Series collection
- from
- 1979=5.86
- to
- 2013=NA
- min:
- 3.382
- max:
- 22.23
- avg:
- 10.342
- σ:
- 5.52
| Series code | 1979 | 1980 | 1981 | 1982 | 1983 | 1984 | 1985 | 1986 | 1987 | 1988 | 1989 | 1990 | 1991 | 1992 | 1993 | 1994 | 1995 | 1996 | 1998 | 1999 | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2008-09-01 | 2009-04-01 | 2010 | 2011-04-01 | 2012-04-01 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| [retraites.independants.salref_rc_art] | 5.86 | 6.58 | 7.46 | 8.41 | 9.25 | 10.13 | 10.86 | 11.21 | 11.6 | 12.13 | 12.59 | 13.19 | 13.77 | 14.24 | 14.64 | 14.87 | 15.04 | 15.38 | 17.46 | 20.36 | 21.53 | 22.23 | 3.382 | 3.731 | 3.882 | 4.104 | 4.145 | 4.207 | 4.324 | 4.692 | 4.79053 | 4.89113 |
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 = []
# Salaire de référence du régime complémentaire des artisans (valeur d'achat du point)
df1 = fetch_series("IPP/taxbenefit_tables/retraites.independants.salref_rc_art")
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()