python replace not na value

Question

I want to create a new column and replace NA and not missing value with 0 and 1.

#df
col1  
1      
3

5

6

what I want:

#df
col1   NewCol
1           1
3           1
            0
5           1
            0
6           1

This is what I tried:

df['NewCol']=df['col1'].fillna(0)
df['NewCol']=df['col1'].replace(df['col1'].notnull(), 1)

It seems that the second line is incorrect.
Any suggestion?

@PeterChen use it as a string: 'int'. or use numpy data types too: https://docs.scipy.org/doc/numpy-1.14.0/reference/arrays.dtypes.html — 123, Jul 01 '19 at 18:23

score 1 · Answer 1 · answered Jul 01 '19 at 18:27

First you will need to convert all 'na's into '0's. How you do this will vary by scope. For a single column you can use:

df['DataFrame Column'] = df['DataFrame Column'].fillna(0)

For the whole dataframe you can use:

df.fillna(0)

After this, you need to replace all nonzeros with '1's. You could do this like so:

for index, entry in enumerate(df['col']):
    if entry != 0:
        df['col'][index] = 1

Note that this method counts 0 as an empty entry, which may or may not be the desired functionality.

score 1 · Accepted Answer · answered Jul 01 '19 at 18:27

1

You can try:

df['NewCol'] = [*map(int, pd.notnull(df.col1))]

Hope this helps.

answered Jul 01 '19 at 18:27

René

2 Answers2