Python for Data Analysis: Difference between revisions
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The set of packages referred from this article focus on structured data, which includes tabular or spreadsheet-like data, in which each column may be a different type (relational database data, spreadsheets and CSV files), multidimensional arrays (matrices), multiple tables or related data joined by key columns, and evenly and unevenly spaced time series. | The set of packages referred from this article focus on structured data, which includes tabular or spreadsheet-like data, in which each column may be a different type (relational database data, spreadsheets and CSV files), multidimensional arrays (matrices), multiple tables or related data joined by key columns, and evenly and unevenly spaced time series. | ||
Python is uniquely positioned for use in data analysis because of many specialized libraries ([[Numpy]], [[Pandas]]) and tools (). | Python is uniquely positioned for use in data analysis because of many specialized data processing libraries ([[Numpy]], [[Pandas]]), visualization libraries ([[matplotlib]], [[plotly]]) and other tools ([[Jupyter Notebook]], [[Jupyter Lab]]). | ||
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Revision as of 23:02, 14 May 2024
External
- Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter 3rd Edition by Wes McKinney
Internal
Overview
This article is loosely based on Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter 3rd Edition by Wes McKinney.
The set of packages referred from this article focus on structured data, which includes tabular or spreadsheet-like data, in which each column may be a different type (relational database data, spreadsheets and CSV files), multidimensional arrays (matrices), multiple tables or related data joined by key columns, and evenly and unevenly spaced time series.
Python is uniquely positioned for use in data analysis because of many specialized data processing libraries (Numpy, Pandas), visualization libraries (matplotlib, plotly) and other tools (Jupyter Notebook, Jupyter Lab).