See this in nbviewer.¶

See this in Binder ¶

Testing https://github.com/m-rossi/jupyter-docx-bundler ¶

Python released 13 versions during 2019 and 12 versions during 2018. Most of programming languages have some kinds of version managements. Node has nvm and Ruby has RVM, etc. pyenv is a Python version management. It installs/uninstalls different Python versions, set global Python version, and set a local/directory Python version. pyenv-virtualenv is a pyenv plugin that manage Python virtual environments on UNIX-like systems.

In [1]:

%run covid-19-data.ipynb

import pandas as pd
import numpy as np
from matplotlib.ticker import ScalarFormatter

Country/Region	US	India	Brazil	Russia	France	Spain	Argentina	Colombia	United Kingdom	Mexico
1/22/20	1	0	0	0	0	0	0	0	0	0
1/23/20	1	0	0	0	0	0	0	0	0	0
1/24/20	2	0	0	0	2	0	0	0	0	0
1/25/20	2	0	0	0	3	0	0	0	0	0
1/26/20	5	0	0	0	3	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...
10/28/20	8856413	8040203	5468270	1553028	1280215	1136503	1130533	1041935	945378	906863
10/29/20	8944934	8088851	5494376	1570446	1327852	1160083	1143800	1053122	968456	912811
10/30/20	9044255	8137119	5516658	1588433	1377347	1185678	1157179	1063151	992874	918811
10/31/20	9133381	8184082	5535605	1606267	1412709	1185678	1166924	1074184	1014793	924962
11/1/20	9206975	8229313	5545705	1624648	1458999	1185678	1173533	1083321	1038054	929392

285 rows × 10 columns

Main title¶

Sub title¶

main 1
- sub 1
- sub 2
main 2
main 3

H3 title¶

Id	Syntax	Description
1	Header	Something very long long long here
2	Long long long paragraph	Text

H4 title¶

$$P(A)=\frac{n(A)}{n(U)}$$$$\overline{x}=\frac{\sum \limits _{i=1} ^k f_i x_i}{n} \text{, where } n=\sum \limits _{i=1} ^k f_i $$

In [2]:

fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
df_confirmed.plot(ax=ax,figsize=(20,20))
ax.set_title(f'COVID-19 Time series Confirmed as of {confirmed_latest}',fontsize= 30)
plt.xlabel('Date',fontsize= 20)
plt.ylabel('Number',fontsize= 20)
plt.rc('legend', fontsize=16)
plt.tick_params(labelsize=16)
fig.autofmt_xdate() # mke space for and rotate the x-axis tick labels
plt.legend(loc=2)
plt.show()

In [3]:

fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
df_deaths.plot(ax=ax,figsize=(20,20))
ax.set_title(f'COVID-19 Time series Death as of {deaths_latest}',fontsize= 30)
plt.xlabel('Date',fontsize= 20)
plt.ylabel('Number',fontsize= 20)
plt.rc('legend', fontsize=16)
plt.tick_params(labelsize=16)
fig.autofmt_xdate() # mke space for and rotate the x-axis tick labels
plt.legend(loc=2)
plt.show()

In [4]:

fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
df_recovered.plot(ax=ax,figsize=(20,20))
ax.set_title(f'COVID-19 Time series Recovered as of {recovered_latest}',fontsize= 30)
plt.xlabel('Date',fontsize= 20)
plt.ylabel('Number',fontsize= 20)
plt.rc('legend', fontsize=16)
plt.tick_params(labelsize=16)
fig.autofmt_xdate() # mke space for and rotate the x-axis tick labels
plt.legend(loc=2)
plt.show()

In [5]:

# Total Confirmed by date
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
df_confirmed_total.plot(ax=ax,figsize=(20,20))
ax.set_title(f'COVID-19 Time series global confirmed as of {confirmed_latest}',fontsize= 30)
plt.xlabel('Date',fontsize= 20)
plt.ylabel('Number',fontsize= 20)
plt.tick_params(labelsize=16)
ax.yaxis.set_major_formatter(ScalarFormatter(useMathText=True))
ax.ticklabel_format(style="plain",  axis="y")
fig.autofmt_xdate() # mke space for and rotate the x-axis tick labels
plt.show()

In [6]:

# Total deaths by date
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
df_deaths_total.plot(ax=ax,figsize=(20,20))
ax.set_title(f'COVID-19 Time series global deaths as of {deaths_latest}',fontsize= 30)
plt.xlabel('Date',fontsize= 20)
plt.ylabel('Number',fontsize= 20)
plt.tick_params(labelsize=16)
ax.yaxis.set_major_formatter(ScalarFormatter(useMathText=True))
ax.ticklabel_format(style="plain",  axis="y",scilimits=(0,0))
fig.autofmt_xdate() # mke space for and rotate the x-axis tick labels
plt.show()

In [7]:

# Total recovered by date
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
df_recovered_total.plot(ax=ax,figsize=(20,20))
ax.set_title(f'COVID-19 Time series global recovered as of {recovered_latest}',fontsize= 30)
plt.xlabel('Date',fontsize= 20)
plt.ylabel('Number',fontsize= 20)
plt.tick_params(labelsize=16)
ax.yaxis.set_major_formatter(ScalarFormatter(useMathText=True))
ax.ticklabel_format(style="plain",  axis="y",scilimits=(0,0))
fig.autofmt_xdate() # mke space for and rotate the x-axis tick labels
plt.show()

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