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index 70425fa43b338d81a006a4962b52ba1d3ca3e9ab..0000000000000000000000000000000000000000
--- a/Ejemplo_nacimientos_2005_2010/Demo_CDS_nacimientos.ipynb
+++ /dev/null
@@ -1,2280 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "7JgsokQzYAJX"
- },
- "source": [
- "# Introducción\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": {},
- "source": [
- "# Qué información podemos obtener"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Link donde obtengo el dataset"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Comentarios sobre el notebooks:\n",
- "* poner que información vamos a averiguar\n",
- "* pasar la descripción de las isntrucciones al final como unasección :referencias técnicas\n",
- "* me parece más claro al quedarse con las columnas en la celda que agregué con la variable *nac_sofia*\n",
- "* poner el link de donde se obtuvo el dataset\n",
- "* agregar el grid en los gráficos"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Herramientas\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",
- "# Funciones importantes\n",
- "---\n",
- "A lo largo de esta demostración vamos a usar 7 funciones principales:\n",
- "\n",
- "# 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.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "# loc:\n",
- "Esta función nos permite obtener ciertas filas en las columnas que nombramos.\n",
- "Por ejemplo, si tenemos un dataframe con colores y dÃas: \n",
- "\n",
- "\n",
- "\n",
- "\n",
- "dataframe.loc[0:2,[\"color\",\"dÃa\"]] nos da:\n",
- "\n",
- "\n",
- "\n",
- "\n",
- "Nosotros la vamos a usar para obtener ciertas columnas y no vamos a cortar filas, para esto se dejan los lugares al lado de los dos puntos vacÃos.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "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": {},
- "source": [
- "\n",
- "# 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": {},
- "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": {},
- "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": {},
- "source": [
- "\n",
- "# plt.legend\n",
- "Esta función nos permite especificar la leyenda que queremos en nuestro gráfico."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "QYEZLjtwiH6p"
- },
- "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": 1,
- "metadata": {
- "id": "gSPpdLmni-mZ"
- },
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "import matplotlib.pyplot as plt\n"
- ]
- },
- {
- "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": 2,
- "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": 3,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 357
- },
- "id": "FDFSoh0Xwh3M",
- "outputId": "91c1ca7e-6677-4cc4-9e06-57c461fc8374"
- },
- "outputs": [
- {
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- "<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",
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- " <th>Sexo</th>\n",
- " <th>nacimientos_cantidad</th>\n",
- " </tr>\n",
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- " <th>0</th>\n",
- " <td>2005</td>\n",
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- " <td>Ciudad Autónoma de Buenos Aires</td>\n",
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- " <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",
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- " <td>masculino</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "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 "
- ]
- },
- "execution_count": 3,
- "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": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "nac_sofia = nacimientos[[\"anio\",\"edad_madre_grupo\",\"instruccion_madre\",\"nacimientos_cantidad\"]]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
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- " 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",
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- "497971 1 \n",
- "497972 1 \n",
- "497973 10 \n",
- "\n",
- "[497974 rows x 4 columns]"
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- "output_type": "execute_result"
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- "nac_sofia"
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- "cell_type": "code",
- "execution_count": null,
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- "execution_count": 6,
- "metadata": {
- "id": "Z9xxlqmM6kc4"
- },
- "outputs": [],
- "source": [
- "nacimientos = nacimientos.loc[:,[\"anio\",\"edad_madre_grupo\",\"instruccion_madre\",\"nacimientos_cantidad\"]]"
- ]
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- {
- "cell_type": "code",
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- "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]"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "nacimientos"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "HY9NHf7Mw2Z8"
- },
- "source": [
- "Pregunta: ¿Cuántos nacimientos hay por año en el paÃs?"
