What is the Right Syntax When Using .notnull() in Pandas?

Question

I want to use .notnull() on several columns of a dataframe to eliminate the rows which contain "NaN" values.

Let say I have the following df:

  A   B   C
0 1   1   1
1 1   NaN 1
2 1   NaN NaN
3 NaN 1   1

I tried to use this syntax but it does not work? do you know what I am doing wrong?

df[[df.A.notnull()],[df.B.notnull()],[df.C.notnull()]]

I get this Error:

TypeError: 'Series' objects are mutable, thus they cannot be hashed

What should I do to get the following output?

  A   B   C
0 1   1   1

Any idea?

Answer 1

You can simply do:

df.dropna()

Answer 2

You can first select subset of columns by df[['A','B','C']], then apply notnull and specify if all values in mask are True:

print (df[['A','B','C']].notnull())
       A      B      C
0   True   True   True
1   True  False   True
2   True  False  False
3  False   True   True

print (df[['A','B','C']].notnull().all(1))
0     True
1    False
2    False
3    False
dtype: bool

print (df[df[['A','B','C']].notnull().all(1)])
     A    B    C
0  1.0  1.0  1.0

Another solution is from Ayhan comment with dropna:

print (df.dropna(subset=['A', 'B', 'C']))
     A    B    C
0  1.0  1.0  1.0

what is same as:

print (df.dropna(subset=['A', 'B', 'C'], how='any'))

and means drop all rows, where is at least one NaN value.

Answer 3

You can apply multiple conditions by combining them with the & operator (this works not only for the notnull() function).

df[(df.A.notnull() & df.B.notnull() & df.C.notnull())]
     A    B    C
0  1.0  1.0  1.0

Alternatively, you can just drop all columns which contain NaN. The original DataFrame is not modified, instead a copy is returned.

df.dropna()