Python memorandum

This article is a record of learning for python beginners. I mainly write what I didn't understand. So the order is messy. For reference ,,,

NumPy np.array

# -*- coding: utf-8 -*-
import numpy as np

#Declaration / initialization of 2D array
A = np.array([[1, 2],
              [3, 4],
              [5, 6]])

#Matrix size
print("Matrix A size:", A.shape)
print("Number of rows in matrix A:", A.shape[0])
print("Number of columns in matrix A:", A.shape[1])

"""
Matrix A size:(3, 2)
Number of rows in matrix A:3
Number of columns in matrix A:2
"""

np.reshape shape [0] displays ** number of rows **, shape [1] displays ** number of columns **.

import numpy as np

a = np.arange(24)

print(a)
# [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]

print(a.shape)
# (24,)

print(a.ndim)
# 1

a_4_6 = a.reshape([4, 6])

print(a_4_6)
# [[ 0  1  2  3  4  5]
#  [ 6  7  8  9 10 11]
#  [12 13 14 15 16 17]
#  [18 19 20 21 22 23]]

print(a_4_6.shape)
# (4, 6)

print(a_4_6.ndim)
# 2

In np.ndim, you can easily find the number of elements. Reshape is as in the above code.

[:, 0] All 0th column. If it is a 2x2 array, if it is written without omission, it will be a [0: 2, 0]. A [a: b, c: d] means to extract columns from c to d (not including b and d) in the rows from a to b.

np.where

import numpy as np

a = np.arange(9).reshape((3, 3))
print(a)
# [[0 1 2]
#  [3 4 5]
#  [6 7 8]]

print(np.where(a < 4, -1, 100))
# [[ -1  -1  -1]
#  [ -1 100 100]
#  [100 100 100]]

print(np.where(a < 4, True, False))
# [[ True  True  True]
#  [ True False False]
#  [False False False]]

print(a < 4)
# [[ True  True  True]
#  [ True False False]
#  [False False False]]

If the following is only a condition, true or false will appear.

np.hstack

Consolidation of arrays with the same number of columns. For rows, np.vstack.

np.unique()

In the sequence in (), extract without fog.

import numpy as np

a = np.array([0, 0, 30, 10, 10, 20])
print(a)
# [ 0  0 30 10 10 20]

print(np.unique(a))
# [ 0 10 20 30]

print(type(np.unique(a)))
# <class 'numpy.ndarray'>