Data science is growing in popularity with the development of technology. Scores of python libraries have been created to help the way scientists, engineers, mathematicians and researchers process data and information. Data science focuses on the use of scientific methods and techniques to unearth information and insights from data.
Over the last decade, Python has gained a lot of popularity in the industry as the primary language used in this field. A variety of industries are increasingly turning to Python libraries as the most useful tool for creating visualisations, processing, data mining and extracting information.
Python libraries are open sourced making them readily available and even more popular in the data science world. If you are starting out in the field of data science or just looking for an effective library to use, we have compiled a list of the top Python data science libraries that are now available.
1. Core Libraries
I) The Jupiter Notebook
Formerly known as the IPython notebook, this Python library is an open source web application that is a core library for scientific computing. It allows the formation and sharing of documents that involve live code, equations, visualizations and explanatory text.
So what are the main functions of this core library?
- Data cleaning and transformation
- Numerical simulation
- Statistical modelling
- Machine learning
Check out the full guide and the components of the Jupiter Notebook to learn more about what this core library offers.
Pandas is a Python library that is quick and flexible. It makes great use of expressive data structures that are created to combine easily with ‘relational’ or ‘labelled’ data intuitively.
Pandas have set the audacious goal of becoming the most influential open source data analysis and manipulation tool available in any language.
So what sort of data manipulation can you do with Panda?
- Tabular data
- Ordered and unordered time series data
- Arbitrary matrix data
- Observational and statistical data sets
Pandas incorporates two main data structures. ‘Series’ data structure is one dimensional, whilst ‘Data Frame’ is two-dimensional and is typically used for statistics, data sciences and engineering.
NumPy is one of the best fundamental Python packages for scientific computing. This library offers a lot of features and tools that make it one of the best going around:
- A formidable N-dimensional array object
- Complex broadcasting functions
- Integration features for C/C++ and Fortran programming languages
- Linear algebra functions and Fourier transform
One of the biggest advantages of NumPy is its ability to integrate with a large number of databases. Discover NumPy’s capabilities and check it out here.
SciPy (pronounced ‘Sigh Pie’) is another top notch Python library for data science and mathematics engineering. SciPy is built on NumPy and the arrays of SciPy use NumPy foundations. It is also important to note the SciPy library is quite different from the SciPy stack. It is definitely worth checking out what is featured in the SciPy stack as well.
So what else does SciPy offer that makes it stand out from the crowd?
- Optimization modules
- A group of numerical algorithms and domain specific toolboxes
- Signal Processing
- Statistics modules
Discover the key features of the SciPy library here.
2. Visualization Libraries
Bokeh makes its mark as being an autonomous interactive visualization library that focuses on the exhibition of modern web browsers. As well as top-level interaction for big data, Bokeh has the ability to provide original graphics in D3.js.
So what are the ideal uses of Bokeh?
- Simple and efficient creation of interactive plots
- Create Dashboards
- Create Data Applications.
Discover why Bokeh is growing in popularity here.
Matplotlib is a 2D plotting library for creating arrays in Python and for creating numerical mathematics extensions from NumPy. Matplotlib can help developers produce a wide variety of 2D graphics in a diverse range of models. Whist the software is exceptional; the library itself operates at a relatively simple function and requires a more hands-on approach and a higher level of coding from users.
However, if you are a skilled developer it won’t take you long to get up to speed to make some awesome graphics. The type of graphics you can create include:
- Line Plots/Graphs
- Pie Charts
- Scatter Plots/Graphs
- Bar Graphs/Histograms
- Stem Plots
- Quiver Plots
- Contour Plots
- Graph Formatting
Nearly everything created in Matplotlib can be custom-built. Check out Matplotlib and see for yourself how much flexibility there is in creating all types of visualizations.
Based on Matplotlib, Seaborn is a visual Python library that is custom-built to bring to life and graphically create eye-catching statistical models. It has support for both NumPy and Pandas data structures and statistical routines.
So how does Seaborn offer a point of difference?
- Built-in themes for styling Matplotlib graphics, such as graphical heat maps.
- A vast amount of colour palette options.
- Functions for visuals comparing univariate and bivariate distributions.
- A toolkit that can create linear regression models for different variables.
- Plotting statistical time series data.
Find out more about Seaborn documentation and features here.
