Exploratory_Data_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Unnamed: 0</th>\n",
       "      <th>fecha</th>\n",
       "      <th>hora</th>\n",
       "      <th>edad</th>\n",
       "      <th>genero</th>\n",
       "      <th>barrio</th>\n",
       "      <th>pregunta_1</th>\n",
       "      <th>pregunta_2</th>\n",
       "      <th>pregunta_3</th>\n",
       "      <th>pregunta_4</th>\n",
       "      <th>...</th>\n",
       "      <th>pregunta_6</th>\n",
       "      <th>pregunta_7</th>\n",
       "      <th>pregunta_8</th>\n",
       "      <th>pregunta_9</th>\n",
       "      <th>pregunta_10</th>\n",
       "      <th>pregunta_11</th>\n",
       "      <th>pregunta_12</th>\n",
       "      <th>pregunta_13</th>\n",
       "      <th>barrio_completo</th>\n",
       "      <th>poblacion_por_barrio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2019-06-14</td>\n",
       "      <td>09:09:00</td>\n",
       "      <td>27</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>Constitucion</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>Constitucion</td>\n",
       "      <td>44107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-06-13</td>\n",
       "      <td>23:20:00</td>\n",
       "      <td>17</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>Otro</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>Coghlan</td>\n",
       "      <td>18604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-06-13</td>\n",
       "      <td>16:33:00</td>\n",
       "      <td>15</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>Recoleta</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>Recoleta</td>\n",
       "      <td>157932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2019-06-13</td>\n",
       "      <td>16:10:00</td>\n",
       "      <td>20</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>Barracas</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>...</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>Barracas</td>\n",
       "      <td>89452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2019-06-13</td>\n",
       "      <td>14:29:00</td>\n",
       "      <td>39</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>Almagro</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>Almagro</td>\n",
       "      <td>131699</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0       fecha      hora  edad  genero        barrio pregunta_1  \\\n",
       "0           0  2019-06-14  09:09:00    27   Mujer  Constitucion          A   \n",
       "1           1  2019-06-13  23:20:00    17   Mujer          Otro          A   \n",
       "2           2  2019-06-13  16:33:00    15   Mujer      Recoleta          B   \n",
       "3           3  2019-06-13  16:10:00    20   Mujer      Barracas          B   \n",
       "4           4  2019-06-13  14:29:00    39  Hombre       Almagro          B   \n",
       "\n",
       "  pregunta_2 pregunta_3 pregunta_4  ... pregunta_6 pregunta_7 pregunta_8  \\\n",
       "0          A          A          B  ...          B          C          C   \n",
       "1          B          B          B  ...          C          C          B   \n",
       "2          B          C          B  ...          B          B          A   \n",
       "3          C          C          C  ...          C          C          C   \n",
       "4          C          B          B  ...          C          B          C   \n",
       "\n",
       "  pregunta_9 pregunta_10 pregunta_11 pregunta_12 pregunta_13 barrio_completo  \\\n",
       "0          B           C           B           C           C    Constitucion   \n",
       "1          B           C           B           C           C         Coghlan   \n",
       "2          A           A           A           A           A        Recoleta   \n",
       "3          C           C           B           C           C        Barracas   \n",
       "4          B           C           C           B           C         Almagro   \n",
       "\n",
       "  poblacion_por_barrio  \n",
       "0                44107  \n",
       "1                18604  \n",
       "2               157932  \n",
       "3                89452  \n",
       "4               131699  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_csv(\"data/dataset_joined.csv\")\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_columnas = df[[\"fecha\",'edad', 'genero', 'barrio','barrio_completo',\n",
    "       'poblacion_por_barrio']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizamos los nulos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "* ```fecha``` tiene 0 nulos, lo cual representa el 0.00%"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "* ```edad``` tiene 0 nulos, lo cual representa el 0.00%"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "* ```genero``` tiene 0 nulos, lo cual representa el 0.00%"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "* ```barrio``` tiene 0 nulos, lo cual representa el 0.00%"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "* ```barrio_completo``` tiene 0 nulos, lo cual representa el 0.00%"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "* ```poblacion_por_barrio``` tiene 0 nulos, lo cual representa el 0.00%"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Markdown, display\n",
    "\n",
    "nulls_by_column = df_columnas.isnull().sum()\n",
    "dataset_size = df.shape[0]\n",
    "\n",
    "for column in nulls_by_column.index:\n",
    "    column_nulls = nulls_by_column[column]\n",
