柚子快報邀請碼778899分享：【數(shù)據(jù)分析】NumPy

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文章目錄

@[toc]ndarray的創(chuàng)建np.array()方法np.arange()方法np.zeros()方法np.ones()方法np.full()方法np.eye()方法np.random模塊np.random.random()方法np.random.randint()方法np.random.choice()方法np.random.shuffle()方法

ndarray的屬性ndarray.dtypendarray.ndimndarray.shapendarray.sizendarray.itemsize

修改ndarray的維度ndarray.reshape()方法ndarray.resize()方法ndarray.flatten()方法

ndarray與數(shù)的運算ndarray與ndarray的運算ndarray索引與切片ndarray索引ndarray切片

ndarray的轉(zhuǎn)置ndarray.Tndarray.transpose()方法

布爾索引

個人主頁：丷從心·

系列專欄：數(shù)據(jù)分析

學(xué)習(xí)指南：Python學(xué)習(xí)指南

ndarray的創(chuàng)建

np.array()方法

import numpy as np

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

print(arr)

print(type(arr))

[1 2 3]

np.arange()方法

import numpy as np

arr = np.arange(6)

print(arr)

[0 1 2 3 4 5]

np.zeros()方法

import numpy as np

arr = np.zeros((2, 2))

print(arr)

[[0. 0.]

[0. 0.]]

np.ones()方法

import numpy as np

arr = np.ones((2, 2))

print(arr)

[[1. 1.]

[1. 1.]]

np.full()方法

import numpy as np

arr = np.full((2, 2), 2)

print(arr)

[[2 2]

[2 2]]

np.eye()方法

import numpy as np

arr = np.eye(3)

print(arr)

[[1. 0. 0.]

[0. 1. 0.]

[0. 0. 1.]]

np.random模塊

np.random.random()方法

import numpy as np

arr = np.random.random((2, 2))

print(arr)

[[0.32149432 0.01896196]

[0.62318947 0.24221268]]

np.random.randint()方法

import numpy as np

arr = np.random.randint(0, 10, size=(2, 2))

print(arr)

[[6 7]

[2 3]]

np.random.choice()方法

choice()方法用于從數(shù)組或列表中隨機采樣

import numpy as np

arr = np.arange(6)

print(np.random.choice(arr, size=(2, 3)))

[[2 1 0]

[0 2 5]]

np.random.shuffle()方法

shuffle()方法用于將數(shù)組隨機打亂

import numpy as np

arr = np.arange(6)

np.random.shuffle(arr)

print(arr)

[1 0 5 4 3 2]

ndarray的屬性

ndarray.dtype

ndarray.dtype表示數(shù)組元素的類型

import numpy as np

arr = np.random.random((2, 2))

print(arr.dtype)

float64

ndarray.ndim

ndarray.ndim表示數(shù)組的維度

import numpy as np

arr = np.random.random((2, 2))

print(arr.ndim)

2

ndarray.shape

ndarray.shape表示數(shù)組的各軸長度

import numpy as np

arr = np.random.random((2, 2))

print(arr.shape)

(2, 2)

ndarray.size

ndarray.size表示數(shù)組的元素總數(shù)量

import numpy as np

arr = np.random.random((2, 2))

print(arr.size)

4

ndarray.itemsize

ndarray.itemsize表示數(shù)組每個元素占的字節(jié)數(shù)

import numpy as np

arr = np.random.random((2, 2))

print(arr.itemsize)

8

修改ndarray的維度

ndarray.reshape()方法

reshape()方法不會修改數(shù)組本身，只返回修改后的結(jié)果

import numpy as np

arr_1 = np.arange(6)

arr_1.reshape((2, 3))

print(arr_1)

arr_2 = arr_1.reshape((2, 3))

print(arr_2)

[0 1 2 3 4 5]

[[0 1 2]

[3 4 5]]

ndarray.resize()方法

resize()方法修改數(shù)組本身，沒有返回值

import numpy as np

arr = np.arange(6)

arr.resize((2, 3))

print(arr)

[[0 1 2]

