| 1 |
NumPy |
Numerical Operations |
Supporting the scientific computing that is high-level mathematical functions over large, multi-dimensional arrays and matrices |
| 2 |
Matplotlib, Seaborn, Bokeh, Plotly, NetworkX, Basemap,d3py, ggplot, prettyplotlib |
Visualization |
Visualizing the data from Python quickly Plotting 2D graphs in various formats such as bar charts, plots, histograms, error charts, power spectra, and scatter plots across platforms using a few lines of code |
| 3 |
Scikit- Learn, Shogun, Pattern, PyLearn2, PyMC |
Machine Learning (ML) Algorithms |
Providing machine learning algorithms such as classification, clustering, and regression Interoperating with the numerical and scientific libraries such as NumPy and SciPy |
| 4 |
Pandas |
Data Analysis |
Offering high-performance operations and data structures for time series and numerical tables manipulation |
| 5 |
NLTK |
Natural Language Processing (NLP) |
Analyzing and understanding the English written human language data Providing easy interfaces over 50 lexical resources and corpora Supporting functionalities include tokenization, stemming, tagging, parsing, and semantic reasoning |
| 6 |
Statsmodel |
Statistical Analysis |
Conducting statistical data exploration and statistical tests Performing statistical computations such as descriptive statistics and providing classes and functions to estimate different statistical models |
| 7 |
PyBrain |
Neural Network |
Providing algorithms for reinforcement learning, neural networks, unsupervised learning, and evolution to analyze large-scale data |
| 8 |
Gensim |
Topic Modeling |
Supporting natural language processing and unsupervised topic modeling through statistical machine learning Supporting automatic extraction of semantic topics from documents |
| 9 |
Keras, TensorFlow, Theano |
Deep Learning |
Providing fast computing of numerical data with deep neural networks Effectively handling mathematical expressions, especially, matrix values |
| 10 |
Scrapy |
Web Crawling |
Extracting the required data from the websites in a simple and fast way |
| 11 |
SciPy, Dask, Numba, HPAT, Cython |
Data Science tools |
Performing scientific computing involves special functions, integration, linear algebra, optimization, interpolation, Ordinary Differential Equation (ODE) solvers, Fast Fourier Transform (FFT), and image processing Optimizing the machine code at runtime |
| 12 |
HDF5 |
Data manipulation |
Enabling the storage of huge amounts of numerical data and manipulating the data easily from NumPy |
| 13 |
SymPy |
Statistical Applications |
Supporting symbolic mathematics and modeling the full-featured Computer Algebra System (CAS) |
| 14 |
csvkit, PyTables, SQLite3 |
Storage and Data Formatting |
Converting to CSV formats from different file formats such as JSON and Excel and working with CSV Managing hierarchical datasets and accessing the large-scale databases |
| 15 |
Cryptography, pyOpenSSL, passlib, requests- oauthlib, ecdsa, PyCryptodome, service- identity |
Security |
Providing low-level primitives Supporting extensive error handling and providing cryptographic authority Reporting bugs and hashing the data |
| 16 |
NumPy, SciPy, matplotlib, OpenCV, scikit-learn, scikit-image, ilastik |
Image Processing |
Providing a set of algorithms for image processing Supporting geometric transformations, segmentation, filtering, color space manipulation, morphology, analysis, and feature detection |
