NumPy数值计算基础实训

编程入门行业动态更新时间:2024-10-25 16:20:14

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NumPy数值计算基础实训

文章目录

实训目的
实训要求
- 1：导入模块
- 2：获取数据
- 3：数据清理：去掉索引号
- 4：数据统计
- - 1：创建数据类型
  - 2：创建二维数组
  - 3：将待处理数据的类型转化为float类型
  - 4：排序
  - 5：数组去重
  - 6：对指定列求和、均值、标准差、方差、最小值和最大值
  - - 6-1：求和
    - 6-2：均值
    - 6-3：标准差
    - 6-4：方差
    - 6-5：最小值
    - 6-6：最大值
完整代码

实训目的

1.熟练掌握NumPy多维数组；
2.熟练掌握NumPy索引和切片；
3.熟练掌握NumPy数组读写及数据统计分析

实训要求

读取iris数据集中鸢尾花的萼片、花瓣长度数据（已保存为CSV格式），并对其进行排序、去重，并求出和、累积和、均值、标准差、方差、最小值、最大值

1：导入模块

import numpy as np
import csv

2：获取数据

实验数据

import numpy as np
import csviris_data = []
with open("iris.csv") as csvfile:csv_reader = csv.reader(csvfile)birth_header = next(csv_reader)for row in csv_reader:iris_data.append(row)

输出数据获取的数据看一下是否正确，为了方便查看每两组数据为一行

i = 0
for x in iris_data:i+=1print(x, end='')if i % 2 == 0:print()

