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Pandas (styled as pandas) is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series .
Comma-separated values (CSV) is a text file format that uses commas to separate values, and newlines to separate records. A CSV file stores tabular data (numbers and text) in plain text, where each line of the file typically represents one data record. Each record consists of the same number of fields, and these are separated by commas in the ...
Python 3.0, released in 2008, was a major revision not completely backward-compatible with earlier versions. Python 2.7.18, released in 2020, was the last release of Python 2. [36] Python consistently ranks as one of the most popular programming languages, and has gained widespread use in the machine learning community. [37] [38] [39] [40]
On March 9, 2011, the Python Software Foundation Board awarded a grant of US$840 to the Read the Docs project for one year of hosting fees. [5] On November 13, 2017, the Linux Mint project announced that they were moving their documentation to Read the Docs. [6] In 2020, Read the Docs received a $200,000 grant from the Chan Zuckerberg ...
Pandas – Python library for data analysis. PAW – FORTRAN/C data analysis framework developed at CERN. R – A programming language and software environment for statistical computing and graphics. [149] ROOT – C++ data analysis framework developed at CERN. SciPy – Python library for scientific computing.
Python data analysis toolkit pandas has the function pivot_table [16] and the xs method useful to obtain sections of pivot tables. [ citation needed ] R has the Tidyverse metapackage, which contains a collection of tools providing pivot table functionality, [ 17 ] [ 18 ] as well as the pivottabler package.
Pydoc is the standard documentation module for the programming language Python.Similar to the functionality of Perldoc within Perl and Javadoc within Java, Pydoc allows Python programmers to access Python's documentation help files, generate text and HTML pages with documentation specifics, and find the appropriate module for a particular job.
Data cleansing or data cleaning is the process of identifying and correcting (or removing) corrupt, inaccurate, or irrelevant records from a dataset, table, or database.It involves detecting incomplete, incorrect, or inaccurate parts of the data and then replacing, modifying, or deleting the affected data. [1]