DataFrame.iterrows的一种用法

import pandas as pd

import numpy as np

help(pd.DataFrame.iterrows)

Help on function iterrows in module pandas.core.frame:

iterrows(self)
Iterate over DataFrame rows as (index, Series) pairs.

Notes
-----

1. Because iterrows returns a Series for each row,
it does not preserve dtypes across the rows (dtypes are
preserved across columns for DataFrames). For example,

>>> df = pd.DataFrame([[1, 1.5]], columns=['int', 'float'])
>>> row = next(df.iterrows())[1]
>>> row
int 1.0
float 1.5
Name: 0, dtype: float64
>>> print(row['int'].dtype)
float64
>>> print(df['int'].dtype)
int64

To preserve dtypes while iterating over the rows, it is better
to use :meth:itertuples which returns namedtuples of the values
and which is generally faster than iterrows.

2. You should never modify something you are iterating over.
This is not guaranteed to work in all cases. Depending on the
data types, the iterator returns a copy and not a view, and writing
to it will have no effect.

Returns
-------
it : generator
A generator that iterates over the rows of the frame.

See also
--------
itertuples : Iterate over DataFrame rows as namedtuples of the values.
iteritems : Iterate over (column name, Series) pairs.

​

运用iterrows()返回的index和row,其中index是行索引，row是包含改行信息的Series的迭代器。运用这个方法，可以一行一行的增加特殊要求的列（前提是首先初始化该特殊要求的列）

xx=np.random.randint(9,size=(6,3))

tests=pd.DataFrame(xx,columns=['one','two','three']);tests

	one	two	three
0	3	0	4
1	1	0	3
2	1	4	4
3	7	3	2
4	7	5	0
5	5	8	8

现在我们想加上一列，这一列的要求如下：如果同行的'one'+'two'+'three'是奇数，则写上奇数，如果是偶数，则写上偶数。

tests['special']='ini'

for index,row in tests.iterrows():
    num=(row.values[:-1]).sum()
    if num%2 :
        row['special']='奇数'
    else:
        row['special']='偶数'
    tests.loc[index]=row          #将Series 迭代器row赋值给tests的index行

tests

	one	two	three	special
0	3	0	4	奇数
1	1	0	3	偶数
2	1	4	4	奇数
3	7	3	2	偶数
4	7	5	0	偶数
5	5	8	8	奇数