eazieda package

Submodules

eazieda.eazieda module

eazieda.eazieda.corr_plot(data, features, method='pearson', plot_width=600, plot_height=400)[source]

Generates a correlation plot for a list of features in a given dataframe

Parameters

  • data (pandas.core.frame.DataFrame) – The input dataframe

  • features (list) – A list of strings that represents numerical feature names len(features) >=2

  • method (str, default = "pearson") – The correlation method Other correlation methods are “spearman” or “kendall”

  • plot_width (int, default = 600) – The width of the plot

  • plot_height (int, default = 400) – The height of the plot

Returns

An interactive altair correlation plot

Return type

altair plot

Examples

>>> from eazieda.eazieda import corr_plot
>>> from vega_datasets import data
>>> df = data.iris()
>>> corr_plot(df, ["petal_length", "petal_width", "sepal_length"])
eazieda.eazieda.missing_impute(data, impute=False, method_num='mean', method_non_num='most_frequent')[source]

Return the number/percentage of missing values for each column in the dataframe as well as giving the choice of imputing the missing values in place

Parameters

  • data (pandas.core.frame.DataFrame) – A Pandas Dataframe for which the missing values need to be detected

  • impute (bool, default = False) – Whether to impute the missing values in place.

  • method_num (str, default = "mean") – The method used for imputing numerical missing values This is only applicable if impute=True One of ‘drop’, mean’, ‘median’

  • method_non_num (str, default = "most_frequent") – The method used for imputing non-numerical missing values This is only applicable if impute=True One of ‘drop’, ‘most_frequent’

Returns

A dataframe containing two columns: the number of missing values and the percentage of missing values for each column

Return type

pandas.core.frame.DataFrame

Examples

>>> from eazieda.eazieda import missing_impute
>>> df = pd.DataFrame([[1, "x"], [np.nan, "y"], [2, np.nan], [3, "y"]], columns = ['a', 'b'])
>>> missing_impute(df)
    n_missing       percent
a   1               25%
b   1               25%

Module contents