Plotly is another Python graphing library and data analysis tool. Plotly allows developers to create interactive graphs, online, in a way that makes the data readily accessible.
The Python API gives developers the opportunity to admit all of Plotly’s functionality from Python. So what can you create with Plotly?
- Line plots
- Scatter plots
- Area charts
- Bar charts
- Box plots
- Heat maps
- Polar charts
- Bubble Charts
Check out Plotly’s cool capabilities here.
3. Machine Learning Libraries
Scikit-learn is a software machine-learning library for Python. The library offers easy and resourceful tools for data mining and data analysis. It is a supplementary package built on top of NumPy, SciPy and Matplotlib.
If you are looking at expanding your understanding of the machine-learning field, Scikit-learn is a great tool and offers some fantastic tutorials and also has a comprehensive user guide to help you on your journey.
4. Deep Learning Libraries
Keras is a very high-level neural networks API. Keras has the remarkable ability, as a python library, to operate over the top of TensorFLow, CNTK or Theano. The developers of Keras created the deep learning library with quick experimentation in mind. Keras is created with a minimalist approach in mind and focuses on the speed and simplicity of experimentation for research and development.
What else does Keras offer as a deep learning library?
- Allows for simple, quick prototyping
- Supports convolutional and/or recurrent networks
- Runs smoothly on CPU and GPU
If you are looking for a deep learning library that is relatively easy to grasp and can still deliver formidable modelling, Find out if Keras could be for you.
Theano is a data science Python library that was created as an open source project by a machine-learning group at the Université de Montréal. Theano is a numerical computation library for Python that operates syntax similar to NumPy. The library excels in evaluating mathematical expressions.
So what does Theano offer as a deep learning library?
- Very close integration with NumPy arrays.
- Transparent use of a GPU allowing much quicker calculations that you would find in CPU.
- Differentiation and derivatives for functions with several inputs
- Exciting C code generation
Check out the latest Theano 0.9.0 update.
TensorFlow is another deep learning data science python library. TensorFlow is released by Google, under the Apache 2.0 open source license, and is a fantastic foundation library for creating deep learning models.
TensorFlow is created for use within both research and development and also for utilisation in production systems. One of the crucial features of TensorFlow is the layered nodes network that allows speedy training of neural networks for big data.
Check out this introduction to TensorFlow to discover more about this adaptable Python deep learning library.
5. Natural language processing libraries
NLTK (Natural Language Toolkit) is a prominent platform for creating Python programmes that utilize human language data. Most commonly, it is used in the fields of language, cognitive sciences, engineering, research and artificial intelligence.
Here are some of the simple things you can do with NLTK:
- Tokenize and tag text
- Identify named entities
- Display a parse tree
The functionality of NTLK is multipurpose and allows developers to use simple building blocks to build complex systems. Discover NLTK here.
Gensim is a Python library with a framework created for fast vector space modelling, topic modelling, document indexing and similarity retrieval with large corpora.
Gensim is based upon NumPy and SciPy making it incredibly efficient and simple for the natural language processing and information retrieval community to grasp. So what are the core features of Gensim?
- Memory independent algorithms
- Intuitive interfaces
- Multicore implementation of popular algorithms
- Distributed computing
- All-encompassing documentation and tutorials.
If you are interested in this area of specialized science check out all there is to know about Gensim.
6. Data Mining
Statsmodels is a Python library that supports classes and functions for the estimation of a variety of statistical models. It also has the ability to perform data mining and statistical tests.
Statsmodels is fantastic for those looking for a Python library that can handle big data. So what are the other main features of Statsmodels?
- Utilizing linear regression models to display descriptive and result statistics
- Generalized linear models
- Time series analysis models
- Plotting functions
Find out all about Statsmodels here.
Scrapy is an open source web crawling framework that is written in Python. It was created initially as a scraping framework but has progressed over time and can now also recruit data from APIs and operate as a multi-purpose web crawler.
Scrapy is a fast, efficient framework for extracting the data that you need from websites. It allows you to set the rules for data extraction and the framework then takes over to do the grunt work. Furthermore, its portability is a huge plus and runs on Mac OS, Windows and Linux.
Scrapy is continually growing in popularity and you can find out how Scrapy operates here.
What Python libraries do you use for data science? And what Python libraries would you recommend as a good starting point for beginners to progress their learning? Join the discussion below.