Series collection
- from
- 2010-S2=6.2
- to
- 2020-S2=4.97
- min:
- 4.97
- max:
- 7.26
- avg:
- 6.141
- σ:
- 0.747
- from
- 2010-S2=4.17
- to
- 2020-S2=2.91
- min:
- 2.91
- max:
- 4.9
- avg:
- 4.057
- σ:
- 0.587
- from
- 2010-S2=4.23
- to
- 2020-S2=2.97
- min:
- 2.97
- max:
- 4.96
- avg:
- 4.117
- σ:
- 0.587
- from
- 2010-S2=4.29
- to
- 2020-S2=3.03
- min:
- 3.03
- max:
- 5.02
- avg:
- 4.177
- σ:
- 0.587
- from
- 2010-S2=4.35
- to
- 2020-S2=3.09
- min:
- 3.09
- max:
- 5.08
- avg:
- 4.237
- σ:
- 0.587
- from
- 2010-S2=4.41
- to
- 2020-S2=3.15
- min:
- 3.15
- max:
- 5.14
- avg:
- 4.297
- σ:
- 0.587
- from
- 2010-S2=4.47
- to
- 2020-S2=3.21
- min:
- 3.21
- max:
- 5.2
- avg:
- 4.357
- σ:
- 0.587
- from
- 2010-S2=4.17
- to
- 2020-S2=2.91
- min:
- 2.91
- max:
- 5.09
- avg:
- 4.088
- σ:
- 0.63
- from
- 2010-S2=4.23
- to
- 2020-S2=2.97
- min:
- 2.97
- max:
- 5.15
- avg:
- 4.148
- σ:
- 0.63
- from
- 2010-S2=4.29
- to
- 2020-S2=3.03
- min:
- 3.03
- max:
- 5.21
- avg:
- 4.208
- σ:
- 0.63
- from
- 2010-S2=4.35
- to
- 2020-S2=3.09
- min:
- 3.09
- max:
- 5.27
- avg:
- 4.268
- σ:
- 0.63
- from
- 2010-S2=4.41
- to
- 2020-S2=3.15
- min:
- 3.15
- max:
- 5.33
- avg:
- 4.328
- σ:
- 0.63
- from
- 2010-S2=4.47
- to
- 2020-S2=3.21
- min:
- 3.21
- max:
- 5.39
- avg:
- 4.388
- σ:
- 0.63
- from
- 2010-S2=7.24
- to
- 2020-S2=4.97
- min:
- 4.97
- max:
- 8.59
- avg:
- 7.175
- σ:
- 1.15
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 kWh B0 - HT
df1 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.prix_unitaire_gdf_par_zone_ht.prix_kwh_b0_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]))
# Prix kWh B1 - zone 1 - HT
df2 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.prix_unitaire_gdf_par_zone_ht.prix_kwh_b1_zone_1_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]))
# Prix kWh B1 - zone 2 - HT
df3 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.prix_unitaire_gdf_par_zone_ht.prix_kwh_b1_zone_2_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]))
# Prix kWh B1 - zone 3 - HT
df4 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.prix_unitaire_gdf_par_zone_ht.prix_kwh_b1_zone_3_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]))
# Prix kWh B1 - zone 4 - HT
df5 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.prix_unitaire_gdf_par_zone_ht.prix_kwh_b1_zone_4_ht")
df5["series_id"] = df5[["provider_code", "dataset_code", "series_code"]].agg('/'.join, axis=1)
dfs.append(df5)
# display(df5)
display(px.line(df5, x="period", y="value", title=df5.series_id[0]))
# Prix kWh B1 - zone 5 - HT
df6 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.prix_unitaire_gdf_par_zone_ht.prix_kwh_b1_zone_5_ht")
df6["series_id"] = df6[["provider_code", "dataset_code", "series_code"]].agg('/'.join, axis=1)
dfs.append(df6)
# display(df6)
display(px.line(df6, x="period", y="value", title=df6.series_id[0]))
# Prix kWh B1 - zone 6 - HT
df7 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.prix_unitaire_gdf_par_zone_ht.prix_kwh_b1_zone_6_ht")
df7["series_id"] = df7[["provider_code", "dataset_code", "series_code"]].agg('/'.join, axis=1)
dfs.append(df7)
# display(df7)
display(px.line(df7, x="period", y="value", title=df7.series_id[0]))
# Prix kWh B2I - zone 1 - HT
df8 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.prix_unitaire_gdf_par_zone_ht.prix_kwh_b2i_zone_1_ht")
df8["series_id"] = df8[["provider_code", "dataset_code", "series_code"]].agg('/'.join, axis=1)
dfs.append(df8)
# display(df8)
display(px.line(df8, x="period", y="value", title=df8.series_id[0]))
# Prix kWh B2I - zone 2 - HT
df9 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.prix_unitaire_gdf_par_zone_ht.prix_kwh_b2i_zone_2_ht")
df9["series_id"] = df9[["provider_code", "dataset_code", "series_code"]].agg('/'.join, axis=1)
dfs.append(df9)
# display(df9)
display(px.line(df9, x="period", y="value", title=df9.series_id[0]))
# Prix kWh B2I - zone 3 - HT
df10 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.prix_unitaire_gdf_par_zone_ht.prix_kwh_b2i_zone_3_ht")
df10["series_id"] = df10[["provider_code", "dataset_code", "series_code"]].agg('/'.join, axis=1)
dfs.append(df10)
# display(df10)
display(px.line(df10, x="period", y="value", title=df10.series_id[0]))
# Prix kWh B2I - zone 4 - HT
df11 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.prix_unitaire_gdf_par_zone_ht.prix_kwh_b2i_zone_4_ht")
df11["series_id"] = df11[["provider_code", "dataset_code", "series_code"]].agg('/'.join, axis=1)
dfs.append(df11)
# display(df11)
display(px.line(df11, x="period", y="value", title=df11.series_id[0]))
# Prix kWh B2I - zone 5 - HT
df12 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.prix_unitaire_gdf_par_zone_ht.prix_kwh_b2i_zone_5_ht")
df12["series_id"] = df12[["provider_code", "dataset_code", "series_code"]].agg('/'.join, axis=1)
dfs.append(df12)
# display(df12)
display(px.line(df12, x="period", y="value", title=df12.series_id[0]))
# Prix kWh B2I - zone 6 - HT
df13 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.prix_unitaire_gdf_par_zone_ht.prix_kwh_b2i_zone_6_ht")
df13["series_id"] = df13[["provider_code", "dataset_code", "series_code"]].agg('/'.join, axis=1)
dfs.append(df13)
# display(df13)
display(px.line(df13, x="period", y="value", title=df13.series_id[0]))
# Prix kWh Base - HT
df14 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.prix_unitaire_gdf_par_zone_ht.prix_kwh_base_ht")
df14["series_id"] = df14[["provider_code", "dataset_code", "series_code"]].agg('/'.join, axis=1)
dfs.append(df14)
# display(df14)
display(px.line(df14, x="period", y="value", title=df14.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()