Package: pynsee
pynseegives a quick access to more than 150 000 macroeconomic series,
a dozen datasets of local data, numerous sources available oninsee.fr,
geographical limits of administrative areas taken from IGN
as well as key metadata and SIRENE database containing data on all French companies.
Have a look at the detailed API pageapi.insee.fr.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 subscriptionThe files available oninsee.frand IGN data, i.e. the use ofdownloadandgeodatamodules, do not require authentication.
Credentials are necessary to access some of the INSEE APIs available throughpynseeby the modulesmacrodata,localdata,metadataandsirene.
API credentials can be created here :api.insee.fr# Download Pypi packagepipinstallpynsee[full]# Get the development version from GitHub# git clone https://github.com/InseeFrLab/pynsee.git# cd pynsee# pip install .[full]# Subscribe to api.insee.fr and get your credentials!# Save your credentials with init_conn function :frompynsee.utils.init_connimportinit_conninit_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 AdviceMacroeconomic data:
First, useget_dataset_listto search what are your datasets of interest and then get the series list withget_series_list.
Alternatively, you can make a keyword-based search withsearch_macrodata, e.g.search_macrodata('GDP').
Then, get the data withget_datasetorget_seriesLocal data: use firstget_local_metadata, then get data withget_local_dataMetadata: e.g. function to get the classification of economic activities (Naf/Nace Rev2)get_activity_listSirene (French companies database): use firstget_dimension_list, then usesearch_sirenewith dimensions as filtering variablesGeodata: get the list of available geographical data withget_geodata_listand then retrieve it withget_geodataFiles on insee.fr: get the list of available files on insee.fr withget_file_listand then download it withdownload_fileFor further advice, have a look at the documentation and gallery of theexamples.Example - Population Mapfrompynsee.geodataimportget_geodata_list,get_geodata,GeoFrDataFrameimportmathimportgeopandasasgpdimportpandasaspdfrompandas.api.typesimportCategoricalDtypeimportmatplotlib.cmascmimportmatplotlib.pyplotaspltimportdescartesimportwarningsfromshapely.errorsimportShapelyDeprecationWarningwarnings.filterwarnings("ignore",category=ShapelyDeprecationWarning)# get geographical data listgeodata_list=get_geodata_list()# get departments geographical limitscom=get_geodata('ADMINEXPRESS-COG-CARTO.LATEST:commune')mapcom=gpd.GeoDataFrame(com).set_crs("EPSG:3857")# area calculations depend on crs which fits metropolitan france but not overseas departements# figures should not be considered as official statisticsmapcom=mapcom.to_crs(epsg=3035)mapcom["area"]=mapcom['geometry'].area/10**6mapcom=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))mapcommapplot=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")foriinrange(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_stringlist_ranges.append(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_axis_off()ax.set(title='Distribution of population in France')plt.show()fig.savefig('pop_france.svg',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 addressfrompynsee.utils.init_connimportinit_conninit_conn(insee_key="my_insee_key",insee_secret="my_insee_secret",http_proxy="http://my_proxy_server:port",https_proxy="http://my_proxy_server:port")# Beware : any change to the keys should be tested after having cleared the cache# Please do : from pynsee.utils import *; clear_all_cache()# Alternativety you can use directly environment variables as follows.# Beware not to commit your credentials!importosos.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"SupportFeel free to open an issue with any question about this package using theGithub repository.ContributingAll contributions, whatever their forms, are welcome. SeeCONTRIBUTING.md