三日目

 #Numpy #Numpyはanacondaに入ったモジュールなので、宣言が必要

import numpy

import numpy as np #asで名前変換



#array()関数を使うことで、簡単にリストが造れる
import numpy as np

#二次元配列
a = np.array([[1, 1, 1], [2,2,2]])
print(a)
print() #改行

#三次元配列
b = np.array([[[1, 1, 1], [2,2,2]], [[3, 3, 3],[4, 4, 4]]])
print(b)

[[1 1 1]
[2 2 2]]

[[[1 1 1]
[2 2 2]]

[[3 3 3]
[4 4 4]]]

#zeros　0
#ones   1
#arange 0~指定の数-1

import numpy as np

a = np.zeros(5) 
b = np.ones(5)
c = np.arange(5)

print(a)
print(b)
print(c)

[0. 0. 0. 0. 0.]
[1. 1. 1. 1. 1.]
[0 1 2 3 4]

import numpy as np

a = np.zeros(3)
b = np.ones(3)
c = np.arange(3)

print(a)
print(b)
print(c)

[0. 0. 0.]
[1. 1. 1.]
[0 1 2]

#d = np.twos(5)

d = 2 * np.ones(5)
print(d)

[2. 2. 2. 2. 2.]

#np.shape() 配列の形状
import numpy as np

a = np.array([1,1,1])
print(np.shape(a))

b = np.array([1,1,100])
print(np.shape(b))

c = np.array([1,1,1,1])
print(np.shape(c))

(3,)
(3,)
(4,)

import numpy as np

a = np.array([[1],[1]])
print(np.shape(a))

b = np.array([[1,1],[1000,1]])
print(np.shape(b))

c = np.array([[1],[1],[1]])
print(np.shape(c))

(2, 1)
(2, 2)
(3, 1)

#np.shape() 配列の形状
#len() 行数を得る
import numpy as np

a = np.array([1,1,1])
print(len(a))

b = np.array([[1,1],[1000,1]])
print(len(b))

３
２

import numpy as np

a = np.array([2])

print(a)
print(a + 98)
print(a ** 3)

[2]
[100]
[8]

import numpy as np

a = np.array([2,2])
b = np.array([3,3])

print(a)
print(a + b)
print(a ** b)

[2 2]
[5 5]
[8 8]

#配列の要素にアクセス
import numpy as np

a = np.array([10,15,20,25,30,99])
print(a[0]) #インデックスの指定

10

#二次元配列にアクセス
import numpy as np

a = np.array([[1, 2], [3, 4]])
print(a[0, 0]) #一つ目の箱の一番目
print(a[1, 0]) #二つ目の箱の一番目
print()
print(a[0,0], a[1,0])

1
3

1 3

#二次元配列の更新
import numpy as np

a = np.array([[1, 2], [3, 4]])
print(a)
print()

a[0, 0] = 99
print(a[0, 0])
print()
print(a)

[[1 2]
[3 4]]

99

[[99 2]
[ 3 4]]

#「:」を使うことで行や列にアクセスする。
a = np.array([[1,10,100], [2,20,200]])
print(a[0, :])
print(a[1, :])
print(a[1, :] * 2)
print()

a[:, 1] = np.array([99,77])
print(a)
print()

a[:, 0] = np.array([0, 0])
print(a)

[ 1 10 100]
[ 2 20 200]
[ 4 40 400]

[[ 1 99 100]
[ 2 77 200]]

[[ 0 99 100]
[ 0 77 200]]