Series collection

Trim NA Values

[IPP/taxbenefit_tables/retraites.secteur_public.pension_civile.revalorisation_pension_au_31_decembre] Coefficient de revalorisation cummulée par année de perception de la valeur des pensions au 31 décembre

from: 1959-01-01=1

to: 2022-01-01=1.011

min:: 1

max:: 1.27

avg:: 1.052

σ:: 0.061

Series code	1959-01-01	1961-01-01	1962-01-01	1963-01-01	1964-01-01	1965-01-01	1966-01-01	1967-01-01	1968-01-01	1969-01-01	1970-01-01	1971-01-01	1972-01-01	1973-01-01	1974-01-01	1975-01-01	1976-01-01	1977-01-01	1978-01-01	1979-01-01	1980-01-01	1981-01-01	1982-01-01	1983-01-01	1984-01-01	1985-01-01	1986-01-01	1987-01-01	1988-01-01	1989-01-01	1990-01-01	1991-01-01	1992-01-01	1993-01-01	1994-01-01	1995-01-01	1996-01-01	1997-01-01	1998-01-01	1999-01-01	2000-01-01	2001-01-01	2002-01-01	2003-01-01	2004-01-01	2005-01-01	2006-01-01	2007-01-01	2008-01-01	2009-01-01	2010-01-01	2011-01-01	2012-01-01	2013-01-01	2014-01-01	2015-01-01	2016-01-01	2017-01-01	2018-01-01	2019-01-01	2020-01-01	2021-01-01	2022-01-01
[retraites.secteur_public.pension_civile.revalorisation_pension_au_31_decembre]	1	1.26985598952651	1.20007302719189	1.1085855422118	1.05808847271553	1.04000732399524	1.05621025858415	1.08489698307579	1.15365025466893	1.10080598060974	1.11471354166667	1.10982658959538	1.08209538702111	1.1342674707343	1.18794774546987	1.17679147036945	1.15924116125323	1.11185682326622	1.09635599159075	1.13750498206457	1.13273137697517	1.11981799797776	1.06002143622722	1.05483323911815	1.04304245283019	1.02353651176826	1	1.02371794871795	1.03723404255319	1.02732240437158	1.01596113809854	1.03297491039427	1	1.01824817518248	1.01181683899557	1.02498107494322	1	1.0108275328693	1.01173708920188	1.00550747442958	1.02006420545746	1.02214930270714	1.01668056713928	1	1.015	1.02	1.018	1.018	1.019088	1.01	1.009	1.021	1.021	1.013	1	1.001	1	1.008	1	1.003	1.01	1.004	1.011

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 = []

# Coefficient de revalorisation cummulée par année de perception de la valeur des pensions au 31 décembre
df1 = fetch_series("IPP/taxbenefit_tables/retraites.secteur_public.pension_civile.revalorisation_pension_au_31_decembre")
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()