Web Scraping and Data Cleaning: Best Practices for Preparing Data for Pandas

Introduction

Web scraping is a powerful technique for gathering data from websites. However, the data collected through web scraping often contains noise, inconsistencies, and missing values. Therefore, it is crucial to clean and preprocess the data before importing it into a Pandas DataFrame. In this article, we will discuss the importance of data cleaning and preprocessing in web scraping and provide practical tips and examples for handling missing data, removing duplicates, and transforming data types.

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Web Scraping with BeautifulSoup

We will start by scraping a simple table from a web page using the requests and BeautifulSoup libraries. For this example, we'll scrape a sample table containing information about top movies.

import requests
from bs4 import BeautifulSoup
import pandas as pd

url = "https://www.example.com/top-movies"
response = requests.get(url)
soup = BeautifulSoup(response.content, "html.parser")

table = soup.find("table")
header = [th.text for th in table.find_all("th")]
rows = table.find_all("tr")[1:]

data = []
for row in rows:
    cells = row.find_all("td")…