Series collection
- from
- 2000-01-01=NA
- to
- 2006-01-01=1,000
- min:
- 1,000
- max:
- 1,000
- avg:
- 1,000
- σ:
- 0
- from
- 2000-01-01=NA
- to
- 2006-01-01=0.5
- min:
- 0.5
- max:
- 0.5
- avg:
- 0.5
- σ:
- 0
- from
- 2000=NA
- to
- 2022=6,250
- min:
- 5,700
- max:
- 6,250
- avg:
- 5,975
- σ:
- 275
- from
- 2000=NA
- to
- 2022=0.25
- min:
- 0.18
- max:
- 0.25
- avg:
- 0.225
- σ:
- 0.029
- from
- 2011-01-01=6,250
- to
- 2011-01-01=6,250
- min:
- 6,250
- max:
- 6,250
- avg:
- 6,250
- σ:
- 0
- from
- 2011=0.9
- to
- 2012=0.76
- min:
- 0.76
- max:
- 0.9
- avg:
- 0.83
- σ:
- 0.07
- from
- 2009-01-01=2,000
- to
- 2009-01-01=2,000
- min:
- 2,000
- max:
- 2,000
- avg:
- 2,000
- σ:
- 0
- from
- 2009-01-01=6,250
- to
- 2009-01-01=6,250
- min:
- 6,250
- max:
- 6,250
- avg:
- 6,250
- σ:
- 0
- from
- 2014-01-01=0.18
- to
- 2023-01-01=0.25
- min:
- 0.18
- max:
- 0.25
- avg:
- 0.215
- σ:
- 0.035
- from
- 2014-01-01=0.25
- to
- 2014-01-01=0.25
- min:
- 0.25
- max:
- 0.25
- avg:
- 0.25
- σ:
- 0
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 des cotisations pour la défense des forêts contre les incendies ouvrant droit à la réduction d'impôt sur le revenu (IR)
df1 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_reductions_impots.investissement_forestier.defense_forets_contre_incendies.plafond")
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 la réduction d'impôt pour la défense des forêts contre les incendies
df2 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_reductions_impots.investissement_forestier.defense_forets_contre_incendies.taux")
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 dépenses pour les acquisitions d'investissement forestier ouvrant droit au crédit de l'impôt sur le revenu (IR)
df3 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_reductions_impots.investissement_forestier.depenses_investissement_forestier.acquisition.plafond")
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]))
# Taux du crédit d'impôt pour les acquisitions d'investissement forestier
df4 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_reductions_impots.investissement_forestier.depenses_investissement_forestier.acquisition.taux")
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 la réduction d'impôt sur la cotisation d'assurance d'investissements forestiers
df5 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_reductions_impots.investissement_forestier.depenses_investissement_forestier.assurance.plafond")
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 du crédit d'impôt sur la cotisation d'assurance d'investissements forestiers
df6 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_reductions_impots.investissement_forestier.depenses_investissement_forestier.assurance.taux")
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]))
# Plafond de dépenses de contrat de gestion (CGA) de dépenses d'investissement forestier
df7 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_reductions_impots.investissement_forestier.depenses_investissement_forestier.plafond_cga")
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]))
# Plafond de dépenses de travaux et assurances d'investissement forestier
df8 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_reductions_impots.investissement_forestier.depenses_investissement_forestier.travaux.plafond")
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 du crédit d'impôt pour les travaux réguliers
df9 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_reductions_impots.investissement_forestier.depenses_investissement_forestier.travaux.taux")
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 pour les travaux avec adhésion à une organisation de producteurs
df10 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_reductions_impots.investissement_forestier.depenses_investissement_forestier.travaux.taux_adhesion_org_producteurs")
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]))
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()