Series collection
- from
- 1983=33.17
- to
- 2020-07-01=101.77
- min:
- 27.71
- max:
- 110.02
- avg:
- 59.042
- σ:
- 28.184
- from
- 1983=223.77
- to
- 2021=248.72
- min:
- 100.8
- max:
- 250.53
- avg:
- 172.242
- σ:
- 53.193
- from
- 1983-01-01=NA
- to
- 2021-01-01=248.72
- min:
- 142.21
- max:
- 250.53
- avg:
- 198.671
- σ:
- 32.143
- from
- 1983=22.09
- to
- 2020-07-01=101.77
- min:
- 17.96
- max:
- 108.03
- avg:
- 45.126
- σ:
- 29.738
| Series code | 1983 | 1984 | 1985 | 1986 | 1987 | 1988 | 1989 | 1990 | 1991 | 1992 | 1993 | 1994 | 1995 | 1996 | 1997 | 1998 | 1999 | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2013-05-01 | 2013-07-01 | 2014 | 2014-07-01 | 2015 | 2015-07-01 | 2016 | 2016-07-01 | 2017 | 2017-07-01 | 2018 | 2018-07-01 | 2019 | 2019-07-01 | 2020 | 2020-07-01 | 2021 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| [tarifs_energie.tarifs_reglementes_gdf.tarif_fixe_gdf_ttc.b0_1000_6000] | 33.17 | 36.8 | 38.76 | 37.58 | 36.15 | 34.69 | 32.07 | - | - | - | - | 31.67 | 31.7 | 31.91 | 33.1 | 33.38 | 27.71 | 28.79 | 32.96 | 32.92 | 34.39 | 35.11 | 35.95 | 41.9 | 47.89 | 53.36 | 58.01 | 61.97 | 64.29 | 64.43 | 69.12 | 70.24 | 78.09 | 78.27 | 89.39 | 89.33 | 95.45 | 95.49 | 92.67 | 94.23 | 109.98 | 110.02 | 107.97 | 108.03 | 101.77 | - |
| [tarifs_energie.tarifs_reglementes_gdf.tarif_fixe_gdf_ttc.b1_6_30000] | 223.77 | 136.33 | 155.01 | 155.66 | 137.14 | 134.93 | 126.35 | 128.39 | 129.34 | 122.33 | 117.8 | 115.23 | 114.75 | 115.54 | 120.09 | 121.45 | 100.8 | 104.04 | 116.6 | 116.36 | 122.08 | 124.15 | 125.21 | 141.58 | 160.15 | 175.41 | 185.18 | 195.45 | 202.42 | - | 210.65 | 214.99 | 226.32 | - | 234.01 | 233.76 | 237.87 | 238.15 | 230.71 | 231.84 | 250.44 | 250.53 | 249.12 | 249.36 | 248.62 | 248.72 |
| [tarifs_energie.tarifs_reglementes_gdf.tarif_fixe_gdf_ttc.base_0_1000] | 22.09 | 22.39 | 24.28 | 23.18 | 21.78 | 20.67 | 18.91 | 18.96 | 20.15 | 20.5 | 20.34 | 20.27 | 20.38 | 20.51 | 21.35 | 21.61 | 17.96 | 18.92 | 22.86 | 23.58 | 24.56 | 25 | 25.32 | 29.99 | 34.56 | 39.81 | 43.89 | 46.93 | 48.85 | - | 57.21 | 58.34 | 66.19 | 66.37 | 74.7 | 74.64 | 80.89 | 80.93 | 85.83 | 87.4 | 97.83 | 97.86 | 107.97 | 108.03 | 101.77 | - |
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 = []
# B0 (1000-6000)
df1 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.tarif_fixe_gdf_ttc.b0_1000_6000")
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]))
# B1 (6-30000)
df2 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.tarif_fixe_gdf_ttc.b1_6_30000")
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]))
# B2I (>30000)
df3 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.tarif_fixe_gdf_ttc.b2i_30000")
df3["series_id"] = df3[["provider_code", "dataset_code", "series_code"]].agg('/'.join, axis=1)
dfs.append(df3)
# display(df3)
display(px.line(df3, x="period", y="value", title=df3.series_id[0]))
# Base (0-1000)
df4 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.tarif_fixe_gdf_ttc.base_0_1000")
df4["series_id"] = df4[["provider_code", "dataset_code", "series_code"]].agg('/'.join, axis=1)
dfs.append(df4)
# display(df4)
display(px.line(df4, x="period", y="value", title=df4.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()