CODEWITHSUNDEEP
pythonpandasnumpy
dodo_2030
dodo_2030

Reputation: 25

how do I use return value from np.where or apply a function in it

I have a dataframe like this how do I use the return value from np.where

samples= pd.DataFrame({'data':['1','2', '5','6','2','1']})

Output :
    data
0     1
1     2
2     6
3     5
4     2
5     1

I can use :

samples['result'] = np.where(samples['data'] > samples['data'].shift(-1), 'High', 'Low')
output : 
    data  result
0     1     Low
1     2    High
2     6    High
3     5     Low
4     2     Low
5     1     Low

I want create a new column and get value from data column if the result is high

expect output :
if result == High, create new column and get value from data on that row,  or call a function
     data  result    final
0     1     Low       NaN
1     2    High        2          if High   do somthing with other function
2     6    High        6          else
3     5     Low       NaN         if Low call other function
4     2     Low       NaN
5     1     Low       NaN

or if I can call a function insdie the np.where()

a = print('this is High')
b = print('this is Low')
final = np.where(samples['data'] > samples['data'].shift(-1), a, b)

Upvotes: 1

Views: 761

Answers (2)

Sander van den Oord
Sander van den Oord

Reputation: 12818

You can do as follows to use a function within np.where():

samples = pd.DataFrame({'data':[1, 2, 5, 6, 2, 1]})

samples['result'] = np.where(
    samples['data'] > samples['data'].shift(-1), 
    'High', 
    'Low',
)

samples['final'] = np.where(
    samples['data'] > samples['data'].shift(-1),
    samples['data'] * 5,
    np.sin(samples['data']),
)

Resulting dataframe:

    data    result  final
0   1       Low     0.841471
1   2       Low     0.909297
2   5       Low     -0.958924
3   6       High    30.000000
4   2       High    10.000000
5   1       Low     0.841471

Upvotes: 0

edvardlindelof
edvardlindelof

Reputation: 226

apply with axis=1 can do it:

>>> samples['final'] = samples.agg(lambda row: row['data'] if row['result'] == 'High' else np.nan, axis=1)
>>> samples
  data result final
0    1    Low   NaN
1    2    Low   NaN
2    5    Low   NaN
3    6   High     6
4    2   High     2
5    1    Low   NaN

You can call other functions inside the lambda expression.

Upvotes: 1

Related Questions