Python. Numpy. Name of columns

[emran]

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.

[Kadir]

Modul https://pandas.pydata.org/pandas-docs/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 called Series♪

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   1.5
In [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