To ignore missing values and change to int type

Asked 2 years ago, Updated 2 years ago, 43 views

Hello, I have the following data frame.

    score
0   0.0
1   9.0
2   NaN
3   1.0
4   4.0
... ... 
211 10.0
212 4.0
213 1.0
214 6.0
215 3.0

It's in float form, but I wanted to get rid of the decimal point in integer form, so I tried to convert it to int type, but there was a missing value, so I got an error. I have to visualize it, so it has to be numeric, so is there a way to get rid of the decimal point?

python pandas

2022-09-20 16:41

2 Answers

I didn't upload the code, so I'm only telling you the approximate direction.

In general, int ('str object') causes an error if the object is a non-numeric character.

For this, you should refer to the following methods.

try:
    n = int(a)
except:
    n = 0


2022-09-20 16:41

Use fillna to change to a specific value (for example, -1000) and convert the column to an integer. After that, you can change the specific value that you changed nan back to the desired value and deal with it.


2022-09-20 16:41

If you have any answers or tips


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