laboratorio.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 🐱 Schrödinger's Data 🐱"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Vamos a cargar nuestras librerias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sidetable\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Comando para ver todas las columnas de nuestros DataFrames.\n",
    "pd.options.display.max_columns = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ahora vamos a cargar nuestros archivos a mergear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Abrir_Archivos:\n",
    "    #Creamos la clase para que por defecto los archivos deben estar guardados en una carpeta \n",
    "    # llamada \"datos\" para no pedir otro parametro en la clase. Aunque también se le podría\n",
    "    #la ruta de cada archivo pero para este ejercicio sería poco practico.\n",
    "    def __init__ (self,csv1,csv2,txt,xml,):\n",
    "        self.csv1=csv1\n",
    "        self.csv2=csv2\n",
    "        self.txt=txt\n",
    "        self.xml=xml\n",
    "    \n",
    "    # Función para abrir el archivo csv1\n",
    "    def abrir_csv1(self):\n",
    "        csv1 = pd.read_csv(f\"./datos/{self.csv1}\", index_col=0)\n",
    "        return csv1\n",
    "    \n",
    "    #Función para abrir el csv 2\n",
    "    def abrir_csv2(self):\n",
    "        csv2 = pd.read_csv(f\"./datos/{self.csv2}\", index_col=0)\n",
    "        return csv2\n",
    "    \n",
    "    #Función para abrir el archivo txt (Hacemos la función con el \n",
    "    #separador \";\" por defecto para no meter otro parametro a la clase o un input)\n",
    "    def abrir_txt(self):\n",
    "        txt = pd.read_csv(f\"./datos/{self.txt}\", sep = \";\",index_col=0)\n",
    "        return txt\n",
    "    \n",
    "    #Funcion para abrir el archivo xml\n",
    "    def abrir_xml(self):\n",
    "        xml = pd.read_xml(f\"./datos/{self.xml}\")\n",
    "        return xml\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "Archivos=Abrir_Archivos(\"data_remaning_kaggle.csv\",\"exported_db_data.csv\",\"data_txt_clean.txt\",\"data_xml_clean.xml\")\n",
    "df_csv1=Archivos.abrir_csv1()\n",
    "df_csv2=Archivos.abrir_csv2()\n",
    "df_txt=Archivos.abrir_txt()\n",
    "df_xml=Archivos.abrir_xml()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HISTORIA DE USUARIA 1\n",
    "### Unión automatizadaa de los ficheros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def merge (csv1,csv2,txt,xml):\n",
    "    merge_1= xml.merge(txt, left_on = \"index\", right_on = \"index\")\n",
    "    merge_2=merge_1.merge(csv2, left_on = \"index\", right_on = \"index\")\n",
    "    merge_final=merge_2.merge(csv1, left_on = \"index\", right_on = \"index\")\n",
    "    return merge_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=merge(df_csv1,df_csv2,df_txt,df_xml)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25973, 260)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Q31_B_Part_5</th>\n",
       "      <th>Q31_B_Part_6</th>\n",
       "      <th>Q31_B_Part_7</th>\n",
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       "      <th>Q32_B_Part_3</th>\n",
       "      <th>Q32_B_Part_4</th>\n",
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       "      <th>Q32_B_Part_6</th>\n",
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       "      <th>Q32_B_Part_15</th>\n",
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       "      <th>Q32_B_Part_17</th>\n",
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       "      <th>Q36_B_Part_7</th>\n",
       "      <th>Q36_B_OTHER</th>\n",
       "      <th>Q37_B_Part_1</th>\n",
       "      <th>Q37_B_Part_2</th>\n",
       "      <th>Q37_B_Part_3</th>\n",
       "      <th>Q37_B_Part_4</th>\n",
       "      <th>Q37_B_Part_5</th>\n",
       "      <th>Q37_B_Part_6</th>\n",
       "      <th>Q37_B_Part_7</th>\n",
       "      <th>Q37_B_OTHER</th>\n",
       "      <th>Q38_B_Part_1</th>\n",
       "      <th>Q38_B_Part_2</th>\n",
       "      <th>Q38_B_Part_3</th>\n",
       "      <th>Q38_B_Part_4</th>\n",
       "      <th>Q38_B_Part_5</th>\n",
       "      <th>Q38_B_Part_6</th>\n",
       "      <th>Q38_B_Part_7</th>\n",
       "      <th>Q38_B_Part_8</th>\n",
       "      <th>Q38_B_Part_9</th>\n",
       "      <th>Q38_B_Part_10</th>\n",
       "      <th>Q38_B_Part_11</th>\n",
       "      <th>Q38_B_OTHER</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6075</th>\n",
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       "      <td>18-21</td>\n",
       "      <td>Man</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>Bachelor’s degree</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>&lt; 1 years</td>\n",
       "      <td>Python</td>\n",
       "      <td>A personal computer / desktop</td>\n",
       "      <td>More than 25 times</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>Other</td>\n",
       "      <td>0-49 employees</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Python</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>None</td>\n",
       "      <td>Matplotlib</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Binder / JupyterHub</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      level_0  index  time    age gender                        Q3  \\\n",
       "6075     6075   6075   170  18-21    Man  United States of America   \n",
       "\n",
       "                     Q4                 Q5         Q6      Q8  \\\n",
       "6075  Bachelor’s degree  Software Engineer  < 1 years  Python   \n",
       "\n",
       "                                Q11                 Q13       Q15    Q20  \\\n",
       "6075  A personal computer / desktop  More than 25 times  < 1 year  Other   \n",
       "\n",
       "                 Q21 Q22  Q23  Q25  Q26  Q33  Q35  Q41      Q7  \\\n",
       "6075  0-49 employees   0  NaN  NaN  NaN  NaN  NaN  NaN  Python   \n",
       "\n",
       "                    Q9    Q12           Q14    Q16    Q17  Q24  Q32  Q34  \\\n",
       "6075  Jupyter Notebook   None   Matplotlib    None   None  NaN  NaN  NaN   \n",
       "\n",
       "     Q10_Part_1 Q10_Part_2 Q10_Part_3 Q10_Part_4             Q10_Part_5  \\\n",
       "6075        NaN        NaN        NaN        NaN   Binder / JupyterHub    \n",
       "\n",
       "     Q10_Part_6 Q10_Part_7 Q10_Part_8 Q10_Part_9 Q10_Part_10 Q10_Part_11  \\\n",
       "6075        NaN        NaN        NaN        NaN         NaN         NaN   \n",
       "\n",
       "     Q10_Part_12 Q10_Part_13 Q10_Part_14 Q10_Part_15 Q10_Part_16 Q10_OTHER  \\\n",
       "6075         NaN         NaN         NaN         NaN         NaN       NaN   \n",
       "\n",
       "     Q18_Part_1 Q18_Part_2 Q18_Part_3 Q18_Part_4 Q18_Part_5 Q18_Part_6  \\\n",
       "6075        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "     Q18_OTHER Q19_Part_1 Q19_Part_2 Q19_Part_3 Q19_Part_4 Q19_Part_5  \\\n",
       "6075       NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "     Q19_OTHER Q27_A_Part_1 Q27_A_Part_2 Q27_A_Part_3 Q27_A_Part_4  \\\n",
       "6075       NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "     Q27_A_Part_5 Q27_A_Part_6 Q27_A_Part_7 Q27_A_Part_8 Q27_A_Part_9  \\\n",
       "6075          NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "     Q27_A_Part_10 Q27_A_Part_11 Q27_A_OTHER  Q28 Q29_A_Part_1 Q29_A_Part_2  \\\n",
       "6075           NaN           NaN         NaN  NaN          NaN          NaN   \n",
       "\n",
       "     Q29_A_Part_3 Q29_A_Part_4 Q29_A_OTHER Q30_A_Part_1 Q30_A_Part_2  \\\n",
       "6075          NaN          NaN         NaN          NaN          NaN   \n",
       "\n",
       "     Q30_A_Part_3 Q30_A_Part_4 Q30_A_Part_5 Q30_A_Part_6 Q30_A_Part_7  \\\n",
       "6075          NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "     Q30_A_OTHER Q31_A_Part_1 Q31_A_Part_2 Q31_A_Part_3 Q31_A_Part_4  \\\n",
       "6075         NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "     Q31_A_Part_5 Q31_A_Part_6 Q31_A_Part_7 Q31_A_Part_8 Q31_A_Part_9  \\\n",
       "6075          NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "     Q31_A_OTHER Q36_A_Part_1 Q36_A_Part_2 Q36_A_Part_3 Q36_A_Part_4  \\\n",
       "6075         NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "     Q36_A_Part_5 Q36_A_Part_6 Q36_A_Part_7 Q36_A_OTHER Q37_A_Part_1  \\\n",
       "6075          NaN          NaN          NaN         NaN          NaN   \n",
       "\n",
       "     Q37_A_Part_2 Q37_A_Part_3 Q37_A_Part_4 Q37_A_Part_5 Q37_A_Part_6  \\\n",
       "6075          NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "     Q37_A_Part_7 Q37_A_OTHER Q38_A_Part_1 Q38_A_Part_2 Q38_A_Part_3  \\\n",
       "6075          NaN         NaN          NaN          NaN          NaN   \n",
       "\n",
       "     Q38_A_Part_4 Q38_A_Part_5 Q38_A_Part_6 Q38_A_Part_7 Q38_A_Part_8  \\\n",
       "6075          NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "     Q38_A_Part_9 Q38_A_Part_10 Q38_A_Part_11 Q38_A_OTHER Q39_Part_1  \\\n",
       "6075          NaN           NaN           NaN         NaN        NaN   \n",
       "\n",
       "     Q39_Part_2 Q39_Part_3 Q39_Part_4 Q39_Part_5 Q39_Part_6 Q39_Part_7  \\\n",
       "6075        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "     Q39_Part_8 Q39_Part_9 Q39_OTHER Q40_Part_1 Q40_Part_2 Q40_Part_3  \\\n",
       "6075        NaN        NaN       NaN        NaN        NaN        NaN   \n",
       "\n",
       "     Q40_Part_4 Q40_Part_5 Q40_Part_6 Q40_Part_7 Q40_Part_8 Q40_Part_9  \\\n",
       "6075        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "     Q40_Part_10 Q40_Part_11 Q40_OTHER Q42_Part_1 Q42_Part_2 Q42_Part_3  \\\n",
       "6075         NaN         NaN       NaN        NaN        NaN        NaN   \n",
       "\n",
       "     Q42_Part_4 Q42_Part_5 Q42_Part_6 Q42_Part_7 Q42_Part_8 Q42_Part_9  \\\n",
       "6075        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "     Q42_Part_10 Q42_Part_11 Q42_OTHER Q27_B_Part_1 Q27_B_Part_2 Q27_B_Part_3  \\\n",
       "6075         NaN         NaN       NaN          NaN          NaN          NaN   \n",
       "\n",
       "     Q27_B_Part_4 Q27_B_Part_5 Q27_B_Part_6 Q27_B_Part_7 Q27_B_Part_8  \\\n",
       "6075          NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "     Q27_B_Part_9 Q27_B_Part_10 Q27_B_Part_11 Q27_B_OTHER Q29_B_Part_1  \\\n",
       "6075          NaN           NaN           NaN         NaN          NaN   \n",
       "\n",
       "     Q29_B_Part_2 Q29_B_Part_3 Q29_B_Part_4 Q29_B_OTHER  Q30_B_Part_1  \\\n",
       "6075          NaN          NaN          NaN         NaN           NaN   \n",
       "\n",
       "      Q30_B_Part_2  Q30_B_Part_3  Q30_B_Part_4  Q30_B_Part_5  Q30_B_Part_6  \\\n",
       "6075           NaN           NaN           NaN           NaN           NaN   \n",
       "\n",
       "      Q30_B_Part_7  Q30_B_OTHER Q31_B_Part_1 Q31_B_Part_2 Q31_B_Part_3  \\\n",
       "6075           NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "     Q31_B_Part_4 Q31_B_Part_5 Q31_B_Part_6 Q31_B_Part_7 Q31_B_Part_8  \\\n",
       "6075          NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "     Q31_B_Part_9 Q31_B_OTHER Q32_B_Part_1 Q32_B_Part_2 Q32_B_Part_3  \\\n",
       "6075          NaN         NaN          NaN          NaN          NaN   \n",
       "\n",
       "     Q32_B_Part_4 Q32_B_Part_5 Q32_B_Part_6 Q32_B_Part_7 Q32_B_Part_8  \\\n",
       "6075          NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "     Q32_B_Part_9 Q32_B_Part_10 Q32_B_Part_11 Q32_B_Part_12 Q32_B_Part_13  \\\n",
       "6075          NaN           NaN           NaN           NaN           NaN   \n",
       "\n",
       "     Q32_B_Part_14 Q32_B_Part_15 Q32_B_Part_16 Q32_B_Part_17 Q32_B_Part_18  \\\n",
       "6075           NaN           NaN           NaN           NaN           NaN   \n",
       "\n",
       "     Q32_B_Part_19 Q32_B_Part_20 Q32_B_OTHER Q34_B_Part_1 Q34_B_Part_2  \\\n",
       "6075           NaN           NaN         NaN          NaN          NaN   \n",
       "\n",
       "     Q34_B_Part_3 Q34_B_Part_4 Q34_B_Part_5 Q34_B_Part_6 Q34_B_Part_7  \\\n",
       "6075          NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "     Q34_B_Part_8 Q34_B_Part_9 Q34_B_Part_10 Q34_B_Part_11 Q34_B_Part_12  \\\n",
       "6075          NaN          NaN           NaN           NaN           NaN   \n",
       "\n",
       "     Q34_B_Part_13 Q34_B_Part_14 Q34_B_Part_15 Q34_B_Part_16 Q34_B_OTHER  \\\n",
       "6075           NaN           NaN           NaN           NaN         NaN   \n",
       "\n",
       "     Q36_B_Part_1 Q36_B_Part_2 Q36_B_Part_3 Q36_B_Part_4 Q36_B_Part_5  \\\n",
       "6075          NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "     Q36_B_Part_6 Q36_B_Part_7 Q36_B_OTHER Q37_B_Part_1 Q37_B_Part_2  \\\n",
