Community location#

This page shows the location (by continent or country) of the OLS community members, given their different roles

import geopandas
import matplotlib.pyplot as plt
import pandas as pd

from pathlib import Path

path = geopandas.datasets.get_path('naturalearth_lowres')
world_df = geopandas.read_file(path)
url = 'https://raw.githubusercontent.com/open-life-science/open-life-science.github.io/main/_data/artifacts/people.csv'
people_df = pd.read_csv(url, index_col=0)

/tmp/ipykernel_2016/1319868172.py:1: FutureWarning: The geopandas.dataset module is deprecated and will be removed in GeoPandas 1.0. You can get the original 'naturalearth_lowres' data from https://www.naturalearthdata.com/downloads/110m-cultural-vectors/.
  path = geopandas.datasets.get_path('naturalearth_lowres')

Number of people

len(people_df)

576

Continents#

Proportion of people without continent information

people_df.continent.isna().sum() / len(people_df.continent)

0.07465277777777778

Continent repartition

continents_df = (
    people_df
    .groupby(["continent"])
    .count()
    .rename(columns={"country_3": "Total"})
    .drop(columns=["city", "first-name", "last-name", "pronouns", "country", "longitude", "latitude", "mastodon"])
)
continents_df["Percentage"] = 100 * continents_df.Total / continents_df.Total.sum(axis = 0)
continents_df = continents_df.sort_values("Percentage", ascending=False)
continents_df
Total Percentage
continent
Europe 257 48.217636
Africa 86 16.135084
North America 70 13.133208
South America 65 12.195122
Asia 46 8.630394
Oceania 9 1.688555 
fig, ax = plt.subplots()
fig.set_dpi(300)
(continents_df.Percentage
     .transpose()
     .plot.bar(ax=ax, color="#139D3D"))
plt.ylabel("People (%)")
plt.xlabel("")

Text(0.5, 0, '')
../_images/afeb194d9357b06fc428b6efc7c906bd1230ecbffd99e197375a2e0f6cde5eac.png

Countries#

Proportion of people without country information

people_df.country.isna().sum() / len(people_df.country)

0.06076388888888889

Country repartition

countries_df = (
    people_df
    .groupby(["country"])
    .count()
    .rename(columns={"country_3": "Total"})
    .drop(columns=["city", "first-name", "last-name", "pronouns", "continent", "longitude", "latitude", "mastodon"])
)
countries_df['Percentage'] = 100 * countries_df.Total / countries_df.Total.sum(axis = 0)
countries_df
Total Percentage
country
Argentina 32 6.003752
Australia 9 1.688555
Austria 1 0.187617
Bangladesh 1 0.187617
Belgium 2 0.375235
Benin 1 0.187617
Bolivia 0 0.000000
Brazil 17 3.189493
Cameroon 11 2.063790
Canada 15 2.814259
Chile 2 0.375235
China 1 0.187617
Colombia 6 1.125704
Czechia 4 0.750469
Denmark 2 0.375235
Egypt 1 0.187617
Estonia 2 0.375235
Eswatini 1 0.187617
Ethiopia 1 0.187617
France 12 2.251407
Germany 37 6.941839
Ghana 4 0.750469
Greece 3 0.562852
Hungary 1 0.187617
India 20 3.752345
India / France 0 0.000000
Indonesia 5 0.938086
Ireland 5 0.938086
Italy 10 1.876173
Japan 1 0.187617
Kazakhstan 1 0.187617
Kenya 22 4.127580
Luxembourg 1 0.187617
Mali 2 0.375235
Mexico 2 0.375235
Nepal 4 0.750469
Netherlands 44 8.255159
Nigeria 21 3.939962
Norway 3 0.562852
Peru 7 1.313321
Poland 1 0.187617
Portugal 2 0.375235
Romania 1 0.187617
Russian Federation 1 0.187617
Saudi Arabia 4 0.750469
Singapore 1 0.187617
South Africa 20 3.752345
South Korea 0 0.000000
Spain 17 3.189493
Sweden 2 0.375235
Switzerland 9 1.688555
Thailand 1 0.187617
Turkey 6 1.125704
Uganda 2 0.375235
United Kingdom 97 18.198874
United Kingdom / Ukraine 0 0.000000
United States 53 9.943715
Uruguay 1 0.187617
Uzbekistan 1 0.187617

Map#

country_code_df = (
    people_df
    .groupby(["country_3"])
    .count()
    .rename(columns = {"country": "total"})
    .drop(columns = ["city", "first-name", "last-name", "pronouns", "continent", "longitude", "latitude", "mastodon"])
)
country_code_df = 100 * country_code_df / country_code_df.sum(axis = 0)
country_code_df = (
    country_code_df
    .rename_axis("iso_a3")
    .reset_index()
)
country_world_df = pd.merge(world_df, country_code_df, on="iso_a3", how="outer")
country_world_df["total"].fillna(0, inplace=True)
country_world_df.head()
pop_est continent name iso_a3 gdp_md_est geometry total
0 889953.0 Oceania Fiji FJI 5496.0 MULTIPOLYGON (((180.00000 -16.06713, 180.00000... 0.000000
1 58005463.0 Africa Tanzania TZA 63177.0 POLYGON ((33.90371 -0.95000, 34.07262 -1.05982... 0.000000
2 603253.0 Africa W. Sahara ESH 907.0 POLYGON ((-8.66559 27.65643, -8.66512 27.58948... 0.000000
3 37589262.0 North America Canada CAN 1736425.0 MULTIPOLYGON (((-122.84000 49.00000, -122.9742... 2.814259
4 328239523.0 North America United States of America USA 21433226.0 MULTIPOLYGON (((-122.84000 49.00000, -120.0000... 9.943715 
fig, ax = plt.subplots(figsize=(15, 10))
fig.set_dpi(300)

country_world_df.plot(
    column="total",
    ax=ax,
    legend=True,
    legend_kwds={"label": "Percentage by Country", "orientation": "horizontal"},
    cmap="YlGn")
ax.set_axis_off()
plt.savefig(f'../results/location.png', bbox_inches='tight')
../_images/d52ec092cee4b923c33ee5b5aa1a7c94c2adb7bdf8fe3cec0257af8da6fc4971.png