Series collection
- from
- 1970-01=173.3
- to
- 2002-01=151.67
- min:
- 151.67
- max:
- 173.3
- avg:
- 164.657
- σ:
- 9.349
- from
- 1970-B1=3.27
- to
- 2024-B6=11.88
- min:
- 3.27
- max:
- 43.72
- avg:
- 15.605
- σ:
- 10.57
- from
- 1970-B1=566.8
- to
- 2024-B1=1,766.92
- min:
- 566.8
- max:
- 7,101.38
- avg:
- 2,610.977
- σ:
- 1,796.072
| Series code | 1970-B1 | 1970-B2 | 1970-B4 | 1971-04-01 | 1971-12-01 | 1971-B1 | 1971-B4 | 1972-B3 | 1972-B4 | 1972-B6 | 1973-02-01 | 1973-10-01 | 1973-12-01 | 1973-B4 | 1974-12-01 | 1974-B2 | 1974-B3 | 1974-B4 | 1974-B5 | 1975-06-01 | 1975-10-01 | 1975-B2 | 1975-B4 | 1976-04-01 | 1976-10-01 | 1976-12-01 | 1976-B1 | 1976-B4 | 1977-04-01 | 1977-06-01 | 1977-10-01 | 1977-12-01 | 1977-B4 | 1978-12-01 | 1978-B3 | 1978-B4 | 1978-B5 | 1979-04-01 | 1979-12-01 | 1979-B4 | 1979-B5 | 1980-12-01 | 1980-B2 | 1980-B3 | 1980-B4 | 1980-B5 | 1981-06-01 | 1981-B2 | 1981-B5 | 1981-B6 | 1982-12-01 | 1982-B1 | 1982-B2 | 1982-B3 | 1982-B4 | 1983-06-01 | 1983-10-01 | 1983-B2 | 1983-B4 | 1984-B1 | 1984-B3 | 1984-B4 | 1984-B6 | 1985-04-01 | 1985-B3 | 1985-B4 | 1986-06-01 | 1986-B4 | 1987-B2 | 1987-B4 | 1988-06-01 | 1988-B4 | 1989-B2 | 1989-B4 | 1990-04-01 | 1990-12-01 | 1990-B4 | 1991-B4 | 1992-B2 | 1992-B4 | 1993-B4 | 1994-B4 | 1995-B4 | 1996-B3 | 1996-B4 | 1997-B4 | 1998-B4 | 1999-B4 | 2000-B4 | 2001-B4 | 2002-B1 | 2002-B4 | 2003-B4 | 2004-B4 | 2005-B4 | 2006-B4 | 2007-B4 | 2008-B3 | 2008-B4 | 2009-B4 | 2010-B1 | 2011-12-01 | 2011-B1 | 2012-B1 | 2012-B4 | 2013-B1 | 2014-B1 | 2015-B1 | 2016-B1 | 2017-B1 | 2018-B1 | 2019-B1 | 2020-B1 | 2021-10-01 | 2021-B1 | 2022-08-01 | 2022-B1 | 2022-B3 | 2023-B1 | 2023-B3 | 2024-B1 | 2024-B6 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| [marche_travail.salaire_minimum.smic.smic_b_horaire] | 3.27 | 3.36 | 3.5 | 3.68 | 3.94 | 3.63 | 3.85 | 4.1 | 4.3 | 4.55 | 4.64 | 5.32 | 5.43 | 5.2 | 6.75 | 5.6 | 5.95 | 6.4 | 6.55 | 7.12 | 7.71 | 6.95 | 7.55 | 8.08 | 8.76 | 8.94 | 7.89 | 8.58 | 9.14 | 9.34 | 9.79 | 10.06 | 9.58 | 11.31 | 10.45 | 10.85 | 11.07 | 11.6 | 12.93 | 12.15 | 12.42 | 14.79 | 13.37 | 13.66 | 14 | 14.29 | 16.72 | 15.2 | 17.34 | 17.76 | 20.29 | 18.15 | 18.62 | 19.03 | 19.64 | 21.65 | 22.33 | 21.02 | 21.89 | 22.78 | 23.56 | 23.84 | 24.36 | 24.9 | 25.54 | 26.04 | 26.59 | 26.92 | 27.57 | 27.84 | 28.48 | 28.76 | 29.36 | 29.91 | 30.51 | 31.94 | 31.28 | 32.66 | 33.31 | 34.06 | 34.83 | 35.56 | 36.98 | 37.72 | 37.91 | 39.43 | 40.22 | 40.72 | 42.02 | 43.72 | 6.67 | 6.83 | 7.19 | 7.61 | 8.03 | 8.27 | 8.44 | 8.63 | 8.71 | 8.82 | 8.86 | 9.19 | 9 | 9.22 | 9.4 | 9.43 | 9.53 | 9.61 | 9.67 | 9.76 | 9.88 | 10.03 | 10.15 | 10.48 | 10.25 | 11.07 | 10.57 | 10.85 | 11.27 | 11.52 | 11.65 | 11.88 |
