📅  最后修改于: 2020-10-29 02:05:51             🧑  作者: Mango
如果要遍历DataFrame以对每行执行一些操作,则可以在Pandas中使用iterrows()函数。
Pandas 使用三个函数来遍历DataFrame的行,即iterrows(),iteritems()和itertuples()。
iterrows()负责遍历DataFrame的每一行。它返回一个迭代器,该迭代器包含作为系列的每一行的索引和数据。
我们具有下一个函数来查看迭代器的内容。
此函数返回每个索引值以及包含每一行数据的序列。
import pandas as pd
import numpy as np
info = pd.DataFrame(np.random.randn(4,2),columns = ['col1','col2'])
for row_index,row in info.iterrows():
print (row_index,row)
输出量
0 name John
degree B.Tech
score 90
Name: 0, dtype: object
1 name Smith
degree B.Com
score 40
Name: 1, dtype: object
2 name Alexander
degree M.Com
score 80
Name: 2, dtype: object
3 name William
degree M.Tech
score 98
Name: 3, dtype: object
# importing pandas module
import pandas as pd
# making data frame from csv file
data = pd.read_csv("aa.csv")
for i, j in data.iterrows():
print(i, j)
print()
输出量
0 Name Hire Date Salary Leaves Remaining 0 John Idle 03/15/14 50...
Name: 0, dtype: object
1 Name Hire Date Salary Leaves Remaining 1 Smith Gilliam 06/01/15 65000...
Name: 1, dtype: object
2 Name Hire Date Salary Leaves Remaining 2 Parker Chapman 05/12/14 45000.0 ...
Name: 2, dtype: object
3 Name Hire Date Salary Leaves Remaining 3 Jones Palin 11/01/13 700...
Name: 3, dtype: object
4 Name Hire Date Salary Leaves Remaining 4 Terry Gilliam 08/12/14 4800...
Name: 4, dtype: object
5 Name Hire Date Salary Leaves Remaining 5 Michael Palin 05/23/13 66000...
Name: 5, dtype: object