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:

import pandas as pd

data = {'A':[2, 4, None, 8], 'B':[5, None, 7, 9], 'C':[12, 13, 14, None]}
df = pd.DataFrame(data)

# Spot missing values
print(df.isnull())

'''Output:
       A      B      C
0  False  False  False
1  False   True  False
2   True  False  False
3  False  False   True
'''

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.
• dropna(): Removes the missing values.

Let's apply these strategies:

# Fill missing values with 0
print(df.fillna(0)) 
'''Output:
     A    B     C
0  2.0  5.0  12.0
1  4.0  0.0  13.0
2  0.0  7.0  14.0
3  8.0  9.0   0.0
'''

# Remove rows with missing values
print(df.dropna()) 
'''Output:
     A    B     C
0  2.0  5.0  12.0
'''

In the last example, all rows with None values were deleted, leaving us just one row.

Note that both functions return a new DataFrame. If you want to update the original df, you'll need to re-assign it:

df = df.fillna(0)

The df.fillna function applies to the whole dataset, which might not be the best strategy. Most of the time, you want to handle missing values in certain columns separately. It's simple! As DataFrame acts like a dictionary, we could access and re-assign separate columns using the key:

# Fill missing values of the "A" column with 0
df["A"] = df["A"].fillna(0)
print(df)
'''Output:
     A    B     C
0  2.0  5.0  12.0
1  4.0  NaN  13.0
2  0.0  7.0  14.0
3  8.0  9.0   NaN
'''

Note that there are no more missing values in the "A" column.