Chapter02/Exercise2.08/Exercise_2_08.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "with open('..\\dtypes.json', 'r') as jsonfile:\n",
    "    dtyp = json.load(jsonfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtyp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('..\\Datasets\\earthquake_data.csv', dtype = dtyp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "description_features = [\n",
    "    'injuries_description', 'damage_description',\n",
    "    'total_injuries_description', 'total_damage_description'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "imp = SimpleImputer(missing_values=np.nan, strategy='constant', fill_value='NA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[description_features] = imp.fit_transform(data[description_features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "<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": 23,
     "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": 24,
   "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": 24,
     "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": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "imputed_values = data.damage_description.map(replacement_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 27,
   "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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_variables = data.select_dtypes(include=[np.number])\n",
    "numeric_variables.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "object_variables = data.select_dtypes(include=[np.object])\n",
    "object_variables.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = data.injuries_description.value_counts(dropna=False)\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>injuries_description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>NA</td>\n",
       "      <td>4723</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  index  injuries_description\n",
       "1     1                   666\n",
       "3     2                   193\n",
       "2     3                   347\n",
       "4     4                   143\n",
       "0    NA                  4723"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts.reset_index().sort_values(by='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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