Top 15 Python Libraries Every Data Scientist Should Master in 2017

Python has become the language of choice for data science, and a growing ecosystem of libraries supports every stage of the workflow. Based on ActiveWizards' experience, the following 15 libraries were the most frequently used by data scientists and engineers in 2017.

Core Libraries

1) NumPy

Website: http://www.numpy.org

NumPy (Numerical Python) provides n‑dimensional array objects and vectorized operations, forming the foundation of the SciPy stack and enabling high‑performance scientific computing.

2) SciPy

Website: https://www.scipy.org

SciPy builds on NumPy and offers modules for linear algebra, optimization, integration, and statistics, delivering efficient numerical routines for engineering and scientific tasks.

3) Pandas

Website: http://pandas.pydata.org

Pandas introduces labeled, relational data structures (Series and DataFrames) that simplify data wrangling, aggregation, and visualization.

Series: one‑dimensional

DataFrames: two‑dimensional

Easy column addition/removal

Conversion between data structures

Missing‑data handling (NaN)

Powerful grouping operations

Visualization

4) Matplotlib

Website: https://matplotlib.org

Matplotlib is a low‑level plotting library that, together with NumPy, SciPy, and Pandas, enables creation of line charts, scatter plots, bar/histograms, pie charts, stem plots, contour plots, area plots, and spectrum plots, all highly customizable.

5) Seaborn

Website: https://seaborn.pydata.org

Built on Matplotlib, Seaborn focuses on statistical visualizations such as heatmaps, providing a high‑level interface for attractive and informative graphics.

6) Bokeh

Website: http://bokeh.pydata.org

Bokeh delivers interactive visualizations that run in modern browsers, independent of Matplotlib, using a D3‑style data‑driven approach.

7) Plotly

Website: https://plot.ly

Plotly is a web‑based toolkit for building interactive visualizations via an API; charts are rendered on a server and can be embedded in web pages.

Machine Learning

8) Scikit‑Learn

Website: http://scikit-learn.org

Scikit‑Learn offers a clean, consistent API for a wide range of supervised and unsupervised learning algorithms, making it the de‑facto standard for Python machine‑learning projects.

Deep Learning

9) Theano

Website: https://github.com/Theano

Theano provides a NumPy‑like array object with symbolic expression compilation, optimizing CPU and GPU performance for deep‑learning workloads.

10) TensorFlow

Website: https://www.tensorflow.org

Developed by Google, TensorFlow is an open‑source data‑flow graph library designed for large‑scale machine‑learning and neural‑network training.

11) Keras

Website: https://keras.io

Keras provides a high‑level, modular API for building neural networks, running on top of Theano, TensorFlow, or Microsoft CNTK.

Natural Language Processing

12) NLTK

Website: http://www.nltk.org

The Natural Language Toolkit supplies tools for tokenization, classification, named‑entity recognition, parsing, stemming, and semantic reasoning, supporting research and teaching in NLP.

13) Gensim

Website: http://radimrehurek.com/gensim

Gensim implements efficient algorithms for vector‑space modeling, topic modeling (LDA, LSA, HDP), and word embeddings (word2vec, doc2vec) on large text corpora.

Data Mining & Statistics

14) Scrapy

Website: https://scrapy.org

Scrapy is an open‑source framework for extracting structured data from websites and APIs, emphasizing reusable, DRY spider components.

15) Statsmodels

Website: http://www.statsmodels.org

Statsmodels provides classes and functions for estimating many statistical models, performing hypothesis tests, and visualizing statistical results on large datasets.

Conclusion

The libraries listed above are widely regarded by data scientists and engineers as essential tools; familiarity with them adds significant value to any data‑science workflow.