How to iterate through rows of a dataframe and check whether value in a column row is NaN

Question

I have a beginner question. I have a dataframe I am iterating over and I want to check if a value in a column2 row is NaN or not, to perform an action on this value if it is not NaN. My DataFrame looks like this:

df:

  Column1  Column2
0    a        hey
1    b        NaN
2    c        up

What I am trying right now is:

for item, frame in df['Column2'].iteritems():
    if frame.notnull() == True:
        print 'frame'

The thought behind that is that I iterate over the rows in column 2 and print frame for every row that has a value (which is a string). What I get however is this:

AttributeError                            Traceback (most recent call last)
<ipython-input-80-8b871a452417> in <module>()
      1 for item, frame in df['Column2'].iteritems():
----> 2     if frame.notnull() == True:
      3         print 'frame'

AttributeError: 'float' object has no attribute 'notnull'

When I only run the first line of my code, I get

0
hey
1
nan
2
up

which suggests that the floats in the output of the first line are the cause of the error. Can anybody tell me how I can accomplish what I want?

Answer 1

As you already understand , frame in

for item, frame in df['Column2'].iteritems():

is every row in the Column, its type would be the type of elements in the column (which most probably would not be Series or DataFrame). Hence, frame.notnull() on that would not work.

You should instead try -

for item, frame in df['Column2'].iteritems():
    if pd.notnull(frame):
        print frame

Answer 2

try this:

df[df['Column2'].notnull()]

The above code will give you the data for which Column2 has not null value

Answer 3

Using iteritems on a Series (which is what you get when you take a column from a DataFrame) iterates over pairs (index, value). So your item will take the values 0, 1, and 2 in the three iterations of the loop, and your frame will take the values 'hey', NaN, and 'up' (so "frame" is probably a bad name for it). The error comes from trying to use the method notnull on NaN (which is represented as a floating-point number).

You can use the function pd.notnull instead:

In [3]: pd.notnull(np.nan)
Out[3]: False

In [4]: pd.notnull('hey')
Out[4]: True

Another way would be to use notnull on the whole Series, and then iterate over those values (which are now boolean):

for _, value in df['Column2'].notnull().iteritems():
    if value:
        print 'frame'