iterate through all the columns of a dataframe and modify the data type
        to reduce memory usage.

def reduce_mem_usage(df):
    """ iterate through all the columns of a dataframe and modify the data type
        to reduce memory usage.
    """
    start_mem = df.memory_usage().sum() / 1024**2
    print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))

    #Removed from debugging
    columns = df.columns
    #.drop('index')

    for col in columns:
        col_type = df[col].dtype
        if str(col_type) != 'category' and col_type != 'datetime64[ns]' and col_type != bool:
            if col_type != object:
                c_min = df[col].min()
                c_max = df[col].max()
                if str(col_type)[:3] == 'int':
                    if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:
                        df[col] = df[col].astype(np.int8)
                    elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:
                        df[col] = df[col].astype(np.int16)
                    elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:
                        df[col] = df[col].astype(np.int32)
                    elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:
                        df[col] = df[col].astype(np.int64)
                else:
                    #if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:
                        #df[col] = df[col].astype(np.float16)
                    #Sometimes causes and error and had to remove
                    if c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:
                        df[col] = df[col].astype(np.float32)
                    else:
                        print('Error '+col+' Value would be a float64. Disregarding.')
            else:
                df[col] = df[col].astype('category')

    end_mem = df.memory_usage().sum() / 1024**2
    print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))
    print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))

    return df