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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# API Covid19 con Pandas"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"La URL https://api.covid19api.com/countries\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Instalacion librerias\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A través de `!pip` instalamos la libreria de pandas, una bilbioteca de software escrito que sirve para manipular y analizar datos en Python"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (1.3.1)\r\n",
"Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (1.21.1)\r\n",
"Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2.8.1)\r\n",
"Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2020.4)\r\n",
"Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7.3->pandas) (1.14.0)\r\n"
]
}
],
"source": [
"!pip install pandas"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configuracion de Pandas"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Configuramos panda como pd cuando importamos la libreria"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Crear variable\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Asignamos a la variable URL, dirección de la que vamos a extraer los datos"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"url = \"https://api.covid19api.com/countries\"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Crear el dataframe\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Creamos un dataframe con el que vamos a visualizar la información del lugar del que extraemos los datos\n",
"\n",
"Decimos que el dataframe sea igual a la lectura que va a hacer pandas (que previamente lo hemos importado como pd), que \n",
"lea los datos de la url con JSON (Java Script Object Notation)\n",
"\n",
"Y decimos que muestre el dataframe poniendo df"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_json(url)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Country</th>\n",
" <th>Slug</th>\n",
" <th>ISO2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>French Polynesia</td>\n",
" <td>french-polynesia</td>\n",
" <td>PF</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Indonesia</td>\n",
" <td>indonesia</td>\n",
" <td>ID</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Malta</td>\n",
" <td>malta</td>\n",
" <td>MT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Sao Tome and Principe</td>\n",
" <td>sao-tome-and-principe</td>\n",
" <td>ST</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Haiti</td>\n",
" <td>haiti</td>\n",
" <td>HT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>243</th>\n",
" <td>Myanmar</td>\n",
" <td>myanmar</td>\n",
" <td>MM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>244</th>\n",
" <td>Turkey</td>\n",
" <td>turkey</td>\n",
" <td>TR</td>\n",
" </tr>\n",
" <tr>\n",
" <th>245</th>\n",
" <td>Western Sahara</td>\n",
" <td>western-sahara</td>\n",
" <td>EH</td>\n",
" </tr>\n",
" <tr>\n",
" <th>246</th>\n",
" <td>Pitcairn</td>\n",
" <td>pitcairn</td>\n",
" <td>PN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>247</th>\n",
" <td>Tanzania, United Republic of</td>\n",
" <td>tanzania</td>\n",
" <td>TZ</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>248 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" Country Slug ISO2\n",
"0 French Polynesia french-polynesia PF\n",
"1 Indonesia indonesia ID\n",
"2 Malta malta MT\n",
"3 Sao Tome and Principe sao-tome-and-principe ST\n",
"4 Haiti haiti HT\n",
".. ... ... ...\n",
"243 Myanmar myanmar MM\n",
"244 Turkey turkey TR\n",
"245 Western Sahara western-sahara EH\n",
"246 Pitcairn pitcairn PN\n",
"247 Tanzania, United Republic of tanzania TZ\n",
"\n",
"[248 rows x 3 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Por defecto al haber tantos datos, aparecen los cinco primeros y los cinco últimos aunque se puede cambiar utilizando \n",
"`df.head`o `df.tail` junto con el número de resultado que queremos que salgan en parentesis (n)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exploración de la tabla"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Country</th>\n",
" <th>Slug</th>\n",
" <th>ISO2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>French Polynesia</td>\n",
" <td>french-polynesia</td>\n",
" <td>PF</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Indonesia</td>\n",
" <td>indonesia</td>\n",
" <td>ID</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Malta</td>\n",
" <td>malta</td>\n",
" <td>MT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Sao Tome and Principe</td>\n",
