From cb7a7fea518089d02542f71442c2e80c5a433e77 Mon Sep 17 00:00:00 2001
From: arturos <arturos@cern.ch>
Date: Sun, 28 Feb 2021 09:58:48 +0100
Subject: [PATCH] Entrega de proyecto
---
codigo/Proyecto_Datos.ipynb | 1397 ++
data-used/surveys.csv | 35550 ++++++++++++++++++++++++++++++++
reporte/Analisis_de_datos.pdf | Bin 0 -> 334402 bytes
3 files changed, 36947 insertions(+)
create mode 100644 codigo/Proyecto_Datos.ipynb
create mode 100644 data-used/surveys.csv
create mode 100644 reporte/Analisis_de_datos.pdf
diff --git a/codigo/Proyecto_Datos.ipynb b/codigo/Proyecto_Datos.ipynb
new file mode 100644
index 0000000..162e8d1
--- /dev/null
+++ b/codigo/Proyecto_Datos.ipynb
@@ -0,0 +1,1397 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 145,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import pandas as pd\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Lectura de datos"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>record_id</th>\n",
+ " <th>month</th>\n",
+ " <th>day</th>\n",
+ " <th>year</th>\n",
+ " <th>plot_id</th>\n",
+ " <th>species_id</th>\n",
+ " <th>sex</th>\n",
+ " <th>hindfoot_length</th>\n",
+ " <th>weight</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1</td>\n",
+ " <td>7</td>\n",
+ " <td>16</td>\n",
+ " <td>1977</td>\n",
+ " <td>2</td>\n",
+ " <td>NL</td>\n",
+ " <td>M</td>\n",
+ " <td>32.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>2</td>\n",
+ " <td>7</td>\n",
+ " <td>16</td>\n",
+ " <td>1977</td>\n",
+ " <td>3</td>\n",
+ " <td>NL</td>\n",
+ " <td>M</td>\n",
+ " <td>33.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>3</td>\n",
+ " <td>7</td>\n",
+ " <td>16</td>\n",
+ " <td>1977</td>\n",
+ " <td>2</td>\n",
+ " <td>DM</td>\n",
+ " <td>F</td>\n",
+ " <td>37.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>4</td>\n",
+ " <td>7</td>\n",
+ " <td>16</td>\n",
+ " <td>1977</td>\n",
+ " <td>7</td>\n",
+ " <td>DM</td>\n",
+ " <td>M</td>\n",
+ " <td>36.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>5</td>\n",
+ " <td>7</td>\n",
+ " <td>16</td>\n",
+ " <td>1977</td>\n",
+ " <td>3</td>\n",
+ " <td>DM</td>\n",
+ " <td>M</td>\n",
+ " <td>35.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " record_id month day year plot_id species_id sex hindfoot_length \\\n",
+ "0 1 7 16 1977 2 NL M 32.0 \n",
+ "1 2 7 16 1977 3 NL M 33.0 \n",
+ "2 3 7 16 1977 2 DM F 37.0 \n",
+ "3 4 7 16 1977 7 DM M 36.0 \n",
+ "4 5 7 16 1977 3 DM M 35.0 \n",
+ "\n",
+ " weight \n",
+ "0 NaN \n",
+ "1 NaN \n",
+ "2 NaN \n",
+ "3 NaN \n",
+ "4 NaN "
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df = pd.read_csv(\"surveys.csv\")\n",
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Analisis por columna del peso y la longitud del pie trasero"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 153,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:title={'center':'Histograma del peso'}, xlabel='peso', ylabel='Frequency'>"
+ ]
+ },
+ "execution_count": 153,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure()\n",
+ "plt.title('Histograma de la longitud de pie trasero')\n",
+ "plt.xlabel('Longitud del pie trasero')\n",
+ "df['hindfoot_length'].plot.hist(alpha=0.5,edgecolor='black')\n",
+ "\n",
+ "plt.figure()\n",
+ "plt.title('Histograma del peso')\n",
+ "plt.xlabel('peso')\n",
+ "df['weight'].plot.hist(alpha=0.5,edgecolor='black')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 160,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Longitud del pie trasero</th>\n",
+ " <th>peso</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>31438.000000</td>\n",
+ " <td>32283.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>29.287932</td>\n",
+ " <td>42.672428</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>9.564759</td>\n",
+ " <td>36.631259</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>2.000000</td>\n",
+ " <td>4.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>21.000000</td>\n",
+ " <td>20.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>32.000000</td>\n",
+ " <td>37.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>36.000000</td>\n",
+ " <td>48.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>70.000000</td>\n",
+ " <td>280.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Longitud del pie trasero peso\n",
+ "count 31438.000000 32283.000000\n",
+ "mean 29.287932 42.672428\n",
+ "std 9.564759 36.631259\n",
+ "min 2.000000 4.000000\n",
+ "25% 21.000000 20.000000\n",
+ "50% 32.000000 37.000000\n",
+ "75% 36.000000 48.000000\n",
+ "max 70.000000 280.000000"
+ ]
+ },
+ "execution_count": 160,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Estadistica de las propiedades de la longitud del pie trasero y el peso de todas las muestras\n",
+ "f=df.describe()[['hindfoot_length','weight']]\n",
+ "f.columns=['Longitud del pie trasero','peso']\n",
+ "f"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Funciones de utilidad"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 143,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Funcion que haya el valor medio de la longitud del pie y el paso, para una cierta caracteristica de la tabla con su desviacion\n",
+ "#estandar\n",
+ "def analisis(carac): \n",
+ " \n",
+ " x=[]\n",
+ " y=[]\n",
+ " xerror=[]\n",
+ " yerror=[]\n",
+ " for i in df[carac].unique():\n",
+ " pie=df[df[carac]==i][\"hindfoot_length\"].mean()\n",
+ " piedesv=df[df[carac]==i][\"hindfoot_length\"].std()\n",
+ " peso=df[df[carac]==i][\"weight\"].mean()\n",
+ " pesodesv=df[df[carac]==i][\"weight\"].mean()\n",
+ " x.append(pie)\n",
+ " xerror.append(piedesv)\n",
+ " y.append(peso)\n",
+ " yerror.append(pesodesv)\n",
+ " \n",
+ " return x,xerror,y,yerror\n",
+ "\n",
+ "\n",
