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model-mexico
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updated march 25

master
Gerardo Marx Chávez-Campos 5 years ago
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```python
# Reading data
import os
import git
import shutil
import tempfile
# Create temporary dir
t = tempfile.mkdtemp()
d = 'lwc/topics/covid19/covid-model'
# Clone into temporary dir
git.Repo.clone_from('http://gmarx.jumpingcrab.com:8088/COVID-19/covid19-data.git',
t, branch='master', depth=1)
# Delete files
#os.remove('README.txt')
shutil.rmtree('data')
#shutil.rmtree('secondTest')
# Copy desired file from temporary dir
shutil.move(os.path.join(t, 'data'), '.')
# Remove temporary dir
shutil.rmtree(t)
```
```python
import pandas as pd
import numpy as np
import os
def loadData(path, file):
csvPath=os.path.join(path, file)
return pd.read_csv(csvPath)
```
```python
# import jtplot submodule from jupyterthemes
from jupyterthemes import jtplot
PATH=os.path.join("data")
covid_data=loadData(PATH,"time-series-19-covid-combined.csv")
covid_data.head()
```
<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>Date</th>
<th>Country/Region</th>
<th>Province/State</th>
<th>Lat</th>
<th>Long</th>
<th>Confirmed</th>
<th>Recovered</th>
<th>Deaths</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>2020-01-22</td>
<td>Afghanistan</td>
<td>NaN</td>
<td>33.0</td>
<td>65.0</td>
<td>0</td>
<td>0.0</td>
<td>0</td>
</tr>
<tr>
<th>1</th>
<td>2020-01-23</td>
<td>Afghanistan</td>
<td>NaN</td>
<td>33.0</td>
<td>65.0</td>
<td>0</td>
<td>0.0</td>
<td>0</td>
</tr>
<tr>
<th>2</th>
<td>2020-01-24</td>
<td>Afghanistan</td>
<td>NaN</td>
<td>33.0</td>
<td>65.0</td>
<td>0</td>
<td>0.0</td>
<td>0</td>
</tr>
<tr>
<th>3</th>
<td>2020-01-25</td>
<td>Afghanistan</td>
<td>NaN</td>
<td>33.0</td>
<td>65.0</td>
<td>0</td>
<td>0.0</td>
<td>0</td>
</tr>
<tr>
<th>4</th>
<td>2020-01-26</td>
<td>Afghanistan</td>
<td>NaN</td>
<td>33.0</td>
<td>65.0</td>
<td>0</td>
<td>0.0</td>
<td>0</td>
</tr>
</tbody>
</table>
</div>
```python
from sklearn.model_selection import train_test_split
train_set, test_set=train_test_split(covid_data,test_size=0.2,random_state=42)
train_cp=train_set.copy()
```
```python
%matplotlib inline
covid_data.hist()
```
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x11e46ca50>,
<matplotlib.axes._subplots.AxesSubplot object at 0x116caea90>],
[<matplotlib.axes._subplots.AxesSubplot object at 0x11e65dd10>,
<matplotlib.axes._subplots.AxesSubplot object at 0x11e6a16d0>],
[<matplotlib.axes._subplots.AxesSubplot object at 0x11e6d3ed0>,
<matplotlib.axes._subplots.AxesSubplot object at 0x11e716710>]],
dtype=object)
![png](output_4_1.png)
```python
%matplotlib inline
import matplotlib.pyplot as plt
covid_mexico = covid_data[covid_data['Country/Region']=='Mexico']
covid_mexico.shape
```
(63, 8)
```python
```
```python
covid_data.plot(kind="scatter", x="Long", y="Lat")
```
<matplotlib.axes._subplots.AxesSubplot at 0x12b7c9910>
![png](output_7_1.png)
```python
from datetime import datetime
#covid_mexico['Date'] =pd.to_datetime(covid_mexico.Date, format="%Y-%m-%d")
mexico_sort=covid_mexico.sort_values(by='Date', ascending=True)
mexico_sort
```
<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>Date</th>
<th>Country/Region</th>
<th>Province/State</th>
<th>Lat</th>
<th>Long</th>
<th>Confirmed</th>
<th>Recovered</th>
<th>Deaths</th>
</tr>
</thead>
<tbody>
<tr>
<th>9954</th>
<td>2020-01-22</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>0</td>
<td>0.0</td>
<td>0</td>
</tr>
<tr>
<th>9955</th>
<td>2020-01-23</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>0</td>
<td>0.0</td>
<td>0</td>
</tr>
<tr>
<th>9956</th>
<td>2020-01-24</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>0</td>
<td>0.0</td>
<td>0</td>
</tr>
<tr>
<th>9957</th>
<td>2020-01-25</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>0</td>
<td>0.0</td>
<td>0</td>
</tr>
<tr>
<th>9958</th>
<td>2020-01-26</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>0</td>
<td>0.0</td>
<td>0</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>10012</th>
<td>2020-03-20</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>164</td>
<td>4.0</td>
<td>1</td>
</tr>
<tr>
<th>10013</th>
<td>2020-03-21</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>203</td>
<td>4.0</td>
<td>2</td>
</tr>
<tr>
<th>10014</th>
<td>2020-03-22</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>251</td>
<td>4.0</td>
<td>2</td>
</tr>
<tr>
<th>10015</th>
<td>2020-03-23</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>316</td>
<td>4.0</td>
<td>3</td>
</tr>
<tr>
<th>10016</th>
<td>2020-03-24</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>367</td>
