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<body>
<section id="uso-de-la-api-del-covid19-con-pandas" class="cell markdown">
<h1>Uso de la API del Covid19 con Pandas</h1>
</section>
<div class="cell markdown">
<p>La URL <a href="https://api.covid19api.com/countries" class="uri">https://api.covid19api.com/countries</a></p>
</div>
<section id="instalacion-librerias" class="cell markdown">
<h2>Instalacion librerias</h2>
</section>
<div class="cell markdown">
<p>Instalamos con <code>!pip</code> la libreria de pandas, es una libreria de software diseñada para manipular y analizar estructuras de datos en Python</p>
</div>
<div class="cell code" data-execution_count="2">
<div class="sourceCode" id="cb1"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="op">!</span>pip install pandas</span></code></pre></div>
<div class="output stream stdout">
<pre><code>Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (1.3.1)
Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (1.21.1)
Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2.8.1)
Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2020.4)
Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7.3->pandas) (1.14.0)
</code></pre>
</div>
</div>
<section id="configuracion-de-pandas" class="cell markdown">
<h2>Configuracion de Pandas</h2>
</section>
<div class="cell markdown">
<p>Configuramos panda como pd para importar la libreria</p>
</div>
<div class="cell code" data-execution_count="3">
<div class="sourceCode" id="cb3"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span></code></pre></div>
</div>
<section id="crear-variable" class="cell markdown">
<h2>Crear variable</h2>
</section>
<div class="cell markdown">
<p>Asignamos a la variable URL la dirección de la que queremos extraer los datos</p>
</div>
<div class="cell code" data-execution_count="4">
<div class="sourceCode" id="cb4"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>url <span class="op">=</span> <span class="st">"https://api.covid19api.com/countries"</span></span></code></pre></div>
</div>
<section id="crear-el-dataframe" class="cell markdown">
<h2>Crear el dataframe</h2>
</section>
<div class="cell markdown">
<p>Creamos el dataframe y así podremos visualizar la información del lugar del cual extraemos los datos</p>
<p>Decimos que el dataframe sea igual a la lectura que va a hacer pandas, previamente importado como pd, que lea los datos de la url con JSON (Java Script Object Notation)</p>
<p>poniendo df le pedimos que nos muestre el dataframe</p>
</div>
<div class="cell code" data-execution_count="6">
<div class="sourceCode" id="cb5"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>df <span class="op">=</span> pd.read_json(url)</span></code></pre></div>
</div>
<div class="cell code" data-execution_count="7">
<div class="sourceCode" id="cb6"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>df</span></code></pre></div>
<div class="output execute_result" data-execution_count="7">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
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.dataframe tbody tr th {
vertical-align: top;
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.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Country</th>
<th>Slug</th>
<th>ISO2</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>French Polynesia</td>
<td>french-polynesia</td>
<td>PF</td>
</tr>
<tr>
<th>1</th>
<td>Indonesia</td>
<td>indonesia</td>
<td>ID</td>
</tr>
<tr>
<th>2</th>
<td>Malta</td>
<td>malta</td>
<td>MT</td>
</tr>
<tr>
<th>3</th>
<td>Sao Tome and Principe</td>
<td>sao-tome-and-principe</td>
<td>ST</td>
</tr>
<tr>
<th>4</th>
<td>Haiti</td>
<td>haiti</td>
<td>HT</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>243</th>
<td>Myanmar</td>
<td>myanmar</td>
<td>MM</td>
</tr>
<tr>
<th>244</th>
<td>Turkey</td>
<td>turkey</td>
<td>TR</td>
</tr>
<tr>
<th>245</th>
<td>Western Sahara</td>
<td>western-sahara</td>
<td>EH</td>
</tr>
<tr>
<th>246</th>
<td>Pitcairn</td>
<td>pitcairn</td>
<td>PN</td>
