diff --git a/Ejemplo_nacimientos_2005_2010/Demo_CDS_nacimientos.ipynb b/Ejemplo_nacimientos_2005_2010/Demo_CDS_nacimientos.ipynb
index 1a8d6f1d097281d029c956d58d0766052b3d3eae..c77504eb15f035a7be4ca295e0e1b04ca4cb3130 100644
--- a/Ejemplo_nacimientos_2005_2010/Demo_CDS_nacimientos.ipynb
+++ b/Ejemplo_nacimientos_2005_2010/Demo_CDS_nacimientos.ipynb
@@ -1,2112 +1,3100 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "8s9_BjAlsDvz"
- },
- "source": [
- "# Indice\n",
- "1. [Introducción](#introduction)\n",
- "2. [Preparando la información](#paragraph1)\n",
- "3. [¿Cuántos nacimientos hay por año en el paÃs?](#paragraph2)\n",
- "4. [¿Cuántos nacimientos hay por año en el paÃs según el grupo etario de la madre?](#paragraph3)\n",
- "5. [¿Que proporción de madres tuvo hijos antes de los 20?](#paragraph4)\n",
- "6. [Para cada nivel de instrucción/educación, ¿Cuántos nacimientos hubo en cada grupo etario?](#paragraph5)\n",
- "7. [Referencias técnicas](#paragraph6)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "7JgsokQzYAJX"
- },
- "source": [
- "## Introducción <a name=\"introduction\"></a>\n",
- "\n",
- "---\n",
- "En esta propuesta vamos a usar datos del ministerio de salud sobre nacimientos en el paÃs entre 2005 y 2010 para hacer algunas preguntas y obtener una respuesta visual con gráficos.\n",
- "---\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "fTccwFpMvWpQ"
- },
- "source": [
- "# Qué información podemos obtener:\n",
- "* ¿Cuántos nacimientos hay por año en el paÃs?\n",
- "* ¿Cuántos nacimientos hay por año en el paÃs según el grupo etario de la madre?\n",
- "* ¿Que proporción de madres tuvo hijos antes de los 20?\n",
- "* Para cada nivel de instrucción/educación, ¿Cuántos nacimientos hubo en cada grupo etario?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "O35LytWBsDv0"
- },
- "source": [
- "## Link donde obtengo el dataset\n",
- "El dataset viene del ministerio de salud y puede encontrarse en: \n",
- "http://datos.salud.gob.ar/dataset/nacidos-vivos-registrados-por-jurisdiccion-de-residencia-de-la-madre-republica-argentina-ano-2017/archivo/3c891522-8448-4490-a7da-6deba78d3b32\n",
- "Aunque los datos fueron limpiados para facilitar su uso"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "GqUKO6-k2moi"
- },
- "source": [
- "Antes de empezar, una aclaración: En muchos lugares de la ejecución, se puede ver un SettingWithCopyWarning que nos avisa que estamos tratando de colocar una parte de una copia del dataframe en el dataframe. Esto no es un problema y se puede ignorar"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "QYEZLjtwiH6p"
- },
- "source": [
- "## Preparando la información <a name=\"paragraph1\"></a>\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "q-PphAWuE151"
- },
- "source": [
- "Primero importamos pandas, esto nos permitirá usar las funciones que provee, es costumbre renombrarla como **pd** y también el módulo pyplot de matplotlib normalmente abreviado como **plt**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "id": "gSPpdLmni-mZ"
- },
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "import matplotlib.pyplot as plt"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "mGIGSZnmiTyN"
- },
- "source": [
- "Usamos la función **read_csv** que nos transforma nuestros datos (en formato csv) a un dataframe que podemos manipular fácilmente."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "id": "oanfaLLOvlVG"
- },
- "outputs": [],
- "source": [
- "nacimientos = pd.read_csv(\"Nacimientos_Arg_2005-2010.csv\",encoding = \"UTF-8\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "6cuhJ6w2zbUc"
- },
- "source": [
- "Vamos a ver como vemos la información:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 0
- },
- "id": "FDFSoh0Xwh3M",
- "outputId": "39b9b53f-9cda-4c25-adb7-ad0fa8ca0609"
- },
- "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>anio</th>\n",
- " <th>jurisdiccion_de_residencia_id</th>\n",
- " <th>jurisdicion_residencia_nombre</th>\n",
- " <th>edad_madre_grupo_id</th>\n",
- " <th>edad_madre_grupo</th>\n",
- " <th>instruccion_madre</th>\n",
- " <th>semana_gestacion_id</th>\n",
- " <th>semana_gestacion</th>\n",
- " <th>intervalo_peso_al_nacer</th>\n",
- " <th>Sexo</th>\n",
- " <th>nacimientos_cantidad</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>2005</td>\n",
- " <td>2</td>\n",
- " <td>Ciudad Autónoma de Buenos Aires</td>\n",
- " <td>5</td>\n",
- " <td>30 a 34</td>\n",
- " <td>Secundaria/Polimodal Incompleta</td>\n",
- " <td>4</td>\n",
- " <td>28 a 31</td>\n",
- " <td>1500 a 1999</td>\n",
- " <td>masculino</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>2005</td>\n",
- " <td>2</td>\n",
- " <td>Ciudad Autónoma de Buenos Aires</td>\n",
- " <td>5</td>\n",
- " <td>30 a 34</td>\n",
- " <td>Primaria/C. EGB Completa</td>\n",
- " <td>4</td>\n",
- " <td>28 a 31</td>\n",
- " <td>500 a 999</td>\n",
- " <td>masculino</td>\n",
- " <td>2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>2005</td>\n",
- " <td>2</td>\n",
- " <td>Ciudad Autónoma de Buenos Aires</td>\n",
- " <td>4</td>\n",
- " <td>25 a 29</td>\n",
- " <td>Secundaria/Polimodal Completa</td>\n",
- " <td>4</td>\n",
- " <td>28 a 31</td>\n",
- " <td>1000 a 1499</td>\n",
- " <td>masculino</td>\n",
- " <td>6</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>2005</td>\n",
- " <td>2</td>\n",
- " <td>Ciudad Autónoma de Buenos Aires</td>\n",
- " <td>5</td>\n",
- " <td>30 a 34</td>\n",
- " <td>Secundaria/Polimodal Incompleta</td>\n",
- " <td>5</td>\n",
- " <td>32 a 36</td>\n",
- " <td>1500 a 1999</td>\n",
- " <td>masculino</td>\n",
- " <td>5</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>2005</td>\n",
- " <td>2</td>\n",
- " <td>Ciudad Autónoma de Buenos Aires</td>\n",
- " <td>4</td>\n",
- " <td>25 a 29</td>\n",
- " <td>Secundaria/Polimodal Completa</td>\n",
- " <td>4</td>\n",
- " <td>28 a 31</td>\n",
- " <td>1500 a 1999</td>\n",
- " <td>masculino</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "8s9_BjAlsDvz"
+ },
+ "source": [
+ "# Indice\n",
+ "1. [Introduction](#introduction)\n",
+ "2. [Preparando la información](#paragraph1)\n",
+ "3. [¿Cuántos nacimientos hay por año en el paÃs?](#paragraph2)\n",
+ "4. [¿Cuántos nacimientos hay por año en el paÃs según el grupo etario de la madre?](#paragraph3)\n",
+ "5. [¿Que proporción de madres tuvo hijos antes de los 20?](#paragraph4)\n",
+ "6. [Para cada nivel de instrucción/educación, ¿Cuántos nacimientos hubo en cada grupo etario?](#paragraph5)\n",
+ "7. [Referencias técnicas](#paragraph6)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7JgsokQzYAJX"
+ },
+ "source": [
+ "## Introducción <a name=\"introduction\"></a>\n",
+ "\n",
+ "---\n",
+ "En esta propuesta vamos a usar datos del ministerio de salud sobre nacimientos en el paÃs entre 2005 y 2010 para hacer algunas preguntas y obtener una respuesta visual con gráficos.\n",
+ "---\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Qué información podemos obtener:\n",
+ "* ¿Cuántos nacimientos hay por año en el paÃs?\n",
+ "* ¿Cuántos nacimientos hay por año en el paÃs según el grupo etario de la madre?\n",
+ "* ¿Que proporción de madres tuvo hijos antes de los 20?\n",
+ "* Para cada nivel de instrucción/educación, ¿Cuántos nacimientos hubo en cada grupo etario?"
],
- "text/plain": [
- " anio jurisdiccion_de_residencia_id jurisdicion_residencia_nombre \\\n",
- "0 2005 2 Ciudad Autónoma de Buenos Aires \n",
- "1 2005 2 Ciudad Autónoma de Buenos Aires \n",
- "2 2005 2 Ciudad Autónoma de Buenos Aires \n",
- "3 2005 2 Ciudad Autónoma de Buenos Aires \n",
- "4 2005 2 Ciudad Autónoma de Buenos Aires \n",
- "\n",
- " edad_madre_grupo_id edad_madre_grupo instruccion_madre \\\n",
- "0 5 30 a 34 Secundaria/Polimodal Incompleta \n",
- "1 5 30 a 34 Primaria/C. EGB Completa \n",
- "2 4 25 a 29 Secundaria/Polimodal Completa \n",
- "3 5 30 a 34 Secundaria/Polimodal Incompleta \n",
- "4 4 25 a 29 Secundaria/Polimodal Completa \n",
- "\n",
- " semana_gestacion_id semana_gestacion intervalo_peso_al_nacer Sexo \\\n",
- "0 4 28 a 31 1500 a 1999 masculino \n",
- "1 4 28 a 31 500 a 999 masculino \n",
- "2 4 28 a 31 1000 a 1499 masculino \n",
- "3 5 32 a 36 1500 a 1999 masculino \n",
- "4 4 28 a 31 1500 a 1999 masculino \n",
- "\n",
- " nacimientos_cantidad \n",
- "0 1 \n",
- "1 2 \n",
- "2 6 \n",
- "3 5 \n",
- "4 1 "
+ "metadata": {
+ "id": "fTccwFpMvWpQ"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "O35LytWBsDv0"
+ },
+ "source": [
+ "## Link donde obtengo el dataset\n",
+ "El dataset viene del ministerio de salud y puede encontrarse en: \n",
+ "http://datos.salud.gob.ar/dataset/nacidos-vivos-registrados-por-jurisdiccion-de-residencia-de-la-madre-republica-argentina-ano-2017/archivo/3c891522-8448-4490-a7da-6deba78d3b32\n",
+ "Aunque los datos fueron limpiados para facilitar su uso"
]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "nacimientos.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "B6kS1QTB6aF9"
- },
- "source": [
- "No vamos a trabajar con toda la información, asi que la cortamos a las columnas que nos interesan:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "id": "Z9xxlqmM6kc4"
- },
- "outputs": [],
- "source": [
- "nacimientos = nacimientos[[\"anio\",\"edad_madre_grupo\",\"instruccion_madre\",\"nacimientos_cantidad\"]]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 0
- },
- "id": "4lOR_MP7sDv-",
- "outputId": "f407388c-6bcd-45fe-e984-96be9affface"
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
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- " <td>2017</td>\n",
- " <td>Sin especificar</td>\n",
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- " <td>10</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>497974 rows × 4 columns</p>\n",
- "</div>"
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Antes de empezar, una aclaración: En muchos lugares de la ejecución, se puede ver un SettingWithCopyWarning que nos avisa que estamos tratando de colocar una parte de una copia del dataframe en el dataframe. Esto no es un problema y se puede ignorar"
],
- "text/plain": [
- " anio edad_madre_grupo instruccion_madre \\\n",
- "0 2005 30 a 34 Secundaria/Polimodal Incompleta \n",
- "1 2005 30 a 34 Primaria/C. EGB Completa \n",
- "2 2005 25 a 29 Secundaria/Polimodal Completa \n",
- "3 2005 30 a 34 Secundaria/Polimodal Incompleta \n",
- "4 2005 25 a 29 Secundaria/Polimodal Completa \n",
- "... ... ... ... \n",
- "497969 2017 30 a 34 Secundaria/Polimodal Completa \n",
- "497970 2017 30 a 34 Secundaria/Polimodal Completa \n",
- "497971 2007 25 a 29 Terciaria/Universitaria Completa \n",
- "497972 2017 30 a 34 Terciaria/Universitaria Incompleta \n",
- "497973 2017 Sin especificar Sin especificar \n",
- "\n",
- " nacimientos_cantidad \n",
- "0 1 \n",
- "1 2 \n",
- "2 6 \n",
- "3 5 \n",
- "4 1 \n",
- "... ... \n",
- "497969 1 \n",
- "497970 1 \n",
- "497971 1 \n",
- "497972 1 \n",
- "497973 10 \n",
- "\n",
- "[497974 rows x 4 columns]"
+ "metadata": {
+ "id": "GqUKO6-k2moi"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "QYEZLjtwiH6p"
+ },
+ "source": [
+ "## Preparando la información <a name=\"paragraph1\"></a>\n"
]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "nacimientos"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "HY9NHf7Mw2Z8"
- },
- "source": [
- "## Pregunta: ¿Cuántos nacimientos hay por año en el paÃs? <a name=\"paragraph2\"></a>\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "XJ_i_X3IA5oI"
- },
- "source": [
- "Para esto vamos a necesitar menos información que antes, solo la cantidad de nacimientos y el año en el que ocurrieron.\n",
- "Se abrevia nacimientos como nac para mayor legibilidad:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 206
- },
- "id": "I-PYL_Qez5hV",
- "outputId": "e0c50fba-5ab8-4d94-989c-65714f666c97"
- },
- "outputs": [
- {
- "data": {
- "text/html": [
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- "</div>"
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Primero importamos pandas, esto nos permitirá usar las funciones que provee, es costumbre renombrarla como **pd** y también el módulo pyplot de matplotlib normalmente abreviado como **plt**"
],
- "text/plain": [
- " anio nacimientos_cantidad\n",
- "0 2005 1\n",
- "1 2005 2\n",
- "2 2005 6\n",
- "3 2005 5\n",
- "4 2005 1"
+ "metadata": {
+ "id": "q-PphAWuE151"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "gSPpdLmni-mZ"
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt"
]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "nac_por_año = nacimientos[[\"anio\",\"nacimientos_cantidad\"]]\n",
- "nac_por_año.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "D6Dps9axBQrp"
- },
- "source": [
- "Hay un problema con esta información, como la cantidad de nacimientos no está agregada por año sino que también por otros factores, hay que agrupar por año y sumar los nacimientos de cada grupo:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 238
- },
- "id": "FbY9_hRmBDuW",
- "outputId": "2f9f8d43-6014-4f71-abf5-be997855408d"
- },
- "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>nacimientos_cantidad</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>anio</th>\n",
- " <th></th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>2005</th>\n",
- " <td>712220</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2006</th>\n",
- " <td>696451</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2007</th>\n",
- " <td>700792</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2008</th>\n",
