Chapter02/Exercise2.09/Exercise_2_09.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": [
    {
     "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>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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts.reset_index().sort_values(by='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "counts = data.damage_description.value_counts()\n",
    "counts = counts.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "slices = ax.pie(counts, \n",
    "                labels=counts.index, \n",
    "                colors = ['white'], \n",
    "               wedgeprops = {'edgecolor': 'black'} )\n",
    "patches = slices[0]\n",
    "hatches =  ['/', '\\\\', '|', '-', '+', 'x', 'o', 'O', '\\.', '*']\n",
    "for patch in range(len(patches)):\n",
    "    patches[patch].set_hatch(hatches[patch])\n",
    "plt.title('Pie chart showing counts for\\ndamage_description categories')\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
}