       "6075          NaN          NaN         NaN          NaN          NaN   \n",
       "\n",
       "     Q37_B_Part_3 Q37_B_Part_4 Q37_B_Part_5 Q37_B_Part_6 Q37_B_Part_7  \\\n",
       "6075          NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "     Q37_B_OTHER Q38_B_Part_1 Q38_B_Part_2 Q38_B_Part_3 Q38_B_Part_4  \\\n",
       "6075         NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "     Q38_B_Part_5 Q38_B_Part_6 Q38_B_Part_7 Q38_B_Part_8 Q38_B_Part_9  \\\n",
       "6075          NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "     Q38_B_Part_10 Q38_B_Part_11 Q38_B_OTHER  \n",
       "6075           NaN           NaN         NaN  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "q30=df[['Q30_B_Part_1','Q30_B_Part_2','Q30_B_Part_3','Q30_B_Part_4',\n",
    "                 'Q30_B_Part_5','Q30_B_Part_6','Q30_B_Part_7', 'Q30_B_OTHER']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>missing</th>\n",
       "      <th>total</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Q30_B_Part_1</th>\n",
       "      <td>25973</td>\n",
       "      <td>25973</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q30_B_Part_2</th>\n",
       "      <td>25973</td>\n",
       "      <td>25973</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q30_B_Part_3</th>\n",
       "      <td>25973</td>\n",
       "      <td>25973</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q30_B_Part_4</th>\n",
       "      <td>25973</td>\n",
       "      <td>25973</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q30_B_Part_5</th>\n",
       "      <td>25973</td>\n",
       "      <td>25973</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q30_B_Part_6</th>\n",
       "      <td>25973</td>\n",
       "      <td>25973</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q30_B_Part_7</th>\n",
       "      <td>25973</td>\n",
       "      <td>25973</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q30_B_OTHER</th>\n",
       "      <td>25973</td>\n",
       "      <td>25973</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              missing  total  percent\n",
       "Q30_B_Part_1    25973  25973    100.0\n",
       "Q30_B_Part_2    25973  25973    100.0\n",
       "Q30_B_Part_3    25973  25973    100.0\n",
       "Q30_B_Part_4    25973  25973    100.0\n",
       "Q30_B_Part_5    25973  25973    100.0\n",
       "Q30_B_Part_6    25973  25973    100.0\n",
       "Q30_B_Part_7    25973  25973    100.0\n",
       "Q30_B_OTHER     25973  25973    100.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q30.stb.missing()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 👀 Falta agregar lo de guardar el archivo al terminar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HISTORIA DE USUARIA 2\n",
    "### Limpieza de los datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unificamos y cambiamos el nombre de nuestras columnas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unificamos el nombre de las columnas de nuestro DataFrame\n",
    "nuevas_columnas = {col: col.lower().replace(\" \", \"_\") for col in df.columns}\n",
    "df.rename(columns = nuevas_columnas, inplace = True)\n",
    "\n",
    "df.rename(columns={'age':'q1_edad','gender':'q2_genero','q3':'q3_pais','q4':'q4_nivel_educativo','q5':'q5_puesto_actual',\n",
    " 'q6':'q6_años_programando','q8':'q8_lenguaje_recomend','q20':'q20_sector_empresa','q21':'q21_num_empleados',\n",
    " 'q22':'q22_perso_resp_equip_da','q23':'q23_incor_metod_nego_ml','q25':'q25_salario_anual','q41':'q41_tool_da_principal',\n",
    " 'q7':'q7_lenguaje_mas_usado'},inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Eliminando las columnas que no ocuparemos en nuestro analisis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Q1: What is your age (# years)?\n",
    "- Q2: What is your gender?\n",
    "- Q3: In which country do you currently reside?\n",
    "- Q4: What is the highest level of formal education that you have attained or plan to attain within the next 2 years?\n",
    "- Q5: Select the title most similar to your current role (or most recent title if retired):\n",
    "- Q6: For how many years have you been writing code and/or programming?\n",
    "- Q7: What programming languages do you use on a regular basis? (Select all that apply)\n",
    "- Q8: What programming language would you recommend an aspiring data scientist to learn first?\n",
    "- Q9: Which of the following integrated development environments (IDE's) do you use on a regular basis? **A eliminar**\n",
    "- Q10: Which of the following hosted notebook products do you use on a regular basis? (Select all that apply) **A eliminar**\n",
    "- Q11: What type of computing platform do you use most often for your data science projects? **A eliminar**\n",
    "- Q12: Which types of specialized hardware do you use on a regular basis? (Select all that apply) **A eliminar**\n",
    "- Q13: Approximately how many times have you used a TPU (tensor processing unit)? **A eliminar**\n",
    "- Q14: What data visualization libraries or tools do you use on a regular basis? (Select all that apply) **A eliminar**\n",
    "- Q15: For how many years have you used machine learning methods? **A eliminar**\n",
    "- Q16: Which of the following machine learning frameworks do you use on a regular basis? (Select all that apply) **A eliminar**\n",
    "- Q17: Which of the following ML algorithms do you use on a regular basis? (Select all that apply) **A eliminar**\n",
    "- Q18: Which categories of computer vision methods do you use on a regular basis? (Select all that apply)**A eliminar**\n",
    "- Q19: Which of the following natural language processing (NLP) methods do you use on a regular basis? (Select all that apply) **A eliminar**\n",
    "- Q20: In what industry is your current employer/contract (or your most recent employer if retired)?\n",
    "- Q21: What is the size of the company where you are employed?\n",
    "- Q22: Approximately how many individuals are responsible for data science workloads at your place of business?\n",
    "- Q23: Does your current employer incorporate machine learning methods into their business?\n",
    "- Q24: Select any activities that make up an important part of your role at work: (Select all that apply) **A eliminar**\n",
    "- Q25: What is your current yearly compensation (approximate $USD)?\n",
    "- Q26: Approximately how much money have you (or your team) spent on machine learning and/or cloud computing services at home (or at work) in the past 5 years (approximate $USD)? **A eliminar**\n",
    "- Q27-A: Which of the following cloud computing platforms do you use on a regular basis? (Select all that apply)\n",
    "- Q28: Of the cloud platforms that you are familiar with, which has the best developer experience (most enjoyable to use)? **A eliminar**\n",
    "- Q29-A: Do you use any of the following cloud computing products on a regular basis? (Select all that apply) **A eliminar**\n",
    "- Q30-A: Do you use any of the following data storage products on a regular basis? (Select all that apply)**A eliminar**\n",
    "- Q31-A: Do you use any of the following managed machine learning products on a regular basis? (Select all that apply)\n",
    "- Q32-A: Which of the following big data products (relational databases, data warehouses, data lakes, or similar) do you use on a regular basis? (Select all that apply)\n",
    "- Q33: Which of the following big data products (relational database, data warehouse, data lake, or similar) do you use most often? **A eliminar**\n",
    "- Q34-A: Which of the following business intelligence tools do you use on a regular basis? (Select all that apply)\n",
    "- Q35: Which of the following business intelligence tools do you use most often? **A eliminar**\n",
    "- Q36-A: Do you use any automated machine learning tools (or partial AutoML tools) on a regular basis? (Select all that apply)\n",
    "- Q37-A: Which of the following automated machine learning tools (or partial AutoML tools) do you use on a regular basis? (Select all that apply)8\n",
    "- Q38-A: Do you use any tools to help manage machine learning experiments? (Select all that apply) **A eliminar**\n",
    "- Q39: Where do you publicly share or deploy your data analysis or machine learning applications? (Select all that apply)\n",
    "- Q40: On which platforms have you begun or completed data science courses? (Select all that apply)\n",
    "- Q41: What is the primary tool that you use at work or school to analyze data? (Include text response)\n",
    "- Q42: Who/what are your favorite media sources that report on data science topics? (Select all that apply) **A eliminar**\n",
    "### Supplementary Questions:\n",
    "- Q27-B: Which of the following cloud computing platforms do you hope to become more familiar with in the next 2 years?\n",
    "- Q29-B: In the next 2 years, do you hope to become more familiar with any of these specific cloud computing products? (Select all that apply)\n",
    "- Q30-B: In the next 2 years, do you hope to become more familiar with any of these specific data storage products? (Select all that apply)\n",
    "- Q31-B: In the next 2 years, do you hope to become more familiar with any of these managed machine learning products? (Select all that apply)\n",
    "- Q32-B: Which of the following big data products (relational databases, data warehouses, data lakes, or similar) do you hope to become more familiar with in the next 2 years? (Select all that apply)\n",
    "- Q34-B: Which of the following business intelligence tools do you hope to become more familiar with in the next 2 years? (Select all that apply)\n",
    "- Q36-B: Which categories of automated machine learning tools (or partial AutoML tools) do you hope to become more familiar with in the next 2 years? (Select all that apply)\n",
    "- Q37-B: Which specific automated machine learning tools (or partial AutoML tools) do you hope to become more familiar with in the next 2 years? (Select all that apply)\n",
    "- Q38-B: In the next 2 years, do you hope to become more familiar with any of these tools for managing ML experiments? (Select all that apply)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# La 30B se eliminó porque tenía el 100% de sus datos nulos en todas las partes 30a se eliminó iguaL\n",
    "#Eliminé la 29 porque es redundate con la pregunta q27-A y tenía todos los datos nulos boorro la 29b porque no estáa para comparaar la A\n",
    "#Eliminé la 38 porque tenia el 10% de sus datos nulos en todas sus partes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eliminar_colum(dataframe):\n",
    "    dataframe.drop(columns=['level_0','time','q11','q13','q15','q26','q33','q35','q9','q12','q14',\n",
    "                 'q16','q17','q24','q10_part_1','q10_part_2','q10_part_3','q10_part_4','q10_part_5','q10_part_6',\n",
    "                 'q10_part_7','q10_part_8','q10_part_9','q10_part_10','q10_part_11', 'q10_part_12','q10_part_13',\n",
    "                 'q10_part_14','q10_part_15','q10_part_16','q10_other','q18_part_1','q18_part_2','q18_part_3',\n",
    "                 'q18_part_4','q18_part_5','q18_part_6','q18_other','q19_part_1','q19_part_2','q19_part_3','q19_part_4',\n",
    "                 'q19_part_5','q19_other','q28','q29_a_part_1','q29_a_part_2','q29_a_part_3','q29_a_part_4', 'q29_a_other',\n",
    "                 'q29_b_part_1','q29_b_part_2','q29_b_part_3','q29_b_part_4','q29_b_other','q30_a_part_1','q30_a_part_2',\n",
    "                 'q30_a_part_3','q30_a_part_4','q30_a_part_5','q30_a_part_6','q30_a_part_7','q30_a_other','q30_b_part_1',\n",
    "                 'q30_b_part_2','q30_b_part_3','q30_b_part_4','q30_b_part_5','q30_b_part_6','q30_b_part_7', 'q30_b_other',\n",
    "                 'q38_a_part_1','q38_a_part_2','q38_a_part_3','q38_a_part_4', 'q38_a_part_5', 'q38_a_part_6','q38_a_part_7',\n",
    "                 'q38_a_part_8','q38_a_part_9','q38_a_part_10','q38_a_part_11','q38_a_other','q38_b_part_1','q38_b_part_2',\n",
    "                 'q38_b_part_3','q38_b_part_4','q38_b_part_5','q38_b_part_6','q38_b_part_7','q38_b_part_8','q38_b_part_9',\n",
    "                 'q38_b_part_10','q38_b_part_11','q38_b_other','q42_part_1','q42_part_2','q42_part_3','q42_part_4','q42_part_5', \n",
    "                 'q42_part_6','q42_part_7', 'q42_part_8', 'q42_part_9', 'q42_part_10','q42_part_11', 'q42_other',], axis=1, inplace=True)\n",
