{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Uso de la API del Covid19 con Pandas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En esta práctica, vamos a usar la API [COVID-19](https://api.covid19api.com/countries), desarrollada por Kyle Redelinghuys y basada en [los datos](https://github.com/CSSEGISandData/COVID-19) que proporciona la [Universidad Johns Hopkins](https://systems.jhu.edu/research/public-health/ncov/)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Instalación librerías" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Para empezar, tenemos que **instalar la librería necesaria** para proyectar los datos que obtengamos sobre la incidencia de la COVID-19. Para ello, usamos el comando *pip install pandas*. Para especificar que lo que estamos haciendo es ejecutar un comando bash, ponemos un *!* al inicio de la línea." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (1.3.1)\n", "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2020.4)\n", "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2.8.1)\n", "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (1.21.1)\n", "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7.3->pandas) (1.14.0)\n" ] } ], "source": [ "!pip install pandas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Configuración de Pandas " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Una vez instalada, tenemos que **importarla al proyecto** que estamos realizando. Para ello, usamos la función *import*, e importamos la librería como *pd*, de forma abreviada, tal y como sigue a continuación." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Crear variable" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ya está instalada la librería *Pandas* y la hemos importado. Ahora ya podemos empezar a crear las variables necesarias para, posteriormente, realizar los gráficos con los datos de la API. Lo primero será **definir la variable** *url*, que será la que le diga a Python el origen de los datos. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "url = 'https://api.covid19api.com/countries'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Crear Dataframe " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Una vez tenemos el enlace donde se encuentran los datos, podemos empezar a mostrarlos. Lo primero será **crear un *dataframe***, una tabla con filas y columnas en la que aparecerán los datos. Para ello, usamos la función de *Pandas* que lee el formato .json, en el que están los datos de la API: *pd.read_json(url)*. Al poner (url), le estamos indicando a *Pandas* cuáles son los datos que tiene que leer." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "df = pd.read_json(url)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Ejecutamos el df** para visualizar que todo está correcto." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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0GambiagambiaGM
1ParaguayparaguayPY
2Trinidad and Tobagotrinidad-and-tobagoTT
3AustriaaustriaAT
4Sao Tome and Principesao-tome-and-principeST
............
243Cape Verdecape-verdeCV
244IsraelisraelIL
245BoliviaboliviaBO
246LatvialatviaLV
247Papua New Guineapapua-new-guineaPG
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248 rows × 3 columns

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" ], "text/plain": [ " Country Slug ISO2\n", "0 Gambia gambia GM\n", "1 Paraguay paraguay PY\n", "2 Trinidad and Tobago trinidad-and-tobago TT\n", "3 Austria austria AT\n", "4 Sao Tome and Principe sao-tome-and-principe ST\n", ".. ... ... ...\n", "243 Cape Verde cape-verde CV\n", "244 Israel israel IL\n", "245 Bolivia bolivia BO\n", "246 Latvia latvia LV\n", "247 Papua New Guinea papua-new-guinea PG\n", "\n", "[248 rows x 3 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exploración tabla" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Como vemos, al componerse de 248 filas, solo se nos muestra un extracto, las primeras y las últimas filas. Ahora vamos a seguir explorando la tabla." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Por ejemplo, vamos a **visualizar las 6 primeras filas**. Para ello, a través de *head*, le decimos que nos muestre el dataframe con las X primeras filas, que en este caso le hemos indicado que sean 6." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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CountrySlugISO2
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" ], "text/plain": [ " Country Slug ISO2\n", "0 Gambia gambia GM\n", "1 Paraguay paraguay PY\n", "2 Trinidad and Tobago trinidad-and-tobago TT\n", "3 Austria austria AT\n", "4 Sao Tome and Principe sao-tome-and-principe ST\n", "5 Timor-Leste timor-leste TL" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(6)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ahora lo mismo, pero al contrario, empezando por el final. Para decirle que queremos comenzar por el final, **en lugar de *head* usamos *tail***, con el mismo formato: entre paréntesis indicamos el X número de filas que queremos visualizar." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CountrySlugISO2
246LatvialatviaLV
247Papua New Guineapapua-new-guineaPG
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" ], "text/plain": [ " Country Slug ISO2\n", "246 Latvia latvia LV\n", "247 Papua New Guinea papua-new-guinea PG" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.tail(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si queremos **conocer más datos técnicos** acerca de la composición de lo que estamos usando, podemos usar *info*, que nos indicará de qué tipo son los datos por columnas, el total de filas y columnas, cuánto utiliza de memoria o si existen datos nulos." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 248 entries, 0 to 247\n", "Data columns (total 3 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Country 248 non-null object\n", " 1 Slug 248 non-null object\n", " 2 ISO2 248 non-null object\n", "dtypes: object(3)\n", "memory usage: 5.9+ KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si queremos filtrar por columnas para ver los datos de alguna en particular, seguimos usando el df pero ahora, entre corchetes, ponemos el nombre de la columna que queramos visualizar. Por ejemplo, para ver una lista (abreviada) de países, usamos *'Country'*." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "0 Gambia\n", "1 Paraguay\n", "2 Trinidad and Tobago\n", "3 Austria\n", "4 Sao Tome and Principe\n", " ... \n", "243 Cape Verde\n", "244 Israel\n", "245 Bolivia\n", "246 Latvia\n", "247 Papua New Guinea\n", "Name: Country, Length: 248, dtype: object" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Country']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Además de esto, también podemos cruzar filas con columnas. Es decir, saber qué valor tiene la fila X en la columna Y. Para ello, añadimos otro corchete a los que tenemos especificando el número de la fila. En este caso, queremos saber qué país está codificado en la fila 66 de la tabla o *dataframe*." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Honduras'" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Country'][66]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Datos de España\n", "https://api.covid19api.com/country/spain/status/confirmed/live" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Una vez hemos testeado algunas formas de visualización del *dataframe* de los países, vamos a centrarnos en **algunos territorios concretos** para ver su evolución en tiempo real de los casos confirmados." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Para hacer esto, tenemos que **definir de nuevo el enlace desde el que *Pandas* leerá los datos y crear, a partir de este, el dataframe**. En esta ocasión, veremos los datos de España, por lo que obtenemos desde la API los casos confirmados en tiempo real para el país y los definimos. Posteriormente, los mostramos para ver que todo ha ido bien." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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CountryCountryCodeProvinceCityCityCodeLatLonCasesStatusDate
0SpainES40.46-3.750confirmed2020-01-22 00:00:00+00:00
1SpainES40.46-3.750confirmed2020-01-23 00:00:00+00:00
2SpainES40.46-3.750confirmed2020-01-24 00:00:00+00:00
3SpainES40.46-3.750confirmed2020-01-25 00:00:00+00:00
4SpainES40.46-3.750confirmed2020-01-26 00:00:00+00:00
.................................