" <td>sao-tome-and-principe</td>\n",
" <td>ST</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Haiti</td>\n",
" <td>haiti</td>\n",
" <td>HT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Poland</td>\n",
" <td>poland</td>\n",
" <td>PL</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Country Slug ISO2\n",
"0 French Polynesia french-polynesia PF\n",
"1 Indonesia indonesia ID\n",
"2 Malta malta MT\n",
"3 Sao Tome and Principe sao-tome-and-principe ST\n",
"4 Haiti haiti HT\n",
"5 Poland poland PL"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(6)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Country</th>\n",
" <th>Slug</th>\n",
" <th>ISO2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>246</th>\n",
" <td>Pitcairn</td>\n",
" <td>pitcairn</td>\n",
" <td>PN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>247</th>\n",
" <td>Tanzania, United Republic of</td>\n",
" <td>tanzania</td>\n",
" <td>TZ</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Country Slug ISO2\n",
"246 Pitcairn pitcairn PN\n",
"247 Tanzania, United Republic of tanzania TZ"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.tail(2)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Para mostrar los datos del dataframe usamos `df.info()` O para buscar en este caso el nombre en concreto de nuestra lista `df[\"Country\"][\"N\"]`"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\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": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Somalia'"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[\"Country\"][66]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tiempo real \n",
"https://api.covid19api.com/country/spain/status/confirmed/live"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ahora aplicamos lo visto en un país específico\n",
"\n",
"Creamos la variable España tanto de los datos como del dataframe (url_es y df_es)\n",
"\n",
"Igualamos la url a la dirección de la que queremos extraer los datos y el data frame a la lectura que queremos que emplee pandas (previamente importado como pd)\n",
"de la dirección de la que extraemos los datos con JSON\n",
"\n",
"Ponemos df_es para que se muestre la visualizacion de los datos"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Country</th>\n",
" <th>CountryCode</th>\n",
" <th>Province</th>\n",
" <th>City</th>\n",
" <th>CityCode</th>\n",
" <th>Lat</th>\n",
" <th>Lon</th>\n",
" <th>Cases</th>\n",
" <th>Status</th>\n",
" <th>Date</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Spain</td>\n",
" <td>ES</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" <td>40.46</td>\n",
" <td>-3.75</td>\n",
" <td>0</td>\n",
" <td>confirmed</td>\n",
" <td>2020-01-22 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Spain</td>\n",
" <td>ES</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" <td>40.46</td>\n",
" <td>-3.75</td>\n",
" <td>0</td>\n",
" <td>confirmed</td>\n",
" <td>2020-01-23 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Spain</td>\n",
" <td>ES</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" <td>40.46</td>\n",
" <td>-3.75</td>\n",
" <td>0</td>\n",
" <td>confirmed</td>\n",
" <td>2020-01-24 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Spain</td>\n",
" <td>ES</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" <td>40.46</td>\n",
" <td>-3.75</td>\n",
" <td>0</td>\n",
" <td>confirmed</td>\n",
" <td>2020-01-25 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Spain</td>\n",
" <td>ES</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" <td>40.46</td>\n",
" <td>-3.75</td>\n",
" <td>0</td>\n",
" <td>confirmed</td>\n",
" <td>2020-01-26 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>798</th>\n",
" <td>Spain</td>\n",
" <td>ES</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" <td>40.46</td>\n",
" <td>-3.75</td>\n",
" <td>11508309</td>\n",
" <td>confirmed</td>\n",
" <td>2022-03-30 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>799</th>\n",
" <td>Spain</td>\n",
" <td>ES</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" <td>40.46</td>\n",
" <td>-3.75</td>\n",
" <td>11508309</td>\n",
" <td>confirmed</td>\n",
" <td>2022-03-31 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>800</th>\n",
" <td>Spain</td>\n",
" <td>ES</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" <td>40.46</td>\n",
" <td>-3.75</td>\n",
" <td>11551574</td>\n",
" <td>confirmed</td>\n",
" <td>2022-04-01 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>801</th>\n",
" <td>Spain</td>\n",
" <td>ES</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" <td>40.46</td>\n",
