Series collection
- from
- 2001-05-01=141.96
- to
- 2020-08-01=139.44
- min:
- 108.72
- max:
- 153.84
- avg:
- 132.726
- σ:
- 13.571
- from
- 2001-05-01=185.04
- to
- 2020-08-01=157.32
- min:
- 122.76
- max:
- 200.28
- avg:
- 158.694
- σ:
- 24.741
- from
- 2001-05-01=228.12
- to
- 2020-08-01=175.8
- min:
- 139.44
- max:
- 246.72
- avg:
- 190.584
- σ:
- 33.291
- from
- 2001-05-01=380.52
- to
- 2020-08-01=216.84
- min:
- 168.12
- max:
- 412.32
- avg:
- 311.174
- σ:
- 85.754
- from
- 2001-05-01=532.92
- to
- 2020-08-01=260.28
- min:
- 199.56
- max:
- 577.92
- avg:
- 409.086
- σ:
- 130.638
- from
- 2001-05-01=685.32
- to
- 2020-08-01=289.8
- min:
- 222.96
- max:
- 743.52
- avg:
- 502.458
- σ:
- 179.521
- from
- 2001-05-01=19.68
- to
- 2020-08-01=83.52
- min:
- 19.68
- max:
- 83.52
- avg:
- 46.23
- σ:
- 21.086
- from
- 2001-05-01=49.92
- to
- 2020-08-01=102.24
- min:
- 49.92
- max:
- 102.24
- avg:
- 69.954
- σ:
- 16.428
- from
- 2001-05-01=98.88
- to
- 2020-08-01=120.84
- min:
- 73.32
- max:
- 120.84
- avg:
- 96.318
- σ:
- 13.424
| Series code | 2001-05-01 | 2003 | 2004 | 2006-08-15 | 2007-08-16 | 2008-08-15 | 2009-08-15 | 2010-08-15 | 2011-07-01 | 2012-08-01 | 2013-08-01 | 2014-11-01 | 2015-08-01 | 2016-08-01 | 2017-08-01 | 2018-02-01 | 2018-08-01 | 2019-08-01 | 2020-02-01 | 2020-08-01 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| [tarifs_energie.tarifs_reglementes_edf.tarif_fixe_base_edf_ht.tarif_fixe_12_kva] | 141.96 | 142.56 | 147.12 | 149.64 | 151.08 | 153.84 | 127.68 | 113.88 | 115.8 | 118.08 | 135 | 138.96 | 142.56 | 135.12 | 108.72 | 113.4 | 118.92 | 129.72 | 131.04 | 139.44 |
| [tarifs_energie.tarifs_reglementes_edf.tarif_fixe_base_edf_ht.tarif_fixe_15_kva] | 185.04 | 185.76 | 191.76 | 195 | 196.8 | 200.28 | 156.12 | 130.8 | 133.08 | 135.72 | 153.84 | 158.4 | 162.48 | 155.28 | 122.76 | 127.44 | 133.92 | 145.32 | 146.76 | 157.32 |
| [tarifs_energie.tarifs_reglementes_edf.tarif_fixe_base_edf_ht.tarif_fixe_18_kva] | 228.12 | 228.96 | 236.4 | 240.36 | 242.52 | 246.72 | 184.56 | 178.2 | 181.2 | 184.92 | 176.76 | 182.04 | 186.72 | 177.48 | 139.44 | 144.12 | 149.64 | 163.2 | 164.52 | 175.8 |
| [tarifs_energie.tarifs_reglementes_edf.tarif_fixe_base_edf_ht.tarif_fixe_24_kva] | 380.52 | 382.32 | 394.8 | 401.64 | 405.24 | 412.32 | 299.04 | 288.72 | 293.64 | 299.52 | 366.72 | 377.52 | 387.24 | 373.44 | 168.12 | 182.8 | 185.64 | 203.04 | 204.36 | 216.84 |
| [tarifs_energie.tarifs_reglementes_edf.tarif_fixe_base_edf_ht.tarif_fixe_30_kva] | 532.92 | 535.68 | 553.2 | 562.92 | 567.96 | 577.92 | 413.52 | 399.24 | 406.08 | 414.24 | 453.96 | 467.4 | 479.4 | 449.52 | 199.56 | 204.24 | 221.52 | 240.36 | 241.8 | 260.28 |
| [tarifs_energie.tarifs_reglementes_edf.tarif_fixe_base_edf_ht.tarif_fixe_36_kva] | 685.32 | 689.04 | 711.6 | 724.2 | 730.68 | 743.52 | 528 | 509.76 | 518.4 | 528.84 | 522.84 | 538.32 | 552.12 | 527.4 | 222.96 | 227.64 | 248.28 | 274.56 | 275.88 | 289.8 |
| [tarifs_energie.tarifs_reglementes_edf.tarif_fixe_base_edf_ht.tarif_fixe_3_kva] | 19.68 | 19.8 | 20.4 | 20.76 | 21 | 21.48 | 51.24 | 53.52 | 54.48 | 55.56 | 38.88 | 40.08 | 41.16 | 42.6 | 52.8 | 53.16 | 74.64 | 79.2 | 80.64 | 83.52 |
| [tarifs_energie.tarifs_reglementes_edf.tarif_fixe_base_edf_ht.tarif_fixe_6_kva] | 49.92 | 50.4 | 51.96 | 52.8 | 53.4 | 54.48 | 58.32 | 63.24 | 64.32 | 65.64 | 66.72 | 68.64 | 70.44 | 78 | 81.24 | 85.92 | 88.44 | 95.76 | 97.2 | 102.24 |
| [tarifs_energie.tarifs_reglementes_edf.tarif_fixe_base_edf_ht.tarif_fixe_9_kva] | 98.88 | 99.36 | 102.48 | 104.28 | 105.36 | 107.4 | 73.56 | 73.32 | 74.52 | 76.08 | 89.76 | 92.4 | 94.8 | 89.16 | 94.8 | 99.48 | 103.32 | 112.56 | 114 | 120.84 |
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 = []
# Tarif fixe - 12 kVA
df1 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_edf.tarif_fixe_base_edf_ht.tarif_fixe_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]))
# Tarif fixe - 15 kVA
df2 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_edf.tarif_fixe_base_edf_ht.tarif_fixe_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]))
# Tarif fixe - 18 kVA
df3 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_edf.tarif_fixe_base_edf_ht.tarif_fixe_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]))
# Tarif fixe - 24 kVA
df4 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_edf.tarif_fixe_base_edf_ht.tarif_fixe_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]))
# Tarif fixe - 30 kVA
df5 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_edf.tarif_fixe_base_edf_ht.tarif_fixe_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]))
# Tarif fixe - 36 kVA
df6 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_edf.tarif_fixe_base_edf_ht.tarif_fixe_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]))
# Tarif fixe - 3 kVA
df7 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_edf.tarif_fixe_base_edf_ht.tarif_fixe_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]))
# Tarif fixe - 6 kVA
df8 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_edf.tarif_fixe_base_edf_ht.tarif_fixe_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]))
# Tarif fixe - 9 kVA
df9 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_edf.tarif_fixe_base_edf_ht.tarif_fixe_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()