{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\"AeroPython\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Carga y manipulación de datos con pandas" ] }, { "cell_type": "markdown", "metadata": {}, "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": 2, "metadata": {}, "outputs": [], "source": [ "# Importamos pandas\n", "import pandas as pd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "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": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import HTML\n", "HTML('')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "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": {}, "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": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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 ...
410-12-16 345 16.4 6.3 10.9 ...
\n", "
" ], "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": 5, "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": {}, "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": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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
\n", "
" ], "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": 6, "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": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "TMAX float64\n", "TMIN float64\n", "TMED float64\n", "PRECIP float64\n", "dtype: object" ] }, "execution_count": 7, "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": {}, "source": [ "
Para acordarnos de cómo parsear las fechas: http://strftime.org/
" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "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": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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
50%22.9000009.70000016.0000000.000000
75%29.20000015.10000022.1000000.000000
max42.60000023.80000032.10000066.200000
\n", "
" ], "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": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Descripción estadística\n", "data.describe()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Int64Index([3, 4, 5, 6, 0, 1, 2, 3, 4, 5,\n", " ...\n", " 6, 0, 1, 2, 3, 4, 5, 6, 0, 1],\n", " dtype='int64', name='DATE', length=4732)" ] }, "execution_count": 10, "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": {}, "source": [ "## Accediendo a los datos " ] }, { "cell_type": "markdown", "metadata": {}, "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": 11, "metadata": { "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": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Accediendo como clave\n", "data['TMAX'].head()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "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": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Accediendo como atributo\n", "data.TMIN.head()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
TMAXTMIN
DATE
2004-01-0118.02.5
2004-01-0217.45.7
2004-01-0315.10.8
2004-01-0416.2-0.4
2004-01-0516.40.6
\n", "
" ], "text/plain": [ " TMAX TMIN\n", "DATE \n", "2004-01-01 18.0 2.5\n", "2004-01-02 17.4 5.7\n", "2004-01-03 15.1 0.8\n", "2004-01-04 16.2 -0.4\n", "2004-01-05 16.4 0.6" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Accediendo a varias columnas a la vez\n", "data[['TMAX', 'TMIN']].head()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
TMAXTMIN
DATE
2004-01-011.800.25
2004-01-021.740.57
2004-01-031.510.08
2004-01-041.62-0.04
2004-01-051.640.06
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
.........
2016-11-141.900.41
2016-11-151.680.49
2016-11-161.950.42
2016-11-171.900.14
2016-11-182.010.32
2016-11-191.970.60
2016-11-201.980.37
2016-11-211.851.23
2016-11-221.360.78
2016-11-231.430.41
2016-11-241.180.20
2016-11-251.320.16
2016-11-261.320.75
2016-11-271.420.81
2016-11-281.390.55
2016-11-291.380.43
2016-11-301.401.07
2016-12-011.360.92
2016-12-021.720.55
2016-12-031.340.87
2016-12-041.181.01
2016-12-051.660.79
2016-12-061.770.71
2016-12-071.570.61
2016-12-081.450.54
2016-12-091.360.95
2016-12-101.640.63
2016-12-111.690.50
2016-12-121.590.30
2016-12-131.460.40
\n", "

4732 rows × 2 columns

\n", "
" ], "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": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Modificando valores de columnas\n", "data[['TMAX', 'TMIN']] / 10" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "23.22476129853591" ] }, "execution_count": 15, "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": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "23.22476129853591" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Calculando la media con pandas\n", "data.TMAX.mean()" ] }, { "cell_type": "markdown", "metadata": {}, "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": 17, "metadata": {}, "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": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Accediendo a una fila por índice\n", "data.iloc[1]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "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": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Accediendo a una fila por etiqueta\n", "data.loc[\"2016-09-02\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Puedo incluso hacer secciones basadas en fechas:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.loc[\"2016-12-01\":]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "También puedo indexar utilizando arrays de valores booleanos, por ejemplo procedentes de