如何使用applymap，lambda和dataframe一起过滤/修改python中的数据帧？

Question

So I 'm trying to figure it out how to replace NaN in pd.DataFrame .. ? Here in the example I created the 3x3 dataframe, having value in df[1][2] = 'a' and the rest are NaN

My understanding is that I can use if with in lambda and do something below. But the result was not what I expected, it overwrite 'a' . I still want 'a' to remain as it is and only change to 'o' where is NaN ... Any recommendation would be appreciated.

df = pd.DataFrame(index=range(0,3),columns=range(0,3))
df[1][2] = 'a'
f = lambda x: 'o' if np.nan else x
df.applymap(f)

Answer 1

Instead of using apply , you could use fillna .

df.fillna('o')

For more information about Working with missing data . You could also use apply with pd.isnull() as mentaioned in @Psidom answer. But in this case, you should really use the built-in function fillna .

Answer 2

np.nan does not really return the value you are expecting, in order to create a valid predicate, you can use pd.isnull :

df = pd.DataFrame(index=range(0,3),columns=range(0,3))
df[1][2] = 'a'
f = lambda x: 'o' if pd.isnull(x) else x
df.applymap(f)

#   0   1   2
#0  o   o   o
#1  o   o   o
#2  o   a   o

To see why it is happening, you can convert np.nan to boolean:

bool(np.nan)
# True

So it is always returning true value thus all values in the data frame will be replaced by o .