- " <td>746460</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2009</th>\n",
- " <td>745336</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "mGIGSZnmiTyN"
+ },
+ "source": [
+ "Usamos la función **read_csv** que nos transforma nuestros datos (en formato csv) a un dataframe que podemos manipular fácilmente."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "oanfaLLOvlVG"
+ },
+ "outputs": [],
+ "source": [
+ "nacimientos = pd.read_csv(\"Nacimientos_Arg_2005-2010.csv\",encoding = \"UTF-8\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6cuhJ6w2zbUc"
+ },
+ "source": [
+ "Vamos a ver como vemos la información:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "FDFSoh0Xwh3M",
+ "outputId": "39b9b53f-9cda-4c25-adb7-ad0fa8ca0609"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " anio jurisdiccion_de_residencia_id jurisdicion_residencia_nombre \\\n",
+ "0 2005 2 Ciudad Autónoma de Buenos Aires \n",
+ "1 2005 2 Ciudad Autónoma de Buenos Aires \n",
+ "2 2005 2 Ciudad Autónoma de Buenos Aires \n",
+ "3 2005 2 Ciudad Autónoma de Buenos Aires \n",
+ "4 2005 2 Ciudad Autónoma de Buenos Aires \n",
+ "\n",
+ " edad_madre_grupo_id edad_madre_grupo instruccion_madre \\\n",
+ "0 5 30 a 34 Secundaria/Polimodal Incompleta \n",
+ "1 5 30 a 34 Primaria/C. EGB Completa \n",
+ "2 4 25 a 29 Secundaria/Polimodal Completa \n",
+ "3 5 30 a 34 Secundaria/Polimodal Incompleta \n",
+ "4 4 25 a 29 Secundaria/Polimodal Completa \n",
+ "\n",
+ " semana_gestacion_id semana_gestacion intervalo_peso_al_nacer Sexo \\\n",
+ "0 4 28 a 31 1500 a 1999 masculino \n",
+ "1 4 28 a 31 500 a 999 masculino \n",
+ "2 4 28 a 31 1000 a 1499 masculino \n",
+ "3 5 32 a 36 1500 a 1999 masculino \n",
+ "4 4 28 a 31 1500 a 1999 masculino \n",
+ "\n",
+ " nacimientos_cantidad \n",
+ "0 1 \n",
+ "1 2 \n",
+ "2 6 \n",
+ "3 5 \n",
+ "4 1 "
+ ],
+ "text/html": [
+ "\n",
+ " <div id=\"df-4567b23d-849d-4a0e-a948-ce613b0d640d\">\n",
+ " <div class=\"colab-df-container\">\n",
+ " <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>anio</th>\n",
+ " <th>jurisdiccion_de_residencia_id</th>\n",
+ " <th>jurisdicion_residencia_nombre</th>\n",
+ " <th>edad_madre_grupo_id</th>\n",
+ " <th>edad_madre_grupo</th>\n",
+ " <th>instruccion_madre</th>\n",
+ " <th>semana_gestacion_id</th>\n",
+ " <th>semana_gestacion</th>\n",
+ " <th>intervalo_peso_al_nacer</th>\n",
+ " <th>Sexo</th>\n",
+ " <th>nacimientos_cantidad</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>2005</td>\n",
+ " <td>2</td>\n",
+ " <td>Ciudad Autónoma de Buenos Aires</td>\n",
+ " <td>5</td>\n",
+ " <td>30 a 34</td>\n",
+ " <td>Secundaria/Polimodal Incompleta</td>\n",
+ " <td>4</td>\n",
+ " <td>28 a 31</td>\n",
+ " <td>1500 a 1999</td>\n",
+ " <td>masculino</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>2005</td>\n",
+ " <td>2</td>\n",
+ " <td>Ciudad Autónoma de Buenos Aires</td>\n",
+ " <td>5</td>\n",
+ " <td>30 a 34</td>\n",
+ " <td>Primaria/C. EGB Completa</td>\n",
+ " <td>4</td>\n",
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+ " <td>500 a 999</td>\n",
+ " <td>masculino</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>2005</td>\n",
+ " <td>2</td>\n",
+ " <td>Ciudad Autónoma de Buenos Aires</td>\n",
+ " <td>4</td>\n",
+ " <td>25 a 29</td>\n",
+ " <td>Secundaria/Polimodal Completa</td>\n",
+ " <td>4</td>\n",
+ " <td>28 a 31</td>\n",
+ " <td>1000 a 1499</td>\n",
+ " <td>masculino</td>\n",
+ " <td>6</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>2005</td>\n",
+ " <td>2</td>\n",
+ " <td>Ciudad Autónoma de Buenos Aires</td>\n",
+ " <td>5</td>\n",
+ " <td>30 a 34</td>\n",
+ " <td>Secundaria/Polimodal Incompleta</td>\n",
+ " <td>5</td>\n",
+ " <td>32 a 36</td>\n",
+ " <td>1500 a 1999</td>\n",
+ " <td>masculino</td>\n",
+ " <td>5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>2005</td>\n",
+ " <td>2</td>\n",
+ " <td>Ciudad Autónoma de Buenos Aires</td>\n",
+ " <td>4</td>\n",
+ " <td>25 a 29</td>\n",
+ " <td>Secundaria/Polimodal Completa</td>\n",
+ " <td>4</td>\n",
+ " <td>28 a 31</td>\n",
+ " <td>1500 a 1999</td>\n",
+ " <td>masculino</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>\n",
+ " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4567b23d-849d-4a0e-a948-ce613b0d640d')\"\n",
+ " title=\"Convert this dataframe to an interactive table.\"\n",
+ " style=\"display:none;\">\n",
+ " \n",
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+ " </svg>\n",
+ " </button>\n",
+ " \n",
+ " <style>\n",
+ " .colab-df-container {\n",
+ " display:flex;\n",
+ " flex-wrap:wrap;\n",
+ " gap: 12px;\n",
+ " }\n",
+ "\n",
+ " .colab-df-convert {\n",
+ " background-color: #E8F0FE;\n",
+ " border: none;\n",
+ " border-radius: 50%;\n",
+ " cursor: pointer;\n",
+ " display: none;\n",
+ " fill: #1967D2;\n",
+ " height: 32px;\n",
+ " padding: 0 0 0 0;\n",
+ " width: 32px;\n",
+ " }\n",
+ "\n",
+ " .colab-df-convert:hover {\n",
+ " background-color: #E2EBFA;\n",
+ " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+ " fill: #174EA6;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-convert {\n",
+ " background-color: #3B4455;\n",
+ " fill: #D2E3FC;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-convert:hover {\n",
+ " background-color: #434B5C;\n",
+ " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+ " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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+ "\n",
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+ " const buttonEl =\n",
+ " document.querySelector('#df-4567b23d-849d-4a0e-a948-ce613b0d640d button.colab-df-convert');\n",
+ " buttonEl.style.display =\n",
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+ "\n",
+ " async function convertToInteractive(key) {\n",
+ " const element = document.querySelector('#df-4567b23d-849d-4a0e-a948-ce613b0d640d');\n",
+ " const dataTable =\n",
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+ " [key], {});\n",
+ " if (!dataTable) return;\n",
+ "\n",
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+ " + ' to learn more about interactive tables.';\n",
+ " element.innerHTML = '';\n",
+ " dataTable['output_type'] = 'display_data';\n",
+ " await google.colab.output.renderOutput(dataTable, element);\n",
+ " const docLink = document.createElement('div');\n",
+ " docLink.innerHTML = docLinkHtml;\n",
+ " element.appendChild(docLink);\n",
+ " }\n",
+ " </script>\n",
+ " </div>\n",
+ " </div>\n",
+ " "
+ ]
+ },
+ "metadata": {},
+ "execution_count": 3
+ }
],
- "text/plain": [
- " nacimientos_cantidad\n",
- "anio \n",
- "2005 712220\n",
- "2006 696451\n",
- "2007 700792\n",
- "2008 746460\n",
- "2009 745336"
+ "source": [
+ "nacimientos.head()"
]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "nac_por_año = nac_por_año.groupby(\"anio\").sum()\n",
- "nac_por_año.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "xPLMRoEUmicq"
- },
- "source": [
- "Ahora está mejor.\n",
- "Vamos a graficarlo con un simple gráfico de lÃnea:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 459
},
- "id": "19u3wAvl0jIN",
- "outputId": "97bf73c5-1a0e-4ed4-d2e2-a7d6c12536e7"
- },
- "outputs": [
{
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x7f6a31955540>"
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "B6kS1QTB6aF9"
+ },
+ "source": [
+ "No vamos a trabajar con toda la información, asi que la cortamos a las columnas que nos interesan:"
]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
},
{
- "data": {
- "image/png": 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q6PMz9ey0tTqzTV39cEdfXdq1gduu8Om0NvGReuGSjlqadVC3T1isEtdF6AHAXVAAAQDAnxw4XKC7Jy7VyLFzlV9UorevTtWLl3RUbDgXoj6ZQa3i9M+hLfXN8h168vs1TscBgN9hERgAAHCUtVZfLM7SI1+t0oEjhfpL30b628BmCg7gdX4VcW2vZG3ak6tX0zYouU6oRqQmOh0JACRRAAEAgEvGnlz93xfLNWv9HnVIjNL7F7RlNctTZIzRg8Naa8u+w7rv82VKqB2sHo2jnY4FAJwCCgCAtysoKtHL09frzOdmaMnWA3p4eGt9yqUMTpu/r49eurSTkqNDNea9BdqwO8fpSABAAQQAwJvNz9inc16cqSe/W6OBLWM17Y6+uqJ7knxZ5KVSRAb7662rUuXv66NrxqVrX26B05EAeDkKIAAAXujg4ULd+9kyXfTaz8rNL9abo1P0ymWdFRfBIi+VLbF2iMZemaLtB/M05r0Fyi/i8hAAnEMBBADAi1hr9eWSbRr4zE/6OH2LruuVrO//3kcDW8Y5Ha1G69ywlp6+uL1+ydinez9dJmu5PAQAZ7AIDAAAXmLrvsP6vy+W66e1u9UuIVLjrk5Vm/hIp2N5jXPb19emPbl6ZupaJUeH6paBTZ2OBMALUQABAKjhCotL9OasTXpu2lr5GqMHzm2lK3mdnyNuGdBEm/bk6umpa5UUHapz29d3OhIAL0MBBACgBlu0Zb/u/WyZVu84pEGt4vTvYa1VPyrY6Vheyxijxy5sq8z9h3XHJ0tUPypYnRvWcjoWAC/CawABAKiBsvMKdf+k5brg1Tk6cLhQ/7uis16/MoXy5wYC/Xz1vytSVC8ySDe8O19b9x12OhIAL0IBBAAcVVJilZNfpJ3ZedqwO0dLth7Qqu3ZTsdCBVhr9c2y7Rr0zE96b+5mje6epKm399GZres6HQ1l1A4N0FtXpaqwuETXjEtXdl6h05EAeAlOAQUAD1dcYpVbUKTc/CLl5BUpJ79IufnFyskvVE5+cel4/q/jv21z9PP83+6TW1CkYy1O2LtptO49q6Va1efC4O4s68ARPTBpuaat2qVW9SI09ooUtU+McjoWjqNxTJheu6KzrnzzF930wcKj1wsEgKpEAQQABxQVl5SWtIKype0YRc11e+ltpaUu11XqDrm2O1xQvmuK+fsahQX6KTTQT2Gut6iQACXUCvltPMhPYYG+v9tm4+5cvZy2Xme/OFMXdkrQHYObqV4kpxG6k6LiEo2bk6Fnpq6VtdI/h7bU1T2T5EeZcHs9Gkfr0fPb6O5Pl+nBySv0yHltZAyL8wCoOhRAAKhEeYXFmrNhj2au26NVG/P0Xkb60aJWtuDlFZaUa38Bfj4Kd5Wz0EA/hQf6KTosQEnRoaVFLeDX0vb7Ylf247AgP4UG+irQz/eUntPAltKIlES9krZeb8/O0FdLt+naXska07exwoP8T2mfqDzLMg/q3s+XanlWtga0iNVDw1sroVaI07FQASNTG2jTnsN67acNahQTpmt7JTsdCUANRgEEgNO0+1C+pq/epamrdmrWuj06UlisYH9fRfiXKNrkKSzQT3UjglxF7I8lzVdhgf4KDfT9rawF+Cncta27nA4WGeKve4e21OXdGuqp79fo5ekbNP6XrfrbGU01qksDt8npTXLyi/T092v0zpwMRYcF6pXLOumsNnU5euSh7jqzuTL25OqRr1eqYe0QndEqzulIAGooCiAAVJC1Vut25Wjqyp36YdVOLdp6QNZK9SODdHFKgs5oGaeujWrr51kz1a9fb6fjVqrE2iF6flRHXdsrWY9+vUr/mrRCb8/O0D1ntdCgVnGUj2pQUmL13YodeuirldqRnafLuzbUnUOaK4KjsR7Nx8fo2ZEdNHLsz7p1/CJN+Et3tYmPdDoWgBqIAggA5VBYXKL0Tfs0ddVO/bBql7a4lm1vlxCpv5/RTGe0jFPLeuFeU4DaJURp/A3d9MOqXfrvN6t0w3sL1CWptu47u6U6sOhIlcgrLNYXi7L0+syN2rA7Vy3qhuvlyzqpUwOuIVdTBAf46o0rU3Tey7N13Tvz9cVNPVU3MsjpWABqGAogABzHwcOFSlu7S9NW7VLaml06lFekQD8f9WwSrTF9G2tgy1jFRXjvL2fGGJ3RKk79msdofPpWPTdtrc57ebbObV9fd53ZXIm1eR1aZThwuEAfzNuit2dnaE9OvlrVi9DzozpoaNt6nHpbA8VGBOnNq1J10atzdO076fpkTHeFBPDrGoDKw/8oAFDG5r25mrZql6at3KlfMvapuMQqOixAQ9vU08CWserVNJpfxv7Az9dHl3drqPM6xmvsTxs0duZGfbd8h67s3lA3D2iiqJAApyN6pK37DuvNWZs0Yf5WHS4oVt9mMbqhTyP1aFzHa440e6uW9SL04qUddd0783Xb+MV67fLO8vVhzgFUDn6LAeDVikusFm/df7T0rduVI0lqHheuMX0baWDLOHVIiJIPv3ydVFign24f3FyXdm2oZ6au0ZuzN+mTBZm6uX8TXdmj4SmvQuptlmUe1P9mbNCUZdvlY4yGdaivG/o0Uou6XIPRmwxoEaf7z2mlB79cqce/Xa37hrZ0OhKAGuKkBdAY01zSx2WGGkm6X1KUpOsl7XaN32etneK6z72SrpVULOlWa+13rvEhkp6X5CvpDWvtY67xZEnjJdWRtEDSFdbaAmNMoKR3JXWWtFfSSGttxmk8XwBQbn6RZq7box9W7dSPq3dpb26B/HyMujaqrUu7NtAZLeM4ffE01I0M0hMXtdc1vZL13ymr9eiUVXp3bobuPLOFzm1Xj6NXx2CtVdqa3frfjA2au3GfwgP9dH3vRrqqZxLXXPRiV/VM1qY9uRo7Y6OS6oTq0q4NnI4EoAY4aQG01q6R1EGSjDG+krIkfS7paknPWmufKru9MaaVpFGSWkuqL2maMaaZ6+aXJQ2SlCkp3Rgz2Vq7UtLjrn2NN8a8ptLy+Krr/X5rbRNjzCjXdiNP7ykD8EY7DuZp2qrSVTtnb9irgqISRQT5qX+LWJ3RMk59msUoMphVFCtTi7oReueaLpq5brf+M2W1bv1okd6cuVH3DW2pro3qOB3PLeQXFWvy4m16feZGrd2Zo3qRQfrn0JYa1SWRayxCkvSvc1pp877D+tek5WpQO0S9mkY7HQmAh6voKaADJW2w1m4+wV9wh0sab63Nl7TJGLNeUhfXbeuttRslyRgzXtJwY8wqSQMkXera5h1JD6q0AA53fSxJEyW9ZIwx1lpbwdwAvIy1Viu2ZWvaqp2atmqnlmdlS5Ia1gnRFd0a6oyWcUpJqsUiGtWgd9MYfXVLtD5flKWnvlujkWPnalCrON1zVgs1jglzOp4jDh4p1Ifztujt2Zu061C+WtQN1zMj2uucdvUV4Me/SfzGz9dHL17SURe9+rNu/GCBPv9rDzWJDXc6FgAPZirSpYwxb0laaK19yRjzoKSrJGVLmi/pDmvtfmPMS5LmWmvfd93nTUnfuHYxxFp7nWv8CkldVVrw5lprm7jGEyV9Y61tY4xZ7rpPpuu2DZK6Wmv3/CHXDZJukKS4uLjO48ePr/AXAuWXk5OjsDDv/KXNXTEnpQpLrFbtLdbiXcVavLtY+/KsjKTGUT7qGOurjrF+qhdqqu0UROblz/KLrb7PKNTXGwtVUCL1S/TTeY0DFBHoHXOy90iJvs8o1E+ZRcorllrX8dFZyf5qXcfXq0+NdXpePMGeIyV66Oc8BfpK/+oerIiAqv33wpy4H+bEPbnrvPTv33+BtTblWLeV+wigMSZA0jBJ97qGXpX0sCTrev+0pGtOL+qpsdaOlTRWklJSUmy/fv2ciOE10tLSxNfYvXjznOzNydf0Nbs1beVOzVi3W4cLihUS4Ks+TeM0sGWs+reIVXRYoCPZvHleTuRMSffm5OuFH9bpg3lb9MvOAo3p20jX9mqk4ICqXSjGqTlZnnVQr8/cqK+Wbpcknduuvq7v00it63Ohb4nvlfJq0uaARv7vZ72zIVAfXNdVQf5V9/3CnLgf5sQ9eeK8VOQU0LNUevRvpyT9+l6SjDGvS/rK9WmWpMQy90twjek443slRRlj/Ky1RX/Y/td9ZRpj/CRFurYH4KWstdqwO+foqp0LtuyXtVLdiCBd0CleA1vGqXujOlX6ixFOX3RYoB4a3kajeyTp8W9W66nv1+r9uVt0x+BmuqBTQo1Y8t5aqxnr9mjsjA2avX6vQgN8dXWPJF3dK1nxUSzsgorrkBilZ0d20F8/WKi7Ji7V86M6ePWRYwCnpiIF8BJJH/36iTGmnrV2u+vT8yUtd308WdKHxphnVLoITFNJv0gykpq6VvzMUulCMZdaa60xZrqki1S6EuhoSZPK7Gu0pJ9dt//I6/8A71NUXKL0jP36wfV6voy9hyVJbeIjdNvApjqjZZxa14/gFyEP1DgmTGOvTNEvm/bp0SmrdOfEpXpz1ibdN7Sl+jSLcTreKSkoKtGXS0oXdlm945DiIgJ1z1ktdEmXBiw0hNM2tG093TWkuZ74do2So0P190HNTn4nACijXAXQGBOq0tU7/1Jm+AljTAeVngKa8ett1toVxpgJklZKKpJ0k7W22LWfmyV9p9LLQLxlrV3h2tfdksYbYx6RtEjSm67xNyW951pIZp9KSyOAMj5bmKkX5xzRi6vmKNDPx/Xmq0D/Mh/7+SjQ30dBR8d/Gzv68XHu89vtPvKrxgVTsvMK9dOa3fph1U5NX7NbB48UKsDXRz2a1NF1vRtpYMtYlsevQbok19YXf+2hr5Zu1xPfrdaVb/2i3k2jdd/QlmpZzzOuf3cor1Af/bJFb83K0I7sPDWLC9OTF7XT8A7xLOyCSnVj38batDtXz/+wTsnRoTqvY7zTkQB4kHIVQGttrkqv0Vd27IoTbP+opEePMT5F0pRjjG/UbyuFlh3Pk3RxeTIC3uiXTft018Slig2Rgv19lV9UrJz8IuUXlii/qFh5rvf5RSXKKyxWyWkeP/fzMa5i6HvSslm2OP66fdBJ7ufv56PlWQf1w6pdmrtxr4pKrGqHBmhQqzid0TJOvZtGKzSwoosXw1MYY3Ru+/oa3DpO7/28WS/+uF5DX5ipizol6I7BzVU3MsjpiMe0/eARvT07Qx/N26JD+UXq3qiO/nthW/VrFsNRaVQJY4wePb+ttu4/rLsmLlVCrWClJNV2OhYAD8FvUoCH2pWdp5s+XKiEWsG6q4M0dFDXk96nqLhE+UW/vhW7iuJvJTG/TGH80+1/2Dav8M/3OVJYrANHCo6537yiYpX3BO4msWG6rncjDWoVqw6JtWrE68FQfoF+vrqudyNd3DlRL01fp3fmbNaXS7fp+t6N9Je+jRXmJn8EWLU9W6/P2KjJS7bJqvTUvBt6N1LbBBZ2QdUL8PPRa5d31vmvzNEN75VeHqJhnVCnYwHwAO7xUxRAhRQWl+imDxcqJ69I71/bVdtXLyjX/fx8S0/jDHVgUUxrrYpKrKsQFh+ziOYVFqtB7RAlRfNLDKTIEH/98+xWurJ7kp74bo1e/HG9Pvpli247o5kuSU2s1lOSf2Wt1ez1ezV25kbNWLtbIQG+uqJ7Q13TM1mJtUOqPQ+8W1RIgN66KlXnvzJb14xL12c39lRkCK8zBXBiFEDAA/1nyiqlZ+zX86M6qHndcG1f7XSikzPGyN/XyN/Xx22O4MAzJNYO0YuXdNR1vZL16JRV+tcXyzVu9ibdc1ZLndEytlpOsywsLtHXS7dr7IyNWrk9WzHhgbrzzOa6rGsDRYUEVPnjA8eTHB2q/13eWZe/OU83frBA71zTRf4O/HEEgOfgfwjAw0xanKW3Z2fo6p5JGt6BF/7De7RPjNLHN3TT2Cs6y0q6/t35Gjl2rpZsPVBlj5mTX6Q3Zm5UvyfT9LePFyu/qFiPX9hWs+7ur5v6N6H8wS10bVRHj13QTnM27NX/fb5cLJgO4ET4MzzgQVbvyNY9ny5TalIt3Te0pdNxgGpnjNHg1nXVv0Wsxqdv1XNT12r4y7M1rH193Xlm80o7DXNndp7enp2hD+Zt1qG8InVJrq2HhrdW/+ax8uE1qXBDF3ZO0KY9uXpp+no1ignVX/o2djoSADdFAQQ8RHZeoca8t0BhQX56+dJOnOIDr+bv66MrujXUeR3q638/bdQbszbq2+U7NLpHQ93cv+kpvw5q7c5Den3GRn2xOEvFJVZntamn6/s0UofEqMp9AkAVuH1QM23am6vHvl2thnVCNaRNXacjAXBDFEDAA5SUWN0xYYky9x/RRzd0U2yEey6HD1S38CB//ePM5rqsWwM98/1avTFrkybMz9QtA5roiu4NFejne9J9WGv188a9en3GRk1fs1tB/j66tEsDXdMrmVUV4VF8fIyevri9svYf0d8+XqRPonqwKi2AP+EQAuABXv1pg6au3Kn7hrZUKtd6Av6kXmSwnry4vb6+pbfaJUTqka9X6YxnftJXS7cd9/VQRcUl+nLJNg17abYufX2elmYe1O2Dmunnewbq38PbUP7gkYL8ffX6lSmqExqoa99J17YDR5yOBMDNUAABNzdj7W499f0aDWtfX1f3THI6DuDWWtWP0HvXdtW713RRaICfbv5wkc5/ZY7SM/Yd3SY3v0hvz96kfk+l6ZaPFik3v0j/Ob+tZt8zQLcObKpaoSzsAs8WEx6ot69O1ZGCYl37znzl5Bc5HQmAG+EUUMCNZe4/rNvGL1Kz2HA9dmHbalnuHqgJ+jSLUc8m0fp0Yaae/n6NLn7tZw1uFaeAvALd9tOPOnikUCkNa+n+c1rpjJZxLOyCGqdZXLheuqyTrhmXrls/WqTXr0yRL//OAYgCCLitvMJi3fj+QhUVW712RWeFBPDtClSEr4/RiJREnduuvt6ctVGvpm3Q4YJindm6rq7v00idG9ZyOiJQpfo2i9GDw1rrX18s16Nfr9L957ZyOhIAN8BvlICbenDyCi3LOqixV3RWcjSvRQJOVXCAr24e0FSXd2uo6TNm6fwhnZ2OBFSbK7o11KbduXpr9iYlR4foiu5JTkcC4DAKIOCGxv+yRePTt+qm/o01uDXLeAOVISokQLWCeOk7vM8/z26pzXtz9eCXK5VYO0T9msc6HQmAg/hJCLiZpZkHdP/kFerdNFq3D2rudBwAgIfz9TF64ZKOah4Xrps/XKQ1Ow45HQmAgyiAgBvZl1ugG99fqJiwQD0/qiMv2AcAVIrQQD+9eVWKQgJ8dc24dO0+lO90JAAOoQACbqK4xOq28Yu0Oydfr17eSbVZih4AUInqRQbrzdGp2pdboOvfna+8wmKnIwFwAAUQcBPPTF2jmev26OHhrdUuIcrpOACAGqhtQqSeG9VBSzIP6I4JS1RSYp2OBKCaUQABN/D9ih16efoGjUpN1MjUBk7HAQDUYGe2rqt7z2qhr5dt1zNT1zodB0A1YxVQwGGb9uTqjglL1C4hUg8Oa+10HACAF7i+dyNt2pOrl6avV1J0qC7qnOB0JADVhAIIOOhwQZHGvLdAfr5Gr1zWSUH+vk5HAgB4AWOMHhreRlv2Hda9ny1VQq1gdWtUx+lYAKoBp4ACDrHW6p5Pl2ntrkN64ZKOSqgV4nQkAIAX8ff10SuXdVaD2iEa8/4CbdqT63QkANWAAgg4ZNycDE1esk3/GNxcvZvGOB0HAOCFIoP99dZVqfIxRteMS9eBwwVORwJQxSiAgAPSM/bp0a9XaVCrON3Yt7HTcQAAXqxhnVCNvaKzsvYf0V/eW6CCohKnIwGoQhRAoJrtys7TXz9YqIRawXp6RHv5cLF3AIDDUpJq68mL22nepn2697NlspbLQwA1FYvAANWosLhEN324UDl5RXr/2q6KCPJ3OhIAAJKk4R3itWlPrp6btk5Hkv3Vu0+J/Hw5VgDUNHxXA9XoP1NWKT1jvx67sK2a1w13Og4AAL9z28CmuqRLoqZsKtR5r8zWym3ZTkcCUMkogEA1mbQ4S2/PztDVPZM0vEO803EAAPgTY4z+e0E73dQhUDsO5mnYS7P0zNS1vC4QqEEogEA1WL0jW/d8ukypSbV039CWTscBAOCEUuv6aerf+2pY+/p64Yd1OvfFWVqy9YDTsQBUAgogUMWy8wo15r0FCgvy08uXdpI/r6cAAHiAWqEBemZkB711VYoOHinU+a/M1n+nrFJeYbHT0QCcBn4TBapQSYnVHROWKHP/Eb1yWSfFRgQ5HQkAgAoZ0CJO39/eRyNTE/W/GRt11vMzlZ6xz+lYAE4RBRCoQq/+tEFTV+7UfUNbKjWpttNxAAA4JRFB/vrvBe30wXVdVVRSohH/+1kPTFqu3Pwip6MBqCAKIFBFZqzdrae+X6Nh7evr6p5JTscBAOC09WwSrW9v66PR3ZP07tzNOvO5GZq1bo/TsQBUAAUQqAKZ+w/rtvGL1Cw2XI9d2FbGcLF3AEDNEBropweHtdYnf+muAF8fXf7mPN3z6VJl5xU6HQ1AOVAAgUqWV1isG99fqKJiq9eu6KyQAD+nIwEAUOlSkmprym29NaZvY02Yv1WDn5mhH1btdDoWgJOgAAKV7MHJK7Qs66CeHtFeydGhTscBAKDKBPn76p6zWujzv/ZUZLC/rn1nvv42fpH25xY4HQ3AcVAAgUo0/pctGp++VTf1b6zBres6HQcAgGrRPjFKX97SS7cNbKqvlm7XoGd/0pRl252OBeAYKIBAJVmaeUD3T16h3k2jdfug5k7HAQCgWgX4+ejvg5rpy1t6qV5ksP76wULd+P4C7TqU53Q0AGVQAIFKsC+3QDe+v1AxYYF6flRH+fqw6AsAwDu1rBehz//aQ3cPaaEfVu/SoGdm6LOFmbLWOh0NgCiAwGkrLrG6bfwi7T6Ur1cv76TaoQFORwIAwFF+vj66sV9jTbm1t5rEhun2CUt0zbh0bT94xOlogNejAAKn6ZmpazRz3R49NLy12iVEOR0HAAC30SQ2TBP+0l0PnNtKczfu0+BnZujDeVs4Ggg4iAIInIbvV+zQy9M3aFRqokZ1aeB0HAAA3I6vj9HVPZP13d/6qE18pO77fJkue2Oetuw97HQ0wCtRAIFTtGlPru6YsETtEiL14LDWTscBAMCtNagTog+v76r/nN9WSzMP6sznZujt2ZtUUsLRQKA6UQCBU3C4oEhj3lsgP1+jVy7rpCB/X6cjAQDg9owxurRrA33/9z7q2qi2/v3lSo3438/asDvH6WiA16AAAhVkrdU9ny7T2l2H9MIlHZVQK8TpSAAAeJT6UcF6+6pUPTOivdbtytFZz8/Uq2kbVFRc4nQ0oMajAAIVNG5OhiYv2aZ/DG6u3k1jnI4DAIBHMsbogk4Jmnp7H/VvHqPHv12t81+Zo9U7sp2OBtRoFECgAtIz9unRr1dpUKs43di3sdNxAADweLHhQXrt8s56+dJO2nbgiM59cZaem7ZWBUUcDQSqAgWwGqzYdlC7D+U7HQOnaVd2nv76wUIl1ArW0yPay4eLvQMAUCmMMTq7XT1Nvb2vzm5bT89NW6dhL83S0swDTkcDahwKYDW4+9Ol6vnYj7rzkyWc1uChCotLdNOHC5WTV6T/XZGiiCB/pyMBAFDj1A4N0HOjOuqNK1O0/3CBznt5th77ZrXyCoudjgbUGBTAavD8qI4akZqgr5Zu15DnZuqyN+bqx9U7WfbYg/xnyiqlZ+zXYxe2VfO64U7HAQCgRjujVZy+/3tfXdw5Ua/9tEFDX5ip+Rn7nI4F1AgUwGrQOCZMj5zXVj/fO0B3DWmuDbtydc24+Trj2Z/03tzNOlxQ5HREnMCkxVl6e3aGru6ZpOEd4p2OAwCAV4gM9tfjF7XTe9d2UX5hiS7+38/695cr+L0JOE0UwGoUFRKgv/Zropl399fzozooLNBP//piubr/90c9/u1q7TiY53RE/MGaHYd0z6fLlJpUS/cNbel0HAAAvE7vpjH6/u99dGW3hnp7dobOfG6G5qzf43QswGNRAB3g7+uj4R3iNemmnpo4prt6NK6j//20Qb0e/1G3jV/EC57dRHZeoca8v0BhQX56+dJO8vfl2wUAACeEBvrp38Pb6OMbusnXGF36xjzd+9kyZecVOh0N8Dh+TgfwZsYYpSTVVkpSbW3dd1jj5mTo4/StmrR4m1KTaunaXska1KqufFltstqVlFjdMWGJtu47rI9u6KbYiCCnIwEA4PW6Nqqjb27ro2enrdUbMzcqbc0u/ef8turfItbpaIDH4JCGm0isHaJ/ndNKP987QP86p5V2ZOdpzPsL1e+p6Xpz1iYd4i9c1erVnzZo6sqdum9oS6Um1XY6DgAAcAkO8NV9Q1vqs7/2VHiQn64el67bP16sA4cLnI4GeAQKoJsJD/LXtb2SlfaP/nrt8k6qGxGkh79aqe7//VEPf7VSW/cddjpijTdz3W49/f0aDWtfX1f3THI6DgAAOIYOiVH68pZeunVAE01esk1nPDND3y7f7nQswO1RAN2Ur4/RkDb19MmYHpp8c08NbBmrd+ZkqO+T03Xj+ws0P2OfrOUyEpUtc/9h3frRIjWNDddjF7aVMZx+CwCAuwr089Xtg5tr0s09FRcRqDHvL9RNHyzU7kP5TkcD3BYF0AO0S4jS86M6atbdA/SXvo01Z8NeXfTazzrv5dmatDhLhcUlTkesEfIKi/XXDxaqqNjqtSs6KySAl8gCAOAJWteP1Bc39dSdZzbX1JU7NfjZnzRpcRZ/LAeOgQLoQepGBunuIS30870D9PB5bXQor0i3jV+s3o9P1ytp6zn3/TQ9OHmFlmYe1NMj2is5OtTpOAAAoAL8fX10U/8m+vrWXkqKDtVt4xfrunfmK3M/L58ByqIAeqCQAD9d0a2hpt3eV29dlaLGsaF64ts16v7fH/V/XyzTxt05Tkf0OON/2aLx6Vt1U//GGty6rtNxAADAKWoaF66JY3ro/85uqdkb9mjAUz/p4a9Wal8ufygHJC4D4dF8fIwGtIjTgBZxWrU9W2/N2qQJ6Zl6f+4WDWgRq2t7JatH4zq8ju0klmYe0P2TV6h302jdPqi503EAAMBp8vUxuq53Iw1tW0/PTl2rt2dv0oT0rRrTr7Gu7pnEyzzg1TgCWEO0rBehJy9ur9n3DNDfzmiqpZkHdNkb83TW8zM1Yf5W5RcVOx3RLe3LLdCN7y9UTFignh/VkWsuAgBQg9SPCtaTF7fXt3/ro66N6ujJ79ao75Np+mDeZtZQgNeiANYwMeGB+tsZzTTr7gF64qJ2kqS7Ji5Vz8d+1HPT1mpPDqti/aq4xOq28Yu0+1C+Xr28k2qHBjgdCQAAVIFmceF6Y3SKJo7proa1Q/TPz5frzGdn6Jtl21koBl7npAXQGNPcGLO4zFu2MeZvxpjaxpipxph1rve1XNsbY8wLxpj1xpilxphOZfY12rX9OmPM6DLjnY0xy1z3ecG4zlk83mPg5IL8fTUiJVHf3NZbH1zXVe0SovTctHXq8diPumviEq3eke10RMc9M3WNZq7bo4eGt1a7hCin4wAAgCqWklRbn4zprtevTJGvj9GNHyzUea/M0ZwNe5yOBlSbk54Aba1dI6mDJBljfCVlSfpc0j2SfrDWPmaMucf1+d2SzpLU1PXWVdKrkroaY2pLekBSiiQraYExZrK1dr9rm+slzZM0RdIQSd+c4DFQTsYY9WwSrZ5NorVhd47enr1JExdkasL8TPVqEq1reyWrb7MY+dTAUx/zCouVdeCIsvYfUdaBI8rcf/jox1n7j2jbwTyNSk3UqC4NnI4KAACqiTFGg1rFaUCLWH26MFPPTl2rS1+fp77NYnT3kBZqVT/C6YhAlaroK2AHStpgrd1sjBkuqZ9r/B1JaSotZ8MlvWtLj6fPNcZEGWPqubadaq3dJ0nGmKmShhhj0iRFWGvnusbflXSeSgvg8R4Dp6BxTJgeOa+t/jG4uT78ZYvenbNZV49LV6OYUF3TM1kXdkpQcICv0zHLLTuvsLTQ7XeVuwNHflf49uT8frUvXx+juhFBiq8VrG6N6qhpXLiu7pnkTHgAAOAoXx+jESmJGta+vt79OUMvT9+gs1+cqeHt6+uOwc2VWDvE6YhAlTAVOe/ZGPOWpIXW2peMMQestVGucSNpv7U2yhjzlaTHrLWzXLf9oNLS1k9SkLX2Edf4vyQdUWmpe8xae4ZrvLeku6215xzvMY6R6wZJN0hSXFxc5/Hjx1f06+CVikqs0ncU6/uMQm3KLlGov9Q/0V8DG/ipVtDxzw7OyclRWFhYlWaz1upQgbTnSIn25FntPWK150iJ9h6x2ptX+vGRot/fx99HqhNkVCfYqE6wj6KDjeoEGUUH+6hOsFGtQFNjF3mpjjlBxTEv7oc5cU/Mi/vxxjnJLbT6emOhpm4uVImVBjTw07mNAxQR4B6/O3jjnHgCd52X/v37L7DWphzrtnIfATTGBEgaJuneP95mrbXGmCp9Be2JHsNaO1bSWElKSUmx/fr1q8ooNcoZku6xVvM379ebMzdpysod+jajSOe0q6drezVS24TIP90nLS1Np/s1Li6x2pGd5zpad7jMaZql77cdyFNe4e9X5woP9FN8rRA1iw9W/1rBio8KVkKtEMW7Po4OC/DaS15Uxpyg8jEv7oc5cU/Mi/vx1jk5W9L2g0f0/LR1mjB/q37eId3Qp5Gu7ZWs0EBnLx3hrXPi7jxxXiryL/kslR792+n6fKcxpp61drvrFM9drvEsSYll7pfgGsvSb6dz/jqe5hpPOMb2J3oMVCJjjFKTais1qba27just2dnaML8rfpi8TZ1Saqta3olaVCruhU6epZfVKxtB35f8DLLnJ65/WCeikt+3+frhAYovlawWtQN18AWsYqPClZ8rRDX+2BFBvtX9lMHAAD4nXqRwXrswna6rneynvxujZ6Zulbv/rxZtw1solFdGsjfl0X04dkqUgAvkfRRmc8nSxot6THX+0llxm82xoxX6SIwB10F7jtJ/ymzkudgSfdaa/e5VhbtptJFYK6U9OJJHgNVJLF2iO4/t5X+PqipJszP1Lg5mzTm/YVKrB2sq3oka0RKaVfPyS86brnL2n9Euw79/nITPkZHX3+X0rCW66jdb0fv4qOCPer1hwAAoGZrEhuu/12RogWb9+vxb1brX5NW6M1Zm/SPM5traJt6NXIBPXiHchVAY0yopEGS/lJm+DFJE4wx10raLGmEa3yKpKGS1ks6LOlqSXIVvYclpbu2e+jXBWEk/VXSOEnBKl385ZuTPAaqWHiQv67tlayreiRp6sodenPWJj381Uo98/0ayRYr99vvfrd9gK+P6keVFrx+zWN+V+4SagWrbmQQfzEDAAAep3PDWvr4L900fc0uPf7NGt384SK1jd+oe85qoZ5Nop2OB1RYuQqgtTZXUp0/jO1V6aqgf9zWSrrpOPt5S9JbxxifL6nNMcaP+RioPr4+RkPa1NOQNvW0NPOAxqdv1Y5t29SlTZOjp2YmRAUrOiyQv4QBAIAayRijAS3i1LdZrL5YlKVnpq7VZW/MU++m0bp7SAu1if/zmgmAu3L21azwKO0SotQuIUppaXvVr29jp+MAAABUK18fows7J+jsdvX0/tzNemn6ep3z4iwNa19fdwxupoZ1Qp2OCJwU5+QBAAAAFRDk76vrejfSjLv666b+jfX9yh0a+PRPemDScu3JyT/5DgAHUQABAACAUxAR5K87z2yhn+7srxGpiXp/3hb1fWK6np26Vjn5RSffAeAACiAAAABwGuIigvSf89vq+7/3Ud/mMXr+h3Xq+8R0vTMnQwVFJSffAVCNKIAAAABAJWgcE6ZXLuusz//aQ03jwvTA5BU645mfNGlxlkr+cP1jwCkUQAAAAKASdWxQSx9d303jrk5VSICvbhu/WOe+NEsz1u5W6YL5gHMogAAAAEAlM8aoX/NYTbm1t54d2V4HjxTqyrd+0eVvztPSzANOx4MXowACAAAAVcTHx+j8jgn64Y6+euDcVlq1/ZCGvTRbN324UJv25DodD16I6wACAAAAVSzQz1dX90zWRZ0T9PqMjXpj1iZ9t3yHRnVJ1K0Dmyo2PMjpiPASHAEEAAAAqkl4kL9uH9xcaXf20yVdGmj8L1vV94k0PfP9Gh3KK3Q6HrwABRAAAACoZrHhQXr4vDaaentfDWgZqxd+XK++T6bprVmblF9U7HQ81GAUQAAAAMAhydGhevnSTpp8c0+1qBuuh75aqYFP/6TPF2Vy6QhUCQogAAAA4LB2CVH64LqueveaLooM9tffP16is1+cpelrdnHpCFQqFoEBAAAA3IAxRn2axahXk2h9tWy7nvpuja5+O13dGtXW4Nhi9XM6IGoECiAAAADgRnx8jIa1r68hrevqo1+26IUf1mnexgK1bLtX3RvXcToePByngAIAAABuKMDPR6N7JCntzn6KDja6f9JyFRaXOB0LHo4CCAAAALix8CB/XdoyQOt25eidORlOx4GHowACAAAAbq5DjK/6NY/Rc9PWaVd2ntNx4MEogAAAAICbM8bogXNbq6CoRP/9ZrXTceDBKIAAAACAB0iODtX1fZL1+aIs/bJpn9Nx4KEogAAAAICHuKl/E9WPDNL9k5ariAVhcAoogAAAAICHCAnw07/OaaXVOw7p/bmbnY4DD0QBBAAAADzIkDZ11atJtJ6eulZ7cvKdjgMPQwEEAAAAPIgxRg8Oa628wmI9zoIwqCAKIAAAAOBhmsSG6ZpeyfpkQaYWbtnvdBx4EAogAAAA4IFuGdBUcRGBun/SchWXWKfjwENQAAEAAAAPFBbop3+e3UrLs7L10S9bnI4DD0EBBAAAADzUue3qqWtybT31/Rrtzy1wOg48AAUQAAAA8FDGGD00vI0O5RXpie/WOB0HHoACCAAAAHiw5nXDdVWPJI1P36KlmQecjgM3RwEEAAAAPNxtZzRVndBA3T9phUpYEAYnQAEEAAAAPFxEkL/uG9pCi7ce0CcLtjodB26MAggAAADUAOd3jFdKw1p6/Ns1Oni40Ok4cFMUQAAAAKAG+HVBmAOHC/T0VBaEwbFRAAEAAIAaolX9CF3RraHen7tZK7YddDoO3BAFEAAAAKhBbh/UXLVCAvTApBWylgVh8HsUQAAAAKAGiQzx191DWmj+5v36bGGW03HgZiiAAAAAQA1zUecEdUiM0n+/Wa3sPBaEwW8ogAAAAEAN4+Nj9NDw1tqbm6/npq5zOg7cCAUQAAAAqIHaJUTpki4N9M7PGVqz45DTceAmKIAAAABADXXn4OYKD/LT/ZOWsyAMJFEAAQAAgBqrVmiA7jyzueZt2qfJS7Y5HQdugAIIAAAA1GCjUhuoTXyE/jNllXLyi5yOA4dRAAEAAIAazNfH6KHhbbQzO18v/sCCMN6OAggAAADUcJ0a1NKIlAS9OWuT1u/KcToOHEQBBAAAALzAXUNaKCTAVw9OXsGCMF6MAggAAAB4geiwQN0xuLlmrd+jb5bvcDoOHEIBBAAAALzEZV0bqEXdcD3y1UodLmBBGG9EAQQAAAC8hJ+vjx4+r422HczTy9PXOx0HDqAAAgAAAF4kNam2LugYr9dnbNKmPblOx0E1owACAAAAXuaes1oowM9H//6SBWG8DQUQAAAA8DKxEUH62xlNlbZmt6au3Ol0HFQjCiAAAADghUb3SFKzuDA99NVK5RUWOx0H1YQCCAAAAHghf18f/XtYG2XuP6JX0zY4HQfVhAIIAAAAeKnujevo3Pb19epPG7Rl72Gn46AaUAABAAAAL3bf0Bby8zF66KuVTkdBNaAAAgAAAF6sXmSwbh3YVNNW7dT01bucjoMqRgEEAAAAvNw1PZPVKCZUD365ggVhajgKIAAAAODlAvx89O9hrbV572G9MXOj03FQhSiAAAAAANS7aYzOalNXL01fr6wDR5yOgypCAQQAAAAgSfq/c1pJkh5hQZgaiwIIAAAAQJIUHxWsm/s30TfLd2jmut1Ox0EVKFcBNMZEGWMmGmNWG2NWGWO6G2MeNMZkGWMWu96Gltn+XmPMemPMGmPMmWXGh7jG1htj7ikznmyMmeca/9gYE+AaD3R9vt51e1IlPncAAAAAf3Bd70ZqWCdED0xeoYKiEqfjoJKV9wjg85K+tda2kNRe0irX+LPW2g6utymSZIxpJWmUpNaShkh6xRjja4zxlfSypLMktZJ0iWtbSXrcta8mkvZLutY1fq2k/a7xZ13bAQAAAKgiQf6+evDc1tq4O1dvzd7kdBxUspMWQGNMpKQ+kt6UJGttgbX2wAnuMlzSeGttvrV2k6T1krq43tZbazdaawskjZc03BhjJA2QNNF1/3cknVdmX++4Pp4oaaBrewAAAABVpH+LWJ3RMk4v/LBOOw7mOR0HlcivHNskS9ot6W1jTHtJCyTd5rrtZmPMlZLmS7rDWrtfUrykuWXun+kak6StfxjvKqmOpAPW2qJjbB//632stUXGmIOu7feUDWiMuUHSDZIUFxentLS0cjwtnKqcnBy+xm6GOXFPzIv7YU7cE/PifpgT9+PEnAyOKVHammLd9naabuwQVK2P7Sk88XulPAXQT1InSbdYa+cZY56XdI+klyQ9LMm63j8t6ZqqCnoi1tqxksZKUkpKiu3Xr58TMbxGWlqa+Bq7F+bEPTEv7oc5cU/Mi/thTtyPU3OS5b9Wz/+wTrcltlGPxtHV/vjuzhO/V8rzGsBMSZnW2nmuzydK6mSt3WmtLbbWlkh6XaWneEpSlqTEMvdPcI0db3yvpChjjN8fxn+3L9ftka7tAQAAAFSxG/s1VkKtYD04eYUKi1kQpiY4aQG01u6QtNUY09w1NFDSSmNMvTKbnS9puevjyZJGuVbwTJbUVNIvktIlNXWt+Bmg0oViJltrraTpki5y3X+0pEll9jXa9fFFkn50bQ8AAACgigX5++r+c1pp7c4cvTMnw+k4qATlOQVUkm6R9IGruG2UdLWkF4wxHVR6CmiGpL9IkrV2hTFmgqSVkook3WStLZYkY8zNkr6T5CvpLWvtCtf+75Y03hjziKRFci0443r/njFmvaR9Ki2NAAAAAKrJoFZx6tc8Rs9NW6dhHeorNpzXA3qychVAa+1iSSl/GL7iBNs/KunRY4xPkTTlGOMb9dsppGXH8yRdXJ6MAAAAACqfMUYPnNtaZz47Q49NWa1nRnZwOhJOQ3mvAwgAAADASyVHh+r6Psn6bFGW0jP2OR0Hp4ECCAAAAOCkburfRPUjg/SvL5ariAVhPBYFEAAAAMBJhQT46f/OaaXVOw7pg3lbnI6DU0QBBAAAAFAuZ7Wpq15NovX092u0Jyff6Tg4BRRAAAAAAOVijNGDw1rpcEGxnvh2tdNxcAoogAAAAADKrUlsuK7tlawJ8zO1cMt+p+OggiiAAAAAACrkloFNFRcRqAcmrVBxiXU6DiqAAggAAACgQsIC/XTf0JZalnVQ49NZEMaTUAABAAAAVNiw9vXVNbm2nvxujfbnFjgdB+VEAQQAAABQYcYYPTS8jQ7lFenJ79c4HQflRAEEAAAAcEqa1w3X6O5J+uiXLVqaecDpOCgHCiAAAACAU/a3QU1VJzRQ909aoRIWhHF7FEAAAAAApywiyF/3ntVCi7ce0MQFmU7HwUlQAAEAAACclgs6xSulYS09/u1qHTxc6HQcnAAFEAAAAMBpMcbo38Nba//hAj0zlQVh3BkFEAAAAMBpa10/Upd3a6j35m7Wim0HnY6D46AAAgAAAKgUdwxqrqiQAD0waYWsZUEYd0QBBAAAAFApIkP8dfeQ5pq/eb8+X5TldBwcAwUQAAAAQKW5uHOi2idG6T9TVutQHgvCuBsKIAAAAIBK4+Nj9PDw1tqbm6/npq1zOg7+gAIIAAAAoFK1S4jSqNQGGjcnQ2t2HHI6DsqgAAIAAACodHed2VzhQX56YPJyFoRxIxRAAAAAAJWuVmiA/jG4ueZu3Kcvl253Og5cKIAAAAAAqsQlXRqoTXyEHv16pXLzi5yOA1EAAQAAAFQRXx+jfw9ro53Z+XrhRxaEcQcUQAAAAABVpnPDWrq4c4LenLlJ63flOB3H61EAAQAAAFSpu89qoeAAXz04eQULwjiMAggAAACgSkWHBeqOQc00a/0efbt8h9NxvBoFEAAAAECVu7xbQ7WoG65Hp6xScQlHAZ1CAQQAAABQ5fx8fXTrwKbK3H9EM9btdjqO16IAAgAAAKgWZ7SMU+3QAE1I3+p0FK9FAQQAAABQLQL8fHRBx3hNW7VTe3PynY7jlSiAAAAAAKrNyNREFRZbfb4oy+koXokCCAAAAKDaNI0LV8cGURqfvpVLQjiAAggAAACgWo1KTdT6XTlauOWA01G8DgUQAAAAQLU6u119hQT4shiMAyiAAAAAAKpVWKCfzmlXT18u3aac/CKn43gVCiAAAACAajcytYEOFxTr66XbnI7iVSiAAAAAAKpdpwZRahIbpo85DbRaUQABAAAAVDtjjEamJGrhlgNat/OQ03G8BgUQAAAAgCPO7xQvPx/DUcBqRAEEAAAA4IjosEANahWnzxZlqaCoxOk4XoECCAAAAMAxI1ITtS+3QD+s2ul0FK9AAQQAAADgmD5NY1QvMkjjOQ20WlAAAQAAADjG18fo4s4JmrFut7YdOOJ0nBqPAggAAADAURenJMpaaeKCTKej1HgUQAAAAACOSqwdop5N6mjC/K0qKbFOx6nRKIAAAAAAHDciJVGZ+49ozoa9Tkep0SiAAAAAABx3Zuu6igz218fzWQymKlEAAQAAADguyN9X53eM13fLd2h/boHTcWosCiAAAAAAtzAiJVEFxSX6YnGW01FqLAogAAAAALfQqn6E2iVE6uP0rbKWxWCqAgUQAAAAgNsYkZKo1TsOaVnWQaej1EgUQAAAAABuY1iH+gry99H4dBaDqQoUQAAAAABuIyLIX0Pb1tOXi7fpSEGx03FqHAogAAAAALcyMiVRh/KLNGXZdqej1DgUQAAAAABupUtybSVHh+pjTgOtdBRAAAAAAG7FGKOLUxL0S8Y+bdyd43ScGoUCCAAAAMDtXNQpQb4+RhPmZzodpUahAAIAAABwO7ERQerfPFafLsxUYXGJ03FqDAogAAAAALc0MjVRuw/la/rqXU5HqTEogAAAAADcUv/mMYoND9SE+SwGU1nKVQCNMVHGmInGmNXGmFXGmO7GmNrGmKnGmHWu97Vc2xpjzAvGmPXGmKXGmE5l9jPatf06Y8zoMuOdjTHLXPd5wRhjXOPHfAwAAAAANZ+fr48u7Jyg6Wt2a2d2ntNxaoTyHgF8XtK31toWktpLWiXpHkk/WGubSvrB9bkknSWpqevtBkmvSqVlTtIDkrpK6iLpgTKF7lVJ15e53xDX+PEeAwAAAIAXGJGSqOISq4kLWAymMpy0ABpjIiX1kfSmJFlrC6y1ByQNl/SOa7N3JJ3n+ni4pHdtqbmSoowx9SSdKWmqtXaftXa/pKmShrhui7