{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "\"AeroPython\"" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Carga y manipulación de datos con pandas" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "_ __pandas es una biblioteca de análisis de datos en Python__ que nos provee de las estructuras de datos y herramientas para realizar análisis de manera rápida. Se articula sobre la biblioteca NumPy y nos permite enfrentarnos a situaciones en las que tenemos que manejar datos reales que requieren seguir un proceso de carga, limpieza, filtrado, reduccióń y análisis. _\n", "\n", "_En esta clase veremos como cargar y guardar datos, las características de las pricipales estructuras de pandas y las aplicaremos a algunos problemas._" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Importamos pandas\n", "%matplotlib inline\n", "import pandas as pd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Cargando los datos y explorándolos" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Trabajaremos sobre un fichero de datos metereológicos de la Consejeria Agricultura Pesca y Desarrollo Rural Andalucía." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import HTML\n", "HTML('')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "FECHA DIA Al04TMax Al04TMin Al04TMed Al04Precip \r\n", "-------- --- -------- -------- -------- ---------- \r\n", "13-12-16 348 14.6 4.0 8.9 0.2 \r\n", "12-12-16 347 15.9 3.0 8.7 0.2 \r\n", "11-12-16 346 16.9 5.0 10.2 0.2 \r\n", "10-12-16 345 16.4 6.3 10.9 0.2 \r\n", "09-12-16 344 13.6 9.5 11.2 1.8 \r\n", "08-12-16 343 14.5 5.4 10.4 0.0 \r\n", "07-12-16 342 15.7 6.1 10.1 0.2 \r\n", "06-12-16 341 17.7 7.1 13.4 0.0 \r\n" ] } ], "source": [ "# Vemos qué pinta tiene el fichero\n", "!head ../data/tabernas_meteo_data.txt" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Vemos que los datos no están en formato CSV, aunque sí tienen algo de estructura. Si intentamos cargarlos con pandas no tendremos mucho éxito:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "
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FECHA DIA Al04TMax Al04TMin Al04TMed Al04Precip
0-------- --- -------- -------- -------- ------...
113-12-16 348 14.6 4.0 8.9 ...
212-12-16 347 15.9 3.0 8.7 ...
311-12-16 346 16.9 5.0 10.2 ...
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" ], "text/plain": [ " FECHA DIA Al04TMax Al04TMin Al04TMed Al04Precip \n", "0 -------- --- -------- -------- -------- ------... \n", "1 13-12-16 348 14.6 4.0 8.9 ... \n", "2 12-12-16 347 15.9 3.0 8.7 ... \n", "3 11-12-16 346 16.9 5.0 10.2 ... \n", "4 10-12-16 345 16.4 6.3 10.9 ... " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Tratamos de cargarlo en pandas\n", "pd.read_csv(\"../data/tabernas_meteo_data.txt\").head(5)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Tenemos que hacer los siguientes cambios:\n", "\n", "* Separar los campos por un número arbitrario de espacios en blanco.\n", "* Saltar las primeras líneas.\n", "* Dar nombres nuevos a las columnas.\n", "* Descartar la columna del día del año (podemos calcularla luego).\n", "* Parsear las fechas en el formato correcto." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "
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TMAXTMINTMEDPRECIP
DATE
2004-01-0118.02.511.10.0
2004-01-0217.45.710.60.0
2004-01-0315.10.87.90.0
2004-01-0416.2-0.47.20.0
2004-01-0516.40.67.10.0
