数据处理：Numpy & Pandas（1）

数据处理：Numpy & Pandas

本文来自B站up莫烦python的视频教学，在此感谢
https://www.bilibili.com/video/BV1Ex411L7oT

1 Numpy

导入numpy

import numpy as np
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1.1 Numpy数组

创建数组

a = np.array([2,3,4]) # 一维
b = np.array([[2,3,4],[1,2,3]]) # 二维
c = np.zero((3,4)) # 三行四列元素全为0的矩阵
d = np.ones((3,4)) # 三行四列元素全为1的矩阵
"""
array([[1, 1, 1, 1],
       [1, 1, 1, 1],
       [1, 1, 1, 1]])
"""
e = np.empty((3,4)) # 数据为empty，3行4列
"""
array([[  0.00000000e+000,   4.94065646e-324,   9.88131292e-324,
          1.48219694e-323],
       [  1.97626258e-323,   2.47032823e-323,   2.96439388e-323,
          3.45845952e-323],
       [  3.95252517e-323,   4.44659081e-323,   4.94065646e-323,
          5.43472210e-323]])
"""
f = np.arange(10,20,2) # 10-19 的数据，2步长
"""
array([10, 12, 14, 16, 18])
"""
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指定元素类型

a = np.array([2,3,4],dtype=np.float)
print(a.dtype)
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改变形状

a = np.arange(12).reshape((3,4))    # 3行4列，0到11
"""
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])
"""
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linspace：arrange的“变种”

a = np.linspace(1,10,20)    # 开始端1，结束端10，且分割成20个数据，生成线段
"""
array([  1.        ,   1.47368421,   1.94736842,   2.42105263,
         2.89473684,   3.36842105,   3.84210526,   4.31578947,
         4.78947368,   5.26315789,   5.73684211,   6.21052632,
         6.68421053,   7.15789474,   7.63157895,   8.10526316,
         8.57894737,   9.05263158,   9.52631579,  10.        ])
"""
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1.2 Numpy的运算1

Numpy支持元素的+、-、*运算

此外还有

乘方

c=b**2  # array([0, 1, 4, 9])
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三角函数的运算

c=10*np.sin(a)  
# array([-5.44021111,  9.12945251, -9.88031624,  7.4511316 ])
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逻辑判断

（返回的是一个bool类型的矩阵）

print(b<3)  
# array([ True,  True,  True, False], dtype=bool)
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标准矩阵乘法 dot

a=np.array([[1,1],[0,1]])
b=np.arange(4).reshape((2,2))

print(a)
# array([[1, 1],
#       [0, 1]])

print(b)
# array([[0, 1],
#       [2, 3]])

c_dot = np.dot(a,b)
# array([[2, 4],
#       [2, 3]])

c_dot_2 = a.dot(b)
# array([[2, 4],
#       [2, 3]])
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其他运算

Numpy矩阵对 random、max、min、sum 的应用

a=np.random.random((2,4))
print(a)
# array([[ 0.94692159,  0.20821798,  0.35339414,  0.2805278 ],
#       [ 0.04836775,  0.04023552,  0.44091941,  0.21665268]])
# 生成2行4列矩阵，每个元素0-1的随机数

np.sum(a)   # 4.4043622002745959
np.min(a)   # 0.23651223533671784
np.max(a)   # 0.90438450240606416


print("a =",a)
# a = [[ 0.23651224  0.41900661  0.84869417  0.46456022]
# [ 0.60771087  0.9043845   0.36603285  0.55746074]]

print("sum =",np.sum(a,axis=1))
# sum = [ 1.96877324  2.43558896]
# 按行求合

print("min =",np.min(a,axis=0))
# min = [ 0.23651224  0.41900661  0.36603285  0.46456022]
# 每一列的最小值

print("max =",np.max(a,axis=1))
# max = [ 0.84869417  0.9043845 ]
# 每一行的最大值
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1.3 Numpy的运算3

最大值和最小值

求矩阵最小值和最大值的索引

import numpy as np
A = np.arange(2,14).reshape((3,4)) 

# array([[ 2, 3, 4, 5]
#        [ 6, 7, 8, 9]
#        [10,11,12,13]])
         
print(np.argmin(A))    # 0
print(np.argmax(A))    # 11
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均值与中位数

整个矩阵的均值中位数

print(np.mean(A))        # 7.5
print(np.average(A))     # 7.5
print(A.median())       # 7.5
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axis的操作同样适用与均值，同时还可以指定权重

b = np.array([1, 2, 3, 4])
wts = np.array([4, 3, 2, 1])
print('不指定权重\n', np.average(b))
print('指定权重\n', np.average(b, weights=wts))
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两种矩阵转置

print(np.transpose(A))    
print(A.T)

# array([[14,10, 6]
#        [13, 9, 5]
#        [12, 8, 4]
#        [11, 7, 3]])
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累加函数与累差函数

print(np.cumsum(A)) 

# [2 5 9 14 20 27 35 44 54 65 77 90]

print(np.diff(A))    

# [[1 1 1]
#  [1 1 1]
#  [1 1 1]]

# A = array([[ 2, 3, 4, 5]
#        	[ 6, 7, 8, 9]
#        	[10,11,12,13]])
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其他函数

nonzero()，将矩阵中所有非0元素的行和列拆成两个矩阵

print(np.nonzero(A))    

