Find difference between 2 columns with Nulls using pandas

Question

I want to find the difference between 2 columns of type int in a pandas DataFrame. I am using python 2.7. The columns are as below -

>>> df
   INVOICED_QUANTITY  QUANTITY_SHIPPED
0                 15               NaN
1                 20               NaN
2                  7               NaN
3                  7               NaN
4                  7               NaN

Now, I want to subtract QUANTITY_SHIPPED from INVOICED_QUANTITY & I do the below-

>>> df['Diff'] = df['QUANTITY_INVOICED'] - df['SHIPPED_QUANTITY']
>>> df
   QUANTITY_INVOICED  SHIPPED_QUANTITY  Diff
0                 15               NaN   NaN
1                 20               NaN   NaN
2                  7               NaN   NaN
3                  7               NaN   NaN
4                  7               NaN   NaN

How do I take care of the NaN's? I would like to get the below as result as I want NaN's to be treated as 0 (zero)-

>>> df
       QUANTITY_INVOICED  SHIPPED_QUANTITY  Diff
    0                 15               NaN   15
    1                 20               NaN   20
    2                  7               NaN   7
    3                  7               NaN   7
    4                  7               NaN   7

I do not want to do a df.fillna(0). For sum I would try something like the following & it works but not for difference -

>>> df['Sum'] = df[['QUANTITY_INVOICED', 'SHIPPED_QUANTITY']].sum(axis=1)
>>> df
   INVOICED_QUANTITY  QUANTITY_SHIPPED  Diff  Sum
0                 15               NaN   NaN   15
1                 20               NaN   NaN   20
2                  7               NaN   NaN    7
3                  7               NaN   NaN    7
4                  7               NaN   NaN    7

Answer 1

You can use the sub method to perform the subtraction - this method allows NaN values to be treated as a specified value:

df['Diff'] = df['INVOICED_QUANTITY'].sub(df['QUANTITY_SHIPPED'], fill_value=0)

Which produces:

   INVOICED_QUANTITY  QUANTITY_SHIPPED  Diff
0                 15               NaN    15
1                 20               NaN    20
2                  7               NaN     7
3                  7               NaN     7
4                  7               NaN     7

---

The other neat way to do this is as @JianxunLi suggests: fill in the missing values in the column (creating a copy of the column) and subtract as normal.

The two approaches are almost the same, although sub is a little more efficient because it doesn't need to produce a copy of the column in advance; it just fills the missing values "on the fly":

In [46]: %timeit df['INVOICED_QUANTITY'] - df['QUANTITY_SHIPPED'].fillna(0)
10000 loops, best of 3: 144 µs per loop

In [47]: %timeit df['INVOICED_QUANTITY'].sub(df['QUANTITY_SHIPPED'], fill_value=0)
10000 loops, best of 3: 81.7 µs per loop

Answer 2

I think a simple fill NaN by 0 would help you out.

df['Diff'] = df['INVOICED_QUANTITY'] - df['QUANTITY_SHIPPED'].fillna(0)

Out[153]: 
   INVOICED_QUANTITY  QUANTITY_SHIPPED  Diff
0                 15               NaN    15
1                 20               NaN    20
2                  7               NaN     7
3                  7               NaN     7
4                  7               NaN     7