Series collection
- from
- 1999-06-13=294
- to
- 2014-07-01=NA
- min:
- 294
- max:
- 313
- avg:
- 301.75
- σ:
- 7.067
- from
- 1985-11-05=0.03
- to
- 1985-11-05=0.03
- min:
- 0.03
- max:
- 0.03
- avg:
- 0.03
- σ:
- 0
- from
- 1985-11-05=0.01
- to
- 1985-11-05=0.01
- min:
- 0.01
- max:
- 0.01
- avg:
- 0.01
- σ:
- 0
- from
- 1985-11-05=0
- to
- 1985-11-05=0
- min:
- 0
- max:
- 0
- avg:
- 0
- σ:
- 0
| Series code | 1985-11-05 | 1999-06-13 | 1999-11-14 | 2001-05-01 | 2006-11-01 | 2011-01-01 | 2012-01-01 | 2012-07-07 | 2013-01-01 |
|---|---|---|---|---|---|---|---|---|---|
| [marche_travail.remuneration_dans_fonction_publique.indemnite_residence.min] | - | 294 | 295 | 297 | 298 | 299 | 306 | 312 | 313 |
| [marche_travail.remuneration_dans_fonction_publique.indemnite_residence.taux.zone1] | 0.03 | - | - | - | - | - | - | - | - |
| [marche_travail.remuneration_dans_fonction_publique.indemnite_residence.taux.zone2] | 0.01 | - | - | - | - | - | - | - | - |
| [marche_travail.remuneration_dans_fonction_publique.indemnite_residence.taux.zone3] | 0 | - | - | - | - | - | - | - | - |
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 = []
# Indemnité de résidence : plancher (en indice majoré de la fonction publique)
df1 = fetch_series("IPP/taxbenefit_tables/marche_travail.remuneration_dans_fonction_publique.indemnite_residence.min")
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]))
# Indemnité de résidence dans la fonction publique pour la zone 1
df2 = fetch_series("IPP/taxbenefit_tables/marche_travail.remuneration_dans_fonction_publique.indemnite_residence.taux.zone1")
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]))
# Indemnité de résidence dans la fonction publique pour la zone 2
df3 = fetch_series("IPP/taxbenefit_tables/marche_travail.remuneration_dans_fonction_publique.indemnite_residence.taux.zone2")
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]))
# Indemnité de résidence dans la fonction publique pour la zone 3
df4 = fetch_series("IPP/taxbenefit_tables/marche_travail.remuneration_dans_fonction_publique.indemnite_residence.taux.zone3")
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]))
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()