更多内容参考：pandas官方文档
Pandas的基础结构可以分为两种：数据框和序列。数据框是拥有轴标签的二维链表，换言之数据框是拥有标签的行和列组成的矩阵 - 列标签位列名，行标签为索引。Pandas中的行和列是Pandas序列 - 拥有轴标签的一维链表。
iterrows()是在数据框中的行进行迭代的一个生成器，它返回每行的索引及一个包含行本身的对象。
示例：
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(10, 4), columns=list('ABCD'))
df
| A | B | C | D | |
|---|---|---|---|---|
| 0 | -0.961305 | -0.236265 | 0.232204 | -0.707088 |
| 1 | -0.898214 | 0.514331 | 0.290273 | 0.224386 |
| 2 | -0.290242 | -1.134433 | -1.891123 | 1.121723 |
| 3 | -0.654990 | 0.208918 | 1.443723 | -0.357779 |
| 4 | -1.264342 | -0.555124 | -1.236119 | 0.791272 |
| 5 | -0.579388 | 0.607004 | -0.231509 | 1.061788 |
| 6 | -2.189155 | -0.375116 | -1.213247 | -0.127200 |
| 7 | 0.018989 | 0.152848 | 0.586678 | -0.629936 |
| 8 | 0.843229 | 1.489267 | 0.353259 | -1.073245 |
| 9 | 0.648877 | 2.103916 | 0.053645 | 1.940579 |
for i,r in df.iterrows():
print(i,r)
0 A -0.961305 B -0.236265 C 0.232204 D -0.707088 Name: 0, dtype: float64 1 A -0.898214 B 0.514331 C 0.290273 D 0.224386 Name: 1, dtype: float64 2 A -0.290242 B -1.134433 C -1.891123 D 1.121723 Name: 2, dtype: float64 3 A -0.654990 B 0.208918 C 1.443723 D -0.357779 Name: 3, dtype: float64 4 A -1.264342 B -0.555124 C -1.236119 D 0.791272 Name: 4, dtype: float64 5 A -0.579388 B 0.607004 C -0.231509 D 1.061788 Name: 5, dtype: float64 6 A -2.189155 B -0.375116 C -1.213247 D -0.127200 Name: 6, dtype: float64 7 A 0.018989 B 0.152848 C 0.586678 D -0.629936 Name: 7, dtype: float64 8 A 0.843229 B 1.489267 C 0.353259 D -1.073245 Name: 8, dtype: float64 9 A 0.648877 B 2.103916 C 0.053645 D 1.940579 Name: 9, dtype: float64
其他链接：https://python.freelycode.com/contribution/detail/1083
https://stackoverflow.com/questions/44506473/pandas-df-iterrows-method-to-access-a-set-number-of-rows