Pynsee python package contains tools to easily search and download French data from INSEE and IGN APIs

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pynsee gives a quick access to more than 150 000 macroeconomic series, a dozen datasets of local data, numerous sources available on as well as key metadata and SIRENE database containing data on all French companies. Have a look at the detailed API page

This package is a contribution to reproducible research and public data transparency. It benefits from the developments made by teams working on APIs at INSEE and IGN.

Installation & API subscription

The files available on and IGN data, i.e. the use of download and geodata modules, do not require authentication. Credentials are necessary to access some of the INSEE APIs available through pynsee by the modules macrodata, localdata, metadata and sirene. API credentials can be created here :

# Download Pypi package
pip install pynsee[full]

# Get the development version from GitHub
# git clone
# cd pynsee
# pip install .[full]

# Subscribe to and get your credentials!
# Save once and for all your credentials with init_conn function.
# Then, functions requiring authentication will use the credentials saved locally on your machine by innit_conn
from pynsee.utils.init_conn import init_conn
init_conn(insee_key="my_insee_key", insee_secret="my_insee_secret")

# Beware : any change to the keys should be tested after having cleared the cache
# Please do : from pynsee.utils import clear_all_cache; clear_all_cache()

Data Search and Collection Advice

  • Macroeconomic data :

    First, use get_dataset_list to search what are your datasets of interest and then get the series list with get_series_list. Alternatively, you can make a keyword-based search with search_macrodata, e.g. search_macrodata('GDP'). Then, get the data with get_dataset or get_series

  • Local data : use first get_local_metadata, then get data with get_local_data

  • Metadata : e.g. function to get the classification of economic activities (Naf/Nace Rev2) get_activity_list

  • Sirene (French companies database) : use first get_dimension_list, then use search_sirene with dimensions as filtering variables

  • Geodata : get the list of available geographical data with get_geodata_list and then retrieve it with get_geodata

  • Files on get the list of available files on with get_file_list and then download it with download_file

For further advice, have a look at the documentation and gallery of the examples.

Example - Population Map
from pynsee.geodata import get_geodata_list, get_geodata, GeoFrDataFrame

import math
import geopandas as gpd
import pandas as pd
from pandas.api.types import CategoricalDtype
import as cm
import matplotlib.pyplot as plt
import descartes

import warnings
from shapely.errors import ShapelyDeprecationWarning
warnings.filterwarnings("ignore", category=ShapelyDeprecationWarning)

# get geographical data list
geodata_list = get_geodata_list()
# get departments geographical limits
com = get_geodata('ADMINEXPRESS-COG-CARTO.LATEST:commune')

mapcom = gpd.GeoDataFrame(com).set_crs("EPSG:3857")

mapcom = mapcom.to_crs(epsg=3035)
mapcom["area"] = mapcom['geometry'].area / 10**6
mapcom = mapcom.to_crs(epsg=3857)

mapcom['REF_AREA'] = 'D' + mapcom['insee_dep']
mapcom['density'] = mapcom['population'] / mapcom['area']

mapcom = GeoFrDataFrame(mapcom)
mapcom = mapcom.translate(departement = ['971', '972', '974', '973', '976'],
                          factor = [1.5, 1.5, 1.5, 0.35, 1.5])

mapcom = mapcom.zoom(departement = ["75","92", "93", "91", "77", "78", "95", "94"],
                 factor=1.5, startAngle = math.pi * (1 - 3 * 1/9))

mapplot = gpd.GeoDataFrame(mapcom)
mapplot.loc[mapplot.density < 40, 'range'] = "< 40"
mapplot.loc[mapplot.density >= 20000, 'range'] = "> 20 000"

density_ranges = [40, 80, 100, 120, 150, 200, 250, 400, 600, 1000, 2000, 5000, 10000, 20000]
list_ranges = []
list_ranges.append( "< 40")

for i in range(len(density_ranges)-1):
    min_range = density_ranges[i]
    max_range = density_ranges[i+1]
    range_string = "[{}, {}[".format(min_range, max_range)
    mapplot.loc[(mapplot.density >= min_range) & (mapplot.density < max_range), 'range'] = range_string

list_ranges.append("> 20 000")

mapplot['range'] = mapplot['range'].astype(CategoricalDtype(categories=list_ranges, ordered=True))

fig, ax = plt.subplots(1,1,figsize=[15,15])
mapplot.plot(column='range', cmap=cm.viridis,
legend=True, ax=ax,
legend_kwds={'bbox_to_anchor': (1.1, 0.8),
             'title':'density per km2'})
ax.set(title='Distribution of population in France')

            format='svg', dpi=1200,
            bbox_inches = 'tight',
            pad_inches = 0)

How to avoid proxy issues ?

# Use the proxy_server argument of the init_conn function to change the proxy server address
from pynsee.utils.init_conn import init_conn

# Alternativety you can use directly environment variables as follows.
# Beware not to commit your credentials!
import os
os.environ['insee_key'] = 'my_insee_key'
os.environ['insee_secret'] = 'my_insee_secret'
os.environ['http_proxy'] = "http://my_proxy_server:port"
os.environ['https_proxy'] = "http://my_proxy_server:port"

# Any change to the keys should be tested after having cleared the cache
# Please do : from pynsee.utils import *; clear_all_cache()


Feel free to open an issue with any question about this package using <> Github repository.


All contributions, whatever their forms, are welcome. See