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Economics

World Bank Poverty and Inequality data

`bulkhours.get_data("world.poverty")`

Raw data: pip_dataset.csv (raw🔄)
Enrich data: world.py (raw)
Direct source: https://ourworldindata.org/poverty
Reference site: https://pip.worldbank.org/

Show columns info

> https://github.com/owid/poverty-data/blob/main/datasets/pip_codebook.csv

Column	Info
country
year
reporting_level
welfare_type
ppp_version
survey_year
survey_comparability
headcount_ratio_international_povline
headcount_ratio_lower_mid_income_povline
headcount_ratio_upper_mid_income_povline
headcount_ratio_100
headcount_ratio_1000
headcount_ratio_2000
headcount_ratio_3000
headcount_ratio_4000
headcount_ratio_40_median
headcount_ratio_50_median
headcount_ratio_60_median
headcount_international_povline
headcount_lower_mid_income_povline
headcount_upper_mid_income_povline
headcount_100
headcount_1000
headcount_2000
headcount_3000
headcount_4000
headcount_40_median
headcount_50_median
headcount_60_median
avg_shortfall_international_povline
avg_shortfall_lower_mid_income_povline
avg_shortfall_upper_mid_income_povline
avg_shortfall_100
avg_shortfall_1000
avg_shortfall_2000
avg_shortfall_3000
avg_shortfall_4000
avg_shortfall_40_median
avg_shortfall_50_median
avg_shortfall_60_median
total_shortfall_international_povline
total_shortfall_lower_mid_income_povline
total_shortfall_upper_mid_income_povline
total_shortfall_100
total_shortfall_1000
total_shortfall_2000
total_shortfall_3000
total_shortfall_4000
total_shortfall_40_median
total_shortfall_50_median
total_shortfall_60_median
income_gap_ratio_international_povline
income_gap_ratio_lower_mid_income_povline
income_gap_ratio_upper_mid_income_povline
income_gap_ratio_100
income_gap_ratio_1000
income_gap_ratio_2000
income_gap_ratio_3000
income_gap_ratio_4000
income_gap_ratio_40_median
income_gap_ratio_50_median
income_gap_ratio_60_median
poverty_gap_index_international_povline
poverty_gap_index_lower_mid_income_povline
poverty_gap_index_upper_mid_income_povline
poverty_gap_index_100
poverty_gap_index_1000
poverty_gap_index_2000
poverty_gap_index_3000
poverty_gap_index_4000
mean
median
decile1_avg
decile2_avg
decile3_avg
decile4_avg
decile5_avg
decile6_avg
decile7_avg
decile8_avg
decile9_avg
decile10_avg
decile1_share
decile2_share
decile3_share
decile4_share
decile5_share
decile6_share
decile7_share
decile8_share
decile9_share
decile10_share
decile1_thr
decile2_thr
decile3_thr
decile4_thr
decile6_thr
decile7_thr
decile8_thr
decile9_thr
gini
mld
polarization
palma_ratio
s80_s20_ratio
p90_p10_ratio
p90_p50_ratio
p50_p10_ratio
index
iso
m49
region1
region2
continent

Show code


def get_poverty(self, timeopt=None):
    timeopt = self.data_info["timeopt"] if "timeopt" in self.data_info else None
    df = self.read_raw_data(self.raw_data)
    return geo_format(df, timeopt)

World Bank Poverty and Inequality data (with gpx extra info)

`bulkhours.get_data("world.mappoverty")`

Raw data: pip_dataset.csv (raw🔄)
Enrich data: world.py (raw)
Direct source: https://ourworldindata.org/poverty
Reference site: https://pip.worldbank.org/

Show columns info

> https://github.com/owid/poverty-data/blob/main/datasets/pip_codebook.csv

Column	Info
pop_est
continent
name
iso_a3
gdp_md_est
geometry
year
reporting_level
welfare_type
ppp_version
survey_year
survey_comparability
headcount_ratio_international_povline
headcount_ratio_lower_mid_income_povline
headcount_ratio_upper_mid_income_povline
headcount_ratio_100
headcount_ratio_1000
headcount_ratio_2000
headcount_ratio_3000
headcount_ratio_4000
headcount_ratio_40_median
headcount_ratio_50_median
headcount_ratio_60_median
headcount_international_povline
headcount_lower_mid_income_povline
headcount_upper_mid_income_povline
headcount_100
headcount_1000
headcount_2000
headcount_3000
headcount_4000
headcount_40_median
headcount_50_median
headcount_60_median
avg_shortfall_international_povline
avg_shortfall_lower_mid_income_povline
avg_shortfall_upper_mid_income_povline
avg_shortfall_100
avg_shortfall_1000
avg_shortfall_2000
avg_shortfall_3000
avg_shortfall_4000
avg_shortfall_40_median
avg_shortfall_50_median
avg_shortfall_60_median
total_shortfall_international_povline
total_shortfall_lower_mid_income_povline
total_shortfall_upper_mid_income_povline
total_shortfall_100
total_shortfall_1000
total_shortfall_2000
total_shortfall_3000
total_shortfall_4000
total_shortfall_40_median
total_shortfall_50_median
total_shortfall_60_median
income_gap_ratio_international_povline
income_gap_ratio_lower_mid_income_povline
income_gap_ratio_upper_mid_income_povline
income_gap_ratio_100
income_gap_ratio_1000
income_gap_ratio_2000
income_gap_ratio_3000
income_gap_ratio_4000
income_gap_ratio_40_median
income_gap_ratio_50_median
income_gap_ratio_60_median
poverty_gap_index_international_povline
poverty_gap_index_lower_mid_income_povline
poverty_gap_index_upper_mid_income_povline
poverty_gap_index_100
poverty_gap_index_1000
poverty_gap_index_2000
poverty_gap_index_3000
poverty_gap_index_4000
mean
median
decile1_avg
decile2_avg
decile3_avg
decile4_avg
decile5_avg
decile6_avg
decile7_avg
decile8_avg
decile9_avg
decile10_avg
decile1_share
decile2_share
decile3_share
decile4_share
decile5_share
decile6_share
decile7_share
decile8_share
decile9_share
decile10_share
decile1_thr
decile2_thr
decile3_thr
decile4_thr
decile6_thr
decile7_thr
decile8_thr
decile9_thr
gini
mld
polarization
palma_ratio
s80_s20_ratio
p90_p10_ratio
p90_p50_ratio
p50_p10_ratio
index
iso
m49
region1
region2

Show code


def get_mappoverty(self, **kwargs):
    return get_mapgeneric(get_poverty(self, **kwargs))

World Bank Gdp data

`bulkhours.get_data("world.gdp")`

Raw data: pip_dataset.csv (raw🔄)
Enrich data: world.py (raw)
Direct source: https://ourworldindata.org/poverty
Reference site: https://pip.worldbank.org/

Show columns info

> https://github.com/owid/poverty-data/blob/main/datasets/pip_codebook.csv

Column	Info
country
year
gdp
index
iso
m49
region1
region2
continent

Show code


def get_gdp(self, timeopt=None, **data_info):
    timeopt = self.data_info["timeopt"] if "timeopt" in self.data_info else None
    df = self.read_raw_data(self.raw_data)
    df = df.set_index("country").stack().to_frame().reset_index()
    df.columns = ["country", "year", "gdp"]
return geo_format(df, timeopt)

World Bank Gdp data (with gpx extra info)

`bulkhours.get_data("world.mapgdp")`

Raw data: pip_dataset.csv (raw🔄)
Enrich data: world.py (raw)
Direct source: https://ourworldindata.org/poverty
Reference site: https://pip.worldbank.org/

Show columns info

> https://github.com/owid/poverty-data/blob/main/datasets/pip_codebook.csv

Column	Info
pop_est
continent
name
iso_a3
gdp_md_est
geometry
year
gdp
index
iso
m49
region1
region2

Show code


def get_mapgdp(self, **kwargs):
    return get_mapgeneric(get_gdp(self, **kwargs))

Global economic data

`bulkhours.get_data("world.macro")`

Raw data: corruption.csv, cost_of_living.csv, richest_countries.csv, unemployment.csv, tourism.csv, continent.tsv
Enrich data: world.py (raw)

Show columns info

Column	Info
country
annual_income
corruption_index
cost_index
monthly_income
purchasing_power_index
gdp_per_capita
unemployment_rate
tourists_in_millions
receipts_in_billions
receipts_per_tourist
percentage_of_gdp
index_x
iso_x
m49_x
region1_x
region2_x
continent_x
index_y
iso_y
m49_y
region1_y
region2_y
continent_y

Show code


def get_macro(self, **data_info):
    df = self.read_raw_data(self.raw_data)
    return geo_format(df, None)

Global economic data (with gpx extra info)

`bulkhours.get_data("world.mapmacro")`

Raw data: corruption.csv, cost_of_living.csv, richest_countries.csv, unemployment.csv, tourism.csv, continent.tsv
Enrich data: world.py (raw)

Show columns info

Column	Info
pop_est
continent
name
iso_a3
gdp_md_est
geometry
annual_income
corruption_index
cost_index
monthly_income
purchasing_power_index
gdp_per_capita
unemployment_rate
tourists_in_millions
receipts_in_billions
receipts_per_tourist
percentage_of_gdp
index_x
iso_x
m49_x
region1_x
region2_x
continent_x
index_y
iso_y
m49_y
region1_y
region2_y
continent_y

Show code


def get_mapmacro(self, **kwargs):
    return get_mapgeneric(get_macro(self, **kwargs))

Corruption index per country

`bulkhours.get_data("world.corruption")`

Raw data: corruption.csv, cost_of_living.csv, richest_countries.csv, unemployment.csv, continent.tsv

Show columns info

Column	Info
country
annual_income
corruption_index
gdp_per_capita
unemployment_rate
index
iso
m49
region1
region2
continent

Show code


def get_corruption(self, show_truth=False, **data_info):
    show_truth = self.data_info["show_truth"] if "show_truth" in self.data_info else False
    df = self.read_raw_data(self.raw_data)
if not show_truth:
    df["corruption_index"] = df["corruption_index"].where(
        ~df.index.isin(["Spain", "Japan", "Sweden", "Romania"]), other=np.nan
    )

df = df[["annual_income", "corruption_index", "gdp_per_capita", "unemployment_rate"]]
df = df.dropna(subset=["annual_income", "gdp_per_capita", "unemployment_rate"])

return geo_format(df, None)

Life expectancy versus GDP/capita per country

`bulkhours.get_data("world.life_expectancy_vs_gdp_2018")`

Raw data: life-expectancy-vs-gdp-per-capita.csv (raw🤗)
Enrich data: world.py (raw)
Direct source: https://ourworldindata.org/grapher/life-expectancy-vs-gdp-per-capita
Reference site: Maddison Project Database (2020); UN WPP (2022); Zijdeman et al. (2015)

Show columns info

Column	Info
Country
Code
Year
Life expectancy (years)
GDP per capita ($)
annotations
Population
Continent

Show code


def get_life_expectancy_vs_gdp_2018(self, **data_info):
    return self.read_raw_data(self.raw_data)  # .dropna()

Evolution du PIB et de ses composantes par rapport au trimestre precedent en volume en %

`bulkhours.get_data("gmacro.fr_qgdp")`

Enrich data: gmacro.py (raw)
Direct source: https://www.insee.fr/fr/statistiques/2830547#tableau-figure1

Show columns info

Column	Info
gdp
Importations
consommation_menages
consommation_APU1
capital_fixe
capital_fixe_non_financieres
menages
APU1
Exportations
demaNaNe_interieure
Variations de stocks
Commerce exterieur
date

