Python. Numpy. Name of columns

Let's say there's a mass:
a = np.array([[1.2, 3.5, 0., 10.], [0.4, 2.1, 0.1, 0.5], [0., 1.1, 1., 1.5]])
How do you give the names of columns 1.2,3,4? I used it.
a = np.insert(a, 0, [1, 2, 3, 4], 0)
[[ 1. 2. 3. 4. ]
[ 1.2 3.5 0. 10. ]
[ 0.4 2.1 0.1 0.5]
[ 0. 1.1 1. 1.5]]
After that, I'd like to do some pole surgery, like, one and three pillars.
[[ [1,3] 2. 4. ]
[ 1.2 3.5 10.]
[ 0.3 2.1 0.5]
[ 1. 1.1 1.5]]
Then do as follows:
[[ [1,3,4] 2. ]
[ 8.8 3.5 ]
[ 0.8 2.1 ]
[ 2.5 1.1 ]]
I want to see the poles united. I don't want a numpy. How can that be realized? Maybe some other libraries. I don't think it's good to ask dtypes, or I'm not good at it. But I'd like to use the numpy mass because of convenient functions.

Modul https://pandas.pydata.org/pandasdocs/stable/user_guide/10min.html Established to work with tabular data (2D and 1D sets). 2D Pandas tables
DataFrame
and represent a set of indicted and named pillars. Each pole under the hood is a 1D Numpy vector with a name and indices. In the Pandas, they are calledSeries
♪Example:
In [129]: a = np.array([[1.2, 3.5, 0., 10.], ...: [0.4, 2.1, 0.1, 0.5], ...: [0., 1.1, 1., 1.5]])
In [130]: df = pd.DataFrame(a, columns=[1,2,3,4])
In [131]: df
Out[131]:
1 2 3 4
0 1.2 3.5 0.0 10.0
1 0.4 2.1 0.1 0.5
2 0.0 1.1 1.0 1.5In [133]: res = pd.DataFrame({"sum_1_3_4": df[[1,3,4]].sum(axis=1), 2: df[2]})
In [134]: res
Out[134]:
sum_1_3_4 2
0 8.8 3.5
1 0.8 2.1
2 2.5 1.1