</tr>
<tr>
<th>247</th>
<td>Tanzania, United Republic of</td>
<td>tanzania</td>
<td>TZ</td>
</tr>
</tbody>
</table>
<p>248 rows × 3 columns</p>
</div>
</div>
</div>
<div class="cell markdown">
<p>Nos saldran solo los cinco primeros datos y los cinco últimos porque hay demasiados datos, pero se puede cambiar utilizando <code>df.head</code>o <code>df.tail</code> seguido de el numero de resultado que queremos que salgan en parentesis (n)</p>
</div>
<section id="exploración-de-la-tabla" class="cell markdown">
<h2>Exploración de la tabla</h2>
</section>
<div class="cell code" data-execution_count="9">
<div class="sourceCode" id="cb7"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>df.head(<span class="dv">6</span>)</span></code></pre></div>
<div class="output execute_result" data-execution_count="9">
<div>
<style scoped>
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Country</th>
<th>Slug</th>
<th>ISO2</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>French Polynesia</td>
<td>french-polynesia</td>
<td>PF</td>
</tr>
<tr>
<th>1</th>
<td>Indonesia</td>
<td>indonesia</td>
<td>ID</td>
</tr>
<tr>
<th>2</th>
<td>Malta</td>
<td>malta</td>
<td>MT</td>
</tr>
<tr>
<th>3</th>
<td>Sao Tome and Principe</td>
<td>sao-tome-and-principe</td>
<td>ST</td>
</tr>
<tr>
<th>4</th>
<td>Haiti</td>
<td>haiti</td>
<td>HT</td>
</tr>
<tr>
<th>5</th>
<td>Poland</td>
<td>poland</td>
<td>PL</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="cell code" data-execution_count="12">
<div class="sourceCode" id="cb8"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>df.tail(<span class="dv">2</span>)</span></code></pre></div>
<div class="output execute_result" data-execution_count="12">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Country</th>
<th>Slug</th>
<th>ISO2</th>
</tr>
</thead>
<tbody>
<tr>
<th>246</th>
<td>Pitcairn</td>
<td>pitcairn</td>
<td>PN</td>
</tr>
<tr>
<th>247</th>
<td>Tanzania, United Republic of</td>
<td>tanzania</td>
<td>TZ</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="cell markdown">
<p>Queremos mostrar la información del dataframe asi que utilizamos <code>df.info()</code> O para buscar en el nombre en concreto de la lista <code>df["Country"]["N"]</code></p>
</div>
<div class="cell code" data-execution_count="13" data-scrolled="true">
<div class="sourceCode" id="cb9"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>df.info()</span></code></pre></div>
<div class="output stream stdout">
<pre><code><class 'pandas.core.frame.DataFrame'>
RangeIndex: 248 entries, 0 to 247
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Country 248 non-null object
1 Slug 248 non-null object
2 ISO2 248 non-null object
dtypes: object(3)
memory usage: 5.9+ KB
</code></pre>
</div>
</div>
<div class="cell code" data-execution_count="16">
<div class="sourceCode" id="cb11"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>df[<span class="st">"Country"</span>][<span class="dv">66</span>]</span></code></pre></div>
<div class="output execute_result" data-execution_count="16">
<pre><code>'Somalia'</code></pre>
</div>
</div>
<section id="tiempo-real" class="cell markdown">
<h2>Tiempo real</h2>
<p><a href="https://api.covid19api.com/country/spain/status/confirmed/live" class="uri">https://api.covid19api.com/country/spain/status/confirmed/live</a></p>
</section>
<div class="cell markdown">
<p>Repetimos el ejercicio on un país en especifico</p>
<p>Elijo España, creamos su variable, tanto de los datos como del dataframe (url_es y df_es)</p>
<p>Igualamos la url a la dirección de la que queremos extraer los datos y el data frame a la lectura que queremos que haga pandas (previamente importado como pd) de la dirección de la que queremos extraer los datos gracias a JSON</p>
<p>Ponemos df_es y asi tenemos la visualizacion de los datos</p>
</div>
<div class="cell code" data-execution_count="17">
<div class="sourceCode" id="cb13"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>url_es <span class="op">=</span> <span class="st">"https://api.covid19api.com/country/spain/status/confirmed/live"</span></span>
