Loading... - test1 ```python from sklearn.datasets import load_iris load_data = load_iris() data_X = load_data.data data_Y = load_data.target ``` - test2 ```python import numpy as np a = np.array([1,2,3]) b = np.array([[1,2,3],[3,4,5]]) print(a) print(b) x = np.zeros(5) print(x) y = np.ones(5) print(y) z = np.zeros((5,),dtype = np.int) print(z) x = np.arange(5) print(x) x = np.arange(10,20,2) print(x) a = np.arange(8) print(a) print('') b = a.reshape(4,2) print('') print(b) b = np.arange(10000) print(b) ``` - 求和 ```python import numpy as np a = np.arange(101) sum = 0 for i in range(101): sum = a[i] + sum print(sum) ``` ```python import numpy as np a = np.arange(101) sum = 0 i = 0 while(i < 101): sum = a[i] + sum i = i + 1 print(sum) ``` - 平均数 ```python data = np.array([[78,66,91],[88,76,77],[45,43,62],[65,32,67]]) print(data) ave1 = np.mean(data,axis=1) ave2 = np.mean(data,axis=0) print(ave1) print(ave2) ``` - pandas分析 (没说) - 文件读写 ```python import pandas as pd # 需要先上传 Salary_Data.csv 文件 df = pd.DataFram(pd.head_csv('Salary_Data.csv',header=1)) df.head() ``` 最后修改:2021 年 11 月 21 日 © 允许规范转载 打赏 赞赏作者 支付宝微信 赞 如果觉得我的文章对你有用,请随意赞赏