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" ], "text/plain": [ " TMAX TMIN TMED PRECIP\n", "DATE \n", "2004-01-01 18.0 2.5 11.1 0.0\n", "2004-01-02 17.4 5.7 10.6 0.0\n", "2004-01-03 15.1 0.8 7.9 0.0\n", "2004-01-04 16.2 -0.4 7.2 0.0\n", "2004-01-05 16.4 0.6 7.1 0.0" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_csv(\n", " \"../data/tabernas_meteo_data.txt\",\n", " delim_whitespace=True, # delimitado por espacios en blanco\n", " usecols=(0, 2, 3, 4, 5), # columnas que queremos usar\n", " skiprows=2, # saltar las dos primeras líneas\n", " names=['DATE', 'TMAX', 'TMIN', 'TMED', 'PRECIP'],\n", " parse_dates=['DATE'],\n", "# date_parser=lambda x: pd.datetime.strptime(x, '%d-%m-%y'), # Parseo manual\n", " dayfirst=True, # ¡Importante\n", " index_col=[\"DATE\"] # Si queremos indexar por fechas\n", ")\n", "\n", "# Ordenando de más antigua a más moderna\n", "data.sort_index(inplace=True)\n", "\n", "# Mostrando sólo las primeras o las últimas líneas\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "TMAX float64\n", "TMIN float64\n", "TMED float64\n", "PRECIP float64\n", "dtype: object" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Comprobamos los tipos de datos de la columnas\n", "data.dtypes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Las fechas también se pueden parsear de manera manual con el argumento:\n", "\n", "```\n", "date_parser=lambda x: pd.datetime.strptime(x, '%d-%m-%y'), # Parseo manual\n", "```" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "
Para acordarnos de cómo parsear las fechas: http://strftime.org/
" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "DatetimeIndex: 4732 entries, 2004-01-01 to 2016-12-13\n", "Data columns (total 4 columns):\n", "TMAX 4713 non-null float64\n", "TMIN 4713 non-null float64\n", "TMED 4713 non-null float64\n", "PRECIP 4713 non-null float64\n", "dtypes: float64(4)\n", "memory usage: 184.8 KB\n" ] } ], "source": [ "# Pedomos información general del dataset\n", "data.info()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "
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TMAXTMINTMEDPRECIP
count4713.0000004713.0000004713.0000004713.000000
mean23.2247619.67687216.2763210.650583
std7.3186566.2633036.6385293.273346
min0.000000-8.200000-14.9000000.000000
25%17.3000004.50000010.6000000.000000
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" ], "text/plain": [ " TMAX TMIN TMED PRECIP\n", "count 4713.000000 4713.000000 4713.000000 4713.000000\n", "mean 23.224761 9.676872 16.276321 0.650583\n", "std 7.318656 6.263303 6.638529 3.273346\n", "min 0.000000 -8.200000 -14.900000 0.000000\n", "25% 17.300000 4.500000 10.600000 0.000000\n", "50% 22.900000 9.700000 16.000000 0.000000\n", "75% 29.200000 15.100000 22.100000 0.000000\n", "max 42.600000 23.800000 32.100000 66.200000" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Descripción estadística\n", "data.describe()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([3, 4, 5, ..., 6, 0, 1], dtype=int32)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Una vez convertido en un objeto fecha se pueden obtener cosas como:\n", "data.index.dayofweek" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Accediendo a los datos " ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Tenemos dos formas de acceder a las columnas: por nombre o por atributo (si no contienen espacios ni caracteres especiales)." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "DATE\n", "2004-01-01 18.0\n", "2004-01-02 17.4\n", "2004-01-03 15.1\n", "2004-01-04 16.2\n", "2004-01-05 16.4\n", "Name: TMAX, dtype: float64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Accediendo como clave\n", "data['TMAX'].head()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "DATE\n", "2004-01-01 2.5\n", "2004-01-02 5.7\n", "2004-01-03 0.8\n", "2004-01-04 -0.4\n", "2004-01-05 0.6\n", "Name: TMIN, dtype: float64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Accediendo como atributo\n", "data.TMIN.head()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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TMAXTMIN
DATE
2004-01-0118.02.5
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2004-01-0315.10.8
2004-01-0416.2-0.4
