Filling missing values with fillna() in Pandas - Step-by-Step Execution

Concept Flow - Filling missing values with fillna()

Start with DataFrame

↓

Identify missing values

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Choose fill value or method

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Apply fillna()

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Get DataFrame with no missing values

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End

Start with a DataFrame that has missing values, decide how to fill them, apply fillna(), and get a DataFrame without missing values.

Execution Sample

Pandas

import pandas as pd

df = pd.DataFrame({'A': [1, None, 3], 'B': [None, 2, 3]})
df_filled = df.fillna(0)
print(df_filled)

This code creates a DataFrame with missing values and fills them with 0 using fillna().

Execution Table

Step	DataFrame State	fillna() Argument	Action	Resulting DataFrame
1	{'A': [1, None, 3], 'B': [None, 2, 3]}	0	Identify missing values in columns A and B	{'A': [1, None, 3], 'B': [None, 2, 3]} (missing values present)
2	Same as above	0	Apply fillna(0) to replace all None with 0	{'A': [1, 0, 3], 'B': [0, 2, 3]} (missing values replaced)
3	Filled DataFrame	N/A	Print the filled DataFrame	A B 0 1.0 0.0 1 0.0 2.0 2 3.0 3.0
4	Filled DataFrame	N/A	End of execution	No missing values remain

💡 All missing values replaced by 0, no None left in DataFrame

Variable Tracker

Variable	Start	After fillna(0)	Final
df	{'A': [1, None, 3], 'B': [None, 2, 3]}	{'A': [1, None, 3], 'B': [None, 2, 3]}	Unchanged
df_filled	Not defined	{'A': [1, 0, 3], 'B': [0, 2, 3]}	Filled DataFrame with no missing values

Key Moments - 3 Insights

Why does df not change after fillna(0)?

Because fillna() returns a new DataFrame by default and does not modify df in place. See execution_table step 2 where df remains unchanged.

What happens if we use fillna() without an argument?

fillna() requires a value or method to fill missing data. Without an argument, it will raise an error. This is why we specify 0 in step 2.

Can fillna() fill missing values differently for each column?

Yes, by passing a dictionary with column names as keys and fill values as values. This is not shown here but is a common use.

Visual Quiz - 3 Questions

Look at the execution table at step 2. What value replaces the missing values?

A 1

B 0

C None

D NaN

💡 Hint

Check the 'fillna() Argument' and 'Resulting DataFrame' columns at step 2.

At which step does the DataFrame have no missing values?

A Step 2

B Step 1

C Step 3

D Step 4

💡 Hint

Look at the 'Resulting DataFrame' column to see when missing values are replaced.

If we want to change df directly without creating df_filled, what should we do?

A Use df.fillna(0) without assignment

B Use df.fillna() without arguments

C Use df.fillna(0, inplace=True)

D Use df.fillna(0).copy()

💡 Hint

Recall that fillna() returns a new DataFrame unless inplace=True is used.

Concept Snapshot

fillna() replaces missing values in a DataFrame.
Syntax: df.fillna(value, inplace=False)
By default, returns a new DataFrame.
Use inplace=True to modify original.
Value can be a scalar or dict per column.
Missing values are replaced by the given value.

Full Transcript

We start with a DataFrame that has missing values represented by None. We want to fill these missing spots with a value, here zero. Using fillna(0), we create a new DataFrame where all None values are replaced by 0. The original DataFrame remains unchanged unless we use inplace=True. This method helps clean data by removing missing values in a simple way.