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Scrapy VS Pandas

Compare Scrapy VS Pandas and see what are their differences

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Scrapy logo Scrapy

Scrapy | A Fast and Powerful Scraping and Web Crawling Framework

Pandas logo Pandas

Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
  • Scrapy Landing page
    Landing page //
    2021-10-11
  • Pandas Landing page
    Landing page //
    2023-05-12

Scrapy features and specs

  • Efficiency
    Scrapy is designed to be efficient and robust, capable of handling multiple tasks simultaneously and scraping large websites in a fast and reliable manner.
  • Built-in Tooling
    Scrapy comes with built-in tools for handling common tasks such as following links, extracting data using XPath and CSS, and exporting data in a variety of formats.
  • Customization
    Scrapy offers extensive customization options, allowing users to build complex spiders and modify their behavior through middleware and pipelines.
  • Python Integration
    Being a Python framework, Scrapy integrates seamlessly with the Python ecosystem, enabling the use of libraries like Pandas, NumPy, and others to process and analyze scraped data.
  • Community Support
    Scrapy has a large and active community, providing extensive documentation, tutorials, and third-party extensions to enhance functionality.
  • Asynchronous Processing
    Scrapy’s asynchronous processing model enhances performance by allowing multiple concurrent requests, reducing the time required for crawling sites.

Possible disadvantages of Scrapy

  • Steep Learning Curve
    For beginners, Scrapy's comprehensive feature set and the need for understanding concepts like XPath and CSS selectors can be challenging.
  • Resource Intensive
    Scrapy can be resource-intensive, potentially consuming significant memory and CPU, which can be problematic for scraping very large websites or running multiple spiders simultaneously.
  • Debugging Complexity
    Debugging Scrapy projects can be complex due to its asynchronous nature and the multiple layers of middleware and pipelines that need to be understood.
  • Overhead for Small Projects
    For simple or small-scale scraping tasks, the overhead of setting up and configuring a Scrapy project might be excessive, with simpler alternatives being more suitable.
  • Limited JavaScript Support
    Scrapy's out-of-the-box support for JavaScript-heavy websites is limited, requiring additional tools like Splash or Selenium, which can complicate the setup.
  • Dependency Management
    Managing Scrapy's dependencies and compatibility with other Python packages can sometimes be challenging, leading to potential conflicts and maintenance overhead.

Pandas features and specs

  • Data Wrangling
    Pandas offers robust tools for manipulating, cleaning, and transforming data, making it easier to prepare data for analysis.
  • Flexible Data Structures
    Pandas provides two primary data structures: Series and DataFrame, which are flexible and offer powerful capabilities for handling various types of datasets.
  • Integration with Other Libraries
    Pandas integrates seamlessly with other Python libraries such as NumPy, Matplotlib, and SciPy, facilitating comprehensive data analysis workflows.
  • Performance with Data Size
    For data sizes that fit into memory, Pandas performs excellently with operations and computations being highly optimized.
  • Rich Feature Set
    Pandas provides a wide array of functionalities, including but not limited to group-by operations, merging and joining data sets, time-series functionality, and input/output tools.
  • Community and Documentation
    Pandas has a strong community and extensive documentation, offering a wealth of tutorials, examples, and support for new and experienced users alike.

Possible disadvantages of Pandas

  • Memory Consumption
    Pandas can become memory inefficient with very large datasets because it relies heavily on in-memory operations.
  • Single-threaded
    Many Pandas operations are single-threaded, which can lead to performance bottlenecks when handling very large datasets.
  • Steep Learning Curve
    For users who are new to data analysis or Pandas, there can be a steep learning curve due to its extensive capabilities and complex syntax at times.
  • Less Suitable for Real-time Analytics
    Pandas is not designed for real-time analytics and is better suited for batch processing due to its in-memory operations and single-threaded nature.
  • Error Handling
    Error messages in Pandas can sometimes be cryptic and hard to interpret, making debugging a challenge for users.

Analysis of Scrapy

Overall verdict

  • Yes, Scrapy is a good option for those looking to implement web scraping projects due to its robust set of features, active community, and comprehensive documentation. It is particularly well-suited for projects that require scraping from multiple websites and processing large volumes of data efficiently.

Why this product is good

  • Scrapy is a popular open-source web crawling framework for Python that's designed for extensive, flexible, and efficient web scraping. Its built-in tools and features make it easy to extract data from websites quickly and automatically. Key advantages include its ability to handle requests asynchronously, its support for multiple protocols, its item pipeline feature that allows for data cleaning and storage, and its ease of integration with other Python libraries and databases.

Recommended for

    Scrapy is recommended for developers, data scientists, and businesses that need to gather data from websites efficiently. It's particularly useful for projects involving data aggregation, market research, competitive analysis, and monitoring pricing changes across various platforms.

Analysis of Pandas

Overall verdict

  • Pandas is highly recommended for tasks involving data manipulation and analysis, especially for those working with tabular data. Its efficiency and ease of use make it a staple in the data science toolkit.

Why this product is good

  • Pandas is widely considered a good library for data manipulation and analysis due to its powerful data structures, like DataFrames and Series, which make it easy to work with structured data. It provides a wide array of functions for data cleaning, transformation, and aggregation, which are essential tasks in data analysis. Furthermore, Pandas seamlessly integrates with other libraries in the Python ecosystem, making it a versatile tool for data scientists and analysts. Its extensive documentation and strong community support also contribute to its reputation as a reliable tool for data analysis tasks.

Recommended for

    Pandas is particularly recommended for data scientists, analysts, and engineers who need to perform data cleaning, transformation, and analysis as part of their work. It is also suitable for academics and researchers dealing with data in various formats and needing powerful tools for their data-driven research.

