BestFREE.nl

How to scrape data from feedly

•

BestFREE.nl

Updated on

To scrape data from Feedly, you’ll generally need to utilize programming methods, as Feedly does not provide a direct, user-friendly “export all data” button for comprehensive scraping. Here are the detailed steps:

👉 Skip the hassle and get the ready to use 100% working script (Link in the comments section of the YouTube Video) (Latest test 31/05/2025)

  1. Understand Feedly’s API Feedly Cloud API:

    • Feedly offers a Feedly Cloud API for developers. This is the most legitimate and recommended way to access your Feedly data programmatically.
    • Start by reading their official API documentation: https://developer.feedly.com/
    • You’ll need to register for a developer account and obtain an access token to authenticate your requests. This token acts as your digital key to unlock your Feedly data.

  2. Choose Your Programming Language:

    • Python is highly recommended due to its rich ecosystem of libraries for web requests requests, JSON parsing, and data manipulation pandas.
    • Other options include JavaScript Node.js, Ruby, or even Go, but Python is often the most accessible for this task.

  3. Authentication:

    • After registering on the Feedly developer portal, you’ll obtain a client ID and client secret.
    • Use these to get an access token. The API typically uses OAuth 2.0. This involves making an initial request to an authorization endpoint, often through your web browser, to grant your application permission to access your Feedly account.
    • Once you have the access token, include it in the Authorization header of all subsequent API requests. The format is usually Authorization: Bearer YOUR_ACCESS_TOKEN.

  4. Identify Endpoints:

    • The Feedly API provides various endpoints to access different types of data. Common ones you might be interested in include:
      • /v3/streams/contents: To retrieve articles from specific feeds or categories.
      • /v3/subscriptions: To list all your subscribed feeds.
      • /v3/markers: To mark articles as read, saved, etc. useful for managing your reading state if you’re building a client.
      • /v3/entries: To get details about specific entries by their IDs.
    • Consult the API documentation for the exact URL paths and required parameters.

  5. Make API Requests:

    • Using your chosen programming language’s HTTP client library e.g., Python’s requests: 
      import requests
import json

ACCESS_TOKEN = "YOUR_FEEDLY_ACCESS_TOKEN" # Replace with your actual token

headers = {


   "Authorization": f"Bearer {ACCESS_TOKEN}",
    "Content-Type": "application/json"
}

# Example: Get content from a specific stream e.g., 'user//category/global.all'
# You'll need to find your user ID or the specific feed ID you want to scrape.
# Check Feedly's API for how to list user categories or specific feed IDs.
stream_id = "user//category/global.all" # Example, replace with your actual stream ID
url = f"https://cloud.feedly.com/v3/streams/contents?streamId={stream_id}&count=100" # Get 100 articles

try:


   response = requests.geturl, headers=headers
   response.raise_for_status # Raise an exception for bad status codes 4xx or 5xx
    data = response.json

   # Process the data
    for entry in data.get'items', :


       printf"Title: {entry.get'title'}"


       printf"URL: {entry.get'canonical'.get'href' if entry.get'canonical' else 'N/A'}"


       printf"Published: {entry.get'published'}"
       print"-" * 20



except requests.exceptions.RequestException as e:
    printf"Error fetching data: {e}"


   printf"Response content: {response.text}"

  6. Parse and Store Data:

    • The API responses will typically be in JSON format.
    • Parse the JSON response to extract the desired fields e.g., article title, URL, publication date, summary, author.
    • Store this data in a structured format:
      • CSV Comma Separated Values: Simple and widely compatible for spreadsheets.
      • Pandas DataFrame Python: Excellent for in-memory data manipulation and then exporting to various formats.
      • Database SQLite, PostgreSQL: For larger datasets or more complex querying needs.

  7. Respect API Rate Limits:

    • Feedly, like most APIs, will have rate limits to prevent abuse. This means there’s a maximum number of requests you can make within a certain timeframe e.g., 60 requests per minute.
    • Exceeding these limits will result in temporary blocks or errors.
    • Implement back-off strategies e.g., using time.sleep in Python to pause your requests if you hit a rate limit. Check the Retry-After header in error responses.

  8. Error Handling:

    • Always include robust error handling in your code to deal with network issues, API errors e.g., 401 Unauthorized, 404 Not Found, 429 Too Many Requests, and unexpected data formats.

  9. Iterate for More Data Pagination:

    • APIs often return data in “pages” or “batches” e.g., 100 articles at a time.
    • Look for parameters like continuation or olderThan in the Feedly API documentation. You’ll typically get a continuation token in a response. pass this token in your next request to get the next batch of data.

This approach ensures you’re accessing data in a way that respects Feedly’s terms of service and provides a stable, reliable method for data extraction.

Table of Contents

Understanding the Landscape: Why Scrape Feedly?

When we talk about “scraping” Feedly, we’re not just pulling random web pages. We’re engaging with a sophisticated RSS reader that aggregates vast amounts of content. The motivation behind such an endeavor often stems from a need for structured content aggregation, trend analysis, or personalized data insights beyond what Feedly’s native interface offers. While Feedly itself is a fantastic tool for consumption, its strength lies in delivery, not necessarily in exhaustive data export for external analysis.

The Power of Aggregated Information

Feedly collects articles from thousands of sources, categorizes them, and presents them in a digestible format.

For professionals, researchers, or even curious individuals, this aggregated data represents a goldmine. Imagine being able to:

  • Analyze content trends over time: See how specific keywords or topics evolve across industries.
  • Identify emerging publishers: Discover new, influential voices in a niche.
  • Build custom archives: Create a searchable database of articles relevant to your work or interests, going beyond Feedly’s “saved articles” limit or specific search functions.
  • Integrate with other tools: Feed this scraped data into a CRM, a research database, or a custom analytics dashboard.

Legal and Ethical Considerations: The API vs. Web Scraping

Before into the “how,” it’s crucial to distinguish between two primary methods of data extraction and their implications:

  • Feedly Cloud API The Recommended Path: This is Feedly’s official gateway for developers. It’s designed to provide structured, rate-limited access to user data with user permission and public content. Using the API is generally legal, ethical, and more robust because it respects Feedly’s infrastructure and terms of service. You’re operating within the boundaries they’ve set.
  • Traditional Web Scraping Parsing HTML: This involves writing code to download Feedly’s web pages and extract information directly from the HTML structure. This method is often discouraged for several reasons:
    • Terms of Service Violations: Most websites, including Feedly, explicitly prohibit automated scraping of their public-facing interfaces without prior agreement. Violating these terms can lead to IP bans or legal action.
    • Fragility: Websites frequently update their layouts and HTML structures. A scraper built today might break tomorrow, requiring constant maintenance.
    • Resource Intensive: It puts a higher load on Feedly’s servers compared to API calls, which are optimized for programmatic access.
    • Ethical Concerns: It can be seen as an aggressive act that doesn’t respect the data provider’s wishes or resource allocation.

