Upper and middle class workers in Ile de France

from pynsee import *

import pandas as pd
import geopandas as gpd
from pathlib import Path
from contextlib import closing
# import urllib.request as request
from urllib.request import Request, urlopen
import os
import py7zr
import re
import shutil

import matplotlib.cm as cm
import matplotlib.pyplot as plt
import descartes

import logging
import sys
logging.basicConfig(stream=sys.stdout,
                    level=logging.INFO,
                    format="%(message)s")

meta = get_file_list()
meta[meta.id.str.contains("RP_ACTRES_IRIS")].T.dropna()

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

	1332
id	RP_ACTRES_IRIS
name	RP_ACTRES_IRIS
label	Données du Recensement de Population, données ...
collection	RP
link	https://www.insee.fr/fr/statistiques/fichier/4...
type	xlsx
zip	True
big_zip	False
data_file	base-ic-activite-residents-2017.xlsx
tab	IRIS
first_row	6
api_rest	False
md5	2df77900dcd6544a454d39abee9070a9
size	60768390
date_ref	2017-01-01

dfraw = download_file("RP_ACTRES_IRIS")

Downloading: 100%|██████████| 58.0M/58.0M [00:02<00:00, 20.8MiB/s]
Extracting: 100%|██████████| 58.0M/58.0M [00:00<00:00, 637MB/s]

Data saved:
/home/onyxia/.cache/pynsee/pynsee/f68f175baadf284aadfd854e462a0cce.parquet

# Metadata from insee.fr : https://www.insee.fr/fr/statistiques/4799323#dictionnaire
# P17_ACT1564 : nombre de personnes actives de 15 à 64 ans
# C17_ACT1564 : nombre de personnes actives de 15 à 64 ans
# C17_ACT1564_CS1 : nombre d'agriculteurs exploitants actifs de 15 à 64 ans
# C17_ACT1564_CS2 : nombre d'artisans, commerçants, chefs d'entreprise actifs de 15 à 64 ans
# C17_ACT1564_CS3 : nombre de cadres et professions intellectuelles supérieures actifs de 15 à 64 ans

df = dfraw[["IRIS", "COM", "REG", "DEP", "LIBCOM", "C17_ACT1564", "C17_ACT1564_CS2", "C17_ACT1564_CS3"]]
df = df[(df["REG"] == "11") ] #& ~(df["DEP"] == "77")
df["C17_ACT1564_CS2"] = pd.to_numeric(df["C17_ACT1564_CS2"])
df["C17_ACT1564_CS3"] = pd.to_numeric(df["C17_ACT1564_CS3"])
df["C17_ACT1564"] = pd.to_numeric(df["C17_ACT1564"])
df["pct"] = 100 * (df["C17_ACT1564_CS2"] + df["C17_ACT1564_CS3"]) / df["C17_ACT1564"]

df = df.reset_index(drop=True)
df

	IRIS	COM	REG	DEP	LIBCOM	C17_ACT1564	C17_ACT1564_CS2	C17_ACT1564_CS3	pct
0	751010101	75101	11	75	Paris 1er Arrondissement	520.643927	29.702144	190.009987	42.200076
1	751010102	75101	11	75	Paris 1er Arrondissement	71.145629	9.899243	36.903044	65.783784
2	751010103	75101	11	75	Paris 1er Arrondissement	107.952527	18.057574	55.539455	68.175365
3	751010104	75101	11	75	Paris 1er Arrondissement	0.000000	0.000000	0.000000	NaN
4	751010105	75101	11	75	Paris 1er Arrondissement	0.000000	0.000000	0.000000	NaN
...	...	...	...	...	...	...	...	...	...
5257	956800112	95680	11	95	Villiers-le-Bel	831.396737	54.511641	35.417896	10.816681
5258	956800113	95680	11	95	Villiers-le-Bel	885.164069	26.592849	32.183605	6.640176
5259	956800114	95680	11	95	Villiers-le-Bel	664.336730	24.113011	11.521560	5.363932
5260	956820000	95682	11	95	Villiers-le-Sec	117.460317	4.894180	24.470899	25.000000
5261	956900000	95690	11	95	Wy-dit-Joli-Village	193.914518	20.062030	49.941941	36.100428

5262 rows × 9 columns

import os
import shutil
from pathlib import Path
from urllib.request import Request, urlopen
from contextlib import closing

ign_iris_2022 = "https://data.geopf.fr/telechargement/download/CONTOURS-IRIS/CONTOURS-IRIS_2-1__SHP__FRA_2022-01-01/CONTOURS-IRIS_2-1__SHP__FRA_2022-01-01.7z"

def get_iris_data(url=ign_iris_2022, update=False):
    list_string_split = url.split("/")
    filename = list_string_split[-1]
    list_string_split_filename = filename.split(".")
    filename_clean = list_string_split_filename[0]

    home = Path.home()
    dataDir = os.path.join(home, filename_clean)
    if not os.path.exists(dataDir):
        os.mkdir(dataDir)

    file_iris = os.path.join(dataDir, "file_" + filename_clean)

    req = Request(
        url=url,
        headers={'User-Agent': 'Mozilla/5.0'},
    )

    if not os.path.exists(file_iris) or update:
        with closing(urlopen(req)) as r:
            with open(file_iris, 'wb') as f:
                shutil.copyfileobj(r, f)

    with py7zr.SevenZipFile(file_iris, mode='r') as z:
            z.extractall(dataDir)

    list_file = []
    list_file_size = []

    for root, d_names, f_names in os.walk(dataDir):
        for f in f_names:
            filename = root + "/" + f
            if re.search("CONTOURS-IRIS.shp", filename):
                list_file += [filename]
                list_file_size += [os.path.getsize(filename)]

    list_shp_df = []

    for f in list_file:

        shp = gpd.read_file(f)
        shp = shp.to_crs("EPSG:3857")
        list_shp_df += [shp]

    shpFinal = pd.concat(list_shp_df)

    return shpFinal

# Example call to the function
shapefile = get_iris_data()

ERROR 1: PROJ: proj_create_from_database: Open of /opt/conda/share/proj failed

shp = shapefile.sort_values(by = ["INSEE_COM"]).reset_index(drop=True)

DF = df.merge(shp[["CODE_IRIS", "geometry"]], how = "left", left_on = "IRIS", right_on = "CODE_IRIS")

mapplot = gpd.GeoDataFrame(DF).set_crs("EPSG:3857")

fig, ax = plt.subplots(1,1,figsize=[15,15])
mapplot.plot(column='pct', cmap=cm.jet,
    legend=True, ax=ax, legend_kwds={'shrink': 0.5})
ax.set_axis_off()
ax.set(title='Proportion of upper and middle middle class workers among working population in 2017')
plt.show()