在 Pandas DataFrame 中设置最大值（上限）
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/40836208/
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
Set maximum value (upper bound) in pandas DataFrame
提问by elPastor
I'm trying to set a maximum value of a pandas DataFrame column. For example:
我正在尝试设置 pandas DataFrame 列的最大值。例如：
my_dict = {'a':[10,12,15,17,19,20]}
df = pd.DataFrame(my_dict)
df['a'].set_max(15)
would yield:
会产生：
a
0 10
1 12
2 15
3 15
4 15
5 15
But it doesn't.
但事实并非如此。
There are a million solutions to findthe maximum value, but nothing to setthe maximum value... at least that I can find.
有一百万个解决方案可以找到最大值，但没有设置最大值......至少我能找到。
I could iterate through the list, but I suspect there is a faster way to do it with pandas. My lists will be significantly longer and thus I would expect iteration to take relatively longer amount of time. Also, I'd like whatever solution to be able to handle NaN.
我可以遍历列表，但我怀疑有一种更快的方法可以用 Pandas 来完成。我的列表会更长，因此我希望迭代需要相对更长的时间。另外，我想要任何能够处理NaN.
回答by Psidom
I suppose you can do:
我想你可以这样做：
maxVal = 15
df['a'].where(df['a'] <= maxVal, maxVal) # where replace values with other when the
# condition is not satisfied
#0 10
#1 12
#2 15
#3 15
#4 15
#5 15
#Name: a, dtype: int64
Or:
或者：
df['a'][df['a'] >= maxVal] = maxVal
回答by tommy.carstensen
回答by cs95
numpy.clipis a good, fast alternative.
numpy.clip是一个很好的、快速的替代方案。
df
a
0 10
1 12
2 15
3 17
4 19
5 20
np.clip(df['a'], a_max=15, a_min=None)
0 10
1 12
2 15
3 15
4 15
5 15
Name: a, dtype: int64
# Or,
np.clip(df['a'].to_numpy(), a_max=15, a_min=None)
# array([10, 12, 15, 15, 15, 15])
From v0.21 onwards, you can also use DataFrame.clip_upper.
从 v0.21 开始，您还可以使用DataFrame.clip_upper.
Note
This method (along withclip_lower) has been deprecated from v0.24 and will be removed in a future version.
注意
此方法（连同clip_lower）已从 v0.24 中弃用，并将在未来版本中删除。
df.clip_upper(15)
# Or, for a specific column,
df['a'].clip_upper(15)
a
0 10
1 12
2 15
3 15
4 15
5 15
In similar vein, if you only want to set the lower bound, use DataFrame.clip_lower. These methods are also avaliable on Seriesobjects.
同样，如果您只想设置下限，请使用DataFrame.clip_lower. 这些方法也可用于Series对象。