- ]
- },
- {
- "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": 8,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 206
- },
- "id": "I-PYL_Qez5hV",
- "outputId": "c2f2eca1-7814-459a-92c7-2312ceef12ae"
- },
- "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>nacimientos_cantidad</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>2005</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>2005</td>\n",
- " <td>2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>2005</td>\n",
- " <td>6</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>2005</td>\n",
- " <td>5</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>2005</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "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"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "nac_por_año = nacimientos.loc[:,[\"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": 9,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 238
- },
- "id": "FbY9_hRmBDuW",
- "outputId": "d998fa9e-dd17-4c9c-b6e1-20d89040609c"
- },
- "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>"
- ],
- "text/plain": [
- " nacimientos_cantidad\n",
- "anio \n",
- "2005 712220\n",
- "2006 696451\n",
- "2007 700792\n",
- "2008 746460\n",
- "2009 745336"
- ]
- },
- "execution_count": 9,
- "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": 10,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 459
- },
- "id": "19u3wAvl0jIN",
- "outputId": "c992d64b-5c86-461f-a177-b7b2267985a3"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x7f995d149c60>"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 1080x504 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "nac_por_año.plot(kind= \"line\",figsize= (15,7),)\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": 11,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 459
- },
- "id": "D8TfEws58gvQ",
- "outputId": "bf04b080-2228-419e-f6a7-3fdfeb702573"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x7f995d041930>"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1080x504 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "nac_por_año.plot(kind= \"line\",figsize= (15,7),ylim=(0))\n",
- "plt.legend([\"Cantidad de nacimientos\"])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "_NpC6hVyzwSc"
- },
- "source": [
- "Pregunta: ¿Cuántos nacidos vivos hay por año en el paÃs según el grupo etario de la madre?"
- ]
- },
- {
- "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": 12,
- "metadata": {
- "id": "glA4XLTT86wg"
- },
- "outputs": [],
- "source": [
- "nac_edad_madre = nacimientos.loc[:,[\"anio\",\"edad_madre_grupo\",\"nacimientos_cantidad\"]]\n"
- ]
- },
- {
- "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": 13,
- "metadata": {
- "id": "If8D3jpHC93r"
- },
- "outputs": [],
- "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": [
- "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": 14,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 238
- },
- "id": "-iJyxCfUC2SY",
- "outputId": "ce5721ab-c4ca-48b8-afb5-08f9abeae1ad"
- },
- "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>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>"
- ],
- "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"
- ]
- },
- "execution_count": 14,
- "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": 15,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 269
- },
- "id": "l13_EjwlEWhK",
- "outputId": "22f5d144-62d2-4192-c7d2-08a83caf7dee"
- },
- "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>anio</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>2005</th>\n",
- " <td>2699</td>\n",
- " <td>104410</td>\n",
- " <td>177813</td>\n",
- " <td>182778</td>\n",
- " <td>141689</td>\n",
- " <td>73194</td>\n",
- " <td>21382</td>\n",
- " <td>1575</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2006</th>\n",
- " <td>2766</td>\n",
- " <td>103885</td>\n",
- " <td>174342</td>\n",
- " <td>176931</td>\n",
- " <td>139003</td>\n",
- " <td>73177</td>\n",
- " <td>19866</td>\n",
- " <td>1488</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2007</th>\n",
- " <td>2841</td>\n",
- " <td>106720</td>\n",
- " <td>174679</td>\n",
- " <td>175632</td>\n",
- " <td>139393</td>\n",
- " <td>73532</td>\n",
- " <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>"
- ],
- "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 "
- ]
- },
- "execution_count": 15,
- "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": 16,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 777
- },
- "id": "o6puSivZDjIQ",
- "outputId": "9ad8a5e5-1427-46a3-9b82-6a5b5c9d0c57"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x7f995d0bde70>"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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- "text/plain": [
- "<Figure size 2160x1080 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "nac_edad_madre.plot(kind= \"line\",figsize= (30,15))\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?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "VIiicLlNFoX5"
- },
- "source": [
- "Igual que los ejemplos anteriores, seleccionamos las columnas relevantes,edad_madre_grupo y nacimientos_cantidad filtrando los sin especificar:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {
- "id": "8lqCEEoFF1JP"
- },
- "outputs": [],
- "source": [
- "nac_madre_menor_20 = nacimientos.loc[:,[\"edad_madre_grupo\",\"nacimientos_cantidad\"]]\n",
- "nac_madre_menor_20.drop(nac_madre_menor_20.index[\n",
- " nac_madre_menor_20['edad_madre_grupo'] == \"Sin especificar\"], inplace = True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "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": 18,
- "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)"
- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "nac_sofia_menor_20 = nacimientos[[\"edad_madre_grupo\",\"nacimientos_cantidad\"]]\n",
