Series collection
- from
- 1985-11-01=621
- to
- 2006-11-01=717
- min:
- 621
- max:
- 717
- avg:
- 669.25
- σ:
- 47.257
- from
- 1985-11-01=380
- to
- 2006-11-01=449
- min:
- 380
- max:
- 449
- avg:
- 432.1
- σ:
- 25.688
- from
- 1985-11-01=480
- to
- 2002-Q1=10.67
- min:
- 10.67
- max:
- 840
- avg:
- 443.557
- σ:
- 339.552
- from
- 1985-11-01=240
- to
- 2002-Q1=4.57
- min:
- 4.57
- max:
- 360
- avg:
- 201.523
- σ:
- 147.632
- from
- 1985-B6=180
- to
- 2002-B1=2.29
- min:
- 2.29
- max:
- 180
- avg:
- 91.145
- σ:
- 88.855
- from
- 1985-11-01=0.03
- to
- 1985-11-01=0.03
- min:
- 0.03
- max:
- 0.03
- avg:
- 0.03
- σ:
- 0
- from
- 1985-11-01=0.06
- to
- 1985-11-01=0.06
- min:
- 0.06
- max:
- 0.06
- avg:
- 0.06
- σ:
- 0
- from
- 1985-11-01=0.08
- to
- 1985-11-01=0.08
- min:
- 0.08
- max:
- 0.08
- avg:
- 0.08
- σ:
- 0
| Series code | 1985-11-01 | 1988-02-07 | 1988-03-01 | 1988-Q4 | 1990-02-01 | 1990-Q1 | 1991-08-01 | 1999-03-20 | 2002-Q1 | 2006-11-01 |
|---|---|---|---|---|---|---|---|---|---|---|
| [marche_travail.remuneration_dans_fonction_publique.sft.im_plafond] | 621 | 623 | 716 | - | - | - | - | - | - | 717 |
| [marche_travail.remuneration_dans_fonction_publique.sft.im_plancher] | 380 | 382 | 440 | 442 | 443 | 444 | 446 | 447 | 448 | 449 |
| [marche_travail.remuneration_dans_fonction_publique.sft.part_fixe.deux_enfants] | 480 | 840 | - | - | - | - | - | - | 10.67 | - |
| [marche_travail.remuneration_dans_fonction_publique.sft.part_fixe.enfant_supplementaire] | 240 | 360 | - | - | - | - | - | - | 4.57 | - |
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 = []
# Indice majoré plafond
df1 = fetch_series("IPP/taxbenefit_tables/marche_travail.remuneration_dans_fonction_publique.sft.im_plafond")
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]))
# Indice majoré plancher
df2 = fetch_series("IPP/taxbenefit_tables/marche_travail.remuneration_dans_fonction_publique.sft.im_plancher")
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]))
# Part fixe pour deux enfants du montant du supplément familial de traitement (SFT)
df3 = fetch_series("IPP/taxbenefit_tables/marche_travail.remuneration_dans_fonction_publique.sft.part_fixe.deux_enfants")
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]))
# Part fixe par enfant supplémentaire du montant du supplément familial de traitement (SFT)
df4 = fetch_series("IPP/taxbenefit_tables/marche_travail.remuneration_dans_fonction_publique.sft.part_fixe.enfant_supplementaire")
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]))
# Part fixe pour un enfant du montant du supplément familial de traitement (SFT)
df5 = fetch_series("IPP/taxbenefit_tables/marche_travail.remuneration_dans_fonction_publique.sft.part_fixe.un_enfant")
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]))
# Taux du montant du supplément familial de traitement pour deux enfants
df6 = fetch_series("IPP/taxbenefit_tables/marche_travail.remuneration_dans_fonction_publique.sft.part_proportionnelle.deux_enfants")
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]))
# Taux du montant du supplément familial de traitement par enfant supplémentaire au delà de 3 enfants
df7 = fetch_series("IPP/taxbenefit_tables/marche_travail.remuneration_dans_fonction_publique.sft.part_proportionnelle.enfant_supplementaire")
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]))
# Taux du montant du supplément familial de traitement pour trois enfants
df8 = fetch_series("IPP/taxbenefit_tables/marche_travail.remuneration_dans_fonction_publique.sft.part_proportionnelle.trois_enfants")
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]))
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()