Jupytext in French

README.md

Demonstrate Jupytext translation in French

Test it online:

The demonstration markdown file [World Population](./World Population.md) is copied from Jupytext.

overrides.json

{
  "@jupyterlab/translation-extension:plugin": {"locale": "fr_FR"},
  "@jupyterlab/docmanager-extension:plugin": {
    "defaultViewers": {
      "markdown": "Jupytext Notebook",
      "myst": "Jupytext Notebook",
      "r-markdown": "Jupytext Notebook",
      "quarto": "Jupytext Notebook"
    }
  }
}

postBuild

#!/usr/bin/env bash
set -eux

mkdir -p ${NB_PYTHON_PREFIX}/share/jupyter/lab/settings
cp overrides.json ${NB_PYTHON_PREFIX}/share/jupyter/lab/settings

requirements.txt

jupyterlab>=3.2.0,<4.0.0
jupytext >= 1.13.0,<2.0.0
jupyterlab-language-pack-fr-FR
plotly
matplotlib
wordcloud
pandas
wbdata
bash_kernel

World population.md

jupyter
jupytext kernelspec cell_markers formats text_representation region,endregion ipynb,.pct.py:percent,.lgt.py:light,.spx.py:sphinx,md,Rmd,.pandoc.md:pandoc extension format_name format_version jupytext_version .md markdown 1.1 1.1.0 display_name language name Python 3 python python3

A quick insight at world population

Collecting population data

In the below we retrieve population data from the World Bank using the wbdata python package

import pandas as pd
import wbdata as wb

pd.options.display.max_rows = 6
pd.options.display.max_columns = 20

Corresponding indicator is found using search method - or, directly, the World Bank site.

wb.search_indicators('Population, total')  # SP.POP.TOTL
# wb.search_indicators('area')
# => https://data.worldbank.org/indicator is easier to use

Now we download the population data

indicators = {'SP.POP.TOTL': 'Population, total',
              'AG.SRF.TOTL.K2': 'Surface area (sq. km)',
              'AG.LND.TOTL.K2': 'Land area (sq. km)',
              'AG.LND.ARBL.ZS': 'Arable land (% of land area)'}
data = wb.get_dataframe(indicators, convert_date=True).sort_index()
data

World is one of the countries

data.loc['World']

Can we classify over continents?

data.loc[(slice(None), '2017-01-01'), :]['Population, total'].dropna(
).sort_values().tail(60).index.get_level_values('country')

Extract zones manually (in order of increasing population)

zones = ['North America', 'Middle East & North Africa',
         'Latin America & Caribbean', 'Europe & Central Asia',
         'Sub-Saharan Africa', 'South Asia',
         'East Asia & Pacific'][::-1]

And extract population information (and check total is right)

population = data.loc[zones]['Population, total'].swaplevel().unstack()
population = population[zones]
assert all(data.loc['World']['Population, total'] == population.sum(axis=1))

Stacked area plot with matplotlib

import matplotlib.pyplot as plt

plt.clf()
plt.figure(figsize=(10, 5), dpi=100)
plt.stackplot(population.index, population.values.T / 1e9)
plt.legend(population.columns, loc='upper left')
plt.ylabel('Population count (B)')
plt.show()

Stacked bar plot with plotly

import plotly.offline as offline
import plotly.graph_objs as go

offline.init_notebook_mode()

data = [go.Scatter(x=population.index, y=population[zone], name=zone, stackgroup='World')
        for zone in zones]
fig = go.Figure(data=data,
                layout=go.Layout(title='World population'))
offline.iplot(fig)