la comprobación de una condición:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Búsqueda de valores nulos\n", "data.loc[data.TMIN.isnull()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Podemos agrupar nuestros datos utilizando `groupby`:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "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": 22, "metadata": {}, "outputs": [], "source": [ "# Creamos la agrupación\n", "monthly = data.groupby(by=['year', 'month'])" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dict_keys([(2004, 1), (2004, 2), (2004, 3), (2004, 4), (2004, 5), (2004, 6), (2004, 7), (2004, 8), (2004, 9), (2004, 10), (2004, 11), (2004, 12), (2005, 1), (2005, 2), (2005, 3), (2005, 4), (2005, 5), (2005, 6), (2005, 7), (2005, 8), (2005, 9), (2005, 10), (2005, 11), (2005, 12), (2006, 1), (2006, 2), (2006, 3), (2006, 4), (2006, 5), (2006, 6), (2006, 7), (2006, 8), (2006, 9), (2006, 10), (2006, 11), (2006, 12), (2007, 1), (2007, 2), (2007, 3), (2007, 4), (2007, 5), (2007, 6), (2007, 7), (2007, 8), (2007, 9), (2007, 10), (2007, 11), (2007, 12), (2008, 1), (2008, 2), (2008, 3), (2008, 4), (2008, 5), (2008, 6), (2008, 7), (2008, 8), (2008, 9), (2008, 10), (2008, 11), (2008, 12), (2009, 1), (2009, 2), (2009, 3), (2009, 4), (2009, 5), (2009, 6), (2009, 7), (2009, 8), (2009, 9), (2009, 10), (2009, 11), (2009, 12), (2010, 1), (2010, 2), (2010, 3), (2010, 4), (2010, 5), (2010, 6), (2010, 7), (2010, 8), (2010, 9), (2010, 10), (2010, 11), (2010, 12), (2011, 1), (2011, 2), (2011, 3), (2011, 4), (2011, 5), (2011, 6), (2011, 7), (2011, 8), (2011, 9), (2011, 10), (2011, 11), (2011, 12), (2012, 1), (2012, 2), (2012, 3), (2012, 4), (2012, 5), (2012, 6), (2012, 7), (2012, 8), (2012, 9), (2012, 10), (2012, 11), (2012, 12), (2013, 1), (2013, 2), (2013, 3), (2013, 4), (2013, 5), (2013, 6), (2013, 7), (2013, 8), (2013, 9), (2013, 10), (2013, 11), (2013, 12), (2014, 1), (2014, 2), (2014, 3), (2014, 4), (2014, 5), (2014, 6), (2014, 7), (2014, 8), (2014, 9), (2014, 10), (2014, 11), (2014, 12), (2015, 1), (2015, 2), (2015, 3), (2015, 4), (2015, 5), (2015, 6), (2015, 7), (2015, 8), (2015, 9), (2015, 10), (2015, 11), (2015, 12), (2016, 1), (2016, 2), (2016, 3), (2016, 4), (2016, 5), (2016, 6), (2016, 7), (2016, 8), (2016, 9), (2016, 10), (2016, 11), (2016, 12)])" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Podemos ver los grupos que se han creado\n", "monthly.groups.keys()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Accedemos a un grupo\n", "monthly.get_group((2016,3)).head()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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": 25, "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": {}, "source": [ "Y podemos reorganizar los datos utilizando _pivot tables_:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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": 26, "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": {}, "source": [ "Por último, pandas proporciona métodos para calcular magnitudes como medias móviles usando el método `rolling`:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "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": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Calcular la media de la columna TMAX\n", "monthly.TMAX.mean().head(15)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "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": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Media trimensual centrada\n", "monthly_mean.TMAX.rolling(3, center=True).mean().head(15)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Líneas" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,'Temperaturas')" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "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": 30, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data.loc[:, 'TMAX':'PRECIP'].plot.box()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pintando la temperatura máxima de las máximas, mínima de las mínimas, media de las medias para cada día del año de los años disponnibles" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
TMEDTMAXTMINPRECIP
monthDATE
118.99230820.6-1.60.076923
29.00000020.9-3.00.046154
38.55384621.0-1.60.661538
48.81538522.8-0.60.400000
58.46153821.7-1.00.369231
\n", "
" ], "text/plain": [ " TMED TMAX TMIN PRECIP\n", "month DATE \n", "1 1 8.992308 20.6 -1.6 0.076923\n", " 2 9.000000 20.9 -3.0 0.046154\n", " 3 8.553846 21.0 -1.6 0.661538\n", " 4 8.815385 22.8 -0.6 0.400000\n", " 5 8.461538 21.7 -1.0 0.369231" ] }, "execution_count": 31, "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": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "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": 34, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# scatter_matrix\n", "from pandas.plotting import scatter_matrix\n", "axes = scatter_matrix(data.loc[:, \"TMAX\":\"TMED\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "#### ¡Síguenos en Twitter!\n", "\n", "Follow @AeroPython \n", "\n", "\n", "Este notebook ha sido realizado por: Juan Luis Cano, Álex Sáez\n", "\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": {}, "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": 35, "metadata": {}, "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": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# preserve\n", "# 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())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.7.2" } }, "nbformat": 4, "nbformat_minor": 1 }