《利用Python进行数据分析》第四章Numpy
文章目录
- 前言
- 代码
前言
没什么好说的，直接code
代码
import numpy as np
import datetime
import matplotlib.pyplot as plt
'''
运行结果：
0
3990
'''
def numpy_time_low():
# 创建了ndarray
my_arr = np.arange(10000)
# 创建了list
my_list = list(range(10000))
# 打印出两个变量的类型
print(type(my_arr))
print(type(my_list))
# print(my_arr)
# print(my_list)
'''
numpy的方法比python要快
'''
start_time = datetime.datetime.now()
for _ in range(10):
my_arr2 = my_arr * 2
end_time = datetime.datetime.now()
print((end_time-start_time).microseconds)
start_time = datetime.datetime.now()
for _ in range(10):
ny_list2 = [x * 2 for x in my_list]
end_time = datetime.datetime.now()
print((end_time-start_time).microseconds)
'''
Numpy多维数组对象
[[-0.70708187 -0.11535279 -0.77706864]
[ 0.12345839 -0.25059675 0.6478144 ]]
[[-7.07081866 -1.15352787 -7.77068637]
[ 1.23458394 -2.50596748 6.47814402]]
[[-1.41416373 -0.23070557 -1.55413727]
[ 0.24691679 -0.5011935 1.2956288 ]]
(2, 3)
float64
'''
def multi_dimention_arr():
import numpy as np
# 采用随机数方法构建数组
data = np.random.randn(2,3)
print(data)
# 每个元素 * 10
print(data * 10)
# 数组中对应元素相加
print(data + data)
# 打印矩阵的维度信息
print(data.shape)
# 打印矩阵的data type
print(data.dtype)
"""
如何生成ndarray
[6. 7.5 8. 0. 1. ]
[[1 2 3 4]
[5 6 7 8]]
1
(5,)
float64
2
(2, 4)
int32
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[[0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0.]]
[[[0. 0.]
[0. 0.]
[0. 0.]]
[[0. 0.]
[0. 0.]
[0. 0.]]]
[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14]
"""
def product_ndarray():
data1 = [6, 7.5, 8, 0, 1]
# list to ndarray
arr1 = np.array(data1)
data2 = [[1, 2, 3, 4],
[5, 6, 7, 8]]
arr2 = np.array(data2)
print(type(arr1))
print(type(arr2))
print(arr1)
print(arr2)
print(arr1.ndim)
print(arr1.shape)
print(arr1.dtype)
print(arr2.ndim)
print(arr2.shape)
print(arr2.dtype)
print(np.zeros(10))
print(np.zeros((3,6)))
# 没有初始化的。 python3.6 没有初始化就是随机的
print(np.empty((2,3,2)))
print(np.arange(15))
'''
ndarray的数据类型
float64
int32
float64
[ 1.25 -9.6 42. ]
'''
def ndarray_data_type():
# 强制生命ndarray中的元素的类型为float64
arr1 = np.array([1, 2, 3], dtype=np.float64)
arr2 = np.array([1, 2, 3], dtype=np.int32)
print(arr1.dtype)
print(arr2.dtype)
# astype函数进行类型转换
float_arr = arr2.astype(np.float64)
print(float_arr.dtype)
numeric_strings = np.array(['1.25', '-9.6', '42'], dtype=np.string_)
print(numeric_strings.astype(np.float64))
"""
8位的无符号整形：
0000 0000 ： 0
1111 1111 ： 255
8位的有符号整形：
1 000 0000 ： -0（-128）
1 111 1111
0 000 0000 ： +0
0 111 1111： +127
"""
'''
Numpy的四则运算
运行结果：
[[1. 2. 3.]
[4. 5. 6.]]
[[ 1. 4. 9.]
[16. 25. 36.]]
[[0. 0. 0.]
[0. 0. 0.]]
[[1. 0.5 0.33333333]
[0.25 0.2 0.16666667]]
[[1. 1.41421356 1.73205081]
[2. 2.23606798 2.44948974]]
[[ 0. 4. 1.]