- "nac_sofia_menor_20[\"edad_madre_grupo\"].unique()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Eliminamos las filas que dicen 'Sin especificar' "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [],
- "source": [
- "nac_sofia_menor_20 = nac_sofia_menor_20.drop(nac_sofia_menor_20[nac_sofia_menor_20['edad_madre_grupo'] == \"Sin especificar\"].index)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array(['30 a 34', '25 a 29', '20 a 24', '15 a 19', '40 a 44',\n",
- " 'De 45 y más', ' Menor de 15', '35 a 39'], dtype=object)"
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "nac_sofia_menor_20[\"edad_madre_grupo\"].unique()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:ylabel='nacimientos_cantidad'>"
- ]
- },
- "execution_count": 35,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "nac_sofia_menor_20.groupby(\"edad_madre_grupo\")[\"nacimientos_cantidad\"].count().plot(kind='pie')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "metadata": {},
- "outputs": [
- {
- "ename": "AttributeError",
- "evalue": "'DataFrame' object has no attribute 'edad_madre_grupo'",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
- "Input \u001b[0;32mIn [38]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m nac_sofia_rango_edad \u001b[38;5;241m=\u001b[39m nac_sofia_menor_20\u001b[38;5;241m.\u001b[39mgroupby((\u001b[43mnac_madre_menor_20\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43medad_madre_grupo\u001b[49m \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m Menor de 15\u001b[39m\u001b[38;5;124m\"\u001b[39m) \n\u001b[1;32m 2\u001b[0m \u001b[38;5;241m|\u001b[39m (nac_madre_menor_20\u001b[38;5;241m.\u001b[39medad_madre_grupo \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m15 a 19\u001b[39m\u001b[38;5;124m\"\u001b[39m))[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124medad_madre_grupo\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mcount()\n",
- "File \u001b[0;32m~/gitlab/cd-sec-doc/env/lib/python3.10/site-packages/pandas/core/generic.py:5575\u001b[0m, in \u001b[0;36mNDFrame.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 5568\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 5569\u001b[0m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_internal_names_set\n\u001b[1;32m 5570\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_metadata\n\u001b[1;32m 5571\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_accessors\n\u001b[1;32m 5572\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_info_axis\u001b[38;5;241m.\u001b[39m_can_hold_identifiers_and_holds_name(name)\n\u001b[1;32m 5573\u001b[0m ):\n\u001b[1;32m 5574\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m[name]\n\u001b[0;32m-> 5575\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mobject\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getattribute__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n",
- "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'edad_madre_grupo'"
- ]
- }
- ],
- "source": [
- "nac_sofia_rango_edad = nac_sofia_menor_20.groupby((nac_madre_menor_20.edad_madre_grupo == \" Menor de 15\") \n",
- " | (nac_madre_menor_20.edad_madre_grupo == \"15 a 19\"))[\"edad_madre_grupo\"].count()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x7f9955d8a9b0>"
- ]
- },
- "execution_count": 37,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x720 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "etiquetas= [\"20 o mayor\", \"Menor a 20\"]\n",
- "nac_sofia_rango_edad.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",
- "\n",
- "plt.legend([\"20 o mayor\", \"Menor a 20\"])"
- ]
- },
- {
- "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": 24,
- "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": 25,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 143
- },
- "id": "3HFy7OavMCJU",
- "outputId": "a9c476c6-c382-4746-958e-d08e03c0facd"
- },
- "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>"
- ],
- "text/plain": [
- " nacimientos_cantidad\n",
- "edad_madre_grupo \n",
- "False 9630285\n",
- "True 1657570"
- ]
- },
- "execution_count": 25,
- "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": "DFLoNabhMQG5"
- },
- "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. Hay que renombrarlos para que true sea: Menor a 20 (osea que estaba en uno de los rangos etarios de nuestra condición) o \"20 o mayor\" (osea que estaba en uno de los otros rangos etarios):"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 143
- },
- "id": "IiU4eCi_MwbO",
- "outputId": "0284be02-58d8-4262-f45b-2c57750d6772"
- },
- "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>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>"
- ],
- "text/plain": [
- " nacimientos_cantidad\n",
- "edad_madre_grupo \n",
- "20 o mayor 9630285\n",
- "Menor a 20 1657570"
- ]
- },
- "execution_count": 26,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "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": "UQj6wVmoNjq5"
- },
- "source": [
- "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": 27,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 879
- },
- "id": "fNs2UewvS6Bq",
- "outputId": "a86853c1-bf6f-47c9-87c6-c7186656b47a"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x7f995953b4c0>"
- ]
- },
- "execution_count": 27,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 1080x1080 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "nac_madre_menor_20.plot(kind= \"pie\", y='nacimientos_cantidad', figsize=(15, 15),autopct='%.2f',title = \"Proporción de madres tuvo hijos antes de los 20\",ylabel=\"\")\n",
- "plt.legend([\"20 o mayor\", \"Menor a 20\"])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Jlvd07tY0QyB"
- },
- "source": [
- "Pregunta: Para cada nivel de instrucción/educación, ¿Cuántos nacimientos hubo en cada grupo etario?"
