Fill_NaNs_Catigorical.py

valueCounts = {}
def CountAll():
    global all_columns, nanCounts, valueCounts
    all_columns = list(df)
    nanCounts = df.isnull().sum()
    for x in all_columns:
        valueCounts[x] = df[x].value_counts()        
"""-------------------------------------------------------------------------"""

def Fill_NaNs_Catigorical(col): 
    """Calculating probability and expected value."""
    proportion = np.array(valueCounts[col].values) / valueCounts[col].sum() * nanCounts[col]
    proportion = np.around(proportion).astype('int')
    
    """Adjusting proportion."""
    diff = int(nanCounts[col] - np.sum(proportion))
    if diff > 0:
        for x in range(diff):
            idx = random.randint(0, len(proportion) - 1)
            proportion[idx] =  proportion[idx] + 1
    else:
        diff = -diff
        while(diff != 0):
            idx = random.randint(0, len(proportion) - 1)
            if proportion[idx] > 0:
                proportion[idx] =  proportion[idx] - 1
                diff = diff - 1
        
    """Filling NaNs."""
    nan_indexes = df[df[col].isnull()].index.tolist() 
    for x in range(len(proportion)):
        if proportion[x] > 0:
            random_subset = random.sample(population = nan_indexes, k = proportion[x])
            df.loc[random_subset, col] = valueCounts[col].keys()[x]
            nan_indexes = list(set(nan_indexes) - set(random_subset))