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DATE
2004-01-011.800.25
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2004-01-061.650.04
2004-01-071.600.14
2004-01-081.990.61
2004-01-092.030.72
2004-01-102.040.58
2004-01-112.240.68
2004-01-122.100.47
2004-01-132.170.51
2004-01-141.990.36
2004-01-151.520.51
2004-01-161.720.35
2004-01-171.780.21
2004-01-181.130.31
2004-01-191.10-0.28
2004-01-201.26-0.12
2004-01-211.770.02
2004-01-221.840.16
2004-01-232.06-0.05
2004-01-242.070.71
2004-01-251.880.49
2004-01-262.200.84
2004-01-272.011.29
2004-01-281.680.63
2004-01-291.460.10
2004-01-301.380.28
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2016-11-141.900.41
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4732 rows × 2 columns

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" ], "text/plain": [ " TMAX TMIN\n", "DATE \n", "2004-01-01 1.80 0.25\n", "2004-01-02 1.74 0.57\n", "2004-01-03 1.51 0.08\n", "2004-01-04 1.62 -0.04\n", "2004-01-05 1.64 0.06\n", "2004-01-06 1.65 0.04\n", "2004-01-07 1.60 0.14\n", "2004-01-08 1.99 0.61\n", "2004-01-09 2.03 0.72\n", "2004-01-10 2.04 0.58\n", "2004-01-11 2.24 0.68\n", "2004-01-12 2.10 0.47\n", "2004-01-13 2.17 0.51\n", "2004-01-14 1.99 0.36\n", "2004-01-15 1.52 0.51\n", "2004-01-16 1.72 0.35\n", "2004-01-17 1.78 0.21\n", "2004-01-18 1.13 0.31\n", "2004-01-19 1.10 -0.28\n", "2004-01-20 1.26 -0.12\n", "2004-01-21 1.77 0.02\n", "2004-01-22 1.84 0.16\n", "2004-01-23 2.06 -0.05\n", "2004-01-24 2.07 0.71\n", "2004-01-25 1.88 0.49\n", "2004-01-26 2.20 0.84\n", "2004-01-27 2.01 1.29\n", "2004-01-28 1.68 0.63\n", "2004-01-29 1.46 0.10\n", "2004-01-30 1.38 0.28\n", "... ... ...\n", "2016-11-14 1.90 0.41\n", "2016-11-15 1.68 0.49\n", "2016-11-16 1.95 0.42\n", "2016-11-17 1.90 0.14\n", "2016-11-18 2.01 0.32\n", "2016-11-19 1.97 0.60\n", "2016-11-20 1.98 0.37\n", "2016-11-21 1.85 1.23\n", "2016-11-22 1.36 0.78\n", "2016-11-23 1.43 0.41\n", "2016-11-24 1.18 0.20\n", "2016-11-25 1.32 0.16\n", "2016-11-26 1.32 0.75\n", "2016-11-27 1.42 0.81\n", "2016-11-28 1.39 0.55\n", "2016-11-29 1.38 0.43\n", "2016-11-30 1.40 1.07\n", "2016-12-01 1.36 0.92\n", "2016-12-02 1.72 0.55\n", "2016-12-03 1.34 0.87\n", "2016-12-04 1.18 1.01\n", "2016-12-05 1.66 0.79\n", "2016-12-06 1.77 0.71\n", "2016-12-07 1.57 0.61\n", "2016-12-08 1.45 0.54\n", "2016-12-09 1.36 0.95\n", "2016-12-10 1.64 0.63\n", "2016-12-11 1.69 0.50\n", "2016-12-12 1.59 0.30\n", "2016-12-13 1.46 0.40\n", "\n", "[4732 rows x 2 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Modificando valores de columnas\n", "data[['TMAX', 'TMIN']] / 10" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "23.224761298535967" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Aplicando una función a una columna entera (ej. media numpy)\n", "import numpy as np\n", "np.mean(data.TMAX)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "23.224761298535967" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Calculando la media con pandas\n", "data.TMAX.mean()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Para acceder a las filas tenemos dos métodos: `.loc` (basado en etiquetas), `.iloc` (basado en posiciones enteras) y `.ix` (que combina ambos)." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "TMAX 17.4\n", "TMIN 5.7\n", "TMED 10.6\n", "PRECIP 0.0\n", "Name: 2004-01-02 00:00:00, dtype: float64" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Accediendo a una fila por índice\n", "data.iloc[1]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "TMAX 31.8\n", "TMIN 16.3\n", "TMED 23.2\n", "PRECIP 0.0\n", "Name: 2016-09-02 00:00:00, dtype: float64" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Accediendo a una fila por etiqueta\n", "data.loc[\"2016-09-02\"]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Puedo incluso hacer secciones basadas en fechas:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "
