ineqpy.utils

Useful functions that make easier develop other functions.

Module Contents

Functions

_to_df(→ pandas.DataFrame)
_apply_to_df(func, df, x, weights, *args, **kwargs)	Generalize main arguments as Series of a pd.Dataframe.
not_empty_weights(weights, like)	Create weights.
not_null_condition(income, weights)	Filter not null condition.
_sort_values(values, partner)
_clean_nans_values(this, pair)
normalize(this)	Normalize data by the sum.
extract_values(data, variable, weights)	Extract values.
repeat_data_from_weighted(x, w)	Generate data data (not sampled) from weights.
generate_data_to_test([n_sample_range])	Generate sampled data for testing.

ineqpy.utils._to_df(*args, **kwargs) → pandas.DataFrame

ineqpy.utils._apply_to_df(func, df, x, weights, *args, **kwargs)

Generalize main arguments as Series of a pd.Dataframe.

Parameters:

funcfunction

Function to convert his arguments in Series of an Dataframe.

dfpandas.Dataframe

DataFrame whats contains the Series x_name and w_name.

x_namestr

Name of the column in df.

weights_namestr

Name of the column in df.

Returns:

returnfunc return

It’s depends of func output type.

ineqpy.utils.not_empty_weights(weights, like)

Create weights.

Create normalized weight if it’s None use like to create it.

Parameters:

income, likearray-like

Returns:

weightsarray-like

Filtered array-like.

See also

normalize

ineqpy.utils.not_null_condition(income, weights)

Filter not null condition.

If a negative value is found in the incomes it will dropped.

Parameters:

income, weightsarray-like

Returns:

income, weightsarray-like

Filtered array-like.

ineqpy.utils._sort_values(values, partner)

ineqpy.utils._clean_nans_values(this, pair)

ineqpy.utils.normalize(this)

Normalize data by the sum.

Parameters:

thisarray-like

Returns:

outarray-like

ineqpy.utils.extract_values(data, variable, weights)

Extract values.

Parameters:

datapandas.DataFrame

variablestr

weightsstr

Returns:

variable, weightsarray-like

ineqpy.utils.repeat_data_from_weighted(x, w)

Generate data data (not sampled) from weights.

Parameters:

x, warray-like

Returns:

repeated_x, repeated_wnp.array

ineqpy.utils.generate_data_to_test(n_sample_range=(20, 100))

Generate sampled data for testing.

Parameters:

n_sample_rangetuple[int, int]

It’s a shape, lenght and columns.

Returns:

income, weightsnp.array