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

Descriptive statistics is a branch of statistics that deals with summarizing and describing a dataset. Descriptive statistics helps to understand the characteristics of the data, such as its central tendency, spread, shape, and so on.

There are several ways to get the descriptive statistics of a dataset in Python:

Using the `describe()` function in the `pandas` library: The `describe()` function can be used to calculate various summary statistics of a dataset. It takes a Pandas DataFrame or Series as an input, and returns the count, mean, standard deviation, minimum, and maximum values, as well as the 25th, 50th (median), and 75th percentiles.

Using the `mean()`, `std()`, `min()`, `max()`, `median()`, and other functions in the `numpy` library: The `mean()`, `std()`, `min()`, `max()`, `median()`, and other functions can be used to calculate specific summary statistics of a dataset. They take a Pandas DataFrame or Series as an input, and return the corresponding summary statistic.

Using the `scipy` library: The `scipy.stats` module provides a wide range of statistical functions, including `scipy.stats.describe()` function which provides a detailed summary of statistics of a dataset.

In summary, you can use the `describe()` function from `pandas` library, `mean()`, `std()`, `min()`, `max()`, `median()` functions from `numpy` library or `scipy.stats.describe()` function from `scipy` library to get the descriptive statistics of a dataset in Python. Descriptive statistics can provide a good overview of the data, and also helps to identify outliers, missing values and other characteristics of the dataset.

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