Show code


def get_fr_qgdp(self):
    data = StringIO(
        """quarter;gdp;Importations;consommation_menages;consommation_APU1;capital_fixe;capital_fixe_non_financieres;menages;APU1;Exportations;demande_interieure;Variations de stocks;Commerce exterieur
2022-T4;0,1;-1,9;-0,9;0,2;0,8;1,2;-0,2;0,3;-0,3;-0,2;-0,2;0,5
2022-T3;0,2;3,9;0,5;0,2;2,3;3,8;-0,7;1,0;0,8;0,9;0,3;-1,0
2022-T2;0,5;1,5;0,5;0,0;0,3;0,5;-0,1;-0,2;1,0;0,3;0,3;-0,2
2022-T1;-0,2;1,3;-1,0;0,2;0,4;0,2;0,0;1,1;1,7;-0,4;0,1;0,1
2021-T4;0,6;5,0;0,6;0,6;-0,3;-0,2;-0,7;-0,5;2,9;0,4;0,8;-0,6
2021-T3;3,3;0,6;5,4;3,1;0,5;0,9;1,2;-1,6;2,1;3,7;-0,8;0,4
2021-T2;1,1;1,9;1,2;0,6;2,1;1,8;4,0;0,5;2,6;1,2;-0,3;0,1
2021-T1;0,1;1,4;0,5;-0,6;0,7;0,6;0,4;-1,5;-0,4;0,3;0,3;-0,6
2020-T4;-0,9;0,8;-5,5;-0,1;2,5;2,0;6,1;0,4;3,7;-2,4;0,7;0,8
2020-T3;18,4;17,7;18,2;17,6;24,2;24,8;28,8;17,4;22,4;19,7;-1,9;0,6
2020-T2;-13,5;-19,2;-11,6;-11,8;-14,3;-13,5;-16,3;-12,5;-25,3;-12,3;0,4;-1,6
2020-T1;-5,6;-5,5;-5,2;-3,3;-9,6;-8,8;-14,2;-5,1;-6,8;-5,8;0,6;-0,3
2019-T4;-0,3;-1,0;0,2;0,1;0,3;0,4;0,4;0,3;-1,1;0,2;-0,5;0,0
2019-T3;0,0;-0,4;0,2;0,4;0,8;0,6;0,9;1,2;-1,1;0,4;-0,1;-0,2
2019-T2;0,7;0,0;0,6;0,3;1,7;1,2;1,9;2,6;0,3;0,8;-0,2;0,1
2019-T1;0,7;2,2;0,8;0,1;1,0;0,4;0,4;3,0;0,3;0,7;0,6;-0,6
2018-T4;0,6;1,3;0,2;0,5;0,8;0,3;0,0;3,0;2,2;0,4;-0,1;0,3
2018-T3;0,4;-1,1;0,4;-0,1;0,7;0,6;0,4;1,2;0,2;0,4;-0,4;0,4
2018-T2;0,4;1,0;-0,1;0,3;1,3;1,7;0,7;1,5;0,9;0,3;0,2;-0,1
2018-T1;0,1;0,6;0,4;0,0;0,1;0,1;0,1;0,4;-0,2;0,2;0,1;-0,2
2017-T4;0,6;1,3;0,0;0,1;0,8;1,4;0,4;0,0;2,5;0,2;0,0;0,4
2017-T3;0,9;1,9;0,7;0,5;1,3;1,9;0,6;0,4;1,5;0,8;0,2;-0,1
2017-T2;0,8;-0,1;0,3;0,5;1,1;1,2;1,6;0,1;2,7;0,6;-0,6;0,9
2017-T1;0,8;2,2;0,4;0,1;2,4;3,1;2,1;-0,4;-0,3;0,8;0,8;-0,8
2016-T4;0,6;0,8;0,9;0,4;0,9;0,5;1,5;0,5;1,4;0,8;-0,4;0,2
2016-T3;0,3;2,0;0,0;0,5;0,4;0,0;1,1;-0,5;0,5;0,2;0,6;-0,5
2016-T2;-0,3;-1,7;0,0;0,2;-0,2;-0,9;0,7;0,1;0,2;0,0;-0,9;0,6
2016-T1;0,6;0,6;1,3;0,4;1,1;1,8;0,4;-0,7;-0,2;1,0;-0,1;-0,2
2015-T4;0,2;1,9;-0,1;0,4;1,1;1,1;0,8;1,1;0,9;0,3;0,3;-0,3
2015-T3;0,2;1,8;0,2;0,2;1,2;1,0;0,7;2,8;0,0;0,4;0,4;-0,6
2015-T2;0,1;0,1;0,3;0,2;-0,8;0,4;-0,3;-5,5;1,6;0,0;-0,4;0,5
2015-T1;0,5;2,1;0,5;0,2;1,0;1,6;0,0;0,2;0,9;0,6;0,4;-0,4
2014-T4;0,0;1,1;0,3;0,3;-0,7;-0,6;-0,8;-1,8;1,7;0,1;-0,3;0,2
2014-T3;0,6;1,8;0,3;0,4;0,5;1,5;-1,3;-1,4;1,6;0,4;0,3;-0,1
2014-T2;0,1;1,1;0,6;0,3;-0,3;0,3;-2,1;-1,5;0,0;0,3;0,1;-0,3
2014-T1;0,0;1,0;-0,5;0,3;-0,5;-0,5;-0,8;-1,8;0,7;-0,3;0,4;-0,1
2013-T4;0,6;1,2;0,6;0,3;0,8;1,3;0,4;-1,0;1,2;0,6;0,1;0,0
2013-T3;-0,1;1,0;0,0;0,3;0,1;0,6;-0,1;-1,1;-0,3;0,1;0,2;-0,4
2013-T2;0,7;2,2;0,4;0,5;0,2;0,7;0,0;0,0;2,3;0,4;0,3;0,0
2013-T1;0,1;-0,2;0,0;0,4;-0,6;-0,5;0,3;-0,1;0,0;0,0;0,1;0,1
2012-T4;-0,1;0,0;0,2;0,3;-0,4;-0,2;-0,2;-0,1;0,1;0,1;-0,3;0,0
2012-T3;0,2;0,4;0,1;0,3;0,3;0,6;-0,3;0,7;0,0;0,2;0,2;-0,1
2012-T2;-0,2;0,3;-0,2;0,5;-0,8;-0,9;-2,0;0,6;0,8;-0,2;-0,1;0,1
2012-T1;0,0;0,9;0,2;0,5;-0,5;-1,3;-0,3;0,4;0,5;0,1;0,0;-0,1
2011-T4;0,2;-0,8;-0,4;0,3;1,2;1,5;0,3;0,9;1,8;0,1;-0,7;0,7
2011-T3;0,5;0,1;0,2;0,6;0,6;0,8;-0,5;0,2;0,6;0,4;0,0;0,1
2011-T2;-0,1;-1,2;-1,2;0,1;0,2;0,3;-0,3;-0,6;1,0;-0,6;-0,2;0,6
2011-T1;1,1;3,9;0,5;0,3;0,2;1,0;-0,1;-2,8;2,4;0,4;1,2;-0,5
2010-T4;0,7;0,9;0,8;0,1;0,5;1,2;0,5;-1,7;1,5;0,6;0,0;0,1
2010-T3;0,6;2,8;0,6;0,4;1,3;1,6;1,7;-0,1;1,0;0,7;0,4;-0,5
2010-T2;0,5;4,2;0,3;0,2;0,8;1,4;1,3;-1,0;3,3;0,4;0,3;-0,2
2010-T1;0,3;0,7;0,2;0,1;1,0;1,9;0,9;-0,7;3,7;0,4;-0,8;0,8
2009-T4;0,7;3,7;0,7;0,5;0,5;1,0;-0,1;0,7;0,1;0,7;1,0;-1,0
2009-T3;0,2;1,0;0,0;0,5;-0,6;-0,5;-1,2;0,3;1,8;0,0;0,0;0,2
2009-T2;0,0;-1,9;0,7;0,7;-2,5;-3,5;-2,5;0,9;0,0;0,0;-0,5;0,5
2009-T1;-1,7;-7,1;-0,1;0,7;-4,1;-6,0;-5,3;2,9;-7,5;-0,8;-0,9;0,0
2008-T4;-1,5;-2,7;-0,4;0,8;-3,2;-4,2;-4,5;1,6;-4,7;-0,8;-0,2;-0,5
2008-T3;-0,2;-0,4;0,1;0,5;-0,8;-0,2;-2,9;-0,1;-0,9;0,0;0,0;-0,2
2008-T2;-0,5;-0,8;0,0;0,1;-1,1;-0,9;-1,5;-1,9;-1,5;-0,3;-0,1;-0,2
2008-T1;0,5;1,1;-0,4;0,1;1,6;2,7;-0,3;0,3;2,3;0,2;0,0;0,3
2007-T4;0,2;0,6;0,3;0,2;0,5;1,2;-0,2;-1,3;0,4;0,3;-0,1;-0,1
2007-T3;0,5;0,7;0,9;0,4;1,2;2,4;-0,2;-0,3;0,2;0,9;-0,2;-0,2
2007-T2;0,7;2,8;1,0;0,5;1,2;2,0;0,7;-0,1;1,6;0,9;0,1;-0,4
2007-T1;0,7;1,0;0,6;0,6;1,8;2,7;0,9;1,8;0,3;0,9;0,0;-0,2
2006-T4;0,7;2,6;0,5;0,5;1,5;2,3;0,7;1,0;1,9;0,8;0,1;-0,2
2006-T3;0,1;-0,6;0,2;0,1;0,7;0,9;0,7;-0,1;-1,5;0,3;0,0;-0,2
2006-T2;1,0;1,6;0,6;0,4;1,9;2,5;1,6;0,2;1,5;0,8;0,2;0,0
2006-T1;0,8;0,5;0,8;0,6;0,6;0,3;1,4;-0,9;2,2;0,7;-0,4;0,5
2005-T4;0,7;3,3;0,6;0,1;0,6;0,7;1,3;-1,3;2,3;0,5;0,5;-0,3
2005-T3;0,6;2,5;0,5;0,3;1,0;1,0;1,2;0,7;2,2;0,6;0,0;-0,1
2005-T2;0,3;0,9;0,2;0,4;0,5;0,2;1,7;0,5;1,4;0,3;-0,2;0,1
2005-T1;0,2;0,4;0,6;0,3;0,6;0,8;1,0;0,2;-0,7;0,5;0,0;-0,3
2004-T4;0,8;2,5;1,5;0,3;1,4;1,8;0,8;1,9;2,0;1,1;-0,3;-0,1
2004-T3;0,3;1,8;0,0;0,3;0,3;0,3;0,6;0,3;0,4;0,1;0,5;-0,4
2004-T2;0,6;2,0;0,4;0,5;0,5;0,2;1,1;0,5;1,7;0,5;0,2;0,0
2004-T1;1,0;0,7;0,6;0,6;1,1;1,0;1,3;0,8;0,4;0,7;0,4;-0,1
2003-T4;0,7;1,7;0,3;0,8;0,2;-0,4;0,9;0,7;2,6;0,4;0,0;0,3
2003-T3;0,7;0,9;0,6;0,4;1,7;2,1;1,0;0,9;1,1;0,7;-0,1;0,1
2003-T2;-0,2;-0,7;0,1;0,6;0,0;-0,9;0,5;1,4;-0,8;0,2;-0,3;-0,1
2003-T1;0,1;0,1;0,4;0,0;0,1;-0,5;0,4;1,1;-2,2;0,2;0,5;-0,6
2002-T4;0,2;0,0;0,6;0,7;1,1;0,9;0,7;1,5;0,1;0,7;-0,5;0,0
2002-T3;0,2;0,8;0,6;0,6;0,4;0,3;0,8;0,3;0,4;0,5;-0,2;-0,1
2002-T2;0,4;0,7;0,5;0,9;-0,6;-1,7;0,8;0,7;2,5;0,3;-0,4;0,5
2002-T1;0,6;2,5;0,4;0,2;-0,6;-1,1;0,8;-0,6;1,5;0,2;0,6;-0,2
2001-T4;-0,1;-0,6;0,4;0,3;-0,2;-0,8;0,8;0,6;-1,3;0,3;-0,1;-0,2
2001-T3;0,3;-1,6;0,2;0,3;0,1;0,5;0,4;-0,7;0,1;0,3;-0,5;0,5
2001-T2;0,1;-0,7;0,7;0,2;-0,3;-0,1;-0,1;-0,9;-2,3;0,4;0,2;-0,5
2001-T1;0,5;-1,4;1,0;0,2;0,6;1,2;0,3;-0,3;0,2;0,7;-0,7;0,4
2000-T4;0,8;3,2;0,3;0,2;0,6;1,0;0,0;0,2;4,0;0,3;0,2;0,3
2000-T3;0,7;3,8;0,6;0,3;1,9;3,2;0,1;0,8;2,1;0,8;0,3;-0,4
2000-T2;1,0;4,4;0,7;0,4;1,4;1,3;0,4;2,8;3,5;0,8;0,4;-0,2
2000-T1;1,0;4,3;0,7;0,6;1,7;1,2;1,5;3,2;4,0;0,9;0,2;0,0
1999-T4;1,4;4,4;1,0;0,6;1,9;1,5;1,2;3,3;2,4;1,1;0,8;-0,4
1999-T3;1,1;2,5;2,5;0,6;1,9;2,0;1,4;1,9;3,8;1,9;-1,1;0,4
1999-T2;0,3;1,8;0,4;0,3;1,7;2,1;1,3;1,0;2,1;0,7;-0,5;0,1
1999-T1;1,1;0,9;0,1;0,6;2,3;2,5;2,9;0,8;-0,3;0,7;0,7;-0,3
1998-T4;0,7;0,7;0,6;0,5;1,4;1,3;1,8;0,8;-0,3;0,7;0,2;-0,2
1998-T3;0,7;1,5;1,4;0,0;1,9;1,9;1,8;1,4;1,5;1,1;-0,4;0,0
1998-T2;0,8;2,0;1,0;-0,3;2,0;2,6;1,4;0,8;1,6;0,9;0,0;-0,1
1998-T1;0,9;4,0;0,9;-0,5;1,6;2,2;0,7;0,5;1,7;0,7;0,7;-0,5
1997-T4;1,0;2,8;1,2;-0,2;1,5;2,1;1,0;-0,1;3,0;0,9;-0,1;0,1
1997-T3;1,0;4,3;1,0;0,1;0,6;0,8;1,2;-1,4;3,1;0,6;0,5;-0,2
1997-T2;1,0;2,7;0,2;0,1;1,5;2,4;1,4;-1,3;4,5;0,4;0,1;0,5
1997-T1;0,3;1,8;-0,1;0,1;-1,0;-1,3;0,5;-2,4;3,4;-0,2;0,2;0,4
1996-T4;0,3;1,4;-1,1;0,6;-0,2;0,0;0,7;-1,8;3,1;-0,5;0,3;0,4
1996-T3;0,5;-0,2;1,4;0,4;0,5;0,6;1,1;-0,7;2,0;1,0;-0,9;0,5
1996-T2;0,1;0,4;-0,4;0,7;-0,3;-1,3;0,4;1,0;-0,2;-0,1;0,4;-0,1
1996-T1;0,7;0,9;1,7;0,8;0,8;1,0;0,3;0,2;1,8;1,2;-0,8;0,2
1995-T4;0,0;-0,4;-0,1;0,5;0,1;-0,1;-0,7;1,1;0,7;0,1;-0,4;0,2
1995-T3;0,2;0,6;-0,6;0,4;0,1;0,5;-0,4;-1,0;-0,9;-0,2;0,7;-0,3
1995-T2;0,6;3,3;1,6;0,1;-0,5;-0,7;-0,7;-0,1;2,6;0,8;-0,1;-0,1
1995-T1;0,3;0,0;0,1;-0,3;0,8;1,3;0,5;-1,6;1,5;0,1;-0,2;0,3
1994-T4;0,9;3,8;0,5;0,0;0,4;0,4;1,3;-1,0;4,6;0,4;0,3;0,2
1994-T3;0,9;3,2;0,4;0,0;0,8;1,7;1,5;-1,8;3,1;0,4;0,4;0,0
1994-T2;1,0;3,2;0,8;0,0;1,1;1,5;1,6;0,0;3,6;0,7;0,2;0,1
1994-T1;0,7;2,3;0,0;-0,2;1,6;1,4;1,9;2,7;0,0;0,3;0,8;-0,5
1993-T4;0,2;2,6;0,4;0,2;-0,3;-0,2;0,6;-0,2;3,4;0,2;-0,2;0,2
1993-T3;0,2;0,8;0,1;0,2;-1,3;-1,2;-0,7;-1,1;1,7;-0,2;0,2;0,2
1993-T2;0,0;-3,4;0,7;1,1;-1,8;-1,6;-1,5;-2,1;-2,8;0,2;-0,4;0,1
1993-T1;-0,7;0,2;-1,4;1,1;-1,7;-2,7;-1,3;-0,7;1,8;-0,9;-0,1;0,3
1992-T4;-0,2;-1,8;0,6;1,0;-1,4;-2,2;-1,7;-0,4;-1,3;0,2;-0,5;0,1
1992-T3;-0,1;-1,7;0,5;1,2;-1,1;-1,7;-0,7;-0,1;-0,8;0,3;-0,5;0,2
1992-T2;0,1;-0,8;0,2;0,7;-1,2;-0,9;-2,0;-0,7;0,7;0,0;-0,3;0,3
1992-T1;0,8;3,8;0,1;0,6;0,6;1,0;0,8;1,2;2,2;0,4;0,8;-0,4
1991-T4;0,7;-0,6;0,3;0,9;-0,7;-0,8;-3,4;0,9;2,2;0,2;-0,2;0,6
1991-T3;0,2;0,0;-0,1;0,5;-0,1;-0,5;-1,0;1,2;2,6;0,1;-0,4;0,6
1991-T2;0,4;0,0;0,1;1,3;0,4;0,6;-1,5;1,7;2,8;0,5;-0,6;0,6
1991-T1;0,1;2,9;-0,1;0,9;-0,9;-0,6;-2,7;0,1;0,7;-0,1;0,7;-0,5
1990-T4;0,0;-0,2;0,6;0,6;-0,1;0,0;-0,6;0,0;1,8;0,4;-0,9;0,4
1990-T3;0,5;-0,3;0,0;1,2;0,6;1,4;-2,0;1,0;0,6;0,4;-0,1;0,2
1990-T2;0,5;1,6;0,4;0,3;0,4;0,6;-1,2;1,5;-1,2;0,4;0,7;-0,6
1990-T1;0,4;0,1;1,1;1,2;1,4;1,9;-0,7;2,5;1,3;1,2;-1,0;0,2
1989-T4;1,3;4,2;0,4;0,8;2,0;2,7;0,2;2,1;3,3;0,9;0,7;-0,2
1989-T3;1,1;0,1;1,1;0,6;1,2;1,5;0,5;1,3;-0,4;1,0;0,1;-0,1
1989-T2;1,2;2,6;0,4;0,1;1,6;1,4;3,8;-0,4;2,6;0,6;0,6;0,0
1989-T1;1,2;1,9;1,0;0,6;2,4;3,5;0,7;1,7;4,3;1,2;-0,5;0,5
1988-T4;0,9;1,6;0,8;-0,2;1,6;1,1;3,6;1,0;1,4;0,8;0,1;-0,1
1988-T3;1,3;4,3;1,3;0,6;2,0;2,4;2,2;1,0;3,3;1,3;0,2;-0,2
1988-T2;0,8;0,5;0,5;0,6;1,8;2,0;0,7;2,8;1,5;0,8;-0,2;0,2
1988-T1;1,2;1,8;0,0;1,1;2,5;2,3;2,2;2,9;1,2;0,8;0,6;-0,1
1987-T4;1,4;2,3;1,8;1,3;1,9;2,5;-0,1;2,8;1,9;1,7;-0,2;-0,1
1987-T3;0,7;1,9;0,1;0,8;2,1;2,2;1,4;2,9;2,9;0,7;-0,2;0,2
1987-T2;1,5;2,4;1,3;0,7;2,3;3,0;0,0;3,4;2,6;1,4;0,0;0,0
1987-T1;0,1;4,1;1,0;0,4;0,3;0,4;2,2;-2,3;-0,9;0,7;0,5;-1,0
1986-T4;0,1;-2,5;0,2;0,8;0,8;0,9;0,7;0,9;-1,2;0,5;-0,7;0,4
1986-T3;0,5;3,2;0,5;0,7;0,9;1,3;-0,2;1,2;2,1;0,7;0,1;-0,3