['1', '5.1', '3.5', '1.4', '0.2', 'setosa']['2', '4.9', '3', '1.4', '0.2', 'setosa']
['3', '4.7', '3.2', '1.3', '0.2', 'setosa']['4', '4.6', '3.1', '1.5', '0.2', 'setosa']
['5', '5', '3.6', '1.4', '0.2', 'setosa']['6', '5.4', '3.9', '1.7', '0.4', 'setosa']
['7', '4.6', '3.4', '1.4', '0.3', 'setosa']['8', '5', '3.4', '1.5', '0.2', 'setosa']
['9', '4.4', '2.9', '1.4', '0.2', 'setosa']['10', '4.9', '3.1', '1.5', '0.1', 'setosa']
['11', '5.4', '3.7', '1.5', '0.2', 'setosa']['12', '4.8', '3.4', '1.6', '0.2', 'setosa']
['13', '4.8', '3', '1.4', '0.1', 'setosa']['14', '4.3', '3', '1.1', '0.1', 'setosa']
['15', '5.8', '4', '1.2', '0.2', 'setosa']['16', '5.7', '4.4', '1.5', '0.4', 'setosa']
['17', '5.4', '3.9', '1.3', '0.4', 'setosa']['18', '5.1', '3.5', '1.4', '0.3', 'setosa']
['19', '5.7', '3.8', '1.7', '0.3', 'setosa']['20', '5.1', '3.8', '1.5', '0.3', 'setosa']
['21', '5.4', '3.4', '1.7', '0.2', 'setosa']['22', '5.1', '3.7', '1.5', '0.4', 'setosa']
['23', '4.6', '3.6', '1', '0.2', 'setosa']['24', '5.1', '3.3', '1.7', '0.5', 'setosa']
['25', '4.8', '3.4', '1.9', '0.2', 'setosa']['26', '5', '3', '1.6', '0.2', 'setosa']
['27', '5', '3.4', '1.6', '0.4', 'setosa']['28', '5.2', '3.5', '1.5', '0.2', 'setosa']
['29', '5.2', '3.4', '1.4', '0.2', 'setosa']['30', '4.7', '3.2', '1.6', '0.2', 'setosa']
['31', '4.8', '3.1', '1.6', '0.2', 'setosa']['32', '5.4', '3.4', '1.5', '0.4', 'setosa']
['33', '5.2', '4.1', '1.5', '0.1', 'setosa']['34', '5.5', '4.2', '1.4', '0.2', 'setosa']
['35', '4.9', '3.1', '1.5', '0.2', 'setosa']['36', '5', '3.2', '1.2', '0.2', 'setosa']
['37', '5.5', '3.5', '1.3', '0.2', 'setosa']['38', '4.9', '3.6', '1.4', '0.1', 'setosa']
['39', '4.4', '3', '1.3', '0.2', 'setosa']['40', '5.1', '3.4', '1.5', '0.2', 'setosa']
['41', '5', '3.5', '1.3', '0.3', 'setosa']['42', '4.5', '2.3', '1.3', '0.3', 'setosa']
['43', '4.4', '3.2', '1.3', '0.2', 'setosa']['44', '5', '3.5', '1.6', '0.6', 'setosa']
['45', '5.1', '3.8', '1.9', '0.4', 'setosa']['46', '4.8', '3', '1.4', '0.3', 'setosa']
['47', '5.1', '3.8', '1.6', '0.2', 'setosa']['48', '4.6', '3.2', '1.4', '0.2', 'setosa']
['49', '5.3', '3.7', '1.5', '0.2', 'setosa']['50', '5', '3.3', '1.4', '0.2', 'setosa']
['51', '7', '3.2', '4.7', '1.4', 'versicolor']['52', '6.4', '3.2', '4.5', '1.5', 'versicolor']
['53', '6.9', '3.1', '4.9', '1.5', 'versicolor']['54', '5.5', '2.3', '4', '1.3', 'versicolor']
['55', '6.5', '2.8', '4.6', '1.5', 'versicolor']['56', '5.7', '2.8', '4.5', '1.3', 'versicolor']
['57', '6.3', '3.3', '4.7', '1.6', 'versicolor']['58', '4.9', '2.4', '3.3', '1', 'versicolor']
['59', '6.6', '2.9', '4.6', '1.3', 'versicolor']['60', '5.2', '2.7', '3.9', '1.4', 'versicolor']
['61', '5', '2', '3.5', '1', 'versicolor']['62', '5.9', '3', '4.2', '1.5', 'versicolor']
['63', '6', '2.2', '4', '1', 'versicolor']['64', '6.1', '2.9', '4.7', '1.4', 'versicolor']
['65', '5.6', '2.9', '3.6', '1.3', 'versicolor']['66', '6.7', '3.1', '4.4', '1.4', 'versicolor']
['67', '5.6', '3', '4.5', '1.5', 'versicolor']['68', '5.8', '2.7', '4.1', '1', 'versicolor']
['69', '6.2', '2.2', '4.5', '1.5', 'versicolor']['70', '5.6', '2.5', '3.9', '1.1', 'versicolor']
['71', '5.9', '3.2', '4.8', '1.8', 'versicolor']['72', '6.1', '2.8', '4', '1.3', 'versicolor']
['73', '6.3', '2.5', '4.9', '1.5', 'versicolor']['74', '6.1', '2.8', '4.7', '1.2', 'versicolor']
['75', '6.4', '2.9', '4.3', '1.3', 'versicolor']['76', '6.6', '3', '4.4', '1.4', 'versicolor']
