Series collection
- from
- 2005-01-01=1
- to
- 2005-01-01=1
- min:
- 1
- max:
- 1
- avg:
- 1
- σ:
- 0
- from
- 1977=15,200
- to
- 2005=NA
- min:
- 7,250
- max:
- 46,800
- avg:
- 31,119.2
- σ:
- 13,951.833
- from
- 1977=15,200
- to
- 2014=NA
- min:
- 7,250
- max:
- 46,800
- avg:
- 25,576.364
- σ:
- 15,604.377
- from
- 1977=16,600
- to
- 2014=NA
- min:
- 7,920
- max:
- 51,100
- avg:
- 27,888.091
- σ:
- 16,997.966
| Series code | 1977 | 1978 | 1979 | 1980 | 1981 | 1982 | 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 | 2012 | 2013 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| [impot_revenu.calcul_revenus_imposables.exo_ir.conditions_ressources_exoneration_ir.apprentis_avant_2005] | 15200 | 16800 | 18600 | 21100 | 24000 | NA | 31800 | 33600 | 34500 | 35700 | 36700 | NA | 39300 | 40500 | 41700 | 42500 | 43200 | 44000 | 44900 | 45400 | 45800 | 46100 | 46800 | 7250 | 7380 | 7510 | 7640 | NA | - | - | - | - | - | - | - |
| [impot_revenu.calcul_revenus_imposables.exo_ir.conditions_ressources_exoneration_ir.moins_de_65_ans] | 15200 | 16800 | 18600 | 21100 | 24000 | NA | 31800 | 33600 | 34500 | 35700 | 36700 | NA | 39300 | 40500 | 41700 | 42500 | 43200 | 44000 | 44900 | 45400 | 45800 | 46100 | 46800 | 7250 | 7380 | 7510 | 7640 | 7780 | 7920 | 8030 | 8270 | 8310 | 8440 | 8610 | 8680 |
| [impot_revenu.calcul_revenus_imposables.exo_ir.conditions_ressources_exoneration_ir.plus_de_65_ans] | 16600 | 18300 | 20300 | 23000 | 26200 | NA | 34700 | 36700 | 37600 | 38900 | 40000 | NA | 42800 | 44100 | 45400 | 46300 | 47000 | 47900 | 48900 | 49500 | 50000 | 50300 | 51100 | 7920 | 8060 | 8200 | 8340 | 8507 | 8660 | 8780 | 9040 | 9080 | 9220 | 9410 | 9490 |
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 = []
# Conditions de ressources pour les apprentis pour exonération d'Impôt sur le Revenu à partir de 2005
df1 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_revenus_imposables.exo_ir.conditions_ressources_exoneration_ir.apprentis_a_partir_2005")
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]))
# Conditions de ressources pour les apprentis pour exonération d'Impôt sur le Revenu avant 2005
df2 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_revenus_imposables.exo_ir.conditions_ressources_exoneration_ir.apprentis_avant_2005")
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]))
# Conditions de ressources pour les moins de 65 ans pour exonération d'Impôt sur le Revenu
df3 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_revenus_imposables.exo_ir.conditions_ressources_exoneration_ir.moins_de_65_ans")
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]))
# Conditions de ressources pour les plus de 65 ans pour exonération d'Impôt sur le Revenu
df4 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_revenus_imposables.exo_ir.conditions_ressources_exoneration_ir.plus_de_65_ans")
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()