numpy基础篇-简单入门教程3

Posted 2021-02-10 yangzhaonan

• np

import numpy as np

• np.__version__

print(np.__version__)  # 1.15.2

• np.arange(10)，创建一维数组

print(np.arange(10))                      # [0 1 2 3 4 5 6 7 8 9]

• np.ones((2, 2), dtype=bool)，创建布尔数组

print(np.full((2, 2), True, dtype=bool))  # [[ True  True] [ True  True]]

print(np.ones((2, 2), dtype=bool))        # [[ True  True] [ True  True]]

print(np.full((2, 2), 3, dtype=float))    # [[3. 3.] [3. 3.]]

• 提取所有的奇数

arr = np.arange(10)

a = arr[arr % 2 == 1]
print(a)  # [1 3 5 7 9]

• 所有的奇数赋值为-1

arr[arr % 2 == 1] = -1
print(arr)  # [ 0 -1  2 -1  4 -1  6 -1  8 -1]

• 奇数赋值为 -1，其他数 +1

arr = np.arange(10)
out = np.where(arr % 2 == 1, -1, arr + 1)

print(arr)  # [0  1  2  3  4  5  6  7  8  9]
print(out)  # [1 -1  3 -1  5 -1  7 -1  9 -1]

• np.concatenate([a, b], axis=0)或np.vstack([a, b]),垂直拼接两个数组

a = np.arange(10).reshape(2, 5)
b = np.repeat(1, 10).reshape(2, -1)  # automatically decides the number of cols
print(a)  # [[0 1 2 3 4] [5 6 7 8 9]]
print(b)  # [[1 1 1 1 1] [1 1 1 1 1]]
print(type(a))  # <class 'numpy.ndarray'>

print(np.concatenate([a, b], axis=0))  # [[0 1 2 3 4] [5 6 7 8 9] [1 1 1 1 1] [1 1 1 1 1]]
print(np.vstack([a, b]))  # same
print(np.r_[a, b])  # same

• np.concatenate([a, b], axis=1)或np.hstack([a, b]),水平拼接两个数组

print(np.concatenate([a, b], axis=1))  # [[0 1 2 3 4 1 1 1 1 1] [5 6 7 8 9 1 1 1 1 1]]
print(np.hstack([a, b]))  # same
print(np.c_[a, b])  # same

• np.repeat(a, 3)，np.tile(a, 3)，重复序列的两种方式

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

print(np.repeat(a, 3))  # [1 1 1 2 2 2 3 3 3]

print(np.tile(a, 3))    # [1 2 3 1 2 3 1 2 3]

print(np.r_[np.repeat(a, 3), np.tile(a, 3)])  # 水平拼接 [1 1 1 2 2 2 3 3 3 1 2 3 1 2 3 1 2 3]

• np.intersect1d(a, b)获取共有项，np.setdiff1d(a, b)获取a独有项

a = np.array([1, 2, 3, 6])
b = np.array([1, 2, 8, 9])

print(np.intersect1d(a, b))  # [1 2]

print(np.setdiff1d(a, b))  # [3 6]

• np.where(a == b)获取元素匹配的位置，相同位置元素相同成为匹配

print(a == b)  # [ True  True False False]
print(np.where(a == b))  # (array([0, 1]),)

• np.where((a >= 5) & (a <= 7))提取给定范围的数字

a = np.arange(3, 10)
b = a[(a >= 5) & (a <= 7)]

print((a >= 5) & (a <= 7))       # [False False  True  True  True False False]
print(np.logical_and(a>=5, a<=7))  # [False False  True  True  True False False]
print(b)  # [5 6 7]

index1 = np.where((a >= 5) & (a <= 7))
index2 = np.where(np.logical_and(a>=5, a<=7))  # 两种方法的效果相同

print(a[index1])  # [5 6 7]
print(a[index2])  # [5 6 7]

• np.vectorize(max)，使用（自定义）函数处理数组，提取每一列的最大值

print(max(5, 6))  # 6

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

pair_max = np.vectorize(max, otypes=[float])  # 提取每一列的最大值

print(pair_max(a, b))  # [2 2 3]

• arr[:, [1, 0, 2]]，交换二维数组的两列，理解为列的复制粘贴

arr = np.arange(9).reshape(3, 3)

print(arr[:, [1, 0, 2]])
# [[1 0 2]
#  [4 3 5]
#  [7 6 8]]

• arr[[1, 0, 2], :]，交换二维数组的两行

print(arr[[1, 0, 2], :])
# [[3 4 5]
#  [0 1 2]
#  [6 7 8]]

• arr[:: -1, :]，反转二维数组的行

print(arr[::-1])
# [[6 7 8]
#  [3 4 5]
#  [0 1 2]]
print(arr[:: -1, :])
# [[6 7 8]
#  [3 4 5]
#  [0 1 2]]

• arr[:, ::-1]，反转二维数组的列

print(arr[:, ::-1])
# [[2 1 0]
#  [5 4 3]
#  [8 7 6]]

完