[ 7. 2. 12.]]
[[False True False]
[ True False True]]
'''
def numpy_4_operator():
# arr = np.array([1., 2., 3.], [4., 5., 6.])
arr = np.array([[1., 2., 3.], [4., 5., 6.]])
print(arr)
print(arr * arr) # 元素相乘
print(arr - arr)
print(1/arr)
print(arr ** 0.5) # ** 幂操作
arr2 = np.array([[0., 4., 1.], [7., 2., 12.]])
print(arr2)
print(arr2 > arr)
'''
Traceback (most recent call last):
File "NumPy.py", line 203, in
numpy_4_operator()
File "NumPy.py", line 182, in numpy_4_operator
arr = np.array([1., 2., 3.], [4., 5., 6.])
TypeError: data type not understood
182 arr = np.array([1., 2., 3.], [4., 5., 6.])
arr = np.array([[1., 2., 3.], [4., 5., 6.]])
'''
'''
基本索引和切片
运行结果：
'''
def base_index_slice():
# arr = np.arange(10)
# print(arr)
# print(arr[5])
# print(arr[5:8])
# arr[5:8] = 12
# print(arr)
'''
0 1 2 3 4 12 12 12 8 9(内存里面的)
arr->
arr_slice->
12 12 12
'''
# # arr_slice是引用，修改arr_slice会影响arr的值
# arr_slice = arr[5:8]
# print(arr_slice)
# arr_slice[1] = 12345
# print(arr)
# 二维矩阵
arr2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
# print(arr2d)
# print(arr2d[2])
# print(arr2d[0][2])
# print(arr2d[0,2])
# 三维矩阵 （图像处理）
# arr3d = np.array([[[1,2,3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])
# print(arr3d)
# print(arr3d[0])
# old_values = arr3d[0].copy()
# arr3d[0] = 42
# print(arr3d)
# arr3d[0] = old_values
# print(arr3d)
# print(arr3d[1,0])
# print(arr2d[:2]) # [0:2]
# print(arr2d[:2, 1:])
# print(arr2d[1, :2])
# temp = arr2d[:2, 2]
# print(temp)
# print(temp.ndim)
# print(temp.shape)
# temp = temp.reshape(2,1)
# print(temp)
# print(temp.ndim)
# print(temp.shape)
# temp = temp.reshape(1,2)
# print(temp)
# print(temp.ndim)
# print(temp.shape)
'''
[[1]
[4]
[7]]
[1 4 7]
'''
# print(arr2d[:, :1])
# print(arr2d[:, 0])
'''
[[1 0 0]
[4 0 0]
[7 8 9]]
'''
arr2d[:2, 1:] = 0
print(arr2d)
'''
布尔索引
结果：
['Bob' 'Joe' 'Will' 'Bob' 'Will' 'Joe' 'Joe']
[[ 0.69243252 1.04347406 1.55725763 -0.48528173]
[ 0.08793525 0.02784941 -2.05238174 0.18119838]
[ 0.6287053 0.8985214 -0.41873237 0.40412278]
[ 0.92062871 -0.11166092 0.25947438 1.93028334]
[-0.7594316 -0.63923709 -0.78700976 0.85967505]
[-0.31072595 -0.21578612 -0.71202604 0.69716139]
