# 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.

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