Is this the correct behavior for read_csv and the data value is NA?

Question

(I opened issue on GitHub.)

The following behavior does not seem right to me. It seems that if the default value for read_csvis equal na_values=False, then no values, including "NA", should be interpreted as NaN, but this is not so.

This behavior was seen in this post (see comments on @JianxunLi's answer), where "NA" actually means "North America." Actually, I cannot find a way to read this without changing it to NaN, and there must definitely be some way to do this.

Here is an example of csv.

%more foo.txt
x,y
"NA",NA
"foo",foo

I include "NA" both in quotation marks and externally to find out if that matters, but as you can see below, it doesn't look like that.

pd.read_csv('foo.txt')
Out[56]: 
     x    y
0  NaN  NaN
1  foo  foo

pd.read_csv('foo.txt',na_values=False)
Out[57]: 
     x    y
0  NaN  NaN
1  foo  foo

pd.read_csv('foo.txt',na_values='foo')
Out[58]: 
    x   y
0 NaN NaN
1 NaN NaN

It appears that the data values ​​of "NaN" are processed in the same way as "NA".

Edit to add: I think I better understand this based on @ Marius's answer, although it really doesn't seem right to me (the default behavior, that is, not Marius's answer, which seems to be the correct explanation of what is happening).

na_values=False    =>   NA and NaN are treated as NaN
na_values='foo'    =>   NA, NaN, and foo are treated as NaN

I think I can understand that this is the default behavior in a column of numbers, but it looks like it should not be the default for a row column. I would also really like to understand this from the documentation without seeing Marius' answer.

Change to add (2):

, , Stata Excel, "NA" , NaN/missing. , , pandas ?

Answer 1

keep_default_na=False, , na_values, NA, . NA, NaN:

pd.read_csv('foo.txt', keep_default_na=False)
Out[5]: 
     x    y
0   NA   NA
1  foo  foo