Hits: 30

How to get SKEW statistics of Dataset

Skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. In other words, it measures how much the data is tilted or leaning towards one side of the distribution. A positive skew means that the tail on the right side of the probability density function is longer or fatter, while a negative skew means that the tail on the left side is longer or fatter. A zero skew refers to a symmetric distribution, where the tail on the left and right sides are equal.

In Python, there are several ways to get the skewness statistics of a dataset:

Using the skew() function in the statistics library: The skew() function can be used to calculate the skewness of data. It takes an iterable (list, tuple, etc.) or a Pandas DataFrame or Series as an input, and returns the skewness of the data.

Using the scipy library: The scipy.stats.skew() function can also be used to calculate the skewness of data. It takes an iterable (list, tuple, etc.) or a Pandas DataFrame or Series as an input, and returns the skewness of the data.

Using the pandas library: The pandas.DataFrame.skew() function can also be used to calculate the skewness of data. It takes a Pandas DataFrame or Series as an input, and returns the skewness of the data.

In summary, you can use the skew() function from statistics library, scipy.stats.skew() function or pandas.DataFrame.skew() function to get the skewness of a dataset in Python. Skewness helps to understand the symmetry of the data if the data is symmetric or not and can be used to make better decisions when working with it.

In this Applied Machine Learning & Data Science Recipe, the reader will learn: How to get SKEW statistics of Dataset.

How to get SKEW statistics of Dataset

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.