comparing dtype [float64] array with a scalar of type [bool] in Pandas DataFrame

问题:

I have a Pandas DF where I need to filter out some rows that contains values == 0 for feature 'a' and feature 'b'.

In order to inspect the values, I run the following:

DF1 = DF[DF['a'] == 0] 

Which returns the right values. Similarly, by doing this:

DF2 = DF[DF['b'] == 0] 

I can see the 0 values for feature 'b'.

However, if I try to combine these 2 in a single line of code using the OR operand:

DF3 = DF[DF['a'] == 0 |  DF['b'] == 0] 

I get this:

TypeError: cannot compare a dtyped [float64] array with a scalar of type [bool] 

What's happening here?

回答1:

You can transform either column 'a' or 'b' so they are both either float64 or bool. However, an easier solution that preserves the data type of your features is this:

DF3 = DF[(DF['a'] == 0) |  (DF['b'] == 0)] 

A common operation is the use of boolean vectors to filter the data. The operators are: | for or, & for and, and ~ for not. These must be grouped by using parentheses.

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