Handling Missing Values in Pandas Dataframe

Missing Data can occur when no information is provided for one or more items or for a whole unit. Missing Data is a very big problem in a real-life scenarios. Missing Data can also refer to as NA(Not Available) values in pandas. 

In DataFrame sometimes many datasets simply arrive with missing data, either because it exists and was not collected or it never existed.

 For Example, Suppose different users being surveyed may choose not to share their income, some users may choose not to share the address in this way many datasets went missing.  

In Pandas missing data is represented by two value:

• None: None is a Python singleton object that is often used for missing data in Python code.
• NaN : NaN (an acronym for Not a Number), is a special floating-point value recognized by all systems that use the standard IEEE floating-point representation

Related Article :  https://www.geeksforgeeks.org/working-with-missing-data-in-pandas/