넘파이 기본함수와 사용법

최초 작성일 : 2021-11-05
categories: Numpy

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-넘파이 불러오기

import numpy as np
print(np.__version__)

1.19.5

-넘파이 기본함수 zeros()

zeros_array = np.zeros((3,2))
print(zeros_array)
print("Data Type is:", zeros_array.dtype)
print("Data Shape is:", zeros_array.shape)

[[0. 0.]
 [0. 0.]
 [0. 0.]]
Data Type is: float64
Data Shape is: (3, 2)

-넘파이 기본함수 ones()

ones_array = np.ones((3,4), dtype='int32')
print(ones_array)
print("Data Type is:", ones_array.dtype)
print("Data Shape is:", ones_array.shape)

[[1 1 1 1]
 [1 1 1 1]
 [1 1 1 1]]
Data Type is: int32
Data Shape is: (3, 4)

-넘파이 기본함수 reshape()

# 3 X 4 배열을 Reshape --> 6 x 2
after_reshape = ones_array.reshape(6,2)
print(after_reshape)
print("Data Shape is:", after_reshape.shape)

[[1 1]
 [1 1]
 [1 1]
 [1 1]
 [1 1]
 [1 1]]
Data Shape is: (6, 2)

after_reshape = ones_array.reshape(3,4)

# 3 x 4 12 --> 2 x 3 x 2 = 12
after_reshape = ones_array.reshape(2,3,2)
print(after_reshape)
print("Data Shape is:", after_reshape.shape)

[[[1 1]
  [1 1]
  [1 1]]

 [[1 1]
  [1 1]
  [1 1]]]
Data Shape is: (2, 3, 2)

after_reshape2= ones_array.reshape(2, -1, 2)
print("reshape(-1,2)?", after_reshape2.shape)
print(after_reshape2)

reshape(-1,2)? (2, 3, 2)
[[[1 1]
  [1 1]
  [1 1]]

 [[1 1]
  [1 1]
  [1 1]]]

after_reshape3= ones_array.reshape(3,-1)
print("reshape(3, -1)? \n")
print(after_reshape3)
print("Data Shape is:", after_reshape3.shape)

reshape(3, -1)? 

[[1 1 1 1]
 [1 1 1 1]
 [1 1 1 1]]
Data Shape is: (3, 4)