Extracting Unique Pages from a DataFrame

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In this article, we will explore how to extract unique pages from a DataFrame that contains data about elastic.co. The DataFrame is created by scraping data from the website and extracting the page URLs as well as their corresponding metadata.

Problem Statement

Given a DataFrame with page URLs and their corresponding metadata, we need to extract the unique pages (i.e., the number of times each URL appears in the DataFrame) and store them in a new column.

Solution Overview

To solve this problem, we can use the groupby function from pandas to group the rows by the page URLs and then count the number of occurrences for each URL. This will give us a Series with the unique pages as indices and their counts as values.

Step 1: Importing Libraries and Loading Data

First, we need to import the necessary libraries and load our data into a DataFrame.

import pandas as pd

# Load data from elastic.co
url = 'https://www.elastic.co'
response = requests.get(url)
data = response.text
df = pd.read_html(data)[0]

Step 2: Cleaning and Preprocessing Data

Next, we need to clean and preprocess our data by removing any unwanted characters or duplicates.

# Remove any unwanted characters from the page URLs
df['page_url'] = df['page_url'].str.replace(' ', '')

# Remove any duplicate rows
df.drop_duplicates(inplace=True)

Step 3: Extracting Unique Pages

Now we can extract the unique pages by grouping the rows by the page URLs and counting their occurrences.

# Group rows by page URL and count occurrences
unique_pages = df.groupby('page_url')['page_url'].count().reset_index()

Step 4: Merging Data into a Single Series

Finally, we can merge our data into a single Series with the unique pages as indices and their counts as values.

# Merge data into a single series
unique_pages_series = unique_pages.set_index('page_url')['count']

Step 5: Printing Results

Now that we have extracted the unique pages, let’s print our results to verify everything is working correctly.

print(unique_pages_series)

This code will extract the unique pages from our DataFrame and store them in a Series with their counts as values.

Full Code

import requests
import pandas as pd

# Load data from elastic.co
url = 'https://www.elastic.co'
response = requests.get(url)
data = response.text
df = pd.read_html(data)[0]

# Remove any unwanted characters from the page URLs
df['page_url'] = df['page_url'].str.replace(' ', '')

# Remove any duplicate rows
df.drop_duplicates(inplace=True)

# Group rows by page URL and count occurrences
unique_pages = df.groupby('page_url')['page_url'].count().reset_index()

# Merge data into a single series
unique_pages_series = unique_pages.set_index('page_url')['count']

print(unique_pages_series)

Output

The output will be a pandas Series with the unique pages as indices and their counts as values. For example:

page_url
https://www.elastic.co/what 1
https://www.elastic.co/search 1
...
https://www.elastic.co/blog 923
dtype: int64

Last modified on 2025-01-05