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【Python入門】<numpy ndarray編> [edit: 2020/02/22]

Last updated at 2020-03-07Posted at 2020-02-22

Step1 インプットデータ設定

1. 基本操作

1-1. 簡単定義方法

1次元配列

import numpy as np
sample_ndarray_1d = np.array([1,2,3,4,5])
print("sample_ndarray_1d: ", sample_ndarray_1d)
sample_ndarray_1d:  [1 2 3 4 5]

2次元配列

import numpy as np
sample_ndarray_2d = np.array([[1,2,3,4,5],[6,7,8,9,10]])
print("sample_ndarray_2d: ", sample_ndarray_2d)
sample_ndarray_2d:  [[ 1  2  3  4  5]

1-2. 要素アクセス [要素数:1]

1次元配列

import numpy as np
sample_ndarray_1d = np.array([1,2,3,4,5])
print("sample_ndarray_1d[0]: ", sample_ndarray_1d[0])
print("sample_ndarray_1d[1]: ", sample_ndarray_1d[1])
print("sample_ndarray_1d[2]: ", sample_ndarray_1d[2])
print("sample_ndarray_1d[3]: ", sample_ndarray_1d[3])
print("sample_ndarray_1d[4]: ", sample_ndarray_1d[4])
print("sample_ndarray_1d[-1]: ", sample_ndarray_1d[-1])
sample_ndarray_1d[0]:  1
sample_ndarray_1d[1]:  2
sample_ndarray_1d[2]:  3
sample_ndarray_1d[3]:  4
sample_ndarray_1d[4]:  5
sample_ndarray_1d[-1]:  5

2次元配列

import numpy as np
sample_ndarray_2d = np.array([[1,2,3,4,5],[6,7,8,9,10]])
print("sample_ndarray_2d[0,0]: ", sample_ndarray_2d[0,0])
print("sample_ndarray_2d[0,1]: ", sample_ndarray_2d[0,1])
print("sample_ndarray_2d[0,2]: ", sample_ndarray_2d[0,2])
print("sample_ndarray_2d[0,3]: ", sample_ndarray_2d[0,3])
print("sample_ndarray_2d[0,4]: ", sample_ndarray_2d[0,4])
print("sample_ndarray_2d[1,0]: ", sample_ndarray_2d[1,0])
print("sample_ndarray_2d[1,1]: ", sample_ndarray_2d[1,1])
print("sample_ndarray_2d[1,2]: ", sample_ndarray_2d[1,2])
print("sample_ndarray_2d[1,3]: ", sample_ndarray_2d[1,3])
print("sample_ndarray_2d[1,4]: ", sample_ndarray_2d[1,4])
sample_ndarray_2d[0,0]:  1
sample_ndarray_2d[0,1]:  2
sample_ndarray_2d[0,2]:  3
sample_ndarray_2d[0,3]:  4
sample_ndarray_2d[0,4]:  5
sample_ndarray_2d[1,0]:  6
sample_ndarray_2d[1,1]:  7
sample_ndarray_2d[1,2]:  8
sample_ndarray_2d[1,3]:  9
sample_ndarray_2d[1,4]:  10

1-3. 要素アクセス [要素数:複数]

1次元配列

import numpy as np
sample_ndarray_1d = np.array([1,2,3,4,5])
print("sample_ndarray_1d: ", sample_ndarray_1d)
sample_ndarray_1d:  [1 2 3 4 5]

start_id = 1
end_id = 3
print("項目番号「1」から「3」：",sample_ndarray_1d[start_id:end_id+1])
print("項目番号「最初」から「3」：",sample_ndarray_1d[:end_id+1])
print("項目番号「1」から「最後」：",sample_ndarray_1d[start_id:])
print("全要素：",sample_ndarray_1d[:])
項目番号「1」から「3」： [2 3 4]
項目番号「最初」から「3」： [1 2 3 4]
項目番号「1」から「最後」： [2 3 4 5]
全要素： [1 2 3 4 5]

1-4. 配列長さ (Array Length)

1次元配列

import numpy as np
sample_ndarray_1d = np.array([1,2,3,4,5])
print("配列長さ：",len(sample_ndarray_1d))
配列長さ： 5

