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This python example uses the percentile function from the numerical library numpy and works in Python 2 and 3. import numpy as np def fivenum ( data ): """Five-number summary.""" return np . percentile ( data , [ 0 , 25 , 50 , 75 , 100 ], method = "midpoint" )
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 [5] 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; Plotly – plotting ...
SimpleITK: A Simplified Path to Insight - an online tutorial using Jupyter notebooks in Python. Organization on GitHub; Short examples illustrating how to use some of the library components are available on read the docs. Class and procedure documentation is available via Doxygen. Jupyter notebooks on GitHub with long and extensively documented ...
Descriptive statistics Nonparametric statistics Quality control Survival analysis Data processing Base stat. [Note 2] Normality tests [Note 3] CTA [Note 4] Nonparametric comparison, ANOVA: Cluster analysis Discriminant analysis BDP [Note 5] Ext. [Note 6]
Beautiful Soup is a Python package for parsing HTML and XML documents, including those with malformed markup. It creates a parse tree for documents that can be used to extract data from HTML, [ 3 ] which is useful for web scraping .
IMSL (International Mathematics and Statistics Library) is a commercial collection of software libraries of numerical analysis functionality that are implemented in the computer programming languages C, Java, C#.NET, and Fortran. A Python interface is also available.
CuPy is an open source library for GPU-accelerated computing with Python programming language, providing support for multi-dimensional arrays, sparse matrices, and a variety of numerical algorithms implemented on top of them. [3] CuPy shares the same API set as NumPy and SciPy, allowing it to be a drop-in replacement to run NumPy/SciPy code on GPU.