[ 1.63542645 1.00164738 -0.26152352 -0.00775471]]
[ True False False True False False False]
[[ 0.69243252 1.04347406 1.55725763 -0.48528173]
[ 0.92062871 -0.11166092 0.25947438 1.93028334]]
[[ 1.55725763 -0.48528173]
[ 0.25947438 1.93028334]]
[-0.48528173 1.93028334]
[False True True False True True True]
[[ 0.08793525 0.02784941 -2.05238174 0.18119838]
[ 0.6287053 0.8985214 -0.41873237 0.40412278]
[-0.7594316 -0.63923709 -0.78700976 0.85967505]
[-0.31072595 -0.21578612 -0.71202604 0.69716139]
[ 1.63542645 1.00164738 -0.26152352 -0.00775471]]
[ True False False True False False False]
[[ 0.08793525 0.02784941 -2.05238174 0.18119838]
[ 0.6287053 0.8985214 -0.41873237 0.40412278]
[-0.7594316 -0.63923709 -0.78700976 0.85967505]
[-0.31072595 -0.21578612 -0.71202604 0.69716139]
[ 1.63542645 1.00164738 -0.26152352 -0.00775471]]
[ True False True True True False False]
[[ 0.69243252 1.04347406 1.55725763 -0.48528173]
[ 0.6287053 0.8985214 -0.41873237 0.40412278]
[ 0.92062871 -0.11166092 0.25947438 1.93028334]
[-0.7594316 -0.63923709 -0.78700976 0.85967505]]
[[0.69243252 1.04347406 1.55725763 0. ]
[0.08793525 0.02784941 0. 0.18119838]
[0.6287053 0.8985214 0. 0.40412278]
[0.92062871 0. 0.25947438 1.93028334]
[0. 0. 0. 0.85967505]
[0. 0. 0. 0.69716139]
[1.63542645 1.00164738 0. 0. ]]
[[7. 7. 7. 7. ]
[0.08793525 0.02784941 0. 0.18119838]
[7. 7. 7. 7. ]
[7. 7. 7. 7. ]
[7. 7. 7. 7. ]
[0. 0. 0. 0.69716139]
[1.63542645 1.00164738 0. 0. ]]
'''
def bool_index():
names = np.array(['Bob', 'Joe', 'Will', 'Bob', 'Will', 'Joe', 'Joe'])
print(names)
data = np.random.randn(7,4)
print(data)
print(names =='Bob')
print(data[names == 'Bob'])
print(data[names == 'Bob', 2:])
print(data[names == 'Bob', 3])
print(names !='Bob')
print(data[~(names == 'Bob')])
cond = names == 'Bob'
print(type(cond))
print(cond)
print(data[~cond])
mask = (names == 'Bob') | (names == 'Will')
print(mask)
print(data[mask])
data[data<0] = 0
print(data)
data[names!='Joe'] =7
print(data)
'''
神奇索引
结果：
[[0. 0. 0. 0.]
[1. 1. 1. 1.]
[2. 2. 2. 2.]
[3. 3. 3. 3.]
[4. 4. 4. 4.]
[5. 5. 5. 5.]
[6. 6. 6. 6.]
[7. 7. 7. 7.]]
[[4. 4. 4. 4.]
[3. 3. 3. 3.]
[0. 0. 0. 0.]
[6. 6. 6. 6.]]
[[5. 5. 5. 5.]
[3. 3. 3. 3.]
[1. 1. 1. 1.]]
