# Applied Data Science Coding in Python : How to get SKEW statistics of Dataset # 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.