Overview
Know top 10 libraries in python
Know about the features and uses of these libraries
Introduction
Python is a prevalent programming language. It’s easy to use, highly interpretable, interactive, and object-oriented. Python libraries contain functions and methods that facilitate specific tasks. Also, it saves developers a significant amount of time and headache!
As a newly hired Product Growth Analyst, having a basic understanding of these libraries has eased the transition into my new role. The Python libraries have helped a lot in manipulating and representing data in a much more understandable manner, whether using Scikit-Learn to build models or Matplotlib to visualize data in a graphic format.
Let us now look at some python libraries:
Table of Contents
TensorFlow
Scikit-Learn
PyTorch
Matplotlib
Pandas
Keras
NLTK
Gensim
Statsmodels
Selenium
1. TensorFlow
An open-source library developed by Google to aid in developing and training machine learning models. Data scientists can instantly develop and deploy machine learning models using TensorFlow, developed initially for computing large mathematical operations.
Features
The visualization of computational graphs is exceptional.
Google manages libraries
Parallel Neural Network Training
Uses
Speech and image recognition
Text-based applications
Time-series analysis
Video detection
2. Scikit-Learn
Scikit-Learn is one of the most popular and valuable python libraries in machine learning. It contains all machine learning algorithms that you might need, like linear and logistic regression, gradient boosting, support vector machines, random forests, etc.,
Features
It contains several methods for checking the accuracy of a model on unseen data.
Provides all types of ML models for different types of data
It is an effective tool for predictive data analysis.
Uses
Model selection
Dimensionality reduction
3. PyTorch
It is open-source software used for computer vision and natural language processing. In addition to being fast and inexpensive, PyTorch is the best deep learning framework because it can accelerate the research on deep learning models.
Features
Production Ready
Distributed Training
Robust Ecosystem
Cloud support
Uses
PyTorch is famous for providing two of the most high-level features:
Tensor computations with solid GPU acceleration support
Building deep neural networks on a tape-based auto-grade system
4. Matplotlib
Matplotlib is the most commonly used library for visualization in the Python community. With endless customization in charts and graphs, the developer can use everything from histograms to scatter plots. You can choose from an array of themes and colour schemes. This library is handy for the exploratory analysis of data during machine learning projects.
Features
It’s free and open source.
Complete control of axes properties, font properties, line styles, etc.
Low memory consumption and better runtime behaviour
Uses
Correlation analysis of variables
Visualize 95 per cent confidence intervals of the models
Outlier detection using a scatter plot etc.
Visualize the distribution of data to gain instant insights
5. Pandas
If you want to get into the data science domain, Pandas is the library you should be mastered in. It is an open-sourced library heavily used for data exploration, manipulation, and analysis. It provides fast, flexible, and inexpensive data structures, making them easy to work with.
Features
The capability of performing custom operations
Enhances the ease of data manipulation
Provides aggregations, concatenations, iteration, reindexing, and visualization capabilities
Uses
Used as excellent support for loading CSV files into its data frame format
Time-series-specific functionality includes date range generation, moving window, linear regression, and date shifting
6. Keras
This open-sourced library supports deep learning and neural networks. Model aggregation, graph visualization, and dataset analysis are among the features of Keras. Furthermore, it offers prelabeled datasets that can be imported and loaded directly. Besides being easy to use, it is versatile and suitable for innovative research.
Features
Its Python-based nature makes debugging and exploring easier.
Modular by nature
Combining neural network models can lead to more complex models
It runs smoothly on both CPU and GPU.
Uses
Keras can make predictions and extract features in deep learning models with corresponding weights without using a new train model.
7. NLTK
NLTK stands for Natural Language Toolkit. This library helps in processing text data, and it contains text processing libraries such as classification, tokenization, stemming, tagging, parsing, etc. It also includes 50+ corpora.
Features
It comes with a part-of-speech tagger
N-gram and collocations
Named-entity recognition
Uses
Sentiment analysis
Topic analysis
8. Gensim
This open-source library is used in unsupervised topic modelling and natural language processing. It was specially developed for handling extensive text collections, or corpora, utilizing data streaming and incremental online algorithms. The most distinguishing feature of Gensim is that, unlike its contemporaries, it doesn’t target only in-memory processing.
Features
Streamed parallelized implementation of doc2vec, fastText, and word2vec algorithms
The function can handle latent Dirichlet allocation, latent semantic analysis, non-negative matrix factorization, random projections, and tf-IDF.
9. Statsmodels
Statsmodel is a python library that conducts statistical tests and statistical data exploration. Statsmodels allows users to explore data, estimate statistical models and perform statistical tests.
Features
Time series hypothesis tests: unit root, cointegration, etc.
Descriptive statistics and process models for time series analysis
Uses
Used for statistical testing
10. Selenium
Web browsers can be automated using Selenium, an open-source tool. It supports many browsers such as Firefox, Chrome, IE, and Safari. However, using the Selenium WebDriver, we can only automate testing for web applications.
Features
Multi-Browser Compatibility
Multiple Language Support
Speed and Performance
Uses
Selenium is an open-source and portable Web testing framework.
Selenium commands are categorized into classes, making them easier to comprehend and implement.
Selenium supports parallel test execution that reduces the time to execute similar tests.
Conclusion
There are many helpful Python libraries for data science in addition to these top 10 Python libraries, and which one the user chooses is mainly based on the kind of project they are engaged in.
