I assume your array looks like:

       |(HUE)(VALUE)(CLASS)
row/col|   0     1     2
-------+-----------------
0      |   0     1     2
1      |   3     4     5
2      |   6     7     8
.      |   .     .     .
.      |   .     .     .
3599999|   .     .     .

And here is the sample code. For simplicity I changed the size 3600000 to 5.

a = np.array(xrange(5 * 3))
a.shape = (5, 3)

Now array a look like this:

array([[ 0,  1,  2],
       [ 3,  4,  5],
       [ 6,  7,  8],
       [ 9, 10, 11],
       [12, 13, 14]])

If you want row with HUE=9, do like this:

a[np.where(a[:,0] == 9)]
#array([[ 9, 10, 11]])

If you want row with VALUE=4, do like this:

a[np.where(a[:,1] == 4)]
#array([[3, 4, 5]])

If you want row with HUE=0 and VALUE=1, do like this:

a[np.where((a[:,0] == 0) * (a[:,1] == 1))]
#array([[0, 1, 2]])

IIUC isin with str slice

np.where(df.period.str[:3].isin(l),'month',df.period.str.split(' ').str[0])
Out[1162]: array(['month', 'YTD'], dtype=object)

df['period_type'] = np.where(df.period.str[:3].isin(l),'month',df.period.str.split(' ').str[0])

You can use pandas.DataFrame.any() with axis=1:

listVars=['var1', 'var2', 'var3']
myDF['newVar'] = np.where((myDF[listVars]==1).any(axis=1), 1, 0)

For example:

myDF = pd.DataFrame({
    "var1": [1, 1, 1, 0, 0],
    "var2": [1, 0, 1, 0, 1],
    "var3": [0, 0, 0, 0, 0]
})

listVars=['var1', 'var2', 'var3']
myDF['newVar'] = np.where((myDF[listVars]==1).any(1),1, 0)

print(myDF)
#   var1  var2  var3  newVar
#0     1     1     0       1
#1     1     0     0       1
#2     1     1     0       1
#3     0     0     0       0
#4     0     1     0       1

Syntax -

np.where(condition, value1, value2)

Solution -

np.where(
   df.productView < df.order, df.productView + df.order, df.productView
)
array([9.5, 4. , 4. , 5. ])

As an efficient alternative, you can use loc:

m = df.productView < df.order
df.loc[m, 'productView'] = df.loc[m, ['productView', 'order']].sum(1)