Chapter02/Exercise2.12/Exercise_2_12.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import missingno as msno\n",
    "from sklearn.impute import SimpleImputer\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "with open('..\\dtypes.json', 'r') as jsonfile:\n",
    "    dtyp = json.load(jsonfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': 'float',\n",
       " 'flag_tsunami': 'str',\n",
       " 'year': 'float',\n",
       " 'month': 'float',\n",
       " 'day': 'float',\n",
       " 'hour': 'float',\n",
       " 'minute': 'float',\n",
       " 'second': 'float',\n",
       " 'focal_depth': 'float',\n",
       " 'eq_primary': 'float',\n",
       " 'eq_mag_mw': 'float',\n",
       " 'eq_mag_ms': 'float',\n",
       " 'eq_mag_mb': 'float',\n",
       " 'intensity': 'float',\n",
       " 'country': 'str',\n",
       " 'state': 'str',\n",
       " 'location_name': 'str',\n",
       " 'latitude': 'float',\n",
       " 'longitude': 'float',\n",
       " 'region_code': 'str',\n",
       " 'injuries': 'float',\n",
       " 'injuries_description': 'str',\n",
       " 'damage_millions_dollars': 'float',\n",
       " 'damage_description': 'str',\n",
       " 'total_injuries': 'float',\n",
       " 'total_injuries_description': 'str',\n",
       " 'total_damage_millions_dollars': 'float',\n",
       " 'total_damage_description': 'str'}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtyp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('..\\Datasets\\earthquake_data.csv', dtype = dtyp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "description_features = [\n",
    "    'injuries_description', 'damage_description',\n",
    "    'total_injuries_description', 'total_damage_description'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "imp = SimpleImputer(missing_values=np.nan, strategy='constant', fill_value='NA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[description_features] = imp.fit_transform(data[description_features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "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>damage_millions_dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>damage_description</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.417211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3.078840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>13.818806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3574.998799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>NA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    damage_millions_dollars\n",
       "damage_description                         \n",
       "1                                  0.417211\n",
       "2                                  3.078840\n",
       "3                                 13.818806\n",
       "4                               3574.998799\n",
       "NA                                      NaN"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "category_means = data[['damage_description', 'damage_millions_dollars']].groupby('damage_description').mean()\n",
    "category_means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1': 0.4172105263157895,\n",
       " '2': 3.0788402777777772,\n",
       " '3': 13.818805970149256,\n",
       " '4': 3574.9987991266385,\n",
       " 'NA': -1,\n",
       " '0': 0}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "replacement_values = category_means.damage_millions_dollars.to_dict()\n",
    "replacement_values['NA'] = -1\n",
    "replacement_values['0'] = 0\n",
    "replacement_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "imputed_values = data.damage_description.map(replacement_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['damage_millions_dollars'] = np.where(data.damage_millions_dollars.isnull(),\n",
    "                                           data.damage_description.map(replacement_values),\n",
    "                                           data.damage_millions_dollars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'year', 'month', 'day', 'hour', 'minute', 'second', 'focal_depth',\n",
       "       'eq_primary', 'eq_mag_mw', 'eq_mag_ms', 'eq_mag_mb', 'intensity',\n",
       "       'latitude', 'longitude', 'injuries', 'damage_millions_dollars',\n",
       "       'total_injuries', 'total_damage_millions_dollars'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_variables = data.select_dtypes(include=[np.number])\n",
    "numeric_variables.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['flag_tsunami', 'country', 'state', 'location_name', 'region_code',\n",
       "       'injuries_description', 'damage_description',\n",
       "       'total_injuries_description', 'total_damage_description'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "object_variables = data.select_dtypes(include=[np.object])\n",
    "object_variables.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NA    4723\n",
       "1      666\n",
       "3      347\n",
       "2      193\n",
       "4      143\n",
       "Name: injuries_description, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = data.injuries_description.value_counts(dropna=False)\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "counts = data.damage_description.value_counts()\n",
    "counts = counts.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_to_plot = data[~pd.isnull(data.injuries) & ~pd.isnull(data.eq_primary)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,9))\n",
    "\n",
    "plt.scatter(x=data_to_plot.eq_primary, y=data_to_plot.injuries)\n",
    "\n",
    "plt.xlabel('Primary earthquake magnitude')\n",
    "plt.ylabel('No. of injuries')\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}