    "    null_percentage = 100 * column_nulls / dataset_size\n",
    "    display(Markdown(f\"* ```{column}``` tiene {column_nulls} nulos, lo cual representa el {null_percentage:.2f}%\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizamos columna EDAD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.edad.hist(bins=150);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Podemos escribir en el plot\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plt.hist(df.edad, bins=150);\n",
    "#plt.annotate('¿Qué hacemos con esto?', xy=(15, 50), xytext=(2.5, 300),\n",
    "            #arrowprops=dict(facecolor='black', shrink=15), fontsize=16)\n",
    "\n",
    "#plt.annotate('y esto', xy=(12.5, 600), xytext=(16, 500),\n",
    "            #arrowprops=dict(facecolor='black', shrink=15), fontsize=16\n",
    "            #);\n",
    "#plt.savefig('esto2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f87706869d0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df.edad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quitamos los outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_edad = df[\"edad\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "q1 = df_edad.quantile(0.25)\n",
    "q2 = df_edad.quantile(0.5)\n",
    "q3 = df_edad.quantile(0.75)\n",
    "\n",
    "iqr = q3 - q1\n",
    "\n",
    "up_threshold = q3 + 1.5 * iqr\n",
    "low_threshold = q1 - 1.5* iqr\n",
    "\n",
    "no_outliers_mask = (df_edad > low_threshold) & (df_edad < up_threshold)\n",
    "\n",
    "ax = sns.boxplot(df_edad[no_outliers_mask], orient = 'v').set(\n",
    "    xlabel='', \n",
    "    ylabel='Edad'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El skew es 0.5893768101027447 cuando quitamos los outliers\n"
     ]
    }
   ],
   "source": [
    "print(f\"El skew es {df_edad[no_outliers_mask].skew()} cuando quitamos los outliers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5561.000000\n",
       "mean       27.400108\n",
       "std         7.758342\n",
       "min         4.000000\n",
       "25%        21.000000\n",
       "50%        26.000000\n",
       "75%        32.000000\n",
       "max        50.000000\n",
       "Name: edad, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_edad[no_outliers_mask].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Sin outliers hemos tomado un 97.97% de los datos que teníamos, considerando solamente 115 registros como outliers\n",
      "\n"
     ]
    }
   ],
   "source": [
    "n_without_outliers = df_edad[no_outliers_mask].shape[0]\n",
    "n_including_outliers = df_edad.shape[0]\n",
    "\n",
    "taken_percentage = 100 * n_without_outliers / n_including_outliers\n",
    "print(f\"\"\"\n",
    "Sin outliers hemos tomado un {taken_percentage:.2f}% de los datos que teníamos, considerando solamente {n_including_outliers - n_without_outliers} registros como outliers\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trabajamos sobre la columna BARRIO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "barrio = df.barrio.value_counts().head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8770bd6290>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=barrio.index, y=barrio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Constitucion\n",
       "1               Otro\n",
       "2           Recoleta\n",
       "3           Barracas\n",
       "4            Almagro\n",
       "            ...     \n",
       "5671       Agronomia\n",
       "5672    Villa lugano\n",
       "5673         Almagro\n",
       "5674         Almagro\n",
       "5675       Agronomia\n",
       "Name: barrio, Length: 5676, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.barrio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Podemos tirar un barplot acá"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mujer     4736\n",
       "Hombre     912\n",
       "Otro        28\n",
       "Name: genero, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"genero\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8770b39690>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='genero', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Constitucion\n",
       "1            Coghlan\n",
       "2           Recoleta\n",
       "3           Barracas\n",
       "4            Almagro\n",
       "            ...     \n",
       "5671       Agronomia\n",
       "5672    Villa lugano\n",
       "5673         Almagro\n",
       "5674         Almagro\n",
       "5675       Agronomia\n",
       "Name: barrio_completo, Length: 5676, dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"barrio_completo\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_columnas = df[['fecha', 'edad', 'genero', 'barrio', 'barrio_completo',\n",
    "       'poblacion_por_barrio']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ploteamos cantidad de participantes de encuesta por barrio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Almagro      371\n",
       "Belgrano     365\n",
       "Palermo      342\n",
       "Caballito    296\n",
       "Agronomia    273\n",
       "Name: barrio_completo, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "porbarrio = df.barrio_completo.value_counts()\n",
    "#porbarrio = barioss.rename_axis('BARRIO').reset_index(name='counts')\n",
    "porbarrio.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "porbarrio = porbarrio.to_frame().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#porbarrio.to_csv('dfencuestadosporbarrio', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}