    "    return dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25973, 153)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=eliminar_colum(df)\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ordenando las columnas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hay que plantearse eliminar la cq32 y q34 ya que tienen muchas partes y nos ahorraría muchas columnas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['index', 'q1_edad', 'q2_genero', 'q3_pais', 'q4_nivel_educativo', 'q5_puesto_actual', 'q6_años_programando', 'q8_lenguaje_recomend', 'q20_sector_empresa', 'q21_num_empleados', 'q22_perso_resp_equip_da', 'q23_incor_metod_nego_ml', 'q25_salario_anual', 'q41_tool_da_principal', 'q7_lenguaje_mas_usado', 'q32', 'q34', 'q27_a_part_1', 'q27_a_part_2', 'q27_a_part_3', 'q27_a_part_4', 'q27_a_part_5', 'q27_a_part_6', 'q27_a_part_7', 'q27_a_part_8', 'q27_a_part_9', 'q27_a_part_10', 'q27_a_part_11', 'q27_a_other', 'q31_a_part_1', 'q31_a_part_2', 'q31_a_part_3', 'q31_a_part_4', 'q31_a_part_5', 'q31_a_part_6', 'q31_a_part_7', 'q31_a_part_8', 'q31_a_part_9', 'q31_a_other', 'q36_a_part_1', 'q36_a_part_2', 'q36_a_part_3', 'q36_a_part_4', 'q36_a_part_5', 'q36_a_part_6', 'q36_a_part_7', 'q36_a_other', 'q37_a_part_1', 'q37_a_part_2', 'q37_a_part_3', 'q37_a_part_4', 'q37_a_part_5', 'q37_a_part_6', 'q37_a_part_7', 'q37_a_other', 'q39_part_1', 'q39_part_2', 'q39_part_3', 'q39_part_4', 'q39_part_5', 'q39_part_6', 'q39_part_7', 'q39_part_8', 'q39_part_9', 'q39_other', 'q40_part_1', 'q40_part_2', 'q40_part_3', 'q40_part_4', 'q40_part_5', 'q40_part_6', 'q40_part_7', 'q40_part_8', 'q40_part_9', 'q40_part_10', 'q40_part_11', 'q40_other', 'q27_b_part_1', 'q27_b_part_2', 'q27_b_part_3', 'q27_b_part_4', 'q27_b_part_5', 'q27_b_part_6', 'q27_b_part_7', 'q27_b_part_8', 'q27_b_part_9', 'q27_b_part_10', 'q27_b_part_11', 'q27_b_other', 'q31_b_part_1', 'q31_b_part_2', 'q31_b_part_3', 'q31_b_part_4', 'q31_b_part_5', 'q31_b_part_6', 'q31_b_part_7', 'q31_b_part_8', 'q31_b_part_9', 'q31_b_other', 'q32_b_part_1', 'q32_b_part_2', 'q32_b_part_3', 'q32_b_part_4', 'q32_b_part_5', 'q32_b_part_6', 'q32_b_part_7', 'q32_b_part_8', 'q32_b_part_9', 'q32_b_part_10', 'q32_b_part_11', 'q32_b_part_12', 'q32_b_part_13', 'q32_b_part_14', 'q32_b_part_15', 'q32_b_part_16', 'q32_b_part_17', 'q32_b_part_18', 'q32_b_part_19', 'q32_b_part_20', 'q32_b_other', 'q34_b_part_1', 'q34_b_part_2', 'q34_b_part_3', 'q34_b_part_4', 'q34_b_part_5', 'q34_b_part_6', 'q34_b_part_7', 'q34_b_part_8', 'q34_b_part_9', 'q34_b_part_10', 'q34_b_part_11', 'q34_b_part_12', 'q34_b_part_13', 'q34_b_part_14', 'q34_b_part_15', 'q34_b_part_16', 'q34_b_other', 'q36_b_part_1', 'q36_b_part_2', 'q36_b_part_3', 'q36_b_part_4', 'q36_b_part_5', 'q36_b_part_6', 'q36_b_part_7', 'q36_b_other', 'q37_b_part_1', 'q37_b_part_2', 'q37_b_part_3', 'q37_b_part_4', 'q37_b_part_5', 'q37_b_part_6', 'q37_b_part_7', 'q37_b_other']\n"
     ]
    }
   ],
   "source": [
    "lista_columnas=list(df.columns)\n",
    "print(lista_columnas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "nuevo_orden=['index','q1_edad','q2_genero','q3_pais','q4_nivel_educativo','q5_puesto_actual', 'q6_años_programando',\n",
    "             'q7_lenguaje_mas_usado','q8_lenguaje_recomend','q20_sector_empresa','q21_num_empleados', 'q22_perso_resp_equip_da',\n",
    "             'q23_incor_metod_nego_ml','q25_salario_anual','q27_a_part_1','q27_a_part_2','q27_a_part_3','q27_a_part_4',\n",
    "             'q27_a_part_5','q27_a_part_6','q27_a_part_7','q27_a_part_8','q27_a_part_9','q27_a_part_10','q27_a_part_11', \n",
    "             'q27_a_other','q27_b_part_1','q27_b_part_2','q27_b_part_3','q27_b_part_4','q27_b_part_5','q27_b_part_6', \n",
    "             'q27_b_part_7','q27_b_part_8','q27_b_part_9','q27_b_part_10','q27_b_part_11', 'q27_b_other','q31_a_part_1', \n",
    "             'q31_a_part_2','q31_a_part_3','q31_a_part_4','q31_a_part_5','q31_a_part_6','q31_a_part_7','q31_a_part_8', \n",
    "             'q31_a_part_9','q31_a_other','q31_b_part_1','q31_b_part_2','q31_b_part_3','q31_b_part_4','q31_b_part_5','q31_b_part_6',\n",
    "             'q31_b_part_7','q31_b_part_8','q31_b_part_9','q31_b_other','q32','q32_b_part_1','q32_b_part_2','q32_b_part_3',\n",
    "             'q32_b_part_4','q32_b_part_5','q32_b_part_6','q32_b_part_7','q32_b_part_8','q32_b_part_9','q32_b_part_10',\n",
    "             'q32_b_part_11','q32_b_part_12','q32_b_part_13','q32_b_part_14','q32_b_part_15','q32_b_part_16','q32_b_part_17',##ooojoooo PLANTEARSE ELIMINAR LA 32 TIENE MUCHAS PARTES\n",
    "             'q32_b_part_18','q32_b_part_19','q32_b_part_20','q32_b_other', 'q34','q34_b_part_1','q34_b_part_2','q34_b_part_3',\n",
    "             'q34_b_part_4','q34_b_part_5','q34_b_part_6','q34_b_part_7','q34_b_part_8','q34_b_part_9','q34_b_part_10',\n",
    "             'q34_b_part_11','q34_b_part_12','q34_b_part_13','q34_b_part_14','q34_b_part_15','q34_b_part_16','q34_b_other',\n",
    "             'q36_a_part_1','q36_a_part_2','q36_a_part_3','q36_a_part_4','q36_a_part_5','q36_a_part_6','q36_a_part_7','q36_a_other',\n",
    "             'q36_b_part_1','q36_b_part_2','q36_b_part_3','q36_b_part_4','q36_b_part_5','q36_b_part_6','q36_b_part_7','q36_b_other',\n",
    "             'q37_a_part_1','q37_a_part_2','q37_a_part_3','q37_a_part_4','q37_a_part_5','q37_a_part_6','q37_a_part_7','q37_a_other',\n",
    "             'q37_b_part_1','q37_b_part_2','q37_b_part_3','q37_b_part_4','q37_b_part_5','q37_b_part_6','q37_b_part_7','q37_b_other',\n",
    "             'q39_part_1','q39_part_2','q39_part_3','q39_part_4','q39_part_5','q39_part_6','q39_part_7','q39_part_8','q39_part_9','q39_other',\n",
    "             'q40_part_1','q40_part_2','q40_part_3','q40_part_4','q40_part_5','q40_part_6','q40_part_7','q40_part_8','q40_part_9', \n",
    "             'q40_part_10', 'q40_part_11', 'q40_other','q41_tool_da_principal' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.reindex(columns=nuevo_orden)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>q1_edad</th>\n",
       "      <th>q2_genero</th>\n",
       "      <th>q3_pais</th>\n",
       "      <th>q4_nivel_educativo</th>\n",
       "      <th>q5_puesto_actual</th>\n",
       "      <th>q6_años_programando</th>\n",
       "      <th>q7_lenguaje_mas_usado</th>\n",
       "      <th>q8_lenguaje_recomend</th>\n",
       "      <th>q20_sector_empresa</th>\n",
       "      <th>q21_num_empleados</th>\n",
       "      <th>q22_perso_resp_equip_da</th>\n",
       "      <th>q23_incor_metod_nego_ml</th>\n",
       "      <th>q25_salario_anual</th>\n",
       "      <th>q27_a_part_1</th>\n",
       "      <th>q27_a_part_2</th>\n",
       "      <th>q27_a_part_3</th>\n",
       "      <th>q27_a_part_4</th>\n",
       "      <th>q27_a_part_5</th>\n",
       "      <th>q27_a_part_6</th>\n",
       "      <th>q27_a_part_7</th>\n",
       "      <th>q27_a_part_8</th>\n",
       "      <th>q27_a_part_9</th>\n",
       "      <th>q27_a_part_10</th>\n",
       "      <th>q27_a_part_11</th>\n",
       "      <th>q27_a_other</th>\n",
       "      <th>q27_b_part_1</th>\n",
       "      <th>q27_b_part_2</th>\n",
       "      <th>q27_b_part_3</th>\n",
       "      <th>q27_b_part_4</th>\n",
       "      <th>q27_b_part_5</th>\n",
       "      <th>q27_b_part_6</th>\n",
       "      <th>q27_b_part_7</th>\n",
       "      <th>q27_b_part_8</th>\n",
       "      <th>q27_b_part_9</th>\n",
       "      <th>q27_b_part_10</th>\n",
       "      <th>q27_b_part_11</th>\n",
       "      <th>q27_b_other</th>\n",
       "      <th>q31_a_part_1</th>\n",
       "      <th>q31_a_part_2</th>\n",
       "      <th>q31_a_part_3</th>\n",
       "      <th>q31_a_part_4</th>\n",
       "      <th>q31_a_part_5</th>\n",
       "      <th>q31_a_part_6</th>\n",
       "      <th>q31_a_part_7</th>\n",
       "      <th>q31_a_part_8</th>\n",
       "      <th>q31_a_part_9</th>\n",
       "      <th>q31_a_other</th>\n",
       "      <th>q31_b_part_1</th>\n",
       "      <th>q31_b_part_2</th>\n",
       "      <th>q31_b_part_3</th>\n",
       "      <th>q31_b_part_4</th>\n",
       "      <th>q31_b_part_5</th>\n",
       "      <th>q31_b_part_6</th>\n",
       "      <th>q31_b_part_7</th>\n",
       "      <th>q31_b_part_8</th>\n",
       "      <th>q31_b_part_9</th>\n",
       "      <th>q31_b_other</th>\n",
       "      <th>q32</th>\n",
       "      <th>q32_b_part_1</th>\n",
       "      <th>q32_b_part_2</th>\n",
       "      <th>q32_b_part_3</th>\n",
       "      <th>q32_b_part_4</th>\n",
       "      <th>q32_b_part_5</th>\n",
       "      <th>q32_b_part_6</th>\n",
       "      <th>q32_b_part_7</th>\n",
       "      <th>q32_b_part_8</th>\n",
       "      <th>q32_b_part_9</th>\n",
       "      <th>q32_b_part_10</th>\n",
       "      <th>q32_b_part_11</th>\n",
       "      <th>q32_b_part_12</th>\n",
       "      <th>q32_b_part_13</th>\n",
       "      <th>q32_b_part_14</th>\n",
       "      <th>q32_b_part_15</th>\n",
       "      <th>q32_b_part_16</th>\n",
       "      <th>q32_b_part_17</th>\n",
       "      <th>q32_b_part_18</th>\n",
       "      <th>q32_b_part_19</th>\n",
       "      <th>q32_b_part_20</th>\n",
       "      <th>q32_b_other</th>\n",
       "      <th>q34</th>\n",
       "      <th>q34_b_part_1</th>\n",
       "      <th>q34_b_part_2</th>\n",
       "      <th>q34_b_part_3</th>\n",
       "      <th>q34_b_part_4</th>\n",
       "      <th>q34_b_part_5</th>\n",
       "      <th>q34_b_part_6</th>\n",
       "      <th>q34_b_part_7</th>\n",
       "      <th>q34_b_part_8</th>\n",
       "      <th>q34_b_part_9</th>\n",
       "      <th>q34_b_part_10</th>\n",
       "      <th>q34_b_part_11</th>\n",
       "      <th>q34_b_part_12</th>\n",
       "      <th>q34_b_part_13</th>\n",
       "      <th>q34_b_part_14</th>\n",
       "      <th>q34_b_part_15</th>\n",
       "      <th>q34_b_part_16</th>\n",
       "      <th>q34_b_other</th>\n",
       "      <th>q36_a_part_1</th>\n",
       "      <th>q36_a_part_2</th>\n",
       "      <th>q36_a_part_3</th>\n",
       "      <th>q36_a_part_4</th>\n",
       "      <th>q36_a_part_5</th>\n",
       "      <th>q36_a_part_6</th>\n",
       "      <th>q36_a_part_7</th>\n",
       "      <th>q36_a_other</th>\n",
       "      <th>q36_b_part_1</th>\n",
       "      <th>q36_b_part_2</th>\n",
       "      <th>q36_b_part_3</th>\n",
       "      <th>q36_b_part_4</th>\n",
       "      <th>q36_b_part_5</th>\n",
       "      <th>q36_b_part_6</th>\n",
       "      <th>q36_b_part_7</th>\n",
       "      <th>q36_b_other</th>\n",
       "      <th>q37_a_part_1</th>\n",
       "      <th>q37_a_part_2</th>\n",
       "      <th>q37_a_part_3</th>\n",
       "      <th>q37_a_part_4</th>\n",
       "      <th>q37_a_part_5</th>\n",
       "      <th>q37_a_part_6</th>\n",
       "      <th>q37_a_part_7</th>\n",
       "      <th>q37_a_other</th>\n",
       "      <th>q37_b_part_1</th>\n",
       "      <th>q37_b_part_2</th>\n",
       "      <th>q37_b_part_3</th>\n",
       "      <th>q37_b_part_4</th>\n",
       "      <th>q37_b_part_5</th>\n",
       "      <th>q37_b_part_6</th>\n",
       "      <th>q37_b_part_7</th>\n",
       "      <th>q37_b_other</th>\n",
       "      <th>q39_part_1</th>\n",
       "      <th>q39_part_2</th>\n",
       "      <th>q39_part_3</th>\n",
       "      <th>q39_part_4</th>\n",
       "      <th>q39_part_5</th>\n",
       "      <th>q39_part_6</th>\n",
       "      <th>q39_part_7</th>\n",
       "      <th>q39_part_8</th>\n",
       "      <th>q39_part_9</th>\n",
       "      <th>q39_other</th>\n",
       "      <th>q40_part_1</th>\n",
       "      <th>q40_part_2</th>\n",
       "      <th>q40_part_3</th>\n",
       "      <th>q40_part_4</th>\n",
       "      <th>q40_part_5</th>\n",
       "      <th>q40_part_6</th>\n",
       "      <th>q40_part_7</th>\n",
       "      <th>q40_part_8</th>\n",
       "      <th>q40_part_9</th>\n",
       "      <th>q40_part_10</th>\n",
       "      <th>q40_part_11</th>\n",
       "      <th>q40_other</th>\n",
       "      <th>q41_tool_da_principal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23607</th>\n",
       "      <td>23607</td>\n",
       "      <td>25-29</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>Doctoral degree</td>\n",
       "      <td>Student</td>\n",
       "      <td>3-5 years</td>\n",
       "      <td>Python, C++, MATLAB</td>\n",
       "      <td>Python</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Amazon Web Services (AWS)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Google Cloud Platform (GCP)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Amazon SageMaker</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Google Cloud Vertex AI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MySQL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SQLite</td>\n",
       "      <td>Oracle Database</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Google Cloud BigQuery</td>\n",
       "      <td>Google Cloud SQL</td>\n",
       "      <td>Google Cloud Firestore</td>\n",
       "      <td>Google Cloud BigTable</td>\n",