DWzrXWWknv/mFfx3oMAAAAAF4gOTpUXZJr65P5W1VaF3A6/MqxTbKk3ZLeNsa0l7RA0m2S4qy1213b7JAU5/o4XlLZk3QzXWMnGs88xrhO8Bi/Y4y5QaVHGxUXF6e0tLRyPC2cqpycHL7GboY5cU/Mi/thTtwT8+J+mBP34+1z0j6sUK9vKtD/Pv9RLWr7Oh3nKE+cl/IUQD9JnSTdYq2dZ4x5Xn84FdNaa40xVVrHT/QY1tqxksZKUkpKiu3Xr19VRvF6aWlp4mvsXpgT98S8uB/mxD0xL+6HOXE/3j4nXQuKNX7tNK0trKMx/To4HecoT5yX8rwGMFNSprV2nuvziSothDtdp2/K9f7XtVmzJCWWuX+Ca+xE4wnHGNcJHgMAAACAlwgO8NWwDvX19bLtOnik0Ok4Hu2kBdBau0PSVmNMc9fQQEkrJU2W9OtKnqMlTXJ9PFnSla7VQLtJOug6jfM7SYONMbVci78MlvSd67ZsY0w31+qfV/5hX8d6DAAAAABeZFRqA+UXlWjykm1OR/Fo5TkFVJJukfSBMSZA0kZJV6u0PE4wxlwrabOkEa5tp0gaKmm9pMOubWWt3WeMeVhSumu7h6y1+1wf/1XSOEnBkr5xvUnSY8d5DAAAAABepE18hFrWi9CE9K26oltDp+N4rHIVQGvtYkkpx7hp4DG2tZJuOs5+3pL01jHG50tqc4zxvcd6DAAAAADexRijkSkJevDLlVqx7aBa1490OpJHKu91AAEAAADAUed1jFeAn48mpG89+cY4JgogAAAAAI8QFRKgIa3r6vNFWcorLHY6jkeiAAIAAADwGCNTE5WdV6TvVuxwOopHogACAAAA8BjdG9VRYu1gfcxpoKeEAggAAADAY/j4GI3onKg5G/Zqy97DTsfxOBRAAAAAAB7lopQE+RjpkwUcBawoCiAAAAAAj1IvMlh9msXok/mZKi6xTsfxKBRAAAAAAB5nVGqidmTnacba3U5H8SgUQAAAAAAeZ0CLONUJDWAxmAqiAAIAAADwOAF+PrqgU7ymrdqp3YfynY7jMSiAAAAAADzSyNREFZVYfb4o0+koHoMCCAAAAMAjNYkNV+eGtfRx+lZZy2Iw5UEBBAAAAOCxRqYkasPuXC3cst/pKB6BAggAAADAY53drp5CA3w1/hcWgykPCiAAAAAAjxUa6Kdz29fX18u2Kye/yOk4bo8CCAAAAMCjjUhN1OGCYn21ZJvTUdweBRAAAACAR+uYGKWmsWEazzUBT4oCCAAAAMCjGWM0MjVRi7ce0Nqdh5yO49YogAAAAAA83gWdEuTva/QxRwFPiAIIAAAAwOPVDg3QoFZx+mxhpvKLip2O47YogAAAAABqhBEpidp/uFDTVu5yOorbogACAAAAqBF6N41R/cggfTyf00CPhwIIAAAAoEbw9TG6KCVRM9ftVtaBI07HcUsUQAAAAAA1xsWdEyRJn3AU8JgogAAAAABqjMTaIerVJFqfzM9USYl1Oo7boQACAAAAqFFGpCQq68ARzd6wx+kobocCCAAAAKBGGdw6TlEh/hrPNQH/hAIIAAAAoEYJ9PPV+R3jNXXFTu3PLXA6jluhAAIAAACocUamJqqguESfL8pyOopboQACAAAAqHFa1I1Q+4RIfZy+VdayGMyvKIAAAAAAaqQRqYlas/OQlmQedDqK26AAAgAAAKiRhrWvr2B/X33MYjBHUQABAAAA1EjhQf4a2raevlyyTYcLipyO4xYogAAAAABqrJGpicrJL9LXS7c7HcUtUAABAAAA1FipSbXUKDpUE+ZzGqhEAQQAAABQgxljNCI1UekZ+7Vhd47TcRxHAQQAAABQo13QKV6+PkYTWAyGAggAAACgZosND9KAFrH6dGGmCotLnI7jKAogAAAAgBpvVGqi9uQU6MfVu5yO4igKIAAAAIAar2+zGMWGB3r9aaAUQAAAAAA1np+vjy7qnKDpa3Zpx8E8p+M4hgIIAAAAwCuMSElUiZU+XZjpdBTHUAABAAAAeIWk6FB1a1RbE+ZvVUmJdTqOIyiAAAAAALzGyNREbd57WHM37XU6iiMogAAAAAC8xllt6ik8yM9rF4OhAAIAAADwGkH+vjqvQ7y+Wb5DB48UOh2n2lEAAQAAAHiVkamJyi8q0eTFWU5HqXYUQAAAAABepU18pFrVi9B4LzwNlAIIAAAAwOuM6pKoFduytTzroNNRqhUFEAAAAIDXGd4+XgF+Ppow37uOAlIAAQAAAHidyBB/ndWmrj5flKW8wmKn41QbCiAAAAAArzQyNVGH8or07fIdTkepNhRAAAAAAF6pW3IdNagdoo+9aDEYCiAAAAAAr+TjYzQiJUE/b9yrzXtznY5TLSiAAAAAALzWRZ0T5WPkNYvBUAABAAAAeK26kUHq1zxWExdkqqi4xOk4VY4CCAAAAMCrjUhJ1M7sfM1Yt9vpKFWOAggAAADAqw1sGavosACN/6XmnwZKAQQAAADg1fx9fXRhpwT9uHqXdh/KdzpOlaIAAgAAAPB6F6ckqqjE6rOFmU5HqVIUQAAAAABer0lsmFIa1tLH6VtlrXU6TpWhAAIAAACApJGpidq4J1fzN+93OkqVKVcBNMZkGGOWGWMWG2Pmu8YeNMZkucYWG2OGltn+XmPMemPMGmPMmWXGh7jG1htj7ikznmyMmeca/9gYE+AaD3R9vt51e1KlPXMAAAAAKOPsdvUUFuinj9Nr7mIwFTkC2N9a28Fam1Jm7FnXWAdr7RRJMsa0kjRKUmtJQyS9YozxNcb4SnpZ0lmSWkm6xLWtJD3u2lcTSfslXesav1bSftf4s67tAAAAAKDShQT46dz29fT10u06lFfodJwqURWngA6XNN5am2+t3SRpvaQurrf11tqN1toCSeMlDTfGGEkDJE103f8dSeeV2dc7ro8nShro2h4AAAAAKt2IlEQdKSzWl0u2Ox2lSviVczsr6XtjjJX0P2vtWNf4zcaYKyXNl3SHtXa/pHhJc8vcN9M1Jklb/zDeVVIdSQestUXH2D7+1/tYa4uMMQdd2+8pG84Yc4OkGyQpLi5OaWlp5XxaOBU5OTl8jd0Mc+KemBf3w5y4J+bF/TAn7oc5qT7WWiWEGb3x4wrVP7LxhNt64ryUtwD2stZmGWNiJU01xqyW9Kqkh1VaDh+W9LSka6om5om5CulYSUpJSbH9+vVzIobXSEtLE19j98KcuCfmxf0wJ+6JeXE/zIn7YU6q19X+m/TwVytVt0UntagbcdztPHFeynUKqLU2y/V+l6TPJXWx1u601hZba0skva7SUzwlKUtSYpm7J7jGjje+V1KUMcbvD+O/25fr9kjX9gAAAABQJc7vGC9/X1MjF4M5aQE0xoQaY8J//VjSYEnLjTH1ymx2vqTlro8nSxrlWsEzWVJTSb9ISpfU1LXiZ4BKF4qZbEsvsjFd0kWu+4+WNKnMvka7Pr5I0o+2Jl+UAwAAAIDjaocGaHDruvp8UZbyi4qdjlOpynMEME7SLGPMEpUWua+ttd9KesJ1aYilkvpL+rskWWtXSJogaaWkbyXd5DpSWCTpZknfSVolaYJrW0m6W9Ltxpj1Kn2N35uu8Tcl1XGN3y7p6KUjAAAAAKCqjExJ1IHDhZq6cqfTUSrVSV8DaK3dKKn9McavOMF9HpX06DHGp0iacpzH6HKM8TxJF58sIwAAAABUpl5NohUfFayP07fqnHb1nY5TaariMhAAAAAA4NF8fIwuTknQrPV7lLn/sNNxKg0FEAAAAACO4eKU0jUsP5mf6XCSykMBBAAAAIBjiI8KVq8m0Zq4IFPFJTVjLUoKIAAAAAAcx8jURGUdOKJZ6/c4HaVSUAABAAAA4DgGtYpTrRB/Tagh1wSkAAIAAADAcQT6+er8jgn6fuUO7cstcDrOaaMAAgAAAMAJjExNVGGx1WcLPX8xGAogAAAAAJxA87rh6pAYpQnzt8paz14MhgIIAAAAACcxMjVRa3fmaPHWA05HOS0UQAAAAAA4iXPa1VOwv68mzPfsxWAogAAAAABwEuFB/jq7XT1NXrxNuflFTsc5ZRRAAAAAACiHUamJyi0o1tfLtjsd5ZRRAAEAAACgHDo3rKVGMaEefU1ACiAAAAAAlIMxRiNTEjV/836t33XI6TinhAIIAAAAAOV0QacE+fkYTZjvmdcEpAACAAAAQDnFhAdqYMtYfbYwU0UlnndNQAogAAAAAFTAyNRE7ckp0OJdxU5HqTAKIAAAAABUQJ+mMaobEaSZWZ53OQg/pwMAAAAAgCfx8/XRC5d01LY1i52OUmEcAQQAAACACuqSXFtRQZ5XpzwvMQAAAADglFAAAQAAAMBLUAABAAAAwEtQAAEAAADAS1AAAQAAAMBLUAABAAAAwEtQAAEAAADAS1AAAQAAAMBLUAABAAAAwEtQAAEAAADAS1AAAQAAAMBLUAABAAAAwEtQAAEAAADAS1AAAQAAAMBLUAABAAAAwEsYa63TGSqVMWa3pM1O56jhoiXtcToEfoc5cU/Mi/thTtwT8+J+mBP3w5y4J3edl4bW2phj3VDjCiCqnjFmvrU2xekc+A1z4p6YF/fDnLgn5sX9MCfuhzlxT544L5wCCgAAAABeggIIAAAAAF6CAohTMdbpAPgT5sQ9MS/uhzlxT8yL+2FO3A9z4p48bl54DSAAAAAAeAmOAAIAAACAl6AAAgAAAICXoABCxphEY8x0Y8xKY8wKY8xtrvHaxpipxph1rve1XOPGGPOCMWa9MWapMaZTmX01MMZ8b4xZ5dpfkkNPy6NV8pw84drHKtc2xqnn5elOYV5aGGN+NsbkG2P+8Yd9DTHGrHHN2T1OPJ+aoLLm5Hj7wampzO8V1+2+xphFxpivqvu51BSV/P9XlDFmojFmtetnS3cnnlNNUMnz8nfXPpYbYz4yxgQ58Zw83SnMyWWu372WGWPmGGPal9mXW/6spwBCkook3WGtbSWpm6SbjDGtJN0j6QdrbVNJP7g+l6SzJDV1vd0g6dUy+3pX0pPW2paSukjaVT1PocaplDkxxvSQ1FNSO0ltJKVK6luNz6Omqei87JN0q6Snyu7EGOMr6WWVzlsrSZe49oOKq5Q5OcF+cGoqa15+dZukVVUbucarzDl5XtK31toWktqLuTkdlfVzJd41nmKtbSPJV9Ko6nkKNU5F52STpL7W2raSHpZrURh3/llPAYSstduttQtdHx9S6X/k8ZKGS3rHtdk7ks5zfTxc0ru21FxJUcaYeq5/1H7W2qmufeVYaw9X41OpMSprTiRZSUGSAiQFSvKXtLO6nkdNU9F5sdbustamSyr8w666SFpvrd1orS2QNN61D1RQZc3JCfaDU1CJ3ysyxiRIOlvSG1WfvOaqrDkxxkRK6iPpTdd2BdbaA9XwFGqkyvxekeQnKdgY4ycpRNK2qk1fM53CnMyx1u53jc+VlOD62G1/1lMA8Tum9JTNjpLmSYqz1m533bRDUpzr43hJW8vcLdM11kzSAWPMZ65TdZ50/fUDp+F05sRa+7Ok6ZK2u96+s9byl9pKUM55OZ7jfQ/hNJzmnBxvPzhNlTAvz0m6S1JJVeTzRqc5J8mSdkt62/Wz/g1jTGiVhfUipzMv1toslR4V3KLSn/cHrbXfV11a73AKc3KtpG9cH7vtz3oKII4yxoRJ+lTS36y12WVvs6XXCznZNUP8JPWW9A+VnmrYSNJVlZ/Ue5zunBhjmkhqqdK/RsVLGmCM6V1Fcb1GJXyvoJJV1pycaD+ouEr4P+wcSbustQuqLqV3qaSf9Z0kvWqt7SgpV7+dCodTVAnfK7VUenQpWVJ9SaHGmMurKK5XqOicGGP6q7QA3l1tIU8RBRCSJGOMv0r/kX9grf3MNbzTdRqhXO9/fT1flqTEMndPcI1lSlrsOtRdJOkLlf6QwCmopDk5X9Jc1+m4OSr9qxQv1j8NFZyX4znefOEUVNKcHG8/OEWVNC89JQ0zxmSo9PSpAcaY96soco1XSXOSKSnTWvvrEfKJ4mf9aamkeTlD0iZr7W5rbaGkzyT1qKrMNV1F58QY006lp6kPt9budQ277c96CiBkjDEqPZd/lbX2mTI3TZY02vXxaEmTyoxfaUp1U+lpBtslpav0tWcxru0GSFpZ5U+gBqrEOdkiqa8xxs/1n1lf8WL9U3YK83I86ZKaGmOSjTEBKn2h/uTKzusNKmtOTrAfnILKmhdr7b3W2gRrbZJKv09+tNZyVOMUVOKc7JC01RjT3DU0UPysP2WV+HNli6RuxpgQ1z4Hip/3p6Sic2KMaaDSwn2FtXZtme3d9me9KT2CCW9mjOklaaakZfrtNRb3qfR85wmSGkjaLGmEtXaf6xvjJUlDJB2WdLW1dr5rX4MkPS3JSFog6QbXC19RAZU1J67XYL6i0hfsW5Wu2nZ7tT6ZGuQU5qWupPmSIlzb50hqZa3NNsYMVelrm3wlvWWtfbQ6n0tNUVlzotKVcv+0H2vtlGp6KjVKZX6vlNlnP0n/sNaeU01Po0ap5P+/Oqj0aEeApI0q/ZmzX6iwSp6Xf0saqdJVLBdJus5am1+dz6cmOIU5eUPSha4xSSqy1qa49uWWP+spgAAAAADgJTgFFAAAAAC8BAUQAAAAALwEBRAAAAAAvAQFEAAAAAC8BAUQAAAAALwEBRAAgCpgjBljjLnS6RwAAJTFZSAAAAAAwEtwBBAAgHIyxnxhjFlgjFlhjLnBNZZjjHnUGLPEGDPXGBPnGn/QGPMP18cdXLctNcZ8boyp5eTzAAB4LwogAADld421trOkFEm3GmPqSAqVNNda217SDEnXH+N+70q621rbTtIySQ9UV2AAAMqiAAIAUH63GmOWSJorKVFSU0kFkr5y3b5AUlLZOxhjIiVFWWt/cg29I6lPtaQFAOAP/JwOAACAJzDG9JN0hqTu1trDxpg0SUGSCu1vL6gvFj9bAQBujCOAAACUT6Sk/a7y10JSt/LcyVp7UNJ+Y0xv19AVkn46wV0AAKgy/JUSAIDy+VbSGGPMKklrVHoaaHmNlvSaMSZE0kZJV1dBPgAATorLQAAAAACAl+AUUAAAAADwEhRAAAAAAPASFEAAAAAA8BIUQAAAAADwEhRAAAAAAPASFEAAAAAA8BIUQAAAAADwEv8PPGBbIbkmmDYAAAAASUVORK5CYII=\n",
- "text/plain": [
- "<Figure size 1080x504 with 1 Axes>"
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "Z9xxlqmM6kc4"
+ },
+ "outputs": [],
+ "source": [
+ "nacimientos = nacimientos[[\"anio\",\"edad_madre_grupo\",\"instruccion_madre\",\"nacimientos_cantidad\"]]"
]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "nac_por_año.plot(kind= \"line\",figsize= (15,7),grid=True)\n",
- "plt.legend([\"Cantidad de nacimientos\"])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "ef0DL8Gh8jLf"
- },
- "source": [
- "Hay un problema con el gráfico, el eje y no comienza en 0 y hace que el gráfico se vea mal, esto se soluciona indicando el lÃmite inferior de y:\n",
- "También establecemos la leyenda del gráfico"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 459
},
- "id": "D8TfEws58gvQ",
- "outputId": "eb6842af-3d5b-40f1-fc04-689461f5abb1"
- },
- "outputs": [
{
- "data": {
- "text/plain": [
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+ "execution_count": null,
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+ },
+ "id": "4lOR_MP7sDv-",
+ "outputId": "f407388c-6bcd-45fe-e984-96be9affface"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " anio edad_madre_grupo instruccion_madre \\\n",
+ "0 2005 30 a 34 Secundaria/Polimodal Incompleta \n",
+ "1 2005 30 a 34 Primaria/C. EGB Completa \n",
+ "2 2005 25 a 29 Secundaria/Polimodal Completa \n",
+ "3 2005 30 a 34 Secundaria/Polimodal Incompleta \n",
+ "4 2005 25 a 29 Secundaria/Polimodal Completa \n",
+ "... ... ... ... \n",
+ "497969 2017 30 a 34 Secundaria/Polimodal Completa \n",
+ "497970 2017 30 a 34 Secundaria/Polimodal Completa \n",
+ "497971 2007 25 a 29 Terciaria/Universitaria Completa \n",
+ "497972 2017 30 a 34 Terciaria/Universitaria Incompleta \n",
+ "497973 2017 Sin especificar Sin especificar \n",
+ "\n",
+ " nacimientos_cantidad \n",
+ "0 1 \n",
+ "1 2 \n",
+ "2 6 \n",
+ "3 5 \n",
+ "4 1 \n",
+ "... ... \n",
+ "497969 1 \n",
+ "497970 1 \n",
+ "497971 1 \n",
+ "497972 1 \n",
+ "497973 10 \n",
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+ " const element = document.querySelector('#df-7565c802-91df-4f6a-adc3-3a8a96800402');\n",
+ " const dataTable =\n",
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+ " [key], {});\n",
+ " if (!dataTable) return;\n",
+ "\n",
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
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+ "source": [
+ "nacimientos"
]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
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\n",
- "text/plain": [
- "<Figure size 1080x504 with 1 Axes>"
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "HY9NHf7Mw2Z8"
+ },
+ "source": [
+ "## Pregunta: ¿Cuántos nacimientos hay por año en el paÃs? <a name=\"paragraph2\"></a>\n"
]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "nac_por_año.plot(kind= \"line\",figsize= (15,7),ylim=(0),grid=True)\n",
- "plt.legend([\"Cantidad de nacimientos\"])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "_NpC6hVyzwSc"
- },
- "source": [
- "## Pregunta: ¿Cuántos nacimientos hay por año en el paÃs según el grupo etario de la madre? <a name=\"paragraph3\"></a>\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "IgOe-p4pCly3"
- },
- "source": [
- "En este caso necesitamos saber el año, el grupo etario de la madre y la cantidad:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {
- "id": "glA4XLTT86wg"
- },
- "outputs": [],
- "source": [
- "nac_edad_madre = nacimientos[[\"anio\",\"edad_madre_grupo\",\"nacimientos_cantidad\"]]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "qCnqL52JC-SA"
- },
- "source": [
- "Hay algunos nacimientos donde el grupo etario de la madre no fue especificado, por lo tanto no podemos sacar conclusiones. \n",
- "\n",
- "Si consultamos cuáles son los valores únicos que tiene la columna \"edad_madre:grupo\" nos encontramos con filas que no tienen la información, o sea no son filas significativas."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array(['30 a 34', '25 a 29', '20 a 24', '15 a 19', 'Sin especificar',\n",
- " '40 a 44', 'De 45 y más', ' Menor de 15', '35 a 39'], dtype=object)"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "XJ_i_X3IA5oI"
+ },
+ "source": [
+ "Para esto vamos a necesitar menos información que antes, solo la cantidad de nacimientos y el año en el que ocurrieron.\n",
+ "Se abrevia nacimientos como nac para mayor legibilidad:"
]
- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "nac_edad_madre[\"edad_madre_grupo\"].unique()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Eliminamos las filas que tienen como valor 'Sin especificar'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "If8D3jpHC93r",
- "outputId": "073e094d-4b1a-489b-bc72-e76982078ee7"
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/tmp/ipykernel_47145/1786625126.py:1: SettingWithCopyWarning: \n",
- "A value is trying to be set on a copy of a slice from a DataFrame\n",
- "\n",
- "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
- " nac_edad_madre.drop(nac_edad_madre.index[nac_edad_madre['edad_madre_grupo'] == \"Sin especificar\"], inplace = True)\n"
- ]
- }
- ],
- "source": [
- "nac_edad_madre.drop(nac_edad_madre.index[nac_edad_madre['edad_madre_grupo'] == \"Sin especificar\"], inplace = True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "ccDvLT5BDpKQ"
- },
- "source": [
- "\n",
- "\n",
- "\n",
- "Ahora con la información filtrada, hay que agrupar por dos criterios, primero por el año y luego por el grupo etario y finalmente sumar las cantidades de estos grupos:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 238
- },
- "id": "-iJyxCfUC2SY",
- "outputId": "292a3e1f-01c5-4907-8876-f7aef2a447b2"
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+ " anio nacimientos_cantidad\n",
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+ " [key], {});\n",
+ " if (!dataTable) return;\n",
+ "\n",
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
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+ " + ' to learn more about interactive tables.';\n",
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+ " }\n",
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],
- "text/plain": [
- " nacimientos_cantidad\n",
- "anio edad_madre_grupo \n",
- "2005 Menor de 15 2699\n",
- " 15 a 19 104410\n",
