pandas读取数据时降低内存使用

pandas读取数据时降低内存使用

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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))
for col in df.columns:
col_type = df[col].dtype

    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)
            elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:
                df[col] = df[col].astype(np.float32)
            else:
                df[col] = df[col].astype(np.float64)
    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

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def import_data(file):
    """create a dataframe and optimize its memory usage"""
    df = pd.read_csv(file, parse_dates=True, keep_date_col=True)
    df = reduce_mem_usage(df)
    return df