['77', '6.8', '2.8', '4.8', '1.4', 'versicolor']['78', '6.7', '3', '5', '1.7', 'versicolor']
['79', '6', '2.9', '4.5', '1.5', 'versicolor']['80', '5.7', '2.6', '3.5', '1', 'versicolor']
['81', '5.5', '2.4', '3.8', '1.1', 'versicolor']['82', '5.5', '2.4', '3.7', '1', 'versicolor']
['83', '5.8', '2.7', '3.9', '1.2', 'versicolor']['84', '6', '2.7', '5.1', '1.6', 'versicolor']
['85', '5.4', '3', '4.5', '1.5', 'versicolor']['86', '6', '3.4', '4.5', '1.6', 'versicolor']
['87', '6.7', '3.1', '4.7', '1.5', 'versicolor']['88', '6.3', '2.3', '4.4', '1.3', 'versicolor']
['89', '5.6', '3', '4.1', '1.3', 'versicolor']['90', '5.5', '2.5', '4', '1.3', 'versicolor']
['91', '5.5', '2.6', '4.4', '1.2', 'versicolor']['92', '6.1', '3', '4.6', '1.4', 'versicolor']
['93', '5.8', '2.6', '4', '1.2', 'versicolor']['94', '5', '2.3', '3.3', '1', 'versicolor']
['95', '5.6', '2.7', '4.2', '1.3', 'versicolor']['96', '5.7', '3', '4.2', '1.2', 'versicolor']
['97', '5.7', '2.9', '4.2', '1.3', 'versicolor']['98', '6.2', '2.9', '4.3', '1.3', 'versicolor']
['99', '5.1', '2.5', '3', '1.1', 'versicolor']['100', '5.7', '2.8', '4.1', '1.3', 'versicolor']
['101', '6.3', '3.3', '6', '2.5', 'virginica']['102', '5.8', '2.7', '5.1', '1.9', 'virginica']
['103', '7.1', '3', '5.9', '2.1', 'virginica']['104', '6.3', '2.9', '5.6', '1.8', 'virginica']
['105', '6.5', '3', '5.8', '2.2', 'virginica']['106', '7.6', '3', '6.6', '2.1', 'virginica']
['107', '4.9', '2.5', '4.5', '1.7', 'virginica']['108', '7.3', '2.9', '6.3', '1.8', 'virginica']
['109', '6.7', '2.5', '5.8', '1.8', 'virginica']['110', '7.2', '3.6', '6.1', '2.5', 'virginica']
['111', '6.5', '3.2', '5.1', '2', 'virginica']['112', '6.4', '2.7', '5.3', '1.9', 'virginica']
['113', '6.8', '3', '5.5', '2.1', 'virginica']['114', '5.7', '2.5', '5', '2', 'virginica']
['115', '5.8', '2.8', '5.1', '2.4', 'virginica']['116', '6.4', '3.2', '5.3', '2.3', 'virginica']
['117', '6.5', '3', '5.5', '1.8', 'virginica']['118', '7.7', '3.8', '6.7', '2.2', 'virginica']
['119', '7.7', '2.6', '6.9', '2.3', 'virginica']['120', '6', '2.2', '5', '1.5', 'virginica']
['121', '6.9', '3.2', '5.7', '2.3', 'virginica']['122', '5.6', '2.8', '4.9', '2', 'virginica']
['123', '7.7', '2.8', '6.7', '2', 'virginica']['124', '6.3', '2.7', '4.9', '1.8', 'virginica']
['125', '6.7', '3.3', '5.7', '2.1', 'virginica']['126', '7.2', '3.2', '6', '1.8', 'virginica']
['127', '6.2', '2.8', '4.8', '1.8', 'virginica']['128', '6.1', '3', '4.9', '1.8', 'virginica']
['129', '6.4', '2.8', '5.6', '2.1', 'virginica']['130', '7.2', '3', '5.8', '1.6', 'virginica']
['131', '7.4', '2.8', '6.1', '1.9', 'virginica']['132', '7.9', '3.8', '6.4', '2', 'virginica']
['133', '6.4', '2.8', '5.6', '2.2', 'virginica']['134', '6.3', '2.8', '5.1', '1.5', 'virginica']
['135', '6.1', '2.6', '5.6', '1.4', 'virginica']['136', '7.7', '3', '6.1', '2.3', 'virginica']
['137', '6.3', '3.4', '5.6', '2.4', 'virginica']['138', '6.4', '3.1', '5.5', '1.8', 'virginica']
['139', '6', '3', '4.8', '1.8', 'virginica']['140', '6.9', '3.1', '5.4', '2.1', 'virginica']
['141', '6.7', '3.1', '5.6', '2.4', 'virginica']['142', '6.9', '3.1', '5.1', '2.3', 'virginica']
['143', '5.8', '2.7', '5.1', '1.9', 'virginica']['144', '6.8', '3.2', '5.9', '2.3', 'virginica']
['145', '6.7', '3.3', '5.7', '2.5', 'virginica']['146', '6.7', '3', '5.2', '2.3', 'virginica']
['147', '6.3', '2.5', '5', '1.9', 'virginica']['148', '6.5', '3', '5.2', '2', 'virginica']
['149', '6.2', '3.4', '5.4', '2.3', 'virginica']['150', '5.9', '3', '5.1', '1.8', 'virginica']

