# 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/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 : a = np.array([[1.2, -3.5, 0., -10.],
...:               [0.4, 2.1, -0.1, 0.5],
...:               [0., 1.1, 1., 1.5]])
In : df = pd.DataFrame(a, columns=[1,2,3,4])
In : df
Out:
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 : res = pd.DataFrame({"sum_1_3_4": df[[1,3,4]].sum(axis=1), 2: df})
In : res
Out:
sum_1_3_4    2
0       -8.8 -3.5
1        0.8  2.1
2        2.5  1.1
``````

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