10 Best Python Graph Libraries

Python continues to make headway in the data industry and in the recent years some useful libraries have been developed and deployed in the market.

Some big winners to this development who include engineers and data scientists will attest to the following Python graph libraries.

Python graph libraries

1. SciPy

Dedicated to the scientific and engineering sector, SciPy provides you with a reliable library for numerical routines. 

Scipy library main repository
Forks: 2450
Stars: 4804
Open issues: 1318
Latest tag: v1.1.0
git clone https://github.com/scipy/scipy.git

 

Efficiently work on statistics, optimization, linear algebra, as well as numerical integration using SciPy’s specific modules and sub-modules.


2. Pandas

For easy and fast aggregation, manipulation and visualization of data, Pandas definitely stands out.

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Forks: 6283
Stars: 15570
Open issues: 2724
Latest tag: v0.24.0.dev0
git clone https://github.com/pandas-dev/pandas.git

 

As an ideal tool for data wrangling, Pandas simple and intuitive design will have you effortlessly working on relational and labeled data.


3. NumPy

Numerical Python in full, NumPy is the Python library that will give you the avenue to handle all those scientific tasks you come across.

Numpy main repository
Forks: 2935
Stars: 7952
Open issues: 1932
Latest tag: with_maskna
git clone https://github.com/numpy/numpy.git

 

You will find plenty of features in NumPy, you can handle n-array operations, n-array matrices and also apply the NumPy array for mathematical operation vectorization. You will find it quite handy for random number capabilities, Fourier transform, and linear algebra.


4. Matplotlib

This top Python library will generate for you visualizations that are not only simple but powerful as well.   With its customizable features, Matplotlib will produce for you legends, grids and labels or any other entities with ease.

matplotlib: plotting with Python
Forks: 3565
Stars: 7743
Open issues: 1375
Latest tag: v3.0.0rc1
git clone https://github.com/matplotlib/matplotlib.git

 

 By aiming at making the hard thing possible and easy things easier, a few lines of code will generate bar charts, plots, error charts, histograms, scatterplots, power spectra and much more.


6. Seaborn

Seaborn is a python library whose main focus is on creating visuals for statistical models. An example is a heat map that displays overall distributions though being a summary of data.

Statistical data visualization using matplotlib
Forks: 786
Stars: 5167
Open issues: 65
Latest tag: v0.9.0
git clone https://github.com/mwaskom/seaborn.git

 

With its high-level interface, Seaborn will give you statistical graphics that are quite attractive.


7. TensorFlow

With TensorFlow you will have computation done by the use of data flow graphs which results in machine learning that is scalable.

Computation using data flow graphs for scalable machine learning
Forks: 66491
Stars: 107515
Open issues: 1778
Latest tag: v1.10.0
git clone https://github.com/tensorflow/tensorflow.git

 

TensorFlow’s architecture is flexible and without rewriting of any code, allows you to deploy computation across multiple CPUs or GPUs within a mobile device, desktop or server.


8. SciKit-Learn

 

SciKit-Learn a Python library built on SciPy brings you specific Machine Learning facilitation and image processing functionalities.

scikit-learn: machine learning in Python
Forks: 14834
Stars: 29953
Open issues: 1675
Latest tag: sprint01
git clone https://github.com/scikit-learn/scikit-learn.git

 

SciKit-Learn is easy to use and its library puts together good documentation and quality code to ensure high performance and desired results in math related operations.


9. Theano

This compiled Python package works smoothly on all architectures to enable you to apply its functionalities of defining expressions, math operations, and multi-dimensional arrays.

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.
Forks: 2449
Stars: 8417
Open issues: 627
Latest tag: rel-1.0.2
git clone https://github.com/Theano/Theano.git

 

Theano performs fast data-intensive computation efficient and symbolic differentiation by optimizing CPU and GPU use.


10. Plotly

Plotly is built to create visualizations that are web-based and for you to use this tool you must set up your API key.

An interactive, browser-based graphing library for Python :sparkles:
Forks: 954
Stars: 3879
Open issues: 288
Latest tag: v3.1.1
git clone https://github.com/plotly/plotly.py.git

 

With Plotly you can style compose, edit and share out interactive graphs or visualizations through the web


The above list sure contains most of the popular Python graph libraries though there are several more out there and even more being developed each day

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