Filling missing values (fillna) in Data Analysis Python - Step-by-Step Execution

Concept Flow - Filling missing values (fillna)

Start with DataFrame

↓

Identify missing values

↓

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

We start with data that has missing values, decide how to fill them, apply fillna(), and get a complete dataset.

Execution Sample

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'A': [1, None, 3], 'B': [4, 5, None]})
df_filled = df.fillna(0)
print(df_filled)

This code fills missing values in a DataFrame with 0.

Execution Table

Step	DataFrame State	fillna() Action	Resulting DataFrame
1	{'A': [1, None, 3], 'B': [4, 5, None]}	Identify missing values at positions A[1], B[2]	{'A': [1, None, 3], 'B': [4, 5, None]}
2	Same as step 1	Apply fillna(0) to replace None with 0	{'A': [1, 0, 3], 'B': [4, 5, 0]}
3	No missing values remain	Print filled DataFrame	A B 0 1 4 1 0 5 2 3 0
4	Execution ends	No more steps	End of process

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

Variable Tracker

Variable	Start	After fillna	Final
df	{'A': [1, None, 3], 'B': [4, 5, None]}	No change	Same as start
df_filled	Not defined	{'A': [1, 0, 3], 'B': [4, 5, 0]}	Same as after fillna

Key Moments - 2 Insights

Why does fillna(0) replace None with 0 in the DataFrame?

fillna(0) looks for missing values (None or NaN) and replaces them with 0, as shown in execution_table step 2.

Does fillna() change the original DataFrame?

No, fillna() returns a new DataFrame with filled values. The original stays the same unless you use inplace=True, as seen in variable_tracker.

Visual Quiz - 3 Questions

Look at the execution table at step 2, what value replaces the missing value in column 'A'?

A None

B 0

C 1

D NaN

💡 Hint

Check the 'Resulting DataFrame' column in step 2 of the execution_table.

At which step does the DataFrame have no missing values?

A Step 2

B Step 3

C Step 1

D Step 4

💡 Hint

Look at the 'DataFrame State' and 'Resulting DataFrame' columns in the execution_table.

If we used fillna(method='ffill') instead of fillna(0), what would happen to the missing value in 'B' at index 2?

A It would remain None

B It would be replaced by 0

C It would be replaced by 5

D It would be replaced by 4

💡 Hint

Forward fill (ffill) replaces missing values with the previous non-missing value, check variable_tracker for values in 'B'.

Concept Snapshot

fillna(value) replaces missing values in a DataFrame with the given value.
It returns a new DataFrame unless inplace=True is used.
Missing values include None or NaN.
You can also use methods like 'ffill' or 'bfill' to fill.
Useful to clean data before analysis.

Full Transcript

We start with a DataFrame that has missing values represented by None. We identify these missing spots. Then, we choose a value to fill them, here zero. Using fillna(0), we replace all missing values with zero, creating a new DataFrame. The original DataFrame remains unchanged. Finally, we print the filled DataFrame which has no missing values. This process helps prepare data for analysis by removing gaps.