Series collection
- from
- 2001-05-01=0.078
- to
- 2020-08-01=0.101
- min:
- 0.075
- max:
- 0.101
- avg:
- 0.087
- σ:
- 0.008
- from
- 2001-05-01=0.078
- to
- 2020-08-01=0.101
- min:
- 0.075
- max:
- 0.101
- avg:
- 0.087
- σ:
- 0.008
- from
- 2001-05-01=0.078
- to
- 2020-08-01=0.101
- min:
- 0.075
- max:
- 0.101
- avg:
- 0.086
- σ:
- 0.008
- from
- 2001-05-01=0.078
- to
- 2020-08-01=0.101
- min:
- 0.075
- max:
- 0.101
- avg:
- 0.086
- σ:
- 0.008
- from
- 2001-05-01=0.078
- to
- 2020-08-01=0.101
- min:
- 0.075
- max:
- 0.101
- avg:
- 0.086
- σ:
- 0.008
- from
- 2001-05-01=0.078
- to
- 2020-08-01=0.101
- min:
- 0.075
- max:
- 0.101
- avg:
- 0.086
- σ:
- 0.008
- from
- 2001-05-01=0.095
- to
- 2020-08-01=0.097
- min:
- 0.078
- max:
- 0.099
- avg:
- 0.092
- σ:
- 0.006
- from
- 2001-05-01=0.078
- to
- 2020-08-01=0.097
- min:
- 0.075
- max:
- 0.097
- avg:
- 0.085
- σ:
- 0.007
- from
- 2001-05-01=0.078
- to
- 2020-08-01=0.101
- min:
- 0.075
- max:
- 0.101
- avg:
- 0.087
- σ:
- 0.008
| Series code | 2001-05-01 | 2001-11-12 | 2003-07-04 | 2003-S1 | 2004-S1 | 2006-08-15 | 2007-08-16 | 2008-08-15 | 2009-08-15 | 2010-08-15 | 2011-S2 | 2012-08-01 | 2013-08-01 | 2014-11-01 | 2015-08-01 | 2016-08-01 | 2017-08-01 | 2018-02-01 | 2018-08-01 | 2019-06-01 | 2019-08-01 | 2020-02-01 | 2020-08-01 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| [tarifs_energie.tarifs_reglementes_edf.prix_unitaire_base_edf_ht.prix_kwh_12_kva] | 0.0779 | 0.0787 | 0.0777 | 0.0754 | 0.0765 | 0.0778 | 0.0787 | 0.0803 | 0.0817 | - | 0.0831 | 0.0848 | 0.0883 | 0.0909 | 0.0909 | 0.0932 | 0.0915 | 0.0915 | 0.0902 | 0.098 | 0.0974 | 0.0999 | 0.1008 |
| [tarifs_energie.tarifs_reglementes_edf.prix_unitaire_base_edf_ht.prix_kwh_15_kva] | 0.0779 | 0.0787 | 0.0777 | 0.0754 | 0.0765 | 0.0778 | 0.0787 | 0.0803 | 0.0817 | - | 0.0831 | 0.0848 | 0.0883 | 0.0909 | 0.0909 | 0.0932 | 0.0915 | 0.0915 | 0.0902 | 0.098 | 0.0974 | 0.0999 | 0.1008 |
| [tarifs_energie.tarifs_reglementes_edf.prix_unitaire_base_edf_ht.prix_kwh_18_kva] | 0.0779 | 0.0787 | 0.0777 | 0.0754 | 0.0765 | 0.0778 | 0.0787 | 0.0803 | 0.0817 | - | 0.0831 | 0.0848 | 0.0883 | 0.0909 | 0.0932 | 0.0898 | 0.0915 | 0.0915 | 0.0902 | - | 0.0974 | 0.0999 | 0.1008 |
| [tarifs_energie.tarifs_reglementes_edf.prix_unitaire_base_edf_ht.prix_kwh_24_kva] | 0.0779 | 0.0787 | 0.0777 | 0.0754 | 0.0765 | 0.0778 | 0.0787 | 0.0803 | 0.0817 | - | 0.0831 | 0.0848 | 0.0883 | 0.0909 | 0.0932 | 0.0898 | 0.0915 | 0.0915 | 0.0902 | - | 0.0974 | 0.0999 | 0.1008 |
| [tarifs_energie.tarifs_reglementes_edf.prix_unitaire_base_edf_ht.prix_kwh_30_kva] | 0.0779 | 0.0787 | 0.0777 | 0.0754 | 0.0765 | 0.0778 | 0.0787 | 0.0803 | 0.0817 | - | 0.0831 | 0.0848 | 0.0883 | 0.0909 | 0.0932 | 0.0898 | 0.0915 | 0.0915 | 0.0902 | - | 0.0974 | 0.0999 | 0.1008 |