3：数据清理：去掉索引号

iris_list = []
for row in iris_data:iris_list.append(tuple(row[1:]))

每行三组数据输出是否正确

i = 0
for x in iris_list:i+=1print(x, end=' ')if i % 3 == 0:print()

('5.1', '3.5', '1.4', '0.2', 'setosa') ('4.9', '3', '1.4', '0.2', 'setosa') ('4.7', '3.2', '1.3', '0.2', 'setosa') 
('4.6', '3.1', '1.5', '0.2', 'setosa') ('5', '3.6', '1.4', '0.2', 'setosa') ('5.4', '3.9', '1.7', '0.4', 'setosa') 
('4.6', '3.4', '1.4', '0.3', 'setosa') ('5', '3.4', '1.5', '0.2', 'setosa') ('4.4', '2.9', '1.4', '0.2', 'setosa') 
('4.9', '3.1', '1.5', '0.1', 'setosa') ('5.4', '3.7', '1.5', '0.2', 'setosa') ('4.8', '3.4', '1.6', '0.2', 'setosa') 
('4.8', '3', '1.4', '0.1', 'setosa') ('4.3', '3', '1.1', '0.1', 'setosa') ('5.8', '4', '1.2', '0.2', 'setosa') 
('5.7', '4.4', '1.5', '0.4', 'setosa') ('5.4', '3.9', '1.3', '0.4', 'setosa') ('5.1', '3.5', '1.4', '0.3', 'setosa') 
('5.7', '3.8', '1.7', '0.3', 'setosa') ('5.1', '3.8', '1.5', '0.3', 'setosa') ('5.4', '3.4', '1.7', '0.2', 'setosa') 
('5.1', '3.7', '1.5', '0.4', 'setosa') ('4.6', '3.6', '1', '0.2', 'setosa') ('5.1', '3.3', '1.7', '0.5', 'setosa') 
('4.8', '3.4', '1.9', '0.2', 'setosa') ('5', '3', '1.6', '0.2', 'setosa') ('5', '3.4', '1.6', '0.4', 'setosa') 
('5.2', '3.5', '1.5', '0.2', 'setosa') ('5.2', '3.4', '1.4', '0.2', 'setosa') ('4.7', '3.2', '1.6', '0.2', 'setosa') 
('4.8', '3.1', '1.6', '0.2', 'setosa') ('5.4', '3.4', '1.5', '0.4', 'setosa') ('5.2', '4.1', '1.5', '0.1', 'setosa') 
('5.5', '4.2', '1.4', '0.2', 'setosa') ('4.9', '3.1', '1.5', '0.2', 'setosa') ('5', '3.2', '1.2', '0.2', 'setosa') 
('5.5', '3.5', '1.3', '0.2', 'setosa') ('4.9', '3.6', '1.4', '0.1', 'setosa') ('4.4', '3', '1.3', '0.2', 'setosa') 
('5.1', '3.4', '1.5', '0.2', 'setosa') ('5', '3.5', '1.3', '0.3', 'setosa') ('4.5', '2.3', '1.3', '0.3', 'setosa') 
('4.4', '3.2', '1.3', '0.2', 'setosa') ('5', '3.5', '1.6', '0.6', 'setosa') ('5.1', '3.8', '1.9', '0.4', 'setosa') 
('4.8', '3', '1.4', '0.3', 'setosa') ('5.1', '3.8', '1.6', '0.2', 'setosa') ('4.6', '3.2', '1.4', '0.2', 'setosa') 
('5.3', '3.7', '1.5', '0.2', 'setosa') ('5', '3.3', '1.4', '0.2', 'setosa') ('7', '3.2', '4.7', '1.4', 'versicolor') 
('6.4', '3.2', '4.5', '1.5', 'versicolor') ('6.9', '3.1', '4.9', '1.5', 'versicolor') ('5.5', '2.3', '4', '1.3', 'versicolor') 
('6.5', '2.8', '4.6', '1.5', 'versicolor') ('5.7', '2.8', '4.5', '1.3', 'versicolor') ('6.3', '3.3', '4.7', '1.6', 'versicolor') 
('4.9', '2.4', '3.3', '1', 'versicolor') ('6.6', '2.9', '4.6', '1.3', 'versicolor') ('5.2', '2.7', '3.9', '1.4', 'versicolor') 
('5', '2', '3.5', '1', 'versicolor') ('5.9', '3', '4.2', '1.5', 'versicolor') ('6', '2.2', '4', '1', 'versicolor') 
('6.1', '2.9', '4.7', '1.4', 'versicolor') ('5.6', '2.9', '3.6', '1.3', 'versicolor') ('6.7', '3.1', '4.4', '1.4', 'versicolor') 
('5.6', '3', '4.5', '1.5', 'versicolor') ('5.8', '2.7', '4.1', '1', 'versicolor') ('6.2', '2.2', '4.5', '1.5', 'versicolor') 
('5.6', '2.5', '3.9', '1.1', 'versicolor') ('5.9', '3.2', '4.8', '1.8', 'versicolor') ('6.1', '2.8', '4', '1.3', 'versicolor') 
('6.3', '2.5', '4.9', '1.5', 'versicolor') ('6.1', '2.8', '4.7', '1.2', 'versicolor') ('6.4', '2.9', '4.3', '1.3', 'versicolor') 
