Series collection
- from
- 2012-01-01=5,000
- to
- 2015-01-01=NA
- min:
- 5,000
- max:
- 5,000
- avg:
- 5,000
- σ:
- 0
- from
- 2012-01-01=10,000
- to
- 2015-01-01=NA
- min:
- 10,000
- max:
- 10,000
- avg:
- 10,000
- σ:
- 0
- from
- 2005-01-01=5,000
- to
- 2005-01-01=5,000
- min:
- 5,000
- max:
- 5,000
- avg:
- 5,000
- σ:
- 0
- from
- 2005-01-01=10,000
- to
- 2005-01-01=10,000
- min:
- 10,000
- max:
- 10,000
- avg:
- 10,000
- σ:
- 0
- from
- 2006-01-01=400
- to
- 2006-01-01=400
- min:
- 400
- max:
- 400
- avg:
- 400
- σ:
- 0
- from
- 2005=400
- to
- 2006=NA
- min:
- 400
- max:
- 400
- avg:
- 400
- σ:
- 0
- from
- 2005=500
- to
- 2006=NA
- min:
- 500
- max:
- 500
- avg:
- 500
- σ:
- 0
- from
- 2005=600
- to
- 2006=NA
- min:
- 600
- max:
- 600
- avg:
- 600
- σ:
- 0
- from
- 2015-01-01=20,000
- to
- 2015-01-01=20,000
- min:
- 20,000
- max:
- 20,000
- avg:
- 20,000
- σ:
- 0
- from
- 2010-01-01=0.15
- to
- 2013-01-01=NA
- min:
- 0.15
- max:
- 0.15
- avg:
- 0.15
- σ:
- 0
- from
- 2005-01-01=0.25
- to
- 2005-01-01=0.25
- min:
- 0.25
- max:
- 0.25
- avg:
- 0.25
- σ:
- 0
- from
- 2010-01-01=0.3
- to
- 2013-01-01=0.4
- min:
- 0.3
- max:
- 0.4
- avg:
- 0.35
- σ:
- 0.05
- from
- 2005-01-01=0.15
- to
- 2010-01-01=NA
- min:
- 0.15
- max:
- 0.15
- avg:
- 0.15
- 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 = []
# Célibataires
df1 = fetch_series("IPP/taxbenefit_tables/impot_revenu.credits_impots.equ_hab_princ_aide_personnes.plafond.maj_plaf_risques_techno_avant_2015.celib")
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]))
# Déclarations communes
df2 = fetch_series("IPP/taxbenefit_tables/impot_revenu.credits_impots.equ_hab_princ_aide_personnes.plafond.maj_plaf_risques_techno_avant_2015.couple")
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]))
# Célibataires
df3 = fetch_series("IPP/taxbenefit_tables/impot_revenu.credits_impots.equ_hab_princ_aide_personnes.plafond.plafond_commun.celib")
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]))
# Déclarations communes
df4 = fetch_series("IPP/taxbenefit_tables/impot_revenu.credits_impots.equ_hab_princ_aide_personnes.plafond.plafond_commun.couple")
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]))
# Majoration par personne à charge
df5 = fetch_series("IPP/taxbenefit_tables/impot_revenu.credits_impots.equ_hab_princ_aide_personnes.plafond.plafond_commun.maj_pac")
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]))
# Majoration 1ère personne à charge
df6 = fetch_series("IPP/taxbenefit_tables/impot_revenu.credits_impots.equ_hab_princ_aide_personnes.plafond.plafond_commun.maj_pac1")
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 2nde personne à charge
df7 = fetch_series("IPP/taxbenefit_tables/impot_revenu.credits_impots.equ_hab_princ_aide_personnes.plafond.plafond_commun.maj_pac2")
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]))
# Majoration 3ème personne à charge et plus
df8 = fetch_series("IPP/taxbenefit_tables/impot_revenu.credits_impots.equ_hab_princ_aide_personnes.plafond.plafond_commun.maj_pac3")
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]))
# Plafond de dépenses de travaux de prévention des risques technologiques et diagnostic préalable (à compter de l'imposition des revenus 2015)
df9 = fetch_series("IPP/taxbenefit_tables/impot_revenu.credits_impots.equ_hab_princ_aide_personnes.plafond.plafond_risque_techno_apres_2015")
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 pour les dépenses d'ascenseurs électriques à traction
df10 = fetch_series("IPP/taxbenefit_tables/impot_revenu.credits_impots.equ_hab_princ_aide_personnes.taux.taux_ascenseurs")
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 du crédit d'impôt pour les dépenses d'installation ou de remplacement d'équipements spécialement conçus pour les personnes âgées ou handicapées
df11 = fetch_series("IPP/taxbenefit_tables/impot_revenu.credits_impots.equ_hab_princ_aide_personnes.taux.taux_equ_pers_agees_hand")
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 du crédit d'impôt pour les dépenses de prévention des risques technologiques
df12 = fetch_series("IPP/taxbenefit_tables/impot_revenu.credits_impots.equ_hab_princ_aide_personnes.taux.taux_risques_techno")
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 du crédit d'impôt pour les dépenses de prévention des risques technologiques et au titre des ascenseurs électriques à traction
df13 = fetch_series("IPP/taxbenefit_tables/impot_revenu.credits_impots.equ_hab_princ_aide_personnes.taux.taux_risques_techno_ascenseurs")
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()