- " 20 a 24 177813\n",
- " 25 a 29 182778\n",
- " 30 a 34 141689"
+ "source": [
+ "nac_por_año = nacimientos[[\"anio\",\"nacimientos_cantidad\"]]\n",
+ "nac_por_año.head()"
]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "nac_edad_madre = nac_edad_madre.groupby([\"anio\",\"edad_madre_grupo\"]).sum()\n",
- "nac_edad_madre.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "AO6pJxA6EIKF"
- },
- "source": [
- "La información como está no puede ser graficada, ya que está toda junta en 2 grupos, asi que usamos la función .unstack(), que despliega la información para que se puede visualizar"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 269
- },
- "id": "l13_EjwlEWhK",
- "outputId": "b6f1b498-b439-4a85-9049-5d09dadb6520"
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- "outputs": [
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- " <td>19879</td>\n",
- " <td>1497</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2008</th>\n",
- " <td>2937</td>\n",
- " <td>112034</td>\n",
- " <td>183265</td>\n",
- " <td>184978</td>\n",
- " <td>153805</td>\n",
- " <td>80258</td>\n",
- " <td>20824</td>\n",
- " <td>1630</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2009</th>\n",
- " <td>3346</td>\n",
- " <td>113478</td>\n",
- " <td>182747</td>\n",
- " <td>178935</td>\n",
- " <td>155464</td>\n",
- " <td>81397</td>\n",
- " <td>20840</td>\n",
- " <td>1546</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "D6Dps9axBQrp"
+ },
+ "source": [
+ "Hay un problema con esta información, como la cantidad de nacimientos no está agregada por año sino que también por otros factores, hay que agrupar por año y sumar los nacimientos de cada grupo:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
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+ },
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+ " nacimientos_cantidad\n",
+ "anio \n",
+ "2005 712220\n",
+ "2006 696451\n",
+ "2007 700792\n",
+ "2008 746460\n",
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+ " const element = document.querySelector('#df-c2e7d7ab-d88c-41c9-9fe3-47b19a1d223a');\n",
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+ " [key], {});\n",
+ " if (!dataTable) return;\n",
+ "\n",
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+ " + ' to learn more about interactive tables.';\n",
+ " element.innerHTML = '';\n",
+ " dataTable['output_type'] = 'display_data';\n",
+ " await google.colab.output.renderOutput(dataTable, element);\n",
+ " const docLink = document.createElement('div');\n",
+ " docLink.innerHTML = docLinkHtml;\n",
+ " element.appendChild(docLink);\n",
+ " }\n",
+ " </script>\n",
+ " </div>\n",
+ " </div>\n",
+ " "
+ ]
+ },
+ "metadata": {},
+ "execution_count": 7
+ }
],
- "text/plain": [
- " nacimientos_cantidad \\\n",
- "edad_madre_grupo Menor de 15 15 a 19 20 a 24 25 a 29 30 a 34 35 a 39 \n",
- "anio \n",
- "2005 2699 104410 177813 182778 141689 73194 \n",
- "2006 2766 103885 174342 176931 139003 73177 \n",
- "2007 2841 106720 174679 175632 139393 73532 \n",
- "2008 2937 112034 183265 184978 153805 80258 \n",
- "2009 3346 113478 182747 178935 155464 81397 \n",
- "\n",
- " \n",
- "edad_madre_grupo 40 a 44 De 45 y más \n",
- "anio \n",
- "2005 21382 1575 \n",
- "2006 19866 1488 \n",
- "2007 19879 1497 \n",
- "2008 20824 1630 \n",
- "2009 20840 1546 "
+ "source": [
+ "nac_por_año = nac_por_año.groupby(\"anio\").sum()\n",
+ "nac_por_año.head()"
]
- },
- "execution_count": 22,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "nac_edad_madre = nac_edad_madre.unstack()\n",
- "nac_edad_madre.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "tNJtFS-WEc0l"
- },
- "source": [
- "Finalmente graficamos como en los ejemplos anteriores, con la diferencia de que ahora hay varios grupos lo que nos da varias lÃneas. No existe el mismo problema del eje y ya que ciertos grupos tienen muy pocos nacimientos y esto hace que el eje empiece en 0:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 894
},
- "id": "o6puSivZDjIQ",
- "outputId": "5f231d70-edcc-4eae-b7e2-91bd01715eba"
- },
- "outputs": [
{
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x7f6a31bf91e0>"
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xPLMRoEUmicq"
+ },
+ "source": [
+ "Ahora está mejor.\n",
+ "Vamos a graficarlo con un simple gráfico de lÃnea:"
]
- },
- "execution_count": 23,
- "metadata": {},
- "output_type": "execute_result"
},
{
- "data": {
- "image/png": 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- "text/plain": [
- "<Figure size 2160x1080 with 1 Axes>"
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 459
+ },
+ "id": "19u3wAvl0jIN",
+ "outputId": "97bf73c5-1a0e-4ed4-d2e2-a7d6c12536e7"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x7f705be255d0>"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 8
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<Figure size 1080x504 with 1 Axes>"
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {
+ "needs_background": "light"
+ }
+ }
+ ],
+ "source": [
+ "nac_por_año.plot(kind= \"line\",figsize= (15,7),grid=True)\n",
+ "plt.legend([\"Cantidad de nacimientos\"])"
]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "nac_edad_madre.plot(kind= \"line\",figsize= (30,15),grid=True)\n",
- "plt.legend([\"Menor de 15\", \"15 a 19\", \"20 a 24\", \"25 a 29\", \"30 a 34\", \"35 a 39\", \"40 a 44\", \"De 45 y más\"])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "bPtagRwyz4t4"
- },
- "source": [
- "## Pregunta: ¿Que proporción de madres tuvo hijos antes de los 20? <a name=\"paragraph4\"></a>\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "BoE73R4ysDwD"
- },
- "source": [
- "Igual que los ejemplos anteriores, obtenemos las columnas de interés. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
},
- "id": "s_CVX6d0sDwD",
- "outputId": "96f454ce-3d71-4621-8f0f-b8f5bd69f9aa"
- },
- "outputs": [
{
- "data": {
- "text/plain": [
- "array(['30 a 34', '25 a 29', '20 a 24', '15 a 19', 'Sin especificar',\n",
- " '40 a 44', 'De 45 y más', ' Menor de 15', '35 a 39'], dtype=object)"
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ef0DL8Gh8jLf"
+ },
+ "source": [
+ "Hay un problema con el gráfico, el eje y no comienza en 0 y hace que el gráfico se vea mal, esto se soluciona indicando el lÃmite inferior de y:\n",
+ "También establecemos la leyenda del gráfico"
]
- },
- "execution_count": 24,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "nac_madre_menor_20 = nacimientos[[\"edad_madre_grupo\",\"nacimientos_cantidad\"]]\n",
- "nac_madre_menor_20[\"edad_madre_grupo\"].unique()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "l_yNY0RXsDwD"
- },
- "source": [
- "Eliminamos las filas que dicen 'Sin especificar' "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {
- "id": "lzOk-dbysDwD"
- },
- "outputs": [],
- "source": [
- "nac_madre_menor_20 = nac_madre_menor_20.drop(nac_madre_menor_20[nac_madre_menor_20['edad_madre_grupo'] == \"Sin especificar\"].index)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 265
},
- "id": "9K-2RWhcsDwD",
- "outputId": "6b4de7ce-be2c-47ff-b43f-e392d3deb8cf"
- },
- "outputs": [
{
- "data": {
- "text/plain": [
- "<AxesSubplot:ylabel='nacimientos_cantidad'>"
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 459
+ },
+ "id": "D8TfEws58gvQ",
+ "outputId": "eb6842af-3d5b-40f1-fc04-689461f5abb1"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x7f705bc787d0>"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 9
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<Figure size 1080x504 with 1 Axes>"
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {
+ "needs_background": "light"
+ }
+ }
+ ],
+ "source": [
+ "nac_por_año.plot(kind= \"line\",figsize= (15,7),ylim=(0),grid=True)\n",
+ "plt.legend([\"Cantidad de nacimientos\"])"
]
- },
- "execution_count": 26,
- "metadata": {},
- "output_type": "execute_result"
},
{
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_NpC6hVyzwSc"
+ },
+ "source": [
+ "## Pregunta: ¿Cuántos nacimientos hay por año en el paÃs según el grupo etario de la madre? <a name=\"paragraph3\"></a>\n"
]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "nac_madre_menor_20.groupby(\"edad_madre_grupo\")[\"nacimientos_cantidad\"].count().plot(kind='pie')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Gyor1fguGMyw"
- },
- "source": [
- "Luego agrupamos los nacimientos en dos categorÃas, basado en si cumple o no la condición: Si está en los grupos \" Menor de 15\" o \"15 a 19\", ponerlos en un grupo, sino en otro grupo. (la | es el equivalente a un \"o\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {
- "id": "KzbpAR3kGMPo"
- },
- "outputs": [],
- "source": [
- "nac_madre_menor_20 = nac_madre_menor_20.groupby(\n",
- " (nac_madre_menor_20.edad_madre_grupo == \" Menor de 15\") \n",
- " | (nac_madre_menor_20.edad_madre_grupo == \"15 a 19\"))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "a-9-o4q3MGjm"
- },
- "source": [
- "Luego sumamos los nacimientos de cada grupo:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 143
- },
- "id": "3HFy7OavMCJU",
- "outputId": "04da3989-7e63-49d9-d713-25b8744b708c"
- },
- "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>nacimientos_cantidad</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>edad_madre_grupo</th>\n",
- " <th></th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>False</th>\n",
- " <td>9630285</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>True</th>\n",
- " <td>1657570</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IgOe-p4pCly3"
+ },
+ "source": [
+ "En este caso necesitamos saber el año, el grupo etario de la madre y la cantidad:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "glA4XLTT86wg"
+ },
+ "outputs": [],
+ "source": [
+ "nac_edad_madre = nacimientos[[\"anio\",\"edad_madre_grupo\",\"nacimientos_cantidad\"]]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qCnqL52JC-SA"
+ },
+ "source": [
+ "Hay algunos nacimientos donde el grupo etario de la madre no fue especificado, por lo tanto no podemos sacar conclusiones, asique se ignoran."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "If8D3jpHC93r",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "073e094d-4b1a-489b-bc72-e76982078ee7"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:4913: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " errors=errors,\n"
+ ]
+ }
],
- "text/plain": [
- " nacimientos_cantidad\n",
- "edad_madre_grupo \n",
- "False 9630285\n",
- "True 1657570"
+ "source": [
+ "nac_edad_madre.drop(nac_edad_madre.index[nac_edad_madre['edad_madre_grupo'] == \"Sin especificar\"], inplace = True)"
]
- },
- "execution_count": 28,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "nac_madre_menor_20 = nac_madre_menor_20.sum()\n",
- "nac_madre_menor_20.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "UQj6wVmoNjq5"
- },
- "source": [
- "Hay un problema con esta información, en la columna de grupo dece \"True\" y \"False\", esto es por la operación de clasificación de más arriba. Esto se soluciona en el gráfico usando las etiquetas definidas en la lista etiquetas y pasandoselas al gráfico.\n",
- "\n",
- "Finalmente, graficamos con un gráfico de torta para mostrar la propoción visualmente, agregando algunas cosas como los porcentajes (con autopct ='%.2f'), el tÃtulo y el tamaño."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 608
},
- "id": "fNs2UewvS6Bq",
- "outputId": "693ee5d8-6718-4dfb-cdeb-81b4dcac06ae"
- },
- "outputs": [
{
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x7f6a2ece44f0>"
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ccDvLT5BDpKQ"
+ },
+ "source": [
+ "Ahora con la información filtrada, hay que agrupar por dos criterios, primero por el año y luego por el grupo etario y finalmente sumar las cantidades de estos grupos:"
]
- },
- "execution_count": 29,
- "metadata": {},
- "output_type": "execute_result"
},
{
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x720 with 1 Axes>"
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 238
+ },
+ "id": "-iJyxCfUC2SY",
+ "outputId": "292a3e1f-01c5-4907-8876-f7aef2a447b2"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " nacimientos_cantidad\n",
+ "anio edad_madre_grupo \n",
+ "2005 Menor de 15 2699\n",
+ " 15 a 19 104410\n",
+ " 20 a 24 177813\n",
+ " 25 a 29 182778\n",
+ " 30 a 34 141689"
+ ],
+ "text/html": [
+ "\n",
+ " <div id=\"df-49138619-ddec-4732-863d-fa57c9dd26e0\">\n",
+ " <div class=\"colab-df-container\">\n",
+ " <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></th>\n",
+ " <th>nacimientos_cantidad</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>anio</th>\n",
+ " <th>edad_madre_grupo</th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th rowspan=\"5\" valign=\"top\">2005</th>\n",
+ " <th>Menor de 15</th>\n",
+ " <td>2699</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>15 a 19</th>\n",
+ " <td>104410</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>20 a 24</th>\n",
+ " <td>177813</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25 a 29</th>\n",
+ " <td>182778</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>30 a 34</th>\n",
+ " <td>141689</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>\n",
+ " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-49138619-ddec-4732-863d-fa57c9dd26e0')\"\n",
+ " title=\"Convert this dataframe to an interactive table.\"\n",
+ " style=\"display:none;\">\n",
+ " \n",
+ " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+ " </svg>\n",
+ " </button>\n",