+ "#x,xerror,y,yerror=analisis('day')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Se encarga de pintar los datos\n",
+ "def graficas(carac):\n",
+ "\n",
+ " \n",
+ " x,xerror,y,yerror=analisis(carac)\n",
+ " \n",
+ " \n",
+ " plt.figure(figsize=(10,5))\n",
+ " plt.errorbar(np.array(list(df[carac].unique())),x,xerror,fmt='.k')\n",
+ " plt.ylabel('tamaño del pie trasero')\n",
+ " plt.xlabel(carac)\n",
+ " plt.grid()\n",
+ " \n",
+ " \n",
+ " plt.figure(figsize=(10,5))\n",
+ " plt.errorbar(np.array(list(df[carac].unique())),y,yerror,fmt='.k')\n",
+ " plt.ylabel('peso')\n",
+ " plt.xlabel(carac)\n",
+ " plt.grid()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Analisis por sexo"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 105,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:ylabel='sex'>"
+ ]
+ },
+ "execution_count": 105,
+ "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": [
+ "#Comparacion de los sexos en los datos \n",
+ "#df[\"sex\"].value_counts().plot(kind=\"bar\")\n",
+ "df[\"sex\"].value_counts().plot.pie(autopct=\"%.2f\")\n",
+ "#plt.ylabel('frecuencia')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 149,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Longitud del pie trasero F</th>\n",
+ " <th>Longitud del pie trasero M</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>14894.000000</td>\n",
+ " <td>16476.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>28.836780</td>\n",
+ " <td>29.709578</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>9.463789</td>\n",
+ " <td>9.629246</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>7.000000</td>\n",
+ " <td>2.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>21.000000</td>\n",
+ " <td>21.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>27.000000</td>\n",
+ " <td>34.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>36.000000</td>\n",
+ " <td>36.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>64.000000</td>\n",
+ " <td>58.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Longitud del pie trasero F Longitud del pie trasero M\n",
+ "count 14894.000000 16476.000000\n",
+ "mean 28.836780 29.709578\n",
+ "std 9.463789 9.629246\n",
+ "min 7.000000 2.000000\n",
+ "25% 21.000000 21.000000\n",
+ "50% 27.000000 34.000000\n",
+ "75% 36.000000 36.000000\n",
+ "max 64.000000 58.000000"
+ ]
+ },
+ "execution_count": 149,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Valores estadisticos de longitud del pie trasero para el sexo femenino y masculino\n",
+ "x=df[df[\"sex\"]=='F']['hindfoot_length'].describe()\n",
+ "y=df[df[\"sex\"]=='M']['hindfoot_length'].describe()\n",
+ "f=pd.concat([x, y],axis=1)\n",
+ "f.columns=['Longitud del pie trasero F','Longitud del pie trasero M']\n",
+ "f"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 150,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Peso F</th>\n",
+ " <th>Peso M</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>15303.000000</td>\n",
+ " <td>16879.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>42.170555</td>\n",
+ " <td>42.995379</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>36.847958</td>\n",
+ " <td>36.184981</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>4.000000</td>\n",
+ " <td>4.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>20.000000</td>\n",
+ " <td>20.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>34.000000</td>\n",
+ " <td>39.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>46.000000</td>\n",
+ " <td>49.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>274.000000</td>\n",
+ " <td>280.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Peso F Peso M\n",
+ "count 15303.000000 16879.000000\n",
+ "mean 42.170555 42.995379\n",
+ "std 36.847958 36.184981\n",
+ "min 4.000000 4.000000\n",
+ "25% 20.000000 20.000000\n",
+ "50% 34.000000 39.000000\n",
+ "75% 46.000000 49.000000\n",
+ "max 274.000000 280.000000"
+ ]
+ },
+ "execution_count": 150,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Valores estadisticos de peso trasero para el sexo femenino y masculino\n",
+ "x=df[df[\"sex\"]=='F']['weight'].describe()\n",
+ "y=df[df[\"sex\"]=='M']['weight'].describe()\n",
+ "f=pd.concat([x, y],axis=1)\n",
+ "f.columns=['Peso F','Peso M']\n",
+ "f"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Sexo masculino"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 147,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>hindfoot_length</th>\n",
+ " <th>weight</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>hindfoot_length</th>\n",
+ " <td>1.000000</td>\n",
+ " <td>0.701169</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>weight</th>\n",
+ " <td>0.701169</td>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " hindfoot_length weight\n",
+ "hindfoot_length 1.000000 0.701169\n",
+ "weight 0.701169 1.000000"
+ ]
+ },
+ "execution_count": 147,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Correlacion lineal entre el peso y la longitud del pie trasero para el sexo masculino\n",
+ "df[df[\"sex\"]=='M'][['hindfoot_length','weight']].corr()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 119,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[<AxesSubplot:title={'center':'hindfoot_length'}>,\n",
+ " <AxesSubplot:title={'center':'weight'}>]], dtype=object)"
+ ]
+ },
+ "execution_count": 119,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Histograma de las caracteriticas \n",
+ "df[df[\"sex\"]=='M'][['hindfoot_length','weight']].hist(edgecolor='black')\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Sexo femenino"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 148,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>hindfoot_length</th>\n",
+ " <th>weight</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>hindfoot_length</th>\n",
