Series collection
- from
- 1973-01-01=NA
- to
- 2011-01-01=NA
- min:
- 0.25
- max:
- 0.25
- avg:
- 0.25
- σ:
- 0
- from
- 1973-01-01=NA
- to
- 2011-01-01=NA
- min:
- 20,000
- max:
- 20,000
- avg:
- 20,000
- σ:
- 0
- from
- 2007-01-01=NA
- to
- 2011-01-01=NA
- min:
- 0.4
- max:
- 0.4
- avg:
- 0.4
- 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 en % du revenu net global de la réduction d'impôts pour les sommes versées sur un compte épargne codéveloppement (2010 - 2011)
df1 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_reductions_impots.compte_epargne_co_developpement.plafond.plafond_en_revenu_net_global")
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]))
# Plafond maximum par personne de la réduction d'impôts pour les sommes versées sur un compte épargne codéveloppement (2010 - 2011)
df2 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_reductions_impots.compte_epargne_co_developpement.plafond.plafond_par_personne")
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 la réduction d'impôts pour les sommes versées sur un compte épargne codéveloppement (2010 - 2011)
df3 = fetch_series("IPP/taxbenefit_tables/impot_revenu.calcul_reductions_impots.compte_epargne_co_developpement.taux")
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]))
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()