Series collection
- from
- 1989-10-01=0
- to
- 1989-10-01=0
- min:
- 0
- max:
- 0
- avg:
- 0
- σ:
- 0
- from
- 1989-10-01=0
- to
- 1989-10-01=0
- min:
- 0
- max:
- 0
- avg:
- 0
- σ:
- 0
- from
- 1989-10-01=100,000
- to
- 2002-01-01=23,000
- min:
- 23,000
- max:
- 150,000
- avg:
- 91,000
- σ:
- 52,236.641
- from
- 1989-10-01=0.6
- to
- 2008-08-06=0.02
- min:
- 0.02
- max:
- 0.6
- avg:
- 0.175
- σ:
- 0.246
- from
- 1989-10-01=300,000
- to
- 2002-01-01=107,000
- min:
- 107,000
- max:
- 700,000
- avg:
- 401,750
- σ:
- 221,267.231
- from
- 1989-10-01=0.012
- to
- 2008-08-06=0.006
- min:
- 0.006
- max:
- 0.09
- avg:
- 0.032
- σ:
- 0.03
- from
- 2008-08-06=200,000
- to
- 2008-08-06=200,000
- min:
- 200,000
- max:
- 200,000
- avg:
- 200,000
- σ:
- 0
- from
- 2008-08-06=0.026
- to
- 2008-08-06=0.026
- min:
- 0.026
- max:
- 0.026
- avg:
- 0.026
- σ:
- 0
| Series code | 1989-10-01 | 1991-10-01 | 1993-05-10 | 1999-09-15 | 2002-01-01 | 2005-01-01 | 2008-08-06 |
|---|---|---|---|---|---|---|---|
| [impot_revenu.contributions_exceptionnelles.indemnite_compensatrice_agents_assurance.0.threshold] | 0 | - | - | - | - | - | - |
| [impot_revenu.contributions_exceptionnelles.indemnite_compensatrice_agents_assurance.0.rate] | 0 | - | - | - | - | - | - |
| [impot_revenu.contributions_exceptionnelles.indemnite_compensatrice_agents_assurance.1.threshold] | 100000 | - | 150000 | - | 23000 | - | - |
| [impot_revenu.contributions_exceptionnelles.indemnite_compensatrice_agents_assurance.1.rate] | 0.6 | - | - | 0.038 | - | 0.04 | 0.02 |
| [impot_revenu.contributions_exceptionnelles.indemnite_compensatrice_agents_assurance.2.threshold] | 300000 | 500000 | 700000 | - | 107000 | - | - |
| [impot_revenu.contributions_exceptionnelles.indemnite_compensatrice_agents_assurance.3.threshold] | - | - | - | - | - | - | 200000 |
| [impot_revenu.contributions_exceptionnelles.indemnite_compensatrice_agents_assurance.3.rate] | - | - | - | - | - | - | 0.026 |
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 = []
# Taxe exceptionnelle sur l'indemnité compensatrice des agents d'assurance (ICAA), threshold 0
df1 = fetch_series("IPP/taxbenefit_tables/impot_revenu.contributions_exceptionnelles.indemnite_compensatrice_agents_assurance.0.threshold")
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]))
# Taxe exceptionnelle sur l'indemnité compensatrice des agents d'assurance (ICAA), rate 0
df2 = fetch_series("IPP/taxbenefit_tables/impot_revenu.contributions_exceptionnelles.indemnite_compensatrice_agents_assurance.0.rate")
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]))
# Taxe exceptionnelle sur l'indemnité compensatrice des agents d'assurance (ICAA), threshold 1
df3 = fetch_series("IPP/taxbenefit_tables/impot_revenu.contributions_exceptionnelles.indemnite_compensatrice_agents_assurance.1.threshold")
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]))
# Taxe exceptionnelle sur l'indemnité compensatrice des agents d'assurance (ICAA), rate 1
df4 = fetch_series("IPP/taxbenefit_tables/impot_revenu.contributions_exceptionnelles.indemnite_compensatrice_agents_assurance.1.rate")
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]))
# Taxe exceptionnelle sur l'indemnité compensatrice des agents d'assurance (ICAA), threshold 2
df5 = fetch_series("IPP/taxbenefit_tables/impot_revenu.contributions_exceptionnelles.indemnite_compensatrice_agents_assurance.2.threshold")
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]))
# Taxe exceptionnelle sur l'indemnité compensatrice des agents d'assurance (ICAA), rate 2
df6 = fetch_series("IPP/taxbenefit_tables/impot_revenu.contributions_exceptionnelles.indemnite_compensatrice_agents_assurance.2.rate")
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]))
# Taxe exceptionnelle sur l'indemnité compensatrice des agents d'assurance (ICAA), threshold 3
df7 = fetch_series("IPP/taxbenefit_tables/impot_revenu.contributions_exceptionnelles.indemnite_compensatrice_agents_assurance.3.threshold")
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]))
# Taxe exceptionnelle sur l'indemnité compensatrice des agents d'assurance (ICAA), rate 3
df8 = fetch_series("IPP/taxbenefit_tables/impot_revenu.contributions_exceptionnelles.indemnite_compensatrice_agents_assurance.3.rate")
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]))
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()