从pandas.DataFrame用复杂的条件选择 [英] Selecting with complex criteria from pandas.DataFrame

问题描述

例如，我有一个简单的DF:

For example I have simple DF:

import pandas as pd
from random import randint

df = pd.DataFrame({'A': [randint(1, 9) for x in xrange(10)],
                   'B': [randint(1, 9)*10 for x in xrange(10)],
                   'C': [randint(1, 9)*100 for x in xrange(10)]})

我可以使用Pandas的方法和惯用法从'A'中选择与B对应的值大于50的值，对于C对应的值大于900的值吗?

Can I select values from 'A' for which corresponding values for 'B' will be greater than 50, and for 'C' - not equal 900, using methods and idioms of Pandas?

推荐答案

当然！设置:

>>> import pandas as pd
>>> from random import randint
>>> df = pd.DataFrame({'A': [randint(1, 9) for x in range(10)],
                   'B': [randint(1, 9)*10 for x in range(10)],
                   'C': [randint(1, 9)*100 for x in range(10)]})
>>> df
   A   B    C
0  9  40  300
1  9  70  700
2  5  70  900
3  8  80  900
4  7  50  200
5  9  30  900
6  2  80  700
7  2  80  400
8  5  80  300
9  7  70  800

我们可以应用列操作并获取布尔Series对象:

We can apply column operations and get boolean Series objects:

>>> df["B"] > 50
0    False
1     True
2     True
3     True
4    False
5    False
6     True
7     True
8     True
9     True
Name: B
>>> (df["B"] > 50) & (df["C"] == 900)
0    False
1    False
2     True
3     True
4    False
5    False
6    False
7    False
8    False
9    False

[更新，以切换到新样式.loc]:

[Update, to switch to new-style .loc]:

然后我们可以使用它们来索引对象.对于读取访问，您可以链接索引:

And then we can use these to index into the object. For read access, you can chain indices:

>>> df["A"][(df["B"] > 50) & (df["C"] == 900)]
2    5
3    8
Name: A, dtype: int64

但是由于视图和执行写访问的副本之间的差异，您可能会遇到麻烦.您可以改用.loc:

but you can get yourself into trouble because of the difference between a view and a copy doing this for write access. You can use .loc instead:

>>> df.loc[(df["B"] > 50) & (df["C"] == 900), "A"]
2    5
3    8
Name: A, dtype: int64
>>> df.loc[(df["B"] > 50) & (df["C"] == 900), "A"].values
array([5, 8], dtype=int64)
>>> df.loc[(df["B"] > 50) & (df["C"] == 900), "A"] *= 1000
>>> df
      A   B    C
0     9  40  300
1     9  70  700
2  5000  70  900
3  8000  80  900
4     7  50  200
5     9  30  900
6     2  80  700
7     2  80  400
8     5  80  300
9     7  70  800

请注意，我不小心键入了== 900而不是!= 900或~(df["C"] == 900)，但是我懒得修复它.为读者练习. :^)

Note that I accidentally typed == 900 and not != 900, or ~(df["C"] == 900), but I'm too lazy to fix it. Exercise for the reader. :^)

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