1986-T2;1,1;1,7;1,6;0,7;1,5;1,4;-0,1;3,7;-1,9;1,4;0,6;-0,8
1986-T1;0,4;1,7;0,9;0,5;0,6;1,9;0,2;-2,5;-0,9;0,7;0,2;-0,6
1985-T4;0,4;1,8;1,1;0,8;0,6;0,5;0,7;0,5;1,2;0,9;-0,4;-0,1
1985-T3;0,5;1,9;0,6;0,4;1,4;1,4;1,3;1,5;-1,8;0,7;0,7;-0,9
1985-T2;0,9;0,6;0,5;0,8;1,7;2,7;-1,9;4,0;2,3;0,8;-0,3;0,4
1985-T1;0,3;1,6;1,4;0,8;-0,1;0,0;-0,9;0,6;-0,4;0,9;-0,1;-0,5
1984-T4;-0,1;1,9;-0,7;0,6;0,8;1,7;-1,5;1,5;-0,1;-0,1;0,5;-0,5
1984-T3;0,7;-1,3;0,2;1,2;-0,2;0,6;-3,1;1,5;1,3;0,3;-0,3;0,6
1984-T2;0,4;2,4;0,4;0,7;-0,5;-1,0;0,3;-1,1;3,8;0,2;-0,1;0,3
1984-T1;0,4;1,3;0,4;0,0;0,4;0,8;-1,2;1,0;-0,3;0,3;0,5;-0,4
1983-T4;0,6;0,6;0,5;0,6;-0,5;-0,2;-1,4;-0,4;1,8;0,3;0,0;0,3
1983-T3;0,1;2,6;-0,4;0,7;-0,4;-0,3;-0,5;-1,0;4,0;-0,2;0,0;0,3
1983-T2;0,0;-3,2;-0,2;0,0;-1,1;-1,1;-0,7;-1,9;2,3;-0,4;-0,9;1,2
1983-T1;0,4;-1,3;-0,2;0,5;-0,5;-1,2;0,5;0,0;-1,1;-0,1;0,5;0,1
1982-T4;0,6;-0,2;1,2;0,7;-1,0;0,0;-2,8;-1,2;3,6;0,6;-0,8;0,8
1982-T3;0,0;-1,2;0,0;1,7;-1,5;-2,0;-0,7;-1,5;-0,3;0,0;-0,3;0,2
1982-T2;0,7;2,0;1,0;0,7;0,4;1,4;-2,3;1,8;-2,6;0,8;0,9;-1,0
1982-T1;0,8;0,1;0,8;1,5;-0,6;0,4;-2,8;-0,1;-0,7;0,6;0,4;-0,2
1981-T4;0,6;1,3;1,4;0,7;0,6;1,1;-1,2;2,2;-2,5;1,0;0,4;-0,8
1981-T3;0,7;3,0;0,1;0,9;-0,1;-0,3;-0,5;0,9;3,2;0,2;0,4;0,0
1981-T2;0,7;-0,5;1,5;0,8;0,2;-0,2;0,1;1,1;3,0;1,0;-1,1;0,8
1981-T1;0,3;-1,8;-0,1;0,7;-0,5;-0,8;-1,4;1,2;1,1;0,0;-0,3;0,7
1980-T4;-0,2;-1,7;0,7;0,5;-0,7;-1,1;-0,5;-0,4;1,1;0,3;-1,1;0,6
1980-T3;0,2;0,5;0,7;0,8;-0,1;0,8;-1,6;0,0;-1,0;0,5;-0,1;-0,3
1980-T2;-0,7;-0,5;-1,2;0,8;-0,2;0,7;-1,4;-0,4;-0,9;-0,5;-0,1;-0,1
1980-T1;1,0;3,6;0,8;nd;0,8;1,2;1,3;-0,8;1,5;0,8;0,6;-0,4
1979-T4;0,3;0,2;0,5;nd;1,7;2,5;1,2;0,3;0,5;0,8;-0,6;0,1
1979-T3;1,3;3,9;0,2;nd;2,4;3,7;0,8;1,4;3,8;0,9;0,4;0,0
1979-T2;0,4;0,8;0,7;nd;0,8;1,2;-0,4;1,8;0,6;0,7;-0,2;0,0
1979-T1;1,1;3,3;1,0;nd;-0,7;-1,3;0,5;-1,9;1,9;0,5;0,8;-0,2
1978-T4;1,1;2,7;1,4;nd;1,3;1,3;1,0;1,7;1,6;1,3;0,0;-0,2
1978-T3;0,6;0,4;0,3;nd;0,3;-0,5;1,9;-0,2;1,3;0,5;-0,1;0,2
1978-T2;1,1;1,6;1,6;nd;1,4;1,7;1,1;1,2;1,5;1,5;-0,4;0,0
1978-T1;1,4;2,3;1,2;nd;1,5;1,1;3,9;-1,0;1,3;1,4;0,1;-0,2
1977-T4;0,8;0,3;0,4;nd;-0,4;-1,1;0,9;-0,7;1,7;0,4;0,1;0,3
1977-T3;0,8;-0,4;1,3;nd;0,3;0,1;0,6;0,1;2,3;1,0;-0,7;0,5
1977-T2;0,4;-0,2;0,2;nd;-0,5;0,8;0,5;-6,1;1,8;0,2;-0,2;0,4
1977-T1;1,1;-1,2;0,0;nd;0,9;2,4;-1,9;0,6;0,8;0,3;0,4;0,4
1976-T4;0,7;0,3;1,0;nd;-0,6;-1,1;1,3;-2,6;3,5;0,4;-0,4;0,6
1976-T3;1,2;3,9;1,4;nd;-1,7;-1,3;-2,3;-2,2;1,0;0,5;1,3;-0,6
1976-T2;1,4;4,7;0,9;nd;0,4;-0,7;3,2;-0,8;3,5;0,7;0,9;-0,3
1976-T1;1,0;7,4;1,4;nd;2,3;4,4;0,0;1,2;3,2;1,5;0,2;-0,7
1975-T4;2,1;5,0;1,9;nd;2,0;3,9;-0,6;1,8;1,7;1,9;0,9;-0,7
1975-T3;0,0;1,4;1,6;nd;-1,5;-2,7;-1,6;2,1;-0,1;0,7;-0,5;-0,3
1975-T2;0,0;0,2;0,8;nd;-0,6;-0,5;-2,6;2,6;-0,7;0,6;-0,5;-0,2
1975-T1;-0,8;-6,3;0,2;nd;-2,1;-2,9;-3,0;1,9;-4,1;-0,2;-1,2;0,7
1974-T4;-1,8;-6,1;-0,4;nd;-2,6;-4,5;-1,8;1,1;-0,6;-0,6;-2,2;0,9
1974-T3;1,1;-0,5;0,1;nd;-0,9;-2,1;-0,5;1,8;2,7;0,0;0,5;0,6
1974-T2;0,7;0,7;0,6;nd;0,8;1,4;0,1;-0,1;0,8;0,8;-0,2;0,0
1974-T1;1,6;2,9;0,2;nd;0,4;-0,9;2,5;0,6;4,2;0,4;0,9;0,3
1973-T4;1,2;2,0;2,4;nd;1,2;0,8;2,4;0,2;4,3;1,7;-1,0;0,4
1973-T3;1,5;4,1;0,5;nd;2,3;2,8;2,3;0,3;2,5;1,0;0,8;-0,3
1973-T2;1,4;1,5;0,7;nd;1,8;1,7;2,9;-0,3;2,6;1,1;0,2;0,2
1973-T1;1,8;5,3;2,4;nd;1,4;1,0;1,9;1,1;2,9;1,9;0,2;-0,4
1972-T4;1,6;5,7;0,8;nd;2,0;1,7;2,1;1,7;5,1;1,2;0,4;-0,1
1972-T3;1,4;1,4;2,3;nd;1,5;1,3;2,1;0,6;0,0;1,8;-0,2;-0,2
1972-T2;0,7;1,2;0,2;nd;1,3;1,0;2,1;0,4;3,9;0,7;-0,4;0,5
1972-T1;1,1;8,2;1,6;nd;1,7;0,8;5,0;-0,8;3,9;1,4;0,3;-0,6
1971-T4;1,0;-0,4;1,1;nd;0,9;1,7;0,2;-0,6;0,1;1,0;-0,2;0,1
1971-T3;1,3;5,4;1,5;nd;1,4;3,1;-1,4;1,0;4,3;1,4;0,1;-0,1
1971-T2;1,1;2,6;1,6;nd;1,8;3,0;-0,7;2,3;1,3;1,5;-0,2;-0,2
1971-T1;1,6;-1,3;0,8;nd;3,0;2,1;8,5;-2,1;2,3;1,5;-0,6;0,6
1970-T4;1,3;1,2;2,5;nd;0,2;2,0;-3,3;1,3;2,6;1,7;-0,6;0,2
1970-T3;1,1;4,1;0,6;nd;1,4;0,7;3,1;0,8;1,9;1,0;0,4;-0,3
1970-T2;1,8;3,7;1,2;nd;2,3;1,7;3,6;1,8;4,0;1,5;0,2;0,1
1970-T1;1,5;1,8;2,0;nd;-0,1;1,1;-3,6;1,4;4,6;1,3;-0,2;0,4
1969-T4;1,5;-1,1;-0,8;nd;1,6;-1,2;8,0;-0,2;4,9;0,2;0,4;0,9
1969-T3;1,4;-1,1;1,0;nd;1,4;0,3;3,3;1,7;3,1;1,2;-0,4;0,6
1969-T2;2,1;6,3;2,8;nd;3,7;5,3;1,6;2,1;3,3;2,8;-0,2;-0,5
1969-T1;0,9;4,4;-0,4;nd;-0,4;-0,1;-0,5;0,0;3,6;-0,1;1,1;-0,1
1968-T4;1,1;5,4;2,6;nd;1,7;3,2;-0,5;0,8;-2,6;2,1;0,2;-1,2
1968-T3;8,0;20,1;5,0;nd;9,1;14,3;4,1;2,2;26,5;5,7;1,6;0,8
1968-T2;-5,3;-8,4;-0,5;nd;-5,6;-8,0;0,0;-6,2;-14,1;-1,6;-2,8;-0,9
1968-T1;2,7;5,2;0,1;nd;2,7;2,7;4,4;0,1;6,1;0,9;1,7;0,1
1967-T4;1,0;1,8;0,7;nd;1,7;2,8;0,7;0,0;3,9;1,0;-0,2;0,3
1967-T3;1,1;2,1;1,2;nd;1,3;0,7;3,0;0,5;0,9;1,2;0,1;-0,2
1967-T2;1,2;-1,9;0,8;nd;1,6;1,4;3,2;0,4;2,6;1,0;-0,5;0,6
1967-T1;1,7;4,3;1,5;nd;3,0;2,4;3,2;4,2;3,2;1,9;-0,1;-0,1
1966-T4;0,6;3,4;1,7;nd;0,9;0,9;-0,6;2,8;-1,2;1,4;-0,1;-0,6
1966-T3;1,2;4,2;0,5;nd;1,8;3,0;-0,4;1,7;2,5;0,9;0,4;-0,2
1966-T2;1,6;2,3;1,9;nd;2,1;1,9;3,2;1,3;1,6;1,8;-0,1;-0,1
1966-T1;0,9;3,1;0,9;nd;1,5;2,9;0,9;-1,0;0,4;1,1;0,1;-0,3
1965-T4;1,5;2,2;0,3;nd;3,3;1,8;9,0;0,1;3,0;1,1;0,3;0,1
1965-T3;1,5;1,5;1,7;nd;0,3;0,0;0,3;1,1;2,9;1,2;0,1;0,2
1965-T2;1,8;2,7;2,5;nd;2,3;2,0;2,5;2,6;2,4;2,1;-0,3;0,0
1965-T1;0,5;-1,5;-0,3;nd;0,3;-0,7;0,9;1,8;2,9;0,1;-0,2;0,6
1964-T4;1,4;0,5;1,2;nd;1,4;-0,9;4,6;3,6;4,4;1,2;-0,3;0,5
1964-T3;0,8;-0,3;0,8;nd;2,7;2,0;4,2;2,4;0,7;1,3;-0,7;0,1
1964-T2;1,3;2,1;0,2;nd;1,2;0,8;1,9;1,8;-0,5;0,6;1,0;-0,3
1964-T1;2,2;5,9;2,0;nd;3,3;2,0;5,4;4,5;4,4;2,2;0,2;-0,2
1963-T4;-0,2;4,7;1,3;nd;0,0;2,1;-6,1;1,5;-0,4;0,9;-0,5;-0,7
1963-T3;3,4;2,4;2,0;nd;5,3;3,5;11,4;3,2;3,8;2,7;0,5;0,2
1963-T2;4,5;10,9;2,9;nd;6,0;4,2;3,8;14,2;4,8;3,4;1,7;-0,7
1963-T1;-1,0;0,1;1,7;nd;-0,1;0,5;3,8;-6,3;1,5;1,0;-2,2;0,2
1962-T4;1,1;1,4;0,9;nd;-0,2;-1,1;-0,1;2,3;1,9;0,7;0,4;0,1
1962-T3;1,9;4,1;1,7;nd;2,2;3,1;-0,1;2,5;-0,4;1,7;0,8;-0,5
1962-T2;1,5;-4,1;1,6;nd;1,0;0,7;0,0;2,9;-0,3;1,4;-0,3;0,5
1962-T1;2,2;4,2;3,2;nd;2,0;2,4;0,2;3,3;1,0;2,6;0,0;-0,4
1961-T4;1,8;3,0;1,2;nd;1,5;1,2;1,0;3,4;-0,7;1,4;0,9;-0,5
1961-T3;0,9;0,4;0,9;nd;2,7;2,9;1,8;3,3;0,4;1,4;-0,5;0,0
1961-T2;0,6;4,4;0,9;nd;0,6;-1,1;2,8;3,2;2,4;0,9;-0,1;-0,2
1961-T1;1,4;0,2;2,5;nd;5,3;6,9;3,6;2,8;1,4;2,8;-1,6;0,2
1960-T4;1,1;0,5;1,6;nd;2,3;2,0;3,1;2,3;0,1;1,6;-0,5;0,0
1960-T3;1,7;6,0;1,0;nd;2,7;3,1;2,4;1,7;3,3;1,3;0,6;-0,2
1960-T2;2,5;-1,0;1,4;nd;2,7;3,8;1,5;1,1;0,8;1,5;0,8;0,2
1960-T1;2,0;3,5;2,0;nd;-0,5;-1,5;0,6;0,8;6,8;1,0;0,4;0,6
1959-T4;2,5;7,6;0,8;nd;2,8;4,4;0,3;1,3;5,3;1,2;1,5;-0,2
1959-T3;1,1;0,7;0,7;nd;0,9;0,9;0,0;2,2;4,3;0,7;-0,1;0,5
1959-T2;1,2;4,4;-0,3;nd;2,6;3,8;-0,2;3,2;7,9;0,5;0,2;0,5
1959-T1;0,2;-3,0;0,9;nd;0,3;-0,6;-0,3;4,0;-1,6;0,7;-0,6;0,1
1958-T4;0,3;-2,0;1,7;nd;1,6;1,7;0,0;3,4;4,9;1,4;-1,9;0,9
1958-T3;-0,2;-2,0;-0,5;nd;0,3;-0,6;0,4;2,2;1,7;-0,2;-0,5;0,5
1958-T2;0,4;0,7;0,1;nd;-1,0;-2,6;1,0;0,9;-1,7;-0,2;0,9;-0,3
1958-T1;1,3;0,4;-1,1;nd;3,6;5,8;1,7;-0,6;1,0;0,1;1,1;0,1
1957-T4;0,3;-4,0;0,4;nd;1,1;0,8;2,2;0,0;-0,5;0,4;-0,6;0,5
1957-T3;1,7;-1,4;1,0;nd;1,9;1,8;2,7;1,0;-0,1;1,0;0,4;0,2
1957-T2;0,7;0,9;1,4;nd;-0,9;-3,6;2,9;2,0;1,5;0,7;-0,1;0,1
1957-T1;1,9;4,0;0,7;nd;4,6;6,0;2,6;3,6;5,7;1,5;0,2;0,2
1956-T4;1,5;2,4;1,4;nd;3,4;4,2;1,8;3,3;-0,3;1,7;0,1;-0,4
1956-T3;1,2;4,5;1,8;nd;1,0;0,5;0,6;3,2;1,8;1,5;0,1;-0,4
1956-T2;1,7;9,8;1,6;nd;5,7;9,9;-0,4;3,0;5,1;2,5;-0,2;-0,6
1956-T1;0,8;1,5;2,0;nd;-1,6;-3,3;-0,8;2,5;-6,1;1,2;0,5;-1,0
1955-T4;1,1;4,2;1,6;nd;1,8;2,6;-0,2;2,6;-0,6;1,6;0,1;-0,6
1955-T3;1,5;3,0;1,3;nd;2,5;3,3;1,0;2,5;1,6;1,5;0,1;-0,1
1955-T2;1,4;3,6;2,3;nd;3,8;5,1;2,6;2,3;-0,4;2,3;-0,4;-0,5
1955-T1;1,3;-0,2;1,7;nd;1,0;-1,1;4,1;2,4;0,5;1,3;0,0;0,1
1954-T4;0,7;0,7;0,2;nd;2,0;0,4;5,1;2,6;1,0;0,6;0,1;0,1
1954-T3;1,6;0,5;0,9;nd;4,1;3,8;5,7;3,2;4,1;1,4;-0,3;0,5
1954-T2;1,7;1,4;0,9;nd;4,8;4,9;5,7;3,6;2,6;1,5;0,1;0,2
1954-T1;1,4;3,0;1,2;nd;-0,9;-4,6;5,2;3,6;2,0;0,4;1,1;-0,1
1953-T4;1,6;-1,0;1,1;nd;2,6;1,9;4,1;3,2;5,1;1,2;-0,4;0,8
1953-T3;0,4;0,8;0,5;nd;1,9;1,3;2,8;2,5;-2,9;0,7;0,2;-0,5
1953-T2;1,6;-0,6;1,7;nd;2,1;2,4;1,5;1,6;0,2;1,5;0,0;0,1
1953-T1;1,2;2,6;1,6;nd;3,0;4,5;0,6;1,2;-1,5;1,7;0,2;-0,6
1952-T4;-0,2;-2,2;1,1;nd;-1,5;-3,5;0,7;2,1;-0,3;0,6;-1,1;0,3
1952-T3;1,0;1,0;1,0;nd;0,5;-0,6;1,2;3,6;1,0;0,9;0,1;0,0
1952-T2;-0,5;-4,1;0,6;nd;-2,5;-6,2;2,1;5,4;-0,1;0,1;-1,3;0,6
1952-T1;1,5;-0,7;1,5;nd;0,7;-1,6;3,4;6,9;-1,1;1,4;0,2;-0,1
1951-T4;1,0;3,7;1,0;nd;2,0;0,5;4,9;5,3;-4,4;1,3;1,0;-1,3
1951-T3;1,4;6,2;1,4;nd;1,4;-0,3;6,5;3,0;1,6;1,4;0,7;-0,6
1951-T2;0,8;5,9;0,6;nd;0,1;-1,9;7,7;0,1;-0,6;0,6;1,1;-0,9
1951-T1;0,3;3,2;1,8;nd;5,8;6,9;8,7;-2,3;0,1;2,3;-1,6;-0,4
1950-T4;2,4;6,4;1,3;nd;0,7;-0,4;7,5;-1,7;13,0;1,2;0,0;1,2
1950-T3;2,5;3,5;4,2;nd;2,5;2,5;5,6;-0,6;4,8;3,2;-1,0;0,3
1950-T2;2,5;-2,0;2,2;nd;1,7;1,5;3,3;1,0;3,6;1,9;-0,2;0,8
1950-T1;2,8;9,7;1,6;nd;0,3;-0,6;1,1;3,3;8,1;nd;nd;nd
1949-T4;1,3;-5,6;-0,4;nd;1,1;0,8;0,8;1,9;2,5;nd;nd;nd
1949-T3;1,5;-4,6;2,3;nd;1,3;1,1;1,0;1,9;0,8;nd;nd;nd
1949-T2;0,9;0,0;0,9;nd;1,5;1,3;1,7;1,8;3,1;nd;nd;nd""".replace(
            "nd", "NaN"
        )
        .replace("-T", "-Q")
        .replace(",", ".")
    )
    df = pd.read_csv(data, sep=";")
    df = df.set_index("quarter").astype(float)
    df["date"] = pd.PeriodIndex(df.index, freq="Q").to_timestamp()
# return get_unemployement()
return df.sort_values("date")

