scipy.stats.describe(a, axis=0, ddof=1, bias=True, nan_policy='propagate')[source]# Compute several descriptive statistics of the passed array. Parameters: aarray_like · Input data. axisint or None, optional · Axis along which statistics are calculated. Default is 0.

July 7, 2025 - scipy.stats.describe() function compute a variety of descriptive statistics for a dataset, including count, min/max, mean, variance, skewness and kurtosis.

February 1, 2025 - In SciPy, descriptive statistics refers to summarizing and analyzing a dataset’s key characteristics. It helps summarize essential properties such as central tendency, variability, and distribution shape. The .describe() function in the scipy.stats module is used to calculate common descriptive ...

Seaborn: statistical data visualization. ... Each univariate distribution is an instance of a subclass of rv_continuous (rv_discrete for discrete distributions): The fit method of the univariate continuous distributions uses maximum likelihood estimation to fit the distribution to a data set. The fit method can accept regular data or censored data. Censored data is represented with instances of the CensoredData class. scipy.stats.multivariate_normal methods accept instances of the following class to represent the covariance.

November 11, 2024 - Descriptive statistics summarize and describe the main characteristics of a dataset using numerical measures. These summaries are essential for understanding data patterns, identifying outliers, and communicating key findings effectively.

January 18, 2015 - scipy.stats.describe(a, axis=0, ddof=1)[source]¶ · Computes several descriptive statistics of the passed array. See also · skew, kurtosis · scipy.stats.zipf · scipy.stats.gmean · © Copyright 2008-2014, The Scipy community. Last updated on Jan 18, 2015.

scipy.stats.describe(a, axis=0, ddof=1, bias=True, nan_policy='propagate')[source]¶ · Compute several descriptive statistics of the passed array. Parameters · aarray_like · Input data. axisint or None, optional · Axis along which statistics are calculated.

>>> from scipy import stats >>> a = np.arange(10) >>> stats.describe(a) DescribeResult(nobs=10, minmax=(0, 9), mean=4.5, variance=9.166666666666666, skewness=0.0, kurtosis=-1.2242424242424244) >>> b = [[1, 2], [3, 4]] >>> stats.describe(b) DescribeResult(nobs=2, minmax=(array([1, 2]), array([3, ...

September 19, 2016 - scipy.stats.describe(a, axis=0, ddof=1, bias=True, nan_policy='propagate')[source]¶ · Computes several descriptive statistics of the passed array. See also · skew, kurtosis ·

May 11, 2014 - scipy.stats.describe(a, axis=0)[source]¶ · Computes several descriptive statistics of the passed array. See also · skew, kurtosis · scipy.stats.zipf · scipy.stats.gmean · © Copyright 2008-2009, The Scipy community. Last updated on May 11, 2014.

scipy.stats.describe(a, axis=0, ddof=1, bias=True, nan_policy='propagate')[source]# Compute several descriptive statistics of the passed array. Parameters: aarray_like · Input data. axisint or None, optional · Axis along which statistics are calculated. Default is 0.

scipy.stats.mstats.describe(a, axis=0, ddof=0, bias=True)[source]# Computes several descriptive statistics of the passed array. Parameters: aarray_like · Data array · axisint or None, optional · Axis along which to calculate statistics. Default 0. If None, compute over the whole array a.

February 10, 2019 - scipy.stats.describe(array, axis=0) computes the descriptive statistics of the passed array elements along the specified axis of the array.

scipy.stats.describe(a, axis=0, ddof=1)[source]¶ · Computes several descriptive statistics of the passed array. See also · skew, kurtosis · scipy.stats.zipf · scipy.stats.gmean · © Copyright 2008-2014, The Scipy community. Last updated on Oct 24, 2015.

Descriptive statistics is a branch of statistics that focuses on summarizing and organizing data to reveal meaningful insights. It helps in understanding the distribution, central tendency and variability of data.

Computes several descriptive statistics of the passed array. ... Enter search terms or a module, class or function name. ... © Copyright 2008-2009, The Scipy community. Last updated on Feb 18, 2015.

December 19, 2019 - scipy.stats.describe(a, axis=0, ddof=1, bias=True, nan_policy='propagate')[source]¶ · Compute several descriptive statistics of the passed array. Parameters · aarray_like · Input data. axisint or None, optional · Axis along which statistics are calculated.

April 28, 2025 - # importing the stats and numpy module from scipy import stats as st import numpy as npy # ID input array array = npy.array([10, 20, 30, 40, 50, 60, 70, 80]) # calling the describe function print(st.describe(array)) Output: DescribeResult( nobs=8, minmax=(10, 80), mean=45.0, variance=600.0, skewness=0.0, kurtosis=-1.2380952380952381) Python3 ·

In this tutorial, we discuss many, but certainly not all, features of scipy.stats. The intention here is to provide a user with a working knowledge of this package.