Series collection
- from
- 1983=0.044
- to
- 2020-07-01=0.07
- min:
- 0.038
- max:
- 0.087
- avg:
- 0.056
- σ:
- 0.017
- from
- 1983=0.032
- to
- 2020-07-01=0.045
- min:
- 0.024
- max:
- 0.06
- avg:
- 0.038
- σ:
- 0.012
- from
- 2013-S1=0.059
- to
- 2020-S2=0.046
- min:
- 0.046
- max:
- 0.06
- avg:
- 0.054
- σ:
- 0.005
- from
- 2013-S1=0.06
- to
- 2020-S2=0.047
- min:
- 0.047
- max:
- 0.061
- avg:
- 0.055
- σ:
- 0.005
- from
- 2013-S1=0.061
- to
- 2020-S2=0.047
- min:
- 0.047
- max:
- 0.062
- avg:
- 0.055
- σ:
- 0.005
- from
- 2013-S1=0.062
- to
- 2020-S2=0.048
- min:
- 0.048
- max:
- 0.063
- avg:
- 0.056
- σ:
- 0.005
- from
- 2013-S1=0.062
- to
- 2020-S2=0.049
- min:
- 0.049
- max:
- 0.063
- avg:
- 0.057
- σ:
- 0.005
- from
- 1983-01-01=NA
- to
- 2020-07-01=0.045
- min:
- 0.023
- max:
- 0.061
- avg:
- 0.037
- σ:
- 0.013
- from
- 2013-S1=0.062
- to
- 2020-S2=0.046
- min:
- 0.046
- max:
- 0.062
- avg:
- 0.054
- σ:
- 0.006
- from
- 2013-S1=0.063
- to
- 2020-S2=0.047
- min:
- 0.047
- max:
- 0.063
- avg:
- 0.055
- σ:
- 0.006
- from
- 2013-S1=0.063
- to
- 2020-S2=0.047
- min:
- 0.047
- max:
- 0.063
- avg:
- 0.056
- σ:
- 0.006
- from
- 2013-S1=0.064
- to
- 2020-S2=0.048
- min:
- 0.048
- max:
- 0.064
- avg:
- 0.056
- σ:
- 0.006
- from
- 2013-S1=0.065
- to
- 2020-S2=0.049
- min:
- 0.049
- max:
- 0.065
- avg:
- 0.057
- σ:
- 0.006
- from
- 1983=0.054
- to
- 2020-07-01=0.07
- min:
- 0.05
- max:
- 0.103
- avg:
- 0.068
- σ:
- 0.018
| Series code | 1983 | 1984 | 1985 | 1986 | 1987 | 1988 | 1989 | 1990 | 1991 | 1992 | 1993 | 1994 | 1995 | 1996 | 1997 | 1998 | 1999 | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2013-07-01 | 2014-07-01 | 2015-07-01 | 2016-07-01 | 2017-07-01 | 2018-07-01 | 2019-07-01 | 2020-07-01 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| [tarifs_energie.tarifs_reglementes_gdf.prix_unitaire_gdf_ttc.prix_kwh_b0_ttc] | 0.0438 | 0.047 | 0.0509 | 0.0473 | 0.0384 | 0.0386 | 0.0389 | 0.04 | 0.0421 | 0.0414 | 0.0404 | 0.0397 | 0.0393 | 0.0394 | 0.04099999999999999 | 0.0415 | 0.0394 | 0.04099999999999999 | 0.0484 | 0.0487 | 0.04969999999999999 | 0.0479 | 0.05139999999999999 | 0.059 | 0.0599 | 0.06860000000000001 | 0.0681 | 0.0727 | 0.08070000000000001 | 0.0852 | 0.087 | 0.0871 | 0.0816 | 0.0762 | 0.0683 | 0.0709 | 0.0819 | 0.0795 | 0.0698 |
| [tarifs_energie.tarifs_reglementes_gdf.prix_unitaire_gdf_ttc.prix_kwh_b1_zone1_ttc] | 0.0315 | 0.0333 | 0.0349 | 0.0312 | 0.0245 | 0.0244 | - | 0.0252 | 0.0271 | 0.0269 | 0.0264 | 0.0261 | 0.0259 | 0.026 | 0.0271 | 0.0274 | 0.0259 | 0.0271 | 0.0328 | 0.0329 | 0.0336 | 0.0315 | 0.0346 | 0.04219999999999999 | 0.0431 | 0.0487 | 0.0461 | 0.049 | 0.0558 | 0.05860000000000001 | 0.0586 | 0.0569 | 0.0543 | 0.0525 | 0.0456 | 0.0489 | 0.0597 | 0.0559 | 0.0451 |
| [tarifs_energie.tarifs_reglementes_gdf.prix_unitaire_gdf_ttc.prix_kwh_base_ttc] | 0.0537 | 0.0596 | 0.0639 | 0.0601 | 0.05110000000000001 | 0.0513 | 0.0517 | 0.0528 | 0.0538 | 0.0526 | 0.05139999999999999 | 0.0504 | 0.04969999999999999 | 0.0499 | 0.0518 | 0.05230000000000001 | 0.0496 | 0.0515 | 0.0595 | 0.0593 | 0.0604 | 0.059 | 0.0636 | 0.0711 | 0.07200000000000001 | 0.0819 | 0.0797 | 0.08470000000000001 | 0.094 | 0.09970000000000001 | 0.1027 | 0.1026 | 0.0998 | 0.0972 | 0.0851 | 0.0788 | 0.0959 | 0.0795 | 0.0698 |
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 - TTC
df1 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.prix_unitaire_gdf_ttc.prix_kwh_b0_ttc")
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 - TTC
df2 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.prix_unitaire_gdf_ttc.prix_kwh_b1_zone1_ttc")
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 - TTC
df3 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.prix_unitaire_gdf_ttc.prix_kwh_b1_zone2_ttc")
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 - TTC
df4 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.prix_unitaire_gdf_ttc.prix_kwh_b1_zone3_ttc")
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 - TTC
df5 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.prix_unitaire_gdf_ttc.prix_kwh_b1_zone4_ttc")
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 - TTC
df6 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.prix_unitaire_gdf_ttc.prix_kwh_b1_zone5_ttc")
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 - TTC
df7 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.prix_unitaire_gdf_ttc.prix_kwh_b1_zone6_ttc")
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 - TTC
df8 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.prix_unitaire_gdf_ttc.prix_kwh_b2i_zone1_ttc")
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 - TTC
df9 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.prix_unitaire_gdf_ttc.prix_kwh_b2i_zone2_ttc")
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 - TTC
df10 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.prix_unitaire_gdf_ttc.prix_kwh_b2i_zone3_ttc")
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 - TTC
df11 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.prix_unitaire_gdf_ttc.prix_kwh_b2i_zone4_ttc")
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 - TTC
df12 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.prix_unitaire_gdf_ttc.prix_kwh_b2i_zone5_ttc")
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 - TTC
df13 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.prix_unitaire_gdf_ttc.prix_kwh_b2i_zone6_ttc")
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 - TTC
df14 = fetch_series("IPP/taxbenefit_tables/tarifs_energie.tarifs_reglementes_gdf.prix_unitaire_gdf_ttc.prix_kwh_base_ttc")
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()