Employment by sector and departement

[1]:

from pynsee.localdata import get_local_metadata, get_nivgeo_list, get_local_data
from pynsee.geodata import get_geodata, GeoFrDataFrame
from pynsee.metadata import get_activity_list
from shapely import wkt

import pandas as pd
from pandas.api.types import CategoricalDtype
import seaborn as sns
import math

from matplotlib.pyplot import pie
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import inset_axes

# get a list all data available : datasets and variables
metadata = get_local_metadata()

# geographic metadata
nivgeo = get_nivgeo_list()

[2]:

# get departement map
dep = get_geodata('ADMINEXPRESS-COG-CARTO.LATEST:departement')

dep = dep.translate(departement=['971', '972', '974', '973', '976'],
                   factor=[1.5, 1.5, 1.5, 0.3, 1.5])

dep = dep.zoom(factor = 3.5 , startAngle = math.pi * (1 - 3 * 1/9))

dep = dep[["insee_dep", "geometry"]]
dep = dep.rename(columns={'insee_dep': 'CODEGEO'})

[3]:

# get local data from INSEE API
data = get_local_data(
    dataset_version='GEO2020FLORES2017', variables='NA17', nivgeo='DEP')

data = data.rename(columns={'NA17': 'A17'})

[4]:

# keep only headcounts and remove the total from the data
df = data[data["UNIT"] == "NBSALET"].reset_index(drop=True)
df = df[~df["A17"].isin(["ENS"])].reset_index(drop=True)

[5]:

# compute share of activity in total employment by department
df['employment_share'] = 100 * df["OBS_VALUE"] / df['OBS_VALUE'].groupby(df['CODEGEO']).transform('sum')

[6]:

# merge values and geographic limits
datamap = GeoFrDataFrame(df.merge(dep, how = 'left', on = 'CODEGEO'))

[7]:

# add english activity labels at A17 level
activity = get_activity_list("A17")
activity["TITLE_A17_EN"] = activity["A17"] + " - " + activity["TITLE_A17_EN"]
datamap = datamap.merge(activity, how='left', on='A17')

[8]:

# compute center location for each departement
datamap["center"] = datamap.geometry.centroid

# list of departements
list_dep = datamap["CODEGEO"].unique()

[9]:

# make color palette
colors = sns.color_palette("Set1") + sns.color_palette("Set2")

# order activity sectors according to average share
emplTot = datamap[["employment_share", "TITLE_A17_EN"]].groupby(['TITLE_A17_EN'], observed=True).mean()
emplTot = emplTot.sort_values(by=["employment_share"],  ascending=False)

datamap["TITLE_A17_EN"] = datamap["TITLE_A17_EN"].astype(CategoricalDtype(categories=emplTot.index, ordered=True))
datamap = datamap.sort_values(["TITLE_A17_EN"]).reset_index(drop=True)
datamap

[9]:

	CODEGEO	NIVGEO	UNIT_label_fr	UNIT	A17	OBS_VALUE	NA17_label	DATASET_VERSION	DATASET_NAME	DATA_DATE	employment_share	geometry	A5	A10	TITLE_A17_EN	TITLE_A17_FR	center
0	24	DEP	Postes salariés dans les établissements	NBSALET	OQ	47091.0	Activité économique en 17 postes (NA - A17)	GEO2020FLORES2017	Fichier localisé des rémunérations et de l'emp...	2017.0	39.785237	MULTIPOLYGON (((4538.51 5624971.758, 4634.387 ...	OQ	OQ	OQ - Public administration and defence, educat...	Administration publique, enseignement, santé h...	POINT (82503.886 5638329.411)
1	33	DEP	Postes salariés dans les établissements	NBSALET	OQ	199158.0	Activité économique en 17 postes (NA - A17)	GEO2020FLORES2017	Fichier localisé des rémunérations et de l'emp...	2017.0	32.943017	MULTIPOLYGON (((-128762.061 5717199.24, -12874...	OQ	OQ	OQ - Public administration and defence, educat...	Administration publique, enseignement, santé h...	POINT (-64206.775 5594297.762)
2	71	DEP	Postes salariés dans les établissements	NBSALET	OQ	61315.0	Activité économique en 17 postes (NA - A17)	GEO2020FLORES2017	Fichier localisé des rémunérations et de l'emp...	2017.0	34.317841	MULTIPOLYGON (((531046.916 5928690.377, 531251...	OQ	OQ	OQ - Public administration and defence, educat...	Administration publique, enseignement, santé h...	POINT (505725.921 5884496.094)
3	01	DEP	Postes salariés dans les établissements	NBSALET	OQ	54202.0	Activité économique en 17 postes (NA - A17)	GEO2020FLORES2017	Fichier localisé des rémunérations et de l'emp...	2017.0	29.047782	MULTIPOLYGON (((533820.757 5815685.538, 533972...	OQ	OQ	OQ - Public administration and defence, educat...	Administration publique, enseignement, santé h...	POINT (595403.95 5796511.951)
4	22	DEP	Postes salariés dans les établissements	NBSALET	OQ	66843.0	Activité économique en 17 postes (NA - A17)	GEO2020FLORES2017	Fichier localisé des rémunérations et de l'emp...	2017.0	36.247933	MULTIPOLYGON (((-382905.38 6257752.911, -38289...	OQ	OQ	OQ - Public administration and defence, educat...	Administration publique, enseignement, santé h...	POINT (-318942.652 6180631.791)
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1763	13	DEP	Postes salariés dans les établissements	NBSALET	C2	1446.0	Activité économique en 17 postes (NA - A17)	GEO2020FLORES2017	Fichier localisé des rémunérations et de l'emp...	2017.0	0.182878	MULTIPOLYGON (((582059.984 5342116.74, 582075....	BE	BE	C2 - Manufacture of coke and refined petroleum...	Cokéfaction et raffinage	POINT (566095.867 5395484.607)
1764	26	DEP	Postes salariés dans les établissements	NBSALET	C2	0.0	Activité économique en 17 postes (NA - A17)	GEO2020FLORES2017	Fichier localisé des rémunérations et de l'emp...	2017.0	0.000000	MULTIPOLYGON (((540386.992 5605296.851, 540431...	BE	BE	C2 - Manufacture of coke and refined petroleum...	Cokéfaction et raffinage	POINT (575197.887 5572442.063)
1765	59	DEP	Postes salariés dans les établissements	NBSALET	C2	269.0	Activité économique en 17 postes (NA - A17)	GEO2020FLORES2017	Fichier localisé des rémunérations et de l'emp...	2017.0	0.027313	MULTIPOLYGON (((338476.314 6474007.637, 339089...	BE	BE	C2 - Manufacture of coke and refined petroleum...	Cokéfaction et raffinage	POINT (357482.755 6524765.315)
1766	27	DEP	Postes salariés dans les établissements	NBSALET	C2	0.0	Activité économique en 17 postes (NA - A17)	GEO2020FLORES2017	Fichier localisé des rémunérations et de l'emp...	2017.0	0.000000	MULTIPOLYGON (((35180.302 6327852.729, 34839.6...	BE	BE	C2 - Manufacture of coke and refined petroleum...	Cokéfaction et raffinage	POINT (110910.513 6294369.435)
1767	33	DEP	Postes salariés dans les établissements	NBSALET	C2	0.0	Activité économique en 17 postes (NA - A17)	GEO2020FLORES2017	Fichier localisé des rémunérations et de l'emp...	2017.0	0.000000	MULTIPOLYGON (((-128762.061 5717199.24, -12874...	BE	BE	C2 - Manufacture of coke and refined petroleum...	Cokéfaction et raffinage	POINT (-64206.775 5594297.762)