('6.6', '3', '4.4', '1.4', 'versicolor') ('6.8', '2.8', '4.8', '1.4', 'versicolor') ('6.7', '3', '5', '1.7', 'versicolor') 
('6', '2.9', '4.5', '1.5', 'versicolor') ('5.7', '2.6', '3.5', '1', 'versicolor') ('5.5', '2.4', '3.8', '1.1', 'versicolor') 
('5.5', '2.4', '3.7', '1', 'versicolor') ('5.8', '2.7', '3.9', '1.2', 'versicolor') ('6', '2.7', '5.1', '1.6', 'versicolor') 
('5.4', '3', '4.5', '1.5', 'versicolor') ('6', '3.4', '4.5', '1.6', 'versicolor') ('6.7', '3.1', '4.7', '1.5', 'versicolor') 
('6.3', '2.3', '4.4', '1.3', 'versicolor') ('5.6', '3', '4.1', '1.3', 'versicolor') ('5.5', '2.5', '4', '1.3', 'versicolor') 
('5.5', '2.6', '4.4', '1.2', 'versicolor') ('6.1', '3', '4.6', '1.4', 'versicolor') ('5.8', '2.6', '4', '1.2', 'versicolor') 
('5', '2.3', '3.3', '1', 'versicolor') ('5.6', '2.7', '4.2', '1.3', 'versicolor') ('5.7', '3', '4.2', '1.2', 'versicolor') 
('5.7', '2.9', '4.2', '1.3', 'versicolor') ('6.2', '2.9', '4.3', '1.3', 'versicolor') ('5.1', '2.5', '3', '1.1', 'versicolor') 
('5.7', '2.8', '4.1', '1.3', 'versicolor') ('6.3', '3.3', '6', '2.5', 'virginica') ('5.8', '2.7', '5.1', '1.9', 'virginica') 
('7.1', '3', '5.9', '2.1', 'virginica') ('6.3', '2.9', '5.6', '1.8', 'virginica') ('6.5', '3', '5.8', '2.2', 'virginica') 
('7.6', '3', '6.6', '2.1', 'virginica') ('4.9', '2.5', '4.5', '1.7', 'virginica') ('7.3', '2.9', '6.3', '1.8', 'virginica') 
('6.7', '2.5', '5.8', '1.8', 'virginica') ('7.2', '3.6', '6.1', '2.5', 'virginica') ('6.5', '3.2', '5.1', '2', 'virginica') 
('6.4', '2.7', '5.3', '1.9', 'virginica') ('6.8', '3', '5.5', '2.1', 'virginica') ('5.7', '2.5', '5', '2', 'virginica') 
('5.8', '2.8', '5.1', '2.4', 'virginica') ('6.4', '3.2', '5.3', '2.3', 'virginica') ('6.5', '3', '5.5', '1.8', 'virginica') 
('7.7', '3.8', '6.7', '2.2', 'virginica') ('7.7', '2.6', '6.9', '2.3', 'virginica') ('6', '2.2', '5', '1.5', 'virginica') 
('6.9', '3.2', '5.7', '2.3', 'virginica') ('5.6', '2.8', '4.9', '2', 'virginica') ('7.7', '2.8', '6.7', '2', 'virginica') 
('6.3', '2.7', '4.9', '1.8', 'virginica') ('6.7', '3.3', '5.7', '2.1', 'virginica') ('7.2', '3.2', '6', '1.8', 'virginica') 
('6.2', '2.8', '4.8', '1.8', 'virginica') ('6.1', '3', '4.9', '1.8', 'virginica') ('6.4', '2.8', '5.6', '2.1', 'virginica') 
('7.2', '3', '5.8', '1.6', 'virginica') ('7.4', '2.8', '6.1', '1.9', 'virginica') ('7.9', '3.8', '6.4', '2', 'virginica') 
('6.4', '2.8', '5.6', '2.2', 'virginica') ('6.3', '2.8', '5.1', '1.5', 'virginica') ('6.1', '2.6', '5.6', '1.4', 'virginica') 
('7.7', '3', '6.1', '2.3', 'virginica') ('6.3', '3.4', '5.6', '2.4', 'virginica') ('6.4', '3.1', '5.5', '1.8', 'virginica') 
('6', '3', '4.8', '1.8', 'virginica') ('6.9', '3.1', '5.4', '2.1', 'virginica') ('6.7', '3.1', '5.6', '2.4', 'virginica') 
('6.9', '3.1', '5.1', '2.3', 'virginica') ('5.8', '2.7', '5.1', '1.9', 'virginica') ('6.8', '3.2', '5.9', '2.3', 'virginica') 
('6.7', '3.3', '5.7', '2.5', 'virginica') ('6.7', '3', '5.2', '2.3', 'virginica') ('6.3', '2.5', '5', '1.9', 'virginica') 
('6.5', '3', '5.2', '2', 'virginica') ('6.2', '3.4', '5.4', '2.3', 'virginica') ('5.9', '3', '5.1', '1.8', 'virginica')