+ " \n",
+ " <style>\n",
+ " .colab-df-container {\n",
+ " display:flex;\n",
+ " flex-wrap:wrap;\n",
+ " gap: 12px;\n",
+ " }\n",
+ "\n",
+ " .colab-df-convert {\n",
+ " background-color: #E8F0FE;\n",
+ " border: none;\n",
+ " border-radius: 50%;\n",
+ " cursor: pointer;\n",
+ " display: none;\n",
+ " fill: #1967D2;\n",
+ " height: 32px;\n",
+ " padding: 0 0 0 0;\n",
+ " width: 32px;\n",
+ " }\n",
+ "\n",
+ " .colab-df-convert:hover {\n",
+ " background-color: #E2EBFA;\n",
+ " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+ " fill: #174EA6;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-convert {\n",
+ " background-color: #3B4455;\n",
+ " fill: #D2E3FC;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-convert:hover {\n",
+ " background-color: #434B5C;\n",
+ " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+ " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+ " fill: #FFFFFF;\n",
+ " }\n",
+ " </style>\n",
+ "\n",
+ " <script>\n",
+ " const buttonEl =\n",
+ " document.querySelector('#df-49138619-ddec-4732-863d-fa57c9dd26e0 button.colab-df-convert');\n",
+ " buttonEl.style.display =\n",
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+ "\n",
+ " async function convertToInteractive(key) {\n",
+ " const element = document.querySelector('#df-49138619-ddec-4732-863d-fa57c9dd26e0');\n",
+ " const dataTable =\n",
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+ " [key], {});\n",
+ " if (!dataTable) return;\n",
+ "\n",
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+ " + ' to learn more about interactive tables.';\n",
+ " element.innerHTML = '';\n",
+ " dataTable['output_type'] = 'display_data';\n",
+ " await google.colab.output.renderOutput(dataTable, element);\n",
+ " const docLink = document.createElement('div');\n",
+ " docLink.innerHTML = docLinkHtml;\n",
+ " element.appendChild(docLink);\n",
+ " }\n",
+ " </script>\n",
+ " </div>\n",
+ " </div>\n",
+ " "
+ ]
+ },
+ "metadata": {},
+ "execution_count": 20
+ }
+ ],
+ "source": [
+ "nac_edad_madre = nac_edad_madre.groupby([\"anio\",\"edad_madre_grupo\"]).sum()\n",
+ "nac_edad_madre.head()"
]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "etiquetas= [\"20 o mayor\", \"Menor a 20\"]\n",
- "nac_madre_menor_20.plot(kind='pie', y='nacimientos_cantidad', figsize=(10, 10),\n",
- " autopct='%.2f',title = \"Proporción de madres tuvo hijos antes de los 20\",\n",
- " labels=etiquetas\n",
- " ,ylabel=\"\")\n",
- "\n",
- "plt.legend(etiquetas)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Jlvd07tY0QyB"
- },
- "source": [
- "## Pregunta: Para cada nivel de instrucción/educación, ¿Cuántos nacimientos hubo en cada grupo etario? <a name=\"paragraph5\"></a>\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "g7S4DRKWT5_Y"
- },
- "source": [
- "Primero obtenemos la información necesaria para responder la pregunta, esta está en las columnas: instruccion_madre , edad_madre_grupo y nacimientos_cantidad"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 206
- },
- "id": "eqcTPtN1TPxQ",
- "outputId": "0a6a59d4-cb61-4657-8c36-b8b72b311aa7"
- },
- "outputs": [
- {
- "data": {
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+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AO6pJxA6EIKF"
+ },
+ "source": [
+ "La información como está no puede ser graficada, ya que está toda junta en 2 grupos, asi que usamos la función .unstack(), que despliega la información para que se puede visualizar"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 269
+ },
+ "id": "l13_EjwlEWhK",
+ "outputId": "b6f1b498-b439-4a85-9049-5d09dadb6520"
+ },
+ "outputs": [
+ {
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+ "data": {
+ "text/plain": [
+ " nacimientos_cantidad \\\n",
+ "edad_madre_grupo Menor de 15 15 a 19 20 a 24 25 a 29 30 a 34 35 a 39 \n",
+ "anio \n",
+ "2005 2699 104410 177813 182778 141689 73194 \n",
+ "2006 2766 103885 174342 176931 139003 73177 \n",
+ "2007 2841 106720 174679 175632 139393 73532 \n",
+ "2008 2937 112034 183265 184978 153805 80258 \n",
+ "2009 3346 113478 182747 178935 155464 81397 \n",
+ "\n",
+ " \n",
+ "edad_madre_grupo 40 a 44 De 45 y más \n",
+ "anio \n",
+ "2005 21382 1575 \n",
+ "2006 19866 1488 \n",
+ "2007 19879 1497 \n",
+ "2008 20824 1630 \n",
+ "2009 20840 1546 "
+ ],
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+ " <td>184978</td>\n",
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+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+ " + ' to learn more about interactive tables.';\n",
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],
- "text/plain": [
- " instruccion_madre edad_madre_grupo nacimientos_cantidad\n",
- "0 Secundaria/Polimodal Incompleta 30 a 34 1\n",
- "1 Primaria/C. EGB Completa 30 a 34 2\n",
- "2 Secundaria/Polimodal Completa 25 a 29 6\n",
- "3 Secundaria/Polimodal Incompleta 30 a 34 5\n",
- "4 Secundaria/Polimodal Completa 25 a 29 1"
+ "source": [
+ "nac_edad_madre = nac_edad_madre.unstack()\n",
+ "nac_edad_madre.head()"
]
- },
- "execution_count": 30,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "nac_edad_edu_madre= nacimientos[[\"instruccion_madre\",\"edad_madre_grupo\",\"nacimientos_cantidad\"]]\n",
- "nac_edad_edu_madre.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "4rh4mxCDT5GQ"
- },
- "source": [
- "Como en la pregunta anterior hay dos campos que tienen \"sin especificar\", los ignoramos:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 293
- },
- "id": "don6Rac5TPkY",
- "outputId": "22dbe015-1964-4d0e-8b9a-db15f7414c6c"
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/tmp/ipykernel_47145/61328159.py:1: SettingWithCopyWarning: \n",
- "A value is trying to be set on a copy of a slice from a DataFrame\n",
- "\n",
- "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
- " nac_edad_edu_madre.drop(nac_edad_edu_madre.index[nac_edad_edu_madre['edad_madre_grupo'] == \"Sin especificar\"], inplace = True)\n",
- "/tmp/ipykernel_47145/61328159.py:2: SettingWithCopyWarning: \n",
- "A value is trying to be set on a copy of a slice from a DataFrame\n",
- "\n",
- "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
- " nac_edad_edu_madre.drop(nac_edad_edu_madre.index[nac_edad_edu_madre['instruccion_madre'] == \"Sin especificar\"], inplace = True)\n"
- ]
- },
- {
- "data": {
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+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "tNJtFS-WEc0l"
+ },
+ "source": [
+ "Finalmente graficamos como en los ejemplos anteriores, con la diferencia de que ahora hay varios grupos lo que nos da varias lÃneas. No existe el mismo problema del eje y ya que ciertos grupos tienen muy pocos nacimientos y esto hace que el eje empiece en 0:"
+ ]
+ },
+ {
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+ "execution_count": null,
+ "metadata": {
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+ },
+ "id": "o6puSivZDjIQ",
+ "outputId": "5f231d70-edcc-4eae-b7e2-91bd01715eba"
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+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x7fa99f5fd550>"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 22
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<Figure size 2160x1080 with 1 Axes>"
+ ],
+ "image/png": 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+ },
+ "metadata": {
+ "needs_background": "light"
+ }
+ }
],
- "text/plain": [
- " instruccion_madre edad_madre_grupo nacimientos_cantidad\n",
- "0 Secundaria/Polimodal Incompleta 30 a 34 1\n",
- "1 Primaria/C. EGB Completa 30 a 34 2\n",
- "2 Secundaria/Polimodal Completa 25 a 29 6\n",
- "3 Secundaria/Polimodal Incompleta 30 a 34 5\n",
- "4 Secundaria/Polimodal Completa 25 a 29 1"
+ "source": [
+ "nac_edad_madre.plot(kind= \"line\",figsize= (30,15),grid=True)\n",
+ "plt.legend([\"Menor de 15\", \"15 a 19\", \"20 a 24\", \"25 a 29\", \"30 a 34\", \"35 a 39\", \"40 a 44\", \"De 45 y más\"])"
]
- },
- "execution_count": 31,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "nac_edad_edu_madre.drop(nac_edad_edu_madre.index[nac_edad_edu_madre['edad_madre_grupo'] == \"Sin especificar\"], inplace = True)\n",
- "nac_edad_edu_madre.drop(nac_edad_edu_madre.index[nac_edad_edu_madre['instruccion_madre'] == \"Sin especificar\"], inplace = True)\n",
- "nac_edad_edu_madre.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "jZRnk0HlT6ch"
- },
- "source": [
- "Agrupamos por instrucción/educación de la madre y grupo etario, luego se suma la cantidad de nacimientos por esas categorÃas:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 238
- },
- "id": "0oQCwFn2TSd5",
- "outputId": "624b8961-5a22-45cf-bdd5-28b1fa351270"
- },
- "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></th>\n",
- " <th>nacimientos_cantidad</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>instruccion_madre</th>\n",
- " <th>edad_madre_grupo</th>\n",
- " <th></th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th rowspan=\"5\" valign=\"top\">Primaria/C. EGB Completa</th>\n",
- " <th>Menor de 15</th>\n",
- " <td>13561</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>15 a 19</th>\n",
- " <td>447330</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>20 a 24</th>\n",
- " <td>687506</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>25 a 29</th>\n",
- " <td>594204</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>30 a 34</th>\n",
- " <td>449616</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "bPtagRwyz4t4"
+ },
+ "source": [
+ "## Pregunta: ¿Que proporción de madres tuvo hijos antes de los 20? <a name=\"paragraph4\"></a>\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "BoE73R4ysDwD"
+ },
+ "source": [
+ "Igual que los ejemplos anteriores, obtenemos las columnas de interés. Pero si consultamos cuáles son los valores únicos que tiene la columna \"edad_madre:grupo\" nos encontramos con filas que no tienen información significativa"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "s_CVX6d0sDwD",
+ "outputId": "96f454ce-3d71-4621-8f0f-b8f5bd69f9aa"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array(['30 a 34', '25 a 29', '20 a 24', '15 a 19', 'Sin especificar',\n",
+ " '40 a 44', 'De 45 y más', ' Menor de 15', '35 a 39'], dtype=object)"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 11
+ }
],
- "text/plain": [
- " nacimientos_cantidad\n",
- "instruccion_madre edad_madre_grupo \n",
- "Primaria/C. EGB Completa Menor de 15 13561\n",
- " 15 a 19 447330\n",
- " 20 a 24 687506\n",
- " 25 a 29 594204\n",
- " 30 a 34 449616"
+ "source": [
+ "nac_madre_menor_20 = nacimientos[[\"edad_madre_grupo\",\"nacimientos_cantidad\"]]\n",
+ "nac_madre_menor_20[\"edad_madre_grupo\"].unique()"
]
- },
- "execution_count": 32,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "nac_edad_edu_madre = nac_edad_edu_madre.groupby([\"instruccion_madre\",\"edad_madre_grupo\"]).sum()\n",
- "nac_edad_edu_madre.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "n9cVIZ3pT6yI"
- },
- "source": [
- "Como agrupamos por dos categorÃas usamos unstack para graficar los datos más facilmente:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 269
- },
- "id": "hHta7iM9T0B2",
- "outputId": "e5b7b688-1dd0-44d1-d21a-b8a3517b428b"
- },
- "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 tr th {\n",
- " text-align: left;\n",
- " }\n",
- "\n",
- " .dataframe thead tr:last-of-type th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr>\n",
- " <th></th>\n",
- " <th colspan=\"8\" halign=\"left\">nacimientos_cantidad</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>edad_madre_grupo</th>\n",
- " <th>Menor de 15</th>\n",
- " <th>15 a 19</th>\n",
- " <th>20 a 24</th>\n",
- " <th>25 a 29</th>\n",
- " <th>30 a 34</th>\n",
- " <th>35 a 39</th>\n",
- " <th>40 a 44</th>\n",
- " <th>De 45 y más</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>instruccion_madre</th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>Primaria/C. EGB Completa</th>\n",
- " <td>13561</td>\n",
- " <td>447330</td>\n",
- " <td>687506</td>\n",
- " <td>594204</td>\n",
- " <td>449616</td>\n",
- " <td>271336</td>\n",
- " <td>87279</td>\n",
- " <td>6532</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Primaria/C. EGB Incompleta</th>\n",
- " <td>13424</td>\n",
- " <td>171170</td>\n",
- " <td>172795</td>\n",
- " <td>128707</td>\n",
- " <td>95095</td>\n",
- " <td>60494</td>\n",
- " <td>22362</td>\n",
- " <td>1998</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Secundaria/Polimodal Completa</th>\n",
- " <td>348</td>\n",
- " <td>224291</td>\n",
- " <td>862070</td>\n",
- " <td>875452</td>\n",
- " <td>655385</td>\n",
- " <td>334111</td>\n",
- " <td>80448</td>\n",
- " <td>5187</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Secundaria/Polimodal Incompleta</th>\n",
- " <td>13535</td>\n",
- " <td>679556</td>\n",
- " <td>722392</td>\n",
- " <td>481346</td>\n",
- " <td>305220</td>\n",
- " <td>160782</td>\n",
- " <td>44473</td>\n",
- " <td>2972</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Sin instrucción</th>\n",
- " <td>455</td>\n",
- " <td>6851</td>\n",
- " <td>10413</td>\n",
- " <td>10255</td>\n",
- " <td>8756</td>\n",
- " <td>6030</td>\n",
- " <td>2618</td>\n",
- " <td>317</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "l_yNY0RXsDwD"
+ },
+ "source": [
+ "Eliminamos las filas que dicen 'Sin especificar' "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "lzOk-dbysDwD"
+ },
+ "outputs": [],
+ "source": [
+ "nac_madre_menor_20 = nac_madre_menor_20.drop(nac_madre_menor_20[nac_madre_menor_20['edad_madre_grupo'] == \"Sin especificar\"].index)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 265
