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)

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[2], line 1
----> 1 path = geopandas.datasets.get_path('naturalearth_lowres')
      2 world_df = geopandas.read_file(path)
      3 url = 'https://raw.githubusercontent.com/open-life-science/open-life-science.github.io/main/_data/artifacts/people.csv'

File /usr/share/miniconda/envs/ols-stats/lib/python3.12/site-packages/geopandas/datasets/__init__.py:18, in get_path(dataset)
     12 error_msg = (
     13     "The geopandas.dataset has been deprecated and was removed in GeoPandas "
     14     f"1.0. You can get the original '{dataset}' data from "
     15     f"{ne_message if 'natural' in dataset else nybb_message}"
     16 )
     17 if dataset in _prev_available:
---> 18     raise AttributeError(error_msg)
     19 else:
     20     error_msg = (
     21         "The geopandas.dataset has been deprecated and "
     22         "was removed in GeoPandas 1.0. New sample datasets are now available "
     23         "in the geodatasets package (https://geodatasets.readthedocs.io/en/latest/)"
     24     )

AttributeError: The geopandas.dataset has been deprecated and was removed in GeoPandas 1.0. You can get the original 'naturalearth_lowres' data from https://www.naturalearthdata.com/downloads/110m-cultural-vectors/.

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, '')

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')