Series collection
- from
- 2009=25,000
- to
- 2013=10,000
- min:
- 10,000
- max:
- 25,000
- avg:
- 18,250
- σ:
- 5,402.546
- from
- 2021=3,000
- to
- 2022=NA
- min:
- 3,000
- max:
- 3,000
- avg:
- 3,000
- σ:
- 0
- from
- 2013-01-01=8,000
- to
- 2013-01-01=8,000
- min:
- 8,000
- max:
- 8,000
- avg:
- 8,000
- σ:
- 0
- from
- 2009=0.1
- to
- 2013=NA
- min:
- 0.04
- max:
- 0.1
- avg:
- 0.07
- σ:
- 0.022
| Series code | 2009 | 2010 | 2011 | 2012 | 2013 | 2021 |
|---|---|---|---|---|---|---|
| [impot_revenu.credits_impots.plaf_nich.plafond] | 25000 | 20000 | 18000 | - | 10000 | - |
| [impot_revenu.credits_impots.plaf_nich.plafonnement_des_niches.majoration_esus_sfs] | - | - | - | - | - | 3000 |
| [impot_revenu.credits_impots.plaf_nich.taux] | 0.1 | 0.08 | 0.06 | 0.04 | NA | - |
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 des niches
df1 = fetch_series("IPP/taxbenefit_tables/impot_revenu.credits_impots.plaf_nich.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]))
# Majoration du plafond global des avantages fiscaux pour les souscriptions au capital d'entreprises solidaires d'utilité sociale (ESUS) et de sociétés foncières solidaires (SFS)
df2 = fetch_series("IPP/taxbenefit_tables/impot_revenu.credits_impots.plaf_nich.plafonnement_des_niches.majoration_esus_sfs")
df2["series_id"] = df2[["provider_code", "dataset_code", "series_code"]].agg('/'.join, axis=1)
dfs.append(df2)
# display(df2)
display(px.line(df2, x="period", y="value", title=df2.series_id[0]))
# Majoration du plafonnement des avantages fiscaux (« niches fiscales ») s'agissant des réductions d'impôt outre-mer et SOFICA
df3 = fetch_series("IPP/taxbenefit_tables/impot_revenu.credits_impots.plaf_nich.plafonnement_des_niches.majoration_om")
df3["series_id"] = df3[["provider_code", "dataset_code", "series_code"]].agg('/'.join, axis=1)
dfs.append(df3)
# display(df3)
display(px.line(df3, x="period", y="value", title=df3.series_id[0]))
# Taux du plafonnement des niches
df4 = fetch_series("IPP/taxbenefit_tables/impot_revenu.credits_impots.plaf_nich.taux")
df4["series_id"] = df4[["provider_code", "dataset_code", "series_code"]].agg('/'.join, axis=1)
dfs.append(df4)
# display(df4)
display(px.line(df4, x="period", y="value", title=df4.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()