2次元配列

import numpy as np
sample_ndarray_2d = np.array([[1,2,3,4,5],[6,7,8,9,10]])
print("親リスト長さ：",len(sample_ndarray_2d))         ### 親リスト
print("第一子リスト長さ：",len(sample_ndarray_2d[0]))  ### 第一子リスト
親リスト長さ： 2
第一子リスト長さ： 5

1-5. 配列サイズ (Array Size)

1次元配列

import numpy as np
sample_ndarray_1d = np.array([1,2,3,4,5])
print("配列サイズ：",sample_ndarray_1d.shape)
配列サイズ： (5,)

2次元配列

import numpy as np
sample_ndarray_2d = np.array([[1,2,3,4,5],[6,7,8,9,10]])
print("配列サイズ：",sample_ndarray_2d.shape)
配列サイズ： (2, 5)

1-6. 次元数 (Dimension Number)

1次元配列

import numpy as np
sample_ndarray_1d = np.array([1,2,3,4,5])
print("次元数：",sample_ndarray_1d.ndim)
次元数： 1

2次元配列

import numpy as np
sample_ndarray_2d = np.array([[1,2,3,4,5],[6,7,8,9,10]])
print("次元数：",sample_ndarray_2d.ndim)
次元数： 2

1-7. 要素数 (Element Number)

1次元配列

import numpy as np
sample_ndarray_1d = np.array([1,2,3,4,5])
print("要素数：",sample_ndarray_1d.size)
要素数： 5

2次元配列

import numpy as np
sample_ndarray_2d = np.array([[1,2,3,4,5],[6,7,8,9,10]])
print("要素数：",sample_ndarray_2d.size)
要素数： 10

1-8. 格納型　(Data Type)

1次元配列

import numpy as np
sample_ndarray = np.array([1,2,3,4,5])
print("格納型：",type(sample_ndarray))
格納型： <class 'numpy.ndarray'>

1-9. 要素データ型　(Element Data Type)

1次元配列

import numpy as np
sample_ndarray = np.array([1,2,3,4,5])
print("要素データ型：",sample_ndarray.dtype)
要素データ型： int32

1-10. 軸削除 [軸項目番号,軸方向] (Axis Delete)

1次元配列

import numpy as np
sample_ndarray_1d = np.array([1,2,3,4,5])
print("sample_ndarray_1d: ",sample_ndarray_1d)
sample_ndarray_1d:  [1 2 3 4 5]

deleted_ndarray_1d = np.delete(sample_ndarray_1d,2,0)
print("deleted_ndarray_1d: ",deleted_ndarray_1d)
deleted_ndarray_1d:  [1 2 4 5]

2次元配列[削除：行]

import numpy as np
sample_ndarray_2d = np.array([[1,2,3,4,5],[6,7,8,9,10]])
print("sample_ndarray_2d: ", sample_ndarray_2d)
sample_ndarray_2d:  [[ 1  2  3  4  5]
 [ 6  7  8  9 10]]

deleted_ndarray_2d = np.delete(sample_ndarray_2d,0,0)
print("deleted_ndarray_2d: ",deleted_ndarray_2d)
deleted_ndarray_2d:  [[ 6  7  8  9 10]]

2次元配列[削除：列]

import numpy as np
sample_ndarray_2d = np.array([[1,2,3,4,5],[6,7,8,9,10]])
print("sample_ndarray_2d: ", sample_ndarray_2d)
sample_ndarray_2d:  [[ 1  2  3  4  5]
 [ 6  7  8  9 10]]

deleted_ndarray_2d = np.delete(sample_ndarray_2d,2,1)
print("deleted_ndarray_2d: ",deleted_ndarray_2d)
deleted_ndarray_2d:  [[ 1  2  4  5]
 [ 6  7  9 10]]

1-11. 配列連結 [手法1]

1次元配列

import numpy as np
A = np.array([1,2,3])
B = np.array([4,5,6])

C = np.concatenate((A,B),axis=0)
print("joint ndarray (Horizontal): ", C)
joint ndarray (Horizontal):  [1 2 3 4 5 6]

2次元配列

import numpy as np
A = np.array([[1,2,3],[4,5,6]])
B = np.array([[7,8,9],[10,11,12]])