Given these points, this guide exclusively focuses on using the Feedly Cloud API. It’s the responsible, sustainable, and professional way to extract data. Engaging in practices that disrespect intellectual property or the terms of service of platforms like Feedly goes against ethical conduct. As a professional, especially one mindful of broader societal impacts, choosing the path of integrity and responsible data handling is paramount. How to scrape amazon data using python

Setting Up Your Development Environment

Embarking on a data scraping project, even via an API, requires a proper workspace. Think of it like preparing your tools before building something substantial. The right environment ensures you can write, test, and execute your code efficiently. For most data-related tasks, especially API interactions, Python stands out as the language of choice due to its simplicity, vast libraries, and strong community support.

Python: The Go-To Language for Data Operations

Python’s readability and extensive ecosystem make it ideal for tasks ranging from simple API calls to complex data analysis and machine learning.

Its high-level nature allows you to focus more on the logic of what you want to achieve rather than low-level implementation details.

  • Why Python?
    • requests library: Simplifies HTTP requests, making API interaction a breeze.
    • json library: Built-in support for parsing JSON, the most common data format for APIs.
    • pandas library: Unrivaled for data manipulation, analysis, and exporting to various formats CSV, Excel, databases.
    • Large Community: Abundant resources, tutorials, and ready-made solutions for almost any problem you encounter.

Step-by-Step Environment Setup

  1. Install Python:

    • If you don’t have Python installed, download the latest version from the official website: https://www.python.org/downloads/.
    • Ensure you check the box that says “Add Python X.X to PATH” during installation, as this makes it easier to run Python from your command line.
    • Verify installation by opening your terminal/command prompt and typing: python --version or python3 --version. You should see the installed version number.

  2. Use a Virtual Environment: How to get qualified leads with web scraping

    • This is a critical best practice. A virtual environment creates an isolated Python environment for your project, preventing conflicts between different project dependencies.
    • Open your terminal/command prompt.
    • Navigate to your project directory or create one: mkdir feedly_scraper && cd feedly_scraper
    • Create a virtual environment: python -m venv venv or python3 -m venv venv on macOS/Linux. This creates a folder named venv containing the isolated environment.
    • Activate the virtual environment:
      • Windows: .\venv\Scripts\activate
      • macOS/Linux: source venv/bin/activate
    • You’ll see venv prefixing your command prompt, indicating the environment is active.

  3. Install Necessary Libraries:

    • Once your virtual environment is active, install the libraries we’ll be using:
      • requests: For making HTTP requests to the Feedly API.
      • pandas: For data manipulation and saving optional but highly recommended.
    • Run: pip install requests pandas

  4. Choose an Integrated Development Environment IDE or Text Editor:

    • While you can use Notepad or a basic text editor, an IDE or a more advanced text editor significantly improves productivity.
    • VS Code Visual Studio Code: Free, lightweight, highly customizable, and has excellent Python support install the Python extension. This is a popular choice for many developers.
    • PyCharm Community Edition: A more full-featured IDE specifically designed for Python, offering powerful debugging and code introspection.
    • Jupyter Notebooks: Excellent for exploratory data analysis, testing API calls interactively, and presenting findings. If your goal is primarily data exploration, Jupyter might be a great starting point.

  5. Create Your Project File:

    • Inside your feedly_scraper directory, create a new Python file, e.g., scraper.py. This is where you’ll write all your code.

By following these steps, you’ll have a robust and organized development environment ready to interact with the Feedly API and process the data efficiently.

Remember, a well-prepared setup saves countless hours of troubleshooting down the line. Full guide for scraping real estate

Feedly Cloud API: Authentication and Access

The Feedly Cloud API is your legitimate gateway to accessing Feedly’s vast content ecosystem. Unlike scraping the public website, using the API means you’re playing by their rules, which translates to stable access, structured data, and a lower risk of getting your access blocked. However, this comes with a prerequisite: authentication.

Understanding OAuth 2.0 and Feedly’s API

Feedly’s API utilizes OAuth 2.0, a standard protocol for authorization. In simple terms, it allows an application your scraper to access protected resources your Feedly data on behalf of a user you without ever needing to know the user’s password. Instead, it uses access tokens.

The general flow for obtaining an access token with Feedly is an “authorization code” grant type, which involves:

  1. Client Application Registration: You register your “app” your scraper with Feedly.
  2. Authorization Request: Your app redirects the user you to Feedly’s authorization server.
  3. User Consent: The user logs in to Feedly if not already logged in and grants permission for your app to access their data.
  4. Authorization Code Grant: Feedly redirects the user back to your app with a temporary “authorization code.”
  5. Access Token Request: Your app exchanges this authorization code for an “access token” and often a refresh token directly with Feedly’s token endpoint.
  6. API Calls: Your app uses the access token to make authenticated requests to the Feedly API.

Step-by-Step Authentication Process

  1. Register Your Developer Application:

    • Go to the Feedly Cloud API developer portal: https://developer.feedly.com/
    • You’ll need to create a developer account if you don’t have one.
    • Navigate to the “My Apps” or “Applications” section.
    • Click “Create New Application.”
    • You’ll be asked for details like:
      • Application Name: Something descriptive, e.g., “My Feedly Data Scraper.”
      • Description: A brief explanation of what your app does.
      • Redirect URI: This is crucial. When Feedly authorizes your app, it will redirect the user back to this URL along with the authorization code. For a personal script running locally, you might use http://localhost:8080 or urn:ietf:wg:oauth:2.0:oob for out-of-band/copy-paste authorization if your app has no web server. The urn:ietf:wg:oauth:2.0:oob is often simpler for command-line scripts. Choose urn:ietf:wg:oauth:2.0:oob if you’re building a command-line script without a web server.
    • Upon successful registration, Feedly will provide you with a Client ID and a Client Secret. These are your application’s credentials. Keep them secure and never expose them publicly.

  2. Initiate the Authorization Flow Get Authorization Code: How to build a hotel data scraper when you are not a techie

    • Construct the authorization URL using your Client ID and Redirect URI.

    • The base authorization URL is https://cloud.feedly.com/v3/auth/auth.

    • Example URL:

      https://cloud.feedly.com/v3/auth/auth?response_type=code&client_id=YOUR_CLIENT_ID&redirect_uri=YOUR_REDIRECT_URI&scope=https://cloud.feedly.com/v3/subscriptions

    • Replace YOUR_CLIENT_ID and YOUR_REDIRECT_URI with your actual values. How to scrape crunchbase data

    • Open this URL in your web browser.

    • Feedly will prompt you to log in if not already and ask for permission to grant access to your application.

    • After you grant permission, if you used urn:ietf:wg:oauth:2.0:oob as your redirect URI, Feedly will display the authorization_code directly in your browser. Copy this code. If you used http://localhost:8080, the code would be appended to the URL as a query parameter e.g., http://localhost:8080?code=YOUR_AUTH_CODE.

