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This article introduces the relevant knowledge of "how to use numpy related functions". Many people will encounter such a dilemma in the operation of actual cases, so let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

Np.where

First of all, it is emphasized that the where () function returns different values for different inputs.

1. When the array is an one-dimensional array, the value returned is an one-dimensional index, so there is only one set of index arrays.

2. When the array is a two-dimensional array, the array value that meets the condition returns the location index of the value, so there are two sets of index arrays to represent the position of the value. The following example:

Import numpy as npa=np.reshape (np.arange (20), (4Power5)) aarray ([0, 1, 2, 3, 4], [5, 6, 7, 8, 9], [10, 11, 12, 13, 14], [15, 16, 17, 18, 19]) b = np.where (a > 10) b (array ([2, 2, 2, 2, 3, 3, 3, 3, 3], dtype=int64), array ([1, 2, 3) 4, 0, 1, 2, 3, 4], dtype=int64)) b [0] [:] array ([2, 2, 2, 2, 3, 3, 3, dtype=int64) b [0] array ([2, 2, 2, 2, 3, 3, 3, 3, 3, 3], dtype=int64) b [1] array ([1, 2, 3, 4, 0, 1, 2, 3, 4], dtype=int64)

An is a two-dimensional array, b is the returned index, the index is divided into two parts: row index and column index, b [0] is the row index, b [1] is the column index.

Np.tile

His function is to repeat an array. For example, tile (A _ Query n), the function is to repeat array A n times to form a new array. Example:

Import numpy as npa = np.tile (a, (1,4)) barray ([[1pyr2, (1,4) barray)]) b = np.tile (a, 4) barray ([1pyr2, 1pyr3, 1pyr3, 1pas, 2p3) d = np.tile (a, (2,4)) darray 2, 3], [1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3])

From the above example, we can see that b = np.tile (a, (1,4)) generates a two-dimensional array, while b = np.tile (a, 4) generates an one-dimensional array, which repeats a four times.

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