Series collection
- from
- 1946-01-01=NA
- to
- 1985-01-01=NA
- min:
- 5,000
- max:
- 100,000
- avg:
- 23,808
- σ:
- 26,841.978
- from
- 1946-01-01=NA
- to
- 1984-01-01=NA
- min:
- 10,000
- max:
- 30,000
- avg:
- 17,400
- σ:
- 7,144.228
- from
- 1946-01-01=NA
- to
- 1971-01-01=NA
- min:
- 14,000
- max:
- 20,000
- avg:
- 18,000
- σ:
- 2,828.427
- from
- 1946=NA
- to
- 1966=NA
- min:
- 6,000
- max:
- 600,000
- avg:
- 113,571.429
- σ:
- 199,331.638
- from
- 1946=NA
- to
- 1985=NA
- min:
- 0.01
- max:
- 0.25
- avg:
- 0.089
- σ:
- 0.072
- from
- 1946-01-01=NA
- to
- 1984-01-01=NA
- min:
- 0.02
- max:
- 0.2
- avg:
- 0.102
- σ:
- 0.062
- from
- 1946-01-01=NA
- to
- 1971-01-01=NA
- min:
- 0.03
- max:
- 0.25
- avg:
- 0.126
- σ:
- 0.083
| Series code | 1967-01-01 | 1968-01-01 | 1969-01-01 | 1970-01-01 | 1971-01-01 | 1972-01-01 | 1980-01-01 | 1981-01-01 | 1982-01-01 | 1983-01-01 | 1984-01-01 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| [impot_revenu.contributions_exceptionnelles.majorations_exceptionnelles.seuil_1] | 5000 | 6000 | 7000 | 10000 | 15000 | NA | 100000 | 15000 | 28000 | 20000 | 32080 |
| [impot_revenu.contributions_exceptionnelles.majorations_exceptionnelles.seuil_2] | 10000 | 12000 | - | 15000 | 20000 | NA | - | - | - | 30000 | NA |
| [impot_revenu.contributions_exceptionnelles.majorations_exceptionnelles.seuil_3_superieur] | 20000 | 14000 | - | 20000 | NA | - | - | - | - | - | - |
| [impot_revenu.contributions_exceptionnelles.majorations_exceptionnelles.taux_2] | 0.2 | 0.14 | 0.07 | 0.02 | - | NA | - | - | - | 0.08 | NA |
| [impot_revenu.contributions_exceptionnelles.majorations_exceptionnelles.taux_3_superieur] | 0.25 | 0.15 | 0.075 | 0.03 | NA | - | - | - | - | - | - |
| Series code | 1947 | 1948 | 1955 | 1960 | 1961 | 1962 | 1963 | 1964 | 1965 | 1966 | 1967 | 1968 | 1970 | 1972 | 1980 | 1981 | 1982 | 1983 | 1984 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| [impot_revenu.contributions_exceptionnelles.majorations_exceptionnelles.seuil_revenu] | 50000 | NA | 600000 | 6000 | - | 8000 | 36000 | 45000 | 50000 | NA | - | - | - | - | - | - | - | - | - |
| [impot_revenu.contributions_exceptionnelles.majorations_exceptionnelles.taux_1] | 0.2 | NA | 0.1 | - | 0.05 | - | - | - | - | NA | 0.1 | 0.02 | 0.01 | NA | 0.25 | 0.1 | 0.07 | 0.05 | 0.03 |
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 = []
# Seuil 1 des majorations exceptionnelles d'impôt
df1 = fetch_series("IPP/taxbenefit_tables/impot_revenu.contributions_exceptionnelles.majorations_exceptionnelles.seuil_1")
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]))
# Seuil 2 des majorations exceptionnelles d'impôt
df2 = fetch_series("IPP/taxbenefit_tables/impot_revenu.contributions_exceptionnelles.majorations_exceptionnelles.seuil_2")
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]))
# Seuil 3 (supérieur) des majorations exceptionnelles d'impôt
df3 = fetch_series("IPP/taxbenefit_tables/impot_revenu.contributions_exceptionnelles.majorations_exceptionnelles.seuil_3_superieur")
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]))
# Seuil de revenu des majorations exceptionnelles d'impôt
df4 = fetch_series("IPP/taxbenefit_tables/impot_revenu.contributions_exceptionnelles.majorations_exceptionnelles.seuil_revenu")
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]))
# Taux 1 des majorations exceptionnelles d'impôt
df5 = fetch_series("IPP/taxbenefit_tables/impot_revenu.contributions_exceptionnelles.majorations_exceptionnelles.taux_1")
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]))
# Taux 2 des majorations exceptionnelles d'impôt
df6 = fetch_series("IPP/taxbenefit_tables/impot_revenu.contributions_exceptionnelles.majorations_exceptionnelles.taux_2")
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]))
# Taux 3 (supérieur) des majorations exceptionnelles d'impôt
df7 = fetch_series("IPP/taxbenefit_tables/impot_revenu.contributions_exceptionnelles.majorations_exceptionnelles.taux_3_superieur")
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()