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TMAXTMINTMEDPRECIP
DATE
2016-12-0113.69.211.13.2
2016-12-0217.25.510.80.0
2016-12-0313.48.711.11.0
2016-12-0411.810.110.923.8
2016-12-0516.67.911.70.0
2016-12-0617.77.113.40.0
2016-12-0715.76.110.10.2
2016-12-0814.55.410.40.0
2016-12-0913.69.511.21.8
2016-12-1016.46.310.90.2
2016-12-1116.95.010.20.2
2016-12-1215.93.08.70.2
2016-12-1314.64.08.90.2
\n", "
" ], "text/plain": [ " TMAX TMIN TMED PRECIP\n", "DATE \n", "2016-12-01 13.6 9.2 11.1 3.2\n", "2016-12-02 17.2 5.5 10.8 0.0\n", "2016-12-03 13.4 8.7 11.1 1.0\n", "2016-12-04 11.8 10.1 10.9 23.8\n", "2016-12-05 16.6 7.9 11.7 0.0\n", "2016-12-06 17.7 7.1 13.4 0.0\n", "2016-12-07 15.7 6.1 10.1 0.2\n", "2016-12-08 14.5 5.4 10.4 0.0\n", "2016-12-09 13.6 9.5 11.2 1.8\n", "2016-12-10 16.4 6.3 10.9 0.2\n", "2016-12-11 16.9 5.0 10.2 0.2\n", "2016-12-12 15.9 3.0 8.7 0.2\n", "2016-12-13 14.6 4.0 8.9 0.2" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.loc[\"2016-12-01\":]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "También puedo indexar utilizando arrays de valores booleanos, por ejemplo procedentes de la comprobación de una condición:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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TMAXTMINTMEDPRECIP
DATE
2005-08-21NaNNaNNaNNaN
2005-12-22NaNNaNNaNNaN
2006-01-28NaNNaNNaNNaN
2006-02-16NaNNaNNaNNaN
2006-05-11NaNNaNNaNNaN
2006-06-14NaNNaNNaNNaN
2007-04-19NaNNaNNaNNaN
2007-06-26NaNNaNNaNNaN
2007-12-20NaNNaNNaNNaN
2012-08-03NaNNaNNaNNaN
2012-08-04NaNNaNNaNNaN
2012-08-05NaNNaNNaNNaN
2012-08-06NaNNaNNaNNaN
2012-08-07NaNNaNNaNNaN
2012-08-08NaNNaNNaNNaN
2012-08-09NaNNaNNaNNaN
2012-08-10NaNNaNNaNNaN
2012-08-11NaNNaNNaNNaN
2015-12-31NaNNaNNaNNaN
\n", "
" ], "text/plain": [ " TMAX TMIN TMED PRECIP\n", "DATE \n", "2005-08-21 NaN NaN NaN NaN\n", "2005-12-22 NaN NaN NaN NaN\n", "2006-01-28 NaN NaN NaN NaN\n", "2006-02-16 NaN NaN NaN NaN\n", "2006-05-11 NaN NaN NaN NaN\n", "2006-06-14 NaN NaN NaN NaN\n", "2007-04-19 NaN NaN NaN NaN\n", "2007-06-26 NaN NaN NaN NaN\n", "2007-12-20 NaN NaN NaN NaN\n", "2012-08-03 NaN NaN NaN NaN\n", "2012-08-04 NaN NaN NaN NaN\n", "2012-08-05 NaN NaN NaN NaN\n", "2012-08-06 NaN NaN NaN NaN\n", "2012-08-07 NaN NaN NaN NaN\n", "2012-08-08 NaN NaN NaN NaN\n", "2012-08-09 NaN NaN NaN NaN\n", "2012-08-10 NaN NaN NaN NaN\n", "2012-08-11 NaN NaN NaN NaN\n", "2015-12-31 NaN NaN NaN NaN" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Búsqueda de valores nulos\n", "data.loc[data.TMIN.isnull()]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Podemos agrupar nuestros datos utilizando `groupby`:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Agruparemos por año y día: creemos dos columnas nuevas\n", "data['year'] = data.index.year\n", "data['month'] = data.index.month" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Creamos la agrupación\n", "monthly = data.groupby(by=['year', 'month'])" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "dict_keys([(2006, 3), (2015, 8), (2011, 1), (2007, 10), (2008, 6), (2006, 6), (2004, 1), (2010, 9), (2009, 7), (2006, 4), (2005, 4), (2009, 11), (2011, 4), (2010, 4), (2007, 1), (2006, 11), (2008, 11), (2011, 9), (2013, 4), (2015, 10), (2014, 6), (2009, 10), (2007, 7), (2010, 6), (2015, 7), (2010, 1), (2006, 12), (2011, 12), (2004, 11), (2007, 9), (2014, 11), (2013, 1), (2008, 3), (2005, 10), (2004, 6), (2015, 2), (2012, 10), (2009, 2), (2006, 1), (2005, 7), (2011, 3), (2016, 1), (2014, 12), (2008, 8), (2004, 3), (2015, 9), (2014, 3), (2009, 5), (2010, 10), (2005, 2), (2011, 6), (2010, 2), (2007, 3), (2006, 9), (2012, 1), (2011, 11), (2004, 8), (2007, 12), (2014, 4), (2009, 8), (2012, 12), (2015, 1), (2006, 2), (2009, 4), (2007, 11), (2014, 9), (2008, 5), (2005, 