- ]
- },
- {
- "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": 28,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 206
- },
- "id": "eqcTPtN1TPxQ",
- "outputId": "40babb56-7be5-42ec-f88b-c2aa204db691"
- },
- "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>instruccion_madre</th>\n",
- " <th>edad_madre_grupo</th>\n",
- " <th>nacimientos_cantidad</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>Secundaria/Polimodal Incompleta</td>\n",
- " <td>30 a 34</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>Primaria/C. EGB Completa</td>\n",
- " <td>30 a 34</td>\n",
- " <td>2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>Secundaria/Polimodal Completa</td>\n",
- " <td>25 a 29</td>\n",
- " <td>6</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>Secundaria/Polimodal Incompleta</td>\n",
- " <td>30 a 34</td>\n",
- " <td>5</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>Secundaria/Polimodal Completa</td>\n",
- " <td>25 a 29</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "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"
- ]
- },
- "execution_count": 28,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "nac_edad_edu_madre= nacimientos.loc[:,[\"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": 29,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 215
- },
- "id": "don6Rac5TPkY",
- "outputId": "bcba689b-0288-4564-e8ff-76b9367d6121"
- },
- "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>instruccion_madre</th>\n",
- " <th>edad_madre_grupo</th>\n",
- " <th>nacimientos_cantidad</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>Secundaria/Polimodal Incompleta</td>\n",
- " <td>30 a 34</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>Primaria/C. EGB Completa</td>\n",
- " <td>30 a 34</td>\n",
- " <td>2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>Secundaria/Polimodal Completa</td>\n",
- " <td>25 a 29</td>\n",
- " <td>6</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>Secundaria/Polimodal Incompleta</td>\n",
- " <td>30 a 34</td>\n",
- " <td>5</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>Secundaria/Polimodal Completa</td>\n",
- " <td>25 a 29</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "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"
- ]
- },
- "execution_count": 29,
- "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": 40,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 238
- },
- "id": "0oQCwFn2TSd5",
- "outputId": "8ade4190-9f50-4ef9-8d2c-50776ae80bcf"
- },
- "outputs": [
- {
- "ename": "KeyError",
- "evalue": "'edad_madre_grupo'",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
- "Input \u001b[0;32mIn [40]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m nac_edad_edu_madre \u001b[38;5;241m=\u001b[39m \u001b[43mnac_edad_edu_madre\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minstruccion_madre\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43medad_madre_grupo\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39msum()\n\u001b[1;32m 2\u001b[0m nac_edad_edu_madre\u001b[38;5;241m.\u001b[39mhead()\n",
- "File \u001b[0;32m~/gitlab/cd-sec-doc/env/lib/python3.10/site-packages/pandas/core/frame.py:7712\u001b[0m, in \u001b[0;36mDataFrame.groupby\u001b[0;34m(self, by, axis, level, as_index, sort, group_keys, squeeze, observed, dropna)\u001b[0m\n\u001b[1;32m 7707\u001b[0m axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_axis_number(axis)\n\u001b[1;32m 7709\u001b[0m \u001b[38;5;66;03m# https://github.com/python/mypy/issues/7642\u001b[39;00m\n\u001b[1;32m 7710\u001b[0m \u001b[38;5;66;03m# error: Argument \"squeeze\" to \"DataFrameGroupBy\" has incompatible type\u001b[39;00m\n\u001b[1;32m 7711\u001b[0m \u001b[38;5;66;03m# \"Union[bool, NoDefault]\"; expected \"bool\"\u001b[39;00m\n\u001b[0;32m-> 7712\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mDataFrameGroupBy\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 7713\u001b[0m \u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 7714\u001b[0m \u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mby\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 7715\u001b[0m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 7716\u001b[0m \u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 7717\u001b[0m \u001b[43m \u001b[49m\u001b[43mas_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mas_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 7718\u001b[0m \u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 7719\u001b[0m \u001b[43m \u001b[49m\u001b[43mgroup_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup_keys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 7720\u001b[0m \u001b[43m \u001b[49m\u001b[43msqueeze\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msqueeze\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[1;32m 7721\u001b[0m \u001b[43m \u001b[49m\u001b[43mobserved\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobserved\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 7722\u001b[0m \u001b[43m \u001b[49m\u001b[43mdropna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdropna\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 7723\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
