Overview

Pandas

Stacks1.8K

Followers1.3K

Votes23

Numba

Stacks18

Followers44

Votes0

GitHub Stars0

Forks0

Numba vs Pandas: What are the differences?

Introduction:

Numba and Pandas are two popular libraries used in Python for different purposes. While Numba focuses on accelerating numerical and scientific computations using just-in-time compilation, Pandas is mainly used for data manipulation and analysis.

Performance Optimization: The key difference between Numba and Pandas is that Numba is primarily used for optimizing performance by compiling Python code to machine code, resulting in faster execution times. On the other hand, Pandas is aimed at providing high-level data structures and tools for data analysis, not specifically for performance optimization.
Typing: Numba requires explicit typing of variables for optimization purposes, while Pandas allows for more flexibility in handling data types without the need for explicit typing. This can lead to more efficient code with Numba but also requires more effort from the programmer.
Compatibility: Numba works well with NumPy arrays and functions, making it suitable for numerical computations and scientific computing tasks. In contrast, Pandas is designed for handling tabular data, making it more suitable for data manipulation and analysis tasks in a structured format.
Ease of Use: Pandas is generally considered easier to use for data analysis and manipulation tasks due to its high-level abstractions and comprehensive documentation. Numba, on the other hand, requires a deeper understanding of optimization techniques and explicit typing, making it more challenging for beginners.
Parallel Computing: Numba provides support for parallel computation using features like multithreading and CUDA acceleration, making it suitable for tasks that benefit from parallel processing. Pandas, on the other hand, does not offer built-in support for parallel computing and is more focused on single-threaded data operations.

In Summary, Numba is primarily focused on performance optimization through just-in-time compilation with explicit typing, while Pandas is designed for data manipulation and analysis tasks with high-level abstractions and flexibility in handling data types. Each library serves a different purpose but can be powerful tools in their respective domains.

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Detailed Comparison

Pandas	Numba
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.	It translates Python functions to optimized machine code at runtime using the industry-standard LLVM compiler library. It offers a range of options for parallelising Python code for CPUs and GPUs, often with only minor code changes.
Easy handling of missing data (represented as NaN) in floating point as well as non-floating point data;Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects;Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you in computations;Powerful, flexible group by functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data;Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects;Intelligent label-based slicing, fancy indexing, and subsetting of large data sets;Intuitive merging and joining data sets;Flexible reshaping and pivoting of data sets;Hierarchical labeling of axes (possible to have multiple labels per tick);Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving/loading data from the ultrafast HDF5 format;Time series-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging, etc.	On-the-fly code generation; Native code generation for the CPU (default) and GPU hardware; Integration with the Python scientific software stack
Statistics
GitHub Stars -	GitHub Stars 0
GitHub Forks -	GitHub Forks 0
Stacks 1.8K	Stacks 18
Followers 1.3K	Followers 44
Votes 23	Votes 0
Pros & Cons
Pros 21 Easy data frame management 2 Extensive file format compatibility	No community feedback yet
Integrations
Python	C++ TensorFlow Python GraphPipe Ludwig

What are some alternatives to Pandas, Numba?

TensorFlow

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Kubeflow

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

NumPy

Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.