pandas 读写常见文件性能对比

1. 背景

pandas在数据分析应用中使用频率非常高的python 库，在数据分析的过程中，读写文件是非常基础的操作，它关系到整个数据分析的性能表现。下面就用程序验证pandas 读写常见几种文件的性能表现。

2. 代码验证

import pandas as pd
import time
import numpy as np


def write_data(df):
    store = pd.HDFStore('D:\\test\\store.h5')
    start = time.time()
    store['df'] = df
    store.close()
    print(f'HDF5存储用时{time.time() - start}秒')

    start = time.time()
    df.to_csv('d:\\test\\df.csv', index=False)
    print(f'csv存储用时{time.time() - start}秒')

    start = time.time()
    df.to_pickle("D:\\test\\df.pickle")
    print(f'pickle存储用时{time.time() - start}秒')

    start = time.time()
    df.to_parquet("D:\\test\\df.parquet")
    print(f'parquet存储用时{time.time() - start}秒')

    start = time.time()
    df.to_feather("D:\\test\\df.feather")
    print(f'feather存储用时{time.time() - start}秒')





def read_data():
    start = time.time()
    store = pd.HDFStore('d:\\test\\store.h5', mode='r')
    df1 = store.get('df')
    print(f'HDF5读取用时{time.time() - start}秒')
    store.close()

    start = time.time()
    df1 = pd.read_csv('d:\\test\\df.csv')
    print(f'csv读取用时{time.time() - start}秒')

    start = time.time()
    df1 = pd.read_pickle('d:\\test\\df.pickle')
    print(f'pickle读取用时{time.time() - start}秒')

    start = time.time()
    df1 = pd.read_parquet('d:\\test\\df.parquet')
    print(f'parquet读取用时{time.time() - start}秒')

    start = time.time()
    df1 = pd.read_feather('d:\\test\\df.feather')
    print(f'feather读取用时{time.time() - start}秒')

if __name__ == '__main__':
    # 生成1亿条5列的随机数据
    data = pd.DataFrame(np.random.rand(100000000, 5))
    write_data(data)
    read_data()

3. 运行结果

从以上表格中可以得出hdf5，pickle，parquet等格式的读写性能以及空间占用均比较好，当进行大量数据的分析时可以考虑使用这些格式。