Pandas VS ML.NET

Compare Pandas VS ML.NET and see what are their differences

Momen

Build powerful web apps, and create your custom AI apps, no code needed! featured

Contents:

» Base Details
» Videos
» Reviews
» Alternatives

Pandas

Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

ML.NET

Machine Learning framework by Microsoft in .net framework and C#.

Landing page //
2023-05-12

Landing page //
2023-03-01

Pandas

Website: pandas.pydata.org
$ Details

Edit details

ML.NET

Website: learn.microsoft.com
$ Details: -

Edit details

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.

ML.NET features and specs

Integration with .NET Ecosystem
ML.NET allows seamless integration with the existing .NET ecosystem, leveraging the familiarity and resources available in .NET libraries and frameworks, making it easier for developers familiar with .NET to adopt machine learning practices.
Support for C# and F#
Being built primarily for .NET developers, ML.NET supports C# and F#, which means developers can build, train, and implement machine learning models using languages they are already comfortable with.
Open Source and Free
ML.NET is open source, which means developers can contribute to its development, view the source code, and it's free to use without licensing costs, encouraging a community-centric approach.
Comprehensive Machine Learning Workflows
ML.NET provides end-to-end support for machine learning workflows, from data preparation to model training, evaluation, and deployment, offering a range of tools and features for various types of machine learning tasks.
Support for AutoML
ML.NET includes support for automated machine learning (AutoML), which simplifies model creation by automating the process of selecting algorithms and optimizing hyperparameters, making it accessible to those with less expertise in machine learning.

Possible disadvantages of ML.NET

Limited Community and Resources
Compared to more established frameworks like TensorFlow or PyTorch, ML.NET has a smaller user community and fewer learning resources, which can be a constraint for beginners seeking support and documentation.
Less Mature Compared to Other Frameworks
ML.NET is relatively new compared to alternatives like TensorFlow and PyTorch, which means it may be less stable and optimized for certain complex tasks or scenarios.
Primarily for .NET Developers
While beneficial for .NET developers, ML.NET's strong coupling to the .NET ecosystem may not appeal to those familiar with other programming languages who may find it less intuitive or flexible.
Limited Support for Deep Learning
While ML.NET provides some capabilities for deep learning, its support and performance for deep learning tasks are limited compared to dedicated deep learning frameworks like TensorFlow.
Dependence on .NET Runtime
ML.NET applications require the .NET runtime, which could be seen as a dependency when deploying models outside the typical .NET environment, potentially complicating deployment scenarios across different platforms.

Pandas videos

+ Add

Ozzy Man Reviews: Pandas

ML.NET videos

+ Add

Announcing ML.NET 2.0 | .NET Conf 2022

Category Popularity

0-100% (relative to Pandas and ML.NET)

ML.NET

Data Science And Machine Learning

96 96%

Data Science And Machine Learning

4% 4

Data Science Tools

100 100%

Data Science Tools

0% 0

0 0%

100% 100

Python Tools

100 100%

Python Tools

0% 0

User comments

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

Reviews

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

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

ML.NET Reviews

We have no reviews of ML.NET yet.
Be the first one to post

Social recommendations and mentions

Based on our record, Pandas seems to be a lot more popular than ML.NET. While we know about 219 links to Pandas, we've tracked only 2 mentions of ML.NET. 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.

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 / 5 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 / 21 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 / 25 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

ML.NET mentions (2)

what is the future of ML.NET?
Documentation - You can find tutorials and how-to guides in our documentation site. Probably the easiest way to get started is with the Model Builder extension in Visual Studio. Here's install instructions and a tutorial to help you start out. Source: almost 3 years ago
What is the best way to get started with AI and ML in C#?
I would start right here- ML.Net Documentation. Source: almost 4 years ago