  3. Exchange Authorization Code for Access Token:

    • This step is done programmatically using Python. You’ll make a POST request to Feedly’s token endpoint. Find b2b leads with web scraping

    • Endpoint: https://cloud.feedly.com/v3/auth/token

    • Method: POST

    • Headers: Content-Type: application/json

    • Body JSON:

      {
    "code": "THE_AUTH_CODE_YOU_COPIED",
    "client_id": "YOUR_CLIENT_ID",
    "client_secret": "YOUR_CLIENT_SECRET",
    "redirect_uri": "YOUR_REDIRECT_URI",
    "grant_type": "authorization_code"

    • Python requests example: How to download images from url list

      CLIENT_ID = “your_client_id_here”
      CLIENT_SECRET = “your_client_secret_here”
      REDIRECT_URI = “urn:ietf:wg:oauth:2.0:oob” # Or your localhost URI
      AUTHORIZATION_CODE = “the_code_you_copied_from_browser” # This changes each time you authorize

      Token_url = “https://cloud.feedly.com/v3/auth/token“

      Headers = {“Content-Type”: “application/json”}
      payload = {
      “code”: AUTHORIZATION_CODE,
      “client_id”: CLIENT_ID,
      “client_secret”: CLIENT_SECRET,
      “redirect_uri”: REDIRECT_URI,

      response = requests.posttoken_url, headers=headers, data=json.dumpspayload
      response.raise_for_status
      token_data = response.json

      ACCESS_TOKEN = token_data.get”access_token” Chatgpt and scraping tools

      REFRESH_TOKEN = token_data.get”refresh_token”
      EXPIRES_IN = token_data.get”expires_in” # Token expiry in seconds

      printf”Access Token: {ACCESS_TOKEN}”

      printf”Refresh Token: {REFRESH_TOKEN}”

      printf”Expires in: {EXPIRES_IN} seconds”

      printf”Error getting token: {e}”
      printf”Response: {response.text}” Extract data from website to excel automatically

  4. Using the Access Token:

    • Once you have the access_token, you’ll include it in the Authorization header for all your subsequent API requests.
    • Format: Authorization: Bearer YOUR_ACCESS_TOKEN

Refreshing Tokens for Persistent Access

Access tokens have a limited lifespan e.g., 24 hours. To avoid re-doing the authorization flow every time your token expires, Feedly provides a refresh_token.

  • When your access_token expires, you can use the refresh_token to get a new access_token without user interaction.
  • Method: POST to https://cloud.feedly.com/v3/auth/token
  • Body JSON: 
    {
    "refresh_token": "YOUR_REFRESH_TOKEN",
    "client_id": "YOUR_CLIENT_ID",
    "client_secret": "YOUR_CLIENT_SECRET",
    "grant_type": "refresh_token"
}
  • Implement logic in your script to check for token expiry and refresh it when needed. Store the refresh_token securely e.g., in an environment variable or a configuration file that’s not publicly accessible.

By meticulously following these authentication steps, you establish a secure and authorized connection to the Feedly API, paving the way for reliable data extraction. Remember, security is paramount when handling API keys and tokens. Never hardcode them directly into publicly shared code, and always store them in a secure manner.

Exploring Key Feedly API Endpoints for Data Extraction

With your development environment set up and authentication handled, the next step is to understand which endpoints will give you the data you need. The Feedly Cloud API is extensive, offering access to various aspects of your Feedly account. For scraping purposes, we’re primarily interested in content streams and metadata.

Essential Endpoints for Scraping

  1. /v3/subscriptions – Listing Your Subscribed Feeds Extracting dynamic data with octoparse

    • Purpose: This endpoint is crucial for identifying the specific feeds you want to scrape. It returns a list of all your subscribed RSS feeds, including their feedId, title, and website URL.

    • Method: GET

    • Example Request: https://cloud.feedly.com/v3/subscriptions

    • Key Data Points:

      • feedId: A unique identifier for the feed e.g., feed/http://example.com/rss.xml. This is what you’ll use in other endpoints.
      • title: The name of the feed.
      • website: The main website URL of the feed.
      • categories: The categories you’ve assigned to the feed in Feedly.

    • Why it’s useful: You can iterate through your subscriptions to gather articles from each one, or filter by category. For instance, if you have a category “Tech News” in Feedly, you can scrape all articles from feeds within that category. Contact details scraper

  2. /v3/streams/contents – Retrieving Stream Content Articles

    • Purpose: This is the workhorse endpoint for getting the actual articles. You provide a streamId which can be a feedId, a category ID, or a tag ID, and it returns a list of articles within that stream.

    • Required Parameter: streamId e.g., feed/http://example.com/rss.xml, or user//category/, or user//tag/. Your user_id can be found by inspecting your Feedly account settings or often by making an initial authenticated request to v3/profile.

    • Optional Parameters:

      • count: Number of articles to return max usually 1000, but often capped lower, e.g., 20 or 100, so check documentation.
      • newerThan: Timestamp milliseconds to get articles newer than a specific time.
      • continuation: A token for pagination see ‘Handling Pagination’ below.
      • ranked: newest default or oldest for chronological order.
      • unreadOnly: Boolean, true to get only unread articles.

    • Key Data Points for Each Entry: Email extractor geathering sales leads in minutes

      • id: Unique ID of the article entry.
      • title: Title of the article.
      • originId: The original URL of the article on the source website.
      • canonical: A list of canonical URLs, often including the primary article link.
      • published: Timestamp milliseconds when the article was published.
      • content: The main content of the article can be HTML.
      • summary: A short summary/excerpt can be HTML.
      • author: The author’s name.
      • keywords / tags: Relevant keywords/tags if provided by the source.
      • engagement: A numerical value indicating popularity.
      • visual: Information about the main image if any.
      • unread: Boolean, true if the article is unread.

    • Why it’s useful: This endpoint provides the core article data you’ll want to scrape. You’ll typically loop through your subscribed feeds obtained from /v3/subscriptions, and for each feed, call v3/streams/contents to get its articles.

  3. /v3/entries/:entryId – Getting Specific Entry Details

    • Purpose: If you have an entryId from a streams/contents response and need more granular details about that specific article, this endpoint is useful.
    • Example: https://cloud.feedly.com/v3/entries/article_id_123 where article_id_123 is the id of an article from a previous streams/contents call.
    • Why it’s useful: Less commonly used for bulk scraping unless you’re trying to retrieve additional data fields not available in the stream response for specific articles.

Handling Pagination for Comprehensive Data

Most APIs, including Feedly’s, implement pagination to limit the amount of data returned in a single request. This prevents overwhelming their servers and ensures faster responses.

  • The /v3/streams/contents endpoint will return a limited number of items per request e.g., 20, 100, or whatever the count parameter allows, up to their max.
  • In the response, you’ll often find a continuation token also sometimes referred to as marker or nextPageToken in other APIs.
  • To get the next batch of articles, you include this continuation token in your subsequent request to the same endpoint.
  • You continue making requests with the new continuation token until the response no longer contains a continuation token, indicating you’ve reached the end of the stream.