[[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]
[12 13 14 15]
[16 17 18 19]
[20 21 22 23]
[24 25 26 27]
[28 29 30 31]]
[[ 4 5 6 7]
[20 21 22 23]
[28 29 30 31]
[ 8 9 10 11]]
[ 4 23 29 10]
[[ 4 7 5 6]
[20 23 21 22]
[28 31 29 30]
[ 8 11 9 10]]
[[ 4 5 6 7]
[ 8 9 10 11]
[20 21 22 23]
[28 29 30 31]]
'''
def magical_index():
arr = np.empty((8,4))
for i in range(8):
arr[i] = i
print(arr)
print(arr[[4,3,0,6]])
print(arr[[-3, -5, -7]])
arr = np.arange(32).reshape((8,4))
print(arr)
print(arr[[1,5,7,2]])
print(arr[ [1,5,7,2], [0,3,1,2] ])
print(arr[[1,5,7,2]][:,[0,3,1,2]])
print(arr[[1,5,7,2]][[0,3,1,2]])
'''
数组的切片索引
'''
def arr_slice_index():
arr = np.arange(10)
arr_slice = arr[5:8]
arr_slice[0:3]=64
print(arr)
print(arr[1:6])
'''
作业：
练习sql；
安装python；
命令行运行python；
安装visual studio code；
解决MySQL存储中文；
'''
'''
数组的转置和换轴
'''
def arr_reverse_alter_axies():
# arr = np.arange(15).reshape(3,5)
# print(arr)
# print(arr.T)
# arr = np.random.randn(6,3)
# print(arr)
# print(np.dot(arr.T, arr))
arr = np.arange(16).reshape(2, 2, 4)
print(arr)
# 没看明白
# print(arr.transpose((1,0,2)))
print(arr.swapaxes(1,2))
'''
通用函数
结果：
[0 1 2 3 4 5 6 7 8 9]
[0. 1. 1.41421356 1.73205081 2. 2.23606798
2.44948974 2.64575131 2.82842712 3. ]
[1.00000000e+00 2.71828183e+00 7.38905610e+00 2.00855369e+01
5.45981500e+01 1.48413159e+02 4.03428793e+02 1.09663316e+03
2.98095799e+03 8.10308393e+03]
[-0.38198195 -0.41349334 -0.22681757 1.07143114 0.81742682 0.20248671
-0.70789151 -1.43965095]
[-0.49113055 0.40064256 1.1547515 -0.92835289 0.61822386 -0.76110323
0.44788687 -0.00267016]
[-0.38198195 0.40064256 1.1547515 1.07143114 0.81742682 0.20248671
0.44788687 -0.00267016]
[ 3.93518567 2.70134773 4.04039374 -0.55103264 -1.49523371 -3.24249544
-3.59989243]
[ 0.93518567 0.70134773 0.04039374 -0.55103264 -0.49523371 -0.24249544
-0.59989243]
[ 3. 2. 4. -0. -1. -3. -3.]
'''
def common_function():
arr = np.arange(10)
print(arr)
print(np.sqrt(arr))
print(np.exp(arr))
x = np.random.randn(8)
y = np.random.randn(8)
print(x)
print(y)
print(np.maximum(x,y))
arr = np.random.randn(7) * 5
print(arr)
#modf函数返回浮点数的小数部分和证书部分
remainder, whole_part = np.modf(arr)
print(remainder)
print(whole_part)
'''
面向数组编程
结果：
[-2 -1 0 1]
[[-2 -1 0 1]
[-2 -1 0 1]
[-2 -1 0 1]
[-2 -1 0 1]]
[[-2 -2 -2 -2]
[-1 -1 -1 -1]
[ 0 0 0 0]
[ 1 1 1 1]]
[[2.82842712 2.23606798 2. 2.23606798]
[2.23606798 1.41421356 1. 1.41421356]
[2. 1. 0. 1. ]
[2.23606798 1.41421356 1. 1.41421356]]
'''
def face_to_array():
points = np.arange(-2, 2 , 1)
print(points)
xs,ys = np.meshgrid(points,points)
print(xs)
print(ys)
z = np.sqrt(xs ** 2 + ys ** 2)
print(z)
plt.imshow(z, cmap = plt.cm.gray)
plt.colorbar()
