np.where 在我的 Pandas 中不起作用
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np.where Not Working in my Pandas
提问by Windstorm1981
I have an np.where problem using Pandas that is driving me crazy and I can't seem to solve through Google, the documentation, etc.
我在使用 Pandas 时遇到了 np.where 问题，这让我发疯了，我似乎无法通过 Google、文档等解决。
I'm hoping someone has insight. I'm sure it isn't complex.
我希望有人有洞察力。我相信它并不复杂。
I have a df where I'm checking the value in one column - and if that value is 'n/a' (as a string, not as in .isnull()), changing it to another value.
我有一个 df，我在其中检查一列中的值 - 如果该值是“n/a”（作为字符串，而不是 .isnull()），则将其更改为另一个值。
Full_Names_Test_2['MarketCap'] == 'n/a'
Full_Names_Test_2['MarketCap'] == 'n/a'
returns:
返回：
70 True
88 False
90 True
145 True
156 True
181 True
191 True
200 True
219 True
223 False
Name: MarketCap, dtype: bool
so that part works.
所以那部分工作。
but this:
但是这个：
Full_Names_Test_2['NewColumn'] = np.where(Full_Names_Test_2['MarketCap'] == 'n/a', 7)
returns:
返回：
ValueError: either both or neither of x and y should be given
What is going on?
到底是怎么回事？
回答by Andy Hayden
You need to pass the boolean mask and the (two) values columns:
您需要传递布尔掩码和（两个）值列：
np.where(Full_Names_Test_2['MarketCap'] == 'n/a', 7)
# should be
np.where(Full_Names_Test_2['MarketCap'] == 'n/a', Full_Names_Test_2['MarketCap'], 7)
See the np.wheredocs.
查看np.where文档。
or alternatively use the whereSeries method:
或者可替换地使用的where系列方法：
Full_Names_Test_2['MarketCap'].where(Full_Names_Test_2['MarketCap'] == 'n/a', 7)
