Series collection

Trim NA Values

[IPP/taxbenefit_tables/chomage.preretraites.sr_fne.revalorisation_sr] Revalorisation du salaire de référence pour le calcul des allocations de préretraites FNE-ARPE

from: 1981-10-01=0.074

to: 2010-S2=0.012

min:: 0

max:: 0.083

avg:: 0.019

σ:: 0.016

Series code	1981-10-01	1982-04-01	1983-04-01	1984-04-01	1984-10-01	1985-S1	1986-10-01	1986-S1	1986-S2	1987-S1	1987-S2	1988-S1	1988-S2	1989-S2	1990-S1	1990-S2	1991-S1	1991-S2	1992-S1	1992-S2	1993-S1	1994-S1	1995-S1	1995-S2	1996-S1	1997-S1	1998-S1	1999-S1	2000-S1	2001-S1	2003-S1	2004-S1	2005-S1	2006-S1	2007-S2	2008-S2	2009-S2	2010-S2
[chomage.preretraites.sr_fne.revalorisation_sr]	0.0743	0.0831	0.04	0.018	0.022	0.028	0.005	0.013	0	0.018	0.01	0.026	0.013	0.012	0.0215	0.013	0.017	0.008	0.01	0.018	0.013	0.02	0.012	0.005	0.02	0.012	0.011	0.012	0.005	0.022	0.015	0.017	0.02	0.018	0.0195	0.025	0.01	0.012

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 = []

# Revalorisation du salaire de référence pour le calcul des allocations de préretraites FNE-ARPE
df1 = fetch_series("IPP/taxbenefit_tables/chomage.preretraites.sr_fne.revalorisation_sr")
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()