I tried multiple solutions but none of them gives the desired output.
I have a DataFrame:
tag value
'A' 3.7
'A' 1.5
'E' 9.7
'E' 2.9
'B' -1.2
'B' 0.8
My expected output is a Numpy Array:
array([[3.7, 1.5],
[9.7, 2.9],
[-1.2, 0.8]])
I tried using groupby and converting in numpy array
df.groupby(['tag']).value.apply(np.array).values
But I get output as:
array([array([3.7, 1.5]), array([9.7, 2.9]), array([-1.2, 0.8]))], dtype=object)
If there is always same number of values per groups is possible create nested lists and pass to np.array, also for same order of groups add sort=False parameter to DataFrame.groupby :
arr = np.array(df.groupby(['tag'], sort=False).value.apply(list).tolist())
print (arr)
[[ 3.7 1.5]
[ 9.7 2.9]
[-1.2 0.8]]
L = df.groupby(['tag'], sort=False).value.apply(list).tolist() and then print ([x for x in L if len(x!=2)]) return empty list?df.groupby('tag')['value'].agg(lambda x: x.tolist()).values
