1.跟踪列表中元素的频率

# Track Frequency
import collections
def Track_Frequency(List):return dict(collections.Counter(List))
print(Track_Frequency([10, 10, 12, 12, 10, 13, 13, 14]))
# Output
# {10: 3, 12: 2, 13: 2, 14: 1}

2.不使用 Pandas 读取 CSV 文件

# 简单的类创建
import csv
with open("Test.csv", "r") as file:read = csv.reader(f)for r in read:print(row)
# 输出
# ['Sr', 'Name', 'Profession'] 
# ['1', '小猴子', '数据挖掘工程师'] 
# ['2', '云朵君', '算法工程师']

3.拆分多行字符串

# 拆分多行字符串
string = "Data \n is encrpted \n by Python"
print(string)
# Output
# Data
# is encrpted
# by Python
splited = string.split("\n")
print(splited) # ['Data ', ' is encrpted ', ' by Python']

4.将列表压缩成一个字符串

# 压缩字符串列表
mylist = ["I learn", "Python", "JavaScript", "Dart"]
string = " ".join(mylist)
print(string) # I learn Python JavaScript Dart

5.获取列表中元素的索引

# 获取列表中元素的索引
mylist = [10, 11, 12, 13, 14]
print(mylist.index(10)) # 0 
print(mylist.index(12)) # 2
print(mylist.index(14)) # 4

6.获取网页 HTML 数据

# 使用 pip 安装请求的第一个安装请求导入请求
r = requests.get("https://www.baidu.com/s?wd=数据STUDIO ") 
print(r) # 显示整页html数据

7.获取数据占用内存

# 获取数据占用的内存导入系统
import sys
def memory(data):return sys.getsizeof(data)
print(memory(100)) # 28
print(memory("Pythonnnnnnn")) # 61

8.简单的类创建

# 简单的类
class Employee:def __init__(self, empID):self.empID = empIDself.name = "Haider"self.salary = 50000def getEmpData(self):return self.name, self.salary
emp = Employee(189345)
print(emp.getEmpData()) # ('Haider', 50000)