Overview of Python Libraries for Data Science
According to a recent survey by Kaggle, 83% of data science practitioners opted python as their language of choice. One of the main reasons behind this is the extensive range of available python libraries. But what is a library? We can consider a library as a set of functions, routines or functionalities which helps developers to focus on the problem statement instead of reinventing the wheel.
Suppose you are working on a problem of predicting loan defaulters for a large financial organization. Now instead of writing code from scratch for common operations like data manipulation, visualization, implementing machine learning algorithms, these libraries help you to handle these with customizable and efficient functionalities. In this article, we will discuss the most commonly used python libraries in various areas of operations in data science like machine learning, data visualization, Deep learning, Natural Language Processing, etc.
Python Data Science Libraries
Based on the operations, we will divide python data science libraries into the following areas
1. General Libraries
NumPy: NumPy stands for Numerical Python. It is one of the fundamental libraries for scientific and mathematical computations. It helps us with efficient N-dimensional array operations, integrating C/C++ and Fortran codes, complex mathematical transformations involving linear algebra, Fourier transform, etc.
Pandas: It is the most popular library for reading, manipulating and preparing data. Pandas provide highly efficient easy to use data structures that help in manipulating data between in-memory and external data formats like CSV, JSON, Microsoft Excel, SQL, etc.
Key features of this library are:
- Comes with fast and efficient DataFrame object
- High-performance merging and intelligent indexing of datasets
- Low latency implementation is written in Cython and C etc.
SciPy: SciPy is another popular open-source library for mathematical and statistical operations. The core data structure of scipy is numpy arrays. It helps data scientists and developers with linear algebra, domain transformations, statistical analysis, etc.
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2. Data Visualization
Matplotlib: It is a 2D plotting library for visualization inspired by MATLAB. Matplotlib provides high-quality two-dimensional figures like a bar chart, distribution plots, histograms, scatterplot, etc. with few lines of code. Like MATLAB, it also gives users the flexibility of choosing low-level functionalities like line styles, font properties, axes properties, etc, via an object-oriented interface or via a set of functions.
Seaborn: Seaborn is basically a high-level API built on top of Matplotlib. It comes with visual reacher and informative statistical graphics like heatmap, count plot, violinplot, etc.
Plotly: Plotly is another popular open-source python graphing library for high quality, interactive visualization. In addition to 2D graphs, it also supports 3D plotting. Plotly is used extensively for in-browser visualization of data.
3. Machine Learning and NLP
ScikitLearn: ScikitLearn is probably one of the most widely-used Python libraries for machine learning and predictive analysis. It offers an extensive collection of efficient algorithms for classification, regression, clustering, model tuning, data preprocessing and dimensionality reduction tasks. It is built on top of NumPy, SciPy and Matplotlib hence it is easy to use, open-sourced and reusable for various contexts.
LightGBM: In the later part of your data science learning, you will come across tree-based learning algorithms and ensembles. One of the most important methodologies in today’s machine learning is boosting. LightGBM is a popular open-source gradient boosting framework by Microsoft.
The key features of lightgbm are
- Parallel and GPU enabled execution
- Fastness and better accuracy
- The capability of handling large scale data sets and supports distributed computing
Surprise: The recommendation system is an important area of interest for modern AI-based applications. State of the art Recommendation system enables businesses to provide highly personalized offerings to their clients. The surprise is a useful open-source Python library to build recommendation systems. It provides tools to evaluate, analyze and compare the performance of the algorithm.
NLTK: NLTK stands for Natural Language Toolkit. It is an open-source library to work with the human language data sets. It is very useful for problems like text analytics, sentiment analysis, analyzing linguistic structure, etc.
4. Deep Learning
TensorFlow: TensorFlow is an open-source framework by Google for an end to end machine learning and deep learning solutions. It gives low-level controls to the users to design and train highly scalable and complex neural networks. Tensorflow is available for both desktop and mobile and supports an extensive number of programming languages through wrappers.
Keras: Keras is an open-source high level deep learning library. It gives the flexibility of using either tensorflow or theano (another low-level python library like tensorflow) as backend. Keras provides simple high-level API for developing deep learning models.
It is suitable for quick prototyping and developing neural network models for industrial use. The primary usage of Keras is in classification, text generation, and summarization, tagging, and translation, speech recognition, etc.
5. Miscellaneous
OpenCV: OpenCV is a popular python library for computer vision problems (Task involving image or video data). It is an efficient framework with cross-platform support and ideal for real-time applications.
Dask: If you have low computation power or do not have access to large clusters Dask is a perfect choice for scalable computation. Dask provides low-level APIs to build custom systems for in-house applications. While working with a very large scale dataset in your local box, you can opt for Dask instead of Pandas.
Conclusion
There is a rich set of python libraries available for various data-driven operations in python. In this article, we discussed the most popular and widely used python libraries across the data science community. Based on the problem statement and Organizational practices appropriate python libraries are chosen in practice.
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