Series collection
- from
- 1965-01-11=0.075
- to
- 1974-01=NA
- min:
- 0.02
- max:
- 0.08
- avg:
- 0.052
- σ:
- 0.02
| Series code | 1965-01-11 | 1965-03-08 | 1967-01-09 | 1967-07-10 | 1968-02-08 | 1969-01-06 | 1970-01-05 | 1970-07-06 | 1971-01-04 | 1971-08-02 | 1972-01-03 | 1972-07-03 | 1973-12-31 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| [chomage.allocations_assurance_chomage.reval_chomeurs_plus_61ans.taux] | 0.075 | 0.02 | 0.04 | 0.025 | 0.045 | 0.07 | 0.08 | 0.04 | 0.05 | 0.06 | 0.03 | 0.06 | 0.08 |
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 = []
# Taux de revalorisation du salaire de références des chômeurs de plus de 61 ans pour les allocations chômage
df1 = fetch_series("IPP/taxbenefit_tables/chomage.allocations_assurance_chomage.reval_chomeurs_plus_61ans.taux")
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]))
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()