**Pandas** is an open-source Python library that is used in data analysis. It provides functionalities to manipulate data in the form of table structures called **data frames**. The `describe()` method displays a statistical summary of the data that consists of the mean, the standard deviation, the minimum and maximum value, and so on. 

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## Syntax

dataFrame.describe(percentiles, include, exclude, datetime_is_numeric)

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#  Arguments
1. `percentiles`: Values between 0 and 1. Specifies the percentile to be returned in the result. (Optional)
2. `include`: List of data types to include in the result. Options are None | 'all' | *datatypes*. (Optional)
3. `exclude`: List of data types to exclude in the result. Options are None | 'all' | *datatypes*. (Optional)
4. `datetime_is_numeric`: To treat datetime data as numeric. Set to True or False, with default as False. (Optional)

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## Return Value
The functions return a DataFrame object, where each row has a type of statistic that provides a summary of the columns.

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## Example code 

#import library
import pandas as pd

#define data
data =  {'Name': ['Kris', 'Kelly', 'Josh', 'Bob','Lisa'],
          'Age': [16, 21, 17, 19, 20],
        'Marks': [78, 56, 87, 89, 79]}

#create a DataFrame object
df = pd.DataFrame(data)

#describe the data
print(df.describe())

What is describe() in pandas?

Displays statistical summary of data, including mean, standard deviation, min, max values.