+ " <td>1.000000</td>\n",
+ " <td>0.665526</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>weight</th>\n",
+ " <td>0.665526</td>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " hindfoot_length weight\n",
+ "hindfoot_length 1.000000 0.665526\n",
+ "weight 0.665526 1.000000"
+ ]
+ },
+ "execution_count": 148,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df[df[\"sex\"]=='F'][['hindfoot_length','weight']].corr()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[<AxesSubplot:title={'center':'hindfoot_length'}>,\n",
+ " <AxesSubplot:title={'center':'weight'}>]], dtype=object)"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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RqkE/MnvT5Mj0CuBMsmHkAJYAn0zvZ6Vp0vzpkpTKl0bEmxHxAtmAE6c2YyfMzKw2NbXpSxoh6WmyMUFXAL8AXomIvrTIFuDY9P5YYDNAmr8HOCpfXmYdMzNrgZpSK0fEO8BkSUcC9wIfGqoKSZoLzAXo6emht7d3qD6qZosWLWLUMdn/t55DYf6kviprwFuLFrWk7vm6VVOqe6vq1ix79+7tqvqadbK68ulHxCuSHgY+Chwp6eB0Nj8O2JoW2wocB2yRdDBwBPByrrwkv07+M24DbgOYMmVKTJ06ta4dGgrTpk3bl7N+/qQ+bnim+q9t08IryEbRG1r5ulVTqnur6tYsvb29dMJxYDYc1NJ7533pDB9JhwJnAc8BDwPnp8VmA/el98vTNGn+Q2kM0eXAhal3zwRgIvBYk/bDzMxqUMuZ/lhgSeppcxCwLCLul7QOWCrpWuAp4Pa0/O3AdyVtBHaR9dghItZKWgasA/qAeanZyMzMWqRq0I+INcDJZcqfp0zvm4j4FfDpCtu6Driu/mqatZakF4HXgHeAvoiYImkMcDcwHngRuCAidqfeaTcB5wJvAJdExJNpO7OBL6fNXhsRSzBrIz+Ra1bZtIiYHBFT0vSVwMqImAisTNMA55A1V04k64RwK0D6J3EVcBrZCdJVkka3sP5mB3DQN6td/hmU/s+m3JmeaXmUrJPDWOBsYEVE7IqI3WTdnWe2uM5m+6mr944NrbHjjmf71s3VF7RWCOAfJQXwzdSrrCcitqX524Ge9L7SMyg1P5syUFflUpfVerrn5r3VwHrN7tY7HLrdDod9AAf9jrJ96+aau1+WbFp43hDVpvA+FhFbJf06sELSz/MzIyLSP4SmGKircqnLaj3dc/M2LbyigeOqud16h0O32+GwD+DmHbOyImJr+rmT7IHEU4EdqdmG9HNnWrzSMyg1PZti1koO+mb9SDpM0ntL74EZwLPs/wxK/2dTLlbmdGBPagZ6EJghaXS6gTsjlZm1jZt3zA7UA9yb9cTkYOBvIuJHkh4HlkmaA2wCLkjLP0DWXXMjWZfNSwEiYpeka4DH03JXR8Su1u2G2YEc9M36Sc+gnFSm/GVgepnyAOZV2NZiYHGz62jWKDfvmJkViIO+mVmBOOibmRWIg76ZWYE46JuZFYiDvplZgTjom5kViIO+mVmBOOibmRWIg76ZWYHUMjD6cZIelrRO0lpJl6fyr0jaKunp9Do3t84CSRslrZd0dq58ZirbKOnKcp9nZmZDp5bcO33A/Ih4MmUeXC1pRZp3Y0Qsyi8s6USywdA/DLwf+LGk30qzbwHOIhtM4nFJyyNiXTN2xMzMqqtlYPRtwLb0/jVJz1Fh9J9kFrA0It4EXpC0kXcHUN+YklkhaWla1kHfzKxF6sqyKWk8cDKwCjgDuEzSxcATZFcDu8n+ITyaWy0/RFz/oeNOK/MZFYeNa5f8MHU9h8L8SdWHnmtkuLlGhsOrZyi8Ut2bPRTeUBsuw9SZdYKag76kw4HvA1+IiFcl3QpcQzaW6DXADcDnBluhgYaNa5f8MHXzJ/VxwzPVf22NDDfXyHB49QyFV6p7s4fCG2rDZZg6s05QU9CXNJIs4N8VET8AiIgdufnfAkqRZ6Ah4jx0nJlZG9XSe0fA7cBzEfG1XPnY3GKfIhtODrKh4y6UdIikCcBE4DGy0YMmSpogaRTZzd7lzdkNMzOrRS1n+mcAnwWekfR0KvsL4CJJk8mad14EPg8QEWslLSO7QdsHzIuIdwAkXUY2RugIYHFErG3anpiZWVW19N55BFCZWQ8MsM51wHVlyh8YaD0zMxtafiLXzKxAHPTNzArEQd+sAkkjJD0l6f40PUHSqpRG5O7UIYHUaeHuVL4qPc9S2kbZlCRm7eKgb1bZ5cBzuemFZKlHTgB2A3NS+Rxgdyq/MS3XPyXJTOAbkka0qO5mZTnom5UhaRzwB8C307SAM4F70iJLgE+m97PSNGn+9LT8vpQkEfECkE9JYtYWdaVhMCuQrwNfBN6bpo8CXomIUs6LfHqRY0kpRiKiT9KetPxAKUn2M1D6kVIaikbSdEB9qTry6zQz9cVwSKUxHPYBHPTNDiDpPGBnRKyWNLUVnzlQ+pFSGopG0nRAfak68us0M1XHcEilMRz2ARz0zco5A/hEGiPiPcC/A24CjpR0cDrbz6cRKaUe2SLpYOAI4GUGTkli1hZu0zfrJyIWRMS4iBhPdiP2oYj4DPAwcH5abDZwX3q/PE2T5j8U2WlypZQkZm3jM32z2n0JWCrpWuApspxUpJ/fTWNH7CL7RzFgShKzdnHQNxtARPQCven985TpfRMRvwI+XWH9silJzNrFzTtmZgXioG9mViAO+mZmBeKgb2ZWIA76ZmYF4qBvZlYgtYyRe5ykhyWtk7RW0uWpfIykFZI2pJ+jU7kk3ZzSya6RdEpuW7PT8hskza70mWZmNjRqOdPvA+ZHxInA6cC8lDL2SmBlREwEVqZpgHPInjycSJZA6lbI/kkAVwGnkfV1vqr0j8LMzFqjatCPiG0R8WR6/xpZfvFj2T+dbP80s3dG5lGyfCVjgbOBFRGxKyJ2AyvIcoybmVmL1PVEbhoR6GRgFdATEdvSrO1AT3q/L81sUkonW6m8/2dUTDE7WGvWPMPbb79V93r5lLY9h8L8SdXT1DaSmraR1Ln1pM0t1b3ZaXOH2nBJaWvWCWoO+pIOB74PfCEiXs3GiMhEREhqSh7WgVLMDlbjqWnP27fe/El93PBM9V9bI6lpG6lfPWlzS3VvdtrcoTZcUtqadYKaeu9IGkkW8O+KiB+k4h2p2Yb0c2cqr5RO1mlmzczarJbeOyLLIvhcRHwtNyufTrZ/mtmLUy+e04E9qRnoQWCGpNHpBu6MVGZmZi1SS/POGcBngWckPZ3K/gK4HlgmaQ6wCbggzXsAOJdsPNA3gEsBImKXpGuAx9NyV0fErmbshJmZ1aZq0I+IRwBVmD29zPIBzKuwrcXA4noqaGZmzeMncs3MCsRB38ysQBz0zcwKxEHfzKxAHPTNzArEQd/MrEAc9M36kfQeSY9J+llKJ/7VVD5B0qqUNvxuSaNS+SFpemOaPz63rQWpfL2ks9u0S2b7OOibHehN4MyIOAmYDMxMT5cvBG6MiBOA3cCctPwcYHcqvzEtR0pBfiHwYbKMst+QNKKVO2LWn4O+WT8pLfjeNDkyvQI4E7gnlfdPJ15KM34PMD2lL5kFLI2INyPiBbKn1E8d+j0wq6yu1MpWhxEjyWcite6SzshXAycAtwC/AF6JiFIe63xq8H1pwyOiT9Ie4KhU/mhus2XTiafPq5hSvJRaupHU21Bf+u38Os1MZz0c0mMPh30AB/2h887bDaRJPm+IKmP1ioh3gMmSjgTuBT40xJ9XMaV4KbV046nBa0+/nV+nmem3h0N67OGwD+DmHbMBRcQrwMPAR8lGgSudKOVTg+9LG57mHwG8jNOJWwdy0DfrR9L70hk+kg4FziIbJvRh4Py0WP904qU04+cDD6XEg8uBC1Pvnglk40Y/1pKdMKvAzTtmBxoLLEnt+gcByyLifknrgKWSrgWeIhtngvTzu5I2ArvIeuwQEWslLQPWAX3AvNRsZNY2Dvpm/UTEGrKxoPuXP0+Z3jcR8Svg0xW2dR1wXbPraNYoN++YmRWIg76ZWYHUMkbuYkk7JT2bK/uKpK2Snk6vc3Pzyj52LmlmKtso6crm74qZmVVTy5n+HWSPkPd3Y0RMTq8HoPJj5+mG2C3AOcCJwEVpWTMza6Faxsj9aT6BVBX7HjsHXki9GUo3vjamG2FIWpqWXVd/lc3MrFGDadO/TNKa1PwzOpXtexw9KT12XqnczMxaqNEum7cC15AloboGuAH4XDMqNFAOksFqRu6SnkNh/qTq22g038lQrlOqe7Pzqgy14ZLzxKwTNBT0I2JH6b2kbwGlxB4DPXZe0+PoA+UgGaxm5C6ZP6mPG56p/mtrNN/JUK5Tqnuz86oMteGS88SsEzTUvCNpbG7yU0CpZ0+lx84fByamQShGkd3sXd54tc3MrBFVT1klfQ+YChwtaQtwFTBV0mSy5p0Xgc/DwI+dS7oMeBAYASyOiLXN3hkzMxtYLb13LipTfHuZstLyZR87T906H6irdmZm1lR+ItfMrEAc9M3MCsRB38ysQBz0zcwKxEHfzKxAHPTNzArEQd/MrEAc9M3MCsRB38ysQBz0zcwKxEHfrB9Jx0l6WNI6SWslXZ7Kx0haIWlD+jk6lUvSzWko0DWSTslta3ZafoOk2e3aJ7MSB32zA/UB8yPiROB0YF4a3vNKYGVETARWpmnIhgGdmF5zycabQNIYsgSFp5GNIHdVbsAhs7Zw0DfrJyK2RcST6f1rwHNkI73NApakxZYAn0zvZwF3RuZR4MiUfvxsYEVE7IqI3cAKyo83bdYyDvpmA0jjQ58MrAJ6ImJbmrUd6EnvPUyodY1Gh0s0G/YkHQ58H/hCRLwqad+8iAhJTRt+bKBhQkvDRTZjuM961mnmEJXDYcjL4bAP4KBvVpakkWQB/66I+EEq3iFpbERsS803O1N5pWFCt5INQJQv7y33eQMNE1oaLrIZw33Ws04zh9QcDkNeDod9ADfvmB1A2Sn97cBzEfG13KzlQKkHzmzgvlz5xakXz+nAntQM9CAwQ9LodAN3Riozaxuf6Zsd6Azgs8Azkp5OZX8BXA8skzQH2ARckOY9AJwLbATeAC4FiIhdkq4hGyMa4OqI2NWSPTCroJYxchcD5wE7I+IjqWwMcDcwnmyM3AsiYnc6Q7qJ7AvwBnBJqRdE6qP85bTZayNiCWYdKCIeAVRh9vQyywcwr8K2FgOLm1c7s8GppXnnDg7sZub+ymZmXahq0I+InwL9L0ndX9nMrAs12qY/ZP2VB+q6NljN6PLWcyjMn1R9G412kxvKdUp1b3Z3vKE2XLrKmXWCQd/IbXZ/5YG6rg1WM7q8zZ/Uxw3PVP+1NdpNbijXKdW92d3xhtpw6Spn1gka7bK5IzXbUEd/5XLl1kXGjjseSXW9xo47vt3VNrOcRs/0S/2Vr+fA/sqXSVpKdtN2T3qQ5UHgr3I3b2cACxqvtrXD9q2bG7gSOW+IamNmjaily+b3yJ4qPFrSFrJeOO6vbGbWhaoG/Yi4qMIs91c2M+syTsNgZlYgDvpmZgXioG9mViAO+mZmBeKgb2ZWIA76ZmYF4qBvZlYgDvpmZgXioG9mViAO+mZmBeKgb2ZWIA76ZmYF4qBvZlYgDvpmZgXioG9WhqTFknZKejZXNkbSCkkb0s/RqVySbpa0UdIaSafk1pmdlt8gaXY79sUsz0HfrLw7gJn9yq4EVkbERGBlmgY4B5iYXnOBWyH7J0E26NBpwKnAVbnR48zawkHfrIyI+CnQf3S3WcCS9H4J8Mlc+Z2ReRQ4Mo0dfTawIiJ2RcRuYAUH/iMxa6lGx8gFQNKLwGvAO0BfRExJZzd3A+OBF4ELImK3JAE3kQ2n+AZwSUQ8OZjPN2uxnojYlt5vB3rS+2OBzbnltqSySuUHkDSX7CqBnp4eent7983bu3cvvb29LFq0iFHH9NVd6bcaWO+tRYv2q8Nglfahmw2HfYBBBv1kWkT8MjddugS+XtKVafpL7H8JfBrZJfBpTfh8s5aLiJAUTdzebcBtAFOmTImpU6fum9fb28vUqVOZNm1a3QPTA2xaeEUDA9pfQTb6aXOU9qGbDYd9gKFp3qn3EtisW+woHbPp585UvhU4LrfcuFRWqdysbQZ7ph/AP6Yznm+ms5V6L4G35coGvMwdrGZcHvccCvMnVd9Go5fUQ7lOqe6NXro38vtrRjNBB11WLwdmA9enn/flyi+TtJTs6nVPRGyT9CDwV7mbtzOABS2us9l+Bhv0PxYRWyX9OrBC0s/zMxu5BB7oMnewmnF5PH9SHzc8U/3X1ugl9VCuU6p7o5fujfz+mtFM0I7LaknfA6YCR0vaQtYL53pgmaQ5wCbggrT4A2T3qjaS3a+6FCAidkm6Bng8LXd1RPS/OWzWUoMK+hGxNf3cKelesm5pOySNTWc6tVwCm3WciLiowqzpZZYNYF6F7SwGFjexamaD0nCbvqTDJL239J7s0vVZ3r0EhgMvgS9OD7KcTroEbrjmZmZWt8Gc6fcA92Y9MTkY+JuI+JGkx6njEtjMzFqn4aAfEc8DJ5Upf5k6L4HNrJjGjjue7Vs3V18w55hjj2Pblv83RDUa/prRT9/MrCHbt25uoHPAeUNUm2JwGgYzswJx0DczKxAHfTOzAnHQNzMrEAd9M7MCcdA3MysQB30zswJx0DczKxAHfTOzAnHQN7MDjRiJpLpeY8cd3+5aWw2chsHMDvTO2/WnR1j0KVICxgMsWrSIadOmNaNm+/4h1cs5ezIO+mbWHAP8oxh1TF/ZeQ3l0WngH1LDnzUMuXnHzKxAHPTNzArEQd/MisE3pwG36ZtZUTRyc3oY3gfwmb6ZWYG0POhLmilpvaSNkq5s9ecbDV3mNtJFrtHPGm6X1D7mu1ju+F29evWwOH5b2rwjaQRwC3AWsAV4XNLyiFhX77YaGVvTklZ2eSv4JXUzj3lrg9zxW6nbaX+dfvy2uk3/VGBjGlQdSUuBWUDdXwCPrWldomnHvHWJBh4ea+WDY4qIlnwQgKTzgZkR8Udp+rPAaRFxWW6ZucDcNPlBYH3LKlibo4FftrsSDerWug+23h+IiPc1qzL1qOWYT+UDHffd+nfL8z60VsVjvuN670TEbcBt7a5HJZKeiIgp7a5HI7q17t1a73oMdNwPh/33PnSOVt/I3Qocl5sel8rMhisf89ZRWh30HwcmSpogaRRwIbC8xXUwayUf89ZRWtq8ExF9ki4DHgRGAIsjYm0r69AEHdv0VINurXu31rtZx3zX7n+O96FDtPRGrpmZtZefyDUzKxAHfTOzAnHQH4Ck4yQ9LGmdpLWSLk/lYyStkLQh/Rzd7rqWI2mEpKck3Z+mJ0haldIB3J1uLHYcSUdKukfSzyU9J+mj3fI7b7ZuSeEgabGknZKezZWV/Zspc3PapzWSTmlfzd9V7/e9U/ejGgf9gfUB8yPiROB0YJ6kE4ErgZURMRFYmaY70eXAc7nphcCNEXECsBuY05ZaVXcT8KOI+BBwEtk+dMvvvGlyKRzOAU4ELkrHXye6A5jZr6zS3+wcYGJ6zQVubVEdq6n3+96p+zGwiPCrxhdwH1kOlfXA2FQ2Fljf7rqVqes4sgP0TOB+QGRPEx6c5n8UeLDd9SxT7yOAF0idDHLlHf87H4LfxX5/I2ABsKDd9RqgvuOBZ6v9zYBvAheVW66TXtW+792yH/1fPtOvkaTxwMnAKqAnIralWduBnnbVawBfB74I/FuaPgp4JSL60vQW4Ng21KuaCcC/At9JTVPflnQY3fE7b7ZjgXxWwU79m1VS6W/W8ftV4/e94/ejHAf9Gkg6HPg+8IWIeDU/L7J/8R3V71XSecDOiFjd7ro04GDgFODWiDgZeJ1+TTmd+Du3gXXT36zbvu/1ctCvQtJIsgPgroj4QSreIWlsmj8W2Nmu+lVwBvAJSS8CS8maeG4CjpRUeiCvU9MBbAG2RMSqNH0P2T+BTv+dD4VuT+FQ6W/WsftV5/e9Y/djIA76A1CWH/V24LmI+Fpu1nJgdno/m6ztr2NExIKIGBcR48ke+38oIj4DPAycnxbruHoDRMR2YLOkD6ai6WRpiDv6dz5Euj2FQ6W/2XLg4tT75XRgT675pG0a+L535H5U1e6bCp38Aj5Gdim3Bng6vc4lax9fCWwAfgyMaXddB9iHqcD96f1vAI8BG4G/BQ5pd/0q1Hky8ET6vf8dMLqbfudN/l2cC/wL8Avgv7W7PgPU83vANuBtsqu1OZX+ZmSdCm5J+/QMMKXd9U/1quv73qn7Ue3lNAxmZgXi5h0zswJx0DczKxAHfTOzAnHQNzMrEAd9M7MCcdA3MysQB30zswL5/82rLp9MpXCwAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<Figure size 432x288 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Histograma de las caracteriticas \n",
+ "df[df[\"sex\"]=='F'][['hindfoot_length','weight']].hist(edgecolor='black')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Analisis en el tiempo"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Valor medio y desviacion estandar por dia del pie trasero y el peso"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 107,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "graficas('day')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Valor medio y desviacion estandar por **mes** del pie trasero y el peso"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 144,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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qC0CSs4GrgNcNWZgkSdK86TvHa2Gb+6sGqEOSJGnu9RnxeitwVZKLmS6g+nTgtYNWJUmSNIf6LKD6sSRLwBO7XadV1cZBq5IkSZpDfQ817gPcCkyAf5Tk6Tt/uiRJku5phyNeSZ5XVecneTvwz4GrgS3dwwV8sUF9kiRJc2NnhxpfDJwPvAB4jNdnlCRJ2jM7O9R4ePfzO8D+DWqRJEmaazsb8fqT7udPgfVJLgJ+OepVVacMWZgkSdK82WHwqqpPdnc/1d0kSZK0B/osJ3F2i0IkSZLm3S6DV5JHM11E9UjgwK37q+qRA9YlSZI0d/qs4/Vh4H3A3cCvAR8BPjpkUZIkSfOoT/C6b1VdBKSqbqiqNzG9cLYkSZJ2Q59rNd6VZB/guiR/APwQuP+wZUmSJM2fPiNepwL3A04BfpXpwqonDVmUJEnSPOpzVuNXu7t3AC8ZthxJkqT51eesxnXA64FHbPv8qjpqwLokSZLmTp85XucArwG+zt9dJFuSJEm7qU/wuqWqXLlekiRpD/UJXm9M8gHgntdqPG+wqiRJkuZQn+D1EuCxwP783aHGAgxekiRJu6FP8HpiVT1m8EokSZLmXJ91vP46yZGDVyJJkjTn+ox4PRlYn+R6pnO8ApTLSUiSJO2ePsHrWYNXIUmStAL0Wbn+BoAk/wA4cPCKJEmS5tQu53glOSHJdcD1wBeA7wGfHbguSZKkudNncv2/ZzrP61tVtRY4HvjyoFVJkiTNoT7B6xdV9RNgnyT7VNXFwLqB65IkSZo7fYLXJMn9gS8C5yR5L3Dnrl6U5GFJLk7yzSRXJzm12/+mJD9Msr67PXvP/hMkSZLGoc9Zjc8Dfga8CjgRWAW8ucfr7gZeXVVXJjkYuCLJhd1j766qM+5NwZIkSWPVZ8TrDVW1parurqqzq+pPgdN29aKquqmqruzu3w5cAxy+Z+VKkiSNV5/g9czt7PvN3XmTJGuAY4DLul1/kGRDkg8lecDu/C5JkqSx2uGhxiS/D7wceGSSDds8dDDwpb5v0M0P+wTwyqq6Lcn7mJ4pWd3PdwIv3c7rTgZOBli9ejVLS0t933K3TSYTgEHfYwiTyYTNmzePrm6w563Z77bsd1v2u62x9huWR893NsfrvzNdr+utwGu32X97Vf1Nn1+eZH+moeucqjoPoKp+vM3j7wcu2N5rq+pM4EyAdevW1eLiYp+3vFcWFhYAGPI9hrCwsMBkMhld3WDPW7Pfbdnvtux3W2PtNyyPnu8weFXVJmAT8Dv35hcnCfBB4Jqqetc2+w+rqpu6zRcA37g3v1+SJGls+pzVeG89FXgx8PUk67t9rwN+J8nRTA81fg/4VwPWIEmStGwMFryq6hIg23noM0O9pyRJ0nLW56xGSZIk7QUGL0mSpEYMXpIkSY0YvCRJkhoxeEmSJDVi8JIkSWrE4CVJktSIwUuSJKkRg5ckSVIjBi9JkqRGDF6SJEmNGLwkSZIaMXhJkiQ1YvCSJElqxOAlSZLUiMFLkiSpEYOXJElSIwYvSZKkRgxekiRJjRi8JEmSGjF4SZIkNWLwkiRJasTgJUmS1IjBS5IkqRGDlyRJUiMGL0mSpEYMXpIkSY0YvCRJkhoxeEmSJDVi8JIkSWrE4CVJktSIwUuSJKkRg5ckSVIjBi9JkqRGDF6SJEmNGLwkSZIaMXhJkiQ1YvCSJElqxOAlSZLUiMFLkiSpEYOXJElSIwYvSZKkRgxekiRJjRi8JEmSGjF4SZIkNbLfrAuQNLylpaVZlyBJwhEvSZKkZgxekiRJjRi8JEmSGjF4SZIkNTJY8ErysCQXJ/lmkquTnNrtf2CSC5Nc