| [tarifs_energie.tarifs_reglementes_edf.prix_unitaire_base_edf_ht.prix_kwh_36_kva] | 0.0779 | 0.0787 | 0.0777 | 0.0754 | 0.0765 | 0.0778 | 0.0787 | 0.0803 | 0.0817 | - | 0.0831 | 0.0848 | 0.0883 | 0.0909 | 0.0932 | 0.0898 | 0.0915 | 0.0915 | 0.0902 | - | 0.0974 | 0.0999 | 0.1008 |
| [tarifs_energie.tarifs_reglementes_edf.prix_unitaire_base_edf_ht.prix_kwh_3_kva] | 0.095 | 0.096 | 0.0955 | 0.0927 | 0.0943 | 0.0959 | 0.097 | 0.0989 | 0.0781 | 0.0793 | 0.0806 | 0.0822 | 0.0883 | 0.0909 | 0.0909 | 0.0932 | 0.0968 | 0.0975 | 0.0888 | 0.0954 | 0.0948 | 0.0965 | 0.0974 |
| [tarifs_energie.tarifs_reglementes_edf.prix_unitaire_base_edf_ht.prix_kwh_6_kva] | 0.0779 | 0.0787 | 0.0777 | 0.0754 | 0.0765 | 0.0778 | 0.0787 | 0.0803 | 0.0784 | 0.0798 | 0.0812 | 0.0828 | 0.0883 | 0.0909 | 0.0909 | 0.0932 | 0.0901 | 0.0902 | 0.0888 | 0.0954 | 0.0948 | 0.0965 | 0.0974 |
| [tarifs_energie.tarifs_reglementes_edf.prix_unitaire_base_edf_ht.prix_kwh_9_kva] | 0.0779 | 0.0787 | 0.0777 | 0.0754 | 0.0765 | 0.0778 | 0.0787 | 0.0803 | 0.0817 | - | 0.0831 | 0.0848 | 0.0883 | 0.0909 | 0.0909 | 0.0932 | 0.0915 | 0.0915 | 0.0902 | 0.098 | 0.0974 | 0.0999 | 0.1008 |
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 du kWh - 12 kVA
df1 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_edf.prix_unitaire_base_edf_ht.prix_kwh_12_kva")
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 du kWh - 15 kVA
df2 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_edf.prix_unitaire_base_edf_ht.prix_kwh_15_kva")
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 du kWh - 18 kVA
df3 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_edf.prix_unitaire_base_edf_ht.prix_kwh_18_kva")
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 du kWh - 24 kVA
df4 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_edf.prix_unitaire_base_edf_ht.prix_kwh_24_kva")
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 du kWh - 30 kVA
df5 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_edf.prix_unitaire_base_edf_ht.prix_kwh_30_kva")
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 du kWh - 36 kVA
df6 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_edf.prix_unitaire_base_edf_ht.prix_kwh_36_kva")
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 du kWh - 3 kVA
df7 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_edf.prix_unitaire_base_edf_ht.prix_kwh_3_kva")
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 du kWh - 6 kVA
df8 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_edf.prix_unitaire_base_edf_ht.prix_kwh_6_kva")
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 du kWh - 9 kVA
df9 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_edf.prix_unitaire_base_edf_ht.prix_kwh_9_kva")
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]))
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()