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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Notebook de datos\n",
+ "# Tarea Clase 8\n",
+ "## @britod"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Importamos las librerÃas básicas\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Creamos el camini hacia los datos:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "path_data = './data/data_surveys.csv'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "scrolled": false
+ },
+ "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",
+ " <td>0</td>\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",
+ " <td>1</td>\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",
+ " <td>2</td>\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",
+ " <td>3</td>\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",
+ " <td>4</td>\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",
+ " <tr>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>35544</td>\n",
+ " <td>35545</td>\n",
+ " <td>12</td>\n",
+ " <td>31</td>\n",
+ " <td>2002</td>\n",
+ " <td>15</td>\n",
+ " <td>AH</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>35545</td>\n",
+ " <td>35546</td>\n",
+ " <td>12</td>\n",
+ " <td>31</td>\n",
+ " <td>2002</td>\n",
+ " <td>15</td>\n",
+ " <td>AH</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>35546</td>\n",
+ " <td>35547</td>\n",
+ " <td>12</td>\n",
+ " <td>31</td>\n",
+ " <td>2002</td>\n",
+ " <td>10</td>\n",
+ " <td>RM</td>\n",
+ " <td>F</td>\n",
+ " <td>15.0</td>\n",
+ " <td>14.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>35547</td>\n",
+ " <td>35548</td>\n",
+ " <td>12</td>\n",
+ " <td>31</td>\n",
+ " <td>2002</td>\n",
+ " <td>7</td>\n",
+ " <td>DO</td>\n",
+ " <td>M</td>\n",
+ " <td>36.0</td>\n",
+ " <td>51.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>35548</td>\n",
+ " <td>35549</td>\n",
+ " <td>12</td>\n",
+ " <td>31</td>\n",
+ " <td>2002</td>\n",
+ " <td>5</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>35549 rows × 9 columns</p>\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",
+ "35544 35545 12 31 2002 15 AH NaN NaN \n",
+ "35545 35546 12 31 2002 15 AH NaN NaN \n",
+ "35546 35547 12 31 2002 10 RM F 15.0 \n",
+ "35547 35548 12 31 2002 7 DO M 36.0 \n",
+ "35548 35549 12 31 2002 5 NaN NaN NaN \n",
+ "\n",
+ " weight \n",
+ "0 NaN \n",
+ "1 NaN \n",
+ "2 NaN \n",
+ "3 NaN \n",
+ "4 NaN \n",
+ "... ... \n",
+ "35544 NaN \n",
+ "35545 NaN \n",
+ "35546 14.0 \n",
+ "35547 51.0 \n",
+ "35548 NaN \n",
+ "\n",
+ "[35549 rows x 9 columns]"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Leemos la data usando pandas\n",
+ "surveys_data = pd.DataFrame(pd.read_csv(path_data))\n",
+ "surveys_data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Vamos a definir la función que que calcule los promedios de las últimas dos colummas"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def mean_values_hw(data_frame):\n",
+ " v = True\n",