1/18wFA1SJIkLSdDjnjdDby6qo4Engy8IsmRwGuBi6rq0cBF3bYkSdLcGyx4VdVNVXVld/924BrgcOB5wNnd084Gnj9UDZIkSctJk3W8kqwBjgEuA1ZX1U3dQxuB1S1qkCTt3NLSkmu+SQMbPHgluT/wCeCVVXVbkl8+VlWVpHbwupOBkwFWr1496JfBZDIBxrfI5GQyYfPmzaOrG+y5+hlrv8f6+Qa44447Rlf3WPvt57u95dDzQYNXkv2Zhq5zquq8bvePkxxWVTclOQy4eXuvraozgTMB1q1bV4uLi4PVubCwAMCQ7zGEhYUFJpPJ6OoGe65+1q9fz9LS0uj6PdbPN2C/G/Lz3d5y+A4fLHhlOrT1QeCaqnrXNg99CjgJeFv38/yhapD2Ng/FSJL2xJAjXk8FXgx8Pcn6bt/rmAaujyd5GXAD8FsD1iBJmnP+MaQxGSx4VdUlQHbw8PFDva8kSdJy1eSsRklaSRyBkbQjBi/G+yXpfCNJUmtj/ndnOfy76bUaJUmSGnHESzMx6784JEmaBUe8JEmSGjF4SZIkNWLwkiRJasTgJUmS1IjBS5IkqRGDlyRJUiMGL0mSpEYMXpIkSY0YvCRJkhoxeEmSJDVi8JIkSWrE4CVJktRIqmrWNexSkluAG2ZdxzL1YODWWRexwtjztux3W/a7LfvdXoueP6KqDt3eA6MIXtqxJJdX1bpZ17GS2PO27Hdb9rst+93erHvuoUZJkqRGDF6SJEmNGLzG78xZF7AC2fO27Hdb9rst+93eTHvuHC9JkqRGHPGSJElqxOA1UkkeluTiJN9McnWSU2dd00qQZN8kVyW5YNa1zLskC0nOTXJtkmuSHDvrmuZdkld13yffSPKxJAfOuqZ5kuRDSW5O8o1t9j0wyYVJrut+PmCWNc6THfT7Hd13yoYkn0yy0Loug9d43Q28uqqOBJ4MvCLJkTOuaSU4Fbhm1kWsEO8FPldVjwWegH0fVJLDgVOAdVX1eGBf4LdnW9XcOQt41j32vRa4qKoeDVzUbWvvOIu/3+8LgcdX1VHAt4DTWxdl8Bqpqrqpqq7s7t/O9B+lw2db1XxLcgTwHOADs65l3iVZBTwd+CBAVf28qiYzLWpl2A+4b5L9gPsBP5pxPXOlqr4I/M09dj8POLu7fzbw/JY1zbPt9buq/qqq7u42vwwc0boug9ccSLIGOAa4bMalzLv3AH8EbJlxHSvBWuAW4MPdod0PJDlo1kXNs6r6IXAG8H3gJmBTVf3VbKtaEVZX1U3d/Y3A6lkWs8K8FPhs6zc1eI1ckvsDnwBeWVW3zbqeeZXkucDNVXXFrGtZIfYDfgV4X1UdA9yJh2AG1c0teh7T0PtQ4KAkL5ptVStLTZcZcKmBBpK8numUnXNav7fBa8SS7M80dJ1TVefNup4591TghCTfA/4cOC7JR2db0ly7EbixqraO4p7LNIhpOM8Arq+qW6rqF8B5wFNmXNNK8OMkhwF0P2+ecT1zL8m/AJ4LnFgzWFPL4DVSScJ0/ss1VfWuWdcz76rq9Ko6oqrWMJ1w/PmqcjRgIFW1EfhBksd0u44HvjnDklaC7wNPTnK/7vvleDyhoYVPASd1908Czp9hLXMvybOYThk5oap+OosaDF7j9VTgxUxHXtZ3t2fPuihpL/pD4JwkG4CjgT+ebTnzrRtdPBe4Evg6038fXFV9L0ryMeBS4DFJbkzyMuBtwDOTXMd01PFts6xxnuyg3/8JOBi4sPt388+a1+XK9ZIkSW044iVJktSIwUuSJKkRg5ckSVIjBi9JkqRGDF6SJEmNGLwk6R6SLCR5+Tbbi0kumGVNkuaDwUuS/r4F4OW7epIk7S6Dl6RRS7ImybVJzkryrSTnJHlGki8luS7Jk5I8MMn/TLIhyZeTHNW99k1JPpRkKcl3k5zS/dq3Af+wW2DxHd2++yc5t3uvc7rV3SVpt+w36wIkaS94FPDPgJcCXwV+F3gacALwOuAHwFVV9fwkxwEfYboaPsBjgV9jupr1/0nyPqYX5H58VR0N00ONwDHA44AfAV9ievWISwb/L5M0VxzxkjQPrq+qr1fVFuBq4KLu4rdfB9YwDWH/DaCqPg88KMkh3Wv/sqruqqpbmV6gePUO3uMrVXVj9x7ru98rSbvF4CVpHty1zf0t22xvYdcj+9u+dvNOnt/3eZK0QwYvSSvB/wZOhF8eNry1qm7byfNvZ3roUZL2Kv9ik7QSvAn4UJINwE+Bk3b25Kr6STc5/xvAZ4G/HL5ESStBptMgJEmSNDQPNUqSJDVi8JIkSWrE4CVJktSIwUuSJKkRg5ckSVIjBi9JkqRGDF6SJEmNGLwkSZIa+X9CuGMJLIKX7wAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<Figure size 720x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "graficas('month')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Valor medio y desviacion estandar por año del pie trasero y el peso"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 108,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "graficas('year')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Correlaciones"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 151,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>day</th>\n",
+ " <th>hindfoot_length</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>day</th>\n",
+ " <td>1.000000</td>\n",
+ " <td>-0.001469</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>hindfoot_length</th>\n",
+ " <td>-0.001469</td>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " day hindfoot_length\n",
+ "day 1.000000 -0.001469\n",
+ "hindfoot_length -0.001469 1.000000"
+ ]
+ },
+ "execution_count": 151,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df[['day','hindfoot_length']].corr()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 101,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>month</th>\n",
+ " <th>hindfoot_length</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>month</th>\n",
+ " <td>1.00000</td>\n",
+ " <td>-0.01371</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>hindfoot_length</th>\n",
+ " <td>-0.01371</td>\n",
+ " <td>1.00000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " month hindfoot_length\n",
+ "month 1.00000 -0.01371\n",
+ "hindfoot_length -0.01371 1.00000"
+ ]
+ },
+ "execution_count": 101,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\n",
+ "df[['month','hindfoot_length']].corr()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 161,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>year</th>\n",
+ " <th>hindfoot_length</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>year</th>\n",
+ " <td>1.000000</td>\n",
+ " <td>-0.287259</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>hindfoot_length</th>\n",
+ " <td>-0.287259</td>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " year hindfoot_length\n",
+ "year 1.000000 -0.287259\n",
+ "hindfoot_length -0.287259 1.000000"
+ ]
+ },
+ "execution_count": 161,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df[['year','hindfoot_length']].corr()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 162,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>year</th>\n",
+ " <th>weight</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>year</th>\n",
+ " <td>1.000000</td>\n",
+ " <td>-0.276595</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>weight</th>\n",
+ " <td>-0.276595</td>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " year weight\n",
+ "year 1.000000 -0.276595\n",
+ "weight -0.276595 1.000000"
+ ]
+ },
+ "execution_count": 162,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df[['year','weight']].corr()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 164,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>day</th>\n",
+ " <th>weight</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>day</th>\n",
+ " <td>1.000000</td>\n",
+ " <td>-0.008636</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>weight</th>\n",