Python Loop Example for Pagination:

import requests
import json
import time

ACCESS_TOKEN = "YOUR_FEEDLY_ACCESS_TOKEN"
USER_ID = "your_feedly_user_id" # You can get this from v3/profile endpoint or your account

headers = {
    "Authorization": f"Bearer {ACCESS_TOKEN}",
    "Content-Type": "application/json"
}

# Example: Get all articles from your 'global.all' category all your Feedly articles
stream_id = f"user/{USER_ID}/category/global.all"


base_url = "https://cloud.feedly.com/v3/streams/contents"

all_articles = 
continuation_token = None
page_count = 0



printf"Starting to fetch articles from stream: {stream_id}"

while True:
    params = {
        "streamId": stream_id,
       "count": 100 # Request up to 100 articles per call
    if continuation_token:


       params = continuation_token

    try:


       response = requests.getbase_url, headers=headers, params=params
        response.raise_for_status
        data = response.json

        items = data.get'items', 
        all_articles.extenditems
        printf"Fetched {lenitems} articles. Total so far: {lenall_articles}"



       continuation_token = data.get'continuation'

        if not continuation_token:
            print"No more continuation token. All articles fetched."
            break
        
        page_count += 1
       # Implement a small delay to respect rate limits, e.g., 1 second per 100 articles
        time.sleep1 



   except requests.exceptions.RequestException as e:
        printf"Error fetching data: {e}"
       if response.status_code == 429: # Too Many Requests


           retry_after = response.headers.get'Retry-After'
            printf"Rate limit hit. Retrying after {retry_after or 60} seconds..."
            time.sleepintretry_after or 60
        else:


           break # Exit loop on other errors



printf"Total articles scraped: {lenall_articles}"
# Now 'all_articles' list contains all the articles from the stream

By understanding and effectively using these key endpoints along with robust pagination logic, you can systematically extract large volumes of data from your Feedly account. Octoparse

Remember to be mindful of Feedly’s rate limits and terms of service throughout the process.

Processing and Storing Scraped Feedly Data

Once you’ve successfully extracted data from the Feedly API, the raw JSON response is just a starting point.

To make this data useful for analysis, reporting, or integration, you need to process it effectively and store it in a structured, accessible format.

Python, especially with the pandas library, offers powerful tools for this.

From Raw JSON to Structured Data

The data returned by the Feedly API, particularly from the /v3/streams/contents endpoint, is a list of JSON objects, where each object represents an article. Best web analysis tools

Each article object contains various fields: title, published, canonical for URLs, summary, content, author, origin feed details, etc.

The challenge is to flatten and standardize this hierarchical JSON data into a tabular format that’s easy to work with.

  1. Normalization and Flattening:

    • Many fields in the Feedly API response are nested e.g., canonical is a list of dictionaries, origin is a dictionary.
    • You’ll need to extract specific values from these nested structures. For instance, from canonical, you usually only need the href of the first item. From origin, you might want title and htmlUrl.
    • Consider creating a dictionary for each article, mapping chosen keys to their extracted values.

  2. Handling Missing Data:

    • Not every article will have every field e.g., some might not have an author or a summary.
    • Use .get with a default value like None or an empty string when accessing dictionary keys to prevent KeyError exceptions.

  3. Data Type Conversion: Best shopify scrapers

    • Timestamps from Feedly are often in milliseconds since the Unix epoch. Convert these to readable date/time formats e.g., using Python’s datetime module or pandas‘ to_datetime.

Leveraging Pandas for Data Manipulation

Pandas is a must for data processing in Python. It introduces DataFrames, which are tabular data structures similar to spreadsheets or SQL tables, making data manipulation intuitive and efficient.

Example of Processing with Pandas:

import pandas as pd
from datetime import datetime

Assume ‘all_articles’ is the list of JSON article objects you scraped from Feedly

processed_data =

for article in all_articles:
# Extract and normalize relevant fields
article_id = article.get’id’
title = article.get’title’

# Handle canonical URL - get the first href if available
 canonical_url = None


if article.get'canonical' and lenarticle > 0:


    canonical_url = article.get'href'

# Convert timestamp to readable format


published_timestamp_ms = article.get'published'
 published_date = None
 if published_timestamp_ms:


    published_date = datetime.fromtimestamppublished_timestamp_ms / 1000.strftime'%Y-%m-%d %H:%M:%S'

 author = article.get'author'
summary_content = article.get'summary', {}.get'content' # Extract content from summary object
 
# Extract feed details


feed_title = article.get'origin', {}.get'title'


feed_url = article.get'origin', {}.get'htmlUrl'

# Add other fields you need, e.g., 'content', 'keywords', 'engagement'

 processed_data.append{
     'article_id': article_id,
     'title': title,
     'canonical_url': canonical_url,
     'published_date': published_date,
     'author': author,
     'summary': summary_content,
     'feed_title': feed_title,
     'feed_url': feed_url,
    # ... add more fields here
 }

Create a Pandas DataFrame

df = pd.DataFrameprocessed_data

Display the first few rows to verify

printdf.head
printf”\nDataFrame shape: {df.shape}”
printf”Columns: {df.columns.tolist}”

Example: Basic data cleaning/enhancement optional

Remove duplicate articles based on canonical_url

Df.drop_duplicatessubset=, inplace=True

Printf”DataFrame shape after removing duplicates: {df.shape}”

Convert ‘published_date’ column to datetime objects for easier time-based analysis

Df = pd.to_datetimedf

Storing the Data: Choosing the Right Format

The choice of storage format depends on your intended use case, the volume of data, and your comfort level with different technologies.

  1. CSV Comma Separated Values:

    • Pros: Universal, human-readable, easily opened in spreadsheets Excel, Google Sheets. Simple to implement.
    • Cons: No data types enforced, difficult for large datasets, poor for complex queries, less efficient for storing nested data.
    • Use Case: Small to medium datasets up to a few hundred thousand rows, quick analysis, sharing with non-technical users.
    • Pandas Export: df.to_csv'feedly_articles.csv', index=False, encoding='utf-8'

  2. Excel .xlsx:

    • Pros: Familiar to business users, supports multiple sheets, basic formatting.
    • Cons: Binary format less programmatic control, can be slow for very large datasets, limited advanced querying.
    • Use Case: Similar to CSV but when more rich formatting or multiple sheets are beneficial.
    • Pandas Export: df.to_excel'feedly_articles.xlsx', index=False requires openpyxl library: pip install openpyxl

  3. JSON Lines .jsonl or .json:

    • Pros: Each line is a valid JSON object, good for streaming processing, retains original JSON structure if you choose not to flatten fully.
    • Cons: Not directly tabular, requires parsing for analysis.
    • Use Case: When you need to preserve the original JSON structure, or for feeding data into other systems that prefer JSON.
    • Pandas Export: df.to_json'feedly_articles.jsonl', orient='records', lines=True, date_format='iso'

  4. SQLite Database:

    • Pros: File-based relational database single file, no server required, supports SQL queries, efficient for larger datasets, enforces data types.

    • Cons: Requires SQL knowledge, slightly more setup than flat files.