<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a>df_es <span class="op">=</span> pd.read_json(url_es)</span>
<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a>df_es</span></code></pre></div>
<div class="output execute_result" data-execution_count="17">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Country</th>
<th>CountryCode</th>
<th>Province</th>
<th>City</th>
<th>CityCode</th>
<th>Lat</th>
<th>Lon</th>
<th>Cases</th>
<th>Status</th>
<th>Date</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>Spain</td>
<td>ES</td>
<td></td>
<td></td>
<td></td>
<td>40.46</td>
<td>-3.75</td>
<td>0</td>
<td>confirmed</td>
<td>2020-01-22 00:00:00+00:00</td>
</tr>
<tr>
<th>1</th>
<td>Spain</td>
<td>ES</td>
<td></td>
<td></td>
<td></td>
<td>40.46</td>
<td>-3.75</td>
<td>0</td>
<td>confirmed</td>
<td>2020-01-23 00:00:00+00:00</td>
</tr>
<tr>
<th>2</th>
<td>Spain</td>
<td>ES</td>
<td></td>
<td></td>
<td></td>
<td>40.46</td>
<td>-3.75</td>
<td>0</td>
<td>confirmed</td>
<td>2020-01-24 00:00:00+00:00</td>
</tr>
<tr>
<th>3</th>
<td>Spain</td>
<td>ES</td>
<td></td>
<td></td>
<td></td>
<td>40.46</td>
<td>-3.75</td>
<td>0</td>
<td>confirmed</td>
<td>2020-01-25 00:00:00+00:00</td>
</tr>
<tr>
<th>4</th>
<td>Spain</td>
<td>ES</td>
<td></td>
<td></td>
<td></td>
<td>40.46</td>
<td>-3.75</td>
<td>0</td>
<td>confirmed</td>
<td>2020-01-26 00:00:00+00:00</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>798</th>
<td>Spain</td>
<td>ES</td>
<td></td>
<td></td>
<td></td>
<td>40.46</td>
<td>-3.75</td>
<td>11508309</td>
<td>confirmed</td>
<td>2022-03-30 00:00:00+00:00</td>
</tr>
<tr>
<th>799</th>
<td>Spain</td>
<td>ES</td>
<td></td>
<td></td>
<td></td>
<td>40.46</td>
<td>-3.75</td>
<td>11508309</td>
<td>confirmed</td>
<td>2022-03-31 00:00:00+00:00</td>
</tr>
<tr>
<th>800</th>
<td>Spain</td>
<td>ES</td>
<td></td>
<td></td>
<td></td>
<td>40.46</td>
<td>-3.75</td>
<td>11551574</td>
<td>confirmed</td>
<td>2022-04-01 00:00:00+00:00</td>
</tr>
<tr>
<th>801</th>
<td>Spain</td>
<td>ES</td>
<td></td>
<td></td>
<td></td>
<td>40.46</td>
<td>-3.75</td>
<td>11551574</td>
<td>confirmed</td>
<td>2022-04-02 00:00:00+00:00</td>
</tr>
<tr>
<th>802</th>
<td>Spain</td>
<td>ES</td>
<td></td>
<td></td>
<td></td>
<td>40.46</td>
<td>-3.75</td>
<td>11551574</td>
<td>confirmed</td>
<td>2022-04-03 00:00:00+00:00</td>
</tr>
</tbody>
</table>
<p>803 rows × 10 columns</p>
</div>
</div>
</div>
<div class="cell markdown" data-slideshow="{"slide_type":"slide"}">
<p>Investigamos que tipo de datos nos muestra la propia tabla de visualización de los datos poniendo el df_es seguido de .columns</p>
</div>
<div class="cell code" data-execution_count="18">
<div class="sourceCode" id="cb14"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a>df_es.columns</span></code></pre></div>
<div class="output execute_result" data-execution_count="18">
<pre><code>Index(['Country', 'CountryCode', 'Province', 'City', 'CityCode', 'Lat', 'Lon',
'Cases', 'Status', 'Date'],
dtype='object')</code></pre>
</div>
</div>
<div class="cell code" data-execution_count="19">
<div class="sourceCode" id="cb16"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a>df_es[<span class="st">"Date"</span>].head(<span class="dv">1</span>)</span></code></pre></div>
<div class="output execute_result" data-execution_count="19">
<pre><code>0 2020-01-22 00:00:00+00:00
Name: Date, dtype: datetime64[ns, UTC]</code></pre>
</div>
</div>
<div class="cell code" data-execution_count="20">
<div class="sourceCode" id="cb18"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>df_es[<span class="st">"Date"</span>].tail(<span class="dv">1</span>)</span></code></pre></div>
<div class="output execute_result" data-execution_count="20">
<pre><code>802 2022-04-03 00:00:00+00:00
Name: Date, dtype: datetime64[ns, UTC]</code></pre>
</div>
</div>
<div class="cell code" data-execution_count="21">
<div class="sourceCode" id="cb20"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a>df_es.info()</span></code></pre></div>
