Series collection
- from
- 2005-01-01=26
- to
- 2009-01-01=NA
- min:
- 26
- max:
- 26
- avg:
- 26
- σ:
- 0
- from
- 2005-01-01=10,060
- to
- 2009-01-01=NA
- min:
- 10,060
- max:
- 10,060
- avg:
- 10,060
- σ:
- 0
- from
- 2005-01-01=12,060
- to
- 2009-01-01=NA
- min:
- 12,060
- max:
- 12,060
- avg:
- 12,060
- σ:
- 0
- from
- 2005-01-01=2,970
- to
- 2009-01-01=NA
- min:
- 2,970
- max:
- 2,970
- avg:
- 2,970
- σ:
- 0
- from
- 2005-01-01=1,500
- to
- 2009-01-01=NA
- min:
- 1,500
- max:
- 1,500
- avg:
- 1,500
- σ:
- 0
- from
- 2005-01-01=25
- to
- 2009-01-01=NA
- min:
- 25
- max:
- 25
- avg:
- 25
- σ:
- 0
- from
- 2005-01-01=4,276
- to
- 2009-01-01=NA
- min:
- 4,276
- max:
- 4,276
- avg:
- 4,276
- σ:
- 0
- from
- 2005-01-01=2
- to
- 2009-01-01=NA
- min:
- 2
- max:
- 2
- avg:
- 2
- σ:
- 0
- from
- 2005-01-01=25,000
- to
- 2009-01-01=NA
- min:
- 25,000
- max:
- 25,000
- avg:
- 25,000
- σ:
- 0
- from
- 2005-01-01=0.75
- to
- 2009-01-01=NA
- min:
- 0.75
- max:
- 0.75
- avg:
- 0.75
- 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 = []
# Âge limite pour bénéficier du crédit d'impôt jeunes actifs
df1 = fetch_series("IPP/taxbenefit_tables/impot_revenu.credits_impots.jeunes.age")
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 intermédiaire de revenus pour bénéficier du crédit d'impôt jeunes actifs
df2 = fetch_series("IPP/taxbenefit_tables/impot_revenu.credits_impots.jeunes.int")
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 revenus pour bénéficier du crédit d'impôt jeunes actifs
df3 = fetch_series("IPP/taxbenefit_tables/impot_revenu.credits_impots.jeunes.max")
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]))
# Plancher de revenus pour bénéficier du crédit d'impôt jeunes actifs
df4 = fetch_series("IPP/taxbenefit_tables/impot_revenu.credits_impots.jeunes.min")
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]))
# Montant du crédit d'impôt jeunes actifs
df5 = fetch_series("IPP/taxbenefit_tables/impot_revenu.credits_impots.jeunes.montant")
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]))
# Seuil de non-versement du crédit d'impôt jeunes actifs
df6 = fetch_series("IPP/taxbenefit_tables/impot_revenu.credits_impots.jeunes.nonvers")
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]))
# Majoration du revenu fiscal de référence plafond (par demi-part supplémentaire) du crédit d'impôt jeunes actifs
df7 = fetch_series("IPP/taxbenefit_tables/impot_revenu.credits_impots.jeunes.rfr_maj")
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]))
# Multiplicateur du RFR plafond pour couple bénéficiant du crédit d'impôt jeunes actifs
df8 = fetch_series("IPP/taxbenefit_tables/impot_revenu.credits_impots.jeunes.rfr_mult")
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]))
# Revenu fiscal de référence plafond (personne seule) pour bénéficier du crédit d'impôt jeunes actifs
df9 = fetch_series("IPP/taxbenefit_tables/impot_revenu.credits_impots.jeunes.rfr_plaf")
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 du crédit d'impôt jeunes actifs
df10 = fetch_series("IPP/taxbenefit_tables/impot_revenu.credits_impots.jeunes.taux")
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()