Series collection
- from
- 1990=0.223
- to
- 2020=0.48
- min:
- 0.17
- max:
- 0.754
- avg:
- 0.401
- σ:
- 0.185
| Series code | 1990 | 1991 | 1992 | 1993 | 1994 | 1995 | 1996 | 1997 | 1998 | 1999 | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| [tarifs_energie.prix_fioul_domestique.prix_annuel_moyen_fioul_domestique_ht_livraisons_2000_4999_litres_en_euro_par_litre] | 0.2232 | 0.2307 | 0.1979 | 0.1993 | 0.1877 | 0.1797 | 0.2005 | 0.2128 | 0.1703 | 0.1923 | 0.319 | 0.2889 | 0.2572 | 0.2692 | 0.3196 | 0.4346 | 0.4858 | 0.4877 | 0.64 | 0.4246 | 0.542 | 0.6858 | 0.7535 | 0.7187 | 0.66 | 0.5118 | 0.4351 | 0.499 | 0.603 | 0.619 | 0.48 |
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 = []
# Prix annuel moyen du fioul domestique HT - Livraisons de 2000 à 4999 litres - en euro par litre
df1 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.prix_fioul_domestique.prix_annuel_moyen_fioul_domestique_ht_livraisons_2000_4999_litres_en_euro_par_litre")
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()