pandas 在列值和常量全局值之间取最小值
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/33689714/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
take minimum between column value and constant global value
提问by EdChum
I would like create new column for given dataframe where I calculate minimum between the column value and some global value (in this example 7). so my df has the columns sessionand noteand my desired output column is minValue:
我想为给定的数据框创建新列，在其中计算列值和某个全局值之间的最小值（在本例中为 7）。所以我的DF具有列session和note我期望的输出列minValue：
session note minValue
1 0.726841 0.726841
2 3.163402 3.163402
3 2.844161 2.844161
4 NaN NaN
I'm using the built in Python method min:
我正在使用内置的 Python 方法min：
df['minValue']=min(7, df['note'])
and I have this error:
我有这个错误：
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
回答by EdChum
Use np.minimum:
使用np.minimum：
In [341]:
df['MinNote'] = np.minimum(1,df['note'])
df
Out[341]:
session note minValue MinNote
0 1 0.726841 0.726841 0.726841
1 2 3.163402 3.163402 1.000000
2 3 2.844161 2.844161 1.000000
3 4 NaN NaN NaN
Also mindoesn't understand array-like comparisons hence your error
也min不能理解阵列喜欢攀比，因此你的错误
回答by Marc Garcia
The preferred way to do this in pandasis to use the Series.clip()method.
执行此操作的首选方法pandas是使用该Series.clip()方法。
In your example:
在你的例子中：
import pandas
df = pandas.DataFrame({'session': [1, 2, 3, 4],
'note': [0.726841, 3.163402, 2.844161, float('NaN')]})
df['minVaue'] = df['note'].clip(upper=1.)
df
Will return:
将返回：
note session minVaue
0 0.726841 1 0.726841
1 3.163402 2 1.000000
2 2.844161 3 1.000000
3 NaN 4 NaN
numpy.minimumwill also work, but .clip()has some advantages:
numpy.minimum也可以工作，但.clip()有一些优点：
- It is more readable
- You can apply simultaneously lower and upper bounds:
df['note'].clip(lower=0., upper=10.) - You can pipe it with other methods:
df['note'].abs().clip(upper=1.).round()
- 它更具可读性
- 您可以同时应用下限和上限：
df['note'].clip(lower=0., upper=10.) - 你可以用其他方法管道它：
df['note'].abs().clip(upper=1.).round()
