Why does testing `NaN == NaN` not work for dropping from a pandas dataFrame?
Solution 1
You should use isnull
and notnull
to test for NaN (these are more robust using pandas dtypes than numpy), see "values considered missing" in the docs.
Using the Series method dropna
on a column won't affect the original dataframe, but do what you want:
In [11]: df
Out[11]:
comments
0 VP
1 VP
2 VP
3 TEST
4 NaN
5 NaN
In [12]: df.comments.dropna()
Out[12]:
0 VP
1 VP
2 VP
3 TEST
Name: comments, dtype: object
The dropna
DataFrame method has a subset argument (to drop rows which have NaNs in specific columns):
In [13]: df.dropna(subset=['comments'])
Out[13]:
comments
0 VP
1 VP
2 VP
3 TEST
In [14]: df = df.dropna(subset=['comments'])
Solution 2
You need to test NaN
with math.isnan()
function (Or numpy.isnan
). NaNs cannot be checked with the equality operator.
>>> a = float('NaN')
>>> a
nan
>>> a == 'NaN'
False
>>> isnan(a)
True
>>> a == float('NaN')
False
Help Function ->
isnan(...)
isnan(x) -> bool
Check if float x is not a number (NaN).
idoda
Updated on July 09, 2022Comments
-
idoda almost 2 years
Please explain how NaN's are treated in pandas because the following logic seems "broken" to me, I tried various ways (shown below) to drop the empty values.
My dataframe, which I load from a CSV file using
read.csv
, has a columncomments
, which is empty most of the time.The column
marked_results.comments
looks like this; all the rest of the column is NaN, so pandas loads empty entries as NaNs, so far so good:0 VP 1 VP 2 VP 3 TEST 4 NaN 5 NaN ....
Now I try to drop those entries, only this works:
marked_results.comments.isnull()
All these don't work:
marked_results.comments.dropna()
only gives the same column, nothing gets dropped, confusing.marked_results.comments == NaN
only gives a series of allFalse
s. Nothing was NaNs... confusing.- likewise
marked_results.comments == nan
I also tried:
comments_values = marked_results.comments.unique() array(['VP', 'TEST', nan], dtype=object) # Ah, gotya! so now ive tried: marked_results.comments == comments_values[2] # but still all the results are Falses!!!