Series collection
- from
- 2012-07-23=0.129
- to
- 2020-08-01=0.16
- min:
- 0.129
- max:
- 0.16
- avg:
- 0.147
- σ:
- 0.009
- from
- 2012-07-23=0.129
- to
- 2020-08-01=0.16
- min:
- 0.129
- max:
- 0.16
- avg:
- 0.147
- σ:
- 0.009
- from
- 1983=0.089
- to
- 2020-08-01=0.156
- min:
- 0.089
- max:
- 0.156
- avg:
- 0.131
- σ:
- 0.014
- from
- 1983=0.084
- to
- 2020-08-01=0.156
- min:
- 0.084
- max:
- 0.156
- avg:
- 0.118
- σ:
- 0.02
- from
- 2012-07-23=0.129
- to
- 2020-08-01=0.16
- min:
- 0.129
- max:
- 0.16
- avg:
- 0.147
- σ:
- 0.009
Series code | 1983 | 1984 | 1985 | 1986 | 1987 | 1988 | 1989 | 1990 | 1991 | 1992 | 1993 | 1994 | 1995 | 1996 | 1997 | 1998 | 1999 | 2000 | 2001 | 2002 | 2003 | 2004 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2012-07-23 | 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_ttc.prix_kwh_12_kva] | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 0.1287 | 0.1329 | 0.1401 | 0.1437 | 0.1503 | 0.1483 | 0.1483 | 0.1467 | 0.1562 | 0.1555 | 0.1587 | 0.1597 |
[tarifs_energie.tarifs_reglementes_edf.prix_unitaire_base_edf_ttc.prix_kwh_15_kva] | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 0.1287 | 0.1329 | 0.1401 | 0.1437 | 0.1503 | 0.1483 | 0.1483 | 0.1467 | 0.1562 | 0.1555 | 0.1587 | 0.1597 |
[tarifs_energie.tarifs_reglementes_edf.prix_unitaire_base_edf_ttc.prix_kwh_3_kva] | 0.0891 | 0.1055 | 0.1186 | 0.1177 | 0.1169 | 0.1181 | 0.1206 | 0.1253 | 0.1288 | 0.1315 | 0.1328 | 0.1332 | 0.1343 | 0.1356 | 0.1332 | 0.1305 | 0.1276 | 0.1251 | 0.1247 | 0.1258 | 0.1273 | 0.129 | 0.1299 | 0.1317 | 0.1335 | 0.1159 | 0.1084 | 0.1161 | 0.1218 | 0.1256 | 0.1329 | 0.1401 | 0.1437 | 0.1503 | 0.1546 | 0.1555 | 0.145 | 0.1531 | 0.1524 | 0.1546 | 0.1557 |
[tarifs_energie.tarifs_reglementes_edf.prix_unitaire_base_edf_ttc.prix_kwh_6_kva] | 0.084 | 0.0934 | 0.09849999999999999 | 0.0973 | 0.0969 | 0.0981 | 0.1005 | 0.106 | 0.1096 | 0.1124 | 0.114 | 0.1144 | 0.1153 | 0.1164 | 0.1122 | 0.108 | 0.1053 | 0.1028 | 0.1023 | 0.1032 | 0.1043 | 0.1057 | 0.1064 | 0.1079 | 0.1094 | 0.1096 | 0.1089 | 0.1168 | 0.1225 | 0.1263 | 0.1329 | 0.1401 | 0.1437 | 0.1503 | 0.1466 | 0.1467 | 0.145 | 0.1531 | 0.1524 | 0.1546 | 0.1557 |
[tarifs_energie.tarifs_reglementes_edf.prix_unitaire_base_edf_ttc.prix_kwh_9_kva] | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 0.1287 | 0.1329 | 0.1401 | 0.1437 | 0.1503 | 0.1483 | 0.1483 | 0.1467 | 0.1562 | 0.1555 | 0.1587 | 0.1597 |
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_ttc.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_ttc.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 - 3 kVA
df3 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_edf.prix_unitaire_base_edf_ttc.prix_kwh_3_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 - 6 kVA
df4 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_edf.prix_unitaire_base_edf_ttc.prix_kwh_6_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 - 9 kVA
df5 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_edf.prix_unitaire_base_edf_ttc.prix_kwh_9_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]))
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()