1768 rows × 17 columns

[10]:

emplTot

[10]:

	employment_share
TITLE_A17_EN
OQ - Public administration and defence, education, human health and social work activities	36.017645
GZ - Wholesale and retail trade; repair of motor vehicles and motorcycles	13.797260
MN - Professional, scientific, technical, administrative and support service activities	9.349464
C5 - Other manufacturing	6.822302
FZ - Construction	6.040776
HZ - Transportation and storage	5.366087
IZ - Accommodation and food service activities	4.072490
RU - Other services activities	3.733203
C1 - Manufacture of food products, beverages and tobacco products	3.016751
KZ - Financial and insurance activities	2.961577
JZ - Information and communication	1.843239
C3 - Manufacture of electrical, computer and electronic equipment; Manufacture of machinery	1.714648
DE - Mining and quarrying; energy, water supply, sewerage, waste management and remediation activities	1.606920
AZ - Agriculture, forestry and fishing	1.422385
C4 - Manufacture of transport equipment	1.343475
LZ - Real estate activities	0.872148
C2 - Manufacture of coke and refined petroleum products	0.019630

[11]:

# draw plot

def extract_lon(x):
    return wkt.loads(x).xy[0][0]
def extract_lat(x):
    return wkt.loads(x).xy[1][0]

ax = datamap.plot(color='white', edgecolor='black', figsize = (15,15))

ax_sub_coeff = 80000

for idep in list_dep:

    df = datamap[datamap["CODEGEO"] == idep].reset_index(drop=True)
    df["lon"] = df["center"].apply(lambda p: p.x)
    df["lat"] = df["center"].apply(lambda p: p.y)

    df = df[df["lon"] == min(df["lon"])].reset_index(drop=True)
    center = df.loc[0,"lon"], df.loc[0,"lat"]

    ratios = df["employment_share"]

    ax_sub = ax.inset_axes(
                    bounds=(center[0]-ax_sub_coeff/2,
                            center[1]-ax_sub_coeff/2,
                            ax_sub_coeff, ax_sub_coeff),
                    transform=ax.transData
                   )

    ax_sub.pie(ratios, wedgeprops={'edgecolor': 'gray','linewidth': 0.25}, colors = colors)


plt.suptitle('Employment by sector and departement', x = 0.4, y = 0.85,
            horizontalalignment= 'left',
            fontsize = 12, fontweight='bold')
plt.legend(emplTot.index, loc="lower left", bbox_to_anchor=(-27, -9), ncol=2)
plt.savefig('empl_map_fr.png')
plt.close()

[12]:

from PIL import Image
def resize_pix(img, pct):
    hsize = int((float(img.size[1])*float(pct)))
    wsize = int((float(img.size[0])*float(pct)))
    img = img.resize((wsize,hsize), Image.Resampling.LANCZOS)
    return img

img = Image.open('empl_map_fr.png').convert("RGB")
img = resize_pix(img, pct=0.5)
img

[12]:

../_images/examples_example_empl_sector_dep_12_0.png