Series collection

Trim NA Values

[IPP/taxbenefit_tables/retraites.secteur_public.pension_civile.revalorisation_pension_servie] Coefficient de revalorisation par année de perception des pensions servies par rapport à la pension annuelle servie l'année précédente

from: 1961=1.27

to: 2022=1.011

min:: 1

max:: 1.27

avg:: 1.053

σ:: 0.061

Series code	1961	1962	1963	1964	1965	1966	1967	1968	1969	1970	1971	1972	1973	1974	1975	1976	1977	1978	1979	1980	1981	1982	1983	1984	1985	1986	1987	1988	1989	1990	1991	1992	1993	1994	1995	1996	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
[retraites.secteur_public.pension_civile.revalorisation_pension_servie]	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	1.013696	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 par année de perception des pensions servies par rapport à la pension annuelle servie l'année précédente
df1 = fetch_series("IPP/taxbenefit_tables/retraites.secteur_public.pension_civile.revalorisation_pension_servie")
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()