Évolution du produit intérieur brut et de ses composantes

`bulkhours.get_data("gmacro.fr_unemployement")`

Enrich data: gmacro.py (raw)
Direct source: https://www.insee.fr/fr/statistiques/2830547#tableau-figure1

Show columns info

Column	Info
Femmes
Femmes_15-24
Femmes_25-49
Femmes_plus_50
Hommes
Hommes_15-24
Hommes_25-49
Hommes_plus_50
Ensemble
Ensemble_15-24
Ensemble_25-49
Ensemble_plus_50
Longue_duree
date

Show code


def get_fr_unemployement(self):
    data = StringIO(
        """quarter,1975-T1,1975-T2,1975-T3,1975-T4,1976-T1,1976-T2,1976-T3,1976-T4,1977-T1,1977-T2,1977-T3,1977-T4,1978-T1,1978-T2,1978-T3,1978-T4,1979-T1,1979-T2,1979-T3,1979-T4,1980-T1,1980-T2,1980-T3,1980-T4,1981-T1,1981-T2,1981-T3,1981-T4,1982-T1,1982-T2,1982-T3,1982-T4,1983-T1,1983-T2,1983-T3,1983-T4,1984-T1,1984-T2,1984-T3,1984-T4,1985-T1,1985-T2,1985-T3,1985-T4,1986-T1,1986-T2,1986-T3,1986-T4,1987-T1,1987-T2,1987-T3,1987-T4,1988-T1,1988-T2,1988-T3,1988-T4,1989-T1,1989-T2,1989-T3,1989-T4,1990-T1,1990-T2,1990-T3,1990-T4,1991-T1,1991-T2,1991-T3,1991-T4,1992-T1,1992-T2,1992-T3,1992-T4,1993-T1,1993-T2,1993-T3,1993-T4,1994-T1,1994-T2,1994-T3,1994-T4,1995-T1,1995-T2,1995-T3,1995-T4,1996-T1,1996-T2,1996-T3,1996-T4,1997-T1,1997-T2,1997-T3,1997-T4,1998-T1,1998-T2,1998-T3,1998-T4,1999-T1,1999-T2,1999-T3,1999-T4,2000-T1,2000-T2,2000-T3,2000-T4,2001-T1,2001-T2,2001-T3,2001-T4,2002-T1,2002-T2,2002-T3,2002-T4,2003-T1,2003-T2,2003-T3,2003-T4,2004-T1,2004-T2,2004-T3,2004-T4,2005-T1,2005-T2,2005-T3,2005-T4,2006-T1,2006-T2,2006-T3,2006-T4,2007-T1,2007-T2,2007-T3,2007-T4,2008-T1,2008-T2,2008-T3,2008-T4,2009-T1,2009-T2,2009-T3,2009-T4,2010-T1,2010-T2,2010-T3,2010-T4,2011-T1,2011-T2,2011-T3,2011-T4,2012-T1,2012-T2,2012-T3,2012-T4,2013-T1,2013-T2,2013-T3,2013-T4,2014-T1,2014-T2,2014-T3,2014-T4,2015-T1,2015-T2,2015-T3,2015-T4,2016-T1,2016-T2,2016-T3,2016-T4,2017-T1,2017-T2,2017-T3,2017-T4,2018-T1,2018-T2,2018-T3,2018-T4,2019-T1,2019-T2,2019-T3,2019-T4,2020-T1,2020-T2,2020-T3,2020-T4,2021-T1,2021-T2,2021-T3,2021-T4,2022-T1,2022-T2,2022-T3
Femmes,"4,6","4,9","5,2","5,5","5,8","5,9","5,8","5,8","6,1","6,5","6,6","6,5","6,3","6,3","6,6","7,0","7,1","7,2","7,4","7,7","8,0","8,1","8,2","8,4","8,8","9,1","9,3","9,4","9,4","9,5","9,6","9,7","9,6","9,5","9,7","10,0","10,5","10,8","10,9","11,1","11,2","11,1","11,1","10,9","10,8","10,9","11,1","11,2","11,4","11,5","11,3","11,3","11,2","11,0","11,1","11,0","10,8","10,6","10,4","10,3","10,3","10,2","10,1","10,0","10,0","10,1","10,3","10,6","10,8","10,9","11,0","11,1","11,2","11,3","11,6","11,8","12,0","12,1","12,1","11,9","11,8","11,5","11,3","11,4","11,7","11,9","11,9","11,8","11,8","11,8","11,8","11,8","11,6","11,5","11,4","11,4","11,4","11,3","11,1","10,7","10,3","9,9","9,6","9,4","9,2","9,1","8,9","8,8","8,7","8,6","8,7","8,7","9,3","9,4","9,1","9,5","10,0","9,5","9,6","9,5","9,2","9,5","9,9","10,0","10,0","9,6","9,8","8,6","9,0","8,6","8,2","7,8","7,5","7,7","7,8","8,2","8,8","9,3","9,4","9,4","9,4","9,4","9,4","9,8","9,7","9,4","9,5","9,4","9,4","9,8","9,7","10,1","10,2","10,3","10,1","10,1","9,8","9,9","10,1","10,1","9,9","10,0","9,9","9,8","9,7","9,8","10,0","9,9","9,7","9,5","9,4","8,9","9,3","9,1","8,9","8,8","8,7","8,4","8,3","8,1","8,0","6,8","9,0","8,0","7,8","7,9","8,1","7,3","7,3","7,3","7,1"
Femmes_15-24,"9,8","10,4","11,1","11,8","12,8","13,1","12,7","12,6","13,5","14,6","15,0","14,7","14,4","14,5","15,7","16,6","16,9","17,3","17,8","18,7","19,7","20,2","20,4","20,9","21,8","22,6","23,2","23,5","23,5","23,8","24,1","24,1","23,8","23,9","24,5","25,6","27,2","28,2","28,5","28,7","28,4","27,5","27,2","26,6","26,1","26,1","26,2","26,0","25,8","25,5","25,0","24,7","24,5","24,1","23,9","23,1","22,5","22,1","21,5","21,6","21,6","21,4","21,2","21,5","21,8","22,4","23,0","23,2","23,8","23,9","24,4","25,0","25,6","26,3","27,0","27,5","28,2","28,9","29,1","29,0","28,7","27,7","26,8","27,1","27,5","28,1","28,7","29,0","29,1","28,8","28,3","27,7","26,6","26,2","26,2","26,3","26,7","26,4","25,4","23,9","22,4","21,3","20,5","20,1","20,0","19,8","19,6","19,9","20,0","19,7","19,4","20,1","22,8","23,3","21,4","23,3","24,4","24,6","24,9","24,8","24,1","24,6","27,2","26,9","27,6","27,4","28,9","25,0","26,4","24,0","22,0","22,7","21,4","21,9","22,1","23,8","25,9","26,6","26,9","26,4","25,9","28,7","28,4","28,1","29,2","27,2","26,5","26,4","26,2","26,5","28,0","30,7","30,0","30,3","29,2","28,5","27,7","26,5","27,1","27,2","27,1","28,2","27,5","26,8","27,3","28,4","29,3","26,9","25,2","26,6","24,2","23,8","25,4","23,0","22,6","22,9","21,5","20,9","21,0","23,0","22,8","24,1","26,0","21,5","21,3","19,6","19,7","15,0","15,8","17,5","16,5"
Femmes_25-49,"3,3","3,5","3,7","3,9","4,0","4,0","4,0","4,0","4,1","4,3","4,4","4,4","4,3","4,2","4,4","4,5","4,5","4,6","4,7","4,8","4,9","5,0","5,0","5,2","5,5","5,8","5,9","6,0","6,0","6,1","6,2","6,2","6,2","6,1","6,2","6,4","6,7","6,9","7,0","7,2","7,4","7,6","7,7","7,7","7,7","7,9","8,2","8,4","8,7","8,9","8,8","8,9","8,9","8,8","9,1","9,1","9,1","8,9","8,8","8,7","8,6","8,6","8,5","8,5","8,4","8,4","8,7","9,0","9,2","9,4","9,5","9,5","9,6","9,8","10,0","10,3","10,6","10,7","10,7","10,5","10,3","10,2","10,1","10,2","10,5","10,6","10,6","10,5","10,5","10,6","10,7","10,7","10,6","10,5","10,5","10,5","10,5","10,4","10,2","9,9","9,6","9,3","9,1","8,8","8,7","8,5","8,4","8,2","8,0","8,0","8,1","8,1","8,6","8,6","8,4","8,6","8,9","8,6","8,7","8,6","8,4","8,7","8,7","9,0","8,9","8,2","8,5","7,6","7,7","7,6","7,6","6,9","6,6","6,8","6,9","7,3","7,7","8,1","8,3","8,3","8,3","8,0","8,1","8,6","8,4","8,3","8,6","8,4","8,7","8,9","8,8","8,8","9,0","9,2","8,9","9,0","8,6","9,1","9,2","9,3","9,1","9,0","9,0","8,9","8,9","8,7","8,7","9,1","8,9","8,6","9,0","8,1","8,7","8,5","8,2","7,9","7,8","7,6","7,5","7,1","7,1","5,9","8,0","7,1","7,3","7,2","7,4","6,9","6,7","6,6","6,4"
Femmes_plus_50,"2,1","2,2","2,4","2,5","2,6","2,7","2,8","3,0","3,2","3,3","3,3","3,1","2,9","2,9","3,1","3,3","3,5","3,7","3,9","4,2","4,5","4,6","4,6","4,6","4,7","4,7","4,8","4,8","4,8","4,9","5,1","5,1","5,1","5,1","5,1","5,2","5,4","5,5","5,5","5,6","5,7","5,7","5,9","5,8","5,6","5,6","5,6","5,8","6,2","6,4","6,6","6,7","6,6","6,5","6,4","6,4","6,4","6,3","6,2","6,1","6,1","6,0","6,1","6,1","6,3","6,4","6,6","6,8","6,9","6,9","6,8","6,6","6,3","6,2","6,2","6,3","6,2","6,0","6,0","6,0","6,1","6,1","6,0","6,0","6,4","6,5","6,7","6,8","6,9","6,9","6,9","6,9","7,0","7,0","7,0","7,0","6,9","6,9","6,8","6,6","6,4","6,2","6,0","5,7","5,5","5,5","5,5","5,2","5,2","5,4","5,4","5,2","4,8","5,5","5,5","5,9","6,6","5,3","5,3","5,6","5,4","5,0","5,6","5,4","5,3","5,8","5,4","4,8","5,3","4,9","4,5","4,3","4,0","4,4","4,5","4,4","4,8","5,5","5,1","5,9","5,8","5,5","5,6","5,8","5,8","5,5","5,7","5,9","5,4","6,3","5,8","6,1","6,4","6,5","6,6","6,6","6,9","6,6","6,9","6,5","6,2","6,6","6,4","6,5","6,3","6,2","6,8","6,4","6,8","6,3","6,1","6,0","6,0","6,3","6,1","6,5","6,7","6,2","6,1","5,6","5,4","4,0","6,3","5,7","4,8","5,8","5,7","5,5","5,6","5,1","5,3"
Hommes,"2,3","2,6","2,8","2,9","2,9","2,8","2,7","2,7","2,9","3,1","3,3","3,3","3,2","3,3","3,5","3,7","3,8","3,8","3,8","3,8","3,7","3,7","3,7","4,0","4,4","4,7","4,9","5,1","5,2","5,3","5,5","5,6","5,6","5,7","5,8","6,1","6,5","6,9","7,2","7,5","7,6","7,6","7,7","7,7","7,7","7,7","7,7","7,6","7,6","7,6","7,5","7,4","7,4","7,2","7,1","6,8","6,6","6,4","6,3","6,3","6,3","6,3","6,3","6,3","6,3","6,4","6,6","6,8","7,1","7,3","7,6","7,9","8,2","8,6","9,0","9,4","9,6","9,6","9,4","9,1","8,8","8,6","8,5","8,7","9,0","9,3","9,4","9,6","9,7","9,7","9,6","9,5","9,2","9,1","9,0","9,1","9,2","9,1","8,8","8,4","8,0","7,6","7,2","6,9","6,6","6,5","6,7","6,9","7,1","7,2","7,2","7,3","7,6","7,7","7,8","8,1","8,1","8,1","8,2","8,3","8,2","8,2","8,2","8,2","8,4","8,4","8,1","8,1","8,0","7,7","7,7","7,1","6,9","6,9","7,1","7,3","8,4","9,1","9,0","9,6","9,4","9,2","9,1","8,7","8,7","8,8","9,0","9,3","9,6","9,6","9,8","10,2","10,5","10,6","10,4","10,2","10,5","10,4","10,5","10,8","10,7","11,0","10,8","10,7","10,7","10,3","9,8","10,2","9,6","9,6","9,6","9,1","9,2","9,1","9,1","8,7","8,8","8,5","8,5","8,3","7,8","7,4","9,0","8,2","8,5","8,0","7,9","7,5","7,4","7,6","7,6"
Hommes_15-24,"4,8","5,4","5,8","6,0","6,0","5,8","5,6","5,9","6,6","7,2","7,5","7,2","6,8","6,9","7,5","7,9","8,0","8,0","8,0","8,0","8,2","8,2","8,3","9,0","9,8","10,5","11,1","11,5","11,7","12,0","12,5","12,8","12,9","13,1","13,7","14,6","15,8","17,0","17,8","18,2","18,5","18,0","17,7","17,3","16,9","16,9","16,3","15,8","15,5","15,3","15,1","15,2","15,1","14,6","14,0","13,2","12,5","12,2","12,2","12,5","12,9","13,0","13,0","13,1","12,9","13,2","13,3","13,4","13,7","14,1","15,1","16,0","16,8","17,8","18,6","19,1","19,7","20,1","19,3","18,3","17,4","16,6","16,1","17,0","17,6","18,6","19,1","19,6","19,9","19,8","19,4","18,9","18,4","18,3","18,9","19,5","20,2","19,9","18,9","17,5","16,2","15,4","14,8","14,4","14,2","14,2","14,8","15,3","15,6","15,9","16,6","16,9","16,0","17,7","18,4","19,3","19,2","18,9","19,6","19,4","19,5","18,9","19,9","19,6","21,1","20,7","19,1","21,0","19,1","18,8","18,2","17,7","16,7","17,8","19,1","20,2","22,6","24,1","24,2","24,4","23,5","21,7","23,1","20,4","21,1","21,6","21,0","22,0","22,6","23,7","24,4","25,6","25,1","24,1","23,6","22,5","23,3","24,6","24,4","25,3","25,6","24,3","24,8","25,3","25,3","24,0","23,8","23,7","22,7","23,1","22,2","21,2","21,0","21,3","22,1","19,0","20,7","20,1","19,0","20,4","18,8","20,9","20,3","19,1","20,7","19,4","18,8","17,0","17,3","18,5","19,8"
Hommes_25-49,"1,7","1,9","2,1","2,1","2,1","2,0","1,9","1,9","2,0","2,1","2,3","2,3","2,3","2,4","2,5","2,6","2,8","2,8","2,8","2,7","2,6","2,6","2,6","2,9","3,1","3,4","3,6","3,8","3,8","3,9","4,0","4,0","4,0","4,0","4,2","4,4","4,7","5,0","5,2","5,4","5,6","5,6","5,8","5,9","5,9","6,0","6,1","6,1","6,2","6,2","6,1","6,1","6,1","6,0","6,0","5,8","5,6","5,5","5,5","5,4","5,4","5,4","5,4","5,4","5,5","5,6","5,8","6,0","6,2","6,4","6,7","7,0","7,4","7,8","8,2","8,5","8,7","8,7","8,6","8,4","8,2","8,0","8,0","8,1","8,3","8,5","8,6","8,8","8,9","9,0","9,0","8,8","8,5","8,3","8,2","8,2","8,2","8,2","7,9","7,6","7,3","6,9","6,6","6,3","6,0","6,0","6,1","6,3","6,5","6,6","6,4","6,4","7,0","7,0","6,9","7,3","7,3","7,2","7,3","7,7","7,5","7,6","7,4","7,3","7,4","7,5","7,3","6,9","7,2","7,0","7,0","6,4","6,2","5,9","6,1","6,2","7,3","8,0","7,8","8,5","8,4","8,4","8,1","8,0","7,9","8,0","8,3","8,7","8,9","8,9","9,0","9,2","9,6","9,9","9,8","9,7","10,0","9,7","9,9","10,0","9,9","10,2","9,9","9,8","9,9","9,5","8,6","9,2","8,7","8,7","8,6","8,2","8,3","8,2","8,1","8,1","7,8","7,6","7,7","7,4","7,0","6,7","8,7","7,6","7,6","6,9","6,9","6,7","6,5","6,7","6,5"
Hommes_plus_50,"1,8","2,0","2,1","2,2","2,2","2,2","2,2","2,2","2,3","2,5","2,5","2,6","2,7","2,7","2,8","2,9","2,9","3,0","3,0","3,0","3,0","3,0","3,1","3,2","3,4","3,5","3,6","3,7","3,8","3,8","3,9","3,9","3,9","4,0","4,0","4,0","4,1","4,3","4,4","4,5","4,6","4,8","5,1","5,3","5,3","5,4","5,3","5,3","5,4","5,4","5,4","5,4","5,4","5,2","5,1","5,0","5,0","4,8","4,6","4,5","4,4","4,3","4,3","4,4","4,4","4,5","4,8","5,0","5,3","5,5","5,5","5,4","5,3","5,4","5,6","5,8","5,9","5,8","5,8","5,7","5,8","5,8","5,8","5,8","6,1","6,3","6,3","6,3","6,4","6,4","6,4","6,5","6,5","6,6","6,7","6,7","6,7","6,6","6,4","6,1","5,8","5,5","5,1","4,7","4,4","4,3","4,4","4,5","4,6","4,8","4,9","4,9","4,9","4,7","5,2","5,1","5,0","5,5","4,9","4,7","4,6","4,6","4,9","5,4","5,3","5,2","5,0","5,2","5,2","4,6","4,7","4,2","4,3","4,9","4,4","4,3","4,8","5,1","5,5","5,8","5,7","5,5","5,5","5,4","5,2","5,5","5,8","5,8","6,1","5,9","6,1","6,8","6,9","7,0","7,0","6,6","6,9","6,9","6,6","7,5","7,0","7,9","7,6","7,4","7,2","7,2","7,4","7,4","6,9","6,8","7,1","6,8","6,8","6,5","6,3","6,1","6,6","6,3","6,4","6,0","5,5","4,7","5,9","5,7","6,0","6,1","6,0","5,9","5,5","5,2","5,0"
Ensemble,"3,2","3,5","3,8","3,9","4,0","4,0","3,9","4,0","4,2","4,5","4,7","4,6","4,5","4,5","4,8","5,0","5,1","5,2","5,3","5,4","5,5","5,5","5,6","5,8","6,2","6,5","6,8","6,9","7,0","7,1","7,2","7,3","7,3","7,3","7,5","7,8","8,2","8,6","8,8","9,0","9,1","9,1","9,1","9,1","9,0","9,1","9,1","9,2","9,3","9,3","9,2","9,1","9,0","8,9","8,9","8,7","8,4","8,3","8,1","8,1","8,1","8,0","8,0","8,0","7,9","8,1","8,3","8,5","8,7","8,9","9,1","9,3","9,6","9,9","10,2","10,5","10,7","10,7","10,6","10,4","10,2","9,9","9,8","9,9","10,2","10,5","10,5","10,6","10,7","10,7","10,6","10,5","10,3","10,2","10,1","10,2","10,2","10,2","9,9","9,5","9,1","8,7","8,4","8,0","7,8","7,7","7,7","7,8","7,8","7,9","7,9","7,9","8,4","8,5","8,4","8,8","9,0","8,8","8,9","8,9","8,6","8,8","9,0","9,1","9,2","9,0","8,9","8,4","8,5","8,1","8,0","7,5","7,2","7,3","7,4","7,7","8,6","9,2","9,2","9,5","9,4","9,3","9,2","9,2","9,2","9,1","9,2","9,3","9,5","9,7","9,7","10,2","10,3","10,5","10,3","10,1","10,1","10,2","10,3","10,5","10,3","10,5","10,3","10,2","10,2","10,0","9,9","10,0","9,6","9,5","9,5","9,0","9,3","9,1","9,0","8,7","8,7","8,4","8,4","8,2","7,9","7,1","9,0","8,1","8,2","7,9","8,0","7,4","7,3","7,4","7,3"
Ensemble_15-24,"7,0","7,6","8,2","8,6","9,1","9,1","8,9","8,9","9,7","10,6","11,0","10,6","10,2","10,4","11,2","11,9","12,1","12,3","12,5","12,9","13,5","13,6","13,8","14,4","15,3","16,1","16,7","17,0","17,0","17,4","17,8","17,9","17,8","18,0","18,5","19,6","21,0","22,1","22,6","23,0","23,0","22,3","22,0","21,5","21,1","21,1","20,9","20,5","20,2","20,0","19,7","19,6","19,4","19,0","18,5","17,7","17,0","16,7","16,4","16,7","16,9","16,8","16,7","16,9","17,0","17,4","17,7","17,8","18,3","18,5","19,3","20,1","20,8","21,7","22,5","23,0","23,6","24,0","23,7","23,2","22,6","21,7","21,0","21,6","22,1","22,9","23,4","23,8","24,0","23,8","23,4","22,9","22,1","21,9","22,1","22,5","23,0","22,8","21,7","20,4","19,0","18,0","17,4","16,9","16,8","16,7","17,0","17,3","17,5","17,6","17,8","18,3","19,1","20,3","19,8","21,1","21,6","21,5","22,0","21,8","21,5","21,5","23,2","22,9","24,1","23,7","23,5","22,8","22,5","21,2","19,9","19,9","18,8","19,7","20,4","21,8","24,1","25,2","25,5","25,3","24,6","24,9","25,5","23,8","24,8","24,2","23,5","24,0","24,2","25,0","26,0","27,9","27,3","26,9","26,1","25,3","25,3","25,5","25,6","26,2","26,3","26,1","26,1","26,0","26,2","26,0","26,4","25,2","23,9","24,7","23,0","22,4","23,0","22,1","22,3","20,7","21,1","20,5","19,9","21,6","20,6","22,4","22,9","20,2","21,0","19,5","19,2","16,1","16,6","18,0","18,3"
Ensemble_25-49,"2,3","2,5","2,7","2,8","2,8","2,8","2,7","2,7","2,8","3,0","3,1","3,2","3,1","3,1","3,2","3,4","3,5","3,5","3,6","3,6","3,6","3,5","3,6","3,8","4,1","4,4","4,6","4,7","4,8","4,8","4,9","4,9","4,9","4,9","5,0","5,3","5,6","5,8","6,0","6,2","6,4","6,5","6,6","6,7","6,7","6,8","7,0","7,1","7,3","7,4","7,3","7,3","7,3","7,2","7,3","7,3","7,1","7,0","6,9","6,9","6,9","6,8","6,8","6,8","6,8","6,9","7,1","7,3","7,6","7,8","7,9","8,1","8,4","8,7","9,0","9,4","9,6","9,6","9,6","9,4","9,2","9,0","8,9","9,1","9,3","9,5","9,5","9,6","9,7","9,7","9,8","9,7","9,5","9,3","9,2","9,2","9,3","9,2","9,0","8,7","8,4","8,0","7,8","7,5","7,3","7,2","7,2","7,2","7,2","7,2","7,2","7,2","7,8","7,8","7,6","7,9","8,1","7,9","8,0","8,1","7,9","8,1","8,0","8,1","8,1","7,8","7,9","7,3","7,4","7,3","7,3","6,6","6,4","6,4","6,5","6,7","7,5","8,0","8,0","8,4","8,3","8,2","8,1","8,3","8,2","8,1","8,4","8,6","8,8","8,9","8,9","9,0","9,3","9,5","9,4","9,4","9,3","9,4","9,6","9,7","9,5","9,6","9,5","9,3","9,4","9,1","8,7","9,2","8,8","8,6","8,8","8,1","8,5","8,4","8,2","8,0","7,8","7,6","7,6","7,3","7,1","6,3","8,3","7,4","7,4","7,0","7,1","6,8","6,6","6,6","6,5"
Ensemble_plus_50,"1,9","2,1","2,2","2,3","2,4","2,4","2,4","2,5","2,7","2,8","2,8","2,8","2,8","2,8","2,9","3,0","3,2","3,3","3,4","3,5","3,6","3,6","3,7","3,8","3,9","4,0","4,1","4,1","4,2","4,3","4,4","4,4","4,4","4,4","4,4","4,5","4,6","4,8","4,9","5,0","5,0","5,2","5,4","5,5","5,4","5,5","5,4","5,5","5,7","5,8","5,9","5,9","5,9","5,8","5,7","5,6","5,6","5,4","5,3","5,2","5,1","5,1","5,1","5,1","5,2","5,4","5,6","5,8","6,0","6,1","6,0","5,9","5,7","5,7","5,8","6,0","6,0","5,9","5,9","5,9","5,9","5,9","5,9","5,9","6,2","6,4","6,5","6,5","6,6","6,6","6,7","6,7","6,7","6,8","6,8","6,8","6,8","6,8","6,6","6,3","6,1","5,8","5,5","5,2","4,9","4,9","4,9","4,8","4,9","5,1","5,1","5,0","4,8","5,1","5,3","5,5","5,7","5,4","5,1","5,2","5,0","4,8","5,2","5,4","5,3","5,5","5,2","5,0","5,3","4,7","4,6","4,2","4,1","4,7","4,5","4,3","4,8","5,3","5,3","5,8","5,7","5,5","5,5","5,6","5,5","5,5","5,8","5,8","5,8","6,1","6,0","6,5","6,7","6,8","6,8","6,6","6,9","6,7","6,8","7,0","6,6","7,2","7,0","6,9","6,7","6,7","7,1","6,9","6,8","6,5","6,6","6,4","6,4","6,4","6,2","6,3","6,6","6,2","6,3","5,8","5,5","4,4","6,1","5,7","5,4","5,9","5,8","5,7","5,5","5,2","5,1"
Longue_duree,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,nd,"2,2","2,2","2,2","2,4","2,3","2,3","2,3","2,5","2,4","2,5","2,5","2,3","2,4","2,5","2,5","2,4","2,4","2,1","2,1","1,9","1,9","1,9","1,8","1,7","1,7","2,0","2,1","2,3","2,5","2,4","2,4","2,4","2,4","2,5","2,6","2,6","2,6","2,6","2,6","2,7","2,8","2,8","2,8","3,0","3,0","3,1","3,1","3,1","3,1","3,1","3,1","3,1","3,2","3,2","3,1","3,1","3,0","3,0","3,0","2,7","2,6","2,6","2,5","2,5","2,4","2,3","2,2","2,2","2,0","1,4","2,2","2,1","2,5","2,3","2,4","2,2","2,2","2,1","2,0"
""".replace(
            "nd", "NaN"
        ).replace(
            "-T", "-Q"
        )
    )
df = pd.read_csv(data, sep=",", quotechar='"').set_index("quarter").T
for c in df.columns:
    df[c] = df[c].astype(str).str.replace(",", ".").astype(float)
df["date"] = pd.PeriodIndex(df.index, freq="Q").to_timestamp()
return df.sort_values("date")