+ " # Corroboramos que las entradas son correctas\n",
+ " if type(data_frame) == pd.DataFrame:\n",
+ " print('OK, Data frame aceptado')\n",
+ " else:\n",
+ " v = False\n",
+ " print('Por favor ingresar un data frame')\n",
+ "\n",
+ " \n",
+ " # Aqi viene el algoritmo para calculo de promedios:\n",
+ " no_value_weight = 0 # Contador de valores que no tienen información en el peso\n",
+ " for i in range(0,len(data_frame),1): # Recorremos cada uno de los Ãndices del data-frame\n",
+ " if pd.isnull(data_frame['weight'][i]) == True:\n",
+ " no_value_weight = no_value_weight +1\n",
+ " # Vamos a calcular el promedio de los pesos sin considerar los campos nulos\n",
+ " mean_value_weight = data_frame['weight'].sum()/(len(data_frame)-no_value_weight)\n",
+ " \n",
+ " \n",
+ " no_value_hindfoot = 0 # Contador de valores que no tienen información en el peso\n",
+ " for i in range(0,len(data_frame),1): # Recorremos cada uno de los Ãndices del data-frame\n",
+ " if pd.isnull(data_frame['hindfoot_length'][i]) == True:\n",
+ " no_value_hindfoot = no_value_hindfoot +1\n",
+ " # Vamos a calcular el promedio de los talones\n",
+ " mean_value_hindfoot = data_frame['hindfoot_length'].sum()/(len(data_frame)-no_value_hindfoot)\n",
+ "\n",
+ " if v == True:\n",
+ " return( print('Aquà tiene el promedio del peso', mean_value_weight ),\n",
+ " print('Aquà tiene el promedio del talón', mean_value_hindfoot ))\n",
+ " else:\n",
+ " return(print('Empieza otra vez.'))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Valores medios de las últimas dos columnas"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "OK, Data frame aceptado\n",
+ "Aquà tiene el promedio del peso 42.672428212991356\n",
+ "Aquà tiene el promedio del talón 29.287931802277498\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(None, None)"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mean_values_hw(surveys_data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000002B2C01FEDC8>]],\n",
+ " dtype=object)"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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DoKq+Dzyc5FWtdDrwLWA7sP+OoA3A9e35duCCdlfRacCT7XLSTcAZSZa3F47PaDVJ0iGykMtEAL8FfCbJkcCDwLvpBMy1SS4CHgLe2cbeCJwNTABPt7FU1d4kHwbuaOM+VFV7FzgvSdIcLCgMqupuYLTHqtN7jC3g4hn2sxXYupC5SJLmz3cgS5IMA0mSYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAksQhhkOSIJN9I8pdteXWS25PsSvK5JEe2+lFteaKtX9W1j0ta/YEkZy50TpKkuVmMM4P3Afd3LX8UuKKq1gCPAxe1+kXA41X1SuCKNo4kJwHnAq8F1gEfT3LEIsxLktSnBYVBkpXAW4E/a8sB3gxc14ZsA85pz9e3Zdr609v49cA1VfWTqvouMAGcspB5SZLmZtkCt/+vwPuBl7TllwFPVNVUW54EVrTnK4CHAapqKsmTbfwK4LaufXZv8yxJNgIbAUZGRhgfH+9rkvv27XvW2E1rp2YePGBGju6vn35/VoeT6cdtmNjbYBrm3uYdBkneBjxWVXclGdtf7jG0Zll3oG2eXazaAmwBGB0drbGxsV7DnmN8fJzusRduvqGv7QbBprVTXL5z9sO4+/yxgz+ZRTb9uA0TextMw9zbQs4M3gi8PcnZwM8DL6VzpnBMkmXt7GAl8EgbPwmcCEwmWQb8ArC3q75f9zaSpENg3q8ZVNUlVbWyqlbReQH4y1V1PnAL8I42bANwfXu+vS3T1n+5qqrVz213G60G1gBfm++8JElzt9DXDHr5PeCaJL8PfAO4qtWvAj6dZILOGcG5AFV1X5JrgW8BU8DFVfXMQZiXJGkGixIGVTUOjLfnD9LjbqCq+hvgnTNsfxlw2WLMRZI0d74DWZJkGEiSDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkiQWEQZITk9yS5P4k9yV5X6sfm2RHkl3t6/JWT5Irk0wkuSfJyV372tDG70qyYeFtSZLmYiFnBlPApqp6DXAacHGSk4DNwM1VtQa4uS0DnAWsaY+NwCegEx7ApcCpwCnApfsDRJJ0aMw7DKpqT1V9vT1/CrgfWAGsB7a1YduAc9rz9cDV1XEbcEySE4AzgR1VtbeqHgd2AOvmOy9J0twtW4ydJFkF/DJwOzBSVXugExhJjm/DVgAPd2022Woz1Xt9n410zioYGRlhfHy8r/nt27fvWWM3rZ3qa7tBMHJ0f/30+7M6nEw/bsPE3gbTMPe24DBI8mLg88BvV9WPksw4tEetDlB/brFqC7AFYHR0tMbGxvqa4/j4ON1jL9x8Q1/bDYJNa6e4fOfsh3H3+WMHfzKLbPpxGyb2NpiGubcF3U2U5AV0guAzVfWFVn60Xf6hfX2s1SeBE7s2Xwk8coC6JOkQWcjdRAGuAu6vqj/sWrUd2H9H0Abg+q76Be2uotOAJ9vlpJuAM5Isby8cn9FqkqRDZCGXid4IvAvYmeTuVvt3wEeAa5NcBDwEvLOtuxE4G5gAngbeDVBVe5N8GLijjftQVe1dwLwkSXM07zCoqv9J7+v9AKf3GF/AxTPsayuwdb5zkSQtjO9AliQZBpKkRXqfgXQ4WrVEtxDv/shbl+T7SgvhmYEkyTCQJBkGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEn4cRQ6yBbykRCb1k4N1V+lkw5nhoG0yPoJwIMVdH4ukubLMHieWKoPbZM0GHzNQJJkGEiSvEwkDRX/hoPmyzMDSdLhEwZJ1iV5IMlEks1LPR9Jej45LC4TJTkC+BjwFmASuCPJ9qr61tLOTFI/ui9PHcr3h3h5avEcFmEAnAJMVNWDAEmuAdYDhoGkGR3q10i6g27YgihVtdRzIMk7gHVV9Ztt+V3AqVX13mnjNgIb2+KrgAf6/BbHAT9cpOkebuxtMNnbYBqG3n6pqn5xevFwOTNIj9pzUqqqtgBb5rzz5M6qGp3PxA539jaY7G0wDXNvh8sLyJPAiV3LK4FHlmgukvS8c7iEwR3AmiSrkxwJnAtsX+I5SdLzxmFxmaiqppK8F7gJOALYWlX3LeK3mPOlpQFib4PJ3gbT0PZ2WLyALElaWofLZSJJ0hIyDCRJwx0Gw/YRF0l2J9mZ5O4kd7basUl2JNnVvi5f6nn2I8nWJI8luber1rOXdFzZjuM9SU5eupn3Z4b+Ppjke+343Z3k7K51l7T+Hkhy5tLMenZJTkxyS5L7k9yX5H2tPvDH7gC9Dfxx60tVDeWDzgvR3wFeDhwJfBM4aanntcCedgPHTav9AbC5Pd8MfHSp59lnL28CTgbuna0X4Gzgi3Tej3IacPtSz3+e/X0Q+N0eY09q/z6PAla3f7dHLHUPM/R1AnBye/4S4Ntt/gN/7A7Q28Aft34ew3xm8P8/4qKqfgrs/4iLYbMe2NaebwPOWcK59K2qvgLsnVaeqZf1wNXVcRtwTJITDs1M52eG/mayHrimqn5SVd8FJuj8+z3sVNWeqvp6e/4UcD+wgiE4dgfobSYDc9z6McxhsAJ4uGt5kgMf2EFQwJeS3NU+mgNgpKr2QOcfM3D8ks1u4WbqZZiO5Xvb5ZKtXZf0BrK/JKuAXwZuZ8iO3bTeYIiO20yGOQz6+oiLAfPGqjoZOAu4OMmblnpCh8iwHMtPAK8A3gDsAS5v9YHrL8mLgc8Dv11VPzrQ0B61QettaI7bgQxzGAzdR1xU1SPt62PAn9M5JX10/2l3+/rY0s1wwWbqZSiOZVU9WlXPVNX/Bf6Un11SGKj+kryAzi/Lz1TVF1p5KI5dr96G5bjNZpjDYKg+4iLJi5K8ZP9z4AzgXjo9bWjDNgDXL80MF8VMvWwHLmh3ppwGPLn/ksQgmXat/NfpHD/o9HdukqOSrAbWAF871PPrR5IAVwH3V9Ufdq0a+GM3U2/DcNz6stSvYB/MB507Gb5N51X+Dyz1fBbYy8vp3LnwTeC+/f0ALwNuBna1r8cu9Vz77OezdE65/5bO/7AumqkXOqfjH2vHcScwutTzn2d/n27zv4fOL5ITusZ/oPX3AHDWUs//AH39IzqXQu4B7m6Ps4fh2B2gt4E/bv08/DgKSdJQXyaSJPXJMJAkGQaSJMNAkoRhIEnCMJAkYRhIkoD/B1RbyADQHy/FAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "surveys_data.hist(column='weight')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000002B2BFF509C8>]],\n",
+ " dtype=object)"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "surveys_data.hist(column='hindfoot_length')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Funcion que calcula valores promedios segun los años "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Promedio del año 1977 46.47565543071161\n",
+ "Promedio del año 1978 67.8594470046083\n",
+ "Promedio del año 1979 63.291079812206576\n",
+ "Promedio del año 1980 62.401365705614566\n",
+ "Promedio del año 1981 65.81075110456554\n",
