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Pandas Profiling Tutorial

February 2018

Introduction

  • Library, which creates HTML profiling reports form pandas DataFrame objects
  • Similar to df.describe() but on steroids
  • For each column in a DataFrame, it provides the following statistics - if relevant for the column type:
    • Essentials: type, unique values, missing values
    • Quantile statistics like minimum value, Q1, median, Q3, maximum, range, interquartile range
    • Descriptive statistics like mean, mode, standard deviation, sum, median absolute deviation, coefficient of variation, kurtosis, skewness
    • Most frequent values
    • Histogram
    • Correlations highlighting of highly correlated variables, Spearman and Pearson matrixes
  • Repo: https://github.com/pandas-profiling/pandas-profiling

Installation

  • pip: pip install pandas-profiling
  • conda: conda install pandas-profiling

Usage

  • Recommends using Jupyter Notebook for pandas profiling:
import pandas as pd
import pandas_profiling
df = pd.read_csv('/Users/chrisfeller/Desktop/sandbox/Meteorite_Landings.csv')
pandas_profiling.ProfileReport(df)
  • To save the HTML report file:
profile = pandas_profiling.ProfileReport(df)
profile.to_file(outputfile='/Users/chrisfeller/Desktop/sandbox/profile.html')