replace_all_NaN.py

def replace_all_NaN(df):
    """ If you are confident that numbers can be replaced with 0 and
    objects can be replaced by No_columnname this function will do that over the whole data frame
    will add more data types as I come across them. It prints info() when finished to check it 
    has captured them all"""
    for col in df:
        if df[col].dtype == 'object' and df[col].isna().sum() > 0:
            df[col] = df[col].fillna('No_' + col)
        elif df[col].dtype == 'float64' and df[col].isna().sum() > 0:
            df[col] = df[col].fillna(0.0)
        elif df[col].dtype == 'int64' and df[col].isna().sum() > 0:
            df[col] = df[col].fillna(0)
        elif df[col].isna().sum() > 0:
            print('Uncoded dtype({}), please update function'.format(df[col].dtype))
            
    print(df.info(verbose=True))
    return df