7 Exciting Python Projects to Boost Your Coding Skills

1. Zhihu Image Scraper

This script uses selenium to open a Zhihu question page, scroll to load content, extract image URLs with regular expressions, and download the first ten images.

import re
from selenium import webdriver
import time
import urllib.request

driver = webdriver.Chrome()
driver.maximize_window()
driver.get("https://www.zhihu.com/question/29134042")
for i in range(10):
    driver.execute_script("window.scrollTo(0, document.body.scrollHeight);")
    time.sleep(2)
    try:
        driver.find_element_by_css_selector('button.QuestionMainAction').click()
        print("page" + str(i))
        time.sleep(1)
    except:
        break
    result_raw = driver.page_source
    content_list = re.findall("img src=\"(.+?)\" ", str(result_raw))
    n = 0
    while n < len(content_list):
        local = r"%s.jpg" % i
        urllib.request.urlretrieve(content_list[n], local)
        print("编号：" + str(i))
        n += 1

2. Dual Chatbot Conversation

The program lets a user input a topic, then alternates between the Turing API and the Qingyunke API to simulate two chatbots chatting with each other.

from time import sleep
import requests

s = input("请主人输入话题：")
while True:
    resp = requests.post("http://www.tuling123.com/openapi/api", data={"key":"4fede3c4384846b9a7d0456a5e1e2943","info":s})
    resp = resp.json()
    sleep(1)
    print('小鱼：', resp['text'])
    s = resp['text']
    resp = requests.get("http://api.qingyunke.com/api.php", params={"key":"free","appid":0,"msg":s})
    resp.encoding = 'utf8'
    resp = resp.json()
    sleep(1)
    print('菲菲：', resp['content'])

3. AI Poetry Author Classification

Using jieba for Chinese word segmentation and NLTK's Naive Bayes classifier, the script determines whether a given poem is more likely written by Li Bai or Du Fu.

import jieba
from nltk.classify import NaiveBayesClassifier

# Load poems
text1 = open(r"libai.txt", "rb").read()
list1 = jieba.cut(text1)
text2 = open(r"dufu.txt", "rb").read()
list2 = jieba.cut(text2)

# Feature extraction
def word_feats(words):
    return dict((word, True) for word in words)

libai_features = [(word_feats(lb), 'lb') for lb in list1]
dufu_features = [(word_feats(df), 'df') for df in list2]
train_set = libai_features + dufu_features
classifier = NaiveBayesClassifier.train(train_set)

sentence = input("请输入一句你喜欢的诗：")
seg_list = jieba.cut(sentence)
words = " ".join(seg_list).split()
lb = df = 0
for word in words:
    classResult = classifier.classify(word_feats(word))
    if classResult == 'lb':
        lb += 1
    if classResult == 'df':
        df += 1
x = float(lb) / len(words)
y = float(df) / len(words)
print('李白的可能性：%.2f%%' % (x * 100))
print('杜甫的可能性：%.2f%%' % (y * 100))

4. 35‑Choose‑7 Lottery Generator

Randomly shuffles numbers 1‑35 and selects the first seven as a lottery ticket.

import random

temp = [i + 1 for i in range(35)]
random.shuffle(temp)
list = []
for i in range(7):
    list.append(temp[i])
list.sort()
print('\033[0;31;;1m')
print(*list[0:6], end="")
print('\033[0;34;;1m', end=" ")
print(list[-1])

5. Automatic Apology Letter Generator

Combines user‑provided event description and required length to produce a formatted apology letter using random sentence lengths from an Excel sheet.

import random, xlrd
ExcelFile = xlrd.open_workbook(r'test.xlsx')
sheet = ExcelFile.sheet_by_name('Sheet1')
# ... (code to build the letter based on user input) ...

6. Screen Capture Tool

Captures screenshots at a user‑defined interval using PIL.ImageGrab and saves them as sequential JPEG files.

from time import sleep
from PIL import ImageGrab

m = int(input("请输入想抓屏几分钟：")) * 60
n = 1
while n < m:
    sleep(0.02)
    im = ImageGrab.grab()
    local = r"%s.jpg" % n
    im.save(local, 'jpeg')
    n += 1

7. GIF Creator

Opens a series of images and saves them as an animated GIF.

from PIL import Image
im = Image.open("1.jpg")
images = [Image.open('2.jpg'), Image.open('3.jpg')]
im.save('gif.gif', save_all=True, append_images=images, loop=1, duration=1, comment=b"aaabb")

These seven ready‑to‑run Python scripts demonstrate practical automation, data processing, and simple AI techniques that can be directly applied or extended for personal projects.