Python is a leading programming language which is majorly used for solving data science related tasks and challenges. As a result, there are numerous Python libraries for data science that you can use for effective and easy computation. Machine learning and data science experts are in high demand at the moment. And if you are new to them, then you should start by learning and understanding the top Python libraries for data science.
Top Python Libraries For Data Science
Pandas stand for Python Data Analysis Library. It is an open-source python library that offers high performing data structures and data analysis tools. It works as a fundamental building block for performing real-world analysis on relational or labeled data.
With pandas, it becomes possible to easily compute even the most complex data operations in just a few commands. It has many in-built methods for filtering, grouping, and combining data.
Here are some of the many features of pandas:
- Support for iteration, re-indexing, aggregations, and visualizations
- High functionality and flexibility when combined with other Python tools and libraries
- Support for manipulating time series data
NumPy is one of the most popular and fundamental libraries in Python. It is a high-performance array processing package that works as a container for generic multi-dimensional data. This Python library is mainly used to process arrays that store the same data type values. It can perform math operations and vectorisations in the arrays which can in turn speed up the execution time and improve performance.
Some of the many features of NumPy include:
- Effective numerical routines like integration and optimization
- Support for signal processing
- Perform data manipulations through Fourier transformations and routines
A low-level library, matplotlib is mainly used for creating two-dimensional graphs and diagrams. All the graphs can be generated and viewed in different cross-platform environments and they can even be saved in hardcopy formats. With matplotlib, you can build a variety of charts including scatter plots and histograms. Most of the plotting libraries like Pygal and Plotly can also be used with matplotlib. Though it is important to note that matplotlib can be hard to use, which is why most beginners prefer Seaborn. You can customise the graphs by changing the size, fonts, colors, and legends. Matplotlib can be used in Python scripts, Python Shell, web application servers, Jupyter Notebook, and other GUI toolkits.
Some of the many features of matplotlib include:
- Offers interface similar to MATLAB
- Provides object-oriented API for embedding plots and graphs into applications
- Gives you complete control over the graph properties
A core library mainly used for scientific and statistical computations, SciPy extends the capabilities of NumPy. Just like NumPy, SciPy also supports multi-dimensional arrays. The SciPy library has a wide number of modules for linear algebra, integration, statistics, and optimisation.
The many features of SciPy include:
- Supports signal processing
- Provides modules for performing common scientific calculations like differential equations, integration, linear algebra, and calculus
- Supports mathematical routines like interpolation, integration, and optimisation
It is a high-level API that is built on top of the Matplotlib library and it also integrates seamlessly with pandas’ data structures. Seaborn offers a rich gallery of visualisations like jointplots, time series, and violin diagrams. The main objective of Seaborn is to make visualisation an important part of understanding and exploring the data. It is mostly used to examine the relationship between multiple variables. The Python library also has all the necessary tools required for creating visual graphs with the right colours and patterns.
Some of the many features of Seaborn include:
- Automatically estimating and plotting linear regression models
- Support for categorical variables
- Offers high-level abstractions for multi-plot grids
- Comparatively easier to use than matplotlib and graphs, in the default setting, are more visually pleasing
Designed by Google, TensorFlow is one of the most popular frameworks for deep learning and machine learning. The open-source math library can be used for numerical computations using data flow graphs. With a comparatively flexible architecture, TensorFlow makes it possible to deploy the models to numerous platforms including CPUs, GPUs, servers, browsers, and devices without rewriting the code. While TensorFlow is based on Python, the computations present in it are carried out in C++.
- Multiple levels of abstractions make it easy to build and train machine learning models
- Visualize neural networks through graphical computations
- Train and run models for image recognition, speech recognition, and recurrent neural networks
Perfect for beginners, Scikit-learn offers a wide range of supervised and unsupervised learning algorithms through a consistent interface in Python. The entire library is built upon SciPy, which is why you need to install it before you can use Scikit-learn.
- Focuses on modeling data and not manipulating, loading, or summarizing data
- Offers improved efficiency through parameter tuning
- Identifies meaningful attributes in order to create supervised models
This list sums up the most useful libraries based on Python for data science. As machine learning, deep learning, and data science gain more popularity, newer Python libraries with advanced features are also being readily developed to support them. If you are planning to learn Python libraries, start by developing your machine learning concepts first. Springboard offers a comprehensive 6-month machine learning program that can transform you into an experienced ML engineer. It’s 1:1 mentoring-led, project-driven and comes with a job guarantee to boot! Take a look at the syllabus and get started today.