Series collection
- from
- 2010-S2=45.84
- to
- 2020-S2=86.64
- min:
- 45.84
- max:
- 94.56
- avg:
- 71.771
- σ:
- 16.336
- from
- 2010-S2=146.4
- to
- 2019-S2=202.32
- min:
- 146.4
- max:
- 203.88
- avg:
- 180.588
- σ:
- 17.842
- from
- 2010-S2=146.4
- to
- 2019-S2=202.32
- min:
- 146.4
- max:
- 203.88
- avg:
- 180.588
- σ:
- 17.842
- from
- 2010-S2=33
- to
- 2020-S2=86.64
- min:
- 33
- max:
- 92.52
- avg:
- 61.778
- σ:
- 19.849
| Series code | 2010-S2 | 2012-S1 | 2013-S1 | 2013-S2 | 2014-S2 | 2015-S2 | 2016-07-02 | 2016-S2 | 2017-S2 | 2018-S2 | 2019-S2 | 2020-S2 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| [tarifs_energie.tarifs_reglementes_gdf.tarif_fixe_gdf_ht.b0_1000_6000_ht] | 45.84 | 51.96 | 54.36 | 58.8 | 66.24 | 76.44 | - | 82.32 | 79.8 | 94.56 | 92.52 | 86.64 |
| [tarifs_energie.tarifs_reglementes_gdf.tarif_fixe_gdf_ht.b1_6_30000_ht] | 146.4 | 159.24 | 166.44 | 173.76 | 183.84 | 189.84 | - | 193.32 | 186.84 | 203.88 | 202.32 | - |
| [tarifs_energie.tarifs_reglementes_gdf.tarif_fixe_gdf_ht.b2i_30000_ht] | 146.4 | 159.24 | 166.44 | 173.76 | 183.84 | 189.84 | - | 193.32 | 186.84 | 203.88 | 202.32 | - |
| [tarifs_energie.tarifs_reglementes_gdf.tarif_fixe_gdf_ht.base_0_1000_ht] | 33 | 37.8 | 39.72 | 47.52 | 54.96 | 62.52 | 68.52 | - | 73.32 | 83.04 | 92.52 | 86.64 |
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) - HT
df1 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.tarif_fixe_gdf_ht.b0_1000_6000_ht")
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) - HT
df2 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.tarif_fixe_gdf_ht.b1_6_30000_ht")
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) - HT
df3 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.tarif_fixe_gdf_ht.b2i_30000_ht")
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) - HT
df4 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.tarif_fixe_gdf_ht.base_0_1000_ht")
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()