United States Macroeconomic data (1959q1 - 2009q3)

`bulkhours.get_data("gmacro.us_gdp")`

Enrich data: gmacro.py (raw)
Direct source: https://www.statsmodels.org/0.6.1/datasets/generated/macrodata.html

Show columns info

Column	Info
year	1959q1 - 2009q3
quarter	1-4
realgdp	Real gross domestic product (Bil. of chained 2005$, seasonally adjusted annual rate)
realcons	Real personal consumption expenditures (Bil. of chained 2005$, seasonally adjusted annual rate)
realinv	Real gross private domestic investment (Bil. of chained 2005$, seasonally adjusted annual rate)
realgovt	Real federal consumption expenditures & gross investment(Bil. of chained 2005 US$, seasonally adjusted annual rate)
realdpi	Real private disposable income (Bil. of chained 2005 US$, seasonally adjusted annual rate)
cpi	End of the quarter consumer price index for all urban consumers: all items (1982-84 = 100, seasonally adjusted).
m1	End of the quarter M1 nominal money stock (Seasonally adjusted)
tbilrate	Quarterly monthly average of the monthly 3-month treasury bill: secondary market rate
unemp	Seasonally adjusted unemployment rate (%)
pop	End of the quarter total population: all ages incl. armed forces over seas
infl	Inflation rate (ln(cpi_{t}/cpi_{t-1}) * 400)
realint	Real interest rate (tbilrate - infl)

Show code


def get_us_gdp(self, simplify=True):
    import statsmodels.api as sm  # Statistical models
us_okun = sm.datasets.macrodata.load_pandas().data
if not simplify:
    return us_okun

us_okun["diff(gdp)"] = 100 * us_okun["realgdp"].diff() / us_okun["realgdp"]
us_okun["diff(unemployement)"] = us_okun["unemp"].diff()
us_okun["yquarter"] = us_okun["year"].astype(str).str[:4] + "-Q" + us_okun["quarter"].astype(str).str[0]
us_okun = us_okun[["yquarter", "diff(gdp)", "diff(unemployement)"]]
us_okun = us_okun.dropna()
us_okun["quarter"] = pd.PeriodIndex(us_okun["yquarter"], freq="Q").to_timestamp()

return us_okun.set_index("quarter")

France Macroeconomic data

`bulkhours.get_data("gmacro.fr_gdp")`

Enrich data: gmacro.py (raw)

Show columns info

Column	Info
yquarter
diff(gdp)
diff(unemployement)

Show code


def get_fr_gdp(self, simplify=True):
    gdp = get_fr_qgdp(self).reset_index()
    une = get_fr_unemployement(self).reset_index()
    fr_okun = gdp.merge(une, how="inner", left_on="quarter", right_on="index")
if not simplify:
    return fr_okun

if simplify:
    fr_okun = fr_okun.rename(columns={"gdp": "diff(gdp)", "quarter": "yquarter"})
    fr_okun["diff(unemployement)"] = fr_okun["Ensemble"].diff()
    fr_okun = fr_okun[["yquarter", "diff(gdp)", "diff(unemployement)"]]
    fr_okun = fr_okun.dropna()
    fr_okun["quarter"] = pd.PeriodIndex(fr_okun["yquarter"], freq="Q").to_timestamp()

return fr_okun.set_index("quarter")

World happiness report data (2015-2020)

`bulkhours.get_data("world.happiness")`

Raw data: DataForFigure2.1WHR2023.xls (raw🔄)
Direct source: https://worldhappiness.report/data/

Show columns info

Column	Info
country
Ladder score
Logged GDP per capita
Social support
Healthy life expectancy
Freedom to make life choices
Generosity
Perceptions of corruption

Show code


def get_happiness(self, **data_info):
    df = self.read_raw_data(self.raw_data)
return df.sort_values("Ladder score", ascending=False)[
    [
        "Country name",
        "Ladder score",
        "Logged GDP per capita",
        "Social support",
        "Healthy life expectancy",
        "Freedom to make life choices",
        "Generosity",
        "Perceptions of corruption",
    ]
].rename(columns={"Country name": "country"})

World Bank Poverty and Inequality data (with gpx extra info)

`bulkhours.get_data("world.maphappiness")`

Raw data: DataForFigure2.1WHR2023.xls (raw🔄)
Direct source: https://worldhappiness.report/data/

Show columns info

Column	Info
pop_est
continent
name
iso_a3
gdp_md_est
geometry
Ladder score
Logged GDP per capita
Social support
Healthy life expectancy
Freedom to make life choices
Generosity
Perceptions of corruption

Show code


def get_mappoverty(self, **kwargs):
    return get_mapgeneric(get_happiness(self, **kwargs))

Market prices of SP500 stocks

`bulkhours.get_data("prices-split-adjusted")`

Raw data: prices-split-adjusted.csv (raw)
Direct source: https://github.com/kyi3081/stock-analysis

Show columns info

Column	Info
date
symbol
open
close
low
high
volume

Market fundamentals of SP500 stocks

`bulkhours.get_data("fundamentals")`

Raw data: fundamentals.csv (raw)
Direct source: https://github.com/kyi3081/stock-analysis

Show columns info

Column	Info
Unnamed: 0
Ticker Symbol
Period Ending
Accounts Payable
Accounts Receivable
Add'l income/expense items
After Tax ROE
Capital Expenditures
Capital Surplus
Cash Ratio
Cash and Cash Equivalents
Changes in Inventories
Common Stocks
Cost of Revenue
Current Ratio
Deferred Asset Charges
Deferred Liability Charges
Depreciation
Earnings Before Interest and Tax
Earnings Before Tax
Effect of Exchange Rate
Equity Earnings/Loss Unconsolidated Subsidiary
Fixed Assets
Goodwill
Gross Margin
Gross Profit
Income Tax
Intangible Assets
Interest Expense
Inventory
Investments
Liabilities
Long-Term Debt
Long-Term Investments
Minority Interest
Misc. Stocks
Net Borrowings
Net Cash Flow
Net Cash Flow-Operating
Net Cash Flows-Financing
Net Cash Flows-Investing
Net Income
Net Income Adjustments
Net Income Applicable to Common Shareholders
Net Income-Cont. Operations
Net Receivables
Non-Recurring Items
Operating Income
Operating Margin
Other Assets
Other Current Assets
Other Current Liabilities
Other Equity
Other Financing Activities
Other Investing Activities
Other Liabilities
Other Operating Activities
Other Operating Items
Pre-Tax Margin
Pre-Tax ROE
Profit Margin
Quick Ratio
Research and Development
Retained Earnings
Sale and Purchase of Stock
Sales, General and Admin.
Short-Term Debt / Current Portion of Long-Term Debt
Short-Term Investments
Total Assets
Total Current Assets
Total Current Liabilities
Total Equity
Total Liabilities
Total Liabilities & Equity
Total Revenue
Treasury Stock
For Year
Earnings Per Share
Estimated Shares Outstanding

Stocks information for SP500

`bulkhours.get_data("securities")`

Raw data: securities.csv (raw)
Direct source: https://github.com/kyi3081/stock-analysis

Show columns info

Column	Info
Ticker symbol
Security
SEC filings
GICS Sector
GICS Sub Industry
Address of Headquarters
Date first added
CIK

Standardized country information (iso m49)

`bulkhours.get_data("continent")`

Raw data: continent.tsv (raw)

Show columns info

Column	Info
index
country
iso
m49
region1
region2
continent

Corruption index per country

`bulkhours.get_data("corruption")`

Raw data: corruption.csv (raw)

Show columns info

Column	Info
country
annual_income
corruption_index

Cost of living

`bulkhours.get_data("cost_of_living")`

Raw data: cost_of_living.csv (raw)

Show columns info

Column	Info
country
cost_index
monthly_income
purchasing_power_index

GDP per capita per country

`bulkhours.get_data("richest_countries")`

Raw data: richest_countries.csv (raw)

Show columns info

Column	Info
country
gdp_per_capita

Tourism information per country

`bulkhours.get_data("tourism")`

Raw data: tourism.csv (raw)

Show columns info

Column	Info
country
tourists_in_millions
receipts_in_billions
receipts_per_tourist
percentage_of_gdp

Unemployemnt rates per country

`bulkhours.get_data("unemployment")`

Raw data: unemployment.csv (raw)

Show columns info

Column	Info
country
unemployment_rate

Simple synthetic data for exercice

`bulkhours.get_data("wages")`

Raw data: wages.tsv (raw)

Show columns info

Column	Info
wage
experience
studies

COR data

`bulkhours.get_data("COR_1")`

Raw data: [Données septembre partie 1.xlsx](https://huggingface.co/datasets/guydegnol/bulkhours/blob/main/Données septembre partie 1.xlsx) ([raw](https://huggingface.co/datasets/guydegnol/bulkhours/raw/main/Données septembre partie 1.xlsx)🤗)

COR data

`bulkhours.get_data("COR_2")`

Raw data: Données_RA2022_P2.xlsx (raw🤗)

COR data

`bulkhours.get_data("COR_2bis")`

Raw data: [Données complémentaires partie 2 RA 2022.xlsx](https://huggingface.co/datasets/guydegnol/bulkhours/blob/main/Données complémentaires partie 2 RA 2022.xlsx) ([raw](https://huggingface.co/datasets/guydegnol/bulkhours/raw/main/Données complémentaires partie 2 RA 2022.xlsx)🤗)