    • Use Case: Medium to large datasets, when you need to perform complex queries, join with other datasets, or have persistent storage without a full database server.

    • Pandas Export:
      import sqlite3

      Conn = sqlite3.connect’feedly_database.db’
      df.to_sql’articles’, conn, if_exists=’replace’, index=False # ‘replace’ or ‘append’
      conn.close

    • Example Querying:

      Query_df = pd.read_sql_query”SELECT title, author FROM articles WHERE published_date > ‘2023-01-01’ ORDER BY published_date DESC LIMIT 10″, conn
      printquery_df.head

  5. Other Databases PostgreSQL, MySQL, MongoDB:

    • Pros: Scalable, robust, concurrent access, ideal for production systems and very large datasets.
    • Cons: Requires setting up and managing a database server, more complex configuration.
    • Use Case: Production applications, very large-scale data storage and analysis, multi-user access.
    • Integration: Pandas can write to these via various database connectors e.g., sqlalchemy.

By thoughtfully processing and storing your Feedly data, you transform raw API responses into valuable assets that can power a variety of analytical and reporting tasks.

The pandas library is your primary ally in this transformation, making complex data manipulation straightforward.

Data Analysis and Visualization Best Practices

After meticulously scraping and structuring your Feedly data, the real magic begins: making sense of it. Data analysis reveals patterns, trends, and insights, while visualization makes these findings accessible and compelling. As a professional, your goal isn’t just to collect data, but to extract actionable intelligence.

Cleaning and Pre-processing for Robust Analysis

Even with initial processing, raw scraped data often contains inconsistencies that can skew your analysis. This step is crucial for data integrity.

  • Handling Duplicates:
    • Articles from different feeds might sometimes refer to the same original source. While Feedly’s canonical_url helps, ensure you identify and remove true duplicates based on a unique identifier like the canonical URL or a combination of title and publish date.
    • Example: If two articles have identical canonical_url values, keep only one. df.drop_duplicatessubset=, inplace=True
  • Missing Values:
    • Decide how to handle missing author, summary, or content fields.
    • Imputation: Replace missing values with a placeholder e.g., “N/A”, “Unknown Author”.
    • Exclusion: Drop rows with critical missing data if they are not useful for your analysis.
    • Example: df.fillna'Unknown', inplace=True
  • Text Cleaning:
    • Article titles, summaries, and content often contain HTML tags, special characters, or inconsistent casing.
    • Remove HTML: Use libraries like BeautifulSoup or regular expressions to strip HTML tags from text fields like summary or content.
    • Lowercasing: Convert all text to lowercase for consistent keyword analysis. df = df.str.lower
    • Remove Punctuation/Numbers: Depending on your analysis, you might remove punctuation or numbers to focus on words.
  • Date and Time Normalization:
    • Ensure all published_date columns are actual datetime objects in Pandas. This allows for time-series analysis.
    • Example: df = pd.to_datetimedf

Common Analytical Approaches

With clean data, you can start asking questions and letting the data provide answers.

  1. Content Volume Analysis:
    • Question: How many articles are published daily/weekly/monthly by certain feeds or categories?
    • Approach: Group by published_date or its truncated version for daily/weekly and count articles.
    • Example: df.groupbydf.dt.to_period'W'.count.sort_indexascending=False.head weekly count
  2. Top Feeds/Authors:
    • Question: Which feeds or authors are most prolific?
    • Approach: Group by feed_title or author and count articles.
    • Example: df.value_counts.head10
  3. Keyword and Topic Analysis:

    • Question: What are the most frequently discussed topics or keywords?

    • Approach:

      • Tokenization: Break down titles and summaries into individual words.
      • Stop Word Removal: Filter out common words like “the,” “is,” “and” use NLTK or SpaCy for this.
      • Frequency Counting: Count the occurrences of remaining words.
      • N-grams: Analyze phrases e.g., “artificial intelligence” instead of single words.

    • Tool: Python’s collections.Counter or scikit-learn for TF-IDF if you want more advanced topic modeling.

    • Example simple:
      from collections import Counter
      import re

      All_titles = ‘ ‘.joindf.dropna.lower
      words = re.findallr’\b\w+\b’, all_titles # Extract words

      Common_words = Counterwords.most_common20

      Print”Top 20 words in titles:”, common_words

  4. Engagement Analysis if available:
    • Question: Which articles or feeds generate the most “engagement” based on Feedly’s engagement score?
    • Approach: Sum or average the engagement score by feed, category, or time period.
    • Example: df.groupby'feed_title'.mean.sort_valuesascending=False.head

Effective Data Visualization

Visualizations translate complex data into easily digestible insights. Choose the right chart for the right story.

Python libraries like Matplotlib, Seaborn, and Plotly are excellent.

  • Trend Over Time:
    • Chart: Line chart or area chart.
    • Use Case: Showing article volume per day/week, or changes in keyword frequency over time.
    • Example using Matplotlib:
      import matplotlib.pyplot as plt

      Assuming df_daily_counts is a series from df.groupbydf.dt.date.count

      df_daily_counts.plotkind=’line’, figsize=12, 6
      plt.title’Daily Article Volume’
      plt.xlabel’Date’
      plt.ylabel’Number of Articles’
      plt.gridTrue
      plt.show

  • Distribution/Comparison:

    • Chart: Bar chart, pie chart.

    • Use Case: Top 10 feeds by article count, distribution of articles across categories.

    • Example using Seaborn:
      import seaborn as sns
      plt.figurefigsize=10, 7

      Sns.barplotx=df.value_counts.head10.index, y=df.value_counts.head10.values
      plt.title’Top 10 Feeds by Article Count’
      plt.xlabel’Feed Title’
      plt.xticksrotation=45, ha=’right’ # Rotate labels for readability
      plt.tight_layout

  • Word Clouds:

    • Chart: Word cloud.

    • Use Case: Visually representing the most frequent keywords in titles or summaries.

    • Tool: wordcloud library pip install wordcloud.

    • Example:
      from wordcloud import WordCloud
      text = ‘ ‘.joindf.dropna # Combine all titles

      Wordcloud = WordCloudwidth=800, height=400, background_color=’white’.generatetext
      plt.figurefigsize=10, 5

      Plt.imshowwordcloud, interpolation=’bilinear’
      plt.axis’off’

Ethical Considerations in Analysis

While analyzing data, always remember the ethical implications:

  • Privacy: If you’re scraping public data, ensure you’re not inadvertently collecting personal identifying information, or if you are, that you handle it with utmost care and anonymize it where possible.
  • Misinterpretation: Data can be misleading if not interpreted correctly. Avoid making overly strong claims based on limited data or correlation vs. causation fallacies.
  • Bias: Be aware of potential biases in your data sources e.g., if you only scrape certain types of feeds, your analysis will be biased towards those perspectives. Strive for diverse data sources where appropriate.
  • Responsible Reporting: Present your findings clearly and accurately, acknowledging any limitations of your data or analysis methods.