plt.title("Image plot of $\sqrt{x^2 + y^2}$ for a grid of values")
plt.show()
'''
将条件逻辑作为数组操作
运行结果：
[1.1, 2.2, 1.3, 1.4, 2.5]
[2.1 2.2 2.3 2.4 2.5]
[[ 0.63752465 0.51828278 -0.47482236 0.94612604]
[ 1.09255775 1.19802064 -0.08868469 -0.56213092]
[ 0.13160435 0.94530834 -0.0552738 0.5749255 ]
[ 2.10613667 -0.55368743 -1.1441873 0.41439221]]
[[ True True False True]
[ True True False False]
[ True True False True]
[ True False False True]]
[[ 2 2 -2 2]
[ 2 2 -2 -2]
[ 2 2 -2 2]
[ 2 -2 -2 2]]
[[ 2. 2. -0.47482236 2. ]
[ 2. 2. -0.08868469 -0.56213092]
[ 2. 2. -0.0552738 2. ]
[ 2. -0.55368743 -1.1441873 2. ]]
'''
def condition_array_op():
xarray = np.array([1.1, 1.2, 1.3, 1.4, 1.5])
yarray = np.array([2.1, 2.2, 2.3, 2.4, 2.5])
cond = np.array([True, False, True, True, False])
result = [(x if c else y) for x, y, c in zip(xarray, yarray, cond)]
print(result)
result = np.where(xarray, yarray, cond)
print(result)
arr = np.random.randn(4,4)
print(arr)
print(arr > 0)
print(np.where(arr > 0, 2, -2))
print(np.where(arr > 0, 2, arr))
'''
数学和统计方法
[[0 1 2]
[3 4 5]
[6 7 8]]
[[ 0 1 2]
[ 3 5 7]
[ 9 12 15]]
[[ 0 1 2]
[ 0 4 10]
[ 0 28 80]]
'''
def math_statistic_method():
arr = np.random.randn(5,4)
# print(arr)
# print(arr.mean())
# print(np.mean(arr))
# print(arr.sum())
# print(arr.mean(axis = 1))
# print(arr.sum(axis = 0))
arr = np.array([0, 1, 2, 3, 4, 5, 6, 7])
# print(arr)
# print(arr.cumsum())
arr = np.array([[0,1,2], [3, 4, 5], [6, 7, 8]])
# print(arr)
# print(arr.cumsum(axis = 0))
# print(arr.cumprod(axis = 0))
### bool值数组的方法
arr = np.random.randn(10)
print(arr)
# 统计正数的个数
print((arr > 0).sum())
bools = np.array([False, False, True, False])
# 至少有一个为TRUE
print(bools.any())
# 所有都为true
print(bools.all())
'''
排序
'''
def sort():
arr = np.random.randn(5)
print(arr)
arr.sort()
print(arr)
# 20% percentile
print(arr[int(0.2 * len(arr))])
arr = np.random.randn(5,3)
print(arr)
arr.sort(axis = 1)
print(arr)
'''
集合操作
['Bob' 'Joe' 'Will']
['Bob', 'Joe', 'Will']
[ True False False True True False True]
'''
def set_operate():
names = np.array(['Bob', 'Joe', 'Will', 'Bob', 'Will', 'Joe', 'Joe'])
# distinct
print(np.unique(names))
print(sorted(set(names)))
values = np.array([6, 0, 0, 3, 2, 5, 6])
print(np.in1d(values, [2, 3, 6]))
'''
文件操作
'''
def file_operate():
arr = np.arange(10)
np.save('some_arr', arr)
print(np.load('some_arr.npy'))
# k - v 结构
np.savez('arr_archive.npz', a = arr, b = arr)
arch = np.load('arr_archive.npz')
print(arch)
# k - v 结构
# {'a':[0 1 2 3 4 5 6 7 8 9],
# 'b':[0 1 2 3 4 5 6 7 8 9] }
print(arch['b'])
print(arch['a'])
print(arch['A'])
'''
线性代数
[[ 28. 64.]
[ 67. 181.]]
[[ 28. 64.]
[ 67. 181.]]
