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Cython VS Pandas

Compare Cython VS Pandas and see what are their differences

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Cython logo Cython

Cython is a language that makes writing C extensions for the Python language as easy as Python...

Pandas logo Pandas

Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
  • Cython Landing page
    Landing page //
    2023-10-15
  • Pandas Landing page
    Landing page //
    2023-05-12

Cython features and specs

  • Performance Improvement
    Cython can significantly increase the execution speed of Python code by translating it into C, and allowing for static typing. This can lead to performance gains for computationally intensive tasks.
  • Compatibility with Python
    Cython is designed to be fully compatible with Python, meaning that most Python code can be compiled with Cython without any modifications.
  • Integration with C/C++
    Cython facilitates easy integration with C and C++ code, enabling the use of native libraries and expanding the modularity and capability of Python programs.
  • Ease of Use
    With syntax similar to Python, Cython is relatively easy for Python developers to learn, especially compared to learning C or C++ for performance improvements.
  • Automatic C Extension Modules
    Cython can automatically generate C extension modules, which can be imported and used in Python as regular modules, simplifying the process of creating performant extensions.

Possible disadvantages of Cython

  • Complexity in Debugging
    Debugging in Cython can be more challenging than in pure Python due to the transition from Python to C, requiring tools and knowledge of both languages for effective debugging.
  • Portability Issues
    Code generated by Cython may not be as portable as pure Python code, especially across different operating systems and architectures, due to dependencies on C compilers.
  • Build Process Overhead
    Using Cython introduces additional build process requirements, including the need for a C compiler, which can increase the complexity of the deployment process.
  • Learning Curve
    Although similar to Python, mastering Cython involves understanding C concepts and how Cython compiles Python code into C, which can entail a learning curve.
  • Limited Benefits for I/O Bound Applications
    Cython excels in CPU-bound tasks but may offer limited performance benefits for I/O-bound applications, where the bottleneck is not compute speed but data input/output rates.

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.

Cython videos

Stefan Behnel - Get up to speed with Cython 3.0

More videos:

  • Review - Cython: A First Look
  • Review - Simmi Mourya - Scientific computing using Cython: Best of both Worlds!

Pandas videos

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 Cython and Pandas)
Website Builder
100 100%
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Data Science And Machine Learning
Website Design
100 100%
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Data Science Tools
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100% 100

User comments

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Reviews

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

Cython Reviews

We have no reviews of Cython yet.
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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 Cython. 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.

Cython mentions (48)

  • I Use Nim Instead of Python for Data Processing
    >Not type safe That's the point. Look up what duck typing means in Python. Your program is meant to throw exceptions if you pass in data that doesn't look and act how it needs to. This means that in Python you don't need to do defensive programming. It's not like in C where you spend many hundreds of lines safe-guarding buffer lengths, memory allocation, return codes, static type sizes, and so on. That means that... - Source: Hacker News / 8 months ago
  • Ask HN: C/C++ developer wanting to learn efficient Python
    Https://cython.org can help with that. - Source: Hacker News / about 1 year ago
  • How to make a c++ python extension?
    The approach that I favour is to use Cython. The nice thing with this approach is that your code is still written as (almost) Python, but so long as you define all required types correctly it will automatically create the C extension for you. Early versions of Cython required using Cython specific typing (Python didn't have type hints when Cython was created), but it can now use Python's type hints. Source: almost 2 years ago
  • Codon: Python Compiler
    Just for reference, * Nuitka[0] "is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11." * Pypy[1] "is a replacement for CPython" with builtin optimizations such as on the fly JIT compiles. * Cython[2] "is an optimising static compiler for both the Python programming language and the extended Cython programming language... Makes writing C... - Source: Hacker News / almost 2 years ago
  • Any faster Python alternatives?
    Profile and optimize the hotspots with cython (or whatever the cool kids are using these days... It's been a while.). Source: about 2 years ago
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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 / 8 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 / 24 days 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 / 28 days 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 / 3 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 / 8 months ago
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What are some alternatives?

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

Numba - Numba gives you the power to speed up your applications with high performance functions written...

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

PyInstaller - PyInstaller is a program that freezes (packages) Python programs into stand-alone executables...

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

nuitka - Nuitka is a Python compiler.

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