4：数据统计

1：创建数据类型

datatype = np.dtype([("Sepal.Length", np.str_, 40),("Sepal.Width", np.str_, 40),("Petal.Length", np.str_, 40),("Petal.Width", np.str_, 40),("Species", np.str_, 40)])

print(datatype)

[('Sepal.Length', '<U40'), ('Sepal.Width', '<U40'), ('Petal.Length', '<U40'), ('Petal.Width', '<U40'), ('Species', '<U40')]

2：创建二维数组

iris_data = np.array(iris_list, dtype = datatype)

print(iris_data)

[('5.1', '3.5', '1.4', '0.2', 'setosa')('4.9', '3', '1.4', '0.2', 'setosa')('4.7', '3.2', '1.3', '0.2', 'setosa')('4.6', '3.1', '1.5', '0.2', 'setosa')('5', '3.6', '1.4', '0.2', 'setosa')('5.4', '3.9', '1.7', '0.4', 'setosa')('4.6', '3.4', '1.4', '0.3', 'setosa')('5', '3.4', '1.5', '0.2', 'setosa')('4.4', '2.9', '1.4', '0.2', 'setosa')('4.9', '3.1', '1.5', '0.1', 'setosa')('5.4', '3.7', '1.5', '0.2', 'setosa')('4.8', '3.4', '1.6', '0.2', 'setosa')('4.8', '3', '1.4', '0.1', 'setosa') ('4.3', '3', '1.1', '0.1', 'setosa')('5.8', '4', '1.2', '0.2', 'setosa')('5.7', '4.4', '1.5', '0.4', 'setosa')('5.4', '3.9', '1.3', '0.4', 'setosa')('5.1', '3.5', '1.4', '0.3', 'setosa')('5.7', '3.8', '1.7', '0.3', 'setosa')('5.1', '3.8', '1.5', '0.3', 'setosa')('5.4', '3.4', '1.7', '0.2', 'setosa')('5.1', '3.7', '1.5', '0.4', 'setosa')('4.6', '3.6', '1', '0.2', 'setosa')('5.1', '3.3', '1.7', '0.5', 'setosa')('4.8', '3.4', '1.9', '0.2', 'setosa') ('5', '3', '1.6', '0.2', 'setosa')('5', '3.4', '1.6', '0.4', 'setosa')('5.2', '3.5', '1.5', '0.2', 'setosa')('5.2', '3.4', '1.4', '0.2', 'setosa')('4.7', '3.2', '1.6', '0.2', 'setosa')('4.8', '3.1', '1.6', '0.2', 'setosa')('5.4', '3.4', '1.5', '0.4', 'setosa')('5.2', '4.1', '1.5', '0.1', 'setosa')('5.5', '4.2', '1.4', '0.2', 'setosa')('4.9', '3.1', '1.5', '0.2', 'setosa')('5', '3.2', '1.2', '0.2', 'setosa')('5.5', '3.5', '1.3', '0.2', 'setosa')('4.9', '3.6', '1.4', '0.1', 'setosa')('4.4', '3', '1.3', '0.2', 'setosa')('5.1', '3.4', '1.5', '0.2', 'setosa')('5', '3.5', '1.3', '0.3', 'setosa')('4.5', '2.3', '1.3', '0.3', 'setosa')('4.4', '3.2', '1.3', '0.2', 'setosa')('5', '3.5', '1.6', '0.6', 'setosa')('5.1', '3.8', '1.9', '0.4', 'setosa')('4.8', '3', '1.4', '0.3', 'setosa')('5.1', '3.8', '1.6', '0.2', 'setosa')('4.6', '3.2', '1.4', '0.2', 'setosa')('5.3', '3.7', '1.5', '0.2', 'setosa')('5', '3.3', '1.4', '0.2', 'setosa')('7', '3.2', '4.7', '1.4', 'versicolor')('6.4', '3.2', '4.5', '1.5', 'versicolor')('6.9', '3.1', '4.9', '1.5', 'versicolor')('5.5', '2.3', '4', '1.3', 'versicolor')('6.5', '2.8', '4.6', '1.5', 'versicolor')('5.7', '2.8', '4.5', '1.3', 'versicolor')('6.3', '3.3', '4.7', '1.6', 'versicolor')('4.9', '2.4', '3.3', '1', 'versicolor')('6.6', '2.9', '4.6', '1.3', 'versicolor')('5.2', '2.7', '3.9', '1.4', 'versicolor')('5', '2', '3.5', '1', 'versicolor')('5.9', '3', '4.2', '1.5', 'versicolor')('6', '2.2', '4', '1', 'versicolor')('6.1', '2.9', '4.7', '1.4', 'versicolor')('5.6', '2.9', '3.6', '1.3', 'versicolor')('6.7', '3.1', '4.4', '1.4', 'versicolor')('5.6', '3', '4.5', '1.5', 'versicolor')('5.8', '2.7', '4.1', '1', 'versicolor')('6.2', '2.2', '4.5', '1.5', 'versicolor')('5.6', '2.5', '3.9', '1.1', 'versicolor')('5.9', '3.2', '4.8', '1.8', 'versicolor')('6.1', '2.8', '4', '1.3', 