Series collection

Trim NA Values

[IPP/taxbenefit_tables/chomage.allocations_assurance_chomage.reval_chomeurs_plus_61ans.taux] Taux de revalorisation du salaire de références des chômeurs de plus de 61 ans pour les allocations chômage

from: 1965-01-11=0.075

to: 1974-01=NA

min:: 0.02

max:: 0.08

avg:: 0.052

σ:: 0.02

Series code	1965-01-11	1965-03-08	1967-01-09	1967-07-10	1968-02-08	1969-01-06	1970-01-05	1970-07-06	1971-01-04	1971-08-02	1972-01-03	1972-07-03	1973-12-31
[chomage.allocations_assurance_chomage.reval_chomeurs_plus_61ans.taux]	0.075	0.02	0.04	0.025	0.045	0.07	0.08	0.04	0.05	0.06	0.03	0.06	0.08

This Python snippet uses the DBnomics Python client to download the series of your cart and plot each of them with a line chart.

This is a starting point that you can customize. Plotly is used here, however any other chart library can be used.

You can start by copying it to a Jupyter Notebook , for example.

If you add series to your cart, you will need to copy-paste the new lines of the source code.

import plotly.express as px
import pandas as pd
from dbnomics import fetch_series

dfs = []

# Taux de revalorisation du salaire de références des chômeurs de plus de 61 ans pour les allocations chômage
df1 = fetch_series("IPP/taxbenefit_tables/chomage.allocations_assurance_chomage.reval_chomeurs_plus_61ans.taux")
df1["series_id"] = df1[["provider_code", "dataset_code", "series_code"]].agg('/'.join, axis=1)
dfs.append(df1)
# display(df1)
display(px.line(df1, x="period", y="value", title=df1.series_id[0]))

df_all = pd.concat(dfs)
fig = px.line(df_all, x="period", y="value", color="series_code", title="All the cart")
fig.update_layout(legend={"xanchor": "right", "yanchor": "bottom"})
fig.show()