Series collection
- from
- 1950-09-01=64
- to
- 1966-10-01=NA
- min:
- 1.508
- max:
- 147.35
- avg:
- 65.024
- σ:
- 57.126
- from
- 1950-09-01=78
- to
- 1970-01-01=NA
- min:
- 1.639
- max:
- 160.15
- avg:
- 55.215
- σ:
- 63.429
- from
- 1950-09-01=13,519.74
- to
- 1970-01-01=NA
- min:
- 284.001
- max:
- 27,758.8
- avg:
- 9,570.391
- σ:
- 10,994.095
| Series code | 1950-09-01 | 1951-04-01 | 1951-06-01 | 1951-09-01 | 1954-02-01 | 1954-10-01 | 1955-04-01 | 1956-04-01 | 1957-08-01 | 1958-01-01 | 1958-03-01 | 1958-06-01 | 1959-02-01 | 1959-11-01 | 1960-10-01 | 1961-12-01 | 1962-06-01 | 1962-11-01 | 1963-01-01 | 1963-07-01 | 1964-10-01 | 1965-03-01 | 1965-09-01 | 1966-03-01 | 1966-10-01 | 1967-07-01 | 1968-01-01 | 1968-06-01 | 1968-12-01 | 1969-04-01 | 1969-10-01 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| [marche_travail.salaire_minimum.smig.smig_brut_horaire.zone_abattement_maximale] | 64 | 74 | 75.25 | 86.5 | 99.48 | 105.1 | 110.9 | 115.9 | 122.75 | 128.05 | 133.25 | 137.3 | 143.5 | 147.35 | 1.5075 | 1.5515 | 1.59 | 1.6615 | 1.6975 | 1.769 | 1.8135 | 1.85 | 1.887 | 1.927 | NA | - | - | - | - | - | - |
| [marche_travail.salaire_minimum.smig.smig_brut_horaire.zone_sans_abattement] | 78 | 87 | - | 100 | 115 | 121.5 | 126 | - | 133.45 | 139.2 | 144.8 | 149.25 | 156 | 160.15 | 1.6385 | 1.6865 | 1.728 | 1.806 | - | 1.882 | 1.9295 | 1.968 | 2.0075 | 2.05 | 2.1 | 2.15 | 2.22 | 3 | 3.08 | 3.15 | 3.27 |
| [marche_travail.salaire_minimum.smig.smig_brut_mensuel] | 13519.740000000002 | 15079.710000000001 | - | 17333 | 19932.95 | 21059.595 | 21839.58 | - | 23130.8885 | 24127.536 | 25098.184000000005 | 25869.502500000002 | 27039.480000000003 | 27758.799500000005 | 284.001205 | 292.321045 | 299.51424000000003 | 313.03398000000004 | - | 326.20706 | 334.44023500000003 | 341.11344 | 347.959975 | 355.3265 | 363.99300000000005 | 372.65950000000004 | 384.79260000000005 | 519.99 | 533.8564 | 545.9895 | 566.7891000000001 |
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 = []
# Zone abattement maximale
df1 = fetch_series("IPP/taxbenefit_tables/marche_travail.salaire_minimum.smig.smig_brut_horaire.zone_abattement_maximale")
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]))
# Zone sans abattement du SMIG horaire brut
df2 = fetch_series("IPP/taxbenefit_tables/marche_travail.salaire_minimum.smig.smig_brut_horaire.zone_sans_abattement")
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]))
# Smig brut (mensuel, 173,33h de travail)
df3 = fetch_series("IPP/taxbenefit_tables/marche_travail.salaire_minimum.smig.smig_brut_mensuel")
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]))
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()