【Numpy-矩阵库~python】_python 矩阵库

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# -*- coding = utf-8 -*-
# @Time : 2022/8/7 14:30
# @Author : lxw_pro
# @File : NumPy 矩阵库.py
# @Software : PyCharm

numpy学习续

NumPy 中包含了一个矩阵库 numpy.matlib该模块中的函数返回的是一个矩阵而不是 ndarray 对象。

转置矩阵

import numpy as np

lxw = np.arange(16).reshape(4, 4)
print("原数组为\n", lxw)

print("转置过的数组为\n", lxw.T)

matlib.empty()

matlib.empty() 函数返回一个新的矩阵

import numpy.matlib

kk = np.matlib.empty((3, 3))    # 填充为随机数据
print(kk)

numpy.matlib.zeros()

numpy.matlib.zeros() 函数创建一个以 0 填充的矩阵

ll = np.matlib.zeros((3, 3))
print(ll)

numpy.matlib.ones()

numpy.matlib.ones()函数创建一个以 1 填充的矩阵

yy = np.matlib.ones((3, 3))
print(yy)

numpy.matlib.eye()

numpy.matlib.eye() 函数返回一个矩阵对角线元素为 1其他位置为零

dd = np.matlib.eye(n=3, M=4, k=0, dtype=float)
print(dd)

numpy.matlib.rand()

numpy.matlib.rand() 函数创建一个给定大小的矩阵数据是随机填充的

sj = np.matlib.rand((3, 3))
print(sj)

二维矩阵

e = np.matrix('1, 2;3, 4')
print(e)

n维数组

r = np.asarray(e)
print(r)

以上所有程序对应运行结果如下

原数组为
 [[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]
 [12 13 14 15]]
转置过的数组为
 [[ 0  4  8 12]
 [ 1  5  9 13]
 [ 2  6 10 14]
 [ 3  7 11 15]]
 
[[           nan 0.0000000e+000 1.1581509e-311]
 [2.0236929e-320 0.0000000e+000 0.0000000e+000]
 [0.0000000e+000 0.0000000e+000 0.0000000e+000]]
 
[[0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]]
 
[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]
 
[[1. 0. 0. 0.]
 [0. 1. 0. 0.]
 [0. 0. 1. 0.]]
 
[[0.39836727 0.16983388 0.91118039]
 [0.77283047 0.24608713 0.72451454]
 [0.32447712 0.21523051 0.4374776 ]]
 
[[1 2]
 [3 4]]
 
[[1 2]
 [3 4]]

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pandas 每日一练

# -*- coding = utf-8 -*-
# @Time : 2022/8/7 19:48
# @Author : lxw_pro
# @File : pandas-12 练习.py
# @Software : PyCharm

import pandas as pd
import matplotlib.pyplot as plt

lxw = pd.read_excel("site.xlsx")
print(lxw)

运行结果如下

     Unnamed: 0  Unnamed: 0.1   create_dt  ...   yye  sku_cost_prc     lrl
0             0             1  2016-11-30  ...   8.8          6.77  30.00%
1             1             2  2016-11-30  ...   7.5          5.77  30.00%
2             2             3  2016-11-30  ...   5.0          3.85  30.00%
3             3             4  2016-11-30  ...  19.6          7.54  30.00%
4             4             5  2016-12-02  ...  13.5         10.38  30.00%
..          ...           ...         ...  ...   ...           ...     ...
751         751           752  2016-12-31  ...   1.0          0.77  30.00%
752         752           753  2016-12-31  ...   2.0          1.54  30.00%
753         753           754  2016-12-31  ...   1.0          0.77  30.00%
754         754           755  2016-12-31  ...   7.6          2.92  30.00%
755         755           756  2016-12-31  ...   3.3          2.54  30.00%
[756 rows x 8 columns]

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71、以5个数据作为一个数据滑动窗口计算这五个数据总和sku_cost_prc

zh = lxw['sku_cost_prc'].rolling(5).sum()
print(zh)

运行结果如下

0        NaN
1        NaN
2        NaN
3        NaN
4      34.31
       ...  
751    10.89
752    10.51
753     8.36
754     9.90
755     8.54
Name: sku_cost_prc, Length: 756, dtype: float64

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72、将sku_cost_prc 5日均线、20日均线与原始数据绘制在同一个圈上

lxw['sku_cost_prc'].plot()
lxw['sku_cost_prc'].rolling(5).mean().plot()
lxw['sku_cost_prc'].rolling(20).mean().plot()

plt.show()

运行效果如下

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73、将数据往后移动5天

wh = lxw.shift(5)
print(wh)

运行结果如下

     Unnamed: 0  Unnamed: 0.1   create_dt  ...   yye  sku_cost_prc     lrl
0           NaN           NaN         NaN  ...   NaN           NaN     NaN
1           NaN           NaN         NaN  ...   NaN           NaN     NaN
2           NaN           NaN         NaN  ...   NaN           NaN     NaN
3           NaN           NaN         NaN  ...   NaN           NaN     NaN
4           NaN           NaN         NaN  ...   NaN           NaN     NaN
..          ...           ...         ...  ...   ...           ...     ...
751       746.0         747.0  2016-12-31  ...  20.0          2.00  40.00%
752       747.0         748.0  2016-12-31  ...   5.0          1.92  30.00%
753       748.0         749.0  2016-12-31  ...   3.8          2.92  30.00%
754       749.0         750.0  2016-12-31  ...   1.8          1.38  30.00%
755       750.0         751.0  2016-12-31  ...   3.9          3.90   2.56%
[756 rows x 8 columns]

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74、将数据往前移动5天

wq = lxw.shift(-5)
print(wq)

运行结果如下

     Unnamed: 0  Unnamed: 0.1   create_dt  ...   yye  sku_cost_prc     lrl
0           5.0           6.0  2016-12-02  ...   3.9          3.00  30.00%
1           6.0           7.0         NaN  ...  10.8          8.31  30.00%
2           7.0           8.0  2016-12-02  ...  15.5         11.92  30.00%
3           8.0           9.0  2016-12-02  ...   3.5          2.69  30.00%
4           9.0          10.0  2016-12-02  ...   NaN          7.31  30.00%
..          ...           ...         ...  ...   ...           ...     ...
751         NaN           NaN         NaN  ...   NaN           NaN     NaN
752         NaN           NaN         NaN  ...   NaN           NaN     NaN
753         NaN           NaN         NaN  ...   NaN           NaN     NaN
754         NaN           NaN         NaN  ...   NaN           NaN     NaN
755         NaN           NaN         NaN  ...   NaN           NaN     NaN
[756 rows x 8 columns]

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75、使用expanding函数计算sku_cost_prc的移动窗口均值

yj = lxw['sku_cost_prc'].expanding(min_periods=1).mean()
print(yj)

运行结果如下

0      6.770000
1      6.270000
2      5.463333
3      5.982500
4      6.862000
         ...   
751    9.549093
752    9.538429
753    9.526769
754    9.517995
755    9.508740
Name: sku_cost_prc, Length: 756, dtype: float64

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每日一言

沉缅于虚幻的梦想而忘记现实的生活这是毫无意义的千万记住。

人往往需要说很多话然后才能够归至潜默。

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