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Python Data Science Handbook: A Comprehensive Guide to Analyzing, Transforming, and Visualizing Data in Python

Jack London

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The Python Data Science Handbook is a comprehensive guide to data analysis, transformation, and visualization in Python. Written by Jake VanderPlas, a leading data scientist and author, the handbook provides a thorough to the Python ecosystem for data science, covering topics such as data structures, data manipulation, data visualization, machine learning, and deep learning.

Data Structures

The handbook begins with an to Python data structures, including lists, tuples, dictionaries, and arrays. These structures are essential for storing and manipulating data in Python, and the handbook provides detailed explanations of their properties and uses.

Python Data Science Handbook: Essential Tools for Working with Data

by Jake VanderPlas

4.6 out of 5

Language	:	English
File size	:	19989 KB
Text-to-Speech	:	Enabled
Enhanced typesetting	:	Enabled
Print length	:	550 pages

Data Manipulation

Once data has been stored in Python, it often needs to be manipulated before it can be analyzed. The handbook covers a wide range of data manipulation techniques, including filtering, sorting, grouping, and reshaping. These techniques are essential for cleaning and preparing data for analysis.

Data Visualization

Data visualization is an important part of data science, as it allows us to explore and communicate our findings. The handbook provides a thorough to data visualization in Python, covering topics such as plotting, charting, and interactive visualizations.

Machine Learning

Machine learning is a subfield of data science that involves using algorithms to learn from data. The handbook provides an to machine learning in Python, covering topics such as supervised learning, unsupervised learning, and deep learning.

Deep Learning

Deep learning is a type of machine learning that involves using neural networks to learn from data. The handbook provides an to deep learning in Python, covering topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks.

The Python Data Science Handbook is a comprehensive and well-written guide to data science in Python. It provides a thorough to all aspects of data science, from data structures to deep learning. The handbook is an essential resource for anyone who wants to learn more about data science in Python.

Alt Attributes

* Data Science Handbook Python: A comprehensive guide to data analysis, transformation, and visualization in Python. * Data Structures in Python: Lists, tuples, dictionaries, and arrays for data storage and manipulation. * Data Manipulation in Python: Filtering, sorting, grouping, and reshaping data for analysis. * Data Visualization in Python: Plotting, charting, and interactive visualizations for exploring and communicating data. * Machine Learning in Python: Supervised learning, unsupervised learning, and deep learning for data analysis and prediction. * Deep Learning in Python: Convolutional neural networks, recurrent neural networks, and generative adversarial networks for advanced data analysis and modeling.

Python Data Science Handbook: Essential Tools for Working with Data

by Jake VanderPlas

4.6 out of 5