       "      <td>Google Cloud Spanner</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Microsoft Power BI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Google Data Studio</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tableau</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Automated data augmentation (e.g. imgaug, albu...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Automated model selection (e.g. auto-sklearn, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Automated hyperparameter tuning (e.g. hyperopt...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Google Cloud AutoML</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Amazon Sagemaker Autopilot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>University Courses (resulting in a university ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Basic statistical software (Microsoft Excel, G...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       index q1_edad q2_genero   q3_pais q4_nivel_educativo q5_puesto_actual  \\\n",
       "23607  23607   25-29     Woman  Malaysia    Doctoral degree          Student   \n",
       "\n",
       "      q6_años_programando q7_lenguaje_mas_usado q8_lenguaje_recomend  \\\n",
       "23607           3-5 years   Python, C++, MATLAB               Python   \n",
       "\n",
       "      q20_sector_empresa q21_num_empleados q22_perso_resp_equip_da  \\\n",
       "23607                NaN               NaN                     NaN   \n",
       "\n",
       "      q23_incor_metod_nego_ml q25_salario_anual q27_a_part_1 q27_a_part_2  \\\n",
       "23607                     NaN               NaN          NaN          NaN   \n",
       "\n",
       "      q27_a_part_3 q27_a_part_4 q27_a_part_5 q27_a_part_6 q27_a_part_7  \\\n",
       "23607          NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "      q27_a_part_8 q27_a_part_9 q27_a_part_10 q27_a_part_11 q27_a_other  \\\n",
       "23607          NaN          NaN           NaN           NaN         NaN   \n",
       "\n",
       "                      q27_b_part_1 q27_b_part_2  \\\n",
       "23607   Amazon Web Services (AWS)           NaN   \n",
       "\n",
       "                        q27_b_part_3 q27_b_part_4 q27_b_part_5 q27_b_part_6  \\\n",
       "23607   Google Cloud Platform (GCP)           NaN          NaN          NaN   \n",
       "\n",
       "      q27_b_part_7 q27_b_part_8 q27_b_part_9 q27_b_part_10 q27_b_part_11  \\\n",
       "23607          NaN          NaN          NaN           NaN           NaN   \n",
       "\n",
       "      q27_b_other q31_a_part_1 q31_a_part_2 q31_a_part_3 q31_a_part_4  \\\n",
       "23607         NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "      q31_a_part_5 q31_a_part_6 q31_a_part_7 q31_a_part_8 q31_a_part_9  \\\n",
       "23607          NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "      q31_a_other        q31_b_part_1 q31_b_part_2             q31_b_part_3  \\\n",
       "23607         NaN   Amazon SageMaker           NaN   Google Cloud Vertex AI   \n",
       "\n",
       "      q31_b_part_4 q31_b_part_5 q31_b_part_6 q31_b_part_7 q31_b_part_8  \\\n",
       "23607          NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "      q31_b_part_9 q31_b_other  q32 q32_b_part_1 q32_b_part_2 q32_b_part_3  \\\n",
       "23607          NaN         NaN  NaN       MySQL           NaN      SQLite    \n",
       "\n",
       "           q32_b_part_4 q32_b_part_5 q32_b_part_6 q32_b_part_7 q32_b_part_8  \\\n",
       "23607  Oracle Database           NaN          NaN          NaN          NaN   \n",
       "\n",
       "      q32_b_part_9 q32_b_part_10 q32_b_part_11 q32_b_part_12 q32_b_part_13  \\\n",
       "23607          NaN           NaN           NaN           NaN           NaN   \n",
       "\n",
       "      q32_b_part_14           q32_b_part_15      q32_b_part_16  \\\n",
       "23607           NaN  Google Cloud BigQuery   Google Cloud SQL    \n",
       "\n",
       "                 q32_b_part_17           q32_b_part_18          q32_b_part_19  \\\n",
       "23607  Google Cloud Firestore   Google Cloud BigTable   Google Cloud Spanner    \n",
       "\n",
       "      q32_b_part_20 q32_b_other  q34        q34_b_part_1 q34_b_part_2  \\\n",
       "23607           NaN         NaN  NaN  Microsoft Power BI          NaN   \n",
       "\n",
       "             q34_b_part_3 q34_b_part_4 q34_b_part_5 q34_b_part_6 q34_b_part_7  \\\n",
       "23607  Google Data Studio          NaN      Tableau          NaN          NaN   \n",
       "\n",
       "      q34_b_part_8 q34_b_part_9 q34_b_part_10 q34_b_part_11 q34_b_part_12  \\\n",
       "23607          NaN          NaN           NaN           NaN           NaN   \n",
       "\n",
       "      q34_b_part_13 q34_b_part_14 q34_b_part_15 q34_b_part_16 q34_b_other  \\\n",
       "23607           NaN           NaN           NaN           NaN         NaN   \n",
       "\n",
       "      q36_a_part_1 q36_a_part_2 q36_a_part_3 q36_a_part_4 q36_a_part_5  \\\n",
       "23607          NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "      q36_a_part_6 q36_a_part_7 q36_a_other  \\\n",
       "23607          NaN          NaN         NaN   \n",
       "\n",
       "                                            q36_b_part_1 q36_b_part_2  \\\n",
       "23607  Automated data augmentation (e.g. imgaug, albu...          NaN   \n",
       "\n",
       "                                            q36_b_part_3 q36_b_part_4  \\\n",
       "23607  Automated model selection (e.g. auto-sklearn, ...          NaN   \n",
       "\n",
       "                                            q36_b_part_5 q36_b_part_6  \\\n",
       "23607  Automated hyperparameter tuning (e.g. hyperopt...          NaN   \n",
       "\n",
       "      q36_b_part_7 q36_b_other q37_a_part_1 q37_a_part_2 q37_a_part_3  \\\n",
       "23607          NaN         NaN          NaN          NaN          NaN   \n",
       "\n",
       "      q37_a_part_4 q37_a_part_5 q37_a_part_6 q37_a_part_7 q37_a_other  \\\n",
       "23607          NaN          NaN          NaN          NaN         NaN   \n",
       "\n",
       "                q37_b_part_1 q37_b_part_2 q37_b_part_3 q37_b_part_4  \\\n",
       "23607   Google Cloud AutoML           NaN          NaN          NaN   \n",
       "\n",
       "                        q37_b_part_5 q37_b_part_6 q37_b_part_7 q37_b_other  \\\n",
       "23607    Amazon Sagemaker Autopilot           NaN          NaN         NaN   \n",
       "\n",
       "      q39_part_1 q39_part_2 q39_part_3 q39_part_4 q39_part_5 q39_part_6  \\\n",
       "23607        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "      q39_part_7 q39_part_8 q39_part_9 q39_other q40_part_1 q40_part_2  \\\n",
       "23607        NaN        NaN        NaN       NaN        NaN        NaN   \n",
       "\n",
       "      q40_part_3 q40_part_4 q40_part_5 q40_part_6 q40_part_7 q40_part_8  \\\n",
       "23607        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "      q40_part_9                                        q40_part_10  \\\n",
       "23607        NaN  University Courses (resulting in a university ...   \n",
       "\n",
       "      q40_part_11 q40_other                              q41_tool_da_principal  \n",
       "23607         NaN       NaN  Basic statistical software (Microsoft Excel, G...  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Procesando los nulos..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.replace(np.nan, \"Unknow\", inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>q41_tool_da_principal</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>153 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       missing  total  percent\n",
       "index                        0  25973      0.0\n",
       "q36_a_other                  0  25973      0.0\n",
       "q36_a_part_1                 0  25973      0.0\n",
       "q36_a_part_2                 0  25973      0.0\n",
       "q36_a_part_3                 0  25973      0.0\n",
       "...                        ...    ...      ...\n",
       "q31_b_part_5                 0  25973      0.0\n",
       "q31_b_part_6                 0  25973      0.0\n",
       "q31_b_part_7                 0  25973      0.0\n",
       "q31_b_part_8                 0  25973      0.0\n",
       "q41_tool_da_principal        0  25973      0.0\n",
       "\n",
       "[153 rows x 3 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.stb.missing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HISTORIA DE USUARIA 3\n",
    "### Análisis exploratorio de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def info (df, nombre_archivo):\n",
    "    try:\n",
    "        ejem=df.head(5)\n",
    "        tail=df.tail(5)\n",
    "        shap=pd.DataFrame(df.shape)\n",
    "        describ=df.describe(include=\"O\").T\n",
    "        tipes=df.dtypes\n",
    "        nulos=df.stb.missing()\n",
    "        with pd.ExcelWriter(f\"datos/{nombre_archivo}\") as writer:\n",
    "            ejem.to_excel(writer, sheet_name=\"Head\")\n",
    "            tail.to_excel(writer, sheet_name=\"Tail\")\n",
    "            shap.to_excel(writer, sheet_name=\"Shape\")\n",
    "            describ.to_excel(writer, sheet_name=\"Describe\")\n",
    "            tipes.to_excel(writer, sheet_name=\"Types\")\n",
    "            nulos.to_excel(writer, sheet_name=\"Nulos\")       \n",
    "        return \"Se ha creado tu archivo con los estadisticos de tu DataFrame\"\n",
    "    except:\n",
    "        print(\"No podemos realizar tu petición. Verifica el nombre de tu DataFrame o el nombre de tu archivo y si pusiste la extención\")\n",
    "        return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Se ha creado tu archivo con los estadisticos de tu DataFrame'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info(df,\"prueba.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creamos una función que itere por bloques (preguntas) y muestre los estadisticos de dichos bloques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def info_columnas(df,columnas, nombre_archivo):\n",
    "\n",
    "    for i in columnas:\n",
    "      descrip=df[i].describe()\n",
    "      unicos=pd.DataFrame(df[i].unique())\n",
    "      valores=df.value_counts([i])\n",
    "      frecuencia=df.stb.freq([i])\n",
    "      nulos=df.stb.missing([i])\n",
    "  \n",
    "    with pd.ExcelWriter(f\"datos/{nombre_archivo}\") as writer:  \n",
    "      descrip.to_excel(writer, sheet_name=\"Descripcion\")\n",
    "      unicos.to_excel(writer, sheet_name=\"Unicos\")\n",
    "      valores.to_excel(writer, sheet_name=\"Value counts\")\n",
    "      frecuencia.to_excel(writer, sheet_name=\"Frecuencia\")\n",
    "      nulos.to_excel(writer, sheet_name=\"Nulos\")\n",
    "    return \"Se ha creado tu archivo con los estadisticos de tus columnas seleccionadas\"\n",
    "      \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creamos todos los grupos\n",
    "grupo_bloque1=df.groupby(['q1_edad','q2_genero','q3_pais','q4_nivel_educativo','q5_puesto_actual',\n",
    " 'q6_años_programando','q40_part_1','q40_part_2','q40_part_3','q40_part_4',\n",
    " 'q40_part_5','q40_part_6','q40_part_7','q40_part_8','q40_part_9','q40_part_10',\n",
    " 'q40_part_11','q40_other']) #Es una lista con los nombres\n",
    "\n",
    "grupo_bloque2=['q20_sector_empresa','q21_num_empleados','q22_perso_resp_equip_da','q25_salario_anual']\n",
    "\n",
    "grupo_bloque3_tools=['q39_part_1','q39_part_2','q39_part_3','q39_part_4','q39_part_5',\n",
    "                     'q39_part_6','q39_part_7','q39_part_8','q39_part_9','q39_other','q40_part_1',\n",
    "                     'q40_part_2','q40_part_3','q40_part_4','q40_part_5','q40_part_6',\n",
    "                     'q40_part_7','q40_part_8','q40_part_9','q40_part_10','q40_part_11','q40_other']\n",
    "\n",
    "grupo_programa=['q8_lenguaje_recomend','q7_lenguaje_mas_usado']\n",
    "\n",
    "grupo_nube=['q27_a_part_1','q27_a_part_2','q27_a_part_3','q27_a_part_4','q27_a_part_5','q27_a_part_6','q27_a_part_7',\n",
    "            'q27_a_part_8','q27_a_part_9','q27_a_part_10','q27_a_part_11','q27_a_other','q27_b_part_1','q27_b_part_2',\n",
    "            'q27_b_part_3','q27_b_part_4','q27_b_part_5','q27_b_part_6','q27_b_part_7','q27_b_part_8','q27_b_part_9',\n",
    "            'q27_b_part_10','q27_b_part_11','q27_b_other']\n",
    "\n",
    "grupo_bigdata=['q32','q32_b_part_1','q32_b_part_2','q32_b_part_3','q32_b_part_4','q32_b_part_5','q32_b_part_6',\n",
    "               'q32_b_part_7','q32_b_part_8','q32_b_part_9','q32_b_part_10','q32_b_part_11','q32_b_part_12',\n",
    "               'q32_b_part_13','q32_b_part_14','q32_b_part_15','q32_b_part_16','q32_b_part_17','q32_b_part_18',\n",
    "               'q32_b_part_19','q32_b_part_20','q32_b_other']\n",
    "\n",
    "grupo_admin_ml=['q31_a_part_1','q31_a_part_2','q31_a_part_3','q31_a_part_4','q31_a_part_5','q31_a_part_6',\n",
    "                'q31_a_part_7','q31_a_part_8','q31_a_part_9','q31_a_other','q31_b_part_1','q31_b_part_2',\n",
    "                'q31_b_part_3','q31_b_part_4','q31_b_part_5','q31_b_part_6','q31_b_part_7','q31_b_part_8',\n",
