Series collection
- from
- 1950-01-01=NA
- to
- 2023-01-01=1,050
- min:
- 800
- max:
- 6,220
- avg:
- 1,592.13
- σ:
- 1,766.293
- from
- 2012=120
- to
- 2013=NA
- min:
- 120
- max:
- 120
- avg:
- 120
- σ:
- 0
- from
- 1950-01-01=NA
- to
- 2023-01-01=4,149
- min:
- 3,490
- max:
- 21,930
- avg:
- 9,284.457
- σ:
- 7,000.502
- from
- 1950-01-01=NA
- to
- 1981-01-01=NA
- min:
- 6,000
- max:
- 10,800
- avg:
- 8,114.286
- σ:
- 1,537.557
- from
- 1950-01-01=NA
- to
- 2023-01-01=1,759
- min:
- 1,500
- max:
- 16,380
- avg:
- 6,750.048
- σ:
- 5,418.298
- from
- 1950-01-01=NA
- to
- 2023-01-01=1,753
- min:
- 570
- max:
- 4,336
- avg:
- 1,275.909
- σ:
- 809.065
- from
- 1950-01-01=NA
- to
- 2023-01-01=1,958
- min:
- 672
- max:
- 1,958
- avg:
- 1,659.167
- σ:
- 308.71
| Series code | 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 | 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 | 2023-01-01 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| [impot_revenu.calcul_impot_revenu.plaf_qf.plafond_avantages_procures_par_demi_part.celib] | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 6100 | - | 6130 | 6220 | 964 | 980 | 800 | 814 | 829 | 844 | 855 | 880 | 884 | 897 | - | - | 901 | 902 | 903 | 912 | 927 | 936 | 938 | 951 | 1002 | 1050 |
| [impot_revenu.calcul_impot_revenu.plaf_qf.plafond_avantages_procures_par_demi_part.celib_enf] | - | - | - | - | - | - | - | - | - | - | - | - | 13770 | 14230 | 14600 | 15090 | 15580 | 16050 | 16500 | 19060 | 19330 | 19680 | 20050 | 20270 | - | 20370 | 21930 | 3490 | 3549 | 3609 | 3670 | 3736 | 3803 | 3852 | 3964 | 3980 | 4040 | - | 3540 | 3558 | 3562 | 3566 | 3602 | 3660 | 3697 | 3704 | 3756 | 3959 | 4149 |
| [impot_revenu.calcul_impot_revenu.plaf_qf.plafond_avantages_procures_par_demi_part.enfant_majeur_celibataire] | 6000 | 6700 | 7300 | 7900 | 8600 | 9500 | 10800 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| [impot_revenu.calcul_impot_revenu.plaf_qf.plafond_avantages_procures_par_demi_part.general] | - | - | - | - | - | - | - | 7500 | 8450 | 9250 | 9960 | 10520 | 10770 | 11130 | 11420 | 11800 | 12180 | 12550 | 12910 | 15400 | 15620 | 15900 | 16200 | 16380 | 11000 | 11060 | 12440 | 2017 | 2051 | 2086 | 2121 | 2159 | 2198 | 2227 | 2292 | 2301 | 2336 | 2000 | 1500 | 1508 | 1510 | 1512 | 1527 | 1551 | 1567 | 1570 | 1592 | 1678 | 1759 |
| [impot_revenu.calcul_impot_revenu.plaf_qf.plafond_avantages_procures_par_demi_part.reduc_postplafond] | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 4336 | - | - | 570 | 580 | 590 | 600 | 611 | 622 | 630 | 648 | 661 | - | 997 | 1497 | 1504 | 1506 | 1508 | 1523 | 1547 | 1562 | 1565 | 1587 | 1673 | 1753 |
| [impot_revenu.calcul_impot_revenu.plaf_qf.plafond_avantages_procures_par_demi_part.reduc_postplafond_veuf] | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 672 | 1672 | 1680 | 1682 | 1684 | 1701 | 1728 | 1745 | 1748 | 1772 | 1868 | 1958 |
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 l'avantage en impôt par demi-part supplémentaire délivrée pour les personnes ayant élevé seules un enfant pendant 5 ans
df1 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_impot_revenu.plaf_qf.plafond_avantages_procures_par_demi_part.celib")
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]))
# Non mariés ou non PACS
df2 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_impot_revenu.plaf_qf.plafond_avantages_procures_par_demi_part.celib_E_ou_K")
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]))
# Plafond de l'avantage en impôt pour la part du premier enfant à charge d'un parent isolé
df3 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_impot_revenu.plaf_qf.plafond_avantages_procures_par_demi_part.celib_enf")
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]))
# Enfant majeur célibataire - Plafond des avantages procurés par demi-part de Quotient Familial
df4 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_impot_revenu.plaf_qf.plafond_avantages_procures_par_demi_part.enfant_majeur_celibataire")
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]))
# Plafond de l'avantage en impôt par demi-part supplémentaire attribuée en raison d'une personne à charge (cas général)
df5 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_impot_revenu.plaf_qf.plafond_avantages_procures_par_demi_part.general")
df5["series_id"] = df5[["provider_code", "dataset_code", "series_code"]].agg('/'.join, axis=1)
dfs.append(df5)
# display(df5)
display(px.line(df5, x="period", y="value", title=df5.series_id[0]))
# Réduction d'impôt complémentaire pour les invalides ou les anciens combattants, si le plafond général de la demi-part supplémentaire a été atteint
df6 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_impot_revenu.plaf_qf.plafond_avantages_procures_par_demi_part.reduc_postplafond")
df6["series_id"] = df6[["provider_code", "dataset_code", "series_code"]].agg('/'.join, axis=1)
dfs.append(df6)
# display(df6)
display(px.line(df6, x="period", y="value", title=df6.series_id[0]))
# Réduction d'impôt complémentaire pour les veufs ayant des enfants à charge, si le plafond général de leur demi-part supplémentaire a été atteint.
df7 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_impot_revenu.plaf_qf.plafond_avantages_procures_par_demi_part.reduc_postplafond_veuf")
df7["series_id"] = df7[["provider_code", "dataset_code", "series_code"]].agg('/'.join, axis=1)
dfs.append(df7)
# display(df7)
display(px.line(df7, x="period", y="value", title=df7.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()