| [marche_travail.salaire_minimum.smic.smic_b_mensuel] | 566.7999999999989 | 582.3999999999988 | 606.6666666666655 | 637.8666666666654 | 682.933333333332 | 629.1999999999988 | 667.3333333333321 | 710.6666666666653 | 745.3333333333319 | 788.6666666666652 | 804.2666666666651 | 922.1333333333316 | 941.1999999999981 | 901.3333333333317 | 1169.9999999999977 | 970.6666666666647 | 1031.3333333333314 | 1109.3333333333312 | 1135.3333333333312 | 1234.133333333331 | 1336.3999999999974 | 1204.6666666666645 | 1308.6666666666642 | 1400.5333333333306 | 1518.3999999999971 | 1549.599999999997 | 1367.5999999999974 | 1487.199999999997 | 1584.2666666666637 | 1618.9333333333302 | 1696.93333333333 | 1743.7333333333302 | 1660.5333333333301 | 1960.3999999999962 | 1811.3333333333298 | 1880.666666666663 | 1918.7999999999963 | 2010.6666666666626 | 2241.1999999999957 | 2105.999999999996 | 2152.799999999996 | 2563.599999999995 | 2317.466666666662 | 2367.733333333329 | 2426.666666666662 | 2476.9333333333284 | 2898.1333333333278 | 2634.6666666666615 | 3005.5999999999945 | 3078.399999999994 | 3429.0099999999998 | 3145.9999999999936 | 3146.78 | 3216.07 | 3319.1600000000003 | 3658.85 | 3773.7699999999995 | 3552.38 | 3699.4100000000003 | 3849.82 | 3981.64 | 4028.96 | 4116.84 | 4208.099999999999 | 4316.26 | 4400.76 | 4493.71 | 4549.4800000000005 | 4659.33 | 4704.96 | 4813.12 | 4860.4400000000005 | 4961.84 | 5054.79 | 5156.1900000000005 | 5397.860000000001 | 5286.320000000001 | 5519.539999999999 | 5629.39 | 5756.14 | 5886.2699999999995 | 6009.64 | 6249.62 | 6374.679999999999 | 6406.789999999999 | 6663.67 | 6797.179999999999 | 6881.679999999999 | 7101.38 | 6631.0124 | 1011.6388999999999 | 1035.9061 | 1090.5073 | 1154.2087 | 1217.9100999999998 | 1254.3108999999997 | 1280.0947999999999 | 1308.9121 | 1321.0457000000001 | 1337.7294 | 1343.7961999999998 | 1393.8473 | 1365.03 | 1398.37 | 1425.67 | 1430.22 | 1445.38 | 1457.52 | 1466.62 | 1480.27 | 1498.47 | 1521.22 | 1539.42 | 1589.47 | 1554.58 | 1678.95 | 1603.12 | 1645.58 | 1709.28 | 1747.2 | 1766.92 | - |
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 = []
# Nombre d'heures travaillées forfaitaires (temps plein) au Smic
df1 = fetch_series("IPP/taxbenefit_tables/marche_travail.salaire_minimum.smic.nb_heures_travail_mensuel")
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]))
# Smic brut (horaire)
df2 = fetch_series("IPP/taxbenefit_tables/marche_travail.salaire_minimum.smic.smic_b_horaire")
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]))
# Smic brut mensuel
df3 = fetch_series("IPP/taxbenefit_tables/marche_travail.salaire_minimum.smic.smic_b_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()