    "                'q31_b_part_9','q31_b_other']\n",
    "\n",
    "grupo_tool_comer_ml=['q34','q34_b_part_1','q34_b_part_2','q34_b_part_3','q34_b_part_4','q34_b_part_5','q34_b_part_6',\n",
    "                      'q34_b_part_7','q34_b_part_8','q34_b_part_9','q34_b_part_10','q34_b_part_11','q34_b_part_12',\n",
    "                      'q34_b_part_13','q34_b_part_14','q34_b_part_15','q34_b_part_16','q34_b_other']\n",
    "\n",
    "grupo_tool_catego_ml=['q36_a_part_1','q36_a_part_2','q36_a_part_3','q36_a_part_4','q36_a_part_5',\n",
    "                       'q36_a_part_6','q36_a_part_7','q36_a_other','q36_b_part_1','q36_b_part_2',\n",
    "                       'q36_b_part_3','q36_b_part_4','q36_b_part_5','q36_b_part_6','q36_b_part_7','q36_b_other']\n",
    "\n",
    "grupo_tool_ml=['q37_a_part_1','q37_a_part_2','q37_a_part_3','q37_a_part_4','q37_a_part_5','q37_a_part_6',\n",
    "                'q37_a_part_7','q37_a_other','q37_b_part_1','q37_b_part_2','q37_b_part_3','q37_b_part_4',\n",
    "                'q37_b_part_5','q37_b_part_6','q37_b_part_7','q37_b_other']\n",
    "\n",
    "valores=['index','q2_genero']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f5e55590070>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grupo_bloque1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Se ha creado tu archivo con los estadisticos de tus columnas seleccionadas'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info_columnas(df,grupo_bloque2,\"prueba_columna.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "categoricas = df.select_dtypes(include = \"object\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HISTORIA DE USUARIA 4\n",
    "### Obtencion de gráficos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hacemos un EDA por bloques que vamos a analizar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bloque 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>missing</th>\n",
       "      <th>total</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>q1_edad</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q2_genero</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q40_part_11</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q40_part_10</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q40_part_9</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q40_part_8</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q40_part_7</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q40_part_6</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q40_part_5</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q40_part_4</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q40_part_3</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q40_part_2</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q40_part_1</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q6_años_programando</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q5_puesto_actual</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q4_nivel_educativo</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q3_pais</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q40_other</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     missing  total  percent\n",
       "q1_edad                    0  25973      0.0\n",
       "q2_genero                  0  25973      0.0\n",
       "q40_part_11                0  25973      0.0\n",
       "q40_part_10                0  25973      0.0\n",
       "q40_part_9                 0  25973      0.0\n",
       "q40_part_8                 0  25973      0.0\n",
       "q40_part_7                 0  25973      0.0\n",
       "q40_part_6                 0  25973      0.0\n",
       "q40_part_5                 0  25973      0.0\n",
       "q40_part_4                 0  25973      0.0\n",
       "q40_part_3                 0  25973      0.0\n",
       "q40_part_2                 0  25973      0.0\n",
       "q40_part_1                 0  25973      0.0\n",
       "q6_años_programando        0  25973      0.0\n",
       "q5_puesto_actual           0  25973      0.0\n",
       "q4_nivel_educativo         0  25973      0.0\n",
       "q3_pais                    0  25973      0.0\n",
       "q40_other                  0  25973      0.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bloque1=df[['q1_edad','q2_genero','q3_pais','q4_nivel_educativo','q5_puesto_actual',\n",
    " 'q6_años_programando','q40_part_1','q40_part_2','q40_part_3','q40_part_4',\n",
    " 'q40_part_5','q40_part_6','q40_part_7','q40_part_8','q40_part_9','q40_part_10',\n",
    " 'q40_part_11','q40_other',]]\n",
    "\n",
    "bloque1.stb.missing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x = df[\"q1_edad\"],ec= 'black', color='pink')\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(8,4))\n",
    "fig = sns.countplot(x='q2_genero', data = bloque1)\n",
    "fig.set(xlabel='Genero', ylabel= 'Cantidad de personas' )\n",
    "fig.set_title('Histograma de variable categórica')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(8,4))\n",
    "fig = sns.countplot(x='q25_salario_anual', data = df, hue = 'q2_genero')\n",
    "fig.set(xlabel='Salario', ylabel= 'Total Anual' )\n",
    "fig.set_title('Histograma de variable categórica')\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bloque 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>missing</th>\n",
       "      <th>total</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>q20_sector_empresa</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q21_num_empleados</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q22_perso_resp_equip_da</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q25_salario_anual</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         missing  total  percent\n",
       "q20_sector_empresa             0  25973      0.0\n",
       "q21_num_empleados              0  25973      0.0\n",
       "q22_perso_resp_equip_da        0  25973      0.0\n",
       "q25_salario_anual              0  25973      0.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bloque2=df[['q20_sector_empresa','q21_num_empleados','q22_perso_resp_equip_da','q25_salario_anual']]\n",
    "\n",
    "bloque2.stb.missing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 4, figsize=(20,6))\n",
    "\n",
    "for ax in fig.axes:\n",
    "    ax.tick_params(labelrotation=90)\n",
    "\n",
    "for i in range(len(bloque2.columns)):\n",
    "    sns.countplot(x = bloque2.columns[i], data = df, ax=axes[i]); \n",
    "plt.show(); "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Bloque 3 Herramientas DA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "q39_compart_codig=df[['q39_part_1','q39_part_2','q39_part_3','q39_part_4','q39_part_5',\n",
    "                     'q39_part_6','q39_part_7','q39_part_8','q39_part_9','q39_other']]\n",
    "\n",
    "q40_plataform_cursos=df[['q40_part_1','q40_part_2','q40_part_3','q40_part_4','q40_part_5','q40_part_6',\n",
    "                         'q40_part_7','q40_part_8','q40_part_9','q40_part_10','q40_part_11','q40_other']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bloque 3 actualidad - Futuro"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Programacion (q7, q8 (recomendación))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>missing</th>\n",
       "      <th>total</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>q8_lenguaje_recomend</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q7_lenguaje_mas_usado</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       missing  total  percent\n",
       "q8_lenguaje_recomend         0  25973      0.0\n",
       "q7_lenguaje_mas_usado        0  25973      0.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_prog= df[['q8_lenguaje_recomend','q7_lenguaje_mas_usado']]\n",
    "\n",
    "df_prog.stb.missing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x = df[\"q8_lenguaje_recomend\"],ec= 'black', color='red')\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(20,6))\n",
    "\n",
    "for ax in fig.axes:\n",
    "    ax.tick_params(labelrotation=90)\n",
    "\n",
    "for i in range(len(df_prog.columns)):\n",
    "    sns.countplot(x = df_prog.columns[i], data = df, ax=axes[i]); \n",
    "plt.show(); "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nube (Q27-A, Q27-B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>missing</th>\n",
       "      <th>total</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>q27_a_part_1</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q27_a_part_2</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q27_b_part_11</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q27_b_part_10</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q27_b_part_9</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q27_b_part_8</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q27_b_part_7</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q27_b_part_6</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q27_b_part_5</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q27_b_part_4</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q27_b_part_3</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q27_b_part_2</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q27_b_part_1</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q27_a_other</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q27_a_part_11</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q27_a_part_10</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q27_a_part_9</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q27_a_part_8</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q27_a_part_7</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q27_a_part_6</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q27_a_part_5</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q27_a_part_4</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q27_a_part_3</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q27_b_other</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               missing  total  percent\n",
       "q27_a_part_1         0  25973      0.0\n",
       "q27_a_part_2         0  25973      0.0\n",
       "q27_b_part_11        0  25973      0.0\n",
       "q27_b_part_10        0  25973      0.0\n",
       "q27_b_part_9         0  25973      0.0\n",
       "q27_b_part_8         0  25973      0.0\n",
       "q27_b_part_7         0  25973      0.0\n",
       "q27_b_part_6         0  25973      0.0\n",
       "q27_b_part_5         0  25973      0.0\n",
       "q27_b_part_4         0  25973      0.0\n",
       "q27_b_part_3         0  25973      0.0\n",
       "q27_b_part_2         0  25973      0.0\n",
       "q27_b_part_1         0  25973      0.0\n",
       "q27_a_other          0  25973      0.0\n",
       "q27_a_part_11        0  25973      0.0\n",
       "q27_a_part_10        0  25973      0.0\n",
       "q27_a_part_9         0  25973      0.0\n",
       "q27_a_part_8         0  25973      0.0\n",
       "q27_a_part_7         0  25973      0.0\n",
       "q27_a_part_6         0  25973      0.0\n",
       "q27_a_part_5         0  25973      0.0\n",
       "q27_a_part_4         0  25973      0.0\n",
       "q27_a_part_3         0  25973      0.0\n",
       "q27_b_other          0  25973      0.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nube=df[['q27_a_part_1','q27_a_part_2','q27_a_part_3','q27_a_part_4','q27_a_part_5','q27_a_part_6','q27_a_part_7',\n",
    "            'q27_a_part_8','q27_a_part_9','q27_a_part_10','q27_a_part_11','q27_a_other','q27_b_part_1','q27_b_part_2',\n",
    "            'q27_b_part_3','q27_b_part_4','q27_b_part_5','q27_b_part_6','q27_b_part_7','q27_b_part_8','q27_b_part_9',\n",
    "            'q27_b_part_10','q27_b_part_11','q27_b_other']]\n",
    "\n",
    "df_nube.stb.missing()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "BIG DATA (Q32-A, Q32-B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>missing</th>\n",
       "      <th>total</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "    <tr>\n",
       "      <th>q32_b_part_1</th>\n",
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       "    <tr>\n",
       "      <th>q32_b_part_20</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q32_b_part_19</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
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       "    <tr>\n",
       "      <th>q32_b_part_18</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
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       "    <tr>\n",
       "      <th>q32_b_part_17</th>\n",