COR data

`bulkhours.get_data("COR_3")`

Raw data: [Données septembre 2022 - partie 3.xlsx](https://huggingface.co/datasets/guydegnol/bulkhours/blob/main/Données septembre 2022 - partie 3.xlsx) ([raw](https://huggingface.co/datasets/guydegnol/bulkhours/raw/main/Données septembre 2022 - partie 3.xlsx)🤗)

COR data

`bulkhours.get_data("COR_4")`

Raw data: Données_RA2022_P4.xlsx (raw🤗)

COR data

`bulkhours.get_data("COR_5")`

Raw data: [Données septembre 2022 - partie 5.xlsx](https://huggingface.co/datasets/guydegnol/bulkhours/blob/main/Données septembre 2022 - partie 5.xlsx) ([raw](https://huggingface.co/datasets/guydegnol/bulkhours/raw/main/Données septembre 2022 - partie 5.xlsx)🤗)

Statement of Apple stock (Quarterly)

`bulkhours.get_data("trading.apple")`

Raw data: APPLE_DownloadFPrepStatementQuarter.tsv (raw🤗)
Enrich data: trading.py (raw)

Show columns info

Column	Info
revenue
grossProfit
ebitda
netIncome
eps

Show code


def get_apple(self):
    apple = self.read_raw_data(self.raw_data).iloc[-4 * 5 :]
apple.index = pd.to_datetime(apple.index)
apple = apple[["date", "revenue", "grossProfit", "ebitda", "netIncome", "eps"]].set_index("date")
apple["revenue"] = apple["revenue"].astype(float)
apple.index = pd.date_range("2017-12-30", periods=20, freq="Q")

return apple

Cotisants, retraités et rapport démographique tous régimes en 2020

`bulkhours.get_data("france.retraites")`

Enrich data: france.py (raw)
Direct source: https://www.insee.fr/fr/statistiques/2415121#tableau-figure1

Show columns info

Column	Info
active
retired
rapport

Show code


def get_retraites(self):
    return (
        pd.read_csv(
            StringIO(
                """
year	active	retired	rapport
2020	28,2	16,9	1,67
2019	28,5	16,7	1,71
2018	28,2	16,5	1,71
2017	27,9	16,3	1,72
2016	27,6	16,1	1,71
2015	27,4	16,0	1,71
2014	27,3	15,8	1,73
2013	27,2	15,6	1,74
2012	27,1	15,3	1,77
2011	27,0	15,3	1,77
2010	26,8	15,1	1,78
2009	26,8	14,7	1,82
""".replace(
                    ",", "."
                )
            ),
            sep="\t",
        )
        .set_index("year")
        .astype(float)
        .sort_index()
    )

Distribution des salaires mensuels nets en équivalent temps plein (EQTP) en 2020

`bulkhours.get_data("france.income")`

Enrich data: france.py (raw)
Direct source: https://www.insee.fr/fr/statistiques/6436313#tableau-figure2

Show columns info

Column	Info
index
income
population
xmin
xmax
is_valid

Show code


def get_income(self):
    data = StringIO(
        """income	population
Moins de 1_200	583943
De 1_200 à 1_300	613_321
De 1_300 à 1_400	835_135
De 1_400 à 1_500	969_172
De 1_500 à 1_600	1_052_630
De 1_600 à 1_700	1_008_034
De 1_700 à 1_800	939_538
De 1_800 à 1_900	863_042
De 1_900 à 2_000	782_314
De 2_000 à 2_100	706_339
De 2_100 à 2_200	630_132
De 2_200 à 2_300	563_387
De 2_300 à 2_400	504_240
De 2_400 à 2_500	452_167
De 2_500 à 2_600	407_908
De 2_600 à 2_700	365_648
De 2_700 à 2_800	329_810
De 2_800 à 2_900	294_230
De 2_900 à 3_000	265_925
De 3_000 à 3_100	241_899
De 3_100 à 3_200	218_832
De 3_200 à 3_300	198_266
De 3_300 à 3_400	180_386
De 3_400 à 3_500	164_164
De 3_500 à 3_600	150_218
De 3_600 à 3_700	136_878
De 3_700 à 3_800	124_602
De 3_800 à 3_900	114_322
De 3_900 à 4_000	106_638
De 4_000 à 4_100	97_332
De 4_100 à 4_200	89_173
De 4_200 à 4_300	82_839
De 4_300 à 4_400	76_130
De 4_400 à 4_500	69_887
De 4_500 à 4_600	64_863
De 4_600 à 4_700	60_466
De 4_700 à 4_800	55_998
De 4_800 à 4_900	52_101
De 4_900 à 5_000	48_438
De 5_000 à 5_100	44_831
De 5_100 à 5_200	41_854
De 5_200 à 5_300	38_848
De 5_300 à 5_400	36_480
De 5_400 à 5_500	34_092
De 5_500 à 5_600	31_841
De 5_600 à 5_700	29_948
De 5_700 à 5_800	27_840
De 5_800 à 5_900	26_335
De 5_900 à 6_000	25_270
De 6_000 à 6_100	23_380
De 6_100 à 6_200	21_912
De 6_200 à 6_300	20_313
De 6_300 à 6_400	19_320
De 6_400 à 6_500	18_286
De 6_500 à 6_600	17_333
De 6_600 à 6_700	16_394
De 6_700 à 6_800	15_519
De 6_800 à 6_900	14_700
De 6_900 à 7_000	13_549
De 7_000 à 7_100	13_210
De 7_100 à 7_200	12_278
De 7_200 à 7_300	11_829
De 7_300 à 7_400	11_186
De 7_400 à 7_500	10_567
De 7_500 à 7_600	10_063
De 7_600 à 7_700	9_698
De 7_700 à 7_800	9_178
De 7_800 à 7_900	8_974
De 7_900 à 8_000	8_689
De 8_000 à 8_100	8_170
De 8_100 à 8_200	7_697
De 8_200 à 8_300	7_431
De 8_300 à 8_400	7_057
De 8_400 à 8_500	6_674
De 8_500 à 8_600	6_410
De 8_600 à 8_700	6_138
De 8_700 à 8_800	5_772
De 8_800 à 8_900	5_585
De 8_900 à 9_000	5_408
Plus de 9_000	183_314
"""
    )
df = pd.read_csv(data, sep="\t").reset_index()

df["population"] = df["population"].str.replace(" ", "").astype(float)
df["xvalue"] = (
    df["income"]
    .str.replace("De ", "")
    .str.replace("Moins de", "1_100 à")
    .str.replace("Plus de 9_000", "9_000 à 9_100")
)
df["xvalue"] = df["xvalue"].str.replace(" ", "").str.split("à")
df["xmin"] = df["xvalue"].apply(lambda x: float(x[0]))
df["xmax"] = df["xvalue"].apply(lambda x: float(x[1]))
df["xvalue"] = 0.5 * (df["xmin"] + df["xmax"])
df["is_valid"] = ~df.index.isin([0, len(df) - 1])

return df.set_index("xvalue")

Revenu salarial et salaire en EQTP annuels moyens selon le sexe en 2019

`bulkhours.get_data("france.salaires")`

Enrich data: france.py (raw)
Direct source: https://www.insee.fr/fr/statistiques/6047743?sommaire=6047805

Show columns info

Column	Info
Revenu annuel Femmes moyen	Revenu annuel Femmes moyen
Revenu annuel Hommes moyen	Revenu annuel Hommes moyen
Revenu annuel Femmes moyen Écart relatif (en %)	Revenu annuel Femmes moyen Écart relatif (en %)
Salaire annuel Femmes moyen EQTP	Salaire annuel Femmes moyen EQTP
Salaire annuel Hommes moyen EQTP	Salaire annuel Hommes moyen EQTP
Salaire annuel Femmes moyen EQTP Écart relatif (en % EQTP)	Salaire annuel Femmes moyen EQTP Écart relatif (en % EQTP)

Show code


def get_salaires(self):
    data = StringIO(
        """
categorie	revenu_femme	revenu_homme	revenu_diff	salaire_ajusté_femme	salaire_ajusté_homme	salaire_ajusté_diff
Age: Moins de 25 ans	7 360	9 110	19,2	17 930	19 210	6,7
Age: 25-39 ans	18 220	22 610	19,4	24 460	27 660	11,6
Age: 40-49 ans	22 830	29 710	23,1	28 190	34 270	17,7
Age: 50-54 ans	23 070	31 340	26,4	28 280	35 740	20,9
Age: 55 ans ou plus	21 410	29 430	27,2	29 520	38 740	23,8
Diplôme: Pas de diplôme	12 450	17 400	28,5	19 590	23 260	15,8
Diplôme: inférieur au baccalauréat	15 180	20 510	26,0	21 460	25 650	16,3
Diplôme: Baccalauréat à bac+2	20 480	26 560	22,9	25 570	31 000	17,5
Diplôme: Bac+3 ou plus	30 790	44 410	30,7	36 190	50 140	27,8
SocioPro: Cadres	36 040	45 370	20,6	42 820	52 950	19,1
SocioPro: Professions intermédiaires	21 770	26 040	16,4	27 230	30 690	11,3
SocioPro: Employés	13 900	15 310	9,2	20 860	22 850	8,7
SocioPro: Ouvriers	11 960	17 200	30,5	19 580	22 930	14,6
Secteur: privé et entreprises publiques	18 010	24 260	25,7	26 330	31 580	16,6
Secteur: Fonction publique	21 330	25 290	15,7	26 640	31 090	14,3
Secteur: Ensemble	18 970	24 420	22,3	26 430	31 510	16,1
"""
    )
    df = pd.read_csv(data, sep="\t").set_index("categorie")
    for c in df.columns:
        df[c] = df[c].str.replace(" ", "").str.replace(",", ".").astype(float)
return df

Inégalités salariales entre femmes et hommes de 1995 à 2019

`bulkhours.get_data("france.histsalaires")`

Enrich data: france.py (raw)
Direct source: https://www.insee.fr/fr/statistiques/6047743?sommaire=6047805

Show columns info

Column	Info
delta_rev_legacy	écart relatif du revenu salarial moyen homme/femme
delta_rev	écart relatif (en %) du revenu salarial moyen.1
delta_rev_eqtp_legacy	écart relatif du salaire moyen en EQTP
delta_rev_eqtp	écart relatif du salaire moyen en EQTP
delta_vol_eqtp	écart relatif du volume de travail en EQTP moyen

Show code


def get_histsalaires(self):
    data = StringIO(
        """
Année	écart relatif du revenu salarial moyen	écart relatif du revenu salarial moyen	écart relatif du salaire moyen en EQTP	écart relatif du salaire moyen en EQTP	écart relatif du volume de travail en EQTP moyen
1995	27,4		18,5		10,9
1996	27,8		18,8		11,1
1997	27,6		18,5		11,2
1998	27,8		18,3		11,4
1999	27,9		17,9		11,9
2000	28,2		18,6		11,6
2001	28,2		18,8		11,3
2002	27,8		18,5		11,3
2003	27,6		18,5		11,1
2004	27,3		18,4		10,9
2005	27,1		18,3		10,8
2006	26,9		18,2		10,6
2007	26,8		18,5		10,2
2008	27,1		18,7		10,3
2009	26,1		18,3		9,6
2010	25,5		18,2		9,0
2011	25,6		18,2		9,2
2012	25,3	25,5	18,2	18,5	8,8
2013		24,8		18,2	8,2
2014		24,1		17,9	7,8
2015		23,7		17,8	7,2
2016		23,3		17,0	7,7
2017		22,9		16,7	7,7
2018		22,8		16,6	7,6
2019		22,3		16,1	7,6
"""
    )
    df = pd.read_csv(data, sep="\t").set_index("Année")
    for c in df.columns:
        df[c] = df[c].str.replace(" ", "").str.replace(",", ".").astype(float)
return df

Age de la population au 1er janvier (fin novembre 2022)

`bulkhours.get_data("pyramide")`

Raw data: pyramide.tsv (raw)
Direct source: https://www.insee.fr/fr/statistiques/2381472#tableau-figure1

Show columns info

Column	Info
Année de naissance
Age révolu
Nombre de femmes
Nombre d'hommes
Ensemble

Descriptive statistics of hourly wages in selected EU countries in 2010 (in PPS)

`bulkhours.get_data("mincer.stats")`

Direct source: https://www.nbp.pl/publikacje/materialy_i_studia/226_en.pdf (table 2)

Show columns info

Column	Info
Mean
Minimum
Maximum
Variance
Coefficient
of
variation

Show code


def get_stats(self):
    return pd.read_csv(
        StringIO(
            """Country Mean Minimum Maximum Variance Coefficient of variation
Austria 16.196 7.330 46.400 64.591 0.4962
Belgium 18.741 10.010 42.140 58.243 0.4072
Bulgaria 5.349 2.340 13.050 7.771 0.5212
Czech Republic 8.060 3.700 20.060 14.024 0.4646
Denmark 19.528 11.750 35.650 29.825 0.2797
Estonia 7.552 3.160 17.840 11.965 0.4580
Finland 16.068 8.990 35.360 38.538 0.3863
France 15.106 8.080 40.320 43.449 0.4364
Germany 17.764 7.520 40.000 67.286 0.4618
Hungary 8.055 3.760 19.730 15.993 0.4965
Ireland 19.313 10.180 40.300 58.440 0.3958
Italy 16.040 7.690 42.550 80.968 0.5610
Latvia 6.238 3.160 13.160 5.978 0.3920
Netherlands 16.471 7.230 32.420 34.222 0.3552
Poland 8.821 4.080 22.620 20.516 0.5135
Portugal 11.422 4.150 31.150 54.673 0.6474
Romania 5.903 2.450 15.250 12.481 0.5985
Slovakia 7.703 3.790 18.970 13.479 0.4766
Slovenia 12.708 5.760 33.910 48.510 0.5481
Spain 14.489 7.390 35.940 44.362 0.4597
Sweden 14.651 9.550 28.050 18.079 0.2902
United Kingdom 16.368 7.590 36.390 53.933 0.4487
"""
        ),
        sep=" ",
    ).set_index("Country")

Mincer equation parameters per country

`bulkhours.get_data("mincer.params")`

Enrich data: mincer.py (raw)
Direct source: https://www.nbp.pl/publikacje/materialy_i_studia/226_en.pdf (table 3)

Show columns info

Column	Info
alpha_0i
alpha_1i
alpha_2i
alpha_3i
alpha_0i_e
alpha_1i_e
alpha_2i_e
alpha_3i_e

Show code


def get_params(self):
    return pd.read_csv(
        StringIO(
            """Country alpha_0i alpha_1i alpha_2i alpha_3i alpha_0i_e alpha_1i_e alpha_2i_e alpha_3i_e
Austria 0.804517 0.331677 0.426552 -0.03883 0.218682 0.021472 0.113136 0.01396
Belgium 1.297771 0.285186 0.335843 -0.02938 0.142724 0.014014 0.073839 0.009111
Bulgaria 0.322091 0.416255 0.113577 -0.01772 0.228792 0.022465 0.118367 0.014605
Czech_Republic 0.520228 0.346856 0.289626 -0.03328 0.229066 0.022491 0.118508 0.014622
Denmark 1.545782 0.174215 0.437635 -0.04545 0.140934 0.013838 0.072913 0.008997
Estonia 0.737041 0.352713 0.205628 -0.03139 0.238293 0.023397 0.123282 0.015211
Finland 1.352042 0.258832 0.319167 -0.03292 0.177835 0.017461 0.092004 0.011352
France 1.259472 0.292803 0.224716 -0.01589 0.159000 0.015612 0.08226 0.01015
Germany 0.694024 0.339402 0.54815 -0.05546 0.180304 0.017704 0.093281 0.01151
Hungary 0.770683 0.375923 0.068702 -0.0028 0.204335 0.020063 0.105714 0.013044
Ireland 0.992552 0.26368 0.571297 -0.05996 0.171045 0.016794 0.088491 0.010919
Italy 0.652806 0.340295 0.448101 -0.03797 0.227626 0.02235 0.117763 0.014531
Latvia 0.856984 0.320107 0.07136 -0.01262 0.182072 0.017877 0.094196 0.011623
Netherlands 0.851423 0.248588 0.571987 -0.05838 0.136289 0.013382 0.07051 0.0087
Poland 0.395191 0.383528 0.325102 -0.0356 0.227625 0.02235 0.117763 0.01453
Portugal -0.04775 0.46068 0.463982 -0.04216 0.262584 0.025783 0.135849 0.016762
Romania 0.128356 0.453002 0.158483 -0.01838 0.269076 0.02642 0.139208 0.017177
Slovakia 0.628832 0.341852 0.223241 -0.02638 0.238073 0.023376 0.123168 0.015197
Slovenia 0.764813 0.385382 0.233162 -0.01591 0.190327 0.018688 0.098467 0.01215
Spain 1.181748 0.317969 0.161371 -0.0043 0.187973 0.018457 0.097249 0.011999
Sweden 1.471135 0.19044 0.324012 -0.03441 0.14431 0.014169 0.07466 0.009212
United_Kingdom 0.750457 0.34039 0.540786 -0.06146 0.186361 0.018298 0.096415 0.011896"""
        ),
        sep=" ",
    ).set_index("Country")

Scipy list of available distributions

`bulkhours.get_data("scipy_distributions_list")`

Enrich data: statsdata.py (raw)

Show code


def get_scipy_distributions_list(self):
    import scipy as sp
return [d for d in dir(sp.stats._continuous_distns) if not d in ["levy_stable", "studentized_range"]]

Oil production in Saudi Arabia from 1996 to 2007

`bulkhours.get_data("statsdata.oil")`

Enrich data: statsdata.py (raw)
Direct source: https://www.statsmodels.org/stable/index.html

Show columns info

Column	Info
oil

Show code


def get_oil(self):
    return pd.Series(
        [
            446.6565,
            454.4733,
            455.663,
            423.6322,
            456.2713,
            440.5881,
            425.3325,
            485.1494,
            506.0482,
            526.792,
            514.2689,
            494.211,
        ],
        pd.date_range(start="1996", end="2008", freq="A"),
    ).to_frame("oil")

Air pollution data

`bulkhours.get_data("statsdata.air")`

Enrich data: statsdata.py (raw)
Direct source: https://www.statsmodels.org/stable/index.html

Show columns info

Column	Info
air

Show code


def get_air(self):
    air = pd.Series(
        [
            17.5534,
            21.86,
            23.8866,
            26.9293,
            26.8885,
            28.8314,
            30.0751,
            30.9535,
            30.1857,
            31.5797,
            32.5776,
            33.4774,
            39.0216,
            41.3864,
            41.5966,
        ],
        pd.date_range(start="1990", end="2005", freq="A"),
    )
    return air.to_frame("air")

Forecasting livestock, sheep in Asia: comparing forecasting performance of non-seasonal methods.