Advanced Scraping Techniques and Considerations

While the foundational steps cover most Feedly API scraping needs, a few advanced techniques and considerations can significantly enhance your process, particularly for large-scale or production-ready applications.

1. Robust Error Handling and Rate Limiting

Hitting API rate limits or encountering network issues is a common challenge. Your scraper needs to be resilient.

  • HTTP Status Codes: Always check the response.status_code.
    • 200 OK: Success.
    • 401 Unauthorized: Your access token is invalid or expired. Implement token refresh logic.
    • 404 Not Found: The requested resource e.g., streamId doesn’t exist.
    • 429 Too Many Requests: You’ve exceeded the API’s rate limits.
    • 5xx Server Error: Feedly’s server is having issues.
  • Exponential Backoff for 429 Errors:
    • When you hit a 429 error, Feedly often includes a Retry-After header indicating how many seconds to wait.
    • If Retry-After is absent, implement exponential backoff: wait for a short period e.g., 5 seconds, then double the wait time for subsequent retries up to a maximum. This prevents hammering the server.
    • Jitter: Add a small random delay to your backoff time.sleepbase_delay + random.uniform0, 1 to prevent all clients from retrying simultaneously, which can cause another burst of requests.
  • Connection Errors: Handle requests.exceptions.ConnectionError and requests.exceptions.Timeout for network-related issues.
  • Max Retries: Set a maximum number of retries before giving up on a request.

Python Example for Robustness:

import random

Def fetch_with_retryurl, headers, params=None, max_retries=5, initial_delay=1:
retries = 0
delay = initial_delay
while retries < max_retries:
response = requests.geturl, headers=headers, params=params, timeout=30 # Add timeout
response.raise_for_status # Raise for 4xx or 5xx errors
return response.json
except requests.exceptions.HTTPError as e:
if response.status_code == 429:
retry_after = intresponse.headers.get’Retry-After’, delay * 2 # Use header or double delay
printf”Rate limit hit. Retrying in {retry_after} seconds…”
time.sleepretry_after + random.uniform0, 1 # Add jitter
elif response.status_code == 401:
print”Unauthorized. Token might be expired. Exiting or refreshing token…”
return None # Or trigger token refresh logic
else:

            printf"HTTP Error {response.status_code}: {e}"


            printf"Response content: {response.text}"
            break # Exit on other HTTP errors


    except requests.exceptions.ConnectionError, requests.exceptions.Timeout as e:


        printf"Network error or timeout: {e}. Retrying in {delay} seconds..."


        time.sleepdelay + random.uniform0, 1
     
     retries += 1
    delay *= 2 # Exponential backoff for other errors



printf"Failed to fetch data after {max_retries} retries."
 return None

Usage:

data = fetch_with_retrybase_url, headers, params

if data:

# Process data

pass

2. Incremental Scraping and Change Detection

For ongoing data collection, you don’t want to re-scrape everything every time.

  • newerThan Parameter: The /v3/streams/contents endpoint has a newerThan parameter that accepts a timestamp milliseconds since epoch. Store the timestamp of your last successful scrape, and in the next run, only request articles newer than that timestamp.
  • Tracking Last Scraped Time: When you scrape, save the current timestamp or the timestamp of the newest article you just scraped to a configuration file or database.
  • Duplicate Detection: Even with newerThan, you might get some overlap. Store a unique identifier like canonical_url or Feedly’s id of already-processed articles in a set or a database. Before adding a new article, check if its ID already exists.

3. Asynchronous Scraping for Performance

If you need to scrape hundreds or thousands of feeds, making sequential requests can be very slow due to network latency.

  • asyncio + aiohttp Python: For I/O-bound tasks like API calls, asynchronous programming allows your program to initiate multiple requests concurrently without waiting for each one to complete before starting the next.
  • Concept: Instead of time.sleep1 after each request, an asyncio script can initiate the next request immediately while the previous one is still in transit, significantly speeding up overall execution time, especially when dealing with multiple streams.
  • Warning: This is an advanced topic and adds complexity. Ensure you still respect Feedly’s rate limits. you can’t just unleash thousands of requests simultaneously. aiohttp allows you to create a ClientSession and limit the number of concurrent connections.

4. Headless Browsers When APIs Are Not Enough – Use with Caution!

In rare scenarios, an API might not expose all the data you need, or you might need to interact with dynamic web elements.

This is where headless browsers like Selenium or Playwright come in.

  • How they work: A headless browser is a web browser without a graphical user interface. You can programmatically control it to navigate pages, click buttons, fill forms, and extract content as if a human were doing it.
  • When to consider last resort:
    • If Feedly’s API truly does not provide a critical piece of information that is visible on the web interface.
    • If content is dynamically loaded by JavaScript and not present in the initial HTML though Feedly’s API handles this for articles.
  • Why to be careful:
    • Performance: Much slower and more resource-intensive than API calls.
    • Fragility: Highly susceptible to UI changes.
    • Ethical/Legal: This is more akin to traditional web scraping and might violate Feedly’s Terms of Service for automated access. Only use if no API alternative exists and you have explicit permission or clear legal grounds.
    • Feedly’s case: For scraping article content and metadata, the Feedly API is more than sufficient. You should not need a headless browser for typical Feedly scraping tasks. This point is included for completeness regarding “advanced scraping techniques” in general, but specifically discouraged for Feedly.

5. Managing Credentials Securely

Hardcoding your CLIENT_ID, CLIENT_SECRET, ACCESS_TOKEN, and REFRESH_TOKEN directly into your script is a major security risk.

  • Environment Variables: The most common and recommended way for development and deployment.
    • Before running your script: export FEEDLY_CLIENT_ID="your_id" Linux/macOS or $env:FEEDLY_CLIENT_ID="your_id" PowerShell.
    • In Python: import os. client_id = os.getenv'FEEDLY_CLIENT_ID'
  • Configuration Files .env, .ini, .yml: Store credentials in a .env file e.g., using python-dotenv library or a .ini file. Crucially, add these files to your .gitignore to prevent them from being committed to version control.
  • Key Management Services: For production environments, consider cloud-based secret management services AWS Secrets Manager, Google Cloud Secret Manager.

By adopting these advanced techniques, you can build a more robust, efficient, and secure Feedly data scraping solution capable of handling larger datasets and long-term data collection needs.

Always prioritize API usage over traditional web scraping for ethical and practical reasons.

Integrating Scraped Feedly Data with Other Tools

The value of scraped data multiplies when it can be integrated into existing workflows or combined with other datasets.

Your meticulously extracted Feedly articles can feed into various tools, enhancing your research, content strategy, and overall data intelligence.

1. Business Intelligence BI Dashboards

BI tools are designed to visualize data, track KPIs, and create interactive reports.