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       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q32_b_part_16</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q32_b_part_15</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q32_b_part_14</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q32_b_part_13</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q32_b_part_12</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q32_b_part_11</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q32_b_part_10</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q32_b_part_9</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q32_b_part_8</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q32_b_part_7</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q32_b_part_6</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q32_b_part_5</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q32_b_part_4</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q32_b_part_3</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q32_b_part_2</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q32_b_other</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               missing  total  percent\n",
       "q32                  0  25973      0.0\n",
       "q32_b_part_1         0  25973      0.0\n",
       "q32_b_part_20        0  25973      0.0\n",
       "q32_b_part_19        0  25973      0.0\n",
       "q32_b_part_18        0  25973      0.0\n",
       "q32_b_part_17        0  25973      0.0\n",
       "q32_b_part_16        0  25973      0.0\n",
       "q32_b_part_15        0  25973      0.0\n",
       "q32_b_part_14        0  25973      0.0\n",
       "q32_b_part_13        0  25973      0.0\n",
       "q32_b_part_12        0  25973      0.0\n",
       "q32_b_part_11        0  25973      0.0\n",
       "q32_b_part_10        0  25973      0.0\n",
       "q32_b_part_9         0  25973      0.0\n",
       "q32_b_part_8         0  25973      0.0\n",
       "q32_b_part_7         0  25973      0.0\n",
       "q32_b_part_6         0  25973      0.0\n",
       "q32_b_part_5         0  25973      0.0\n",
       "q32_b_part_4         0  25973      0.0\n",
       "q32_b_part_3         0  25973      0.0\n",
       "q32_b_part_2         0  25973      0.0\n",
       "q32_b_other          0  25973      0.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bigdata=df[['q32','q32_b_part_1','q32_b_part_2','q32_b_part_3','q32_b_part_4','q32_b_part_5','q32_b_part_6',\n",
    "               'q32_b_part_7','q32_b_part_8','q32_b_part_9','q32_b_part_10','q32_b_part_11','q32_b_part_12',\n",
    "               'q32_b_part_13','q32_b_part_14','q32_b_part_15','q32_b_part_16','q32_b_part_17','q32_b_part_18',\n",
    "               'q32_b_part_19','q32_b_part_20','q32_b_other']]\n",
    "\n",
    "df_bigdata.stb.missing()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Machine learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Administración Machine Learning (Q31-A, Q31-B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>missing</th>\n",
       "      <th>total</th>\n",
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       "  <tbody>\n",
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       "      <th>q31_a_part_1</th>\n",
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       "      <td>25973</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>25973</td>\n",
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       "    <tr>\n",
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       "      <td>25973</td>\n",
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       "    <tr>\n",
       "      <th>q31_b_part_6</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>q31_b_part_5</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>q31_b_part_4</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>q31_b_part_3</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q31_b_part_2</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q31_b_part_1</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>q31_a_other</th>\n",
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       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>q31_a_part_9</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>q31_a_part_8</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q31_a_part_7</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q31_a_part_6</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q31_a_part_5</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q31_a_part_4</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q31_a_part_3</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q31_b_other</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              missing  total  percent\n",
       "q31_a_part_1        0  25973      0.0\n",
       "q31_a_part_2        0  25973      0.0\n",
       "q31_b_part_9        0  25973      0.0\n",
       "q31_b_part_8        0  25973      0.0\n",
       "q31_b_part_7        0  25973      0.0\n",
       "q31_b_part_6        0  25973      0.0\n",
       "q31_b_part_5        0  25973      0.0\n",
       "q31_b_part_4        0  25973      0.0\n",
       "q31_b_part_3        0  25973      0.0\n",
       "q31_b_part_2        0  25973      0.0\n",
       "q31_b_part_1        0  25973      0.0\n",
       "q31_a_other         0  25973      0.0\n",
       "q31_a_part_9        0  25973      0.0\n",
       "q31_a_part_8        0  25973      0.0\n",
       "q31_a_part_7        0  25973      0.0\n",
       "q31_a_part_6        0  25973      0.0\n",
       "q31_a_part_5        0  25973      0.0\n",
       "q31_a_part_4        0  25973      0.0\n",
       "q31_a_part_3        0  25973      0.0\n",
       "q31_b_other         0  25973      0.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_admin_ml=df[['q31_a_part_1','q31_a_part_2','q31_a_part_3','q31_a_part_4','q31_a_part_5','q31_a_part_6',\n",
    "                'q31_a_part_7','q31_a_part_8','q31_a_part_9','q31_a_other','q31_b_part_1','q31_b_part_2',\n",
    "                'q31_b_part_3','q31_b_part_4','q31_b_part_5','q31_b_part_6','q31_b_part_7','q31_b_part_8',\n",
    "                'q31_b_part_9','q31_b_other']]\n",
    "\n",
    "df_admin_ml.stb.missing()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Herramientas inteligencia comercial ML (Q34-A, Q34-B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>missing</th>\n",
       "      <th>total</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>q34</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q34_b_part_1</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q34_b_part_16</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q34_b_part_15</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q34_b_part_14</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q34_b_part_13</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q34_b_part_12</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q34_b_part_11</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q34_b_part_10</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q34_b_part_9</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q34_b_part_8</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q34_b_part_7</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q34_b_part_6</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q34_b_part_5</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q34_b_part_4</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q34_b_part_3</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q34_b_part_2</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q34_b_other</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               missing  total  percent\n",
       "q34                  0  25973      0.0\n",
       "q34_b_part_1         0  25973      0.0\n",
       "q34_b_part_16        0  25973      0.0\n",
       "q34_b_part_15        0  25973      0.0\n",
       "q34_b_part_14        0  25973      0.0\n",
       "q34_b_part_13        0  25973      0.0\n",
       "q34_b_part_12        0  25973      0.0\n",
       "q34_b_part_11        0  25973      0.0\n",
       "q34_b_part_10        0  25973      0.0\n",
       "q34_b_part_9         0  25973      0.0\n",
       "q34_b_part_8         0  25973      0.0\n",
       "q34_b_part_7         0  25973      0.0\n",
       "q34_b_part_6         0  25973      0.0\n",
       "q34_b_part_5         0  25973      0.0\n",
       "q34_b_part_4         0  25973      0.0\n",
       "q34_b_part_3         0  25973      0.0\n",
       "q34_b_part_2         0  25973      0.0\n",
       "q34_b_other          0  25973      0.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tools_ml_comercial=df[['q34','q34_b_part_1','q34_b_part_2','q34_b_part_3','q34_b_part_4','q34_b_part_5','q34_b_part_6',\n",
    "                      'q34_b_part_7','q34_b_part_8','q34_b_part_9','q34_b_part_10','q34_b_part_11','q34_b_part_12',\n",
    "                      'q34_b_part_13','q34_b_part_14','q34_b_part_15','q34_b_part_16','q34_b_other']]\n",
    "\n",
    "tools_ml_comercial.stb.missing()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Herramientas Machine Learning categorias (Q36-A, Q36-B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>missing</th>\n",
       "      <th>total</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>q36_a_part_1</th>\n",
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       "    <tr>\n",
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       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q36_a_part_3</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q36_a_part_4</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q36_a_part_5</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q36_a_part_6</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q36_a_part_7</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q36_a_other</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q36_b_part_1</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q36_b_part_2</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q36_b_part_3</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q36_b_part_4</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q36_b_part_5</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q36_b_part_6</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q36_b_part_7</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q36_b_other</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              missing  total  percent\n",
       "q36_a_part_1        0  25973      0.0\n",
       "q36_a_part_2        0  25973      0.0\n",
       "q36_a_part_3        0  25973      0.0\n",
       "q36_a_part_4        0  25973      0.0\n",
       "q36_a_part_5        0  25973      0.0\n",
       "q36_a_part_6        0  25973      0.0\n",
       "q36_a_part_7        0  25973      0.0\n",
       "q36_a_other         0  25973      0.0\n",
       "q36_b_part_1        0  25973      0.0\n",
       "q36_b_part_2        0  25973      0.0\n",
       "q36_b_part_3        0  25973      0.0\n",
       "q36_b_part_4        0  25973      0.0\n",
       "q36_b_part_5        0  25973      0.0\n",
       "q36_b_part_6        0  25973      0.0\n",
       "q36_b_part_7        0  25973      0.0\n",
       "q36_b_other         0  25973      0.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tools_catego_ml=df[['q36_a_part_1','q36_a_part_2','q36_a_part_3','q36_a_part_4','q36_a_part_5',\n",
    "                       'q36_a_part_6','q36_a_part_7','q36_a_other','q36_b_part_1','q36_b_part_2',\n",
    "                       'q36_b_part_3','q36_b_part_4','q36_b_part_5','q36_b_part_6','q36_b_part_7','q36_b_other']]\n",
    "\n",
    "df_tools_catego_ml.stb.missing()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Herramientas Machine Learning (Q37-A, Q37-B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>missing</th>\n",
       "      <th>total</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>q37_a_part_2</th>\n",
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       "    <tr>\n",
       "      <th>q37_a_part_3</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q37_a_part_4</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q37_a_part_5</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>q37_a_part_6</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q37_a_part_7</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q37_a_other</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q37_b_part_1</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q37_b_part_2</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q37_b_part_3</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q37_b_part_4</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q37_b_part_5</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q37_b_part_6</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q37_b_part_7</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q37_b_other</th>\n",
       "      <td>0</td>\n",
