Python has no doubt one of the wide variety of packages in the Data Science Industry but there are a few Python packages for data science that are a must to learn if you want to grow your knowledge and working capability around this area, that’s what we are going to look at in this article. By the end of this blog post, you’ll have a good overview of 10 Python packages for data science you should learn in 2024.
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List of Python Packages for Data Science
Here is the list of Python libraries for data science, try reading their documentation, and get your hands dirty with them by building projects that utilize these packages.
TensorFlow is an end-to-end open-source platform originally developed by Google, it’s the first package on our list as its usage and demand have become popular in recent times.
TensorFlow uses data flow graphs to represent mathematical computations, which enables parallel and distributed executions. It includes a comprehensive stack of tooling that facilitates building and training ML models at scale with high performance. This covers the full machine learning pipeline from data ingestion through model building, training, and deployment.
TensorFlow abstracts away much complexity around distributed computing and provides portability through its unified API available across a multitude of languages and platforms. This has made TensorFlow hugely popular among machine learning practitioners and one of the most widely adopted platforms for production ML today.
Scikit-learn provides a robust set of ML tools for Python which enables efficient data modeling and analysis without needing to build models from scratch. With the help of its consistent API, vast module ecosystem, and integration across scientific Python stacks, Scikit-learn powers machine-learning workflows in academic research and industry production systems alike.
Here are some core capabilities Scikit-learn delivers for machine learning:
- Classification: Support for SVMs, random forests, gradient boosting ensemble models amongst many others. Includes metrics like accuracy, precision, and recall.
- Regression: Linear regression, decision trees, regularized models like Lasso and Ridge, and more. Metrics like R-squared evaluate performance.
- Clustering: K-Means, spectral clustering methods, and affinity propagation algorithms group unlabelled datasets based on inherent structure.
- Dimensionality reduction: Techniques like PCA extract informative low-dimensional representations simplifying high-variance datasets.
- Preprocessing: Functions for data cleaning, normalization, splitting, imputing missing values, and pipelines to chain operations.
NumPy provides efficient array and matrix manipulation capabilities ideal for scientific computing tasks in Python. At the core of NumPy is the ndarray (n-dimensional array) class providing contiguous allocated memory blocks to store and manipulate homogeneous dense datasets efficiently. These arrays enable fast vectorized operations without Python for-loops while retaining conveniences like indexing/slicing akin to Python lists.
The tailored C implementations and memory optimizations result in order of magnitude faster computations than regular Python sequences built atop NumPy arrays. This makes NumPy indispensable for numerical programming.
Here are some key capabilities NumPy arrays unlock:
- Elementwise arithmetic, exponentials, trig functions, etc
- Linear Algebra operations like matrix multiplication, eigenvalues
- Statistical methods covering regression, FFT, histograms
- Vectorized string functions and datetime features
- Masked arrays with missing value handling
- Random number generation for sampling
- Save and load from efficient binary formats like .npy
SciPy builds on top of foundational NumPy arrays and provides a rich collection of modules addressing common needs in scientific, engineering, and technical computing domains.
Here are some key capability areas covered in SciPy:
- Numerical integration routines including quadrature and ordinary differential equations solvers
- Signal processing module with filtering, spectral analysis (FFT), waveform generation
- Statistical distributions and descriptive metrics with support for large datasets
- Multivariate generalization of mathematical operations on NumPy arrays
- Sparse matrix representations and linear algebra operations like LU decomposition
- Clustering algorithms with hierarchical, spectral, and K-Means methods
- Special functions from mathematical physics like Bessel, gamma, erf, etc.
- Optimizers for scalar and multi-dimensional root finding, curve fitting, and minimization
SciPy works seamlessly with Python’s core scientific computing packages like NumPy arrays for data representation and manipulation as well as Pandas for structured data wrangling. The integration enables building advanced analytic workflows leveraging the best of breeding.
Matplotlib enables Python developers to create a wide array of publication-quality charts and plots easily configured to convey insights effectively. Matplotlib promotes effortless integration within Python’s scientific computing stacks like NumPy and Pandas leveraging their capabilities for robust visualization needs.
Matplotlib promotes customization through object-oriented usage where each element is represented as a customizable object rather than a declarative API call.