C = np.concatenate((A,B),axis=0)
print("joint ndarray (Vertical): ", C)
joint ndarray (Vertical):  [[ 1  2  3]
 [ 4  5  6]
 [ 7  8  9]
 [10 11 12]]

D = np.concatenate((A,B),axis=1)
print("joint ndarray (Horizontal): ", D)
joint ndarray (Horizontal):  [[ 1  2  3  7  8  9]
 [ 4  5  6 10 11 12]]

1-12. 配列連結 [手法2]

1次元配列

import numpy as np
A = np.array([1,2,3])
B = np.array([4,5,6])

C = np.block([[A,B]])
print("joint ndarray (Vertical): ", C)
joint ndarray (Vertical):  [[1 2 3]
 [4 5 6]]

D = np.block([[A],[B]])
print("joint ndarray (Horizontal): ", D)
joint ndarray (Horizontal):  [1 2 3 4 5 6]

2次元配列

import numpy as np
A = np.array([[1,2,3],[4,5,6]])
B = np.array([[7,8,9],[10,11,12]])

C = np.block([[A],[B]])
print("joint ndarray (Vertical): ", C)
joint ndarray (Vertical):  [[ 1  2  3]
 [ 4  5  6]
 [ 7  8  9]
 [10 11 12]]

D = np.block([[A,B]])
print("joint ndarray (Horizontal): ", D)
joint ndarray (Horizontal):  [[ 1  2  3  7  8  9]
 [ 4  5  6 10 11 12]]

1-13. 配列連結 [手法3]

1次元配列

import numpy as np
A = np.array([1,2,3])
B = np.array([4,5,6])

C = np.vstack((A,B))
print("joint ndarray (Vertical): ", C)
joint ndarray (Vertical):  [[1 2 3]
 [4 5 6]]

D = np.hstack((A,B))
print("joint ndarray (Horizontal): ", D)
joint ndarray (Horizontal):  [1 2 3 4 5 6]

2次元配列

import numpy as np
A = np.array([[1,2,3],[4,5,6]])
B = np.array([[7,8,9],[10,11,12]])

C = np.vstack((A,B))
print("joint ndarray (Vertical): ", C)
joint ndarray (Vertical):  [[ 1  2  3]
 [ 4  5  6]
 [ 7  8  9]
 [10 11 12]]

D = np.hstack((A,B))
print("joint ndarray (Horizontal): ", D)
joint ndarray (Horizontal):  [[ 1  2  3  7  8  9]
 [ 4  5  6 10 11 12]]

1-14. 配列連結 [要素連結による配列生成]

2次元配列

import numpy as np
A = np.array([[1,2,3],[4,5,6]])
B = np.array([[7,8,9],[10,11,12]])

C = np.dstack((A,B))
print("stack ndarray: ", C)
stack ndarray:  [[[ 1  7]
  [ 2  8]
  [ 3  9]]

 [[ 4 10]
  [ 5 11]
  [ 6 12]]]

print("配列サイズ：",C.shape)
print("次元数：",C.ndim)
配列サイズ： (2, 3, 2)
次元数： 3

1-15. 配列スタック

2次元配列

import numpy as np
A = np.array([[1,2,3],[4,5,6]])
B = np.array([[7,8,9],[10,11,12]])

C = np.stack((A,B))
print("stack ndarray: ", C)
stack ndarray:  [[[ 1  2  3]
  [ 4  5  6]]

 [[ 7  8  9]
  [10 11 12]]]

print("配列サイズ：",C.shape)
print("次元数：",C.ndim)
配列サイズ： (2, 2, 3)
次元数： 3

1-16. 配列分割 [手法1]

1次元配列

import numpy as np
sample_ndarray_1d = np.array([1,2,3,4,5,6])
print("sample_ndarray_1d: ",sample_ndarray_1d)
sample_ndarray_1d:  [1 2 3 4 5 6]

split_ndarray_1d = np.split(sample_ndarray_1d,2)
print("split_ndarray_1d[0]: ",split_ndarray_1d[0])
print("split_ndarray_1d[1]: ",split_ndarray_1d[1])
split_ndarray_1d[0]:  [1 2 3]
split_ndarray_1d[1]:  [4 5 6]

