6. Pandas系列 - 迭代

迭代DataFrame
迭代DataFrame - 遍历数据帧
- iteritems()示例
- iterrows()示例
- itertuples()示例

Pandas对象之间的基本迭代的行为取决于类型。当迭代一个系列时，它被视为数组式，基本迭代产生这些值

注意: 不要尝试在迭代时修改任何对象。迭代是用于读取，迭代器返回原始对象(视图)的副本，因此更改将不会反映在原始对象上。

迭代DataFrame

import pandas as pd
import numpy as np

N=20

df = pd.DataFrame({
    'A': pd.date_range(start='2016-01-01',periods=N,freq='D'),
    'x': np.linspace(0,stop=N-1,num=N),
    'y': np.random.rand(N),
    'C': np.random.choice(['Low','Medium','High'],N).tolist(),
    'D': np.random.normal(100, 10, size=(N)).tolist()
    })

for col in df:
   print (col)

res:

A
C
D
x

迭代DataFrame - 遍历数据帧

迭代器	details	备注
iteritems()	将列迭代(col，value)对	列值
iterrows()	将行迭代(index，value)对	行值
itertuples()	以namedtuples的形式迭代行	行pandas形式

iteritems()示例

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(4,3),columns=['col1','col2','col3'])
print df
for key,value in df.iteritems():
   print (key,value)

结果：

       col1      col2      col3
0  2.040860  3.054064  0.294766
1 -0.545032  0.484716 -0.127386
2 -0.647270  0.246625 -1.215398
3  1.236336  0.945946 -1.313925

<========================================
('col1', 0    2.040860
1   -0.545032
2   -0.647270
3    1.236336
Name: col1, dtype: float64)
('col2', 0    3.054064
1    0.484716
2    0.246625
3    0.945946
Name: col2, dtype: float64)
('col3', 0    0.294766
1   -0.127386
2   -1.215398
3   -1.313925
Name: col3, dtype: float64)

iterrows()示例

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(4,3),columns = ['col1','col2','col3'])
print df
for row_index,row in df.iterrows():
   print (row_index,row)

结果：

       col1      col2      col3
0  1.317360  0.209008 -1.406420
1 -1.410877  0.549579  0.114726
2 -0.625855  0.759171  1.128685
3 -0.726843  0.936854 -0.088602

<===================================
(0, col1    1.317360
col2    0.209008
col3   -1.406420
Name: 0, dtype: float64)
(1, col1   -1.410877
col2    0.549579
col3    0.114726
Name: 1, dtype: float64)
(2, col1   -0.625855
col2    0.759171
col3    1.128685
Name: 2, dtype: float64)
(3, col1   -0.726843
col2    0.936854
col3   -0.088602
Name: 3, dtype: float64)

itertuples()示例

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(4,3),columns = ['col1','col2','col3'])
print df
for row in df.itertuples():
    print (row)

结果：

       col1      col2      col3
0  2.344358  0.995072 -0.854100
1 -1.753913  0.116023 -0.695364
2  0.683273 -1.420054 -1.135608
3  0.704008 -0.805667 -1.470546
<==========================================
Pandas(Index=0, col1=2.344358114509865, col2=0.9950716436632336, col3=-0.8540998901850537)
Pandas(Index=1, col1=-1.753912851201583, col2=0.11602289315026405, col3=-0.6953643685628161)
Pandas(Index=2, col1=0.6832726480890194, col2=-1.4200541327635743, col3=-1.1356075254300841)
Pandas(Index=3, col1=0.7040080214990596, col2=-0.8056672789772055, col3=-1.4705455721132779)