+ " <td>-0.008636</td>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " day weight\n",
+ "day 1.000000 -0.008636\n",
+ "weight -0.008636 1.000000"
+ ]
+ },
+ "execution_count": 164,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df[['day','weight']].corr()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 163,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>month</th>\n",
+ " <th>weight</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>month</th>\n",
+ " <td>1.00000</td>\n",
+ " <td>-0.00297</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>weight</th>\n",
+ " <td>-0.00297</td>\n",
+ " <td>1.00000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " month weight\n",
+ "month 1.00000 -0.00297\n",
+ "weight -0.00297 1.00000"
+ ]
+ },
+ "execution_count": 163,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df[['month','weight']].corr()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Analisis por especie"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Frecuencia de cada una de las especies\n",
+ "plt.figure(figsize=(14,5))\n",
+ "df[\"species_id\"].value_counts().plot(kind=\"bar\")\n",
+ "plt.ylabel('frecuencia')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1008x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1008x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "graficas('species_id')"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/data-used/surveys.csv b/data-used/surveys.csv
new file mode 100644
index 0000000..7131902
--- /dev/null
+++ b/data-used/surveys.csv
@@ -0,0 +1,35550 @@
+record_id,month,day,year,plot_id,species_id,sex,hindfoot_length,weight
+1,7,16,1977,2,NL,M,32,
+2,7,16,1977,3,NL,M,33,
+3,7,16,1977,2,DM,F,37,
+4,7,16,1977,7,DM,M,36,
+5,7,16,1977,3,DM,M,35,
+6,7,16,1977,1,PF,M,14,
+7,7,16,1977,2,PE,F,,
+8,7,16,1977,1,DM,M,37,
+9,7,16,1977,1,DM,F,34,
+10,7,16,1977,6,PF,F,20,
+11,7,16,1977,5,DS,F,53,
+12,7,16,1977,7,DM,M,38,
+13,7,16,1977,3,DM,M,35,
+14,7,16,1977,8,DM,,,
+15,7,16,1977,6,DM,F,36,
+16,7,16,1977,4,DM,F,36,
+17,7,16,1977,3,DS,F,48,
+18,7,16,1977,2,PP,M,22,
+19,7,16,1977,4,PF,,,
+20,7,17,1977,11,DS,F,48,
+21,7,17,1977,14,DM,F,34,
+22,7,17,1977,15,NL,F,31,
+23,7,17,1977,13,DM,M,36,
+24,7,17,1977,13,SH,M,21,
+25,7,17,1977,9,DM,M,35,
+26,7,17,1977,15,DM,M,31,
+27,7,17,1977,15,DM,M,36,
+28,7,17,1977,11,DM,M,38,
+29,7,17,1977,11,PP,M,,
+30,7,17,1977,10,DS,F,52,
+31,7,17,1977,15,DM,F,37,
+32,7,17,1977,10,DM,F,35,
+33,7,17,1977,11,DM,F,36,
+34,7,17,1977,17,DM,,,
+35,7,17,1977,13,DM,F,38,
+36,7,17,1977,16,OT,F,22,
+37,7,17,1977,11,DM,F,35,
+38,7,17,1977,17,NL,M,33,
+39,7,17,1977,11,DM,F,36,
+40,7,18,1977,20,DM,M,36,
+41,7,18,1977,23,DM,F,34,
+42,7,18,1977,18,DS,F,46,
+43,7,18,1977,21,DM,M,36,
+44,7,18,1977,18,DM,F,35,
+45,7,18,1977,22,DM,M,36,
+46,7,18,1977,19,DM,M,35,
+47,7,18,1977,18,DM,M,32,
+48,7,18,1977,20,DM,F,36,
+49,7,18,1977,12,PP,F,17,
+50,7,18,1977,18,DM,F,32,
+51,7,18,1977,21,DM,F,36,
+52,7,18,1977,12,DM,F,26,
+53,7,18,1977,22,DM,M,36,
+54,7,18,1977,18,DM,M,37,
+55,7,18,1977,23,DM,M,36,
+56,7,18,1977,20,DM,M,34,
+57,7,18,1977,22,DM,,,
+58,7,18,1977,12,DS,M,45,
+59,7,18,1977,19,DM,M,33,
+60,7,18,1977,19,PE,M,20,
+61,7,18,1977,23,DM,M,35,
+62,7,18,1977,19,DM,M,35,
+63,8,19,1977,3,DM,M,35,40
+64,8,19,1977,7,DM,M,37,48
+65,8,19,1977,4,DM,F,34,29
+66,8,19,1977,4,DM,F,35,46
+67,8,19,1977,7,DM,M,35,36
+68,8,19,1977,8,DO,F,32,52
+69,8,19,1977,2,PF,M,15,8
+70,8,19,1977,3,OX,F,21,22
+71,8,19,1977,7,DM,F,36,35
+72,8,19,1977,2,NL,M,31,
+73,8,19,1977,3,DS,F,44,
+74,8,19,1977,8,PF,M,12,7
+75,8,19,1977,8,DM,F,32,22
+76,8,19,1977,9,DS,F,47,
+77,8,19,1977,4,SS,,,
+78,8,19,1977,1,PF,M,16,9
+79,8,19,1977,7,DM,F,34,42
+80,8,19,1977,1,DS,M,48,
+81,8,19,1977,4,PF,F,14,8
+82,8,19,1977,4,DM,F,35,41
+83,8,20,1977,6,DM,F,37,37
+84,8,20,1977,19,DM,F,35,43
+85,8,20,1977,23,DM,F,35,41
+86,8,20,1977,18,DM,F,33,40
+87,8,20,1977,5,PF,F,11,9
+88,8,20,1977,18,DM,F,35,45
+89,8,20,1977,12,PP,F,20,15
+90,8,20,1977,18,DM,M,35,29
+91,8,20,1977,11,DS,F,50,
+92,8,20,1977,6,DM,M,35,39
+93,8,20,1977,18,DM,,,42
+94,8,20,1977,18,DM,F,36,43
+95,8,20,1977,23,DM,M,38,46
+96,8,20,1977,12,DM,M,36,41
+97,8,20,1977,18,DM,M,36,41
+98,8,20,1977,5,DM,M,38,40
+99,8,20,1977,11,DM,M,37,45
+100,8,20,1977,5,DS,F,54,
+101,8,20,1977,5,DM,F,35,46
+102,8,20,1977,23,DM,F,35,40
+103,8,20,1977,18,DM,F,35,30
+104,8,20,1977,11,DS,M,43,
+105,8,20,1977,19,DM,M,35,39
+106,8,20,1977,12,NL,,,
+107,8,20,1977,18,NL,,,
+108,8,20,1977,11,PP,M,21,
+109,8,20,1977,6,DM,M,35,34
+110,8,20,1977,10,DS,,,
+111,8,20,1977,11,DM,F,37,42
+112,8,20,1977,23,DM,M,37,42
+113,8,20,1977,5,PF,F,13,8
+114,8,21,1977,15,DM,F,32,
+115,8,21,1977,21,DM,M,34,
+116,8,21,1977,13,DM,F,37,31
+117,8,21,1977,21,OX,F,19,
+118,8,21,1977,17,DM,M,33,
+119,8,21,1977,20,DM,M,35,
+120,8,21,1977,20,DM,M,35,
+121,8,21,1977,15,NL,,,
+122,8,21,1977,22,DM,F,33,
+123,8,21,1977,17,DM,M,37,
+124,8,21,1977,14,DM,M,36,
+125,8,21,1977,22,DM,M,35,
+126,8,21,1977,20,DM,F,30,
+127,8,21,1977,14,DM,M,33,
+128,8,21,1977,15,DM,M,34,
+129,8,21,1977,14,DM,F,34,
+130,8,21,1977,16,DM,F,34,
+131,8,21,1977,22,DM,M,37,
+132,8,21,1977,22,DM,F,33,
+133,8,21,1977,15,DM,M,37,
+134,8,21,1977,17,DS,F,45,
+135,8,21,1977,15,DM,F,35,
+136,8,21,1977,13,PF,F,16,
+137,8,21,1977,20,DM,F,35,
+138,8,21,1977,22,DM,M,37,
+139,8,21,1977,14,PF,M,15,
+140,8,21,1977,20,DM,M,33,
+141,8,21,1977,16,DM,M,36,
+142,8,21,1977,17,DM,F,34,
+143,8,21,1977,15,DM,M,35,
+144,8,21,1977,15,DS,M,40,
+145,8,21,1977,13,DM,F,37,
+146,8,21,1977,13,DM,F,36,40
+147,8,21,1977,13,DM,M,37,
+148,8,21,1977,14,DM,M,37,
+149,8,21,1977,20,DS,M,50,
+150,8,21,1977,21,DM,M,35,
+151,8,21,1977,13,DM,M,35,
+152,8,21,1977,14,DM,M,38,
+153,8,21,1977,16,DS,F,50,
+154,8,21,1977,20,DS,F,47,
+155,8,21,1977,20,DS,,,
+156,8,21,1977,21,DM,M,35,
+157,9,11,1977,20,DM,M,36,37