  • Tools: Tableau, Power BI, Google Data Studio, Metabase, Looker.
  • Integration:
    • CSV/Excel Upload: The simplest method. Export your Pandas DataFrame to CSV or Excel, then manually upload it to your BI tool. Good for ad-hoc analysis.
    • Database Connection: If you’ve stored your data in SQLite, PostgreSQL, or MySQL, most BI tools can directly connect to these databases. This allows for automated data refreshes and more dynamic dashboards.
    • API/Custom Connectors: Some BI tools allow you to write custom connectors. If you expose your scraped data via a simple web API e.g., using Flask or FastAPI, you could build a connector.
  • Use Cases:
    • Track daily/weekly article volume from key industry feeds.
    • Visualize top authors or publishers over time.
    • Monitor keyword trends across different categories.
    • Create a “news hub” dashboard for your team.

2. Content Management Systems CMS or Publishing Platforms

If you’re curating content or building a knowledge base, scraped articles can be imported.

  • Tools: WordPress, Drupal, HubSpot, custom CMS.
    • API: Many CMS platforms have APIs REST or GraphQL for creating posts, pages, or custom content types. Your Python script could process scraped Feedly articles and then use the CMS API to publish them e.g., as drafts for review.
    • Database Import: If your CMS uses a standard database e.g., MySQL, you might directly insert processed article data into its tables use with caution and deep understanding of the CMS’s schema.
    • RSS Feed Generation: You could process your scraped data and generate a new RSS feed from it, which other systems can then subscribe to.
    • Automatically populate a “curated news” section on your website.
    • Build an internal knowledge base of industry articles.
    • Pre-fill content for social media scheduling tools though always review and add human touch!.

3. Data Science and Machine Learning Workflows

Scraped Feedly data is a rich source for text analysis, topic modeling, and recommendation systems.

HubSpot

  • Tools: Jupyter Notebooks, Google Colab, scikit-learn, TensorFlow, PyTorch.
  • Integration: Pandas DataFrames are the standard input for most Python data science libraries.
    • Topic Modeling: Use techniques like Latent Dirichlet Allocation LDA to identify underlying themes in a large corpus of articles.
    • Sentiment Analysis: Analyze the sentiment of articles positive, negative, neutral regarding specific topics or entities.
    • Recommendation Systems: Build a basic content recommendation engine based on article keywords or user reading history.
    • Named Entity Recognition NER: Extract names of people, organizations, locations, etc., from article text.
    • Clustering: Group similar articles together automatically.

4. Customer Relationship Management CRM or Sales Tools

Contextual news can enrich customer profiles.

  • Tools: Salesforce, HubSpot CRM, Zoho CRM.
  • Integration: Most CRMs have APIs. You could potentially link relevant industry news to specific company accounts.
    • Provide sales teams with recent news about target companies or industries before a meeting.
    • Monitor mentions of your company or competitors across news sources.

5. Research and Archiving Systems

For long-term storage and retrieval of information.

  • Tools: Custom databases e.g., PostgreSQL, document management systems, search engines Elasticsearch, Solr.
  • Integration: Direct database inserts or indexing into search engines.
    • Build a searchable archive of industry news for historical analysis.
    • Create a personalized research database with advanced search capabilities.

6. Alerting and Notification Systems

Be notified when specific content appears.

  • Tools: Custom Python scripts, Zapier, IFTTT with custom webhooks.
  • Integration: Your script can check for new articles matching certain criteria e.g., keywords, specific feeds and then send an email, a Slack message, or trigger a webhook.
    • Receive an email alert whenever a new article mentions your company or key competitors.
    • Get a daily summary of articles from your top 5 feeds via Slack.

General Advice for Integration

  • APIs are King: Prioritize using official APIs of the target tools for robust and sanctioned integration.
  • ETL Extract, Transform, Load: Think about your process in terms of ETL. Your scraper extracts, Pandas transforms, and then you load into the target system.
  • Security and Authentication: Always handle API keys and credentials for target systems with the same care as your Feedly API keys.
  • Incremental Updates: For continuous integration, focus on pushing only new or updated data to avoid redundant processing and storage.
  • Testing: Thoroughly test your integrations to ensure data fidelity and correct functionality.

By integrating your scraped Feedly data, you move beyond mere collection to create dynamic, insightful, and actionable information streams that can significantly enhance various aspects of your professional life and organizational efficiency.

Legal and Ethical Considerations in Data Scraping

As professionals, particularly within a framework that emphasizes integrity and accountability, understanding these boundaries is paramount.

When it comes to scraping data from platforms like Feedly, always prioritize methods that are compliant and respectful.

The Foundation: Terms of Service ToS

Every online platform, including Feedly, has a Terms of Service ToS or User Agreement. This document outlines the rules for using their service, including how data can be accessed.

  • Feedly’s Stance: Feedly explicitly provides a Feedly Cloud API for programmatic access. This is their sanctioned method. Their ToS which developers agree to when registering for the API will define the limits, rate limits, and permissible uses of data obtained via the API.
  • Direct Web Scraping: Most ToS explicitly prohibit automated scraping of their public-facing website i.e., parsing HTML without prior written permission. This is because such activities can:
    • Overload their servers.
    • Circumvent their business models e.g., ads, premium features.
    • Violate intellectual property rights.
  • Violation Consequences: Disregarding ToS can lead to:
    • IP address bans.
    • Account suspension.
    • In extreme cases, legal action e.g., trespass to chattels, breach of contract.

Key Takeaway: Always consult and adhere to the platform’s API documentation and Terms of Service. The Feedly API is the ethical and legal path for data extraction from Feedly. Avoid direct web scraping of Feedly’s user interface.

Intellectual Property Rights

The content you scrape articles, images, text is subject to intellectual property laws, primarily copyright.

  • Copyright Ownership: The original publishers and authors of the articles retain copyright over their work.
  • Fair Use/Fair Dealing: In some jurisdictions, limited use of copyrighted material without permission may be allowed under “fair use” U.S. or “fair dealing” U.K., Canada. This typically applies to transformative uses like commentary, criticism, news reporting, teaching, scholarship, or research.
    • Personal Archiving: If you are scraping your own Feedly content for personal research or archiving, and not republishing it, it is generally considered a low-risk use case.
    • Commercial Use/Republication: Do NOT scrape content and republish it for commercial gain without explicit permission from the copyright holder. This includes monetizing it, incorporating it into products, or distributing it broadly. This is a clear copyright infringement.
  • Attribution: Even when permissible, always attribute the original source. It’s a professional and ethical imperative.

Data Privacy and Personal Data

If the data you’re scraping contains information about individuals, data privacy laws come into play e.g., GDPR in Europe, CCPA in California.

  • Public vs. Private Data: Data that is truly public e.g., a news article published on a public website is generally less restricted than private data. However, aggregating public data in a way that identifies individuals or creates new profiles can still raise privacy concerns.
  • Feedly and User Data: When using the Feedly API, you are typically accessing your own Feedly user data your subscriptions, your saved articles. Feedly’s API ensures you only access what you are authorized to see. You are not typically accessing other users’ private data.
  • Content of Articles: Be mindful if the articles themselves contain sensitive personal information though major news publishers usually redact this. If you are processing this data, ensure you have a legitimate purpose and handle it securely, potentially anonymizing it.

