python学习——pandas

iterrows，iteritems ，itertuples区别

iterrows

Iterate over DataFrame rows as (index, Series) pairs.
迭代(iterate)覆盖整个DataFrame的行中，返回(index, Series)对，以实现对整个Data进行遍历。
官方文档的例子

>>> 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

借鉴官方文档，测试以下代码

>>>df = pd.DataFrame([[1,2.1,3],[4,5,6],[7,8,9]],index = 
['1','2','3'],columns = ['A','B','C'])
>>>df
   A    B  C
1  1  2.1  3
2  4  5.0  6
3  7  8.0  9
>>>a = df.iterrows()
>>>next(a)
('1', A    1.0
B    2.1
C    3.0
Name: 1, dtype: float64)
>>>next(a)[1]
A    4.0
B    5.0
C    6.0
Name: 2, dtype: float64
>>>next(a)[1]
A    7.0
B    8.0
C    9.0
Name: 3, dtype: float64

iterrows生成的一个由(index, Series)对组成的一个迭代器，第一次next(a)时返回第一行，index = 1，Series =

A    4.0
B    5.0
C    6.0

由于DataFrame中2.1的存在，生成的迭代器会将所有value统一为float型，

iteritems

Iterator over (column name, Series) pairs.
与iterrows类似，对整个DataFrame遍历，生成一个由（column name ,Series）对组成的一个迭代器

测试代码如下

>>>df = pd.DataFrame([[1,2.1,3],[4,5,6],[7,8,9]],index = 
['1','2','3'],columns = ['A','B','C'])
>>>df
   A    B  C
1  1  2.1  3
2  4  5.0  6
3  7  8.0  9
>>>b = df.iteritems()
>>>next(b)
('A', 1    1
2    4
3    7
Name: A, dtype: int64)
>>>next(b)[1]
1    2.1
2    5.0
3    8.0
Name: B, dtype: float64
>>>next(b)[1]
1    3
2    6
3    9
Name: C, dtype: int64

官方文档中提到“Because iterrows returns a Series for each row, it does not preserve dtypes across the rows (dtypes are preserved across columns for DataFrames). ”
DataFrame行方向数据格式统一，列方向的数据格式保持不变。

itertuples

Iterate over DataFrame rows as namedtuples, with index value as first element of the tuple.

>>> df = pd.DataFrame({'col1': [1, 2], 'col2': [0.1, 0.2]},
                      index=['a', 'b'])
>>> df
   col1  col2
a     1   0.1
b     2   0.2
>>> for row in df.itertuples():
...     print(row)
...
Pandas(Index='a', col1=1, col2=0.10000000000000001)
Pandas(Index='b', col1=2, col2=0.20000000000000001)