" <td>-3.75</td>\n",
" <td>11551574</td>\n",
" <td>confirmed</td>\n",
" <td>2022-04-02 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>802</th>\n",
" <td>Spain</td>\n",
" <td>ES</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td></td>\n",
" <td>40.46</td>\n",
" <td>-3.75</td>\n",
" <td>11551574</td>\n",
" <td>confirmed</td>\n",
" <td>2022-04-03 00:00:00+00:00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>803 rows × 10 columns</p>\n",
"</div>"
],
"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",
"798 Spain ES 40.46 -3.75 11508309 \n",
"799 Spain ES 40.46 -3.75 11508309 \n",
"800 Spain ES 40.46 -3.75 11551574 \n",
"801 Spain ES 40.46 -3.75 11551574 \n",
"802 Spain ES 40.46 -3.75 11551574 \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",
"798 confirmed 2022-03-30 00:00:00+00:00 \n",
"799 confirmed 2022-03-31 00:00:00+00:00 \n",
"800 confirmed 2022-04-01 00:00:00+00:00 \n",
"801 confirmed 2022-04-02 00:00:00+00:00 \n",
"802 confirmed 2022-04-03 00:00:00+00:00 \n",
"\n",
"[803 rows x 10 columns]"
]
},
"execution_count": 17,
"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": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Indagamos en la visualización de los datos buscando que tipo de datos se muestran o el df_es seguido de .columns"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Country', 'CountryCode', 'Province', 'City', 'CityCode', 'Lat', 'Lon',\n",
" 'Cases', 'Status', 'Date'],\n",
" dtype='object')"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_es.columns\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 2020-01-22 00:00:00+00:00\n",
"Name: Date, dtype: datetime64[ns, UTC]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_es[\"Date\"].head(1)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"802 2022-04-03 00:00:00+00:00\n",
"Name: Date, dtype: datetime64[ns, UTC]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_es[\"Date\"].tail(1)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 803 entries, 0 to 802\n",
"Data columns (total 10 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Country 803 non-null object \n",
" 1 CountryCode 803 non-null object \n",
" 2 Province 803 non-null object \n",
" 3 City 803 non-null object \n",
" 4 CityCode 803 non-null object \n",
" 5 Lat 803 non-null float64 \n",
" 6 Lon 803 non-null float64 \n",
" 7 Cases 803 non-null int64 \n",
" 8 Status 803 non-null object \n",
" 9 Date 803 non-null datetime64[ns, UTC]\n",
"dtypes: datetime64[ns, UTC](1), float64(2), int64(1), object(6)\n",
"memory usage: 62.9+ KB\n"
]
}
],
"source": [
"df_es.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Le pedimos que el dataframe haga una descripción de los datos "
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Lat</th>\n",
" <th>Lon</th>\n",
" <th>Cases</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>8.030000e+02</td>\n",
" <td>803.00</td>\n",
" <td>8.030000e+02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>4.046000e+01</td>\n",
" <td>-3.75</td>\n",
" <td>3.242248e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>7.109856e-15</td>\n",
" <td>0.00</td>\n",
" <td>3.109442e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>4.046000e+01</td>\n",
" <td>-3.75</td>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>4.046000e+01</td>\n",
" <td>-3.75</td>\n",
" <td>3.186710e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>4.046000e+01</td>\n",
" <td>-3.75</td>\n",
" <td>3.149012e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>4.046000e+01</td>\n",
" <td>-3.75</td>\n",
" <td>4.920388e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>4.046000e+01</td>\n",
" <td>-3.75</td>\n",
" <td>1.155157e+07</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Lat Lon Cases\n",
"count 8.030000e+02 803.00 8.030000e+02\n",
"mean 4.046000e+01 -3.75 3.242248e+06\n",
"std 7.109856e-15 0.00 3.109442e+06\n",
"min 4.046000e+01 -3.75 0.000000e+00\n",
"25% 4.046000e+01 -3.75 3.186710e+05\n",
"50% 4.046000e+01 -3.75 3.149012e+06\n",
"75% 4.046000e+01 -3.75 4.920388e+06\n",
"max 4.046000e+01 -3.75 1.155157e+07"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_es.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"En este caso decimos que se muestren los datos haciendo un índice de los mismos clasificados por la fecha"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Country</th>\n",
" <th>CountryCode</th>\n",
" <th>Province</th>\n",
" <th>City</th>\n",
" <th>CityCode</th>\n",
" <th>Lat</th>\n",
" <th>Lon</th>\n",
" <th>Cases</th>\n",
" <th>Status</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",