Ethical Considerations Beyond Legality

Beyond the strict letter of the law, consider the broader ethical implications of your actions:

  • Resource Burden: Even with API usage, making an excessive number of requests can strain a server. Adhere to rate limits strictly.
  • Transparency: If you’re building a service based on scraped data, be transparent about the sources and how the data is used.
  • Benefit vs. Harm: Does your scraping activity contribute positively? Is it creating value or simply extracting it without benefit to the original creators or platform?
  • “Do Unto Others”: Would you want someone scraping your website or service in the same way? This simple thought experiment can often guide ethical decisions.
  • Islamic Principles: From an Islamic perspective, actions should be undertaken with integrity Amana, justice ‘Adl, and without causing harm Darar. Misrepresenting data, infringing on intellectual property, or causing undue burden on others’ resources would generally go against these principles. Seeking permissible means and adhering to agreements like ToS aligns with these values.

In summary: For scraping Feedly, stick to the official Feedly Cloud API, respect its rate limits, understand the terms of service, and be mindful of copyright and privacy laws, especially if you plan to use the data beyond personal analysis. Always choose the path of integrity and respect for intellectual property.

Frequently Asked Questions

How do I legally scrape data from Feedly?

The most legal and ethical way to scrape data from Feedly is by using their official Feedly Cloud API. This method respects their terms of service and provides structured access to the data, typically requiring authentication via an access token obtained after registering a developer application.

Can I use traditional web scraping tools like Beautiful Soup or Selenium to scrape Feedly?

No, it is highly discouraged and generally a violation of Feedly’s Terms of Service to use traditional web scraping tools like Beautiful Soup or Selenium to parse their public-facing website HTML. This can lead to IP bans or account suspension and is less robust than using their official API.

What data can I get from the Feedly API?

You can get various types of data, including:

  • Your subscribed feeds titles, URLs, categories.
  • Articles from specific feeds, categories, or your entire Feedly stream titles, URLs, publication dates, summaries, content, authors, engagement scores.
  • Information about specific articles by their IDs.

Do I need a developer account to use the Feedly API?

Yes, you need to register for a developer account on the Feedly developer portal https://developer.feedly.com/ to obtain a Client ID and Client Secret, which are essential for authentication and accessing the API.

How do I authenticate with the Feedly API?

Feedly’s API uses OAuth 2.0. You’ll typically obtain an authorization code via a browser redirect, then exchange this code for an access_token and a refresh_token by making a POST request to their token endpoint.

The access_token is then used in the Authorization header of all subsequent API requests.

What is an API rate limit, and how do I handle it?

An API rate limit is a restriction on the number of requests you can make to an API within a certain timeframe e.g., 60 requests per minute. To handle it, implement pauses time.sleep in Python between your requests, especially when fetching large amounts of data.

If you hit a 429 Too Many Requests error, check the Retry-After header in the response and wait for that duration before retrying.

What is pagination in API scraping, and how does Feedly handle it?

Pagination is when an API returns data in limited batches or “pages.” Feedly’s /v3/streams/contents endpoint uses a continuation token.

You include this token in your next request to get the subsequent batch of articles.

You continue until no continuation token is returned, indicating the end of the stream.

What programming language is best for scraping Feedly data via API?

Python is highly recommended due to its powerful libraries like requests for API calls, json for data parsing, and pandas for data processing and storage.

How do I store the scraped Feedly data?

Common storage formats include:

  • CSV: Simple, widely compatible for spreadsheets.
  • Excel .xlsx: For spreadsheet-friendly data, supports multiple sheets.
  • JSON Lines .jsonl: If you want to retain the original JSON structure per line.
  • SQLite Database: A file-based relational database ideal for larger datasets and SQL querying without a server.
  • Other Databases PostgreSQL, MySQL: For very large-scale or production environments.

Can I scrape historical data from Feedly?

Yes, the Feedly API allows you to retrieve historical data.

The /v3/streams/contents endpoint typically supports parameters like newerThan to get articles published after a specific timestamp and pagination to retrieve older articles.

However, the depth of available history might be limited by Feedly’s retention policies for certain accounts.

How can I get my Feedly User ID?

Your Feedly User ID is typically a part of stream IDs for personalized streams e.g., user//category/global.all. You can often find it by making an authenticated request to a general user-specific endpoint like v3/profile which returns user details including the ID.

Is it possible to scrape only unread articles?

Yes, the /v3/streams/contents endpoint has an unreadOnly parameter.

Setting it to true will return only articles that are marked as unread in your Feedly account.

How do I extract the full content of an article, not just the summary?

The content field within the article object returned by the /v3/streams/contents endpoint often contains the full HTML content of the article.

You’ll need to parse this HTML to extract plain text if desired.

What should I do if my access token expires?

If your access_token expires you’ll typically get a 401 Unauthorized error, you should use your refresh_token to request a new access_token from Feedly’s token endpoint. This process doesn’t require user interaction.

Can I scrape articles from specific categories I’ve set up in Feedly?

Yes, you can specify a category ID as the streamId in the /v3/streams/contents endpoint.

A category ID typically looks like user//category/.

How can I avoid overwhelming Feedly’s servers?

Always adhere to Feedly’s API rate limits.

Implement delays between your requests, especially when iterating through many feeds or pages.

Use exponential backoff if you encounter 429 Too Many Requests errors.

This ensures your scraping is respectful of their infrastructure.

Is it ethical to scrape data?

Scraping data can be ethical if done responsibly.

Using official APIs, respecting terms of service, honoring intellectual property rights especially copyright, being mindful of data privacy, and not causing undue burden on servers are key ethical considerations. For Feedly, using their API is the ethical route.

Can I automate the authentication process?

Obtaining the initial authorization_code usually requires a one-time manual step where you open a URL in your browser and grant permission.

However, once you have the refresh_token, subsequent access token renewals can be fully automated programmatically without manual intervention.

What are common issues when scraping Feedly data?

Common issues include:

  • Hitting API rate limits 429 errors.
  • Expired access tokens 401 errors.
  • Incorrect streamId or other parameters.
  • Network connectivity issues.
  • Changes in API response structure though less frequent with official APIs.
  • Problems parsing nested JSON data.

How can I make my Feedly scraping script more efficient for large datasets?

For large datasets, consider:

  • Incremental Scraping: Use newerThan to only fetch new articles.
  • Batch Processing: Request the maximum count allowed per API call e.g., 100.
  • Asynchronous Requests Advanced: Use libraries like aiohttp with asyncio to make concurrent API calls, improving overall speed while still respecting rate limits.
  • Efficient Data Storage: Choose a database like SQLite or PostgreSQL over flat files for better performance with large volumes.

0.0
0.0 out of 5 stars (based on 0 reviews)
Excellent0%
Very good0%
Average0%
Poor0%
Terrible0%

There are no reviews yet. Be the first one to write one.

​
Amazon.com: Check Amazon for How to scrape
Latest Discussions & Reviews:

•

Leave a Reply

Your email address will not be published. Required fields are marked *

Recent Posts

Social Media

https://developer.feedly.com/
Skip / Close