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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 .
NLOGIT – comprehensive statistics and econometrics package; nQuery Sample Size Software – Sample Size and Power Analysis Software [7] O-Matrix – programming language; OriginPro – statistics and graphing, programming access to NAG library; PASS Sample Size Software (PASS) – power and sample size software from NCSS
Sturges's rule [1] is a method to choose the number of bins for a histogram.Given observations, Sturges's rule suggests using ^ = + bins in the histogram. This rule is widely employed in data analysis software including Python [2] and R, where it is the default bin selection method.
The five-number summary gives information about the location (from the median), spread (from the quartiles) and range (from the sample minimum and maximum) of the observations. Since it reports order statistics (rather than, say, the mean) the five-number summary is appropriate for ordinal measurements, as well as interval and ratio measurements.
Exploratory data analysis, robust statistics, nonparametric statistics, and the development of statistical programming languages facilitated statisticians' work on scientific and engineering problems. Such problems included the fabrication of semiconductors and the understanding of communications networks, which concerned Bell Labs.
McKinney made the pandas project public in 2009. [6] McKinney left AQR in 2010 to start a PhD in Statistics at Duke University. He went on leave from Duke in the summer of 2011 to devote more time to developing Pandas, [6] culminating in the writing of Python for Data Analysis in 2012. In 2012, he co-founded Lambda Foundry Inc. [7]
The four datasets composing Anscombe's quartet. All four sets have identical statistical parameters, but the graphs show them to be considerably different. Anscombe's quartet comprises four datasets that have nearly identical simple descriptive statistics, yet have very different distributions and appear very different when graphed.
In statistics, an empirical distribution function (commonly also called an empirical cumulative distribution function, eCDF) is the distribution function associated with the empirical measure of a sample. [1] This cumulative distribution function is a step function that jumps up by 1/n at each of the n data points. Its value at any specified ...