'versicolor')('6.3', '2.5', '4.9', '1.5', 'versicolor')('6.1', '2.8', '4.7', '1.2', 'versicolor')('6.4', '2.9', '4.3', '1.3', 'versicolor')('6.6', '3', '4.4', '1.4', 'versicolor')('6.8', '2.8', '4.8', '1.4', 'versicolor')('6.7', '3', '5', '1.7', 'versicolor')('6', '2.9', '4.5', '1.5', 'versicolor')('5.7', '2.6', '3.5', '1', 'versicolor')('5.5', '2.4', '3.8', '1.1', 'versicolor')('5.5', '2.4', '3.7', '1', 'versicolor')('5.8', '2.7', '3.9', '1.2', 'versicolor')('6', '2.7', '5.1', '1.6', 'versicolor')('5.4', '3', '4.5', '1.5', 'versicolor')('6', '3.4', '4.5', '1.6', 'versicolor')('6.7', '3.1', '4.7', '1.5', 'versicolor')('6.3', '2.3', '4.4', '1.3', 'versicolor')('5.6', '3', '4.1', '1.3', 'versicolor')('5.5', '2.5', '4', '1.3', 'versicolor')('5.5', '2.6', '4.4', '1.2', 'versicolor')('6.1', '3', '4.6', '1.4', 'versicolor')('5.8', '2.6', '4', '1.2', 'versicolor')('5', '2.3', '3.3', '1', 'versicolor')('5.6', '2.7', '4.2', '1.3', 'versicolor')('5.7', '3', '4.2', '1.2', 'versicolor')('5.7', '2.9', '4.2', '1.3', 'versicolor')('6.2', '2.9', '4.3', '1.3', 'versicolor')('5.1', '2.5', '3', '1.1', 'versicolor')('5.7', '2.8', '4.1', '1.3', 'versicolor')('6.3', '3.3', '6', '2.5', 'virginica')('5.8', '2.7', '5.1', '1.9', 'virginica')('7.1', '3', '5.9', '2.1', 'virginica')('6.3', '2.9', '5.6', '1.8', 'virginica')('6.5', '3', '5.8', '2.2', 'virginica')('7.6', '3', '6.6', '2.1', 'virginica')('4.9', '2.5', '4.5', '1.7', 'virginica')('7.3', '2.9', '6.3', '1.8', 'virginica')('6.7', '2.5', '5.8', '1.8', 'virginica')('7.2', '3.6', '6.1', '2.5', 'virginica')('6.5', '3.2', '5.1', '2', 'virginica')('6.4', '2.7', '5.3', '1.9', 'virginica')('6.8', '3', '5.5', '2.1', 'virginica')('5.7', '2.5', '5', '2', 'virginica')('5.8', '2.8', '5.1', '2.4', 'virginica')('6.4', '3.2', '5.3', '2.3', 'virginica')('6.5', '3', '5.5', '1.8', 'virginica')('7.7', '3.8', '6.7', '2.2', 'virginica')('7.7', '2.6', '6.9', '2.3', 'virginica')('6', '2.2', '5', '1.5', 'virginica')('6.9', '3.2', '5.7', '2.3', 'virginica')('5.6', '2.8', '4.9', '2', 'virginica')('7.7', '2.8', '6.7', '2', 'virginica')('6.3', '2.7', '4.9', '1.8', 'virginica')('6.7', '3.3', '5.7', '2.1', 'virginica')('7.2', '3.2', '6', '1.8', 'virginica')('6.2', '2.8', '4.8', '1.8', 'virginica')('6.1', '3', '4.9', '1.8', 'virginica')('6.4', '2.8', '5.6', '2.1', 'virginica')('7.2', '3', '5.8', '1.6', 'virginica')('7.4', '2.8', '6.1', '1.9', 'virginica')('7.9', '3.8', '6.4', '2', 'virginica')('6.4', '2.8', '5.6', '2.2', 'virginica')('6.3', '2.8', '5.1', '1.5', 'virginica')('6.1', '2.6', '5.6', '1.4', 'virginica')('7.7', '3', '6.1', '2.3', 'virginica')('6.3', '3.4', '5.6', '2.4', 'virginica')('6.4', '3.1', '5.5', '1.8', 'virginica')('6', '3', '4.8', '1.8', 'virginica')('6.9', '3.1', '5.4', '2.1', 'virginica')('6.7', '3.1', '5.6', '2.4', 'virginica')('6.9', '3.1', '5.1', '2.3', 'virginica')('5.8', '2.7', '5.1', '1.9', 'virginica')('6.8', '3.2', '5.9', '2.3', 'virginica')('6.7', '3.3', '5.7', '2.5', 'virginica')('6.7', '3', '5.2', '2.3', 'virginica')('6.3', '2.5', '5', '1.9', 'virginica')('6.5', '3', '5.2', '2', 'virginica')('6.2', '3.4', '5.4', '2.3', 'virginica')('5.9', '3', '5.1', '1.8', 'virginica')]