- "File \u001b[0;32m~/gitlab/cd-sec-doc/env/lib/python3.10/site-packages/pandas/core/groupby/groupby.py:882\u001b[0m, in \u001b[0;36mGroupBy.__init__\u001b[0;34m(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze, observed, mutated, dropna)\u001b[0m\n\u001b[1;32m 879\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m grouper \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 880\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgroupby\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgrouper\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_grouper\n\u001b[0;32m--> 882\u001b[0m grouper, exclusions, obj \u001b[38;5;241m=\u001b[39m \u001b[43mget_grouper\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 883\u001b[0m \u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 884\u001b[0m \u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 885\u001b[0m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 886\u001b[0m \u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 887\u001b[0m \u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 888\u001b[0m \u001b[43m \u001b[49m\u001b[43mobserved\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobserved\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 889\u001b[0m \u001b[43m \u001b[49m\u001b[43mmutated\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmutated\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 890\u001b[0m \u001b[43m \u001b[49m\u001b[43mdropna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdropna\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 891\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 893\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj \u001b[38;5;241m=\u001b[39m obj\n\u001b[1;32m 894\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;241m=\u001b[39m obj\u001b[38;5;241m.\u001b[39m_get_axis_number(axis)\n",
- "File \u001b[0;32m~/gitlab/cd-sec-doc/env/lib/python3.10/site-packages/pandas/core/groupby/grouper.py:882\u001b[0m, in \u001b[0;36mget_grouper\u001b[0;34m(obj, key, axis, level, sort, observed, mutated, validate, dropna)\u001b[0m\n\u001b[1;32m 880\u001b[0m in_axis, level, gpr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m, gpr, \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 881\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 882\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(gpr)\n\u001b[1;32m 883\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(gpr, Grouper) \u001b[38;5;129;01mand\u001b[39;00m gpr\u001b[38;5;241m.\u001b[39mkey \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 884\u001b[0m \u001b[38;5;66;03m# Add key to exclusions\u001b[39;00m\n\u001b[1;32m 885\u001b[0m exclusions\u001b[38;5;241m.\u001b[39madd(gpr\u001b[38;5;241m.\u001b[39mkey)\n",
- "\u001b[0;31mKeyError\u001b[0m: 'edad_madre_grupo'"
- ]
- }
- ],
- "source": [
- "nac_edad_edu_madre = nac_edad_edu_madre.groupby([\"instruccion_madre\",\"edad_madre_grupo\"]).sum()\n",
- "nac_edad_edu_madre.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 49,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x7f99521d71f0>"
- ]
- },
- "execution_count": 49,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1800x936 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "nac_edad_edu_madre.plot(kind='bar',figsize= (25,13),xlabel=\"\",\n",
- " title = \"Cantidad de nacimientos por grupo etario y educación de la madre\")\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": "n9cVIZ3pT6yI"
- },
- "source": [
- "Como Agrupamos por dos categorÃas usamos unstack para graficar los datos más facilmente:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 269
- },
- "id": "hHta7iM9T0B2",
- "outputId": "4017426a-ab33-47e4-ad10-d70045e890d6"
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
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- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
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- " 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>"
- ],
- "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 "
- ]
- },
- "execution_count": 31,
- "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": 32,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 946
- },
- "id": "bf6v9x7gAwku",
- "outputId": "7f8bec90-7f0c-462d-877f-42317d86ee19"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x7f995aedf2e0>"
- ]
- },
- "execution_count": 32,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 1800x936 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "nac_edad_edu_madre.plot.bar(figsize= (25,13),xlabel=\"\",title = \"Cantidad de nacimientos por grupo etario y educación de la madre\")\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\"])"
- ]
- }
- ],
- "metadata": {
- "colab": {
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