816SpainES40.46-3.7511662214confirmed2022-04-17 00:00:00+00:00
817SpainES40.46-3.7511662214confirmed2022-04-18 00:00:00+00:00
818SpainES40.46-3.7511736893confirmed2022-04-19 00:00:00+00:00
819SpainES40.46-3.7511736893confirmed2022-04-20 00:00:00+00:00
820SpainES40.46-3.7511736893confirmed2022-04-21 00:00:00+00:00
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" ], "text/plain": [ " Country CountryCode Province City CityCode Lat Lon Cases \\\n", "0 Spain ES 40.46 -3.75 0 \n", "1 Spain ES 40.46 -3.75 0 \n", "2 Spain ES 40.46 -3.75 0 \n", "3 Spain ES 40.46 -3.75 0 \n", "4 Spain ES 40.46 -3.75 0 \n", ".. ... ... ... ... ... ... ... ... \n", "816 Spain ES 40.46 -3.75 11662214 \n", "817 Spain ES 40.46 -3.75 11662214 \n", "818 Spain ES 40.46 -3.75 11736893 \n", "819 Spain ES 40.46 -3.75 11736893 \n", "820 Spain ES 40.46 -3.75 11736893 \n", "\n", " Status Date \n", "0 confirmed 2020-01-22 00:00:00+00:00 \n", "1 confirmed 2020-01-23 00:00:00+00:00 \n", "2 confirmed 2020-01-24 00:00:00+00:00 \n", "3 confirmed 2020-01-25 00:00:00+00:00 \n", "4 confirmed 2020-01-26 00:00:00+00:00 \n", ".. ... ... \n", "816 confirmed 2022-04-17 00:00:00+00:00 \n", "817 confirmed 2022-04-18 00:00:00+00:00 \n", "818 confirmed 2022-04-19 00:00:00+00:00 \n", "819 confirmed 2022-04-20 00:00:00+00:00 \n", "820 confirmed 2022-04-21 00:00:00+00:00 \n", "\n", "[821 rows x 10 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "url_es = 'https://api.covid19api.com/country/spain/status/confirmed/live'\n", "df_es = pd.read_json(url_es)\n", "df_es" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Al igual que hicimos anteriormente, vamos a ver diferentes datos de la tabla. Para empezar, queremos listar las columnas existentes y saber qué datos contiene cada una. Lo hacemos **mostrando el *dataframe* de España y añadiendo *columns***. " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "Index(['Country', 'CountryCode', 'Province', 'City', 'CityCode', 'Lat', 'Lon',\n", " 'Cases', 'Status', 'Date'],\n", " dtype='object')" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_es.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "De la misma manera, podemos cruzar filas con las columnas para saber un dato concreto. Por ejemplo, para saber cuál es el dato que se encuentra en la primera fila, empezando por el principio, filtramos por la columna 'Date', y con .head(x) le indicamos el número que queremos." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "0 2020-01-22 00:00:00+00:00\n", "Name: Date, dtype: datetime64[ns, UTC]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_es['Date'].head(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Además, para saber los tipos de datos contenidos en el *dataframe*, si existen datos nulos y el tamaño total de la tabla, usamos *info*." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 821 entries, 0 to 820\n", "Data columns (total 10 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Country 821 non-null object \n", " 1 CountryCode 821 non-null object \n", " 2 Province 821 non-null object \n", " 3 City 821 non-null object \n", " 4 CityCode 821 non-null object \n", " 5 Lat 821 non-null float64 \n", " 6 Lon 821 non-null float64 \n", " 7 Cases 821 non-null int64 \n", " 8 Status 821 non-null object \n", " 9 Date 821 non-null datetime64[ns, UTC]\n", "dtypes: datetime64[ns, UTC](1), float64(2), int64(1), object(6)\n", "memory usage: 64.3+ KB\n" ] } ], "source": [ "df_es.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si profundizamos en cuestiones estadísticas, podemos saber, por ejemplo, datos como la media, la desviación estándar, los diferentes cuartiles y el valor mínimo y máximo, usamos *describe*." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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LatLonCases
count8.210000e+02821.008.210000e+02
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" ], "text/plain": [ " Lat Lon Cases\n", "count 8.210000e+02 821.00 8.210000e+02\n", "mean 4.046000e+01 -3.75 3.426515e+06\n", "std 1.421952e-14 0.00 3.312558e+06\n", "min 4.046000e+01 -3.75 0.000000e+00\n", "25% 4.046000e+01 -3.75 3.428130e+05\n", "50% 4.046000e+01 -3.75 3.180212e+06\n", "75% 4.046000e+01 -3.75 4.953930e+06\n", "max 4.046000e+01 -3.75 1.173689e+07" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_es.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Aquí volvemos a mostrar el *dataframe* entero, aunque aparece de forma abrevada por cuestión de espacio, sin filtrar por columna o tipo de datos." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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CountryCountryCodeProvinceCityCityCodeLatLonCasesStatusDate
0SpainES40.46-3.750confirmed2020-01-22 00:00:00+00:00
1SpainES40.46-3.750confirmed2020-01-23 00:00:00+00:00
2SpainES40.46-3.750confirmed2020-01-24 00:00:00+00:00
3SpainES40.46-3.750confirmed2020-01-25 00:00:00+00:00
4SpainES40.46-3.750confirmed2020-01-26 00:00:00+00:00
.................................
