Marie as a firstname over time and departements¶
[1]:
import pandas as pd
import numpy as np
import geopandas as gpd
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import descartes
from pynsee.download import download_file, get_file_list
from pynsee.geodata import get_geodata_list, get_geodata
[2]:
import logging
import sys
logging.basicConfig(stream=sys.stdout,
level=logging.INFO,
format="%(message)s")
[3]:
meta = get_file_list()
dfraw = download_file("PRENOM_DEP")
pynsee.download's metadata rely on volunteering contributors and their manual updates. get_file_list does not provide data from official Insee's metadata API
Consequently, please report any issue
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/home/coder/local/bin/conda/envs/basesspcloud/lib/python3.10/site-packages/pynsee/download/_download_store_file.py:68: UserWarning: File in insee.fr modified or corrupted during download
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Data file missing in the zip file:
dpt2020.csv
Following file has been used instead:
dpt2021.csv
Please report this issue
[4]:
list_dep = list(dfraw.dpt.unique()) + ['2A', '2B']
list_year = ["1900", "1936", "1965", "2015"]
[5]:
# compute frequency of marie as a firstname given to female babies
df = dfraw[(dfraw["sexe"] == "2") & (dfraw["annais"].isin(list_year))].reset_index(drop=True)
df["nombre"] = pd.to_numeric(df["nombre"])
df.loc[(df["dpt"] == "78"), 'dpt'] = '75'
df = df.groupby(['dpt', 'annais', 'preusuel'], as_index=False).nombre.agg('sum')
df["tot"] = df.groupby(['dpt', 'annais'], as_index=False).nombre.transform('sum')
marie = df[(df["preusuel"] == "MARIE")].reset_index(drop=True)
marie['pct'] = marie.nombre / marie.tot * 100
[6]:
# get geographical limits of departements
geodata_list = get_geodata_list()
dep = get_geodata('ADMINEXPRESS-COG-CARTO.LATEST:departement')
dep = dep.translate()
dep = dep.rename(columns={'insee_dep': 'dpt'})
Previously saved data has been used:
/home/onyxia/.cache/pynsee/pynsee/2ac583be54e866b2b1b30fb0113c1fd0
Set update=True to get the most up-to-date data
Previously saved data has been used:
/home/onyxia/.cache/pynsee/pynsee/399f6d97ce4631c4e02c487406744440
Set update=True to get the most up-to-date data
[7]:
DF = pd.concat([pd.DataFrame({'dpt': list_dep, 'annais': y}) for y in list_year])
DF = DF.merge(marie, how = 'left', on = ['dpt', 'annais'])
[8]:
# data correction
# due to administrative changes in Paris region
# for all years a common value is computed for the following departement : '75', '91', '92', '93', '94', '95', '78'
# departement in Corsica are reported together
for y in list_year:
valParis = DF.loc[(DF['annais'] == y) & (DF['dpt'] == '75'), 'pct'].values
valCorse = DF.loc[(DF['annais'] == y) & (DF['dpt'] == '20'), 'pct'].values
for d in ['91', '92', '93', '94', '95', '78']:
valdep = DF.loc[(DF['annais'] == y) & (DF['dpt'] == d), 'pct'].values
if np.isnan(valdep):
DF.loc[(DF['annais'] == y) & (DF['dpt'] == d), 'pct'] = valParis
for d in ['2A', '2B']:
valdep = DF.loc[(DF['annais'] == y) & (DF['dpt'] == d), 'pct'].values
if np.isnan(valdep):
DF.loc[(DF['annais'] == y) & (DF['dpt'] == d), 'pct'] = valCorse
DF['pct'] = DF['pct'].fillna(0)
[9]:
DF = DF.merge(dep, how = 'left', on = 'dpt')
datamap = gpd.GeoDataFrame(DF)
[10]:
# make plot
fig, axes = plt.subplots(ncols=2, nrows=2, figsize=(12, 12))
axes = axes.flatten()
normalize = mcolors.Normalize(vmin=datamap.pct.min(), vmax=datamap.pct.max())
colormap = cm.jet
plt.suptitle("Percentage of new female babies whose name is Marie", x = 0.3, y = 0.9,
horizontalalignment= 'left',
fontsize = 12, fontweight='bold')
for a, ax in zip(np.unique(datamap.annais.values), axes):
ax.set_axis_off()
dt = datamap[datamap.annais == a]
p = dt.plot(column='pct', ax=ax, cmap=colormap,norm=normalize)
ax.set_title(a)
bbox = ax.get_position()
cax = fig.add_axes([bbox.x1 + 0.01, bbox.y0 + 0.2, 0.01, bbox.height])
fig.colorbar(cm.ScalarMappable(norm=normalize, cmap=colormap), cax=cax)
plt.subplots_adjust(hspace=-0.2, wspace=-0.1)
plt.show()
