Series collection
- from
- 2000-S1=NA
- to
- 2005-S2=NA
- min:
- 1,154.27
- max:
- 1,197.37
- avg:
- 1,174.793
- σ:
- 17.655
- from
- 2000-S1=6,881.68
- to
- 2005-S2=NA
- min:
- 1,094.65
- max:
- 7,180.83
- avg:
- 3,654.559
- σ:
- 2,911.18
- from
- 2000-S1=NA
- to
- 2005-S2=NA
- min:
- 1,113.45
- max:
- 7,303.97
- avg:
- 3,166.825
- σ:
- 2,854.472
- from
- 2000-S1=NA
- to
- 2005-S2=NA
- min:
- 1,127.23
- max:
- 7,388.68
- avg:
- 2,405.324
- σ:
- 2,491.779
- from
- 2000-S1=6,797.18
- to
- 2005-S2=NA
- min:
- 1,081.26
- max:
- 7,092.27
- avg:
- 3,611.687
- σ:
- 2,873.614
| Series code | 2000-S1 | 2000-S2 | 2001-S2 | 2002-S1 | 2002-S2 | 2003-S2 | 2004-S2 |
|---|---|---|---|---|---|---|---|
| [marche_travail.salaire_minimum.gmr.rtt_apres_01_07_02_gmr5] | NA | - | - | - | 1154.27 | 1172.74 | 1197.37 |
| [marche_travail.salaire_minimum.gmr.rtt_avant_30_06_00_gmr2] | 6881.68 | 6981.46 | 7180.83 | 1094.65 | 1114.35 | 1145.54 | 1183.4 |
| [marche_travail.salaire_minimum.gmr.rtt_avant_30_06_01_gmr3] | NA | 7101.38 | 7303.97 | 1113.45 | 1133.49 | 1158.52 | 1190.14 |
| [marche_travail.salaire_minimum.gmr.rtt_avant_30_06_02_gmr4] | NA | - | 7388.68 | 1127.23 | 1147.52 | 1168.16 | 1195.03 |
| [marche_travail.salaire_minimum.gmr.rtt_avant_30_06_99_gmr1] | 6797.18 | 6895.74 | 7092.27 | 1081.26 | 1100.67 | 1136.15 | 1178.54 |
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 = []
# RTT après 01/07/02 (GMR5)
df1 = fetch_series("IPP/taxbenefit_tables/marche_travail.salaire_minimum.gmr.rtt_apres_01_07_02_gmr5")
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]))
# RTT avant 30/06/00 (GMR2)
df2 = fetch_series("IPP/taxbenefit_tables/marche_travail.salaire_minimum.gmr.rtt_avant_30_06_00_gmr2")
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]))
# RTT avant 30/06/01 (GMR3)
df3 = fetch_series("IPP/taxbenefit_tables/marche_travail.salaire_minimum.gmr.rtt_avant_30_06_01_gmr3")
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]))
# RTT avant 30/06/02 (GMR4)
df4 = fetch_series("IPP/taxbenefit_tables/marche_travail.salaire_minimum.gmr.rtt_avant_30_06_02_gmr4")
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]))
# RTT avant 30/06/99 (GMR1)
df5 = fetch_series("IPP/taxbenefit_tables/marche_travail.salaire_minimum.gmr.rtt_avant_30_06_99_gmr1")
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]))
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()