{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Romell D.Z. \n", "last updated: 2018-11-24 \n", "\n", "numpy 1.15.4\n", "pandas 0.23.4\n", "matplotlib 2.2.2\n", "seaborn 0.9.0\n", "statsmodels 0.10.0.dev0+3261eea\n" ] } ], "source": [ "%load_ext watermark\n", "%watermark -a \"Romell D.Z.\" -u -d -p numpy,pandas,matplotlib,seaborn,statsmodels" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "import warnings\n", "warnings.simplefilter('ignore')\n", "\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "from matplotlib.collections import LineCollection\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "sns.set('notebook')\n", "from __future__ import division\n", "import statsmodels.api as sm\n", "plt.rcParams['figure.figsize'] = (18,8)\n", "plt.rcParams['axes.titlesize'] = 40\n", "plt.rcParams['axes.labelsize'] = 25\n", "plt.rcParams['ytick.labelsize'] = 15\n", "plt.rcParams['xtick.labelsize'] = 15\n", "%config InlineBackend.figure_format = 'retina'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2004 to 2018" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "47\n" ] }, { "data": { "text/plain": [ "Index(['Doméstico', 'Internacional', 'Total', 'Doméstico.1', 'Internacional.1',\n", " 'Total.1', 'Doméstico.2', 'Internacional.2', 'Unnamed: 9',\n", " 'Unnamed: 10', 'Total.2', 'Doméstico.3', 'Internacional.3', 'Total.3',\n", " 'Doméstico.4', 'Internacional.4', 'Total.4', 'Doméstico.5',\n", " 'Internacional.5', 'Total.5', 'Doméstico.6', 'Internacional.6',\n", " 'Total.6', 'Doméstico.7', 'Internacional.7', 'Total.7', 'Doméstico.8',\n", " 'Internacional.8', 'Total.8', 'Doméstico.9', 'Internacional.9',\n", " 'Total.9', 'Doméstico.10', 'Internacional.10', 'Total.10',\n", " 'Doméstico.11', 'Internacional.11', 'Total.11', 'Doméstico.12',\n", " 'Internacional.12', 'Total.12', 'Doméstico.13', 'Internacional.13',\n", " 'Total.13', 'Doméstico.14', 'Internacional.14', 'Total.14'],\n", " dtype='object')" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "iqutos_FAP = pd.read_excel('aero_iquitos_FAP.xls',sheet_name='rptaniomes3',nrows=12,\n", " skiprows=4,index_col=0,)\n", "print(iqutos_FAP.columns.size)\n", "iqutos_FAP.columns" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "28\n" ] }, { "data": { "text/plain": [ "Index(['Doméstico', 'Internacional', 'Doméstico.1', 'Internacional.1',\n", " 'Doméstico.2', 'Internacional.2', 'Doméstico.3', 'Internacional.3',\n", " 'Doméstico.4', 'Internacional.4', 'Doméstico.5', 'Internacional.5',\n", " 'Doméstico.6', 'Internacional.6', 'Doméstico.7', 'Internacional.7',\n", " 'Doméstico.8', 'Internacional.8', 'Doméstico.9', 'Internacional.9',\n", " 'Doméstico.10', 'Internacional.10', 'Doméstico.11', 'Internacional.11',\n", " 'Doméstico.12', 'Internacional.12', 'Doméstico.13', 'Internacional.13'],\n", " dtype='object')" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "iqutos_FAP.dropna(axis=1,inplace=True)\n", "iqutos_FAP = iqutos_FAP.drop(iqutos_FAP.columns[iqutos_FAP.columns.str.contains('Total')],axis=1)\n", "print(iqutos_FAP.columns.size)\n", "iqutos_FAP.columns" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " | Doméstico | \n", "Internacional | \n", "Doméstico.1 | \n", "Internacional.1 | \n", "Doméstico.2 | \n", "Internacional.2 | \n", "Doméstico.3 | \n", "Internacional.3 | \n", "Doméstico.4 | \n", "Internacional.4 | \n", "... | \n", "Doméstico.9 | \n", "Internacional.9 | \n", "Doméstico.10 | \n", "Internacional.10 | \n", "Doméstico.11 | \n", "Internacional.11 | \n", "Doméstico.12 | \n", "Internacional.12 | \n", "Doméstico.13 | \n", "Internacional.13 | \n", "
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Enero | \n", "28822 | \n", "360 | \n", "32605 | \n", "151 | \n", "31327 | \n", "148 | \n", "40428 | \n", "52 | \n", "42190 | \n", "14 | \n", "... | \n", "70786 | \n", "461 | \n", "81146 | \n", "782 | \n", "98316 | \n", "848 | \n", "90916 | \n", "0 | \n", "85915 | \n", "0 | \n", "
Febrero | \n", "29726 | \n", "176 | \n", "32260 | \n", "644 | \n", "31819 | \n", "5 | \n", "42795 | \n", "584 | \n", "46428 | \n", "201 | \n", "... | \n", "73173 | \n", "494 | \n", "78689 | \n", "1282 | \n", "95931 | \n", "856 | \n", "86757 | \n", "0 | \n", "82169 | \n", "0 | \n", "
Marzo | \n", "30758 | \n", "164 | \n", "35746 | \n", "627 | \n", "32829 | \n", "9 | \n", "47506 | \n", "32 | \n", "46876 | \n", "10 | \n", "... | \n", "71404 | \n", "492 | \n", "76894 | \n", "1542 | \n", "87289 | \n", "873 | \n", "76759 | \n", "0 | \n", "75312 | \n", "0 | \n", "
Abril | \n", "27026 | \n", "330 | \n", "29495 | \n", "695 | \n", "29282 | \n", "9 | \n", "38321 | \n", "129 | \n", "39943 | \n", "138 | \n", "... | \n", "60304 | \n", "331 | \n", "65828 | \n", "1862 | \n", "78133 | \n", "893 | \n", "68944 | \n", "0 | \n", "68176 | \n", "0 | \n", "
Mayo | \n", "26447 | \n", "127 | \n", "28853 | \n", "0 | \n", "29228 | \n", "0 | \n", "39179 | \n", "0 | \n", "41626 | \n", "127 | \n", "... | \n", "65520 | \n", "620 | \n", "69645 | \n", "1982 | \n", "82050 | \n", "1111 | \n", "72753 | \n", "0 | \n", "72053 | \n", "0 | \n", "
5 rows × 28 columns
\n", "\n", " | Domestic | \n", "International | \n", "
---|---|---|
Date | \n", "\n", " | \n", " |
2016-08-01 | \n", "84458 | \n", "0 | \n", "
2006-12-01 | \n", "40728 | \n", "5 | \n", "
2008-10-01 | \n", "46017 | \n", "0 | \n", "
2012-09-01 | \n", "58749 | \n", "347 | \n", "