2次元配列[分割：行]

import numpy as np
sample_ndarray_2d = np.array([[1,2,3,4,5,6],[7,8,9,10,11,12]])
print("sample_ndarray_2d: ",sample_ndarray_2d)
sample_ndarray_2d:  [[ 1  2  3  4  5  6]
 [ 7  8  9 10 11 12]]

split_ndarray_2d = np.split(sample_ndarray_2d,2,axis=0)
print("split_ndarray_2d[0]: ",split_ndarray_2d[0])
print("split_ndarray_2d[1]: ",split_ndarray_2d[1])
split_ndarray_2d[0]:  [[1 2 3 4 5 6]]
split_ndarray_2d[1]:  [[ 7  8  9 10 11 12]]

2次元配列[分割：列]

import numpy as np
sample_ndarray_2d = np.array([[1,2,3,4,5,6],[7,8,9,10,11,12]])
print("sample_ndarray_2d: ",sample_ndarray_2d)
sample_ndarray_2d:  [[ 1  2  3  4  5  6]
 [ 7  8  9 10 11 12]]

split_ndarray_2d = np.split(sample_ndarray_2d,2,axis=1)
print("split_ndarray_2d[0]: ",split_ndarray_2d[0])
print("split_ndarray_2d[1]: ",split_ndarray_2d[1])
split_ndarray_2d[0]:  [[1 2 3]
 [7 8 9]]
split_ndarray_2d[1]:  [[ 4  5  6]
 [10 11 12]]

1-16. 配列分割 [手法2]

2次元配列[分割：行]

import numpy as np
sample_ndarray_2d = np.array([[1,2,3,4,5,6],[7,8,9,10,11,12]])
print("sample_ndarray_2d: ",sample_ndarray_2d)
sample_ndarray_2d:  [[ 1  2  3  4  5  6]
 [ 7  8  9 10 11 12]]

split_ndarray_2d = np.vsplit(sample_ndarray_2d,2)
print("split_ndarray_2d[0]: ",split_ndarray_2d[0])
print("split_ndarray_2d[1]: ",split_ndarray_2d[1])
split_ndarray_2d[0]:  [[1 2 3 4 5 6]]
split_ndarray_2d[1]:  [[ 7  8  9 10 11 12]]

2次元配列[分割：列]

import numpy as np
sample_ndarray_2d = np.array([[1,2,3,4,5,6],[7,8,9,10,11,12]])
print("sample_ndarray_2d: ",sample_ndarray_2d)
sample_ndarray_2d:  [[ 1  2  3  4  5  6]
 [ 7  8  9 10 11 12]]

split_ndarray_2d = np.hsplit(sample_ndarray_2d,2)
print("split_ndarray_2d[0]: ",split_ndarray_2d[0])
print("split_ndarray_2d[1]: ",split_ndarray_2d[1])
split_ndarray_2d[0]:  [[1 2 3]
 [7 8 9]]
split_ndarray_2d[1]:  [[ 4  5  6]
 [10 11 12]]

1-17. 配列分割 [要素分割による配列生成]

3次元配列

import numpy as np
sample_ndarray_3d = np.array([[[1,2],[3,4]],[[5,6],[7,8]]])
print("sample_ndarray_3d: ",sample_ndarray_3d)
sample_ndarray_3d:  [[[1 2]
  [3 4]]

 [[5 6]
  [7 8]]]

split_ndarray_3d = np.dsplit(sample_ndarray_3d,2)
print("split_ndarray_3d[0]: ",split_ndarray_3d[0])
print("split_ndarray_3d[1]: ",split_ndarray_3d[1])
split_ndarray_3d[0]:  [[[1]
  [3]]

 [[5]
  [7]]]
split_ndarray_3d[1]:  [[[2]
  [4]]

 [[6]
  [8]]]

2. データ定義

2-1. 零配列 (Zero Array)

1次元配列

import numpy as np
zero_ndarray_1d = np.zeros(5)
print("zero_ndarray_1d: ",zero_ndarray_1d)
zero_ndarray_1d:  [0. 0. 0. 0. 0.]

2次元配列

import numpy as np
zero_ndarray_2d = np.zeros((2,3))
print("zero_ndarray_2d: ",zero_ndarray_2d)
zero_ndarray_2d:  [[0. 0. 0.]
 [0. 0. 0.]]