3：将待处理数据的类型转化为float类型

PetalLength = iris_data["Petal.Length"].astype(float)

print(PetalLength)

[1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 1.5 1.6 1.4 1.1 1.2 1.5 1.3 1.41.7 1.5 1.7 1.5 1.  1.7 1.9 1.6 1.6 1.5 1.4 1.6 1.6 1.5 1.5 1.4 1.5 1.21.3 1.4 1.3 1.5 1.3 1.3 1.3 1.6 1.9 1.4 1.6 1.4 1.5 1.4 4.7 4.5 4.9 4.4.6 4.5 4.7 3.3 4.6 3.9 3.5 4.2 4.  4.7 3.6 4.4 4.5 4.1 4.5 3.9 4.8 4.4.9 4.7 4.3 4.4 4.8 5.  4.5 3.5 3.8 3.7 3.9 5.1 4.5 4.5 4.7 4.4 4.1 4.4.4 4.6 4.  3.3 4.2 4.2 4.2 4.3 3.  4.1 6.  5.1 5.9 5.6 5.8 6.6 4.5 6.35.8 6.1 5.1 5.3 5.5 5.  5.1 5.3 5.5 6.7 6.9 5.  5.7 4.9 6.7 4.9 5.7 6.4.8 4.9 5.6 5.8 6.1 6.4 5.6 5.1 5.6 6.1 5.6 5.5 4.8 5.4 5.6 5.1 5.1 5.95.7 5.2 5.  5.2 5.4 5.1]

4：排序

np.sort(PetalLength)

print(np.sort(PetalLength))

[1.  1.1 1.2 1.2 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.4 1.4 1.4 1.4 1.4 1.4 1.41.4 1.4 1.4 1.4 1.4 1.4 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.51.5 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.7 1.7 1.7 1.7 1.9 1.9 3.  3.3 3.3 3.53.5 3.6 3.7 3.8 3.9 3.9 3.9 4.  4.  4.  4.  4.  4.1 4.1 4.1 4.2 4.2 4.24.2 4.3 4.3 4.4 4.4 4.4 4.4 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.6 4.6 4.64.7 4.7 4.7 4.7 4.7 4.8 4.8 4.8 4.8 4.9 4.9 4.9 4.9 4.9 5.  5.  5.  5.5.1 5.1 5.1 5.1 5.1 5.1 5.1 5.1 5.2 5.2 5.3 5.3 5.4 5.4 5.5 5.5 5.5 5.65.6 5.6 5.6 5.6 5.6 5.7 5.7 5.7 5.8 5.8 5.8 5.9 5.9 6.  6.  6.1 6.1 6.16.3 6.4 6.6 6.7 6.7 6.9]

5：数组去重

np.unique(PetalLength)

print(np.unique(PetalLength))

[1.  1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.9 3.  3.3 3.5 3.6 3.7 3.8 3.9 4.  4.14.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 5.  5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.96.  6.1 6.3 6.4 6.6 6.7 6.9]

6：对指定列求和、均值、标准差、方差、最小值和最大值

6-1：求和

print(np.sum(PetalLength))

563.7

6-2：均值

print(np.mean(PetalLength))

3.7580000000000005

6-3：标准差

print(np.std(PetalLength))

1.759404065775303

6-4：方差

print(np.var(PetalLength))

3.0955026666666665

6-5：最小值

print(np.min(PetalLength))

1.0

6-6：最大值

print(np.max(PetalLength))

6.9

完整代码

import numpy as np
import csviris_data = []
with open("iris.csv") as csvfile:csv_reader = csv.reader(csvfile)birth_header = next(csv_reader)for row in csv_reader:iris_data.append(row)iris_list = []
for row in iris_data:iris_list.append(tuple(row[1:]))datatype = np.dtype([("Sepal.Length", np.str_, 40),("Sepal.Width", np.str_, 40),("Petal.Length", np.str_, 40),("Petal.Width", np.str_, 40),("Species", np.str_, 40)])iris_data = np.array(iris_list, dtype = datatype)PetalLength = iris_data["Petal.Length"].astype(float)np.sort(PetalLength)np.unique(PetalLength)print(np.sum(PetalLength))#求和print(np.mean(PetalLength))#均值print(np.std(PetalLength))#标准差print(np.var(PetalLength))#方差print(np.min(PetalLength))#最小值print(np.max(PetalLength))#最大值

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