Python None in dataframe range meaning

Question

Can someone tell me what does None mean in this code?

s = df.A.values[:, None]

I don't know what else can I write about this problem.

Answer 1

df.A.values converts df.A to a numpy array. To answer this, let's create a test dataframe:

>>> from pandas import util
>>> df= util.testing.makeDataFrame()
>>> df.head()

                   A         B         C         D
BMdjymcTHC -0.684721  1.622097 -2.525634  1.627290
0e7Mekvkf7  0.003399  0.152074 -0.095163 -0.276664
q0E6te3rF9  1.639105 -1.935913  1.733587 -0.729493
w7d1NGfq1p -0.496669 -1.182373 -0.950125  2.201667
RPqDHEGhxs -1.169309  0.608857 -0.748978  0.270510

Your code gives the following output:

>>> df.A.values[:, None]
array([[-0.68472066],
       [ 0.00339929],
       [ 1.63910531],
       [-0.49666918],
       [-1.16930896],
       [ 0.18225299],
       [ 0.88957142],
       [ 0.97299314],
       [ 0.67984743],
       [ 1.11192848],
       [-1.43273161],
       [-0.59633832],
       [ 0.81591342],
       [ 1.26188783],
       [ 0.08789735],
       [-0.37412069],
       [ 0.15285941],
       [-0.14208735],
       [ 0.37897237],
       [ 0.49208469],
       [ 0.86949863],
       [-0.98972967],
       [ 0.66001405],
       [-1.69139314],
       [ 1.18512158],
       [ 1.47981638],
       [ 1.21812138],
       [ 0.82375357],
       [-0.4896989 ],
       [ 0.53701562]])

Let's check it's shape:

>>> df.A.values[:, None].shape
(30, 1)

If you don't have None:

>>> df.A.values[:]
array([-0.68472066,  0.00339929,  1.63910531, -0.49666918, -1.16930896,
        0.18225299,  0.88957142,  0.97299314,  0.67984743,  1.11192848,
       -1.43273161, -0.59633832,  0.81591342,  1.26188783,  0.08789735,
       -0.37412069,  0.15285941, -0.14208735,  0.37897237,  0.49208469,
        0.86949863, -0.98972967,  0.66001405, -1.69139314,  1.18512158,
        1.47981638,  1.21812138,  0.82375357, -0.4896989 ,  0.53701562])

and the shape is :

>>> df.A.values[:].shape
(30,)

So, it is essentially adding one more dimension/axis to the numpy array and creates an array of dimension (30x1) in my case. Your code is equivalent to:

>>> df.A.values.reshape(-1,1)
>>> df.A.values.reshape(-1,1).shape
(30, 1)