dataframe_explo.py

import pandas as pd
import numpy as np

def describe_df(df, cols=None, max_distinct=10):
    """Describe columns of given dataframe.
    
    Return a dataframe with column name, type, nb of distinct values and nb of missing values
    Missing values are counted as one distinct value.
    
    Issue: if a column contains integer and missing values, pandas cast it in float and add .0 to the value.
    This misleads type column and distinct values.
    
    Parameters
    ----------
    df : pandas dataframe 
    cols : iterable, {str}, optional
        list of column names, if None (default) consider all columns
    
    Returns
    -------
    df : dataframe
        dataframe with descriptive figures for each columns.
    
    """
    if cols is None:
        cols = df.columns
    else:
        unknown_cols = set(cols) - set(df.columns)
        
        # presence of unknown columns
        if len(unknown_cols) != 0:
            print("Unkown columns:", unknown_cols)
            return

    typ = []
    nb_missing = []
    nb_distinct = []
    distinct_values = []
    
    for col in cols:
        # type
        typ.append(df[col].dtype)
        
        # distinct values
        distincts = df[col].unique()
        nb_dist = len(distincts)
        nb_distinct.append(nb_dist)
        if nb_dist <= max_distinct:
            distinct_values.append(distincts)
        else:
            distinct_values.append(None)
        
        # missing values
        if df[col].dtype is 'object':
            nb_missing.append((df[col] == '').sum() + (df[col] is None).sum())
        else:
            nb_missing.append(df[col].isnull().sum())
            
    result = pd.DataFrame({'column': cols, 'type': typ, 'nb_missing': nb_missing, 'nb_distinct': nb_distinct, 'distinct_values': distinct_values})
    return result[['column', 'type', 'nb_missing', 'nb_distinct', 'distinct_values']]