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Show Notes
Python for Data Analysis (Wes McKinney)
- Amazon USA Store: https://www.amazon.com/dp/1491957662?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/Python-for-Data-Analysis-Wes-McKinney.html
- Apple Books: https://books.apple.com/us/audiobook/python-for-data-analysis-real-world-projects-and/id1827577561?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree
- eBay: https://www.ebay.com/sch/i.html?_nkw=Python+for+Data+Analysis+Wes+McKinney+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1
- Read more: https://english.9natree.com/read/1491957662/
#IPythonandJupyternotebooks #NumPyvectorizedcomputation #pandasDataFramesandSeries #datacleaningandreshaping #groupbyandtimeseries #PythonforDataAnalysis
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython is a technical, practice-oriented guide to working with structured data in the Python ecosystem. Written by Wes McKinney, creator of pandas, it is designed for analysts learning Python as well as Python users moving into scientific computing and data analysis. The book concentrates on the operational stages that often determine whether analysis is reliable: loading data, inspecting it, correcting inconsistencies, restructuring tables, calculating summaries, and communicating findings. Rather than treating data science chiefly as model building, it presents analysis as an iterative process of exploration and transformation. Its principal tools are NumPy for efficient numerical arrays, pandas for labeled tabular data, IPython and Jupyter for interactive work, and matplotlib for basic visualization. Practical examples connect individual commands to common analytical tasks involving files, databases, web data, time-indexed observations, and messy real-world records. The result is both an instructional introduction and a durable reference to the core workflows of Python-based data wrangling.
- Amazon USA Store: https://www.amazon.com/dp/1491957662?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/Python-for-Data-Analysis-Wes-McKinney.html
- Apple Books: https://books.apple.com/us/audiobook/python-for-data-analysis-real-world-projects-and/id1827577561?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree
- eBay: https://www.ebay.com/sch/i.html?_nkw=Python+for+Data+Analysis+Wes+McKinney+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1
- Read more: https://english.9natree.com/read/1491957662/
#IPythonandJupyternotebooks #NumPyvectorizedcomputation #pandasDataFramesandSeries #datacleaningandreshaping #groupbyandtimeseries #PythonforDataAnalysis
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython is a technical, practice-oriented guide to working with structured data in the Python ecosystem. Written by Wes McKinney, creator of pandas, it is designed for analysts learning Python as well as Python users moving into scientific computing and data analysis. The book concentrates on the operational stages that often determine whether analysis is reliable: loading data, inspecting it, correcting inconsistencies, restructuring tables, calculating summaries, and communicating findings. Rather than treating data science chiefly as model building, it presents analysis as an iterative process of exploration and transformation. Its principal tools are NumPy for efficient numerical arrays, pandas for labeled tabular data, IPython and Jupyter for interactive work, and matplotlib for basic visualization. Practical examples connect individual commands to common analytical tasks involving files, databases, web data, time-indexed observations, and messy real-world records. The result is both an instructional introduction and a durable reference to the core workflows of Python-based data wrangling.
