Series collection
- from
- 2008=52.493
- to
- 2016=55.564
- min:
- 52.493
- max:
- 55.564
- avg:
- 54.152
- σ:
- 1.075
- from
- 2008=54.375
- to
- 2019=56.232
- min:
- 54.375
- max:
- 56.232
- avg:
- 55.405
- σ:
- 0.631
- from
- 2008=0.068
- to
- 2020=0.038
- min:
- 0.014
- max:
- 0.079
- avg:
- 0.048
- σ:
- 0.02
| Series code | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| [marche_travail.remuneration_dans_fonction_publique.gipa.indice_1] | 52.4933 | 52.7558 | 53.2012 | 53.8453 | 54.3753 | 54.6791 | 55.026 | 55.4253 | 55.5635 | - | - | - | - |
| [marche_travail.remuneration_dans_fonction_publique.gipa.indice_2] | 54.3753 | 54.6791 | 55.026 | 55.4253 | 55.5635 | - | - | - | - | 55.7302 | 56.2044 | 56.2323 | - |
| [marche_travail.remuneration_dans_fonction_publique.gipa.inflation] | 0.068 | 0.079 | 0.062 | 0.059 | 0.065 | 0.055 | 0.063 | 0.0516 | 0.0308 | 0.0138 | 0.0164 | 0.0285 | 0.0377 |
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 1
df1 = fetch_series("IPP/taxbenefit_tables/marche_travail.remuneration_dans_fonction_publique.gipa.indice_1")
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 2
df2 = fetch_series("IPP/taxbenefit_tables/marche_travail.remuneration_dans_fonction_publique.gipa.indice_2")
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]))
# Inflation
df3 = fetch_series("IPP/taxbenefit_tables/marche_travail.remuneration_dans_fonction_publique.gipa.inflation")
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()