`bulkhours.get_data("statsdata.livestock2")`

Enrich data: statsdata.py (raw)
Direct source: https://www.statsmodels.org/stable/index.html

Show columns info

Column	Info
livestock2

Show code


def get_livestock2(self):
    index = pd.date_range(start="1970", end="2001", freq="A")
    return pd.Series(
        [
            263.9177,
            268.3072,
            260.6626,
            266.6394,
            277.5158,
            283.834,
            290.309,
            292.4742,
            300.8307,
            309.2867,
            318.3311,
            329.3724,
            338.884,
            339.2441,
            328.6006,
            314.2554,
            314.4597,
            321.4138,
            329.7893,
            346.3852,
            352.2979,
            348.3705,
            417.5629,
            417.1236,
            417.7495,
            412.2339,
            411.9468,
            394.6971,
            401.4993,
            408.2705,
            414.2428,
        ],
        index,
    ).to_frame("livestock2")

Forecasting livestock, sheep in Asia: comparing forecasting performance of non-seasonal methods. (3)

`bulkhours.get_data("statsdata.livestock3")`

Enrich data: statsdata.py (raw)
Direct source: https://www.statsmodels.org/stable/index.html

Show columns info

Column	Info
livestock3

Show code


def get_livestock3(self):
    data = [407.9979, 403.4608, 413.8249, 428.105, 445.3387, 452.9942, 455.7402]
    return pd.Series(data, pd.date_range(start="2001", end="2008", freq="A")).to_frame("livestock3")

International visitor night in Australia (millions) < 2005

`bulkhours.get_data("statsdata.aust")`

Enrich data: statsdata.py (raw)

Show columns info

Column	Info
aust

Show code


def get_aust(self):
    data = [
        41.7275,
        24.0418,
        32.3281,
        37.3287,
        46.2132,
        29.3463,
        36.4829,
        42.9777,
        48.9015,
        31.1802,
        37.7179,
        40.4202,
        51.2069,
        31.8872,
        40.9783,
        43.7725,
        55.5586,
        33.8509,
        42.0764,
        45.6423,
        59.7668,
        35.1919,
        44.3197,
        47.9137,
    ]
    return pd.Series(data, pd.date_range(start="2005", end="2010-Q4", freq="QS-OCT")).to_frame("aust")

International visitor night in Australia (millions) > 2005

`bulkhours.get_data("air_passengers")`

Raw data: AirPassengers.csv (raw🤗)
Enrich data: statsdata.py (raw)

Show columns info

Column	Info
#Passengers
is_test

Show code


def get_air_passengers(self):
    df = self.read_raw_data(self.raw_data).set_index("Month")
df.index = pd.to_datetime(df.index)
df.index.freq = "MS"
df["is_test"] = df.index >= df.index[120]

return df

All-Transactions House Price Index for Houston

`bulkhours.get_data("statsdata.hhousing")`

Enrich data: statsdata.py (raw)
Direct source: https://fred.stlouisfed.org/series/ATNHPIUS26420Q

Show code


def get_hhousing(self):
    from pandas_datareader import data as pdr  # To get data
data = pdr.get_data_fred("HOUSTNSA", "1959-01-01")  # , "2019-06-01")
housing = data.HOUSTNSA.pct_change().dropna()
# Scale by 100 to get percentages
housing = 100 * housing.asfreq("MS")
return housing.to_frame()

Predictive_Maintenance

Synthetic data for machine failure data (1)

`bulkhours.get_data("maintenance1")`

Raw data: fe_equipment_failure_data_1.csv (raw)

Show columns info

Column	Info
ID
DATE
MANUFACTURER
S15
EQUIPMENT_FAILURE

Synthetic data for machine failure data (2)

`bulkhours.get_data("maintenance2")`

Raw data: fe_equipment_failure_data_2.csv (raw)

Show columns info

Column	Info
ID
DATE
MANUFACTURER
S15
EQUIPMENT_FAILURE

Synthetic data for machine failure data (3)

`bulkhours.get_data("maintenance3")`

Raw data: equipment_failure_data_1.csv (raw)

Show columns info

Column	Info
ID
DATE
REGION_CLUSTER
MAINTENANCE_VENDOR
MANUFACTURER
WELL_GROUP
S15
S17
S13
S5
S16
S19
S18
EQUIPMENT_FAILURE
S8
AGE_OF_EQUIPMENT

Synthetic data for machine failure data (4)

`bulkhours.get_data("maintenance4")`

Raw data: equipment_failure_data_2.csv (raw)

Show columns info

Column	Info
ID
DATE
REGION_CLUSTER
MAINTENANCE_VENDOR
MANUFACTURER
WELL_GROUP
S15
S17
S13
S5
S16
S19
S18
EQUIPMENT_FAILURE
S8
AGE_OF_EQUIPMENT

Computing

transistor_count

`bulkhours.get_data("hpc.transistor_count")`

Reference site: https://en.wikipedia.org/wiki/Transistor_count

Show columns info

Column	Info
processor
count
date
designer
manufacturer
engraving_scale
area
density
ref

Show code


def get_dfistor_count(self):
    columns = ["processor", "count", "date", "designer", "manufacturer", "engraving_scale", "area", "density", "ref"]
    df = flops.get_table_from_wiki(wpage="Transistor_count", in_table="Voodoo Graphics", columns=columns)
    return df.iloc[:-1]

Semiconductor device fabrication: MOSFET scaling

`bulkhours.get_data("hpc.engraving_scale")`

Reference site: https://en.wikipedia.org/wiki/Transistor_count

Show columns info

Column	Info
year
scale

Show code


def get_engraving_scale(self):
    return flops.get_engraving_scale(verbose=True)

FLOPS sub-units

`bulkhours.get_data("hpc.FLOPS_units")`

Reference site: https://en.wikipedia.org/wiki/FLOPS

Show columns info

Column	Info
Name
Unit
Value

Show code


def get_FLOPS(self):
    return flops.get_table_from_wiki("FLOPS", "Computer performance")

FLOPS for gpus

`bulkhours.get_data("hpc.FLOPS_gpus")`

Reference site: https://en.wikipedia.org/wiki/FLOPS

Show columns info

Column	Info
date
un_costs
costs
platform
comments

Show code


def get_gpus(self):
    return flops.get_table_from_wiki("FLOPS", "NVIDIA", columns=["date", "un_costs", "costs", "platform", "comments"])

FLOPS for cpus

`bulkhours.get_data("hpc.FLOPS_cpus")`

Reference site: https://en.wikipedia.org/wiki/FLOPS

Show code


def get_cpus(self):
    columns = ["processor", "count", "date", "designer", "engraving_scale", "area", "density"]
    df = flops.get_table_from_wiki(
        wpage="Transistor_count", in_table="20-bit, 6-chip, 28 chips total", columns=columns
    )
    df = df.iloc[:-1]
    df["date"] = df["date"].str.replace("March ", "").str.replace("November ", "")
    df["date"] = df["date"].str.replace("March ", "").str.replace("November ", "")
    df["date"] = df["date"].str.split("[").str[0].astype(int)
df["count"] = df["count"].str.replace(",", "")
df["count"] = df["count"].str.split("[").str[0]
df["count"] = df["count"].str.split("+").str[0]
df["count"] = df["count"].str.split(" ").str[0]
df["count"] = pd.to_numeric(df["count"], errors="coerce")

df["engraving_scale"] = df["engraving_scale"].str.split("[").str[0]
df["engraving_scale"] = df["engraving_scale"].str.replace(",", "")
df["engraving_scale"] = df["engraving_scale"].str.split("(").str[0]
df["engraving_scale"] = df["engraving_scale"].str.replace("\xa0nm", "")
df["engraving_scale"] = df["engraving_scale"].str.replace("nm", "")
df["engraving_scale"] = pd.to_numeric(df["engraving_scale"], errors="coerce")

l = [3, 4, 6, 8, 15, 23, 50, 80, 150, 300, 500, 800, 1000, 3000, 5000, 10000]
df["engraving_scale2"] = pd.cut(df["engraving_scale"], bins=l, include_lowest=True)
df["engraving_scale3"] = df["engraving_scale2"].map(
    dict(zip(df["engraving_scale2"].unique(), range(len(df["engraving_scale2"].unique()))))
)
df["engraving_scale3"] = df["engraving_scale3"].fillna(1).astype(float)

return df

Costs of FLOPS

`bulkhours.get_data("hpc.FLOPS_costs")`

Reference site: https://en.wikipedia.org/wiki/FLOPS

Show columns info

Column	Info
('Date', 'Date')
('Approximate USD per GFLOPS', 'Unadjusted')
('Approximate USD per GFLOPS', '2022[69]')
('Platform providing the lowest cost per GFLOPS', 'Platform providing the lowest cost per GFLOPS')
('Comments', 'Comments')

Show code


def get_costs(self):
    return flops.get_table_from_wiki("FLOPS", "Approximate USD per GFLOPS")#, columns=["date", "un_costs", "costs", "platform", "comments"])

Energy Efficiency (GFlops/watts)

`bulkhours.get_data("hpc.green500")`

Raw data: green500_top_202306.xlsx (raw🤗)
Reference site: https://www.top500.org/lists/green500/2023/06/

Show columns info

Column	Info
Rank
TOP500 Rank
Name
Computer
Site
Manufacturer
Country
Year
Segment
Total Cores
Accelerator/Co-Processor Cores
Rmax [TFlop/s]
Rpeak [TFlop/s]
Power (kW)
Power Source
Energy Efficiency [GFlops/Watts]
Power Source.1
Power Quality Level
Optimized Run (HPL)
Optimized Run (Peak Power)
Memory
Architecture
Processor
Processor Technology
Processor Speed (MHz)
Operating System
OS Family
Accelerator/Co-Processor
Cores per Socket
Processor Generation
System Model
System Family
Interconnect Family
Interconnect
Continent
Site ID
System ID

Energy Efficiency (GFlops/watts)

`bulkhours.get_data("hpc.top500")`

Raw data: TOP500_202306.xlsx (raw🤗)
Reference site: https://www.top500.org/lists/top500/2023/06/

Show columns info

Column	Info
Rank
Previous Rank
First Appearance
First Rank
Name
Computer
Site
Manufacturer
Country
Year
Segment
Total Cores
Accelerator/Co-Processor Cores
Rmax [TFlop/s]
Rpeak [TFlop/s]
Nmax
Nhalf
HPCG [TFlop/s]
Power (kW)
Power Source
Energy Efficiency [GFlops/Watts]
Memory
Architecture
Processor
Processor Technology
Processor Speed (MHz)
Operating System
OS Family
Accelerator/Co-Processor
Cores per Socket
Processor Generation
System Model
System Family
Interconnect Family
Interconnect
Continent
Site ID
System ID

Computational capacity of the fastest supercomputers

`bulkhours.get_data("supercomputers")`

Raw data: supercomputer-power-flops.csv (raw🤗)
Direct source: https://ourworldindata.org/grapher/supercomputer-power-flops

Show columns info

> The number of floating-point operations per second (GigaFLOPS) by the fastest supercomputer in any given year

Column	Info
Entity	The number of GigaFLOP/S by the fastest supercomputer in any given year

Physics

Celsius en Kelvin: $273.15°K=0°C$

from bulkhours import constants as bkc
bkc.c2k := 273.15  # K.C-1
bkc.kelvin := 273.15  # K.C-1

Celerité de la lumière: $c = 3 \cdot 10^{5}m\cdot s^{-1}$

from bulkhours import constants as bkc
bkc.c := 3e+05  # m.s-1
bkc.vitesse_lumiere := 3e+05  # m.s-1

Distance parcourue par la lumière en 1an: $al = 9.461 \cdot 10^{15}m$

from bulkhours import constants as bkc
bkc.al := 9.461e+15  # m
bkc.annee_lumiere := 9.461e+15  # m

Une Unité astrononique faisant un angle d'une seconde d'arc (ancienne déf.): $1pc \equiv \frac{180\cdot60\cdot60}{\pi} = 3.086 \cdot 10^{16}m = 3.26al$

from bulkhours import constants as bkc
bkc.parsec := 3.086e+16  # m.pc-1

Une Unité astrononique faisant un angle d'une seconde d'arc (ancienne déf.): $1kpc \equiv \frac{1000\cdot180\cdot60\cdot60}{\pi}$

from bulkhours import constants as bkc
bkc.kparsec := 3.086e+19  # m.kpc-1

Constante de la gravitation: $G = 6.67 \cdot 10^{-11}N\cdot m^2\cdot kg^{-2}$ [6.6743e-11N.m2.kg-2]

from bulkhours import constants as bkc
bkc.G := 6.67e-11  # N.m2.kg-2

Acceleration standard de la gravitation: $g = 9.8m\cdot s^{-2}$ [9.80665m.s-2]

from bulkhours import constants as bkc
bkc.g := 9.8  # m.s-2

Constante de Planck: $h = 6.626 \cdot 10^{-34}J\cdot s$ [6.62607015e-34J.s]

from bulkhours import constants as bkc
bkc.h := 6.626e-34  # J.s

Constante de Planck réduite: $\bar{h} = \frac{h}{2\pi}$

from bulkhours import constants as bkc
bkc.hbar := 1.055e-34  # J.s

Nombre d'Avogadro: $N_\mathcal{A} = 6.02 \cdot 10^{23}mol-1$ (Carbone: $12g\Leftrightarrow 1mol$)

from bulkhours import constants as bkc
bkc.N_A := 6.02e+23  # mol-1
bkc.A := 6.02e+23  # mol-1

Constante de Stefan-Boltzmann: $\sigma = 5.67 \cdot 10^{-8} W\cdot m^{-2}\cdot K^{-4}$

from bulkhours import constants as bkc
bkc.sigma := 5.67e-08  # W.m-2.K-4
bkc.stefan := 5.67e-08  # W.m-2.K-4

Constante de Wien: $\lambda_{\text{max}} \cdot T = 0.003m\cdot K$ [0.002897771955m.K]

from bulkhours import constants as bkc
bkc.Wien := 0.003  # m.K
bkc.wien := 0.003  # m.K
bkc.lambda_max := 0.003  # m.K

Constante de Rydberg: $R_H({\text{Hydrogene}}) = 1.1 \cdot 10^{7}m-1$

from bulkhours import constants as bkc
bkc.Rydberg := 1.1e+07  # m-1
bkc.rydberg := 1.1e+07  # m-1
bkc.R_H := 1.1e+07  # m-1

Energie cinetique e sous 1Volt: $\mathrm{eV} = 1.6 \cdot 10^{-19}J\cdot eV^{-1}$ [1.602176634e-19J.eV-1]

from bulkhours import constants as bkc
bkc.eV := 1.6e-19  # J.eV-1
bkc.ev := 1.6e-19  # J.eV-1

Masse electron: $m_e = 9.109 \cdot 10^{-31}kg$ [9.1093837015e-31kg]

from bulkhours import constants as bkc
bkc.m_e := 9.109e-31  # kg

Rayon de Bohr: $a = 5.3 \cdot 10^{-11} m$

from bulkhours import constants as bkc
bkc.r_bohr := 5.300e-11  # m
bkc.a := 5.300e-11  # m

Masse proton: $m_p = 1.673 \cdot 10^{-27}kg$ [1.67262192369e-27kg]

from bulkhours import constants as bkc
bkc.m_p := 1.673e-27  # kg

Masse proton: $m_p = 1.007276uma$ ($1uma \equiv \frac{M(^{12}C)}{12}$) [1.007276466621uma]

from bulkhours import constants as bkc
bkc.m_puma := 1.007276  # uma

Masse neutron: $m_n = 1.008663uma$ ($1uma \equiv \frac{M(^{12}C)}{12}$) [1.00866491595uma]

from bulkhours import constants as bkc
bkc.m_numa := 1.008663  # uma

Unité de Masse Atomique: $m_{nuc} = 1.660 \cdot 10^{-27}kg\cdot uma^{-1}$ ($1uma \equiv \frac{M(^{12}C)}{12}$) [1.6605390666e-27kg.uma-1]

from bulkhours import constants as bkc
bkc.uma := 1.660e-27  # kg.uma-1

Unité de Masse Atomique (MeV): $m_{nuc} = 931.500MeV\cdot uma^{-1}$ ($1uma \equiv \frac{M(^{12}C)}{12}$)

from bulkhours import constants as bkc
bkc.uma_mev := 931.500  # MeV.uma-1

Masse: $M_{\mathrm{mercure}} = 3.301 \cdot 10^{23}kg$

from bulkhours import constants as bkc
bkc.M_mercure := 3.301e+23  # kg

Distance au soleil: $d_{\odot \mathrm{mercure}} = 0.38ua$

from bulkhours import constants as bkc
bkc.d_mercure := 0.38  # ua

Rayon: $R_{\mathrm{mercure}} = 2439km$

from bulkhours import constants as bkc
bkc.R_mercure := 2439  # km

Albedo: $A_{\mathrm{mercure}} = 0.09$

from bulkhours import constants as bkc
bkc.A_mercure := 0.09  # Sans unité (entre 0 et 1)

