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03491nam a2200481 4500 |
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978-1-4842-3913-1 |
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DE-He213 |
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20191220130319.0 |
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180927s2018 xxu| s |||| 0|eng d |
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|a 9781484239131
|9 978-1-4842-3913-1
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|a 10.1007/978-1-4842-3913-1
|2 doi
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|a QA76.73.P98
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|a 005.133
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|a Nelli, Fabio.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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|a Python Data Analytics
|h [electronic resource] :
|b With Pandas, NumPy, and Matplotlib /
|c by Fabio Nelli.
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|a 2nd ed. 2018.
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|a Berkeley, CA :
|b Apress :
|b Imprint: Apress,
|c 2018.
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|a XIX, 569 p. 648 illus.
|b online resource.
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|a text
|b txt
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|a computer
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|2 rdamedia
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|a online resource
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|a text file
|b PDF
|2 rda
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|a 1. An Introduction to Data Analysis -- 2. Introduction to the Python's World -- 3. The NumPy Library -- 4. The pandas Library-- An Introduction -- 5. pandas: Reading and Writing Data -- 6. pandas in Depth: Data Manipulation -- 7. Data Visualization with matplotlib -- 8. Machine Learning with scikit-learn -- 9. Deep Learning with TensorFlow -- 10. An Example - Meteorological Data -- 11. Embedding the JavaScript D3 Library in IPython Notebook -- 12. Recognizing Handwritten Digits -- 13. Textual data Analysis with NLTK -- 14. Image Analysis and Computer Vision with OpenCV -- Appendix A -- Appendix B.
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|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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| 650 |
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|a Python (Computer program language).
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| 650 |
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|a Big data.
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| 650 |
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|a Artificial intelligence.
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| 650 |
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|a Python.
|0 http://scigraph.springernature.com/things/product-market-codes/I29080
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|a Big Data.
|0 http://scigraph.springernature.com/things/product-market-codes/I29120
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|a Artificial Intelligence.
|0 http://scigraph.springernature.com/things/product-market-codes/I21000
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| 710 |
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|a SpringerLink (Online service)
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|t Springer eBooks
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| 776 |
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|i Printed edition:
|z 9781484239124
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| 776 |
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|i Printed edition:
|z 9781484239148
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| 776 |
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|i Printed edition:
|z 9781484247372
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| 856 |
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|u https://doi.org/10.1007/978-1-4842-3913-1
|z Full Text via HEAL-Link
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| 912 |
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|a ZDB-2-CWD
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| 950 |
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|a Professional and Applied Computing (Springer-12059)
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