Greetings! Our topic today is 'Identifying and Handling Missing Values', a critical step in data cleaning that ensures our dataset is complete. Essential for accurate analysis, we'll unravel the intricacies of identifying and treating missing values.

Imagine untangling a heap of necklaces — it's tedious but necessary to use each piece. Similarly, datasets may contain confusion like misspellings, incorrect data types, and even missing values, all needing to be sorted. This sorting process is known as 'Data Cleaning'.

Missing values often pose as 'NA', 'None', 'NaN', or zeros. Python's Pandas library simplifies the process of spotting them using the isnull() function: this function returns a DataFrame, replacing missing cells with True and non-missing cells with False.

Take a look at this mini-dataset:

Using this, we can identify the missing values.

After identification, missing values need to be dealt with. Python provides several strategies:

• fillna(): Fills the missing values.