Practical Data Science with Python by Nathan George
Author:Nathan George [Nathan George]
Language: eng
Format: epub, pdf
Publisher: Packt Publishing
Published: 2021-09-29T16:00:00+00:00
Here, we loop through all column names and convert any non-interval columns to a category datatype. Now we can calculate the phik correlations and p-values:
phik_correlations = [] phik_significances = [] columns = loan_df_epoch_time.columns y = loan_df_epoch_time['LOAN_DEFAULT'] for c in columns: x = loan_df_epoch_time[c] if c in interval_columns: phik_correlations.append(phik.phik_from_array(x, y, [c])) phik_significances.append( phik.significance.significance_from_array(x, y, [c])[0]) else: phik_correlations.append(phik.phik_from_array(x, y)) phik_significances.append( phik.significance.significance_from_array(x, y)[0])
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