Python

Numpy 정리

1. Numpy 기본함수

  • arange
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    arrange_array = np.arange(5)
    arrange_array
      array([0, 1, 2, 3, 4])
  # array [0, 1, 2, 3, 4]의 형태로 반환

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    arrange_array2 = np.arange(1, 9, 3)
    arrange_array2
      array([1, 4, 7])
  # 1부터 시작 9까지 3간격으로 array[1, 4, 7] 형태로 반환

  • zoroes
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    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)

  • ones
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    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)

  • reshape
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    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)
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