Python data analytics : with Pandas, NumPy, and Matplotlib / Fabio Nelli.
Explore the latest Python tools and techniques to help you tackle the world of data acquisition and analysis. You'll review scientific computing with NumPy, visualization with matplotlib, and machine learning with scikit-learn. This revision is fully updated with new content on social media dat...
Saved in:
| Online Access: |
Full Text (via Skillsoft) |
|---|---|
| Main Author: | |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
New York, NY :
Apress,
[2018]
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| Edition: | Second edition. |
| Subjects: |
MARC
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| 100 | 1 | |a Nelli, Fabio, |e author. | |
| 245 | 1 | 0 | |a Python data analytics : |b with Pandas, NumPy, and Matplotlib / |c Fabio Nelli. |
| 250 | |a Second edition. | ||
| 264 | 1 | |a New York, NY : |b Apress, |c [2018] | |
| 300 | |a 1 online resource (xix, 569 pages) : |b illustrations (some color) | ||
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| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a An introduction to data analysis -- Introduction to the Python world -- The NumPy library -- The pandas library : an introduction -- Pandas : reading and writing data -- Pandas in depth : data manipulation -- Data visualization with matplotlib -- Machine learning with scikit-learn -- Deep learning with TensorFlow -- An example : meteorological data -- Embedding the JavaScript D3 library in the IPython notebook -- Recognizing handwritten digits -- Textual data analysis with NLTK -- Image analysis and computer vision with OpenCV -- Appendix A: Writing mathematical expressions with LaTeX -- Appendix B: Open data sources. | |
| 520 | |a Explore the latest Python tools and techniques to help you tackle the world of data acquisition and analysis. You'll review scientific computing with NumPy, visualization with matplotlib, and machine learning with scikit-learn. This revision is fully updated with new content on social media data analysis, image analysis with OpenCV, and deep learning libraries. Each chapter includes multiple examples demonstrating how to work with each library. At its heart lies the coverage of pandas, for high-performance, easy-to-use data structures and tools for data manipulation Author Fabio Nelli expertly demonstrates using Python for data processing, management, and information retrieval. Later chapters apply what you've learned to handwriting recognition and extending graphical capabilities with the JavaScript D3 library. Whether you are dealing with sales data, investment data, medical data, web page usage, or other data sets, Python Data Analytics, Second Edition is an invaluable reference with its examples of storing, accessing, and analyzing data. | ||
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