Python数据分析与可视化NumPy数值计算（实训一）
Posted ZSYL
tags:
篇首语：本文由小常识网(cha138.com)小编为大家整理，主要介绍了Python数据分析与可视化NumPy数值计算（实训一）相关的知识，希望对你有一定的参考价值。
NumPy数值计算（实训一）
1.导入模块
import numpy as np
import csv
2.获取数据
iris_data = []
with open("data//iris.csv") as csvfile:
# 使用csv.reader读取csvfile中的文件
csv_reader = csv.reader(csvfile)
# 读取第一行每一列的标题
birth_header = next(csv_reader)
# 将csv 文件中的数据保存到birth_data中
for row in csv_reader:
iris_data.append(row)
iris_data
[['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:]))
iris_list
[('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)
iris_data
array([('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')],
dtype=[('Sepal.Length', '<U40'), ('Sepal.Width', '<U40'), ('Petal.Length', '<U40'), ('Petal.Width', '<U40'), ('Species', '<U40')])
（3）将待处理数据待类型转化为float类型
PetalLength = iris_data["Petal.Length"].astype(float)
PetalLength
array([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, 1.4, 1.7, 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, 1.2, 1.3, 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, 6.3, 5.8, 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,
5.9, 5.7, 5.2, 5. , 5.2, 5.4, 5.1])
（4）数据排序
np.sort(PetalLength)
array([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, 1.4, 1.4, 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, 1.5, 1.5, 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, 3.5, 3.5, 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, 4.2, 4.2, 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, 4.6, 4.7,
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, 5.6, 5.6, 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,
6.1, 6.3, 6.4, 6.6, 6.7, 6.7, 6.9])
（5）数据去重
np.unique(PetalLength)
array([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. , 4.1, 4.2, 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, 5.9, 6. , 6.1, 6.3,
6.4, 6.6, 6.7, 6.9])
sum: 计算数组的和
mean 计算数组均值
std 计算数组标准差
var 计算数组方差
min 计算数组最小值
max 计算数组最大值
argmin 返回数组最小元素的索引
argmax 返回数组最小元素的索引
cumsum 计算所有元素的累计和
cumprod 计算所有元素的累计积
对指定列求和、均值、标准差、方差、最小值、最大值
np.sum(PetalLength)
563.7
np.mean(PetalLength)
3.7580000000000005
np.std(PetalLength)
1.759404065775303
np.var(PetalLength)
3.0955026666666665
np.min(PetalLength)
1.0
np.max(PetalLength)
6.9
加油!
感谢!
努力!
以上是关于Python数据分析与可视化NumPy数值计算（实训一）的主要内容，如果未能解决你的问题，请参考以下文章