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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 .
Wes McKinney is an American software developer and businessman. He is the creator and "Benevolent Dictator for Life" (BDFL) of the open-source pandas package for data analysis in the Python programming language, and has also authored three versions of the reference book Python for Data Analysis.
Python is a multi-paradigm programming language. Object-oriented programming and structured programming are fully supported, and many of their features support functional programming and aspect-oriented programming (including metaprogramming [71] and metaobjects). [72]
In computer programming, data-driven programming is a programming paradigm in which the program statements describe the data to be matched and the processing required rather than defining a sequence of steps to be taken. [1]
The iterative proportional fitting procedure (IPF or IPFP, also known as biproportional fitting or biproportion in statistics or economics (input-output analysis, etc.), RAS algorithm [1] in economics, raking in survey statistics, and matrix scaling in computer science) is the operation of finding the fitted matrix which is the closest to an initial matrix but with the row and column totals of ...
Alpha Books, publisher of the Complete Idiot's Guides, is a member of Penguin Group. It began as a division of Macmillan. Pearson Education acquired Macmillan General Reference (MGR) from Simon & Schuster in 1998 and retained the line while the rest of MGR was sold to IDG Books. [1] Alpha moved from Pearson Education to Penguin Group in 2003 ...
To process this statement without an index the database software must look at the last_name column on every row in the table (this is known as a full table scan). With an index the database simply follows the index data structure (typically a B-tree ) until the Smith entry has been found; this is much less computationally expensive than a full ...
Kernel density estimation of 100 normally distributed random numbers using different smoothing bandwidths.. In statistics, kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method to estimate the probability density function of a random variable based on kernels as weights.