Matplotlib renders visualizations across a spectrum of categories including:
- Histograms, scatter plots, area plots, and bar charts
- Line plots with automatic, Bézier curves and stacked variants
- Pie charts, box plots, and violin plots
- Heatmaps, contour plots, and polar axis coordinates
- Errorbars, custom legends, and annotated text points
- 3D plots – wireframes, curves, surfaces and scatter
- Subplot grids for combining customized view configurations
- AxAxis control for fine-grained tuning of composition
- Scalable vector graphics (SVG) output exporting
Pandas provides versatile data structures, analysis functions, and visualization capabilities for working with structured and tabular data in Python. Its tight integration with NumPy, compatibility across scientific Python stacks, and rich tooling around missing data, aggregation, filtering, and statistical analysis make Pandas a must-have tool for data scientists.
Pandas introduces the DataFrame providing an intuitive tabular data container with column names, indices, data types, and size flexibility akin to spreadsheets. Built on NumPy arrays, DataFrames enable speed gains through vectorization. Index objects further empower slicing, selecting, assigning, and faster reordering leveraging orientation along an axis.
Together they facilitate wrangling heterogeneous, messy data from varied sources into uniformly structured datasets ready for analysis and visualization using native operations for joining, statistics, and graphing alongside NumPy/SciPy/Matplotlib interoperability.
Keras offers an easy starting point for building neural networks and experimenting with deep learning using Python without boilerplate code. It provides simple APIs using just backend TensorFlow, PyTorch or CNTK engines while enabling great flexibility to customize models.
Here are some key features that make Keras extremely popular:
- User-friendly APIs to quickly build and train models ranging from sequential to complex topologies
- Support for CNN and RNN architectures frequently used in computer vision and NLP
- Runs seamlessly on CPU and GPU allowing prototyping before distribution
- Modular and composable – add custom layers, loss functions, optimizers, callbacks
- Save, load, and restore full models avoiding repeat training cycles
- Pretrained models like VGG16, LSTM, and GPT-2 ready for fine-tuning
- Built-in utilities like callbacks for checkpointing, early stopping, LR decay
PyTorch is an open-source machine learning framework providing flexible computational graphs and autograd systems for building neural networks with Python. It focuses on enabling fast experiment iteration vital for areas like computer vision and NLP with strong GPU acceleration support.
PyTorch promotes getting models running quickly by removing boilerplate allowing more cycles of learning.
Some major features that make PyTorch widely used:
- Dynamic computational graphs enable easier debugging and inspection
- Hybrid frontend providing eager and graph execution modes
- Distributed training harnessing multi-GPU or cluster parallelism
- Strong Python integration with custom extensions using native code
- Interoperability with libraries like Cython and Numba for performance
- Robust ecosystem of components like TorchVision, TorchText, Ignite
PyBrain provides modular toolkits addressing the broad field of neural networks and reinforcement learning using Python without requiring deep math knowledge.
PyBrain delivers features including:
- Supervised & unsupervised neural network architectures
- Reinforcement learning algorithms like Q-Learning
- Support for backpropagation and evolution strategies-based training
- Flexible custom network configurations and plugins
- Integration of OpenAI Gym environments as possible problems
- Normalization and encoding transforms out of the box
- Built-in benchmarking tasks and evaluation metrics
While more constrained than PyTorch, for education and simpler ML experimentation, PyBrain lowers barriers and allows focusing on learning applied ML concepts rather than low-level math.
BeautifulSoup is a Python library that excels in web scraping tasks. BeautifulSoup provides a convenient way to parse HTML and XML documents when dealing with unstructured data on the web which makes it easier to extract the information you need. Its simplicity and elegance in handling HTML traversal and searching make it a great option for web scraping projects.
BeautifulSoup supports various parsers, each with its strengths and weaknesses. The default parser is ‘html.parser’, but you might encounter situations where using a different parser is more suitable. For example, ‘lxml’ and ‘html5lib’ are popular alternatives.
We discussed the list of 10 Python Packages for Data Science that will be good to practice in 2024 and will add a lot of value to your data science portfolio. You should start out by learning the basics about them. ideologies behind them and then jump onto the projects that will boost your skill level.