<div class="output stream stdout">
<pre><code><class 'pandas.core.frame.DataFrame'>
RangeIndex: 803 entries, 0 to 802
Data columns (total 10 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Country 803 non-null object
1 CountryCode 803 non-null object
2 Province 803 non-null object
3 City 803 non-null object
4 CityCode 803 non-null object
5 Lat 803 non-null float64
6 Lon 803 non-null float64
7 Cases 803 non-null int64
8 Status 803 non-null object
9 Date 803 non-null datetime64[ns, UTC]
dtypes: datetime64[ns, UTC](1), float64(2), int64(1), object(6)
memory usage: 62.9+ KB
</code></pre>
</div>
</div>
<div class="cell markdown">
<p>Pedimos que el dataframe haga una descripción de los datos</p>
</div>
<div class="cell code" data-execution_count="22">
<div class="sourceCode" id="cb22"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a>df_es.describe()</span></code></pre></div>
<div class="output execute_result" data-execution_count="22">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Lat</th>
<th>Lon</th>
<th>Cases</th>
</tr>
</thead>
<tbody>
<tr>
<th>count</th>
<td>8.030000e+02</td>
<td>803.00</td>
<td>8.030000e+02</td>
</tr>
<tr>
<th>mean</th>
<td>4.046000e+01</td>
<td>-3.75</td>
<td>3.242248e+06</td>
</tr>
<tr>
<th>std</th>
<td>7.109856e-15</td>
<td>0.00</td>
<td>3.109442e+06</td>
</tr>
<tr>
<th>min</th>
<td>4.046000e+01</td>
<td>-3.75</td>
<td>0.000000e+00</td>
</tr>
<tr>
<th>25%</th>
<td>4.046000e+01</td>
<td>-3.75</td>
<td>3.186710e+05</td>
</tr>
<tr>
<th>50%</th>
<td>4.046000e+01</td>
<td>-3.75</td>
<td>3.149012e+06</td>
</tr>
<tr>
<th>75%</th>
<td>4.046000e+01</td>
<td>-3.75</td>
<td>4.920388e+06</td>
</tr>
<tr>
<th>max</th>
<td>4.046000e+01</td>
<td>-3.75</td>
<td>1.155157e+07</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="cell markdown">
<p>Ahora le pedimos que nos muestre los datos en un índice con los datos clasificados por la fecha</p>
</div>
<div class="cell code" data-execution_count="23">
<div class="sourceCode" id="cb23"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a>df_es.set_index(<span class="st">"Date"</span>)</span></code></pre></div>
<div class="output execute_result" data-execution_count="23">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Country</th>
<th>CountryCode</th>
<th>Province</th>
<th>City</th>
<th>CityCode</th>
<th>Lat</th>
<th>Lon</th>
<th>Cases</th>
<th>Status</th>
</tr>
<tr>
<th>Date</th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>2020-01-22 00:00:00+00:00</th>
<td>Spain</td>
<td>ES</td>
<td></td>
<td></td>
<td></td>
<td>40.46</td>
<td>-3.75</td>
<td>0</td>
<td>confirmed</td>
</tr>
<tr>
<th>2020-01-23 00:00:00+00:00</th>
<td>Spain</td>
<td>ES</td>
<td></td>
<td></td>
<td></td>
<td>40.46</td>
<td>-3.75</td>
<td>0</td>
<td>confirmed</td>
</tr>
<tr>
<th>2020-01-24 00:00:00+00:00</th>
<td>Spain</td>
<td>ES</td>
<td></td>
<td></td>
<td></td>
<td>40.46</td>
<td>-3.75</td>
<td>0</td>
<td>confirmed</td>
</tr>
<tr>
<th>2020-01-25 00:00:00+00:00</th>
<td>Spain</td>
<td>ES</td>
<td></td>
<td></td>
<td></td>
<td>40.46</td>
<td>-3.75</td>
<td>0</td>
<td>confirmed</td>
</tr>
<tr>
<th>2020-01-26 00:00:00+00:00</th>
<td>Spain</td>
<td>ES</td>
<td></td>
<td></td>
<td></td>
<td>40.46</td>
<td>-3.75</td>
<td>0</td>
<td>confirmed</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>2022-03-30 00:00:00+00:00</th>
<td>Spain</td>
<td>ES</td>
<td></td>
<td></td>
<td></td>
<td>40.46</td>
<td>-3.75</td>
<td>11508309</td>
<td>confirmed</td>
</tr>
<tr>
<th>2022-03-31 00:00:00+00:00</th>
<td>Spain</td>
<td>ES</td>
<td></td>
<td></td>
<td></td>
<td>40.46</td>
<td>-3.75</td>
<td>11508309</td>
<td>confirmed</td>
</tr>
<tr>
<th>2022-04-01 00:00:00+00:00</th>
<td>Spain</td>
<td>ES</td>
<td></td>
<td></td>
<td></td>
<td>40.46</td>
<td>-3.75</td>
<td>11551574</td>
<td>confirmed</td>
</tr>
<tr>
<th>2022-04-02 00:00:00+00:00</th>
<td>Spain</td>
<td>ES</td>
<td></td>
<td></td>
<td></td>
<td>40.46</td>
<td>-3.75</td>
<td>11551574</td>