       "      <td>25973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              missing  total  percent\n",
       "q37_a_part_1        0  25973      0.0\n",
       "q37_a_part_2        0  25973      0.0\n",
       "q37_a_part_3        0  25973      0.0\n",
       "q37_a_part_4        0  25973      0.0\n",
       "q37_a_part_5        0  25973      0.0\n",
       "q37_a_part_6        0  25973      0.0\n",
       "q37_a_part_7        0  25973      0.0\n",
       "q37_a_other         0  25973      0.0\n",
       "q37_b_part_1        0  25973      0.0\n",
       "q37_b_part_2        0  25973      0.0\n",
       "q37_b_part_3        0  25973      0.0\n",
       "q37_b_part_4        0  25973      0.0\n",
       "q37_b_part_5        0  25973      0.0\n",
       "q37_b_part_6        0  25973      0.0\n",
       "q37_b_part_7        0  25973      0.0\n",
       "q37_b_other         0  25973      0.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tools_ml=df[['q37_a_part_1','q37_a_part_2','q37_a_part_3','q37_a_part_4','q37_a_part_5','q37_a_part_6',\n",
    "                'q37_a_part_7','q37_a_other','q37_b_part_1','q37_b_part_2','q37_b_part_3','q37_b_part_4',\n",
    "                'q37_b_part_5','q37_b_part_6','q37_b_part_7','q37_b_other']]\n",
    "\n",
    "df_tools_ml.stb.missing()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Uniendo columnas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hemos creado una función que nos permite unir las columnas que son respuestas a la misma pregunta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def unir_columnas(df,nombre_columna,lista_columnas): ## No funciona\n",
    "    df[nombre_columna]=df[[lista_columnas]].agg('_'.join, axis=1)\n",
    "    df[nombre_columna] = df[nombre_columna].str.replace('UNKKNOWN_', '')\n",
    "    df[nombre_columna] = df[nombre_columna].str.replace('_UNKNOWN', '')\n",
    "    df[nombre_columna] = df[nombre_columna].apply(lambda x: x.split('_'))\n",
    "    df=df.explode(nombre_columna)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "También hemos creado otra función que nos permite contabilizar las respuestas de las columnas sin necesidad de utilizar el explode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def contar_programas(dfA):\n",
    "    lista1 =[]\n",
    "    lista2 =[]\n",
    "    for i in range(len(dfA.columns)):\n",
    "        x = pd.DataFrame(dfA[dfA.columns[i]]).groupby(dfA.columns[i]).size().reset_index()\n",
    "        \n",
    "        lista1.append(x.iloc[0,0])\n",
    "        lista2.append(x.iloc[0,1])\n",
    "    return pd.DataFrame(list(zip(lista1, lista2)), columns = ['programa','usuarios'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GRÁFICAS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hemos explorado diferentes tipos de gráficas y evaluada cual sería la más adecuada para exponer la información extraída de los datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gráfica brecha de género"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "\n",
    "sns.countplot(data=df, x='q2_genero', palette='rocket')\n",
    "\n",
    "ax.set_title('Gráfico de barras de la cantidad de personas por género trabajando en DS', fontsize=15, pad=20)\n",
    "ax.set_ylabel('Género')\n",
    "ax.set_xlabel('Cantidad de trabajadores')\n",
    "#ax.set_xticklabels(df_sectores['index'], rotation = 80)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gráfica Data Analyst en el conjunto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "puestos = df.groupby('q5_puesto_actual').size().reset_index()\n",
    "puestos.reset_index()\n",
    "\n",
    "puestos['percent'] = [round(i*100/sum(puestos[0]),1) for i in puestos[0]]\n",
    "\n",
    "1:29\n",
    "plt.figure(figsize=(15,20))\n",
    "plt.bar(puestos['q5_puesto_actual'],puestos[0])\n",
    "plt.xticks(rotation = 90)\n",
    "plt.title('Distribución de puestos en el sector', color = 'black', fontsize = 20, fontweight = 'bold') # para poner el título\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gráfica Data Analyst en los diferentes sectores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_analitics = df[df['q5_puesto_actual'] == 'Data Analyst']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sectores = pd.DataFrame(df_analitics['q20_sector_empresa'].value_counts())\n",
    "df_sectores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sectores.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,15))\n",
    "\n",
    "sns.barplot(data=df_sectores, y='index', x='q20_sector_empresa', palette='rocket')\n",
    "\n",
    "ax.set_title('Gráfico de barras de la cantidad de personas trabajando por sector', fontsize=20, pad=20)\n",
    "ax.set_ylabel('Sector')\n",
    "ax.set_xlabel('Cantidad de trabajadores')\n",
    "#ax.set_xticklabels(df_sectores['index'], rotation = 80)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gráfica de los estudios de los DA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nivel_edu = df_analitics[['q4_nivel_educativo']]\n",
    "nivel_edu = pd.DataFrame(nivel_edu.value_counts())\n",
    "nivel_edu.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "\n",
    "sns.barplot(data=nivel_edu, x='q4_nivel_educativo', y=0, palette='rocket')\n",
    "\n",
    "ax.set_title('Gráfico de barras del nivel de estudios de las personas trabajando en DA', fontsize=15, pad=20)\n",
    "ax.set_xlabel('Nivel educativo')\n",
    "ax.set_ylabel('Cantidad de trabajadores')\n",
    "ax.set_xticklabels(['Masters degree', 'Bachelors degree', 'Some studies without dregree', 'Doctoral degree', 'Prefer not answer', 'High school', 'Professional Doctorate'], rotation = 80)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gráfica Experiencia programando de los DA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_exp = df_analitics[['q6_años_programando']]\n",
    "df_exp = pd.DataFrame(df_exp.value_counts())\n",
    "df_exp.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "\n",
    "sns.barplot(data=df_exp, x='q6_años_programando', y=0, palette='rocket')\n",
    "\n",
    "ax.set_title('Gráfico de barras de la experiencia programando de los DAs', fontsize=15, pad=20)\n",
    "ax.set_ylabel('Años programando')\n",
    "ax.set_xlabel('Cantidad de trabajadores')\n",
    "ax.set_xticklabels(df_exp['q6_años_programando'], rotation = 80)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gráfica de Herramientas diarias ML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gráfica de Herramientas ML que se buscan aprender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2= df[df['q5_puesto_actual'] == 'Data Analyst']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_prog= df2[['q8_lenguaje_recomend','q7_lenguaje_mas_usado']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ml=pd.DataFrame()\n",
    "df_ml_fut=pd.DataFrame()\n",
    "#unimos las columnas correspondiententes al presente de q37\n",
    "df_ml[\"presente\"] = pd.DataFrame(df2[['q37_a_part_1','q37_a_part_2','q37_a_part_3','q37_a_part_4','q37_a_part_5','q37_a_part_6',\\\n",
    "    'q37_a_part_7','q37_a_other']].agg('_'.join, axis=1))\n",
    "df_ml['presente'] = df_ml['presente'].apply(lambda x: x.split('_'))\n",
    "\n",
    "#hacemos lo mismo para el futuro o sea las b\n",
    "df_ml_fut[\"futuro\"] = pd.DataFrame(df2[['q37_b_part_1','q37_b_part_2','q37_b_part_3','q37_b_part_4','q37_b_part_5','q37_b_part_6',\\\n",
    "    'q37_b_part_7','q37_b_other']].agg('_'.join, axis=1))\n",
    "df_ml_fut['futuro'] = df_ml_fut['futuro'].apply(lambda x: x.split('_'))\n",
    "\n",
    "#aplicamos el método explode a ambos dataframes\n",
    "df_ml = df_ml.explode('presente')\n",
    "df_ml_fut = df_ml_fut.explode('futuro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prog_reco=pd.DataFrame()\n",
    "prog_reco[\"recomendado\"]=pd.DataFrame(df2[[\"q8_lenguaje_recomend\"]])\n",
    "prog_reco=prog_reco[prog_reco[\"recomendado\"]!=\"Unknow\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prog_usado=pd.DataFrame()\n",
    "prog_usado[\"lenguaje mas usado\"]=pd.DataFrame(df2[[\"q7_lenguaje_mas_usado\"]])\n",
    "prog_usado[\"lenguaje mas usado\"]=prog_usado[\"lenguaje mas usado\"].apply(lambda x: x.split(\",\"))\n",
    "prog_usado=prog_usado.explode(\"lenguaje mas usado\")\n",
    "prog_usado=prog_usado[prog_usado[\"lenguaje mas usado\"]!= \"Unknow\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Volvemos a filtrar para sacar los diferentes a Unknow\n",
    "ml_presente=pd.DataFrame(df_ml[df_ml[\"presente\"]!= \"Unknow\"])\n",
    "\n",
    "#Lo mismo para el otro dataframe\n",
    "ml_futuro=pd.DataFrame(df_ml_fut[df_ml_fut[\"futuro\"]!= \"Unknow\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "manuela=ml_presente.value_counts().reset_index()\n",
    "carmela=ml_futuro.value_counts().reset_index()\n",
    "mas_usado=prog_usado.value_counts().reset_index()\n",
    "mas_reco=prog_reco.value_counts().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lista = [\"forestgreen\", \"limegreen\", \"darkgreen\", \"green\", \"lime\", \"seagreen\", \"lightgreen\", \"darkseagreen\", \"mediumseagreen\", \"turquoise\"]\n",
    "colors1 = sns.color_palette('pastel')[0:5]\n",
    "colors2=sns.color_palette(\"flare\")[0:5]\n",
    "colors3=sns.color_palette(\"viridis\")\n",
    "colors4=sns.color_palette(\"Spectral\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "explode = ( 0 ,0.1,0.02,0.02,0.02,0.02,0.02,0.02) # para sacar los quesitos hacia fuera\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "plt.pie(manuela[0], \n",
    "       labels = manuela[\"presente\"], \n",
    "       colors = colors1, # para cambiar el color de la gráfica\n",
    "       autopct=\"%1.1f%%\",\n",
    "       explode = explode)  # para sacar los quesitos hacia fuera\n",
    "\n",
    "plt.title(\"Herramientas de Machine Learning en la actualidad\", color = \"black\", fontsize = 20, fontweight = \"bold\") # para poner el título\n",
    "plt.legend(bbox_to_anchor=(1.2, 1)) # sacar leyenda y donde colocarla. El primer índice (derecha-izquierda), segundo índice(arriba, abajo) )\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "explode = ( 0.1 ,0.02,0.02,0.02,0.02,0.02,0.02,0.02) # para sacar los quesitos hacia fuera\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "plt.pie(carmela[0], \n",
    "       labels = carmela[\"futuro\"], \n",
    "       colors = colors1,# para cambiar el color de la gráfica\n",
    "       autopct=\"%1.1f%%\",\n",
    "       explode = explode)  # para sacar los quesitos hacia fuera\n",
    "\n",
    "plt.title(\"Herramientas de Machine Learning en el futuro\", color = \"black\", fontsize = 20, fontweight = \"bold\") # para poner el título\n",
    "plt.legend(bbox_to_anchor=(1.2, 1)) # sacar leyenda y donde colocarla. El primer índice (derecha-izquierda), segundo índice(arriba, abajo) )\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "explode = ( 0.1 ,0.01,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02) # para sacar los quesitos hacia fuera\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "plt.pie(mas_usado[0], \n",
    "       labels = mas_usado[\"lenguaje mas usado\"], \n",
    "       colors = colors3, # para cambiar el color de la gráfica\n",
    "       autopct=\"%1.1f%%\",\n",
    "       explode = explode)  # para sacar los quesitos hacia fuera\n",
    "\n",
    "plt.title(\"Lenguajes más usados en la actualidad\", color = \"black\", fontsize = 20, fontweight = \"bold\") # para poner el título\n",
    "plt.legend(bbox_to_anchor=(1.2, 1)) # sacar leyenda y donde colocarla. El primer índice (derecha-izquierda), segundo índice(arriba, abajo) )\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "explode = ( 0.1 ,0.01,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02) # para sacar los quesitos hacia fuera\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "plt.pie(mas_reco[0], \n",
    "       labels = mas_reco[\"recomendado\"], \n",
    "       colors = colors3, # para cambiar el color de la gráfica\n",
    "       autopct=\"%1.1f%%\",\n",
    "       explode = explode)  # para sacar los quesitos hacia fuera\n",
    "\n",
    "plt.title(\"Lenguajes más recomendados\", color = \"black\", fontsize = 20, fontweight = \"bold\") # para poner el título\n",
    "plt.legend(bbox_to_anchor=(1.2, 1)) # sacar leyenda y donde colocarla. El primer índice (derecha-izquierda), segundo índice(arriba, abajo) )\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gráfica Categorías de ML más utilizadas en DA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ml_futuro = df_analitics[['q36_b_part_1','q36_b_part_2',\n",
    "                       'q36_b_part_3','q36_b_part_4','q36_b_part_5','q36_b_part_6','q36_b_part_7','q36_b_other']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ml_futuro_1 = contar_programas(df_ml_futuro)\n",
    "df_ml_futuro_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ml_futuro_1['programa'] = df_ml_futuro_1['programa'].map({'Automated data augmentation (e.g. imgaug, albumentations)': 'Automated data augmentation' ,\n",