+ "Promedio del año 1982 53.74361759913091\n",
+ "Promedio del año 1983 55.08226221079691\n",
+ "Promedio del año 1984 50.93181818181818\n",
+ "Promedio del año 1985 46.659382064807836\n",
+ "Promedio del año 1986 54.98946135831382\n",
+ "Promedio del año 1987 49.41858932102834\n",
+ "Promedio del año 1988 45.0241145440844\n",
+ "Promedio del año 1989 35.72550382209868\n",
+ "Promedio del año 1990 35.47642679900744\n",
+ "Promedio del año 1991 32.03104575163399\n",
+ "Promedio del año 1992 33.29125138427464\n",
+ "Promedio del año 1993 34.205607476635514\n",
+ "Promedio del año 1994 34.48479427549195\n",
+ "Promedio del año 1995 29.50321987120515\n",
+ "Promedio del año 1996 28.20160791589363\n",
+ "Promedio del año 1997 31.748760330578513\n",
+ "Promedio del año 1998 34.805734767025086\n",
+ "Promedio del año 1999 36.46933085501859\n",
+ "Promedio del año 2000 32.37214137214137\n",
+ "Promedio del año 2001 36.444290657439446\n",
+ "25\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "43.857890384645245"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "year_sum_value = 0\n",
+ "\n",
+ "j=0 # Contador de campos nulos\n",
+ "index_interval = []\n",
+ "mean_value_list = []\n",
+ "\n",
+ "for i in range(1,len(surveys_data)-1):\n",
+ " \n",
+ " if surveys_data['year'][i-1] == surveys_data['year'][i]:\n",
+ " if pd.isnull(surveys_data['weight'][i-1]) == True:\n",
+ " j = j + 1\n",
+ " else:\n",
+ " year_sum_value = year_sum_value +surveys_data['weight'][i-1]\n",
+ " else:\n",
+ " if surveys_data['year'][i-1] != max(surveys_data['year']):\n",
+ " index_interval.append(i)\n",
+ " if len(index_interval) < 2:\n",
+ " year_mean_value = year_sum_value/(index_interval[-1] - j) \n",
+ " print('Promedio del año ', surveys_data['year'][i-1], year_mean_value )\n",
+ " mean_value_list.append(year_mean_value)\n",
+ " j = 0 \n",
+ " year_sum_value = 0\n",
+ " else:\n",
+ " year_mean_value = year_sum_value/(index_interval[-1] - index_interval[-2] - j) \n",
+ " print('Promedio del año ', surveys_data['year'][i-1], year_mean_value )\n",
+ " mean_value_list.append(year_mean_value)\n",
+ " j = 0 \n",
+ " year_sum_value = 0\n",
+ " else:\n",
+ " j = 0\n",
+ " year_sum_value = 0\n",
+ " if pd.isnull(surveys_data['weight'][i-1]) == True:\n",
+ " j = j + 1\n",
+ " else:\n",
+ " print('Aca estoy')\n",
+ " lastyear_sum_value = year_sum_value + surveys_data['year'][i]\n",
+ " index_interval.append(len(surveys_data))\n",
+ " lastyear_mean_value = lastyear_sum_value / (len(surveys_data) - index_interval[-2] - j)\n",
+ " print('Promedio del año ', surveys_data['year'][i], lastyear_mean_value )\n",
+ " mean_value_list.append(lastyear_mean_value)\n",
+ " \n",
+ " \n",
+ "print(len(mean_value_list)) \n",
+ "sum(mean_value_list)/(len(mean_value_list))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2002"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "max(surveys_data['year'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}