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+3034,6,2,1980,2,DS,M,50,102
+3035,6,2,1980,2,DM,M,38,46
+3036,6,2,1980,7,DM,M,,46
+3037,6,2,1980,7,DM,F,36,53
+3038,6,2,1980,7,DS,,49,126
+3039,6,2,1980,9,DS,M,48,122
+3040,6,2,1980,9,OL,F,19,28
+3041,6,2,1980,9,DM,F,36,51
+3042,6,2,1980,9,DS,F,50,69
+3043,6,22,1980,10,OL,M,20,35
+3044,6,22,1980,17,DM,M,34,41
+3045,6,22,1980,20,DS,F,52,151
+3046,6,22,1980,11,DS,M,52,109
+3047,6,22,1980,18,DS,F,49,93
+3048,6,22,1980,18,DS,M,47,82
+3049,6,22,1980,24,PE,M,20,22
+3050,6,22,1980,11,DO,F,36,56
+3051,6,22,1980,24,DM,F,34,45
+3052,6,22,1980,17,DM,M,35,46
+3053,6,22,1980,11,DO,M,33,26
+3054,6,22,1980,20,DM,M,37,48
+3055,6,22,1980,17,DS,F,,109
+3056,6,22,1980,22,DS,M,48,103
+3057,6,22,1980,18,DS,M,50,136
+3058,6,22,1980,24,NL,F,34,187
+3059,6,22,1980,11,PF,M,15,7
+3060,6,22,1980,8,DO,F,35,51
+3061,6,22,1980,18,SS,,,
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+3063,6,22,1980,9,DS,M,47,101
+3064,6,22,1980,11,DS,M,46,54
+3065,6,22,1980,8,DS,M,54,133
+3066,6,22,1980,17,DO,F,35,58
+3067,6,22,1980,22,DS,M,50,141
+3068,6,22,1980,17,DS,M,50,144
+3069,6,22,1980,20,DS,M,47,123
+3070,6,22,1980,9,DS,M,50,118
+3071,6,22,1980,19,PE,M,19,18
+3072,6,22,1980,9,DS,F,51,80
+3073,6,22,1980,17,DO,M,34,49
+3074,6,22,1980,20,NL,M,32,209
+3075,6,22,1980,18,DM,M,35,37
+3076,6,22,1980,9,DS,F,47,97
+3077,6,22,1980,22,DS,F,50,102
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+3079,6,22,1980,18,DO,M,36,44
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+3083,6,23,1980,2,DS,F,51,90
+3084,6,23,1980,5,DM,F,34,52
+3085,6,23,1980,4,OL,F,20,32
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+3087,6,23,1980,13,NL,M,35,241
+3088,6,23,1980,12,DS,M,51,132
+3089,6,23,1980,3,NL,F,32,150
+3090,6,23,1980,14,DM,F,35,48
+3091,6,23,1980,14,DS,M,50,77
+3092,6,23,1980,1,PP,F,21,7
+3093,6,23,1980,4,DM,F,34,38
+3094,6,23,1980,6,DS,M,52,135
+3095,6,23,1980,15,OT,M,,26
+3096,6,23,1980,4,DS,M,50,124
+3097,6,23,1980,1,PP,M,21,17
+3098,6,23,1980,12,DM,F,35,39
+3099,6,23,1980,2,DM,M,35,42
+3100,6,23,1980,6,PF,M,15,7
+3101,6,23,1980,4,DS,M,51,96
+3102,6,23,1980,13,DM,M,35,43
+3103,6,23,1980,4,DS,F,49,124
+3104,6,23,1980,4,DS,M,49,106
+3105,6,23,1980,12,DM,F,35,29
+3106,6,23,1980,13,DS,M,47,111
+3107,6,23,1980,2,DM,F,35,53
+3108,6,23,1980,13,DM,M,35,28
+3109,6,23,1980,14,DM,F,36,25
+3110,6,23,1980,5,DM,M,34,20
+3111,6,23,1980,14,DS,F,46,106
+3112,6,23,1980,4,DS,M,51,111
+3113,6,23,1980,4,OT,F,20,33
+3114,6,23,1980,14,DM,M,36,
+3115,6,23,1980,13,PF,M,16,7
+3116,6,23,1980,16,OT,F,,26
+3117,6,23,1980,14,DM,F,37,42
+3118,6,23,1980,1,DS,M,52,131
+3119,6,23,1980,12,DM,M,36,40
+3120,6,23,1980,4,OT,M,,30
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+3124,7,21,1980,17,DM,M,36,45
+3125,7,21,1980,18,DS,M,47,90
+3126,7,21,1980,8,AB,,,
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+3129,7,21,1980,11,PF,M,16,7
+3130,7,21,1980,11,DS,M,52,113
+3131,7,21,1980,17,DO,M,34,24
+3132,7,21,1980,17,OT,F,20,32
+3133,7,21,1980,24,NL,F,34,191
+3134,7,21,1980,17,DS,F,50,104
+3135,7,21,1980,18,DM,M,37,47
+3136,7,21,1980,8,DS,M,53,135
+3137,7,21,1980,17,DS,M,51,143
+3138,7,21,1980,22,DS,M,50,143
+3139,7,21,1980,18,DS,F,50,93
+3140,7,21,1980,22,DS,F,50,108
+3141,7,21,1980,24,DM,F,35,41
+3142,7,21,1980,11,DO,M,34,34
+3143,7,21,1980,17,DO,M,36,49
+3144,7,21,1980,10,SS,,,
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+3146,7,21,1980,24,AB,,,
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+3148,7,21,1980,11,DS,M,53,122
+3149,7,21,1980,17,DO,F,35,47
+3150,7,21,1980,11,DS,M,45,71
+3151,7,21,1980,18,DO,M,36,44
+3152,7,21,1980,19,AB,,,
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+3158,7,22,1980,14,DM,M,38,42
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+3160,7,22,1980,1,SS,,,
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+3162,7,22,1980,14,NL,F,33,80
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+3164,7,22,1980,4,DM,M,36,48
+3165,7,22,1980,12,DS,M,52,134
+3166,7,22,1980,14,DS,M,50,95
+3167,7,22,1980,13,DM,F,34,32
+3168,7,22,1980,12,DM,F,35,46
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+3170,7,22,1980,1,PP,F,21,14
+3171,7,22,1980,4,DS,M,49,136
+3172,7,22,1980,13,PF,F,15,7
+3173,7,22,1980,4,DM,M,35,29
+3174,7,22,1980,4,DS,F,50,128
+3175,7,22,1980,15,PF,F,14,8
+3176,7,22,1980,16,OT,M,19,18
+3177,7,22,1980,4,DS,M,49,114
+3178,7,22,1980,4,DS,M,50,92
+3179,7,22,1980,14,DM,M,35,45
+3180,7,22,1980,14,DM,F,34,19
+3181,7,22,1980,13,OL,F,21,33
+3182,7,22,1980,2,DM,M,36,43
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+3184,7,22,1980,3,,,,
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+3187,8,13,1980,11,DO,M,34,39
+3188,8,13,1980,17,DM,M,36,46
+3189,8,13,1980,23,CB,,,
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+3192,8,13,1980,19,PF,M,15,8
+3193,8,13,1980,17,DO,M,34,32
+3194,8,13,1980,18,DS,M,51,140
+3195,8,13,1980,24,NL,F,31,108
+3196,8,13,1980,18,DS,M,46,99
+3197,8,13,1980,20,DS,M,49,134
+3198,8,13,1980,22,DS,M,49,115
+3199,8,13,1980,8,OL,F,21,34
+3200,8,13,1980,19,CQ,,,
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+3202,8,13,1980,18,DO,M,36,46
+3203,8,13,1980,8,DS,M,53,134
+3204,8,13,1980,11,DS,M,47,102
+3205,8,13,1980,17,DO,M,35,51
+3206,8,13,1980,24,DM,M,37,43
+3207,8,13,1980,17,DS,M,50,149
+3208,8,13,1980,8,CM,,,
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+3210,8,13,1980,8,OL,M,21,35
+3211,8,13,1980,11,DM,M,36,29
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+3213,8,13,1980,17,SS,,,
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+3216,8,14,1980,3,NL,F,33,154
+3217,8,14,1980,12,NL,F,33,173
+3218,8,14,1980,12,PP,F,20,13
+3219,8,14,1980,12,DM,M,37,55
+3220,8,14,1980,14,DM,F,37,50
+3221,8,14,1980,15,PF,F,16,9
+3222,8,14,1980,3,PF,M,16,10
