Series collection
- from
- 1947-01-01=NA
- to
- 1970-01-01=NA
- min:
- 30,000
- max:
- 30,000
- avg:
- 30,000
- σ:
- 0
- from
- 1947-01-01=NA
- to
- 1969-01-01=NA
- min:
- 3,000
- max:
- 3,000
- avg:
- 3,000
- σ:
- 0
- from
- 1947-01-01=NA
- to
- 1969-01-01=NA
- min:
- 4,400
- max:
- 4,400
- avg:
- 4,400
- σ:
- 0
- from
- 1947=NA
- to
- 1959=NA
- min:
- 200,000
- max:
- 440,000
- avg:
- 320,000
- σ:
- 120,000
- from
- 1947-01-01=NA
- to
- 1959-01-01=NA
- min:
- 300,000
- max:
- 300,000
- avg:
- 300,000
- σ:
- 0
- from
- 1947=NA
- to
- 1959=NA
- min:
- 5,000
- max:
- 5,000
- avg:
- 5,000
- σ:
- 0
- from
- 1947=NA
- to
- 1959=NA
- min:
- 15,000
- max:
- 15,000
- avg:
- 15,000
- σ:
- 0
- from
- 1947=NA
- to
- 1959=NA
- min:
- 0.1
- max:
- 0.1
- avg:
- 0.1
- σ:
- 0
- from
- 1947=NA
- to
- 1959=NA
- min:
- 0.3
- max:
- 0.3
- avg:
- 0.3
- σ:
- 0
- from
- 1947=NA
- to
- 1959=NA
- min:
- 0.75
- max:
- 0.75
- avg:
- 0.75
- σ:
- 0
- from
- 1947=NA
- to
- 1959=NA
- min:
- 1
- max:
- 1
- avg:
- 1
- σ:
- 0
- from
- 1947=NA
- to
- 1970=NA
- min:
- 0.06
- max:
- 0.22
- avg:
- 0.126
- σ:
- 0.062
- from
- 1947=NA
- to
- 1970=NA
- min:
- 0.03
- max:
- 0.09
- avg:
- 0.057
- σ:
- 0.025
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 = []
# Abattement général
df1 = fetch_series("IPP/taxbenefit_tables/impot_revenu.contributions_exceptionnelles.taxe_prop.abattement_general")
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]))
# abattement
df2 = fetch_series("IPP/taxbenefit_tables/impot_revenu.contributions_exceptionnelles.taxe_prop.autres_revenus_non_salariaux.abattement")
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]))
# abattement
df3 = fetch_series("IPP/taxbenefit_tables/impot_revenu.contributions_exceptionnelles.taxe_prop.bnc_autres_produits_charges_offices_bic_artisans_assimiles.abattement")
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
df4 = fetch_series("IPP/taxbenefit_tables/impot_revenu.contributions_exceptionnelles.taxe_prop.bnc_autres_produits_charges_offices_bic_artisans_assimiles.seuil")
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]))
# seuil
df5 = fetch_series("IPP/taxbenefit_tables/impot_revenu.contributions_exceptionnelles.taxe_prop.bnc_produits_charges_offices_bic_autres_que_artisans_assimiles_ba.seuil")
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]))
# Jusqu'à 2 pacs
df6 = fetch_series("IPP/taxbenefit_tables/impot_revenu.contributions_exceptionnelles.taxe_prop.plafond_maximum_reduction_par_pac.jusqu_2_pacs")
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]))
# Plus de 3 pacs
df7 = fetch_series("IPP/taxbenefit_tables/impot_revenu.contributions_exceptionnelles.taxe_prop.plafond_maximum_reduction_par_pac.plus_3_pacs")
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]))
# 1 pac
df8 = fetch_series("IPP/taxbenefit_tables/impot_revenu.contributions_exceptionnelles.taxe_prop.reduction_impot_charge_famille.1_pac")
df8["series_id"] = df8[["provider_code", "dataset_code", "series_code"]].agg('/'.join, axis=1)
dfs.append(df8)
# display(df8)
display(px.line(df8, x="period", y="value", title=df8.series_id[0]))
# 2 pac
df9 = fetch_series("IPP/taxbenefit_tables/impot_revenu.contributions_exceptionnelles.taxe_prop.reduction_impot_charge_famille.2_pac")
df9["series_id"] = df9[["provider_code", "dataset_code", "series_code"]].agg('/'.join, axis=1)
dfs.append(df9)
# display(df9)
display(px.line(df9, x="period", y="value", title=df9.series_id[0]))
# 3 pac
df10 = fetch_series("IPP/taxbenefit_tables/impot_revenu.contributions_exceptionnelles.taxe_prop.reduction_impot_charge_famille.3_pac")
df10["series_id"] = df10[["provider_code", "dataset_code", "series_code"]].agg('/'.join, axis=1)
dfs.append(df10)
# display(df10)
display(px.line(df10, x="period", y="value", title=df10.series_id[0]))
# 4 pac
df11 = fetch_series("IPP/taxbenefit_tables/impot_revenu.contributions_exceptionnelles.taxe_prop.reduction_impot_charge_famille.4_pac")
df11["series_id"] = df11[["provider_code", "dataset_code", "series_code"]].agg('/'.join, axis=1)
dfs.append(df11)
# display(df11)
display(px.line(df11, x="period", y="value", title=df11.series_id[0]))
# Taux
df12 = fetch_series("IPP/taxbenefit_tables/impot_revenu.contributions_exceptionnelles.taxe_prop.taux")
df12["series_id"] = df12[["provider_code", "dataset_code", "series_code"]].agg('/'.join, axis=1)
dfs.append(df12)
# display(df12)
display(px.line(df12, x="period", y="value", title=df12.series_id[0]))
# Taux réduit
df13 = fetch_series("IPP/taxbenefit_tables/impot_revenu.contributions_exceptionnelles.taxe_prop.taux_reduit")
df13["series_id"] = df13[["provider_code", "dataset_code", "series_code"]].agg('/'.join, axis=1)
dfs.append(df13)
# display(df13)
display(px.line(df13, x="period", y="value", title=df13.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()