8), (2009, 9), (2015, 12), (2010, 8), (2013, 12), (2006, 7), (2005, 5), (2011, 5), (2010, 7), (2007, 6), (2006, 10), (2008, 10), (2015, 6), (2015, 11), (2014, 1), (2013, 7), (2015, 4), (2012, 9), (2016, 4), (2016, 3), (2012, 3), (2004, 10), (2016, 11), (2014, 10), (2013, 2), (2008, 2), (2005, 11), (2007, 4), (2015, 3), (2009, 3), (2012, 7), (2004, 5), (2007, 5), (2008, 7), (2016, 9), (2004, 2), (2009, 6), (2014, 2), (2013, 10), (2006, 5), (2005, 3), (2011, 7), (2010, 5), (2016, 5), (2006, 8), (2013, 9), (2011, 8), (2016, 8), (2014, 7), (2013, 5), (2016, 12), (2012, 4), (2008, 12), (2007, 8), (2014, 8), (2009, 12), (2012, 8), (2005, 9), (2004, 7), (2010, 11), (2009, 1), (2013, 6), (2005, 6), (2007, 2), (2011, 2), (2008, 4), (2016, 2), (2012, 5), (2004, 12), (2013, 11), (2012, 11), (2013, 8), (2016, 6), (2005, 1), (2015, 5), (2010, 3), (2016, 7), (2012, 6), (2012, 2), (2011, 10), (2004, 9), (2016, 10), (2014, 5), (2013, 3), (2008, 1), (2005, 12), (2004, 4), (2008, 9), (2010, 12)])" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Podemos ver los grupos que se han creado\n", "monthly.groups.keys()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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TMAXTMINTMEDPRECIPyearmonth
DATE
2016-03-0120.50.09.90.020163
2016-03-0223.52.913.60.020163
2016-03-0320.92.912.50.020163
2016-03-0420.32.012.60.020163
2016-03-0517.37.112.50.020163
\n", "
" ], "text/plain": [ " TMAX TMIN TMED PRECIP year month\n", "DATE \n", "2016-03-01 20.5 0.0 9.9 0.0 2016 3\n", "2016-03-02 23.5 2.9 13.6 0.0 2016 3\n", "2016-03-03 20.9 2.9 12.5 0.0 2016 3\n", "2016-03-04 20.3 2.0 12.6 0.0 2016 3\n", "2016-03-05 17.3 7.1 12.5 0.0 2016 3" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Accedemos a un grupo\n", "monthly.get_group((2016,3)).head()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "
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TMAXTMINTMEDPRECIP
yearmonth
2004117.5677423.4322589.9000000.025806
216.0172414.6724149.8034480.531034
317.0741946.18709711.3709682.619355
419.0166677.04333313.1900003.233333
521.28387110.51935515.8838711.019355
630.75666715.91666723.3233330.206667
731.66451617.91290324.7580650.006452
833.48387119.00322626.2419350.000000
930.06666716.32333322.6566670.020000
1026.02258111.60000018.4516130.122581
1118.0566674.76666710.9200000.366667
1214.5000003.7903238.8000001.606452
2005114.587097-0.0677426.4258060.090323
212.7285710.7750006.7464291.821429
317.6354845.57419411.3322580.858065
421.9100008.16333315.0433330.073333
526.77096812.03548419.7322580.109677
630.71000015.55000023.7433330.033333
733.44516117.99677426.2064520.000000
832.19333317.97666724.7066670.040000
927.80333314.30333320.7566670.553333
1023.90000011.48064517.2354840.187097
1117.0533335.55000010.9133330.793333
1214.8566672.7300008.6100000.306667
\n", "
" ], "text/plain": [ " TMAX TMIN TMED PRECIP\n", "year month \n", "2004 1 17.567742 3.432258 9.900000 0.025806\n", " 2 16.017241 4.672414 9.803448 0.531034\n", " 3 17.074194 6.187097 11.370968 2.619355\n", " 4 19.016667 7.043333 13.190000 3.233333\n", " 5 21.283871 10.519355 15.883871 1.019355\n", " 6 30.756667 15.916667 23.323333 0.206667\n", " 7 31.664516 17.912903 24.758065 0.006452\n", " 8 33.483871 19.003226 26.241935 0.000000\n", " 9 30.066667 16.323333 22.656667 0.020000\n", " 10 26.022581 11.600000 18.451613 0.122581\n", " 11 18.056667 4.766667 10.920000 0.366667\n", " 12 14.500000 3.790323 8.800000 1.606452\n", "2005 1 14.587097 -0.067742 6.425806 0.090323\n", " 2 12.728571 0.775000 6.746429 1.821429\n", " 3 17.635484 5.574194 11.332258 0.858065\n", " 4 21.910000 8.163333 15.043333 0.073333\n", " 5 26.770968 12.035484 19.732258 0.109677\n", " 6 30.710000 15.550000 23.743333 0.033333\n", " 7 33.445161 17.996774 26.206452 0.000000\n", " 8 32.193333 17.976667 