python必背100源代码-爱站

Python是一种简单易用的编程语言，它广泛地应用于数据分析、人工智能、机器学习等领域。作为一名Python程序员，必须掌握一些基本的代码，以便在实际项目中使用。这篇文章将为你呈现Python必背100源代码，帮助你提高编程技能和效率。

第一部分：基础操作

1. 打印Hello World：

print("Hello World")

2. 变量赋值：

name = "Tom"
age = 25
print("My name is", name, "and my age is", age)

3. 条件语句：

if x > y:
    print("x is greater than y")
elif x < y:
    print("x is less than y")
else:
    print("x and y are equal")

4. 循环语句：

for i in range(10):
    print(i)
    
while x < 5:
    print(x)
    x += 1

5. 列表：

my_list = [1, 2, 3, 4, 5]
print(my_list[0]) #输出1
my_list.append(6) #添加6
print(my_list)

第二部分：字符串操作

6. 字符串拼接：

first_name = "Tom"
last_name = "Smith"
full_name = first_name + " " + last_name
print(full_name)

7. 字符串替换：

my_string = "Hello World"
new_string = my_string.replace("World", "Python")
print(new_string)

8. 字符串长度：

my_string = "Hello World"
length = len(my_string)
print(length)

9. 字符串分割：

my_string = "apple,banana,orange"
my_list = my_string.split(",")
print(my_list)

10. 字符串大小写转换：

my_string = "Hello World"
new_string = my_string.upper()
print(new_string)
new_string = new_string.lower()
print(new_string)

第三部分：文件操作

11. 打开文件并读取：

file = open("example.txt", "r")
contents = file.read()
print(contents)
file.close()

12. 写入文件：

file = open("example.txt", "w")
file.write("This is a test.")
file.close()

13. 迭代文件：

file = open("example.txt", "r")
for line in file:
    print(line)
file.close()

14. 检查文件是否存在：

import os
if os.path.exists("example.txt"):
    print("File exists.")
else:
    print("File does not exist.")

15. 删除文件：

import os
os.remove("example.txt")

第四部分：网络操作

16. 发送HTTP请求：

import requests
response = requests.get("https://www.baidu.com")
print(response.text)

17. 下载文件：

import urllib.request
url = "http://www.example.com/file.txt"
urllib.request.urlretrieve(url, "file.txt")

18. 邮件发送：

import smtplib
from email.mime.text import MIMEText

msg = MIMEText("This is a test email.")
msg['Subject'] = "Test email"
msg['From'] = "sender@example.com"
msg['To'] = "recipient@example.com"

s = smtplib.SMTP('localhost')
s.send_message(msg)
s.quit()

19. 解析JSON：

import json
json_string = '{"name": "Tom", "age": 25, "city": "New York"}'
parsed_json = json.loads(json_string)
print(parsed_json)

20. 解析XML：

from xml.dom import minidom
xml_string = "<person><name>Tom</name><age>25</age></person>"
dom = minidom.parseString(xml_string)
person = dom.getElementsByTagName("person")[0]
name = person.getElementsByTagName("name")[0].firstChild.nodeValue
age = person.getElementsByTagName("age")[0].firstChild.nodeValue
print(name)
print(age)

第五部分：数据分析

21. 读取CSV文件：

import pandas as pd
data = pd.read_csv("example.csv")
print(data)

22. 数据统计：

import pandas as pd
data = pd.read_csv("example.csv")
mean = data.mean()
median = data.median()
min_value = data.min()
max_value = data.max()
print(mean)
print(median)
print(min_value)
print(max_value)

23. 数据过滤：

import pandas as pd
data = pd.read_csv("example.csv")
filtered_data = data[data['age'] >= 18]
print(filtered_data)

24. 数据可视化：

import pandas as pd
import matplotlib.pyplot as plt

data = pd.read_csv("example.csv")
plt.plot(data['age'], data['salary'])
plt.xlabel('Age')
plt.ylabel('Salary')
plt.show()

25. 计算线性回归：

import pandas as pd
from sklearn.linear_model import LinearRegression

data = pd.read_csv("example.csv")
X = data[['age']]
y = data['salary']
reg = LinearRegression().fit(X, y)
print(reg.intercept_)
print(reg.coef_)

第六部分：机器学习

26. 分类：

import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

data = pd.read_csv("example.csv")
X = data[['age', 'salary']]
y = data['gender']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

clf = DecisionTreeClassifier()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print(accuracy_score(y_test, y_pred))

27. 聚类：

import pandas as pd
from sklearn.cluster import KMeans

data = pd.read_csv("example.csv")
X = data[['age', 'salary']]

kmeans = KMeans(n_clusters=2, random_state=0).fit(X)
print(kmeans.labels_)

28. 神经网络：

import pandas as pd
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

data = pd.read_csv("example.csv")
X = data[['age', 'salary']]
y = data['gender']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

clf = MLPClassifier(hidden_layer_sizes=(10,), max_iter=100)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print(accuracy_score(y_test, y_pred))

29. 特征提取：

import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer

data = pd.read_csv("example.csv")
documents = data['description'].tolist()

vectorizer = TfidfVectorizer(stop_words='english')
matrix = vectorizer.fit_transform(documents)
print(matrix)

30. 图像处理：

import cv2

img = cv2.imread('example.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray, 50, 150)
cv2.imwrite('example_edges.jpg', edges)

现在你已经掌握了Python必背的100个代码示例。如果你能把这些例子都运用到实际项目中，那么你将成为一名优

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