Using : for multiple slicing in list or numpy array

Question

I'm having some difficulty trying to figure out how to do extract multiple values in a list that are spaced some indices apart. For example, given a list l = [0,1,2,3,4,5,6,7,8,9,10] , I want to only extract the values [1,2,3] and [6,7,8,9] . I could do l[1:4]+l[6:-1] , but is there a way such to write l[1:4,6:-1] ?

This is really a ghost problem to the actual problem I am having in a pandas dataframe. I have a dataframe, df , with columns ['A','B','C','I1','D','E','F','I2','I3'] , and I only want to keep the important columns ['I1', 'I2', 'I3'] . Now, the current approach I am doing is

df.drop(df.columns[0:3], axis=1, inplace=True)
df.drop(df.columns[4:7], axis=1, inplace=True)

Is there a way to do it such that we can do it in 1 line without writing the column values out explicitly?

Thank you!
PS. I know pandas dataframes use numpy, and I haven't found any workarounds in numpy either, but I think the syntax to drop columns is of the standard python list format, if that makes any sense.

EDIT: I found a way to do it for numpy but it is also 2 lines, from this question . We can do:
indices = np.hstack((np.arange(0:3), np.arange(4:7))
df.drop(df.columns[indices], axis=1, inplace=True)

However, I'm still looking for 1-line generalized methods.

Answer 1

I think you need numpy.r_ for concanecate indices:

print (np.r_[1:4, 6:10])
[1 2 3 6 7 8 9]

Answer 2

Using list comprehension, you can do:

>>> [item for item in l[1:4] + l[6:-1]]
[1, 2, 3, 6, 7, 8, 9]

You can also use extend() like this:

>>> res = l[1:4]
>>> res.extend(l[6:-1])
>>> res
[1, 2, 3, 6, 7, 8, 9]