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Winsorizing or winsorization is the transformation of statistics by limiting extreme values in the statistical data to reduce the effect of possibly spurious outliers. It is named after the engineer-turned-biostatistician Charles P. Winsor (1895–1951). The effect is the same as clipping in signal processing.
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 .
Data compression aims to reduce the size of data files, enhancing storage efficiency and speeding up data transmission. K-means clustering, an unsupervised machine learning algorithm, is employed to partition a dataset into a specified number of clusters, k, each represented by the centroid of its points. This process condenses extensive ...
reStructuredText (RST, ReST, or reST) is a file format for textual data used primarily in the Python programming language community for technical documentation.. It is part of the Docutils project of the Python Doc-SIG (Documentation Special Interest Group), aimed at creating a set of tools for Python similar to Javadoc for Java or Plain Old Documentation (POD) for Perl.
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 Initiative. [7] For 2021, Read the Docs reported 700 million page views and 196 million unique visitors. [8] In 2013, a "Write the Docs" conference for ...
In 2000, Microsoft released an initial version of an XML-based format for Microsoft Excel, which was incorporated in Office XP. In 2002, a new file format for Microsoft Word followed. [9] The Excel and Word formats—known as the Microsoft Office XML formats—were later incorporated into the 2003 release of Microsoft Office.
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.
Data are plotted on a scale of half width, relative to the peak maximum at zero. The smoothed curve (red line) and 1st derivative (green) were calculated with 7-point cubic Savitzky–Golay filters. Linear interpolation of the first derivative values at positions either side of the zero-crossing gives the position of the peak maximum. 3rd ...