def att_dtype(column, types, info):
    for np_type in types:
        if column.min() > info(np_type).min and column.max() < info(np_type).max:
            column = column.astype(np_type)
    return column
def reduce_mem_usage(column, verbose=True):
    col_type = str(column.dtype)
    if 'int' in col_type:
        np_int = [np.int8, np.int16, np.int32, np.int64][::-1]
        info = np.iinfo
        att_column = att_dtype(column, np_int, info)
    elif 'float' in col_type:
        np_float = [np.float16, np.float32, np.float64][::-1]
        info = np.finfo
        att_column = att_dtype(column, np_float, info) 
    if verbose:
        start_mem = column.memory_usage() / 1024**2  
        end_mem = att_column.memory_usage() / 1024**2
        reduction = 100 * (start_mem - end_mem) / start_mem
        print(f'Column {column.name} to type {str(att_column.dtype)} with {reduction}% reduction')
    return att_column
df = df.apply(reduce_mem_usage, axis = 1)
df.dtypes