【9-1】
▲array関数で変換
-------------------
>>> import numpy as np
>>> x = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
>>> x
array([1., 2., 3., 4., 5.])
>>> print(type(x))
<class 'numpy.ndarray'>
-------------------


▲ネストしたリストの場合
-------------------
>>> import numpy as np
>>> list1 = [1, 2, 3, 4, 5]
>>> list2 = [10, 20, 30, 40, 50]
>>> lists = [list1, list2]
>>> array1 = np.array(lists)
>>> array1
array([[ 1,  2,  3,  4,  5],
       [10, 20, 30, 40, 50]])
-------------------


【9-2】
▲要素数を数える
-------------------
>>> import numpy as np
>>> a = np.array([1, 2, 3, 4])
>>> np.shape(a)
(4,)
-------------------


▲ネストされたリスト
-------------------
>>> import numpy as np
>>> a = np.array([[1, 2, 3],[4, 5, 6]])
>>> a.shape
(2, 3)
-------------------


【9-3】
▲等差数列
-------------------
>>> import numpy as np
>>> x = np.arange(10)
>>> print(x)
[0 1 2 3 4 5 6 7 8 9]
-------------------


▲配列同士の計算
-------------------
>>> import numpy as np
>>> x = np.arange(1, 10).reshape(3, 3)
>>> y = x
>>> print(x)
[[1 2 3]
 [4 5 6]
 [7 8 9]]
>>> print(y)
[[1 2 3]
 [4 5 6]
 [7 8 9]]
>>> print(x + y)
[[ 2  4  6]
 [ 8 10 12]
 [14 16 18]]
>>> print(x - y)
[[0 0 0]
 [0 0 0]
 [0 0 0]]
>>> print(x * y)
[[ 1  4  9]
 [16 25 36]
 [49 64 81]]
-------------------


【9-4】
▲1次元配列を多次元配列に変換
-------------------
>>> import numpy as np
>>> a = np.arange(10)
>>> print(a)
[0 1 2 3 4 5 6 7 8 9]
>>> print(np.reshape(a, (2, 5)))
[[0 1 2 3 4]
 [5 6 7 8 9]]
-------------------


▲配列の転置（行と列の入れ替え）
-------------------
>>> import numpy as np
>>> a = np.arange(10)
>>> a.reshape(2, 5)
array([[0, 1, 2, 3, 4],
       [5, 6, 7, 8, 9]])
>>> a = np.transpose(a)
>>> print(a)
[0 1 2 3 4 5 6 7 8 9]
-------------------


▲配列の結合
-------------------
>>> import numpy as np
>>> a = np.array([[1, 2, 3],[4, 5, 6]])
>>> b = np.array([[7, 8, 9],[10, 11, 12]])
>>> print(np.concatenate([a, b], axis=1))
[[ 1  2  3  7  8  9]
 [ 4  5  6 10 11 12]]
>>> print(np.concatenate([a, b], axis=0))
[[ 1  2  3]
 [ 4  5  6]
 [ 7  8  9]
 [10 11 12]]
-------------------


▲配列の要素を削除
-------------------
>>> import numpy as np
>>> a = np.arange(12).reshape(3, 4)
>>> print(a)
[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]]
>>> a = np.delete(a, 1, 0)
>>> print(a)
[[ 0  1  2  3]
 [ 8  9 10 11]]
-------------------