+ },
+ "id": "9K-2RWhcsDwD",
+ "outputId": "6b4de7ce-be2c-47ff-b43f-e392d3deb8cf"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "<matplotlib.axes._subplots.AxesSubplot at 0x7fa9a1378810>"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 13
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ }
],
- "text/plain": [
- " nacimientos_cantidad \\\n",
- "edad_madre_grupo Menor de 15 15 a 19 20 a 24 25 a 29 \n",
- "instruccion_madre \n",
- "Primaria/C. EGB Completa 13561 447330 687506 594204 \n",
- "Primaria/C. EGB Incompleta 13424 171170 172795 128707 \n",
- "Secundaria/Polimodal Completa 348 224291 862070 875452 \n",
- "Secundaria/Polimodal Incompleta 13535 679556 722392 481346 \n",
- "Sin instrucción 455 6851 10413 10255 \n",
- "\n",
- " \n",
- "edad_madre_grupo 30 a 34 35 a 39 40 a 44 De 45 y más \n",
- "instruccion_madre \n",
- "Primaria/C. EGB Completa 449616 271336 87279 6532 \n",
- "Primaria/C. EGB Incompleta 95095 60494 22362 1998 \n",
- "Secundaria/Polimodal Completa 655385 334111 80448 5187 \n",
- "Secundaria/Polimodal Incompleta 305220 160782 44473 2972 \n",
- "Sin instrucción 8756 6030 2618 317 "
+ "source": [
+ "nac_madre_menor_20.groupby(\"edad_madre_grupo\")[\"nacimientos_cantidad\"].count().plot(kind='pie')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Gyor1fguGMyw"
+ },
+ "source": [
+ "Luego agrupamos los nacimientos en dos categorÃas, basado en si cumple o no la condición: Si está en los grupos \" Menor de 15\" o \"15 a 19\", ponerlos en un grupo, sino en otro grupo. (la | es el equivalente a un \"o\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "KzbpAR3kGMPo"
+ },
+ "outputs": [],
+ "source": [
+ "nac_madre_menor_20 = nac_madre_menor_20.groupby(\n",
+ " (nac_madre_menor_20.edad_madre_grupo == \" Menor de 15\") \n",
+ " | (nac_madre_menor_20.edad_madre_grupo == \"15 a 19\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "a-9-o4q3MGjm"
+ },
+ "source": [
+ "Luego sumamos los nacimientos de cada grupo:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 143
+ },
+ "id": "3HFy7OavMCJU",
+ "outputId": "04da3989-7e63-49d9-d713-25b8744b708c"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " nacimientos_cantidad\n",
+ "edad_madre_grupo \n",
+ "False 9630285\n",
+ "True 1657570"
+ ],
+ "text/html": [
+ "\n",
+ " <div id=\"df-e984a7a8-85d4-4cae-b227-95540334dcb8\">\n",
+ " <div class=\"colab-df-container\">\n",
+ " <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>nacimientos_cantidad</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>edad_madre_grupo</th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>False</th>\n",
+ " <td>9630285</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>True</th>\n",
+ " <td>1657570</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>\n",
+ " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e984a7a8-85d4-4cae-b227-95540334dcb8')\"\n",
+ " title=\"Convert this dataframe to an interactive table.\"\n",
+ " style=\"display:none;\">\n",
+ " \n",
+ " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+ " width=\"24px\">\n",
+ " <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+ " <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+ " </svg>\n",
+ " </button>\n",
+ " \n",
+ " <style>\n",
+ " .colab-df-container {\n",
+ " display:flex;\n",
+ " flex-wrap:wrap;\n",
+ " gap: 12px;\n",
+ " }\n",
+ "\n",
+ " .colab-df-convert {\n",
+ " background-color: #E8F0FE;\n",
+ " border: none;\n",
+ " border-radius: 50%;\n",
+ " cursor: pointer;\n",
+ " display: none;\n",
+ " fill: #1967D2;\n",
+ " height: 32px;\n",
+ " padding: 0 0 0 0;\n",
+ " width: 32px;\n",
+ " }\n",
+ "\n",
+ " .colab-df-convert:hover {\n",
+ " background-color: #E2EBFA;\n",
+ " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+ " fill: #174EA6;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-convert {\n",
+ " background-color: #3B4455;\n",
+ " fill: #D2E3FC;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-convert:hover {\n",
+ " background-color: #434B5C;\n",
+ " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+ " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+ " fill: #FFFFFF;\n",
+ " }\n",
+ " </style>\n",
+ "\n",
+ " <script>\n",
+ " const buttonEl =\n",
+ " document.querySelector('#df-e984a7a8-85d4-4cae-b227-95540334dcb8 button.colab-df-convert');\n",
+ " buttonEl.style.display =\n",
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+ "\n",
+ " async function convertToInteractive(key) {\n",
+ " const element = document.querySelector('#df-e984a7a8-85d4-4cae-b227-95540334dcb8');\n",
+ " const dataTable =\n",
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+ " [key], {});\n",
+ " if (!dataTable) return;\n",
+ "\n",
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+ " + ' to learn more about interactive tables.';\n",
+ " element.innerHTML = '';\n",
+ " dataTable['output_type'] = 'display_data';\n",
+ " await google.colab.output.renderOutput(dataTable, element);\n",
+ " const docLink = document.createElement('div');\n",
+ " docLink.innerHTML = docLinkHtml;\n",
+ " element.appendChild(docLink);\n",
+ " }\n",
+ " </script>\n",
+ " </div>\n",
+ " </div>\n",
+ " "
+ ]
+ },
+ "metadata": {},
+ "execution_count": 15
+ }
+ ],
+ "source": [
+ "nac_madre_menor_20 = nac_madre_menor_20.sum()\n",
+ "nac_madre_menor_20.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "UQj6wVmoNjq5"
+ },
+ "source": [
+ "Hay un problema con esta información, en la columna de grupo dece \"True\" y \"False\", esto es por la operación de clasificación de más arriba. Esto se soluciona en el gráfico usando las etiquetas definidas en la lista etiquetas y pasandoselas al gráfico.\n",
+ "\n",
+ "Finalmente, graficamos con un gráfico de torta para mostrar la propoción visualmente, agregando algunas cosas como los porcentajes (con autopct ='%.2f'), el tÃtulo y el tamaño."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 608
+ },
+ "id": "fNs2UewvS6Bq",
+ "outputId": "693ee5d8-6718-4dfb-cdeb-81b4dcac06ae"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x7fa99f6b6910>"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 16
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<Figure size 720x720 with 1 Axes>"
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "etiquetas= [\"20 o mayor\", \"Menor a 20\"]\n",
+ "nac_madre_menor_20.plot(kind='pie', y='nacimientos_cantidad', figsize=(10, 10),\n",
+ " autopct='%.2f',title = \"Proporción de madres tuvo hijos antes de los 20\",\n",
+ " labels=etiquetas\n",
+ " ,ylabel=\"\")\n",
+ "\n",
+ "plt.legend(etiquetas)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 143
+ },
+ "id": "IiU4eCi_MwbO",
+ "outputId": "6f4a02c6-f291-43e1-f019-3d17288fedf2"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " nacimientos_cantidad\n",
+ "edad_madre_grupo \n",
+ "20 o mayor 9630285\n",
+ "Menor a 20 1657570"
+ ],
+ "text/html": [
+ "\n",
+ " <div id=\"df-420ead63-ee68-474f-9634-9ddad5bd0dec\">\n",
+ " <div class=\"colab-df-container\">\n",
+ " <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>nacimientos_cantidad</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>edad_madre_grupo</th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>20 o mayor</th>\n",
+ " <td>9630285</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Menor a 20</th>\n",
+ " <td>1657570</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>\n",
+ " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-420ead63-ee68-474f-9634-9ddad5bd0dec')\"\n",
+ " title=\"Convert this dataframe to an interactive table.\"\n",
+ " style=\"display:none;\">\n",
+ " \n",
+ " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+ " </svg>\n",
+ " </button>\n",
+ " \n",
+ " <style>\n",
+ " .colab-df-container {\n",
+ " display:flex;\n",
+ " flex-wrap:wrap;\n",
+ " gap: 12px;\n",
+ " }\n",
+ "\n",
+ " .colab-df-convert {\n",
+ " background-color: #E8F0FE;\n",
+ " border: none;\n",
+ " border-radius: 50%;\n",
+ " cursor: pointer;\n",
+ " display: none;\n",
+ " fill: #1967D2;\n",
+ " height: 32px;\n",
+ " padding: 0 0 0 0;\n",
+ " width: 32px;\n",
+ " }\n",
+ "\n",
+ " .colab-df-convert:hover {\n",
+ " background-color: #E2EBFA;\n",
+ " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+ " fill: #174EA6;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-convert {\n",
+ " background-color: #3B4455;\n",
+ " fill: #D2E3FC;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-convert:hover {\n",
+ " background-color: #434B5C;\n",
+ " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+ " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+ " fill: #FFFFFF;\n",
+ " }\n",
+ " </style>\n",
+ "\n",
+ " <script>\n",
+ " const buttonEl =\n",
+ " document.querySelector('#df-420ead63-ee68-474f-9634-9ddad5bd0dec button.colab-df-convert');\n",
+ " buttonEl.style.display =\n",
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+ "\n",
+ " async function convertToInteractive(key) {\n",
+ " const element = document.querySelector('#df-420ead63-ee68-474f-9634-9ddad5bd0dec');\n",
+ " const dataTable =\n",
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+ " [key], {});\n",
+ " if (!dataTable) return;\n",
+ "\n",
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+ " + ' to learn more about interactive tables.';\n",
+ " element.innerHTML = '';\n",
+ " dataTable['output_type'] = 'display_data';\n",
+ " await google.colab.output.renderOutput(dataTable, element);\n",
+ " const docLink = document.createElement('div');\n",
+ " docLink.innerHTML = docLinkHtml;\n",
+ " element.appendChild(docLink);\n",
+ " }\n",
+ " </script>\n",
+ " </div>\n",
+ " </div>\n",
+ " "
+ ]
+ },
+ "metadata": {},
+ "execution_count": 17
+ }
+ ],
+ "source": [
+ "nac_madre_menor_20 = nac_madre_menor_20.rename({True:'Menor a 20',False:'20 o mayor'})\n",
+ "nac_madre_menor_20.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Jlvd07tY0QyB"
+ },
+ "source": [
+ "##Pregunta: Para cada nivel de instrucción/educación, ¿Cuántos nacimientos hubo en cada grupo etario? <a name=\"paragraph5\"></a>\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "g7S4DRKWT5_Y"
+ },
+ "source": [
+ "Primero obtenemos la información necesaria para responder la pregunta, esta está en las columnas: instruccion_madre , edad_madre_grupo y nacimientos_cantidad"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
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+ "0 Secundaria/Polimodal Incompleta 30 a 34 1\n",
+ "1 Primaria/C. EGB Completa 30 a 34 2\n",
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+ " + ' to learn more about interactive tables.';\n",
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+ " "
+ ]
+ },
+ "metadata": {},
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+ }
+ ],
+ "source": [
+ "nac_edad_edu_madre= nacimientos[[\"instruccion_madre\",\"edad_madre_grupo\",\"nacimientos_cantidad\"]]\n",
+ "nac_edad_edu_madre.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "4rh4mxCDT5GQ"
+ },
+ "source": [
+ "Como en la pregunta anterior hay dos campos que tienen \"sin especificar\", los ignoramos:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
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+ "id": "don6Rac5TPkY",
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+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:4913: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " errors=errors,\n"
+ ]
+ },
+ {
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+ "0 Secundaria/Polimodal Incompleta 30 a 34 1\n",
+ "1 Primaria/C. EGB Completa 30 a 34 2\n",
+ "2 Secundaria/Polimodal Completa 25 a 29 6\n",
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+ " [key], {});\n",
+ " if (!dataTable) return;\n",
+ "\n",
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+ " + ' to learn more about interactive tables.';\n",
+ " element.innerHTML = '';\n",
+ " dataTable['output_type'] = 'display_data';\n",
+ " await google.colab.output.renderOutput(dataTable, element);\n",
+ " const docLink = document.createElement('div');\n",
+ " docLink.innerHTML = docLinkHtml;\n",
+ " element.appendChild(docLink);\n",
+ " }\n",
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+ " </div>\n",
+ " </div>\n",
+ " "
+ ]
+ },
+ "metadata": {},
+ "execution_count": 24
+ }
+ ],
+ "source": [
+ "nac_edad_edu_madre.drop(nac_edad_edu_madre.index[nac_edad_edu_madre['edad_madre_grupo'] == \"Sin especificar\"], inplace = True)\n",
+ "nac_edad_edu_madre.drop(nac_edad_edu_madre.index[nac_edad_edu_madre['instruccion_madre'] == \"Sin especificar\"], inplace = True)\n",
+ "nac_edad_edu_madre.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "jZRnk0HlT6ch"
+ },
+ "source": [
+ "Agrupamos por instrucción/educación de la madre y grupo etario, luego se suma la cantidad de nacimientos por esas categorÃas:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
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+ },
+ "id": "0oQCwFn2TSd5",
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+ },
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+ {
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+ "text/plain": [
+ " nacimientos_cantidad\n",
+ "instruccion_madre edad_madre_grupo \n",
+ "Primaria/C. EGB Completa Menor de 15 13561\n",
+ " 15 a 19 447330\n",
+ " 20 a 24 687506\n",
+ " 25 a 29 594204\n",
+ " 30 a 34 449616"
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+ " dataTable['output_type'] = 'display_data';\n",
+ " await google.colab.output.renderOutput(dataTable, element);\n",
