how to apply Numpy Vectorize instead of apply function-CodePudding

I have a pandas Dataframe as follows

data = {

'ID' : [0,0,0,0,0,1],
        
'DAYS': [293,1111,3020,390,210,10],
 

}



df = pd.DataFrame(data, columns = ['ID','DAYS'])
    ID  DAYS
0   0   293
1   0   1111
2   0   3020
3   0   390
4   0   210
5   1   10

What I am trying to do is the simple apply function with the following condition and outputs column as boolean :

df['bool'] = df.apply(lambda x:( x['DAYS'] < 365),axis =1  )

and i would like to optimize this apply-lambda part.. I managed to do in numpy array

df['bool_numpy'] = np.where(df['DAYS'] <365 ,True ,False)

But I am struggling applying same thing for np.vectorize method.

def copy_filter(df):
    if df['DAYS'] <365:
        return True
    else:
        return False

a= np.vectorize(copy_filter, otypes = [bool])
df['bool_vectorize'] = a(df['DAYS'])

but gave me an error. Any help would be appreciated. and also, any other optimization technique on this problem would be great as well!

CodePudding user response：

You don't need apply nor vectorize for this:

df['bool'] = df['DAYS'] < 365

output:

   ID  DAYS   bool
0   0   293   True
1   0  1111  False
2   0  3020  False
3   0   390  False
4   0   210   True
5   1    10   True

CodePudding user response：

Change your function to

def copy_filter(x):
    if x <365:
        return True
    else:
        return False
a= np.vectorize(copy_filter, otypes = [bool])
df['bool_vectorize'] = a(df['DAYS'])
df
   ID  DAYS  bool_vectorize
0   0   293            True
1   0  1111           False
2   0  3020           False
3   0   390           False
4   0   210            True
5   1    10            True