describr 0.0.6

Describr is a Python library that provides a convenient way to generate descriptive statistics for datasets.

README.md

describr is a Python library that provides functionality for descriptive statistics and outlier detection in pandas DataFrames.

Installation

You can install describr using pip:

pip install describr

Example usage

import pandas as pd

import numpy as np

from describr import FindOutliers, DescriptiveStats

Create a sample dataframe

np.random.seed(0)

n = 500



data = {

    'MCID': ['MCID_' + str(i) for i in range(1, n + 1)],

    'Age': np.random.randint(18, 90, size=n),

    'Race': np.random.choice(['White', 'Black', 'Asian', 'Hispanic',''], size=n),

    'Educational_Status': np.random.choice(['High School', 'Bachelor', 'Master', 'PhD',''], size=n),

    'Gender': np.random.choice(['Male', 'Female', ''], size=n),

    'ER_COST': np.random.uniform(500, 5000, size=n),

    'ER_VISITS': np.random.randint(0, 10, size=n),

    'IP_COST': np.random.uniform(5000, 20000, size=n),

    'IP_ADMITS': np.random.randint(0, 5, size=n),

    'CHF': np.random.choice([0, 1], size=n),

    'COPD': np.random.choice([0, 1], size=n),

    'DM': np.random.choice([0, 1], size=n),

    'ASTHMA': np.random.choice([0, 1], size=n),

    'HYPERTENSION': np.random.choice([0, 1], size=n),

    'SCHIZOPHRENIA': np.random.choice([0, 1], size=n),

    'MOOD_DEPRESSED': np.random.choice([0, 1], size=n),

    'MOOD_BIPOLAR': np.random.choice([0, 1], size=n),

    'TREATMENT': np.random.choice(['Yes', 'No'], size=n)

}



df = pd.DataFrame(data)

Parameters

df: name of dataframe

id_col: Primary key of the dataframe; accepts string or integer or float.

group_col: A Column to group by, It must be a binary column. Strings or integers are acceptable.

positive_class: This is the response value for the primary outcome of interest. For instance, positive value for a Treatment cohort is 'Yes' or 1 otherwise 'No' or 0, respectively. Strings or integers are acceptable.

continuous_var_summary: Users specifies measures of central tendency, only mean and median are acceptable. This parameter is case insensitive.

Example usage of FindOutliers Class

This returns a dataframe (outliers_flag_df) with outlier_flag column (outlier_flag =1: record contains one or more ouliers). Tukey's IQR method is used to detect outliers in the data

outliers_flag=FindOutliers(df=df, id_col='MCID', group_col='TREATMENT')

outliers_flag_df=outliers_flag.flag_outliers()

This example counts number of rows with outliers stratified by a defined grouping variable

outliers_flag.count_outliers()

This example removes all outliers

df2=outliers_flag.remove_outliers()

df2.shape

Example usage of DescriptiveStats Class

descriptive_stats = DescriptiveStats(df=df, id_col='MCID', group_col='TREATMENT', positive_class='Yes', continuous_var_summary='median')

Gets statistics for binary and categorical variables and returns a dataframe.

binary_stats_df = descriptive_stats.get_binary_stats()

Gets mean and standard deviation for continuous variables and returns a dataframe.

continuous_stats_mean_df = descriptive_stats.get_continuous_mean_stats()

Gets median and interquartile range for continuous variables and returns a dataframe.

continuous_stats_median_df = descriptive_stats.get_continuous_median_stats()

Computes summary statistics for binary and continuous variables based on defined measure of central tendency. Method returns a dataframe.

descriptive_stats.compute_descriptive_stats()

summary_stats = descriptive_stats.summary_stats()