[[ 1.16999411 -0.10067219 -1.33726426 -1.20643037 -1.32953707]
[-0.10067219 0.50586977 0.42394564 0.27183461 -0.10028987]
[-1.33726426 0.42394564 2.92391913 2.01479364 1.78308665]
[-1.20643037 0.27183461 2.01479364 1.95243865 1.81276902]
[-1.32953707 -0.10028987 1.78308665 1.81276902 2.71003925]]
[[ 1.00000000e+00 1.00804182e-17 3.63016207e-16 5.32506845e-16
-3.21293394e-16]
[-9.83318096e-17 1.00000000e+00 2.08552662e-18 1.73960705e-16
3.96910012e-17]
[-2.59141111e-16 -1.65073195e-17 1.00000000e+00 -2.85411106e-16
3.59974278e-16]
[ 1.62130244e-16 -1.61380005e-16 -3.56803605e-16 1.00000000e+00
-2.30618373e-17]
[ 2.55489996e-16 1.93773878e-16 -8.54602643e-17 1.86392195e-16
1.00000000e+00]]
[[-0.9338917 -0.05630678 -0.09346094 0.05038271 -0.33675283]
[ 0.02899543 -0.90409277 0.12760495 -0.40601384 -0.025402 ]
[-0.12679454 0.11498625 -0.70762233 -0.51575367 0.45163049]
[-0.27264825 0.26225619 0.68774028 -0.41602943 0.45914861]
[-0.19128938 -0.31216287 -0.03558053 0.6273213 0.6864144 ]]
[[-2.91756504 0.29133256 -0.2324026 -1.32121277 -0.45447127]
[ 0. -3.32956346 0.20826961 1.93050414 -1.66676745]
[ 0. 0. -1.7613716 2.91880761 -0.80664011]
[ 0. 0. 0. -1.12946828 0.98699176]
[ 0. 0. 0. 0. 0.25328578]]
'''
def liner_daishu():
x = np.array([[1.,2.,3.], [4., 5., 6.]])
y = np.array([[6., 23.], [-1, 7], [8, 9]])
# 矩阵乘法
# *
print(x.dot(y))
print(np.dot(x, y))
from numpy.linalg import inv,qr
X = np.random.randn(5, 5)
mat = X.T.dot(X)
# inv 求逆矩阵
print(inv(mat))
print()
# E矩阵
# 数值解：3.33333333333333333333333333333333
# 分析解：10/3
print(mat.dot(inv(mat)))
q, r = qr(mat)
print(q)
print(r)
'''
伪随机数
[[-0.6397935 0.47535849 0.43555024 1.18810862]
[ 0.17208923 -1.31364949 -0.1641457 0.40799942]
[-0.37597055 -0.92800192 1.1709309 1.85358984]
[ 1.08287133 1.18566971 -0.69085958 1.70358103]]
-2.198533078572999
'''
def random_num():
# 正态分布
samples = np.random.normal(size = (4,4))
print(samples)
from random import normalvariate
print(normalvariate(0,1))
'''
随机漫步
'''
def random_walk():
import random
#
position = 0
walk = [position] # walk = [0]
steps = 1000
for i in range(steps):
step = 1 if random.randint(0, 1) else -1
# step = 0
# if random.randint(0, 1) == 1:
# step = 1
# else:
# step = -1
position += step
walk.append(position)
print(len(walk))
plt.plot(walk[:100])
plt.show()
'''
效果同上
'''
nsteps = 1000
draws = np.random.randint(0, 2, size = nsteps)
steps = np.where(draws >0 , 1, -1)
walk = steps.cumsum()
print(len(walk))
print(walk.min())
print(walk.max())
#朝同一方向，连续走了10步的第一次出现的位置
# +8 -2 +4
print((np.abs(walk) >= 10).argmax())
#一次性模拟多个随机漫步
nwalks = 5000
nsteps = 1000
draws = np.random.randint(0,2, size=(nwalks, nsteps))
steps = np.where(draws >0 , 1, -1)
walks = np.cumsum(1)
# 声明主函数入口
if __name__ == "__main__":
# multi_dimention_arr()
# product_ndarray()
# ndarray_data_type()
# numpy_4_operator()
# base_index_slice()
# arr_slice_index()
# bool_index()
# magical_index()
# arr_reverse_alter_axies()
# common_function()
# face_to_array()
# condition_array_op()
# math_statistic_method()
# sort()
# set_operate()
# file_operate()
# liner_daishu()
# random_num()
random_walk()