816SpainES40.46-3.7511662214confirmed2022-04-17 00:00:00+00:00
817SpainES40.46-3.7511662214confirmed2022-04-18 00:00:00+00:00
818SpainES40.46-3.7511736893confirmed2022-04-19 00:00:00+00:00
819SpainES40.46-3.7511736893confirmed2022-04-20 00:00:00+00:00
820SpainES40.46-3.7511736893confirmed2022-04-21 00:00:00+00:00
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821 rows × 10 columns

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" ], "text/plain": [ " Country CountryCode Province City CityCode Lat Lon Cases \\\n", "0 Spain ES 40.46 -3.75 0 \n", "1 Spain ES 40.46 -3.75 0 \n", "2 Spain ES 40.46 -3.75 0 \n", "3 Spain ES 40.46 -3.75 0 \n", "4 Spain ES 40.46 -3.75 0 \n", ".. ... ... ... ... ... ... ... ... \n", "816 Spain ES 40.46 -3.75 11662214 \n", "817 Spain ES 40.46 -3.75 11662214 \n", "818 Spain ES 40.46 -3.75 11736893 \n", "819 Spain ES 40.46 -3.75 11736893 \n", "820 Spain ES 40.46 -3.75 11736893 \n", "\n", " Status Date \n", "0 confirmed 2020-01-22 00:00:00+00:00 \n", "1 confirmed 2020-01-23 00:00:00+00:00 \n", "2 confirmed 2020-01-24 00:00:00+00:00 \n", "3 confirmed 2020-01-25 00:00:00+00:00 \n", "4 confirmed 2020-01-26 00:00:00+00:00 \n", ".. ... ... \n", "816 confirmed 2022-04-17 00:00:00+00:00 \n", "817 confirmed 2022-04-18 00:00:00+00:00 \n", "818 confirmed 2022-04-19 00:00:00+00:00 \n", "819 confirmed 2022-04-20 00:00:00+00:00 \n", "820 confirmed 2022-04-21 00:00:00+00:00 \n", "\n", "[821 rows x 10 columns]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_es" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A continuación, colocamos como referencia la fecha de los datos, para empezar a obtener una imagen general de la evolución de los datos. Para ello, fijamos con *set_index* el nombre de la columna que queremos colocar como referencia, que en este caso es la fecha (*'Date'*)." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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CountryCountryCodeProvinceCityCityCodeLatLonCasesStatus
Date
2020-01-22 00:00:00+00:00SpainES40.46-3.750confirmed
2020-01-23 00:00:00+00:00SpainES40.46-3.750confirmed
2020-01-24 00:00:00+00:00SpainES40.46-3.750confirmed
2020-01-25 00:00:00+00:00SpainES40.46-3.750confirmed
2020-01-26 00:00:00+00:00SpainES40.46-3.750confirmed
..............................
2022-04-17 00:00:00+00:00SpainES40.46-3.7511662214confirmed
2022-04-18 00:00:00+00:00SpainES40.46-3.7511662214confirmed
2022-04-19 00:00:00+00:00SpainES40.46-3.7511736893confirmed
2022-04-20 00:00:00+00:00SpainES40.46-3.7511736893confirmed
2022-04-21 00:00:00+00:00SpainES40.46-3.7511736893confirmed
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821 rows × 9 columns

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" ], "text/plain": [ " Country CountryCode Province City CityCode Lat \\\n", "Date \n", "2020-01-22 00:00:00+00:00 Spain ES 40.46 \n", "2020-01-23 00:00:00+00:00 Spain ES 40.46 \n", "2020-01-24 00:00:00+00:00 Spain ES 40.46 \n", "2020-01-25 00:00:00+00:00 Spain ES 40.46 \n", "2020-01-26 00:00:00+00:00 Spain ES 40.46 \n", "... ... ... ... ... ... ... \n", "2022-04-17 00:00:00+00:00 Spain ES 40.46 \n", "2022-04-18 00:00:00+00:00 Spain ES 40.46 \n", "2022-04-19 00:00:00+00:00 Spain ES 40.46 \n", "2022-04-20 00:00:00+00:00 Spain ES 40.46 \n", "2022-04-21 00:00:00+00:00 Spain ES 40.46 \n", "\n", " Lon Cases Status \n", "Date \n", "2020-01-22 00:00:00+00:00 -3.75 0 confirmed \n", "2020-01-23 00:00:00+00:00 -3.75 0 confirmed \n", "2020-01-24 00:00:00+00:00 -3.75 0 confirmed \n", "2020-01-25 00:00:00+00:00 -3.75 0 confirmed \n", "2020-01-26 00:00:00+00:00 -3.75 0 confirmed \n", "... ... ... ... \n", "2022-04-17 00:00:00+00:00 -3.75 11662214 confirmed \n", "2022-04-18 00:00:00+00:00 -3.75 