Scrapy videos

+ Add

Python Scrapy Tutorial - 22 - Web Scraping Amazon

More videos:

  • Demo - Scrapy - Overview and Demo (web crawling and scraping)
  • Review - GFuel LemoNADE Taste Test & Review! | Scrapy

Pandas videos

+ Add

Ozzy Man Reviews: Pandas

More videos:

  • Review - Ozzy Man Reviews: PANDAS Part 2
  • Review - Trash Pandas Review with Sam Healey

Category Popularity

0-100% (relative to Scrapy and Pandas)

Scrapy

Pandas

Web Scraping
100 100%
Web Scraping
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Data Extraction
100 100%
Data Extraction
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

Share your experience with using Scrapy and Pandas. For example, how are they different and which one is better?
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Reviews

These are some of the external sources and on-site user reviews we've used to compare Scrapy and Pandas

Scrapy Reviews

Top 15 Best TinyTask Alternatives in 2022
The software is simply deployable via the cloud, or you can host the spiders on your server using Scrapy. Only the rules need to be written; Scrapy will take care of the rest to separate the facts. With Scrapy’s portability and ability to run on Windows, Linux, Mac, and BSD platforms, new features can be added without affecting the program’s core.
Source: www.dashtech.org

Pandas Reviews

25 Python Frameworks to Master
Pandas is a powerful and flexible open-source library used to perform data analysis in Python. It provides high-performance data structures (i.e., the famous DataFrame) and data analysis tools that make it easy to work with structured data.
Source: kinsta.com
Python & ETL 2020: A List and Comparison of the Top Python ETL Tools
When it comes to ETL, you can do almost anything with Pandas if you're willing to put in the time. Plus, pandas is extraordinarily easy to run. You can set up a simple script to load data from a Postgre table, transform and clean that data, and then write that data to another Postgre table.
Source: www.xplenty.com

Social recommendations and mentions

Based on our record, Pandas should be more popular than Scrapy. It has been mentiond 219 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Scrapy mentions (97)

  • Current problems and mistakes of web scraping in Python and tricks to solve them!
    One might ask, what about Scrapy? I'll be honest: I don't really keep up with their updates. But I haven't heard about Zyte doing anything to bypass TLS fingerprinting. So out of the box Scrapy will also be blocked, but nothing is stopping you from using curl_cffi in your Scrapy Spider. - Source: dev.to / 9 months ago
  • Automate Spider Creation in Scrapy with Jinja2 and JSON
    Install scrapy (Offical website) either using pip or conda (Follow for detailed instructions):. - Source: dev.to / 10 months ago
  • Analyzing Svenskalag Data using DBT and DuckDB
    Using Scrapy I fetched the data needed (activities and attendance). Scrapy handled authentication using a form request in a very simple way:. - Source: dev.to / 12 months ago
  • Scrapy Vs. Crawlee
    Scrapy is an open-source Python-based web scraping framework that extracts data from websites. With Scrapy, you create spiders, which are autonomous scripts to download and process web content. The limitation of Scrapy is that it does not work very well with JavaScript rendered websites, as it was designed for static HTML pages. We will do a comparison later in the article about this. - Source: dev.to / about 1 year ago
  • What is SERP? Meaning, Use Cases and Approaches
    While there is no specific library for SERP, there are some web scraping libraries that can do the Google Search Page Ranking. One of them which is quite famous is Scrapy - It is a fast high-level web crawling and web scraping framework, used to crawl websites and extract structured data from their pages. It offers rich developer community support and has been used by more than 50+ projects. - Source: dev.to / over 1 year ago
View more

Pandas mentions (219)

  • Top Programming Languages for AI Development in 2025
    Libraries for data science and deep learning that are always changing. - Source: dev.to / 29 days ago
  • How to import sample data into a Python notebook on watsonx.ai and other questions…
    # Read the content of nda.txt Try: Import os, types Import pandas as pd From botocore.client import Config Import ibm_boto3 Def __iter__(self): return 0 # @hidden_cell # The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials. # You might want to remove those credentials before you share the notebook. Cos_client = ibm_boto3.client(service_name='s3', ... - Source: dev.to / about 2 months ago
  • How I Hacked Uber’s Hidden API to Download 4379 Rides
    As with any web scraping or data processing project, I had to write a fair amount of code to clean this up and shape it into a format I needed for further analysis. I used a combination of Pandas and regular expressions to clean it up (full code here). - Source: dev.to / about 2 months ago
  • Must-Know 2025 Developer’s Roadmap and Key Programming Trends
    Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python,... - Source: dev.to / 4 months ago
  • Sample Super Store Analysis Using Python & Pandas
    This tutorial provides a concise and foundational guide to exploring a dataset, specifically the Sample SuperStore dataset. This dataset, which appears to originate from a fictional e-commerce or online marketplace company's annual sales data, serves as an excellent example for learning and how to work with real-world data. The dataset includes a variety of data types, which demonstrate the full range of... - Source: dev.to / 9 months ago
View more

What are some alternatives?

When comparing Scrapy and Pandas, you can also consider the following products

Apify - Apify is a web scraping and automation platform that can turn any website into an API.

NumPy - NumPy is the fundamental package for scientific computing with Python

ParseHub - ParseHub is a free web scraping tool. With our advanced web scraper, extracting data is as easy as clicking the data you need.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Octoparse - Octoparse provides easy web scraping for anyone. Our advanced web crawler, allows users to turn web pages into structured spreadsheets within clicks.

OpenCV - OpenCV is the world's biggest computer vision library

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