<td>NaN</td>
<td>4</td>
</tr>
</tbody>
</table>
<p>63 rows × 8 columns</p>
</div>
```python
mexico_filter = mexico_sort[mexico_sort['Confirmed']!=0]
mexico_filter
```
<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>Date</th>
<th>Country/Region</th>
<th>Province/State</th>
<th>Lat</th>
<th>Long</th>
<th>Confirmed</th>
<th>Recovered</th>
<th>Deaths</th>
</tr>
</thead>
<tbody>
<tr>
<th>9991</th>
<td>2020-02-28</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>1</td>
<td>0.0</td>
<td>0</td>
</tr>
<tr>
<th>9992</th>
<td>2020-02-29</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>4</td>
<td>0.0</td>
<td>0</td>
</tr>
<tr>
<th>9993</th>
<td>2020-03-01</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>5</td>
<td>0.0</td>
<td>0</td>
</tr>
<tr>
<th>9994</th>
<td>2020-03-02</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>5</td>
<td>0.0</td>
<td>0</td>
</tr>
<tr>
<th>9995</th>
<td>2020-03-03</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>5</td>
<td>1.0</td>
<td>0</td>
</tr>
<tr>
<th>9996</th>
<td>2020-03-04</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>5</td>
<td>1.0</td>
<td>0</td>
</tr>
<tr>
<th>9997</th>
<td>2020-03-05</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>5</td>
<td>1.0</td>
<td>0</td>
</tr>
<tr>
<th>9998</th>
<td>2020-03-06</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>6</td>
<td>1.0</td>
<td>0</td>
</tr>
<tr>
<th>9999</th>
<td>2020-03-07</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>6</td>
<td>1.0</td>
<td>0</td>
</tr>
<tr>
<th>10000</th>
<td>2020-03-08</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>7</td>
<td>1.0</td>
<td>0</td>
</tr>
<tr>
<th>10001</th>
<td>2020-03-09</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>7</td>
<td>1.0</td>
<td>0</td>
</tr>
<tr>
<th>10002</th>
<td>2020-03-10</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>7</td>
<td>4.0</td>
<td>0</td>
</tr>
<tr>
<th>10003</th>
<td>2020-03-11</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>8</td>
<td>4.0</td>
<td>0</td>
</tr>
<tr>
<th>10004</th>
<td>2020-03-12</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>12</td>
<td>4.0</td>
<td>0</td>
</tr>
<tr>
<th>10005</th>
<td>2020-03-13</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>12</td>
<td>4.0</td>
<td>0</td>
</tr>
<tr>
<th>10006</th>
<td>2020-03-14</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>26</td>
<td>4.0</td>
<td>0</td>
</tr>
<tr>
<th>10007</th>
<td>2020-03-15</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>41</td>
<td>4.0</td>
<td>0</td>
</tr>
<tr>
<th>10008</th>
<td>2020-03-16</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>53</td>
<td>4.0</td>
<td>0</td>
</tr>
<tr>
<th>10009</th>
<td>2020-03-17</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>82</td>
<td>4.0</td>
<td>0</td>
</tr>
<tr>
<th>10010</th>
<td>2020-03-18</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>93</td>
<td>4.0</td>
<td>0</td>
</tr>
<tr>
<th>10011</th>
<td>2020-03-19</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>118</td>
<td>4.0</td>
<td>1</td>
</tr>
<tr>
<th>10012</th>
<td>2020-03-20</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>164</td>
<td>4.0</td>
<td>1</td>
</tr>
<tr>
<th>10013</th>
<td>2020-03-21</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>203</td>
<td>4.0</td>
<td>2</td>
</tr>
<tr>
<th>10014</th>
<td>2020-03-22</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>251</td>
<td>4.0</td>
<td>2</td>
</tr>
<tr>
<th>10015</th>
<td>2020-03-23</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>316</td>
<td>4.0</td>
<td>3</td>
</tr>
<tr>
<th>10016</th>
<td>2020-03-24</td>
<td>Mexico</td>
<td>NaN</td>
<td>23.6345</td>
<td>-102.5528</td>
<td>367</td>
<td>NaN</td>
<td>4</td>
</tr>
</tbody>
</table>
</div>
```python
n=mexico_filter.shape[0]
days=np.arange(1,n+1,1)
days
```
array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
18, 19, 20, 21, 22, 23, 24, 25, 26])
```python
#mexico_filter = mexico_sort[mexico_sort['Confirmed']!=0]
plt.scatter(x=days, y=mexico_filter['Confirmed'])
```
<matplotlib.collections.PathCollection at 0x12acc8290>
![png](output_11_1.png)
```python
```
```python
from scipy.optimize import curve_fit
def exponential(x, a,k, b):
return a*np.exp(x*k) + b
potp, pcov = curve_fit(exponential, days, mexico_filter['Confirmed'])
potp
```
array([ 1.07768657, 0.22640743, -3.90363561])
```python
```
```python
```
```python
```
```python
```
```python
# Plot outputs
plt.scatter(days, mexico_filter['Confirmed'], color='black')
plt.plot(days,exponential(days,*potp), color='blue', linewidth=2)
```
[<matplotlib.lines.Line2D at 0x12b888fd0>]
![png](output_18_1.png)

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