[3 4 5]]

ndarray.flatten()方法

flatten()方法用于將多維數(shù)組轉(zhuǎn)變?yōu)橐痪S數(shù)組

import numpy as np

arr = np.arange(6)

arr.resize((2, 3))

print(arr.flatten())

[0 1 2 3 4 5]

ndarray與數(shù)的運算

import numpy as np

arr = np.random.random((3, 4))

arr *= 10

print(arr.round(2))

[[6.61 5.87 3.59 9.65]

[9.91 5.98 2.7 5.72]

[2.76 3.68 2.4 2.79]]

round()方法用于修改保留小數(shù)的位數(shù)

ndarray與ndarray的運算

import numpy as np

arr_1 = np.array([[1, 2, 3],

[4, 5, 6],

[7, 8, 9]])

arr_2 = np.array([[1, 2, 3],

[4, 5, 6],

[7, 8, 9]])

print(arr_1 * arr_2)

[[ 1 4 9]

[16 25 36]

[49 64 81]]

import numpy as np

arr_1 = np.random.randint(0, 10, size=(3, 4)) # 3 行 4 列

arr_2 = np.random.randint(0, 10, size=(3, 1)) # 3 行 1 列

arr_3 = arr_1 - arr_2

print(arr_1)

print(arr_2)

print(arr_3)

[[9 5 1 6]

[4 4 9 5]

[9 0 7 3]]

[[3]

[7]

[8]]

[[ 6 2 -2 3]

[-3 -3 2 -2]

[ 1 -8 -1 -5]]

import numpy as np

arr_1 = np.random.randint(0, 10, size=(3, 4)) # 3 行 4 列

arr_2 = np.random.randint(0, 10, size=(1, 4)) # 1 行 4 列

arr_3 = arr_1 - arr_2

print(arr_1)

print(arr_2)

print(arr_3)

[[0 1 1 8]

[3 3 6 3]

[4 9 8 6]]

[[3 5 9 0]]

[[-3 -4 -8 8]

[ 0 -2 -3 3]

[ 1 4 -1 6]]

ndarray索引與切片

ndarray索引

import numpy as np

arr = np.array([[1, 2, 3],

[4, 5, 6],

[7, 8, 9]])

print(arr[0]) # 打印第 0 行的元素

print(arr[0, 0]) # 打印第 0 行第 0 列的元素

[1 2 3]

1

ndarray切片

import numpy as np

arr = np.array([[1, 2, 3],

[4, 5, 6],

[7, 8, 9]])

print(arr[:2]) # 打印第 0 ~ 1 行的元素

print(arr[:2, :2]) # 打印第 0 ~ 1 行的第 0 ~ 1 列的元素

[[1 2 3]

[4 5 6]]

[[1 2]

[4 5]]

ndarray的轉(zhuǎn)置

ndarray.T

ndarray.T不會修改數(shù)組本身，只返回轉(zhuǎn)置后的結(jié)果

import numpy as np

arr = np.arange(6).reshape((2, 3))

print(arr)

print(arr.T)

[[0 1 2]

[3 4 5]]

[[0 3]

[1 4]

[2 5]]

ndarray.transpose()方法

transpose()方法修改數(shù)組本身，并返回轉(zhuǎn)置后的結(jié)果

import numpy as np

arr = np.arange(6).reshape((2, 3))

print(arr)

print(arr.transpose())

[[0 1 2]

[3 4 5]]

[[0 3]

[1 4]

[2 5]]

布爾索引

布爾索引是指將一個布爾數(shù)組作為數(shù)組索引，返回的數(shù)據(jù)是布爾數(shù)組中Ture對應(yīng)位置的值

import numpy as np

arr_1 = np.arange(0, 12).reshape((3, 4))

arr_2 = (arr_1 < 3) | (arr_1 > 8)

print(arr_1)

print(arr_2)

print(arr_1[arr_2])

[[ 0 1 2 3]

[ 4 5 6 7]

[ 8 9 10 11]]

[[ True True True False]

[False False False False]

[False True True True]]

[ 0 1 2 9 10 11]

柚子快報邀請碼778899分享：【數(shù)據(jù)分析】NumPy

http://yzkb.51969.com/

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