11662214 confirmed \n", "2022-04-19 00:00:00+00:00 -3.75 11736893 confirmed \n", "2022-04-20 00:00:00+00:00 -3.75 11736893 confirmed \n", "2022-04-21 00:00:00+00:00 -3.75 11736893 confirmed \n", "\n", "[821 rows x 9 columns]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_es.set_index('Date')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Para ceñirnos estrictamente a los datos que queremos ver, y así dejar fuera los que son innecesarios, como el país, la latitud y la longitud o el código del país, entre corchetes especificamos la(s) columna(s) que queramos mostrar además de la de la fecha." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "Date\n", "2020-01-22 00:00:00+00:00 0\n", "2020-01-23 00:00:00+00:00 0\n", "2020-01-24 00:00:00+00:00 0\n", "2020-01-25 00:00:00+00:00 0\n", "2020-01-26 00:00:00+00:00 0\n", " ... \n", "2022-04-17 00:00:00+00:00 11662214\n", "2022-04-18 00:00:00+00:00 11662214\n", "2022-04-19 00:00:00+00:00 11736893\n", "2022-04-20 00:00:00+00:00 11736893\n", "2022-04-21 00:00:00+00:00 11736893\n", "Name: Cases, Length: 821, dtype: int64" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_es.set_index('Date')['Cases']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vamos a pasar a la representación gráfica. Manteniendo lo anterior, es decir, los datos que queremos visualizar (fecha y número de casos, para ver la evolución temporal), **añadimos *plot* y le ponemos un título al gráfico, dentro de los paréntesis a partir de *plot***." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df_es.set_index('Date')['Cases'].plot(title=\"Casos de Covid19 en España\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Datos de Italia" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ahora repetimos el mismo proceso que hemos hecho hasta ahora pero con otro país, pero centrándonos solo en la representación visual de los datos.En este caso se ha elegido Italia." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "url_it = 'https://api.covid19api.com/country/italy/status/confirmed/live'\n", "df_it = pd.read_json(url_it)\n", "df_it.set_index('Date')['Cases'].plot(title=\"Casos de Covid19 Ben Italia\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Datos de México" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "De nuevo, escogemos un tercer país de la API y volvemos a visualizarlo a través de *plot* de su *dataframe*. Se ha elegido México." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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+VxkpBicMSdzTy+tTWItIq1J+oJoH3tpAdvcOtGuTGu9yIqawFpFWZUNBOQBnjOwV50qaRmEtIq3K9uL9AFyek5i372qIwlpEWpWXFuYB0K9repwraRqFtYi0Gu7O7LUFAHRJb8otaONPYS0ircbefVXUOvzwwtFJcYp5XQprEWk1dpZUAsnXBQIKaxFpRZ6atxWA3hnt41xJ0ymsRaRVWJ5XwswFufTrms7I3ol9V5jDUViLSKvw7Ifb6JCWyiu3nUrHdsm1cxEU1iLSSmzbU8GQXp3ISE+LdylHRWEtIi1eVU0t728oIrt74t++qyEKaxFp8Z7/KBeAIZkd41zJ0VNYi0iL987aQtq1SeGWM4fGu5SjprAWkRatqqaWeZuK+Lcp/ZPqKnv1KaxFpEVbmltM+YFqpg1LjjvCNERhLSItVsWBam5/YSkAJw/tEedqjo3CWkRarIfe2cjWon1cc9JAunZoG+9yjknEYW1mqWa22MxejmVBIiLRUFVTy/Mf5XL2qF7cffG4eJdzzJrSsr4NWB2rQkREounlZdspLDvAlSdkx7uUqIgorM2sP3Ah8EhsyxEROXYl+6r4zb/WMzqrC2cm2e27GhLpCfK/Ae4AGrz6iZlNB6YDZGe3jL9kIpJ8nvtwGz/6ywqqa50nr59KSkpyXbe6IY2GtZldBBS4+0IzO6Oh6dx9BjADICcnx6NVoIhIQ9ydnaWVLM0tZvaaQvKL9zNnw25OHNKd73xuBCcMSe4jQOqKpGV9CvBFM7sAaA90MbOn3f1rsS1NROQT7k5B2QE+2FjEivwSluQWs2pHKfsO1gDQtUMaA7p14IZpg/k/Zw9P2gs2NaTRsHb37wPfBwhb1t9VUItILLk7q3eUsb6gjLU7y5izYTcbC8qpCIO5fVoKQzI7ccXxAxiS2ZGhPTuRM6g7bdu03KORk++iriLS4rg7O0oqmbepiGV5JSzetpeleSUAtEkxxvTtwmVT+pPdoyMnDO7OmKwuLaYvOlJNCmt3fxt4OyaViEirUV1Ty5LcYt5bv5t5m4pYt6uMvfuqgKDVPLJPF+66YBSnj+jFoMwOSX1Nj2hRy1pEmsXcDbv5+7LtbCysYPWOUsoqq0kxGNSjI+eM6c2YrC4cP7g7w3p1UjgfhsJaRGKitLKKf63axcwFuWwoKGd3+UE6t2vD6KwuXDAui9NG9GTasEwyOrSsHYGxorAWkahxd95aU8Djc7fw/obd1DoMzgxazsN6deaqE7Jpn6ZW89FQWIvIMTtQXcOTc7cyc0Eu6wvK6dc1nZtOH8oZI3tx/KBumLWunYGxoLAWkaNWVlnFW2sKmLkgl/c3FDGsVyd+cck4Ls8ZQFpqyz2MLh4U1iJyVBZt28utzy4mv3g/aanGjy4aww3TBse7rBZLYS0iTbK34iC/fH0NLyzIo09Gex6/7nhOHprZok9ISQQKaxGJ2MbCcq76w3x2llZy8aS+3P3FcTqao5korEUkIrPXFPC9F5fiDk9eP5XTRvSMd0mtisJaRI5oa1EFP5i1gjkbdtOvazqPX3c8w3s3eLVkiRGFtYg0aPaaAn701xWUVVZzyxlD+fqpQ+jWMbnvZZisFNYiclgvL9vOt55dTP9u6Tx5/VQmDuga75JaNYW1iHzGktxivvOnJbRtk8KsW06hZ+d28S6p1dOxNiLyKe+sK+SmpxbSq3N75t55loI6QSisReRjq3eU8vUnFtChbSozrplCZicFdaJQN4iIAMH1Pb7zpyV0SU/jxZtPprt2JCYUtaxFhJ0lldz4xALW7Czjl5eNV1AnILWsRVq5A9U1XPXIPLYXV/LzL43jrFG9412SHIbCWqSV+/3bG9lYWMHj1x3PGSN7xbscaYC6QURasdw9+/jftzdy0YQsBXWCU1iLtFK5e/Zx63OLSTXjBxeOjnc50gh1g4i0QrW1zreeW8zGgnJ+edkEsjLS412SNEJhLdIKvbpiJ0tzi/mfL0/kCxP7xrsciYC6QURaGXfn9+9sYEjPjlw6uV+8y5EIKaxFWpkXFuaxIr+UG6cNISVFN7JNFo2GtZm1N7MPzWypma00s/9qjsJEJPqemLuFO15cxpSB3fhyTv94lyNNEEmf9QHgLHcvN7M0YI6Zveru82Jcm4hE0f6DNfz2rfVkd+/AI9fk6O7jSabRsHZ3B8rDp2nhj8eyKBGJvmfmb2V3+UFeuGmKbiCQhCL602pmqWa2BCgA3nD3+TGtSkSiqmRfFQ/O3sC0YZkcP6h7vMuRoxBRWLt7jbtPAvoDU81sXP1pzGy6mS0wswWFhYVRLlNEjtbOkkq+8od5FO+v4q4LdPJLsmpSp5W7FwOzgfMOM26Gu+e4e07PnrrrsUii+Pk/VrF5dzkPfW0KY/p2iXc5cpQiORqkp5l1DR+nA+cAa2Jcl4hEwYeb9/Dysh1MP20onx/bJ97lyDGI5GiQLOAJM0slCPeZ7v5ybMsSkWNVU+v8199XkpXRnptOHxLvcuQYRXI0yDJgcjPUIiJRUn6gmpufXsjK7aU88NXJdGirK0skOx1oKdIC/eqfa5mzYTc/vHA0X5iQFe9yJAr051akhVmSW8zjc7fwtRMGcuOp6v5oKdSyFmlBSiur+P6fl9Orczu+d97IeJcjUaSWtUgLsf9gDef/5j3yi/cz4+opdGmfFu+SJIrUshZpAaprarnmsfnkF+/n4auncK4O02txFNYiSa66ppZfvLKaj7bs5YqcAZwzWncnb4nUDSKSxIrKD3DVI/NZs7OMa04ayN0Xf+ZKENJCKKxFktTeioNc9ch8Nu+u4P6vTOILE3R7rpZMYS2SZNydp+dvY8a7G9lVeoBHr83h1OG6Hk9Lp7AWSSJF5Qe4a9ZyXl+5i0kDunLvpRM4ZVhmvMuSZqCwFkkCa3eW8fC7G/nbku0A/PDC0dwwbTBmuodia6GwFklQeyoO8udFecxanM/K7aWkp6Vy5QnZXHH8AMb2zYh3edLMFNYiCaKqppbl+SW8vbaQ2WsKWLm9hFqHiQO6csd5I/nq8dm6HVcrprAWiZPSyipW5JewMr+U+ZuL+GBjERUHa0gxOC67G7eeNZwLxmcxsk/neJcqCUBhLdIM3J1dpQdYu6uMxdv2snhbMXM27KamNrj39MAeHbjkuH6cNCSTk4b2oLta0FKPwlokyqpqasnfu5/FuUEor99VzuqdpRTvqwLADAZnduSGaYM5ZVgm4/p2oUendnGuWhKdwlrkGOUX72fuht2syC9hSW4xq3aUUlUTtJg7tWvDsF6dOH9cH0ZndWFYz06M759BZ11kSZpIYS3SRHsqDjJ7TQFvrtnF8vwScvfsB6Bj21TG9cvg+mmDGZoZhPKI3p1JTdHhdXLsFNYijXB3NhaW8/KyHfxtyXY27a4AICujPZOzu3LtSYOYNjyTEb06k6JglhhRWIschruTu2c/b67ZxXMfbmPdrnLM4KQhPbj8+AGcMLg7kwZ01Ukp0mwU1iKhyqoaPtqyh7fWFPCXxfnsDXcITuyfwc++NI6zR/Wib9f0OFcprZXCWlo1d2fdrnJeXbGDpz7YSlHFQdJSjXPH9uGUoZkcN7Aro/p0iXeZIgpraZ12llQyb1MRj8/dwpLcYgDOGtWLq08cSM6gbjpaQxKOwlpalby9+7j31TW8vGwHAP26pnP3xWP53Oje6uKQhKawllZhwZY93PevdXy0ZS8pBt88cyjnj8tidFYXHVonSUFhLS3a/E1FvLAwjxcX5pGV0Z6vnTCQr582mKwMtaIluTQa1mY2AHgS6A04MMPd7491YSLHomRfFT/+2wr+umQ7bVKMb5w2hNs+N5wObdU+keQUyZpbDdzu7ovMrDOw0MzecPdVMa5NpMlK9ldx76treHXFDsorq/mPc0Zww7TBdGynkJbk1uga7O47gB3h4zIzWw30AxTWkjBqa533N+7mzpeWs7O0kgvHZzH9tCGM66eL9EvL0KTmhpkNAiYD8w8zbjowHSA7OzsatYlEpLDsADc+uYClucVkd+/AizedxOTsbvEuSySqIg5rM+sEvAR8291L64939xnADICcnByPWoUiR7Asr5hvPruI3WUHueeS8Vw8qa+6PKRFimitNrM0gqB+xt3/HNuSRCJz/7/W85s315HZqR3PTT+RSQO6xrskkZiJ5GgQAx4FVrv7r2Nfkkjjnv9wG/f9ax1fmNiXey4ZpzMOpcWLpGV9CnA1sNzMloTD7nL3V2JWlUgD8ov38+O/rODNNQV0bteGu784VkEtrUIkR4PMAXSKl8RdVU0ttzy9kA0F5fzHOSOYftoQ2qelxrsskWahPTGSNB6cvYGleSU8eOVxXDghK97liDSrlHgXIBKJf67cyW/f2sAlk/spqKVVUlhLwlu0bS/ffHYR4/sFNwEQaY0U1pLQamudn/x1JZmd2vHE9VPppGOopZVSWEtCe3FhHsvzS7jz/FFkpOuoD2m9FNaSsMoqq/jl62uZMrAbX5zYN97liMSVwloSUlllFVc9Mp89FQf48UVjdBdxafXUASgJ6cHZG1mWV8JDX5vCRJ1GLqKWtSSe5XklPDZnM5ce14/zxvWJdzkiCUFhLQkld88+bn5mIT07t+NHF46JdzkiCUPdIJIwCsoq+cqMeZTsr+KpG6bSrWPbeJckkjAU1pIQKg5Uc8PjC9hTcZCZ3ziJ8f11hxeRutQNInG3ZXcF1/3xI1btKOV3V05WUIschlrWElczP8rl+7OWk5Zq/PryiZw9une8SxJJSApriQt359kPt/HDv6xg2rBMfnX5RHp1bh/vskQSlsJaml3unn3c8swilueXcOrwTP5wTY6uSy3SCIW1NKstuyu48g/zqDhYwy8vm8Alk/uRlqpdJyKNUVhLs3nqgy3c++oa2rZJ4dmvn8DYvtqRKBIphbXEnLvz6zfW8du3NnD6iJ7cffFYBvboGO+yRJKKwlpiqrbW+cnfVvLUvK1ckTOAey4dT2qKLsok0lQKa4mZg9W13P7CUv6+dDvfOH0Id543SlfPEzlKCmuJiX0Hq7nlmUW8vbaQO88fxU2nD413SSJJTWEtUbepsJzrHv+I3D37uPfS8Xxlana8SxJJegpriaqFW/dy4xMfkWLGMzeeyElDe8S7JJEWQWEtUVFVU8vD72zkgbc2kJXRnieum8qgTB3xIRItjYa1mT0GXAQUuPu42Jckyaa0sopbnl7EnA27+fzY3vzfSyfQXZc3FYmqSFrWjwO/A56MbSmSjDYUlHHz04vYvLuC/75sAl/OGRDvkkRapEbD2t3fNbNBzVCLJInqmloWbN3LG6t28dQHW+nUvg1PXj+Vk4dlxrs0kRZLfdYSscqqGp78YAuPzdnCztJKUlOMiyZk8cMLx9Czc7t4lyfSokUtrM1sOjAdIDtbh2q1JBUHqnl2/jb+8N4mCsoOMG1YJj/+whhOHZ5J5/Zp8S5PpFWIWli7+wxgBkBOTo5Ha74SP7vLD/D0vK08PncLxfuqOHloD+7/ymQdjicSB+oGkc9Yub2EJ+Zu4S9LtnOwupazR/Xim2cN47jsbvEuTaTViuTQveeAM4BMM8sDfuLuj8a6MGl+a3eWce+rq5m9tpD0tFQum9Kf608ZzLBeneJdmkirF8nRIF9tjkIkPtydd9YV8vA7m/hgUxGd27fhjvNGctXUgWR0UH+0SKJQN0grtb14P7MW5/Piwjw2766gX9d0bj9nBF87cSDddEKLSMJRWLcyJfuq+PafFvP2ukLcYerg7nzzzGF8cWJf2rbR7bVEEpXCuhXZWFjOr99Yx+y1hdx61jAum9Jfd2wRSRIK6xautLKKfyzbwQsLclm0rZjUFOOGaYO5/dyR8S5NRJpAYd0C1dY6H2wq4oUFuby2cieVVbUM79WJuy4YxZcm96NX5/bxLlFEmkhh3YLsrTjIsx9u49n528gv3k/n9m34t+P68+WcAUzsn6FbaokkMYV1C7B2Zxl/fH8zsxbnc6C6lpOH9uCO80by+bF9aJ+WGu/yRCQKFNZJqqbWeWtNAX98fzNzNxbRrk0Klx7Xj38/eTAj+3SOd3kiEmUK6yRTVlnFzAV5PDF3C9v27CMroz13nDeSrx6freOjRVowhXWSyC/ezx/nbOb5j3IpP1DNlIHdPu7qSEvV8dEiLZ3COsEt2LKHP87dwmsrdgJw4fgsbpg2mIkDusa3MBFpVgrrBLVo217ue2Md763fTUZ6GtedPIjrpg2mX9f0eJcmInGgsE4wy/KKuS88y7B7x7bcdcEorj5xEOltdVSHSGumsE4Q24r28fN/rOKfq3bRtUMad5w3kmtPGkTHdvqKRERhHXcl+6t45L1NPPzuJtqkGN/53AiunzZIt8sSkU9RWMdJVU0tj87ZzIOzN1BWWc0XJvblBxeMpk+GTgUXkc9SWDczd+fttYX8v9fWsGZnGWeP6sV3zhnBuH4Z8S5NRBKYwroZLdq2l3tfXcOHm/cwsEcHHvraFM4b1yfeZYlIElBYN4MFW/bwq3+u44NNRWR2asfPLh7LFcdn62L/IhIxhXUMrdtVxn1vrOPVFTvp1bkdd10wiqtOGKgjPESkyZQaMbBuVxkPvbORWYvz6di2DbedPZxvnD6EDm31cYvI0VF6REltrfPqip08OmcTi7YV065NCl8/dQg3nz5UF1gSkWOmsD5GFQeqmbU4n8fnbmFDQTmDMzvygwtGc+lx/ejRqV28yxORFkJhfRTcnRX5pby0KI+XFuZRdqCacf268NuvTuaC8VmkpuiOLCISXQrrCNXUOktyi5m9poDXVu5kQ0E5bVNTOH98H649eRCTB3TVbbNEJGYU1kewp+Ig764rZPbaAt5ZV0jxvipSU4wpA7txzyXjuXB8FhkddFq4iMReRGFtZucB9wOpwCPufm9Mq4qDmlpna1EFq3eUsWpHCR9sLGJxbjHu0KNjW84a1YuzRvXi1GE9FdAi0uwaDWszSwUeBM4B8oCPzOxv7r4q1sVFW22tU1RxkF2llewqrSS/eD+rd5Sxekcpa3eWsb+qBoDUFGNc3y7cdvZwzhzZi/H9MkhRP7SIxFEkLeupwAZ33wRgZs8DFwMNhvXu8gM88t4mANzB8fD/Tz8/xP3w4/3QDDj8uEPP+fi5U1XjVFbVsO9gDfurath/sIayyioKyg5QWHaA6lr/VK0Z6WmMzurMV6YOYHRWF8ZkdWFYr066K7iIJJRIwrofkFvneR5wQv2JzGw6MB2gbZ9h/Pwfq6NSYDDv8P9gOeH/YBjhv4+ft0k1OrRNJT0tlfS2bUhPS6FLehrDe3emd5d29O7S/uOfrIz29OrcTjsGRSThRW0Ho7vPAGYATD5uir/9k3PDAP1swH4cvh+HsB1+WoWoiAgQWVjnAwPqPO8fDmtQaoqRka6dcCIi0RLJZd8+Aoab2WAzawt8BfhbbMsSEZG6Gm1Zu3u1mX0LeJ3g0L3H3H1lzCsTEZGPRdRn7e6vAK/EuBYREWmArn4vIpIEFNYiIklAYS0ikgQU1iIiScDcvfGpmjpTszJgbZ1BGUBJhC9vyrSHZAK7mzD90Syjqa9pak1Hs4yjeU1z1NUaa2op393RvKYlfH9Hs4xo1zTS3Ts3ODa4Lkd0f4AF9Z7PaMJrI562oeXFaBlNek1Ta2pJdbXGmlrKd5eodbWGmhob31zdIH+P0bRH62iWobpiN/3RSLSaWsp3d7SvifUyWmtNH4tVN8gCd8+J+owTZHmRSMSaIDHrUk2RScSaIDHrSsaaGhsfq5b1jBjNN1GWF4lErAkSsy7VFJlErAkSs65krOmI42PSshYRkejSoXsiIklAYS0ikgSSKqzN7Etm5mY2Kt611Gdm5Y2Mf9vMmmWHh5n1N7O/mtl6M9toZveHl7dtaPpvm1mHZqjriJ9RPGidirgWrVMRitU6lVRhDXwVmBP+H7Hwpr+tggW31/kz8Bd3Hw6MADoBvzjCy74NxPwXK0FpnWqE1qkmi8061dQDx+P1Q7By5BOsKGvDYWcA7wL/IDhj8iEgJRxXDvwKWApMa4b6ysN6Xq4z7HfAv4eP3wZymqGOs4F36w3rAhQBHYH/AVYAy4Bbgf8DHASWA7Ob4TPqBLwJLAqXeXE4bhCwGvgDsBL4J5CudUrrlNap4CeZWtYXA6+5+zqgyMymhMOnEqwgY4ChwKXh8I7AfHef6O5zmr3a+BkLLKw7wN1LgW3AjQQr8CR3nwA84+4PANuBM939zGaorxK4xN2PA84EfmWf3GxzOPCgu48FioF/i3EtWqcio3UqcjFbp5IprL8KPB8+fp5PNjE+dPdN7l4DPAdMC4fXAC81b4kJ7wzgYXevBnD3PXGowYB7zGwZ8C+gH9A7HLfZ3ZeEjxcShEAsaZ06dmegdaqumK1TUbu7eSyZWXfgLGC8mTnB7cWcYLOi/oHih55Xhh9Mc6rm038A2zfz8gFWAZfVHWBmXYBsYEsc6qnvKqAnMMXdq8xsC598TgfqTFcDpMeqCK1TTaJ1KgKxXqeSpWV9GfCUuw9090HuPgDYDJwKTA1v5psCXEHQsR8vW4ExZtbOzLoS9PU1tzeBDmZ2DXy80+JXwOME99H8hpm1Ccd1D19TBjR8ta/oygAKwl+qM4GBzbTc+rRORU7rVGRiuk4lS1h/FZhVb9hL4fCPCHa6rCb4YOpPF3PhinrA3XOBmQQ7W2YCi5u7Fg/2WlwCfNnM1gPrCPr07gIeIehnXGZmS4Erw5fNAF4zs9mxquvQZwQ8A+SY2XLgGmBNrJbZCK1TEdI6FbGYrlNJfbq5mZ0BfNfdL4pzHROBP7j71HjWkciS5TPSOpU8kuUzitY6lSwt64RlZjcR7DD4YbxrSVT6jJpGn1fjWuNnlNQtaxGR1kIta4k6MxtgZrPNbJWZrTSz28Lh3c3sjfCU5TfMrFs4/CozW2Zmy81sbrh5e2hej5lZgZmtiNf7kcQQrfWqofkkOrWsJerMLAvIcvdFZtaZ4PjWLwH/Duxx93vN7E6gm7v/p5mdDKx2971mdj7wU3c/IZzXaQRneT3p7uPi8X4kMURrvWpoPu6+Ki5vLEJqWUvUufsOd18UPi4j2APej+DsrifCyZ4g+EXD3ee6+95w+Dygf515vQvE40QLSTDRWq+OMJ+EprCWmDKzQcBkYD7Q2913hKN28slZZnXdALzaPNVJsorWelVvPgktKc5glORkZp0IjjP9truXfnK5huDY3fAsr7rTn0nwSzUNkQZEa72qP5+YF36M1LKWmDCzNIJfhGfc/c/h4F1hf+Gh/seCOtNPIDjB4mJ3L2rueiU5RGu9amA+CU1hLVFnQVPnUYKdO7+uM+pvwLXh42uBv4bTZxNcL/nq8GplIp8RrfXqCPNJaDoaRKLOzKYB7xFcW7g2HHwXQb/gTIILAG0FLnf3PWb2CMGlK7eG01a7e044r+cIruyWCewCfuLujzbTW5EEEq31qqH5uPsrzfNOjo7CWkQkCagbREQkCSisRUSSgMJaRCQJKKxFRJKAwlpEJAkorCXpmVmNmS0Jr6C21MxuD2+fdKTXDDKzK480jUgiUVhLS7Df3Se5+1jgHOB84CeNvGYQn9yCSiTh6ThrSXpmVu7uneo8H0Jwz7tMgpunPgV0DEd/y93nmtk8YDTB/fCeAB4A7iU4Aacd8KC7P9xsb0KkEQprSXr1wzocVgyMJLjLdq27V5rZcOC58Cy2M6hzXzwzmw70cvefm1k74H3gy+6+uRnfikiDdNU9aenSgN+Z2SSgBhjRwHTnAhPM7LLweQYwnKDlLRJ3CmtpccJukBqCq6/9hOCaIhMJ9tFUNvQy4FZ3f71ZihRpIu1glBbFzHoCDwG/86CPLwPY4e61wNVAajhpGdC5zktfB24OL52JmY0ws46IJAi1rKUlSDezJQRdHtUEOxQPXfryf4GXzOwa4DWgIhy+DKgxs6XA48D9BEeILAovoVlIeHsokUSgHYwiIklA3SAiIklAYS0ikgQU1iIiSUBhLSKSBBTWIiJJQGEtIpIEFNYiIklAYS0ikgT+P9v+It5yPuE9AAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "url_mex = 'https://api.covid19api.com/country/mexico/status/confirmed/live'\n", "df_mex = pd.read_json(url_mex)\n", "df_mex.set_index('Date')['Cases'].plot(title=\"Casos de Covid19 en México\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparación" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Una vez que hemos visto diversos países por separados, es el momento de unir todos sus datos para poder compararlos y ver cómo han ido evolucionando sus casos confirmados de coronavirus a lo largo de un periodo temporal." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Para ello, **creamos una nueva variable por cada país que contenga solo los datos que hemos ido necesitando y trabajando anteriormente: fecha y número de casos confirmados**. " ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "casos_it = df_it.set_index('Date')['Cases']\n", "casos_es = df_es.set_index('Date')['Cases']\n", "casos_mex = df_mex.set_index('Date')['Cases']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Una vez definidas estas variables, empezamos a jugar con ellas y mostrándolas, esta vez haciendo uso de la concatenación de la librería de *Pandas*." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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CasesCasesCases
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2020-01-22 00:00:00+00:00000
2020-01-23 00:00:00+00:00000
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2022-04-17 00:00:00+00:0015712088116622145727668
2022-04-18 00:00:00+00:0015730676116622145727668
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2022-04-20 00:00:00+00:0015858442117368935730560
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" ], "text/plain": [ " Cases Cases Cases\n", "Date \n", "2020-01-22 00:00:00+00:00 0 0 0\n", "2020-01-23 00:00:00+00:00 0 0 0\n", "2020-01-24 00:00:00+00:00 0 0 0\n", "2020-01-25 00:00:00+00:00 0 0 0\n", "2020-01-26 00:00:00+00:00 0 0 0\n", "... ... ... ...\n", "2022-04-17 00:00:00+00:00 15712088 11662214 5727668\n", "2022-04-18 00:00:00+00:00 15730676 11662214 5727668\n", "2022-04-19 00:00:00+00:00 15758002 11736893 5729270\n", "2022-04-20 00:00:00+00:00 15858442 11736893 5730560\n", "2022-04-21 00:00:00+00:00 15934437 11736893 5731635\n", "\n", "[821 rows x 3 columns]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.concat([casos_it, casos_es, casos_mex],axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Para simplificar esta agrupación, creamos una nueva variable a partir de la concatenación anterior, a la que llamaremos 'vs' y la mostramos." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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CasesCasesCases
