Series collection
- from
- 2004-06-01=20,000
- to
- 2020-01-01=31,865
- min:
- 20,000
- max:
- 31,865
- avg:
- 30,043.083
- σ:
- 3,130.585
| Series code | 2004-06-01 | 2005-02-08 | 2006-01-01 | 2007-08-22 | 2008-01-01 | 2008-05-01 | 2010-01-01 | 2011-01-01 | 2011-07-31 | 2012-01-01 | 2012-08-17 | 2020-01-01 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| [droits_mutation_titre_gratuit.abattement_don_numeraire] | 20000 | 30000 | 30000 | 30000 | 30000 | 30390 | 31272 | 31395 | 31865 | 31865 | 31865 | 31865 |
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 forfaitaire sur les dons numéraires au titre des droits de mutation à titre gratuit
df1 = fetch_series("IPP/taxbenefit_tables/droits_mutation_titre_gratuit.abattement_don_numeraire")
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()