Series collection
[IPP/taxbenefit_tables/retraites.secteur_prive.regime_general_cnav.majoreversion_plafond] Plafond de ressources pour la majoration de la retraite de réversion
- from
- 2010-Q1=2,400
- to
- 2025-Q1=2,993.14
- min:
- 2,400
- max:
- 2,993.14
- avg:
- 2,630.471
- σ:
- 164.989
| Series code | 2010-Q1 | 2010-Q2 | 2011-Q2 | 2012-Q2 | 2013-Q2 | 2015-Q4 | 2017-Q4 | 2019-Q1 | 2020-Q1 | 2021-Q1 | 2022-Q1 | 2022-Q3 | 2023-Q1 | 2024-Q1 | 2025-Q1 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| [retraites.secteur_prive.regime_general_cnav.majoreversion_plafond] | 2400 | 2421.6 | 2472.45 | 2524.37 | 2557.18 | 2559.73 | 2580.2 | 2587.94 | 2613.81 | 2624.26 | 2653.12 | 2759.24 | 2781.31 | 2928.71 | 2993.14 |
Loading chart
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 = []
# Plafond de ressources pour la majoration de la retraite de réversion
df1 = fetch_series("IPP/taxbenefit_tables/retraites.secteur_prive.regime_general_cnav.majoreversion_plafond")
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()