Date
2020-01-22 00:00:00+00:00000
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2020-01-24 00:00:00+00:00000
2020-01-25 00:00:00+00:00000
2020-01-26 00:00:00+00:00000
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2022-04-17 00:00:00+00:0015712088116622145727668
2022-04-18 00:00:00+00:0015730676116622145727668
2022-04-19 00:00:00+00:0015758002117368935729270
2022-04-20 00:00:00+00:0015858442117368935730560
2022-04-21 00:00:00+00:0015934437117368935731635
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821 rows × 3 columns

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" ], "text/plain": [ " Cases Cases Cases\n", "Date \n", "2020-01-22 00:00:00+00:00 0 0 0\n", "2020-01-23 00:00:00+00:00 0 0 0\n", "2020-01-24 00:00:00+00:00 0 0 0\n", "2020-01-25 00:00:00+00:00 0 0 0\n", "2020-01-26 00:00:00+00:00 0 0 0\n", "... ... ... ...\n", "2022-04-17 00:00:00+00:00 15712088 11662214 5727668\n", "2022-04-18 00:00:00+00:00 15730676 11662214 5727668\n", "2022-04-19 00:00:00+00:00 15758002 11736893 5729270\n", "2022-04-20 00:00:00+00:00 15858442 11736893 5730560\n", "2022-04-21 00:00:00+00:00 15934437 11736893 5731635\n", "\n", "[821 rows x 3 columns]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vs = pd.concat([casos_it, casos_es, casos_mex],axis=1)\n", "vs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ahora, damos nombre a las columnas para organizar bien los datos y el gráfico. Una vez hecho esto, mostramos el gráfico con *plot* y elegimos el tipo de gráfico, que en este caso, será de área." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "vs.columns = ['Italia', 'España', 'Mexico']\n", "vs.plot(title=\"Italia vs España vs Mexico\",kind='area')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Expportamos nuestra nueva variable, que hemos limpiado para que contenga únicamente los datos necesarios, y la exportamos a un archivo separado por comas (.csv), con la función *to_csv*." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "vs.to_csv('esvsit.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Verificamos que se haya guardado ejecutando el comando bash *ls*." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "api-pandas-folium.ipynb pruebas-r.ipynb\t\t Shared_Resources\r\n", "esvsit.csv\t\t python-api-covid19-pandas.ipynb Untitled1.ipynb\r\n", "esvsit.png\t\t python-pruebas.ipynb\t\t Untitled.ipynb\r\n" ] } ], "source": [ "!ls" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Para guardar el gráfico como imagen PNG, tenemos que importar la librería *pyplot* de *matplotlib*, que ya se encuentra instalada. Generamos el gráfico de nuevo y con plt.savefig('nombre-grafico.png') se crea la imagen." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "vs.plot()\n", "plt.savefig('esvsit.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Volvemos a verificar que se ha exportado correctamente y que el gráfico existe como imagen PNG con el nombre que le hemos dado." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "api-pandas-folium.ipynb pruebas-r.ipynb\t\t Shared_Resources\r\n", "esvsit.csv\t\t python-api-covid19-pandas.ipynb Untitled1.ipynb\r\n", "esvsit.png\t\t python-pruebas.ipynb\t\t Untitled.ipynb\r\n" ] } ], "source": [ "!ls" ] } ], "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 4 }