Trouble Creating Pandas DataFrame from Lists

As a web scraper, one of the most challenging tasks is to convert raw data into a structured format that can be easily analyzed and manipulated. In this article, we will explore how to create a pandas DataFrame from lists generated while scraping data from the web.

Introduction to Web Scraping and Beautiful Soup

Before diving into creating DataFrames from lists, let’s take a quick look at what web scraping and Beautiful Soup are all about.

Web scraping involves navigating through websites, identifying specific pieces of information, and extracting them for further analysis or processing. Beautiful Soup is a Python library used to parse HTML and XML documents, making it easy to navigate and extract data from web pages.

In the given Stack Overflow post, the user is using Beautiful Soup to scrape data about local farms from localharvest.org. They are able to successfully extract the farm names, cities, and descriptions from the website, but are having trouble creating a pandas DataFrame from these lists.

Understanding Pandas DataFrames

A pandas DataFrame is a two-dimensional table of data with rows and columns. It is similar to an Excel spreadsheet or a SQL table. DataFrames are used extensively in data analysis, machine learning, and data science applications.

To create a DataFrame from a list, we need to understand the structure of the data. In this case, the user has three lists: fname, fcity, and fdesc. These lists contain the farm names, cities, and descriptions, respectively.

Creating DataFrames from Lists

Now that we have an understanding of what a DataFrame is and how it’s created, let’s dive into the code. The original code looks like this:

import requests
from bs4 import BeautifulSoup
import pandas

url = "http://www.localharvest.org/search.jsp?jmp&amp;lat=44.80798&amp;lon=-69.22736&amp;scale=8&amp;ty=6"
r = requests.get(url)
soup = BeautifulSoup(r.content)

data = soup.find_all("div", {'class': 'membercell'})

fname = []
fcity = []
fdesc = []

for item in data:
    name = item.contents[1].text
    fname.append(name)
    city = item.contents[3].text
    fcity.append(city)
    desc = item.find_all("div", {'class': 'short-desc'})[0].text
    fdesc.append(desc)

df = pandas.DataFrame({'fname': fname, 'fcity': fcity, 'fdesc': fdesc})

print(df)

df.to_csv('farmdata.csv')

The issue with this code is that the name, city, and desc variables contain newline characters (\n) and spaces. When these lists are passed to the DataFrame constructor, they are treated as separate columns instead of being merged into a single column.

To fix this, we need to remove the newline characters and spaces from each string before appending it to the list. We can do this by using the split() function with no arguments, which splits a string at any amount of whitespace. Then, we join the resulting strings back together using the join() function.

Here’s the corrected code:

import requests
from bs4 import BeautifulSoup
import pandas

url = "http://www.localharvest.org/search.jsp?jmp&amp;lat=44.80798&amp;lon=-69.22736&amp;scale=8&amp;ty=6"
r = requests.get(url)
soup = BeautifulSoup(r.content)

data = soup.find_all("div", {'class': 'membercell'})

fname = []
fcity = []
fdesc = []

for item in data:
    name = item.contents[1].text.split()
    fname.append(' '.join(name))
    city = item.contents[3].text.split()
    fcity.append(' '.join(city))
    desc = item.find_all("div", {'class': 'short-desc'})[0].text.split()
    fdesc.append(' '.join(desc))

df = pandas.DataFrame({'fname': fname, 'fcity': fcity, 'fdesc': fdesc})

print(df)

df.to_csv('farmdata.csv')

In this corrected code, we’ve replaced the original name, city, and desc variables with name.split(), city.split(), and desc.split() respectively. We then join these strings back together using ' '.join(name), which removes any whitespace and joins the resulting list into a single string.

Conclusion

Creating DataFrames from lists is an essential skill for any data analyst or scientist working with web scraping and data manipulation. By understanding how to remove newline characters and spaces from each string, we can create clean and structured DataFrames that are ready for analysis or processing.

Troubleshooting Common Issues

Here are some common issues that may arise when creating DataFrames from lists:

Empty DataFrames: Check if the list is empty before passing it to the DataFrame constructor.
Extra Columns: Make sure to remove any extra columns by specifying only the required columns when creating the DataFrame.
Invalid Data Types: Ensure that all data types in the list are consistent. For example, if one column contains integers and another column contains strings, they should be converted to a consistent type.

By following these tips and best practices, you’ll be able to create clean and structured DataFrames from lists generated while scraping data from the web.

Last modified on 2023-07-11