Series collection
- from
- 2002-01-01=1,525
- to
- 2008-01-01=NA
- min:
- 1,525
- max:
- 2,000
- avg:
- 1,762.5
- σ:
- 237.5
- from
- 2002-01-01=2,300
- to
- 2008-01-01=NA
- min:
- 2,300
- max:
- 3,000
- avg:
- 2,650
- σ:
- 350
| Series code | 2002-01-01 | 2006-01-01 |
|---|---|---|
| [impot_revenu.credits_impots.acqgpl.mont_up] | 1525 | 2000 |
| [impot_revenu.credits_impots.acqgpl.mont_uq] | 2300 | 3000 |
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 = []
# Case UP - Dépenses d'acquisition ou de transformation d'un véhicule GPL ou mixte
df1 = fetch_series("IPP/taxbenefit_tables/impot_revenu.credits_impots.acqgpl.mont_up")
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]))
# Case UQ - Dépenses d'acquisition ou de transformation d'un véhicule GPL ou mixte
df2 = fetch_series("IPP/taxbenefit_tables/impot_revenu.credits_impots.acqgpl.mont_uq")
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]))
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()