24.706667 0.040000\n", " 9 27.803333 14.303333 20.756667 0.553333\n", " 10 23.900000 11.480645 17.235484 0.187097\n", " 11 17.053333 5.550000 10.913333 0.793333\n", " 12 14.856667 2.730000 8.610000 0.306667" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# O hacemos una agregación de los datos:\n", "monthly_mean = monthly.mean()\n", "monthly_mean.head(24)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Y podemos reorganizar los datos utilizando _pivot tables_:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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TMAX...PRECIP
month12345678910...3456789101112
year
200417.56774216.01724117.07419419.01666721.28387130.75666731.66451633.48387130.06666726.022581...2.6193553.2333331.0193550.2066670.0064520.0000000.0200000.1225810.3666671.606452
200514.58709712.72857117.63548421.91000026.77096830.71000033.44516132.19333327.80333323.900000...0.8580650.0733330.1096770.0333330.0000000.0400000.5533330.1870970.7933330.306667
200612.11000014.32222220.72258122.33333325.28000028.38620733.90000031.99032328.63333325.483871...0.0709681.9600002.0266670.3517240.0000000.0000001.7200000.2322581.3333330.322581
200716.48709718.10000018.39032317.99310325.76774229.57931032.55161331.76451626.80666721.919355...0.6258061.2482760.2516130.0000000.0000000.0709682.1333332.0516130.3800001.280000
200816.29354815.26206920.14838721.96000023.20000028.72000032.59677432.38064527.34333321.548387...0.4645160.1200001.5032260.0933330.2838710.0000002.1466673.2967740.6466670.000000
200913.60967714.62500018.01935520.54666726.08387132.06666734.96451632.36774226.36333325.945161...1.4258060.7200000.1032260.0200000.0000000.0774191.3066670.0903230.2333333.503226
201013.83871023.36428616.10000020.03333324.40322628.78333333.07096833.06774228.72666723.980645...2.5483870.4866670.3677420.8533330.0000000.0322580.1466670.6774191.5466671.877419
201114.25806517.00714316.21290322.09000024.14516129.21666732.97741933.68709728.87000024.216129...1.0129030.9933331.6064520.0800000.0000000.0774190.5733330.1419351.2933330.458065
201215.79677414.13448318.52258121.57666727.13871032.57666732.88064535.75454528.10666723.506452...0.0258060.0133330.0000000.0066670.0000000.0000002.2333330.7870972.1733330.058065
201316.91935515.72500018.56774221.28000023.42580627.97666731.84193531.71612928.01666726.603226...1.1419350.3733330.5741940.0000000.0000000.8000000.6733330.0838710.6733330.683871
201416.50645217.54285718.80967724.88666725.11290329.03333332.15483932.64516129.60333325.287097...0.1225810.0133330.0516130.5800000.0000000.0000001.2400000.6645160.6333330.419355
201515.81935514.01428618.79354820.10333327.17419429.25000035.17419432.20322627.96666723.664516...1.3741941.0800000.1032260.1133330.0129030.0064521.1600001.3225810.6333330.058065
201617.54193517.25172418.90645221.50000024.63225830.54000032.18064530.92903229.01666724.567742...0.1677420.1266670.4258060.0066670.0000000.0322580.3133330.5677421.6800002.369231
\n", "

13 rows × 48 columns

\n", "
" ], "text/plain": [ " TMAX \\\n", "month 1 2 3 4 5 6 \n", "year \n", "2004 17.567742 16.017241 17.074194 19.016667 21.283871 30.756667 \n", "2005 14.587097 12.728571 17.635484 21.910000 26.770968 30.710000 \n", "2006 12.110000 14.322222 20.722581 22.333333 25.280000 28.386207 \n", "2007 16.487097 18.100000 18.390323 17.993103 25.767742 29.579310 \n", "2008 16.293548 15.262069 20.148387 21.960000 23.200000 28.720000 \n", "2009 13.609677 14.625000 18.019355 20.546667 26.083871 32.066667 \n", "2010 13.838710 23.364286 16.100000 20.033333 24.403226 28.783333 \n", "2011 14.258065 17.007143 16.212903 22.090000 24.145161 29.216667 \n", "2012 15.796774 14.134483 18.522581 21.576667 27.138710 32.576667 \n", "2013 16.919355 15.725000 18.567742 21.280000 23.425806 27.976667 \n", "2014 16.506452 17.542857 18.809677 24.886667 25.112903 29.033333 \n", "2015 15.819355 14.014286 18.793548 20.103333 27.174194 29.250000 \n", "2016 17.541935 17.251724 18.906452 21.500000 24.632258 30.540000 \n", "\n", " ... PRECIP \\\n", "month 7 8 9 10 ... 