    "       'Automated feature engineering/selection (e.g. tpot, boruta_py)':   'Automated feature engineering/selection' ,\n",
    "       'Automated model selection (e.g. auto-sklearn, xcessiv)': 'Automated model selection',\n",
    "       'Automated model architecture searches (e.g. darts, enas)': 'Automated model architecture searches',\n",
    "       'Automated hyperparameter tuning (e.g. hyperopt, ray.tune, Vizier)':'Automated hyperparameter tuning',\n",
    "       'Automation of full ML pipelines (e.g. Google Cloud AutoML, H2O Driverless AI)':'Automation of full ML pipelines',\n",
    "       'None': 'None', 'Other':'Other'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors_ml = sns.color_palette('pastel')[0:7]\n",
    "explode_ml = (0.01,0.01,0.2,0.2,0.1,0.01,0.1, 0.01) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.pie(df_ml_futuro_1['usuarios'],\n",
    "       labels = df_ml_futuro_1[\"programa\"],\n",
    "       colors = colors_ml, # para cambiar el color de la gráfica\n",
    "       autopct=\"%1.1f%%\",\n",
    "       explode = explode_ml)  # para sacar los quesitos hacia fuera\n",
    "plt.title(\"Categorías de ML que buscan aprender los DA\", color = \"black\", fontsize = 20, fontweight = \"bold\") # para poner el título\n",
    "plt.legend(bbox_to_anchor=(1.2, 1)) # sacar leyenda y donde colocarla. El primer índice (derecha-izquierda), segundo índice(arriba, abajo) )\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gráfica Categorías de ML que buscan aprender los DA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ml_catego = df_analitics[['q36_a_part_1','q36_a_part_2','q36_a_part_3','q36_a_part_4','q36_a_part_5',\n",
    "                       'q36_a_part_6','q36_a_part_7','q36_a_other']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ml_catego_1 = contar_programas(df_ml_catego)\n",
    "df_ml_catego_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ml_catego_1['programa'] = df_ml_catego_1['programa'].map({'Automated data augmentation (e.g. imgaug, albumentations)': 'Automated data augmentation' ,\n",
    "       'Automated feature engineering/selection (e.g. tpot, boruta_py)':   'Automated feature engineering/selection' ,\n",
    "       'Automated model selection (e.g. auto-sklearn, xcessiv)': 'Automated model selection',\n",
    "       'Automated model architecture searches (e.g. darts, enas)': 'Automated model architecture searches',\n",
    "       'Automated hyperparameter tuning (e.g. hyperopt, ray.tune, Vizier)':'Automated hyperparameter tuning',\n",
    "       'Automation of full ML pipelines (e.g. Google AutoML, H2O Driverless AI)':'Automation of full ML pipelines',\n",
    "       'No / None': 'No / None', 'Other':'Other'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors_ml = sns.color_palette('pastel')[0:7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "explode_ml = ( 0.1,0.02,0.1,0.02,0.02,0.02,0.1, 0.02) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.pie(df_ml_catego_1['usuarios'],\n",
    "       labels = df_ml_catego_1[\"programa\"],\n",
    "       colors = colors_ml, # para cambiar el color de la gráfica\n",
    "       autopct=\"%1.1f%%\",\n",
    "       explode = explode_ml)  # para sacar los quesitos hacia fuera\n",
    "plt.title(\"Categorías de ML utilizadas en el día a día de un DA\", color = \"black\", fontsize = 20, fontweight = \"bold\") # para poner el título\n",
    "plt.legend(bbox_to_anchor=(1.8, 1)) # sacar leyenda y donde colocarla. El primer índice (derecha-izquierda), segundo índice(arriba, abajo) )\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### Gráfica herramientas administrativas de ML de uso diario"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df[df['q5_puesto_actual'] == 'Data Analyst'].reset_index()\n",
    "df2\n",
    "\n",
    "\n",
    "df_admin_ml_a = df2[['q31_a_part_1','q31_a_part_2','q31_a_part_3','q31_a_part_4','q31_a_part_5','q31_a_part_6',\n",
    "                'q31_a_part_7','q31_a_part_8','q31_a_part_9','q31_a_other']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "admin_ml_a = contar_programas(df_admin_ml_a).sort_values('usuarios', ascending = False)\n",
    "admin_ml_a\n",
    "\n",
    "explode = ( 0 ,0.1,0.1,0.02,0.02,0.02,0.02,0.02, 0.02, 0.02) # para sacar los quesitos hacia fuera\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.pie(admin_ml_a['usuarios'],\n",
    "       labels = admin_ml_a['programa'],\n",
    "       colors = colors_ml # para cambiar el color de la gráfica\n",
    "       autopct='%1.1f%%',\n",
    "       explode = explode)  # para sacar los quesitos hacia fuera\n",
    "plt.title('Herramientas de administración de Machine Learning en la actualidad', color = 'black', fontsize = 20, fontweight = 'bold')\n",
    "plt.legend(bbox_to_anchor=(1.2, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### Gráfica herramientas administrativas de ML que se buscan aprender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_admin_ml_b= df2[['q31_b_part_1','q31_b_part_2',\n",
    "                'q31_b_part_3','q31_b_part_4','q31_b_part_5','q31_b_part_6','q31_b_part_7','q31_b_part_8',\n",
    "                'q31_b_part_9','q31_b_other']]\n",
    "\n",
    "admin_ml_b = contar_programas(df_admin_ml_b).sort_values('usuarios', ascending = False)\n",
    "admin_ml_b\n",
    "\n",
    "explode = ( 0.1 ,0.05,0.05,0.02,0.02,0.02,0.02,0.02, 0.02, 0.02) # para sacar los quesitos hacia fuera\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.pie(admin_ml_b['usuarios'],\n",
    "       labels = admin_ml_b['programa'],\n",
    "       colors = colors_ml, # para cambiar el color de la gráfica\n",
    "       autopct='%1.1f%%',\n",
    "       explode = explode)  # para sacar los quesitos hacia fuera\n",
    "plt.title('Herramientas de administración de Machine Learning en dos años', color = 'black', fontsize = 20, fontweight = 'bold') # para poner el título\n",
    "plt.legend(bbox_to_anchor=(1.2, 1)) # sacar leyenda y donde colocarla. El primer índice (derecha-izquierda), segundo índice(arriba, abajo) )\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gráfica de Competencia Adalab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df_40 = df2[['q40_part_1','q40_part_2','q40_part_3','q40_part_4','q40_part_5','q40_part_6','q40_part_7','q40_part_8','q40_part_9','q40_part_10','q40_part_11','q40_other']]\n",
    "\n",
    "def contar_programas2(dataframe):\n",
    "    lista1 =[]\n",
    "    lista2 =[]\n",
    "    for i in range(len(dataframe.columns)):\n",
    "        lista1.append(dataframe[dataframe.columns[i]].unique())\n",
    "        lista2.append(dataframe[dataframe.columns[i]].value_counts())\n",
    "    return pd.DataFrame(list(zip(lista1, lista2)), columns = ['programa','usuarios'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plataformas_formacion = contar_programas2(df_40)\n",
    "plataformas_formacion\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def contar_programas3(dataframe):\n",
    "    lista3 = []\n",
    "    lista4 = []\n",
    "    for i in range(len(dataframe)):\n",
    "        for i in dataframe.loc[i]['programa']:\n",
    "            if i != 'Unknow':\n",
    "                lista3.append(i)\n",
    "    for i in range(len(dataframe)):\n",
    "        x = 2301 - dataframe['usuarios'][i][0]\n",
    "        lista4.append(x)\n",
    "    return pd.DataFrame(list(zip(lista3, lista4)), columns = ['programa','usuarios'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "plataformas = contar_programas3(plataformas_formacion).sort_values('usuarios', ascending = False)\n",
    "plataformas\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plataformas['percent'] = [round(i*100/sum(plataformas['usuarios']),1) for i in plataformas['usuarios']]\n",
    "\n",
    "label = [i+'<br>'+str(j)+'<br>' + str(k) +'%' for i,j,k in zip(plataformas.programa, plataformas.usuarios, plataformas.percent)]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.scatter(plataformas, x='programa', y='usuarios',\n",
    "                 color='programa',\n",
    "                 size='usuarios', text=label, size_max=90\n",
    "                )\n",
    "fig.update_layout(width=1200, height=1200,\n",
    "                  margin = dict(t=50, l=0, r=0, b=0),\n",
    "                  showlegend=False\n",
    "                 )\n",
    "fig.update_traces(textposition='top center')\n",
    "fig.update_xaxes(showgrid=False, zeroline=False, visible=False)\n",
    "fig.update_yaxes(showgrid=False, zeroline=False, visible=False)\n",
    "fig.update_layout({'plot_bgcolor': 'white',\n",
    "                   'paper_bgcolor': 'white'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Gráfica programas utilizados en la nube\n",
    "plt.figure(figsize=(12,12))\n",
    "ax = plt.subplot(111, polar=True)\n",
    "plt.axis()\n",
    "#set min and max value\n",
    "lowerLimit = 0\n",
    "max_v = df_nube2['usuarios'].max()\n",
    "#set heights and width\n",
    "heights = df_nube2['usuarios']\n",
    "width = 2*np.pi / len(df_nube2.index)\n",
    "#set index and angle\n",
    "indexes = list(range(1, len(df_nube2.index)+1))\n",
    "angles = [element * width for element in indexes]\n",
    "bars = ax.bar(x=angles, height=heights, width=width, bottom=lowerLimit,\n",
    "              linewidth=1, edgecolor=\"white\", color=color_blue)\n",
    "labelPadding = 15\n",
    "plt.title(\"Programas de la nube utilizados por analistas de datos\",\n",
    "          fontdict={'size': 18},\n",
    "          loc='left')\n",
    "for bar, angle, height, label in zip(bars,angles, heights, df_nube2['programa']):\n",
    "    rotation = np.rad2deg(angle)\n",
    "    alignment = \"\"\n",
    "    #deal with alignment\n",
    "    if angle >= np.pi/2 and angle < 3*np.pi/2:\n",
    "        alignment = \"right\"\n",
    "        rotation = rotation + 220\n",
    "    else:\n",
    "        alignment = \"left\"\n",
    "    ax.text(x=angle, y=lowerLimit + bar.get_height() + labelPadding,\n",
    "            s=label, ha=alignment, va='center', rotation=rotation,\n",
    "            rotation_mode=\"anchor\")\n",
    "    ax.set_thetagrids([], labels=[])\n",
    "plt.show()\n",
    "\n",
    "#Programas de big data utilizados con regularidad\n",
    "explode = ( 0 ,0.1,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02) # para sacar los quesitos hacia fuera\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.pie(df_bigdata3[\"presente\"],\n",
    "       labels = df_bigdata3[\"index\"],\n",
    "       colors = colors2, # para cambiar el color de la gráfica\n",
    "       autopct=\"%1.1f%%\",\n",
    "       explode = explode)  # para sacar los quesitos hacia fuera\n",
    "plt.title(\"Programas de big data utilizados regularmente por analistas de datos\", color = \"black\", fontsize = 20, fontweight = \"bold\") # para poner el título\n",
    "plt.legend(bbox_to_anchor=(1.2, 1)) # sacar leyenda y donde colocarla. El primer índice (derecha-izquierda), segundo índice(arriba, abajo) )\n",
    "plt.show();\n",
    "\n",
    "#Programas de big data que interesan en el futuro\n",
    "explode = ( 0 ,0.1,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02) # para sacar los quesitos hacia fuera\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.pie(df_bd_32b[\"usuarios\"],\n",
    "       labels = df_bd_32b[\"programa\"],\n",
    "       colors = colors2, # para cambiar el color de la gráfica\n",
    "       autopct=\"%1.1f%%\",\n",
    "       explode = explode)  # para sacar los quesitos hacia fuera\n",
    "plt.title(\"Programas de big data que interesa aprender a analistas de datos\", color = \"black\", fontsize = 20, fontweight = \"bold\") # para poner el título\n",
    "plt.legend(bbox_to_anchor=(1.2, 1)) # sacar leyenda y donde colocarla. El primer índice (derecha-izquierda), segundo índice(arriba, abajo) )\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GATITO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = path.dirname(__file__) if \"__file__\" in locals() else os.getcwd()\n",
    "text = open(path.join(d, 'gato.txt')).read()\n",
    "alice_mask = np.array(Image.open(path.join(d, \"cat_mask.jpg\")))\n",
    "stopwords = set(STOPWORDS)\n",
    "stopwords.add(\"said\")\n",
    "wc = WordCloud(background_color=\"white\", max_words=100, mask=alice_mask,\n",
    "               stopwords=stopwords, contour_width=6, contour_color='rebeccapurple')\n",
    "wc.generate(text)\n",
    "\n",
    "wc.to_file(path.join(d, \"gato.png\"))\n",
    "\n",
    "plt.imshow(wc, interpolation='bilinear')\n",
    "plt.axis(\"off\")\n",
    "plt.figure()\n",
    "plt.imshow(alice_mask, cmap=plt.cm.gray, interpolation='bilinear')\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  }
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