Series collection
- from
- 1996-01-01=0.3
- to
- 1999-01-01=NA
- min:
- 0.3
- max:
- 0.3
- avg:
- 0.3
- σ:
- 0
- from
- 1996-01-01=0.02
- to
- 1999-01-01=NA
- min:
- 0.02
- max:
- 0.02
- avg:
- 0.02
- σ:
- 0
- from
- 1996-01-01=0.1
- to
- 1999-01-01=NA
- min:
- 0.1
- max:
- 0.1
- avg:
- 0.1
- σ:
- 0
- from
- 1996-01-01=NA
- to
- 2003-01-01=NA
- min:
- 0.3
- max:
- 0.3
- avg:
- 0.3
- σ:
- 0
- from
- 1996-01-01=NA
- to
- 2003-01-01=NA
- min:
- 0.025
- max:
- 0.025
- avg:
- 0.025
- σ:
- 0
- from
- 1996-01-01=NA
- to
- 2003-01-01=NA
- min:
- 0.08
- max:
- 0.08
- avg:
- 0.08
- σ:
- 0
- from
- 1996-01-01=NA
- to
- 2006-01-01=NA
- min:
- 0.025
- max:
- 0.025
- avg:
- 0.025
- σ:
- 0
- from
- 1996-01-01=NA
- to
- 2006-01-01=NA
- min:
- 0.08
- max:
- 0.08
- avg:
- 0.08
- σ:
- 0
- from
- 1996-01-01=NA
- to
- 2009-01-01=NA
- min:
- 0.04
- max:
- 0.04
- avg:
- 0.04
- σ:
- 0
- from
- 1996-01-01=NA
- to
- 2009-01-01=NA
- min:
- 0.025
- max:
- 0.025
- avg:
- 0.025
- σ:
- 0
- from
- 1996-01-01=NA
- to
- 2009-01-01=NA
- min:
- 0.06
- max:
- 0.06
- avg:
- 0.06
- σ:
- 0
- from
- 1996-01-01=100,000
- to
- 2001-01-01=10,700
- min:
- 10,700
- max:
- 100,000
- avg:
- 60,233.333
- σ:
- 37,104.926
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 minimum d'imposition sur le revenu pour être éligible
df1 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_revenus_imposables.deduc_invest_locatif.amortissement_perissol.seuil")
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]))
# Taux de déduction de l'investissment les 20 années suivantes
df2 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_revenus_imposables.deduc_invest_locatif.amortissement_perissol.taux_20_ans")
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]))
# Taux de déduction de l'investissement les 4 premières années
df3 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_revenus_imposables.deduc_invest_locatif.amortissement_perissol.taux_4_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]))
# Seuil minimum d'imposition pour être éligible
df4 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_revenus_imposables.deduc_invest_locatif.dispositif_besson_neuf.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]))
# Taux de déduction de l'investissement les 10 années suivantes
df5 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_revenus_imposables.deduc_invest_locatif.dispositif_besson_neuf.taux_10_ans")
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 de déduction de l'investissement les 5 premières années
df6 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_revenus_imposables.deduc_invest_locatif.dispositif_besson_neuf.taux_5_ans")
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 de déduction de l'investissement les 10 années suivantes
df7 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_revenus_imposables.deduc_invest_locatif.dispositif_robien.taux_10_ans")
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]))
# Taux de déduction de l'investissement les 5 premières années
df8 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_revenus_imposables.deduc_invest_locatif.dispositif_robien.taux_5_ans")
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]))
# Taux de déduction de l'investissement les 2 années suivantes
df9 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_revenus_imposables.deduc_invest_locatif.dispositif_robien_recentre.taux_2_ans")
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]))
# Taux de déduction de l'investissement les 6 années suivantes en cas d'éligibilité au dispositif Borloo neuf
df10 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_revenus_imposables.deduc_invest_locatif.dispositif_robien_recentre.taux_6_ans_borloo")
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]))
# Taux de déduction de l'investissement les 7 premières années
df11 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_revenus_imposables.deduc_invest_locatif.dispositif_robien_recentre.taux_7_ans")
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]))
# Plafond des déficits fonciers déductibles des revenus
df12 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_revenus_imposables.deduc_invest_locatif.plafond")
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]))
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()