3 \n", "year ... \n", "2004 31.664516 33.483871 30.066667 26.022581 ... 2.619355 \n", "2005 33.445161 32.193333 27.803333 23.900000 ... 0.858065 \n", "2006 33.900000 31.990323 28.633333 25.483871 ... 0.070968 \n", "2007 32.551613 31.764516 26.806667 21.919355 ... 0.625806 \n", "2008 32.596774 32.380645 27.343333 21.548387 ... 0.464516 \n", "2009 34.964516 32.367742 26.363333 25.945161 ... 1.425806 \n", "2010 33.070968 33.067742 28.726667 23.980645 ... 2.548387 \n", "2011 32.977419 33.687097 28.870000 24.216129 ... 1.012903 \n", "2012 32.880645 35.754545 28.106667 23.506452 ... 0.025806 \n", "2013 31.841935 31.716129 28.016667 26.603226 ... 1.141935 \n", "2014 32.154839 32.645161 29.603333 25.287097 ... 0.122581 \n", "2015 35.174194 32.203226 27.966667 23.664516 ... 1.374194 \n", "2016 32.180645 30.929032 29.016667 24.567742 ... 0.167742 \n", "\n", " \\\n", "month 4 5 6 7 8 9 10 \n", "year \n", "2004 3.233333 1.019355 0.206667 0.006452 0.000000 0.020000 0.122581 \n", "2005 0.073333 0.109677 0.033333 0.000000 0.040000 0.553333 0.187097 \n", "2006 1.960000 2.026667 0.351724 0.000000 0.000000 1.720000 0.232258 \n", "2007 1.248276 0.251613 0.000000 0.000000 0.070968 2.133333 2.051613 \n", "2008 0.120000 1.503226 0.093333 0.283871 0.000000 2.146667 3.296774 \n", "2009 0.720000 0.103226 0.020000 0.000000 0.077419 1.306667 0.090323 \n", "2010 0.486667 0.367742 0.853333 0.000000 0.032258 0.146667 0.677419 \n", "2011 0.993333 1.606452 0.080000 0.000000 0.077419 0.573333 0.141935 \n", "2012 0.013333 0.000000 0.006667 0.000000 0.000000 2.233333 0.787097 \n", "2013 0.373333 0.574194 0.000000 0.000000 0.800000 0.673333 0.083871 \n", "2014 0.013333 0.051613 0.580000 0.000000 0.000000 1.240000 0.664516 \n", "2015 1.080000 0.103226 0.113333 0.012903 0.006452 1.160000 1.322581 \n", "2016 0.126667 0.425806 0.006667 0.000000 0.032258 0.313333 0.567742 \n", "\n", " \n", "month 11 12 \n", "year \n", "2004 0.366667 1.606452 \n", "2005 0.793333 0.306667 \n", "2006 1.333333 0.322581 \n", "2007 0.380000 1.280000 \n", "2008 0.646667 0.000000 \n", "2009 0.233333 3.503226 \n", "2010 1.546667 1.877419 \n", "2011 1.293333 0.458065 \n", "2012 2.173333 0.058065 \n", "2013 0.673333 0.683871 \n", "2014 0.633333 0.419355 \n", "2015 0.633333 0.058065 \n", "2016 1.680000 2.369231 \n", "\n", "[13 rows x 48 columns]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Dejar los años como índices y ver la media mensual en cada columna\n", "monthly_mean.reset_index().pivot(index='year', columns='month')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Por último, pandas proporciona métodos para calcular magnitudes como medias móviles usando el método `rolling`:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "year month\n", "2004 1 17.567742\n", " 2 16.017241\n", " 3 17.074194\n", " 4 19.016667\n", " 5 21.283871\n", " 6 30.756667\n", " 7 31.664516\n", " 8 33.483871\n", " 9 30.066667\n", " 10 26.022581\n", " 11 18.056667\n", " 12 14.500000\n", "2005 1 14.587097\n", " 2 12.728571\n", " 3 17.635484\n", "Name: TMAX, dtype: float64" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Calcular la media de la columna TMAX\n", "monthly.TMAX.mean().head(15)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "year month\n", "2004 1 NaN\n", " 2 16.886392\n", " 3 17.369367\n", " 4 19.124910\n", " 5 23.685735\n", " 6 27.901685\n", " 7 31.968351\n", " 8 31.738351\n", " 9 29.857706\n", " 10 24.715305\n", " 11 19.526416\n", " 12 15.714588\n", "2005 1 13.938556\n", " 2 14.983717\n", " 3 17.424685\n", "Name: TMAX, dtype: float64" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Media