+3223,8,14,1980,4,DS,M,49,137
+3224,8,14,1980,12,DM,F,35,44
+3225,8,14,1980,1,DS,M,51,131
+3226,8,14,1980,1,DM,F,36,53
+3227,8,14,1980,12,DS,M,53,138
+3228,8,14,1980,3,PF,F,16,10
+3229,8,14,1980,12,DM,M,34,35
+3230,8,14,1980,13,PF,M,14,6
+3231,8,14,1980,14,DM,M,35,41
+3232,8,14,1980,2,OT,M,21,30
+3233,8,14,1980,1,PP,F,21,18
+3234,8,14,1980,4,DM,F,34,42
+3235,8,14,1980,12,OT,F,19,25
+3236,8,14,1980,2,DM,M,35,42
+3237,8,14,1980,1,DS,M,51,143
+3238,8,14,1980,5,DM,F,37,42
+3239,8,14,1980,13,PF,F,14,10
+3240,8,14,1980,4,DS,F,51,136
+3241,8,14,1980,14,DM,F,36,28
+3242,8,14,1980,4,DS,M,47,120
+3243,8,14,1980,14,DS,F,50,111
+3244,8,14,1980,14,DM,M,36,45
+3245,8,14,1980,6,,,,
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+3248,9,7,1980,7,DM,M,,48
+3249,9,7,1980,20,DM,,,
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+3252,9,7,1980,18,DM,M,,54
+3253,9,7,1980,18,DS,M,45,115
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+3255,9,7,1980,17,DM,,37,35
+3256,9,7,1980,20,PP,F,21,9
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+3258,9,7,1980,17,DM,M,37,51
+3259,9,7,1980,11,DS,M,51,131
+3260,9,7,1980,13,DS,M,53,108
+3261,9,7,1980,11,PP,F,23,16
+3262,9,7,1980,18,SS,,,
+3263,9,7,1980,10,CM,,,
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+3265,9,7,1980,22,DS,M,52,111
+3266,9,7,1980,9,DS,M,49,114
+3267,9,7,1980,22,DS,M,52,120
+3268,9,7,1980,8,OL,F,20,35
+3269,9,7,1980,20,DS,F,54,150
+3270,9,7,1980,17,DS,M,51,145
+3271,9,7,1980,9,DS,F,50,124
+3272,9,7,1980,11,NL,M,31,143
+3273,9,7,1980,13,PF,F,15,9
+3274,9,7,1980,22,DS,F,51,122
+3275,9,7,1980,19,PF,M,16,8
+3276,9,7,1980,18,DM,F,35,33
+3277,9,7,1980,23,CM,,,
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+3280,9,7,1980,17,DO,M,35,52
+3281,9,7,1980,8,OT,M,21,21
+3282,9,7,1980,17,SS,,,
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+3290,9,7,1980,20,NL,M,,205
+3291,9,7,1980,18,CM,,,
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+3297,9,8,1980,12,DS,M,53,141
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+3299,9,8,1980,5,DM,F,35,52
+3300,9,8,1980,12,NL,F,32,172
+3301,9,8,1980,12,PP,F,21,13
+3302,9,8,1980,2,DM,M,35,45
+3303,9,8,1980,4,DS,M,49,139
+3304,9,8,1980,12,DM,F,35,55
+3305,9,8,1980,1,DM,F,,47
+3306,9,8,1980,2,CM,,,
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+3308,9,8,1980,15,PF,F,15,6
+3309,9,8,1980,1,DS,M,52,138
+3310,9,8,1980,14,DS,F,47,109
+3311,9,8,1980,2,OT,F,20,21
+3312,9,8,1980,6,DM,M,37,44
+3313,9,8,1980,15,PF,F,14,8
+3314,9,8,1980,14,DM,F,37,51
+3315,9,8,1980,4,DM,F,36,39
+3316,9,8,1980,1,PP,F,21,14
+3317,9,8,1980,3,CM,,,
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+3319,9,8,1980,3,CM,,,
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+3322,9,8,1980,14,DM,M,36,47
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+3324,9,8,1980,4,OT,F,21,29
+3325,9,8,1980,1,DS,M,52,144
+3326,9,8,1980,7,,,,
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+3328,10,11,1980,8,OL,F,23,38
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+3336,10,11,1980,20,CQ,,,
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+3338,10,11,1980,20,DM,M,37,38
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+3340,10,11,1980,17,DS,F,49,128
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+3342,10,11,1980,21,OT,F,20,22
+3343,10,11,1980,1,NL,M,31,171
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+3345,10,11,1980,11,DM,M,37,39
+3346,10,11,1980,17,DS,M,51,149
+3347,10,11,1980,24,DM,M,38,52
+3348,10,11,1980,22,DS,F,51,124
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+3351,10,11,1980,20,NL,M,31,212
+3352,10,11,1980,10,CM,,,
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+3355,10,11,1980,1,PF,F,15,
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+3357,10,11,1980,1,DS,M,52,150
+3358,10,11,1980,11,DS,M,51,130
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+3364,10,11,1980,19,,,,
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+3369,10,12,1980,12,DS,M,51,140
+3370,10,12,1980,15,OT,F,20,26
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+3373,10,12,1980,12,DM,F,36,45
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+3376,10,12,1980,12,OT,M,19,26
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+3378,10,12,1980,18,DS,M,51,140
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+3380,10,12,1980,2,DS,F,53,137
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+3388,10,12,1980,2,DM,M,34,45
+3389,10,12,1980,18,DM,M,36,49
+3390,10,12,1980,9,DS,F,49,120
+3391,10,12,1980,9,DM,M,37,44
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+3395,10,12,1980,5,DM,F,36,42
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+3398,10,12,1980,15,OT,M,21,23
+3399,10,12,1980,14,DM,F,35,62
+3400,10,12,1980,12,OT,F,19,25
+3401,10,12,1980,14,DM,M,36,47
+3402,10,12,1980,5,PP,F,21,14
+3403,10,12,1980,12,DM,M,37,40
+3404,10,12,1980,16,,,,
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+3406,11,8,1980,17,DM,F,37,40
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+3418,11,8,1980,1,DS,M,52,154
+3419,11,8,1980,24,DM,F,36,43
+3420,11,8,1980,21,OT,F,19,33
+3421,11,8,1980,1,DS,M,51,155
+3422,11,8,1980,10,DM,M,35,40
+3423,11,8,1980,8,DS,F,48,115
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+3425,11,8,1980,1,DM,,,
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+3428,11,8,1980,24,DM,M,37,52
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+3431,11,8,1980,20,DM,M,37,35
+3432,11,8,1980,13,DS,F,48,122
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diff --git a/reporte/Analisis_de_datos.pdf b/reporte/Analisis_de_datos.pdf
new file mode 100644
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HcmV?d00001
--
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