# (array([0,0,0,0,1,1,1,1,2,2,2,2]),array([0,1,2,3,0,1,2,3,0,1,2,3]))
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排序

print(np.sort(A))    

# array([[11,12,13,14]
#        [ 7, 8, 9,10]
#        [ 3, 4, 5, 6]])
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clip()函数：将矩阵中的元素都转换为固定区间的元素

print(A)
# array([[14,13,12,11]
#        [10, 9, 8, 7]
#        [ 6, 5, 4, 3]])

print(np.clip(A,5,9))    
# array([[ 9, 9, 9, 9]
#        [ 9, 9, 8, 7]
#        [ 6, 5, 5, 5]])
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1.4 Numpy 索引

Numpy 支持[]索引，和数组一样

如果矩阵是二维的，则有

A = np.arange(3,15).reshape((3,4))
"""
array([[ 3,  4,  5,  6]
       [ 7,  8,  9, 10]
       [11, 12, 13, 14]])
"""
         
print(A[2])         
# [11 12 13 14]
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二维索引

访问单个元素的两种办法

print(A[1][1])      # 8
print(A[1, 1])      # 8
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切片操作

print(A[1, 1:3])    # [8 9]
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逐行输出和逐列输出

for row in A:
    print(row)
"""    
[ 3,  4,  5, 6]
[ 7,  8,  9, 10]
[11, 12, 13, 14]
"""

for column in A.T:
    print(column)
"""  
[ 3,  7,  11]
[ 4,  8,  12]
[ 5,  9,  13]
[ 6, 10,  14]
"""
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迭代输出

A = np.arange(3,15).reshape((3,4))
         
print(A.flatten())   
# array([3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])

for item in A.flat:
    print(item)
    
# 3
# 4
……
# 14
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1.5 Numpy array 合并

上下合并 vstack()

import numpy as np
A = np.array([1,1,1])
B = np.array([2,2,2])
         
print(np.vstack((A,B)))    # vertical stack
"""
[[1,1,1]
 [2,2,2]]
"""
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左右合并 hstack()

D = np.hstack((A,B))       # horizontal stack

print(D)
# [1,1,1,2,2,2]

print(A.shape,D.shape)
# (3,) (6,)
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newaxis()

有些矩阵可能无法通过 .T 进行转置，这时候可以借助newaxis()

print(A[np.newaxis,:])
# [[1 1 1]]

print(A[np.newaxis,:].shape)
# (1,3)

print(A[:,np.newaxis])
"""
[[1]
[1]
[1]]
"""

print(A[:,np.newaxis].shape)
# (3,1)
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多个矩阵操作：concatenate()

C = np.concatenate((A,B,B,A),axis=0)

print(C)
"""
array([1 1 1 2 2 2 2 2 2 1 1 1])
"""

D = np.concatenate((A,B,B,A),axis=1)

print(D)
"""
array([[1, 2, 2, 1],
       [1, 2, 2, 1],
       [1, 2, 2, 1]])
"""
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1.6 Numpy Array分割

创建array

A = np.arange(12).reshape((3, 4))
print(A)
"""
array([[ 0,  1,  2,  3],
    [ 4,  5,  6,  7],
    [ 8,  9, 10, 11]])
"""

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等量分割

纵向分割

print(np.split(A, 2, axis=1))
"""
[array([[0, 1],
        [4, 5],
        [8, 9]]), array([[ 2,  3],
        [ 6,  7],
        [10, 11]])]
"""
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横向分割

print(np.split(A, 3, axis=0))

# [array([[0, 1, 2, 3]]), array([[4, 5, 6, 7]]), array([[ 8,  9, 10, 11]])]
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这两种也可以有其他的实现方式

print(np.vsplit(A, 3)) #等于 print(np.split(A, 3, axis=0))

# [array([[0, 1, 2, 3]]), array([[4, 5, 6, 7]]), array([[ 8,  9, 10, 11]])]


print(np.hsplit(A, 2)) #等于 print(np.split(A, 2, axis=1))
"""
[array([[0, 1],
       [4, 5],
       [8, 9]]), array([[ 2,  3],
        [ 6,  7],
        [10, 11]])]
"""
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不等量的分割

print(np.array_split(A, 3, axis=1))
"""
[array([[0, 1],
        [4, 5],
        [8, 9]]), array([[ 2],
        [ 6],
        [10]]), array([[ 3],
        [ 7],
        [11]])]
"""
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1.7 Numpy copy

创建变量

import numpy as np

a = np.arange(4)
# array([0, 1, 2, 3])

b = a
c = a
d = b
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试着改变值

a[0] = 11
print(a)
# array([11,  1,  2,  3])

b is a  # True
c is a  # True
d is a  # True

d[1:3] = [22, 33]   # array([11, 22, 33,  3])
print(a)            # array([11, 22, 33,  3])
print(b)            # array([11, 22, 33,  3])
print(c)            # array([11, 22, 33,  3])
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使用copy() 则可以使这种关联失效

b = a.copy()    # deep copy
print(b)        # array([11, 22, 33,  3])
a[3] = 44
print(a)        # array([11, 22, 33, 44])
print(b)        # array([11, 22, 33,  3])
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