trimensual centrada\n", "monthly_mean.TMAX.rolling(3, center=True).mean().head(15)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Plotting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Líneas" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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70u+pcVvxRBK/fGGBVHRGfV8GcuS8J9l1b2d9CyY8+RV21vEXO1mRTiBibO+b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\no43thNudUOhcT44ry7B4eR4Qslw6EcPlk0snGX0Ozu3xfCEPwzIjjL0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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Pintar la temperatura máx, min, med\n", "data.plot(y=[\"TMAX\", \"TMIN\", \"TMED\"])\n", "plt.title('Temperaturas')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cajas" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ], "text/plain": [ " TMIN PRECIP TMED TMAX\n", "month \n", "1 1 -1.6 0.076923 8.992308 20.6\n", " 2 -3.0 0.046154 9.000000 20.9\n", " 3 -1.6 0.661538 8.553846 21.0\n", " 4 -0.6 0.400000 8.815385 22.8\n", " 5 -1.0 0.369231 8.461538 21.7" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "group_daily = data.groupby(['month', data.index.day])\n", "\n", "daily_agg = group_daily.agg({'TMED': 'mean', 'TMAX': 'max', 'TMIN': 'min', 'PRECIP': 'mean'})\n", "daily_agg.head()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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XVBqgrVqom/BE7zo9Kj9cVdeqitGgCKMRToQX4x8UAWGW8e9wUMbcYTekN0Cq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\nFEvvncJtp/cnJNCfYanRfHr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nQoQp510JM238yNhXsBtyN0PaWHmeMkKCt6qNm/qwVbaP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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "daily_agg.plot(y=['TMED', 'TMAX', 'TMIN'])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualizaciones especiales" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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kzi7uu61bCf4q0NsWmHcTnp1IMJPKYzku0Yy1IF5jVRD+gFdw65YIb9i3VR3H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\nV0uSz5r/R4DdwJdEBOCzwIsi8jLQDXwtj99ddM8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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# scatter_matrix\n", "from pandas.tools.plotting import scatter_matrix\n", "axes = scatter_matrix(data.loc[:, \"TMAX\":\"TMED\"])" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "---\n", "
\n", "####

¡Síguenos en Twitter!\n", "
\n", "###### Follow @AeroPython \n", "
\n", "###### Este notebook ha sido realizado por: Juan Luis Cano, y Álex Sáez \n", "
\n", "##### \"Licencia
Curso AeroPython por Juan Luis Cano Rodriguez y Alejandro Sáez Mollejo se distribuye bajo una Licencia Creative Commons Atribución 4.0 Internacional." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "---\n", "_Las siguientes celdas contienen configuración del Notebook_\n", "\n", "_Para visualizar y utlizar los enlaces a Twitter el notebook debe ejecutarse como [seguro](http://ipython.org/ipython-doc/dev/notebook/security.html)_\n", "\n", " File > Trusted Notebook" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "/* This template is inspired in the one used by Lorena Barba\n", "in the numerical-mooc repository: https://github.com/numerical-mooc/numerical-mooc\n", "We thank her work and hope you also enjoy the look of the notobooks with this style */\n", "\n", "\n", "\n", "El estilo se ha aplicado =)\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Esta celda da el estilo al notebook\n", "from IPython.core.display import HTML\n", "css_file = '../styles/aeropython.css'\n", "HTML(open(css_file, \"r\").read())" ] } ], "metadata": { "anaconda-cloud": {}, "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": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 0 }