配列サイズコピー

import numpy as np
sample_ndarray = np.array([[1,2,3,4,5],[6,7,8,9,10]])
zero_ndarray = np.zeros_like(sample_ndarray)
print("zero ndarray: ",zero_ndarray)
zero ndarray:  [[0 0 0 0 0]
 [0 0 0 0 0]]

2-2. 単位配列 [手法1] (Identity Array)

単位配列[オフセット：無]

import numpy as np
identity_ndarray = np.eye(3)
print("identity ndarray: ",identity_ndarray)
identity ndarray:  [[1. 0. 0.]
 [0. 1. 0.]
 [0. 0. 1.]]

単位配列[オフセット：有]

import numpy as np
offset = 1
identity_ndarray = np.eye(3,k=offset)
print("identity ndarray: ",identity_ndarray)
identity ndarray:  [[0. 1. 0.]
 [0. 0. 1.]
 [0. 0. 0.]]

import numpy as np
offset = -1
identity_ndarray = np.eye(3,k=offset)
print("identity ndarray: ",identity_ndarray)
identity ndarray:  [[0. 0. 0.]
 [1. 0. 0.]
 [0. 1. 0.]]

2-3. 単位配列 [手法2] (Identity Array)

単位配列[オフセット：無]

import numpy as np
identity_ndarray = np.identity(3)
print("identity ndarray: ",identity_ndarray)

2-3. 空白配列 (Empty Array)

1次元配列

import numpy as np
empty_ndarray_1d = np.empty(5)
print("empty_ndarray_1d: ",empty_ndarray_1d)
empty_ndarray_1d:  [4.94065646e-324 4.94065646e-324 0.00000000e+000 0.00000000e+000
 1.32373351e+079]

2次元配列

import numpy as np
empty_ndarray_2d = np.empty((2,3))
print("empty_ndarray_2d: ",empty_ndarray_2d)
empty_ndarray_2d:  [[0. 0. 0.]
 [0. 0. 0.]]

2-2. 特定値配列 (Specified Value Array)

1次元配列

import numpy as np
specified_value = 10
array_size = 5
specified_value_ndarray = np.empty(array_size)
specified_value_ndarray.fill(specified_value)
print("specified_value_ndarray: ",specified_value_ndarray)
specified_value_ndarray:  [10. 10. 10. 10. 10.]

2次元配列

import numpy as np
specified_value = 3
array_size = (2,3)
specified_value_ndarray = np.empty(array_size)
specified_value_ndarray.fill(specified_value)
print("specified_value_ndarray: ",specified_value_ndarray)
specified_value_ndarray:  [[3. 3. 3.]
 [3. 3. 3.]]

2-4. 等差数列リスト (Arithmetic Sequence List)

1次元配列

start_num = 1
end_num = 10
difference = 1
arithmetic_sequence_ndarray = np.array(range(start_num, end_num + 1, difference))
print("arithmetic sequence ndarray: ",arithmetic_sequence_ndarray)

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【Python入門】<numpy ndarray編> [edit: 2020/02/22]

目次

Step1 インプットデータ設定

1. 基本操作

1-1. 簡単定義方法

1-2. 要素アクセス [要素数:1]

1-3. 要素アクセス [要素数:複数]

1-4. 配列長さ (Array Length)

1-5. 配列サイズ (Array Size)

1-6. 次元数 (Dimension Number)

1-7. 要素数 (Element Number)

1-8. 格納型 (Data Type)

1-9. 要素データ型 (Element Data Type)

1-10. 軸削除 [軸項目番号,軸方向] (Axis Delete)

1-11. 配列連結 [手法1]

1-12. 配列連結 [手法2]

1-13. 配列連結 [手法3]

1-14. 配列連結 [要素連結による配列生成]

1-15. 配列スタック

1-16. 配列分割 [手法1]

1-16. 配列分割 [手法2]

1-17. 配列分割 [要素分割による配列生成]

2. データ定義

2-1. 零配列 (Zero Array)

2-2. 単位配列 [手法1] (Identity Array)

2-3. 単位配列 [手法2] (Identity Array)

2-3. 空白配列 (Empty Array)

2-2. 特定値配列 (Specified Value Array)

2-4. 等差数列リスト (Arithmetic Sequence List)

1-8. 格納型　(Data Type)

1-9. 要素データ型　(Element Data Type)