Effet de serre: $S_{\mathrm{mercure}} = 0$

from bulkhours import constants as bkc
bkc.S_mercure := 0  # Sans unité (entre 0 et 1)

Temperature moyenne: $T_{\mathrm{mercure}} = 167.0°C$

from bulkhours import constants as bkc
bkc.T_mercure := 167.0  # °C

Masse: $M_{\mathrm{venus}} = 4.867 \cdot 10^{24}kg$

from bulkhours import constants as bkc
bkc.M_venus := 4.867e+24  # kg

Distance au soleil: $d_{\odot \mathrm{venus}} = 0.72ua$

from bulkhours import constants as bkc
bkc.d_venus := 0.72  # ua

Rayon: $R_{\mathrm{venus}} = 3390km$

from bulkhours import constants as bkc
bkc.R_venus := 3390  # km

Albedo: $A_{\mathrm{venus}} = 0.77$

from bulkhours import constants as bkc
bkc.A_venus := 0.77  # Sans unité (entre 0 et 1)

Effet de serre: $S_{\mathrm{venus}} = 0.991$

from bulkhours import constants as bkc
bkc.S_venus := 0.991  # Sans unité (entre 0 et 1)

Temperature moyenne: $T_{\mathrm{venus}} = 464.0°C$

from bulkhours import constants as bkc
bkc.T_venus := 464.0  # °C

Masse: $M_{\mathrm{terre}} = 5.972 \cdot 10^{24}kg$

from bulkhours import constants as bkc
bkc.M_terre := 5.972e+24  # kg

Distance au soleil: $d_{\odot \mathrm{terre}} = 1.00ua$

from bulkhours import constants as bkc
bkc.d_terre := 1.00  # ua

Rayon: $R_{\mathrm{terre}} = 6371km$

from bulkhours import constants as bkc
bkc.R_terre := 6371  # km

Albedo: $A_{\mathrm{terre}} = 0.30$

from bulkhours import constants as bkc
bkc.A_terre := 0.30  # Sans unité (entre 0 et 1)

Effet de serre: $S_{\mathrm{terre}} = 0.394$

from bulkhours import constants as bkc
bkc.S_terre := 0.394  # Sans unité (entre 0 et 1)

Temperature moyenne: $T_{\mathrm{terre}} = 15.0°C$

from bulkhours import constants as bkc
bkc.T_terre := 15.0  # °C

Distance au soleil: $d_{\odot \mathrm{terre}} = 1ua = 1.500 \cdot 10^{11}m$ [149597870700.0m]

from bulkhours import constants as bkc
bkc.d_terre_solm := 1.500e+11  # m
bkc.d_terresoleil := 1.500e+11  # m

Masse: $M_{\mathrm{mars}} = 6.417 \cdot 10^{23}kg$

from bulkhours import constants as bkc
bkc.M_mars := 6.417e+23  # kg

Distance au soleil: $d_{\odot \mathrm{mars}} = 1.52ua$

from bulkhours import constants as bkc
bkc.d_mars := 1.52  # ua

Rayon: $R_{\mathrm{mars}} = 3390km$

from bulkhours import constants as bkc
bkc.R_mars := 3390  # km

Albedo: $A_{\mathrm{mars}} = 0.25$

from bulkhours import constants as bkc
bkc.A_mars := 0.25  # Sans unité (entre 0 et 1)

Effet de serre: $S_{\mathrm{mars}} = 0.010$

from bulkhours import constants as bkc
bkc.S_mars := 0.010  # Sans unité (entre 0 et 1)

Temperature moyenne: $T_{\mathrm{mars}} = -62.8°C$

from bulkhours import constants as bkc
bkc.T_mars := -62.8  # °C

Masse: $M_{\odot} = 1.988 \cdot 10^{30}kg$

from bulkhours import constants as bkc
bkc.M_soleil := 1.988e+30  # kg

Rayon: $R_{\odot} = 7 \cdot 10^{5}km$

from bulkhours import constants as bkc
bkc.R_soleil := 7e+05  # km

Luminosité: $L_{\odot} = 3.83 \cdot 10^{26}W$

from bulkhours import constants as bkc
bkc.L_soleil := 3.83e+26  # W
bkc.L_sol := 3.83e+26  # W
bkc.L_sun := 3.83e+26  # W

Temperature moyenne: $T_{\odot} = 5800.0°C$

from bulkhours import constants as bkc
bkc.T_soleil := 5800.0  # °C

Masse: $M_{\mathrm{lune}} = 7.350 \cdot 10^{22}kg$

from bulkhours import constants as bkc
bkc.M_lune := 7.350e+22  # kg

Distance au soleil: $d_{\odot \mathrm{lune}} = 1.00ua$

from bulkhours import constants as bkc
bkc.d_lune := 1.00  # ua

Rayon: $R_{\mathrm{lune}} = 6371km$

from bulkhours import constants as bkc
bkc.R_lune := 6371  # km

Albedo: $A_{\mathrm{lune}} = 0.11$

from bulkhours import constants as bkc
bkc.A_lune := 0.11  # Sans unité (entre 0 et 1)

Distance à la lune: $d_{\mathrm{terre} \mathrm{lune}} = 3.844 \cdot 10^{8}m$

from bulkhours import constants as bkc
bkc.d_terre_lune := 3.844e+08  # m

Perimètre d'un cercle de rayon 1/2🙂: $pi = 3.141593$

from bulkhours import constants as bkc
bkc.pi := 3.141593  #

Quarterly sunspots activity (ssn)

`bulkhours.get_data("sunspots")`

Raw data: observed-solar-cycle-indices.json (raw🔄)
Enrich data: statsdata.py (raw)
Direct source: https://www.swpc.noaa.gov/products/solar-cycle-progression
Reference site: https://services.swpc.noaa.gov/json/solar-cycle/observed-solar-cycle-indices.json

Show columns info

Column	Info
ssn	SunSpot Number (aka Wolf Number or Zürich number): number of sunspots and groups of sunspots present on the surface of the Sun (source: S.I.D.C. Brussels International Sunspot Number)
smoothed_ssn	smoothed ssn (source: S.I.D.C. Brussels International Sunspot Number)
observed_swpc_ssn	mean monthly SWPC/SWO ssn (source: SWPC Space Weather Operations)
smoothed_swpc_ssn	smoothed ssn (source: SWPC Space Weather Operations)
f10.7	mean monthly flux values (sfu) (source: Penticton, B.C. 10.7cm radio)
smoothed_f10.7	smoothed radio flux values (source: Penticton, B.C. 10.7cm radio)

Show code


def get_sunspots(self):
    dta = pd.read_json(self.raw_data)
    sunspots = dta.set_index("time-tag")
    sunspots.index = pd.to_datetime(sunspots.index)
    sunspots.index.freq = sunspots.index.inferred_freq
sunspots = sunspots.resample("MS").mean()
sunspots = sunspots.resample("Q").mean().iloc[-400:]

return sunspots

Health

Coronavirus Pandemic (COVID-19) data

`bulkhours.get_data("vaccinations")`

Raw data: vaccinations.csv (raw)
Direct source: https://ourworldindata.org/coronavirus
Reference site: https://covid19.who.int/data

Show columns info

> https://github.com/owid/covid-19-data/tree/master/public/data/

Column	Info
location
iso_code
date
total_vaccinations
people_vaccinated
people_fully_vaccinated
total_boosters
daily_vaccinations_raw
daily_vaccinations
total_vaccinations_per_hundred
people_vaccinated_per_hundred
people_fully_vaccinated_per_hundred
total_boosters_per_hundred
daily_vaccinations_per_million
daily_people_vaccinated
daily_people_vaccinated_per_hundred

Prostate cancer data

`bulkhours.get_data("prostate")`

Raw data: prostate.tsv (raw)
Direct source: https://hastie.su.domains/ElemStatLearn/data.html

Show columns info

Column	Info
lcavol
lweight
lbph
svi
lcp
gleason
pgg45
[outcome]

Coronavirus Pandemic (COVID-19) data

`bulkhours.get_data("covid")`

Raw data: owid-covid-data.csv (raw🔄)
Direct source: https://ourworldindata.org/coronavirus
Reference site: https://covid19.who.int/data

Show columns info

> https://github.com/owid/covid-19-data/tree/master/public/data/

Column	Info
iso_code
continent
location
date
total_cases
new_cases
new_cases_smoothed
total_deaths
new_deaths
new_deaths_smoothed
total_cases_per_million
new_cases_per_million
new_cases_smoothed_per_million
total_deaths_per_million
new_deaths_per_million
new_deaths_smoothed_per_million
reproduction_rate
icu_patients
icu_patients_per_million
hosp_patients
hosp_patients_per_million
weekly_icu_admissions
weekly_icu_admissions_per_million
weekly_hosp_admissions
weekly_hosp_admissions_per_million
total_tests
new_tests
total_tests_per_thousand
new_tests_per_thousand
new_tests_smoothed
new_tests_smoothed_per_thousand
positive_rate
tests_per_case
tests_units
total_vaccinations
people_vaccinated
people_fully_vaccinated
total_boosters
new_vaccinations
new_vaccinations_smoothed
total_vaccinations_per_hundred
people_vaccinated_per_hundred
people_fully_vaccinated_per_hundred
total_boosters_per_hundred
new_vaccinations_smoothed_per_million
new_people_vaccinated_smoothed
new_people_vaccinated_smoothed_per_hundred
stringency_index
population_density
median_age
aged_65_older
aged_70_older
gdp_per_capita
extreme_poverty
cardiovasc_death_rate
diabetes_prevalence
female_smokers
male_smokers
handwashing_facilities
hospital_beds_per_thousand
life_expectancy
human_development_index
population
excess_mortality_cumulative_absolute
excess_mortality_cumulative
excess_mortality
excess_mortality_cumulative_per_million

Climate_Evolution

Data on CO2 and Greenhouse Gas Emissions by Our World in Data

`bulkhours.get_data("co2.main")`

Raw data: owid-co2-data.csv (raw)
Enrich data: world.py (raw)

Show columns info

> https://github.com/owid/co2-data/blob/master/owid-co2-codebook.csv

Column	Info
country
year
iso_code
population
gdp
cement_co2
cement_co2_per_capita
co2
co2_growth_abs
co2_growth_prct
co2_including_luc
co2_including_luc_growth_abs
co2_including_luc_growth_prct
co2_including_luc_per_capita
co2_including_luc_per_gdp
co2_including_luc_per_unit_energy
co2_per_capita
co2_per_gdp
co2_per_unit_energy
coal_co2
coal_co2_per_capita
consumption_co2
consumption_co2_per_capita
consumption_co2_per_gdp
cumulative_cement_co2
cumulative_co2
cumulative_co2_including_luc
cumulative_coal_co2
cumulative_flaring_co2
cumulative_gas_co2
cumulative_luc_co2
cumulative_oil_co2
cumulative_other_co2
energy_per_capita
energy_per_gdp
flaring_co2
flaring_co2_per_capita
gas_co2
gas_co2_per_capita
ghg_excluding_lucf_per_capita
ghg_per_capita
land_use_change_co2
land_use_change_co2_per_capita
methane
methane_per_capita
nitrous_oxide
nitrous_oxide_per_capita
oil_co2
oil_co2_per_capita
other_co2_per_capita
other_industry_co2
primary_energy_consumption
share_global_cement_co2
share_global_co2
share_global_co2_including_luc
share_global_coal_co2
share_global_cumulative_cement_co2
share_global_cumulative_co2
share_global_cumulative_co2_including_luc
share_global_cumulative_coal_co2
share_global_cumulative_flaring_co2
share_global_cumulative_gas_co2
share_global_cumulative_luc_co2
share_global_cumulative_oil_co2
share_global_cumulative_other_co2
share_global_flaring_co2
share_global_gas_co2
share_global_luc_co2
share_global_oil_co2
share_global_other_co2
share_of_temperature_change_from_ghg
temperature_change_from_ch4
temperature_change_from_co2
temperature_change_from_ghg
temperature_change_from_n2o
total_ghg
total_ghg_excluding_lucf
trade_co2
trade_co2_share

Data on CO2 and Greenhouse Gas Emissions by Our World in Data (with extra gpx data)

`bulkhours.get_data("co2.mapmain")`

Raw data: owid-co2-data.csv (raw)
Enrich data: world.py (raw)

Show columns info

> https://github.com/owid/co2-data/blob/master/owid-co2-codebook.csv

Column	Info
country
year
iso_code
population
gdp
cement_co2
cement_co2_per_capita
co2
co2_growth_abs
co2_growth_prct
co2_including_luc
co2_including_luc_growth_abs
co2_including_luc_growth_prct
co2_including_luc_per_capita
co2_including_luc_per_gdp
co2_including_luc_per_unit_energy
co2_per_capita
co2_per_gdp
co2_per_unit_energy
coal_co2
coal_co2_per_capita
consumption_co2
consumption_co2_per_capita
consumption_co2_per_gdp
cumulative_cement_co2
cumulative_co2
cumulative_co2_including_luc
cumulative_coal_co2
cumulative_flaring_co2
cumulative_gas_co2
cumulative_luc_co2
cumulative_oil_co2
cumulative_other_co2
energy_per_capita
energy_per_gdp
flaring_co2
flaring_co2_per_capita
gas_co2
gas_co2_per_capita
ghg_excluding_lucf_per_capita
ghg_per_capita
land_use_change_co2
land_use_change_co2_per_capita
methane
methane_per_capita
nitrous_oxide
nitrous_oxide_per_capita
oil_co2
oil_co2_per_capita
other_co2_per_capita
other_industry_co2
primary_energy_consumption
share_global_cement_co2
share_global_co2
share_global_co2_including_luc
share_global_coal_co2
share_global_cumulative_cement_co2
share_global_cumulative_co2
share_global_cumulative_co2_including_luc
share_global_cumulative_coal_co2
share_global_cumulative_flaring_co2
share_global_cumulative_gas_co2
share_global_cumulative_luc_co2
share_global_cumulative_oil_co2
share_global_cumulative_other_co2
share_global_flaring_co2
share_global_gas_co2
share_global_luc_co2
share_global_oil_co2
share_global_other_co2
share_of_temperature_change_from_ghg
temperature_change_from_ch4
temperature_change_from_co2
temperature_change_from_ghg
temperature_change_from_n2o
total_ghg
total_ghg_excluding_lucf
trade_co2
trade_co2_share

CO2 transportation info

`bulkhours.get_data("co2.travel_mode")`

Raw data: carbon-footprint-travel-mode.csv (raw🤗)
Direct source: https://ourworldindata.org/grapher/carbon-footprint-travel-mode

Show columns info

Column	Info
Entity
Code
Year
GHG emissions (gCO2e/km)

Greenhouse effect gaz concentrations

`bulkhours.get_data("co2.concentrations")`

Raw data: climate-change.csv (raw)
Enrich data: world.py (raw)
Direct source: https://ourworldindata.org/atmospheric-concentrations

Show columns info

Column	Info
country
year
CO2 concentrations
CH4 concentrations
N2O concentrations
February
September
Mass U.S. glaciers
CSIRO
IAP
MRIJMA
NOAA
Snow cover
Sea surface temp
Sea surface temp (lower-bound)
Sea surface temp (upper-bound)
IAP.1
NOAA.1
MRIJMA.1
February.1
September.1

Show code


def get_concentrations(self, zone="World", **data_info):
    df = self.read_raw_data(self.raw_data)
df = df.rename(columns={"Entity": "country", "Year": "year"})

if zone is not None:
    df = df.query(f"country == '{zone}'")
return df

Greenhouse effect gaz concentrations

`bulkhours.get_data("co2.mapconcentrations")`

Raw data: climate-change.csv (raw)
Enrich data: world.py (raw)
Direct source: https://ourworldindata.org/atmospheric-concentrations

Show columns info

Column	Info
pop_est
continent
name
iso_a3
gdp_md_est
geometry
year
CO2 concentrations
CH4 concentrations
N2O concentrations
February
September
Mass U.S. glaciers
CSIRO
IAP
MRIJMA
NOAA
Snow cover
Sea surface temp
Sea surface temp (lower-bound)
Sea surface temp (upper-bound)
IAP.1
NOAA.1
MRIJMA.1
February.1
September.1

Show code


def get_mapconcentrations(self, **kwargs):
    return get_mapgeneric(get_concentrations(self, **kwargs))

Les incidences économique de l'action pour le climat

`bulkhours.get_data("climate.pisaniferry")`

Raw data: 2023-incidences-economiques-transition-climat-rapport-de-synthese_0.pdf (raw🔄)

La contribution des émissions importées à l'empreinte carbone de la France

`bulkhours.get_data("climate.francecarbone")`

Raw data: Rapport-OFCE-HCC-2020.pdf (raw🔄)

Machine_learning

Cat or not training data: keys=[train_set_x, train_set_y]

`bulkhours.get_data("train_catvnoncat")`

Raw data: train_catvnoncat.h5 (raw🤗)

Cat or not test data: keys=[test_set_x, test_set_y, list_classes]

`bulkhours.get_data("test_catvnoncat")`

Raw data: test_catvnoncat.h5 (raw🤗)

🔗Sigmoid: evaluation automation

📋Course Edition

🌍Global Warming: dataset studies

🧮C++/CUDA review

🧠A bit of Machine Learning

🦠A bit of data science

🧑‍🎓Main methods

📱bulkhours.init_env

🔗evaluation_cell_id

🧮compile_and_exec

📈bulkhours.get_data

👨‍🏫Evaluators'methods

❓student_evaluation_function

💡student_explanation_function

💻bulkhours.is_equal

📋bulkhours.admin.dashboard

📰bulkhours.admin.summary

✏️bulkhours.admin.evaluate

📈Available data