从 pandas.DataFrame 中选择复杂的标准 [英] Selecting with complex criteria from pandas.DataFrame

问题描述

例如我有简单的 DF:

将pandas导入为pd来自随机导入 randintdf = 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 的方法和习惯用法从 'A' 中选择值，其中 'B' 的对应值将大于 50，而 'C' 的对应值不等于 900?

解决方案

好的！设置:

<预><代码>>>>将熊猫导入为 pd>>>来自随机导入 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乙丙0 9 40 3001 9 70 7002 5 70 9003 8 80 9004 7 50 2005 9 30 9006 2 80 7007 2 80 4008 5 80 3009 7 70 800

我们可以应用列操作并获得布尔系列对象:

<预><代码>>>>df["B"] >500 错误1 真2 真3 真4 错误5 错误6 真7 真8 真9 真姓名:乙>>>(df["B"] > 50) &(df["C"] == 900)0 错误1 错误2 真3 真4 错误5 错误6 错误7 错误8 错误9 错误

[更新，切换到新样式.loc]:

然后我们可以使用这些来索引对象.对于读取访问，您可以链接索引:

<预><代码>>>>df["A"][(df["B"] > 50) &(df["C"] == 900)]2 53 8名称:A，数据类型:int64

但是由于视图和副本之间的差异，您可能会遇到麻烦，这样做是为了写访问.您可以使用 .loc 代替:

<预><代码>>>>df.loc[(df["B"] > 50) &(df["C"] == 900), "A"]2 53 8名称:A，数据类型:int64>>>df.loc[(df["B"] > 50) &(df["C"] == 900), "A"].values数组([5, 8], dtype=int64)>>>df.loc[(df["B"] > 50) &(df["C"] == 900), "A"] *= 1000>>>df乙丙0 9 40 3001 9 70 7002 5000 70 9003 8000 80 9004 7 50 2005 9 30 9006 2 80 7007 2 80 4008 5 80 3009 7 70 800

请注意，我不小心输入了 == 900 而不是 != 900，或者 ~(df["C"] == 900)，但我懒得修.为读者练习.:^)

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)]})

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?

解决方案

Sure! Setup:

>>> 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

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

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

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

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

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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