國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Python data analyticswith Pandas, Nu...

Nelli, Fabio.

Python data analyticswith Pandas, NumPy, and Matplotlib /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Python data analyticsby Fabio Nelli.
其他題名:	with Pandas, NumPy, and Matplotlib /
作者:	Nelli, Fabio.
出版者:	Berkeley, CA :Apress :2018.
面頁冊數:	xix, 569 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Python (Computer program language)
電子資源:	https://doi.org/10.1007/978-1-4842-3913-1
ISBN:	9781484239131$q(electronic bk.)

Python data analyticswith Pandas, NumPy, and Matplotlib /
Nelli, Fabio.

Python data analyticswith Pandas, NumPy, and Matplotlib /[electronic resource] :by Fabio Nelli. - 2nd ed. - Berkeley, CA :Apress :2018. - xix, 569 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9781484239131$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-3913-1doiSubjects--Topical Terms:

215247
Python (Computer program language)

LC Class. No.: QA76.73.P98 / N455 2018

Dewey Class. No.: 005.133

Python data analyticswith Pandas, NumPy, and Matplotlib /
LDR:02682nmm a2200337 a 4500 001 544856
003 DE-He213
005 20190318170227.0
006 m d
007 cr nn 008maaau
008 190508s2018 cau s 0 eng d
020 $a 9781484239131$q(electronic bk.)
020 $a 9781484239124$q(paper)
024 7 $a 10.1007/978-1-4842-3913-1 $2 doi
035 $a 978-1-4842-3913-1
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.73.P98 $b N455 2018
072 7 $a UMX $2 bicssc
072 7 $a COM051360 $2 bisacsh
072 7 $a UMX $2 thema
082 0 4 $a 005.133 $2 23
090 $a QA76.73.P98 $b N422 2018
100 1 $a Nelli, Fabio. $3 702979
245 1 0 $a Python data analytics $h [electronic resource] : $b with Pandas, NumPy, and Matplotlib / $c by Fabio Nelli.
250 $a 2nd ed.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2018.
300 $a xix, 569 p. : $b ill., digital ; $c 24 cm.
505 0 $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.
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.
650 0 $a Python (Computer program language) $3 215247
650 0 $a Data mining. $3 184440
650 1 4 $a Python. $3 763308
650 2 4 $a Big Data. $3 760530
650 2 4 $a Computing Methodologies. $3 274528
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-1-4842-3913-1
950 $a Professional and Applied Computing (Springer-12059)