+ " const docLink = document.createElement('div');\n",
+ " docLink.innerHTML = docLinkHtml;\n",
+ " element.appendChild(docLink);\n",
+ " }\n",
+ " </script>\n",
+ " </div>\n",
+ " </div>\n",
+ " "
+ ]
+ },
+ "metadata": {},
+ "execution_count": 25
+ }
+ ],
+ "source": [
+ "nac_edad_edu_madre = nac_edad_edu_madre.groupby([\"instruccion_madre\",\"edad_madre_grupo\"]).sum()\n",
+ "nac_edad_edu_madre.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "n9cVIZ3pT6yI"
+ },
+ "source": [
+ "Como agrupamos por dos categorÃas usamos unstack para graficar los datos más facilmente:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 269
+ },
+ "id": "hHta7iM9T0B2",
+ "outputId": "e5b7b688-1dd0-44d1-d21a-b8a3517b428b"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " nacimientos_cantidad \\\n",
+ "edad_madre_grupo Menor de 15 15 a 19 20 a 24 25 a 29 \n",
+ "instruccion_madre \n",
+ "Primaria/C. EGB Completa 13561 447330 687506 594204 \n",
+ "Primaria/C. EGB Incompleta 13424 171170 172795 128707 \n",
+ "Secundaria/Polimodal Completa 348 224291 862070 875452 \n",
+ "Secundaria/Polimodal Incompleta 13535 679556 722392 481346 \n",
+ "Sin instrucción 455 6851 10413 10255 \n",
+ "\n",
+ " \n",
+ "edad_madre_grupo 30 a 34 35 a 39 40 a 44 De 45 y más \n",
+ "instruccion_madre \n",
+ "Primaria/C. EGB Completa 449616 271336 87279 6532 \n",
+ "Primaria/C. EGB Incompleta 95095 60494 22362 1998 \n",
+ "Secundaria/Polimodal Completa 655385 334111 80448 5187 \n",
+ "Secundaria/Polimodal Incompleta 305220 160782 44473 2972 \n",
+ "Sin instrucción 8756 6030 2618 317 "
+ ],
+ "text/html": [
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+ " <th></th>\n",
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+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>Primaria/C. EGB Completa</th>\n",
+ " <td>13561</td>\n",
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+ " <td>87279</td>\n",
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+ " <tr>\n",
+ " <th>Primaria/C. EGB Incompleta</th>\n",
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+ " <td>5187</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Secundaria/Polimodal Incompleta</th>\n",
+ " <td>13535</td>\n",
+ " <td>679556</td>\n",
+ " <td>722392</td>\n",
+ " <td>481346</td>\n",
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+ " <td>44473</td>\n",
+ " <td>2972</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Sin instrucción</th>\n",
+ " <td>455</td>\n",
+ " <td>6851</td>\n",
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+ " <td>10255</td>\n",
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+ " <td>317</td>\n",
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+ " </svg>\n",
+ " </button>\n",
+ " \n",
+ " <style>\n",
+ " .colab-df-container {\n",
+ " display:flex;\n",
+ " flex-wrap:wrap;\n",
+ " gap: 12px;\n",
+ " }\n",
+ "\n",
+ " .colab-df-convert {\n",
+ " background-color: #E8F0FE;\n",
+ " border: none;\n",
+ " border-radius: 50%;\n",
+ " cursor: pointer;\n",
+ " display: none;\n",
+ " fill: #1967D2;\n",
+ " height: 32px;\n",
+ " padding: 0 0 0 0;\n",
+ " width: 32px;\n",
+ " }\n",
+ "\n",
+ " .colab-df-convert:hover {\n",
+ " background-color: #E2EBFA;\n",
+ " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+ " fill: #174EA6;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-convert {\n",
+ " background-color: #3B4455;\n",
+ " fill: #D2E3FC;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-convert:hover {\n",
+ " background-color: #434B5C;\n",
+ " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+ " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+ " fill: #FFFFFF;\n",
+ " }\n",
+ " </style>\n",
+ "\n",
+ " <script>\n",
+ " const buttonEl =\n",
+ " document.querySelector('#df-47f6543f-f112-4253-b4e7-65799cca68be button.colab-df-convert');\n",
+ " buttonEl.style.display =\n",
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+ "\n",
+ " async function convertToInteractive(key) {\n",
+ " const element = document.querySelector('#df-47f6543f-f112-4253-b4e7-65799cca68be');\n",
+ " const dataTable =\n",
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+ " [key], {});\n",
+ " if (!dataTable) return;\n",
+ "\n",
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+ " + ' to learn more about interactive tables.';\n",
+ " element.innerHTML = '';\n",
+ " dataTable['output_type'] = 'display_data';\n",
+ " await google.colab.output.renderOutput(dataTable, element);\n",
+ " const docLink = document.createElement('div');\n",
+ " docLink.innerHTML = docLinkHtml;\n",
+ " element.appendChild(docLink);\n",
+ " }\n",
+ " </script>\n",
+ " </div>\n",
+ " </div>\n",
+ " "
+ ]
+ },
+ "metadata": {},
+ "execution_count": 26
+ }
+ ],
+ "source": [
+ "nac_edad_edu_madre = nac_edad_edu_madre.unstack()\n",
+ "nac_edad_edu_madre.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "YIh4bD50T7VR"
+ },
+ "source": [
+ "Finalmente graficamos con un gáfico de barras, donde cada grupo corresponde a un nivel de educación y cada barra a un grupo etario, mientras más alta la barra, más nacimientos. También agregamos un tÃtulo y la leyenda:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 946
+ },
+ "id": "bf6v9x7gAwku",
+ "outputId": "d76f0e80-b08b-4590-e897-1a067fcccb60"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x7fa99f5a3150>"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 27
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<Figure size 1800x936 with 1 Axes>"
+ ],
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\n"
+ },
+ "metadata": {
+ "needs_background": "light"
+ }
+ }
+ ],
+ "source": [
+ "nac_edad_edu_madre.plot.bar(figsize= (25,13),xlabel=\"\",title = \"Cantidad de nacimientos por grupo etario y educación de la madre\",grid=True)\n",
+ "plt.legend([\"Menor de 15\", \"15 a 19\", \"20 a 24\", \"25 a 29\", \"30 a 34\", \"35 a 39\", \"40 a 44\", \"De 45 y más\"])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ ""
+ ],
+ "metadata": {
+ "id": "mqrIQcbnDVGQ"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Referencias técnicas <a name=\"paragraph6\"></a>"
+ ],
+ "metadata": {
+ "id": "dPsfdz_yEXOq"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "HusbZdgnsDv0"
+ },
+ "source": [
+ "\n",
+ "---\n",
+ "Este es un apartado más técnico sobre las funciones que se ven en la demostración\n",
+ "---\n",
+ "El lenguaje de programación que estamos utilizando es **Python**, un lenguaje muy popular para ciencia de datos, combinado con la librerÃa *pandas*, también muy popular, ya que nos permite manejar los datos fácilmente y finalmente usamos *matplotlib* para graficar los datos.\n",
+ "\n",
+ "Pandas trabaja con dataframes, estos son la estructura básica que vamos a manipular y funcionan como una tabla con filas y columnas.\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "---\n",
+ "## Funciones importantes\n",
+ "---\n",
+ "A lo largo de esta demostración vamos a usar 7 funciones principales:"
+ ],
+ "metadata": {
+ "id": "osqzdhscEsR4"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### head:\n",
+ "Esta función nos permite ver las primeras 5 filas de un dataframe, además de los nombres de columnas. Es muy útil para visualizar una operación."
+ ],
+ "metadata": {
+ "id": "Jlkc7qhmEiGN"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xa6NE0LLsDv5"
+ },
+ "source": [
+ "\n",
+ "### groupby\n",
+ "\n",
+ "Esta potente función, nos permite agrupar nuestra información basados en los valores de una columna y luego realizar operaciones con esos grupos.\n",
+ "\n",
+ "\n"
]
- },
- "execution_count": 33,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "nac_edad_edu_madre = nac_edad_edu_madre.unstack()\n",
- "nac_edad_edu_madre.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "YIh4bD50T7VR"
- },
- "source": [
- "Finalmente graficamos con un gáfico de barras, donde cada grupo corresponde a un nivel de educación y cada barra a un grupo etario, mientras más alta la barra, más nacimientos. También agregamos un tÃtulo y la leyenda:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 946
},
- "id": "bf6v9x7gAwku",
- "outputId": "d76f0e80-b08b-4590-e897-1a067fcccb60"
- },
- "outputs": [
{
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x7f6a2ebeff40>"
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ZYFkU_DLsDv6"
+ },
+ "source": [
+ "### sum\n",
+ "Nos permite sumar los valores de un conjunto de datos, columna, fila, o en nuestro caso de los grupos de un groupby.\n"
]
- },
- "execution_count": 34,
- "metadata": {},
- "output_type": "execute_result"
},
{
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1800x936 with 1 Axes>"
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "LhFsPjissDv7"
+ },
+ "source": [
+ "\n",
+ "### drop\n",
+ "Esta función nos permite eliminar filas de un dataframe, hay que indicarle una condición para seleccionar cuales se borran.\n"
]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "XwrmQ5MGsDv7"
+ },
+ "source": [
+ "\n",
+ "### plot\n",
+ "Nos permite graficar los datos de un dataframe, le indicamos el tipo de gráfico con la instrucción *kind*, y tiene otros parámetros para cosas como tamaño, titulo, etc.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RC12jKRmsDv7"
+ },
+ "source": [
+ "### plt.legend\n",
+ "Esta función nos permite especificar la leyenda que queremos en nuestro gráfico."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### unstack\n",
+ "Esta función nos permite desagrupar un dataframe compuesto de dataframes en uno solo con toda la información. Nos sirve para graficar datos que requieren de agrupación por más de una categorÃa."
+ ],
+ "metadata": {
+ "id": "23uSeoIWPf8K"
+ }
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "collapsed_sections": [
+ "O35LytWBsDv0",
+ "QYEZLjtwiH6p",
+ "HY9NHf7Mw2Z8",
+ "_NpC6hVyzwSc",
+ "bPtagRwyz4t4",
+ "Jlvd07tY0QyB"
+ ],
+ "name": "Demo_CDS_nacimientos.ipynb",
+ "provenance": [],
+ "toc_visible": true
+ },
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.4"
}
- ],
- "source": [
- "nac_edad_edu_madre.plot.bar(figsize= (25,13),xlabel=\"\",title = \"Cantidad de nacimientos por grupo etario y educación de la madre\",grid=True)\n",
- "plt.legend([\"Menor de 15\", \"15 a 19\", \"20 a 24\", \"25 a 29\", \"30 a 34\", \"35 a 39\", \"40 a 44\", \"De 45 y más\"])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "dPsfdz_yEXOq"
- },
- "source": [
- "# Referencias técnicas <a name=\"paragraph6\"></a>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "HusbZdgnsDv0"
- },
- "source": [
- "\n",
- "---\n",
- "Este es un apartado más técnico sobre las funciones que se ven en la demostración\n",
- "---\n",
- "El lenguaje de programación que estamos utilizando es **Python**, un lenguaje muy popular para ciencia de datos, combinado con la librerÃa *pandas*, también muy popular, ya que nos permite manejar los datos fácilmente y finalmente usamos *matplotlib* para graficar los datos.\n",
- "\n",
- "Pandas trabaja con dataframes, estos son la estructura básica que vamos a manipular y funcionan como una tabla con filas y columnas.\n",
- "\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "osqzdhscEsR4"
- },
- "source": [
- "---\n",
- "## Funciones importantes\n",
- "---\n",
- "A lo largo de esta demostración vamos a usar 7 funciones principales:"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Jlkc7qhmEiGN"
- },
- "source": [
- "### head:\n",
- "Esta función nos permite ver las primeras 5 filas de un dataframe, además de los nombres de columnas. Es muy útil para visualizar una operación."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "xa6NE0LLsDv5"
- },
- "source": [
- "\n",
- "### groupby\n",
- "\n",
- "Esta potente función, nos permite agrupar nuestra información basados en los valores de una columna y luego realizar operaciones con esos grupos.\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "ZYFkU_DLsDv6"
- },
- "source": [
- "### sum\n",
- "Nos permite sumar los valores de un conjunto de datos, columna, fila, o en nuestro caso de los grupos de un groupby.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "LhFsPjissDv7"
- },
- "source": [
- "\n",
- "### drop\n",
- "Esta función nos permite eliminar filas de un dataframe, hay que indicarle una condición para seleccionar cuales se borran.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "XwrmQ5MGsDv7"
- },
- "source": [
- "\n",
- "### plot\n",
- "Nos permite graficar los datos de un dataframe, le indicamos el tipo de gráfico con la instrucción *kind*, y tiene otros parámetros para cosas como tamaño, titulo, etc.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "RC12jKRmsDv7"
- },
- "source": [
- "### plt.legend\n",
- "Esta función nos permite especificar la leyenda que queremos en nuestro gráfico."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "23uSeoIWPf8K"
- },
- "source": [
- "### unstack\n",
- "Esta función nos permite desagrupar un dataframe compuesto de dataframes en uno solo con toda la información. Nos sirve para graficar datos que requieren de agrupación por más de una categorÃa."
- ]
- }
- ],
- "metadata": {
- "colab": {
- "collapsed_sections": [
- "O35LytWBsDv0",
- "QYEZLjtwiH6p",
- "HY9NHf7Mw2Z8",
- "_NpC6hVyzwSc",
- "bPtagRwyz4t4",
- "Jlvd07tY0QyB"
- ],
- "name": "Demo_CDS_nacimientos.ipynb",
- "provenance": []
- },
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
},
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.4"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file