Impute missing values to 0, and create indicator columns in Pandas

Question

I have a very simple dataframe in Pandas,

testdf = [{'name' : 'id1', 'W': np.NaN, 'L':   0, 'D':0},
          {'name' : 'id2', 'W':   0, 'L': np.NaN, 'D':0},
          {'name' : 'id3', 'W':  np.NaN, 'L':  10, 'D':0},
          {'name' : 'id4', 'W':  75, 'L':  20, 'D':0}
          ]
testdf = pd.DataFrame(testdf)
testdf = testdf[['name', 'W', 'L', 'D']]

which looks like this:

| name | W   | L   | D |
|------|-----|-----|---|
| id1  | NaN | 0   | 0 |
| id2  | 0   | NaN | 0 |
| id3  | NaN | 10  | 0 |
| id4  | 75  | 20  | 0 |

My goal is simple:
1) I want to impute all the missing values by simply replacing them with a 0.
2) Next I want to create indicator columns with a 0 or 1 to indicate that the new value (the 0) is indeed created by the imputation process.

It's probably easier to just show instead of explain with words:

| name | W  | W_indicator | L  | L_indicator | D | D_indicator |
|------|----|-------------|----|-------------|---|-------------|
| id1  | 0  | 1           | 0  | 0           | 0 | 0           |
| id2  | 0  | 0           | 0  | 1           | 0 | 0           |
| id3  | 0  | 1           | 10 | 0           | 0 | 0           |
| id4  | 75 | 0           | 20 | 0           | 0 | 0           |

My attempts have failed, since I get stuck trying to change all non-NaN values to some placeholder value, then change all NaNs to a 0, then change back the placeholder value to NaN, etc etc. It gets messy so fast. Then I keep getting all kinds of slice warnings. And the masks get all jumbled. I'm sure there's a much more elegant way to do this than my wonky heuristical methods.

If my answer was helpful, don't forget `accept` it. Thanks. (And also you can check linked question and great answers and upvote them) — jezrael, Jul 17 '17 at 05:31

jezrael · Accepted Answer · 2017-07-15T20:08:26.027

You can use isnull with convert to int by astype and add_prefix for new df and then concat with reindex_axis by cols created by some solution from this answers:

cols = ['W','L','D']
df = testdf[cols].isnull().astype(int).add_suffix('_indicator')
print (df)
   W_indicator  L_indicator  D_indicator
0            1            0            0
1            0            1            0
2            1            0            0
3            0            0            0

Solution with generator:

def mygen(lst):
    for item in lst:
        yield item
        yield item + '_indicator'

df1 = pd.concat([testdf.fillna(0), df], axis=1) \
        .reindex_axis(['name'] + list(mygen(cols)), axis=1)
print (df1)

  name     W  W_indicator     L  L_indicator  D  D_indicator
0  id1   0.0            1   0.0            0  0            0
1  id2   0.0            0   0.0            1  0            0
2  id3   0.0            1  10.0            0  0            0
3  id4  75.0            0  20.0            0  0            0

And solution with list comprehenion:

cols = ['name'] + [item for x in cols for item in (x, x + '_indicator')]
df1 = pd.concat([testdf.fillna(0), df], axis=1).reindex_axis(cols, axis=1)
print (df1)
  name     W  W_indicator     L  L_indicator  D  D_indicator
0  id1   0.0            1   0.0            0  0            0
1  id2   0.0            0   0.0            1  0            0
2  id3   0.0            1  10.0            0  0            0
3  id4  75.0            0  20.0            0  0            0

score 1 · Answer 2 · answered Aug 13 '21 at 13:53

A few years late to the party, but this is how I do it:

transformer = FeatureUnion(
     transformer_list=[
         ('features', SimpleImputer(strategy='mean')),
         ('indicators', MissingIndicator())])
transformer = transformer.fit(Xnum, df.fraud)
results = transformer.transform(Xnum)
results.shape

Impute missing values to 0, and create indicator columns in Pandas

2 Answers2