代码
- 一万行测试
# -*- coding: utf-8 -*-
# @Author : zbz
import time
import pandas as pd
curr = lambda: time.time()
file = "./files/person.xlsx"
df = pd.DataFrame(pd.read_excel(file, engine="openpyxl"))
begin = curr()
cols = df.columns.tolist()
for val in df.itertuples(index=False):
vals = list(val)
docu = {k: v for k, v in zip(cols, vals)}
print("{} cons {}".format("itertuples".ljust(20), curr() - begin))
begin = curr()
for i, ser in df.iterrows():
docu = dict(ser)
print("{} cons {}".format("iterrows".ljust(20), curr() - begin))
结果

结论
- 使用itertuples方法要快的多
本文通过实际代码演示了在Python中使用Pandas库处理Excel文件时,itertuples与iterrows两种不同数据迭代方法的效率差异,并得出itertuples方法在性能上更胜一筹的结论。
1万+





