Series collection
- from
- 1979-01-01=200,000
- to
- 2013-01-01=159,325
- min:
- 46,000
- max:
- 300,000
- avg:
- 175,308.313
- σ:
- 93,193.219
| Series code | 1979-01-01 | 1981-01-01 | 1983-01-01 | 1991-01-01 | 1999-01-01 | 2000-01-01 | 2002-01-01 | 2005-01-01 | 2006-01-01 | 2007-01-01 | 2008-01-01 | 2009-01-01 | 2010-01-01 | 2011-01-01 | 2012-01-01 | 2013-01-01 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| [droits_mutation_titre_gratuit.abattement.personnes_handicapees] | 200000 | 275000 | 300000 | 300000 | 300000 | 300000 | 46000 | 50000 | 50000 | 50000 | 150000 | 151950 | 156359 | 156974 | 159325 | 159325 |
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 = []
# Abattement pour les personnes handicapées
df1 = fetch_series("IPP/taxbenefit_tables/droits_mutation_titre_gratuit.abattement.personnes_handicapees")
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]))
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()