Pandas is primarily a data manipulation and analysis library, but it can also be used for web scraping in combination with other libraries such as Requests, Beautiful Soup, and lxml. Here is an example of how to scrape data from a webpage and load it into a Pandas dataframe:
import pandas as pd
import requests
from bs4 import BeautifulSoup
# send a GET request to the webpage and get the HTML content
response = requests.get('https://www.example.com/')
html = response.content
# parse the HTML content with Beautiful Soup
soup = BeautifulSoup(html, 'lxml')
# find the table element in the HTML content
table = soup.find('table')
# convert the HTML table to a Pandas dataframe
df = pd.read_html(str(table))[0]
# print the dataframe
print(df)
In this example, we use the requests library to send a GET request to a webpage and get the HTML content. We then use Beautiful Soup to parse the HTML content and find the table element. We use the read_html() function from Pandas to convert the HTML table to a Pandas dataframe. The [0] at the end of the line is used to get the first table on the webpage, in case there are multiple tables.
Once you have a Pandas dataframe, you can manipulate and analyze the data using the various functions available in Pandas. For example, you can filter rows based on a condition, group the data by one or more columns, aggregate the data using various functions, and more.
Finally, you can write the Pandas dataframe to a file or database using various Pandas functions such as to_csv(), to_excel(), to_sql(), and more. Here’s an example:
# write the dataframe to a CSV file
df.to_csv('output.csv', index=False)
# write the dataframe to a SQL database
import sqlite3
conn = sqlite3.connect('example.db')
df.to_sql('data', conn, if_exists='replace', index=False)
In this example, we use the to_csv() function to write the Pandas dataframe to a CSV file called ‘output.csv’. The index=False parameter is used to prevent writing the Pandas dataframe index to the CSV file. We also use the to_sql() function to write the Pandas dataframe to a SQLite database. The if_exists=’replace’ parameter is used to replace any existing data in the ‘data’ table in the database. Check out the pandas documentation for more details on web scraping with Pandas.