python replace not na value [duplicate]

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: docs.scipy.org/doc/numpy-1.14.0/reference/arrays.dtypes.html — 123
– 123, Commented Jul 1, 2019 at 18:23

René · Accepted Answer · 2019-07-01 18:27:39Z

1

You can try:

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

Hope this helps.

answered Jul 1, 2019 at 18:27

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Comments

user5880984 · Accepted Answer · 2019-07-01 18:27:24Z

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.