
       "wandsworth                3051.0\n",
       "waltham forest            2941.0\n",
       "brent                     2937.0\n",
       "barnet                    2893.0\n",
       "greenwich                 2853.0\n",
       "hillingdon                2819.0\n",
       "hounslow                  2817.0\n",
       "lewisham                  2813.0\n",
       "enfield                   2798.0\n",
       "kensington and chelsea    2778.0\n",
       "hammersmith and fulham    2645.0\n",
       "bromley                   2637.0\n",
       "redbridge                 2560.0\n",
       "barking and dagenham      2049.0\n",
       "havering                  1956.0\n",
       "bexley                    1914.0\n",
       "harrow                    1763.0\n",
       "merton                    1623.0\n",
       "richmond upon thames      1551.0\n",
       "sutton                    1425.0\n",
       "kingston upon thames      1379.0\n",
       "city of london              10.0\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": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('area').no_of_crimes.max().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "area\n",
       "city of london               0.0\n",
       "kingston upon thames       692.0\n",
       "richmond upon thames       700.0\n",
       "sutton                     787.0\n",
       "merton                     819.0\n",
       "bexley                     860.0\n",
       "harrow                     937.0\n",
       "havering                  1130.0\n",
       "barking and dagenham      1217.0\n",
       "hammersmith and fulham    1323.0\n",
       "kensington and chelsea    1347.0\n",
       "bromley                   1441.0\n",
       "hillingdon                1445.0\n",
       "redbridge                 1487.0\n",
       "greenwich                 1513.0\n",
       "hounslow                  1529.0\n",
       "haringey                  1536.0\n",
       "waltham forest            1575.0\n",
       "wandsworth                1582.0\n",
       "enfield                   1635.0\n",
       "tower hamlets             1646.0\n",
       "lewisham                  1675.0\n",
       "barnet                    1703.0\n",
       "brent                     1850.0\n",
       "hackney                   1870.0\n",
       "ealing                    1871.0\n",
       "islington                 1871.0\n",
       "croydon                   2031.0\n",
       "camden                    2079.0\n",
       "newham                    2130.0\n",
       "southwark                 2267.0\n",
       "lambeth                   2381.0\n",
       "westminster               3504.0\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": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('area').no_of_crimes.min().sort_values(ascending = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  (G) Show the Total count of records of each area, where average price is less than 100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "north east              112\n",
       "north west              111\n",
       "yorks and the humber    110\n",
       "east midlands            96\n",
       "west midlands            94\n",
       "england                  87\n",
       "barking and dagenham     85\n",
       "south west               78\n",
       "east of england          76\n",
       "newham                   72\n",
       "bexley                   64\n",
       "waltham forest           64\n",
       "lewisham                 62\n",
       "havering                 60\n",
       "greenwich                59\n",
       "south east               59\n",
       "croydon                  57\n",
       "enfield                  54\n",
       "sutton                   54\n",
       "hackney                  53\n",
       "redbridge                52\n",
       "southwark                48\n",
       "tower hamlets            47\n",
       "outer london             46\n",
       "hillingdon               44\n",
       "hounslow                 41\n",
       "lambeth                  41\n",
       "brent                    40\n",
       "london                   39\n",
       "merton                   35\n",
       "bromley                  33\n",
       "haringey                 33\n",
       "ealing                   31\n",
       "inner london             31\n",
       "kingston upon thames     30\n",
       "harrow                   30\n",
       "wandsworth               26\n",
       "barnet                   25\n",
       "islington                19\n",
       "city of london           11\n",
       "Name: area, dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.average_price < 100000].area.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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