Dropping missing values (dropna) in Data Analysis Python - Step-by-Step Execution

Concept Flow - Dropping missing values (dropna)

Start with DataFrame

↓

Check each row/column for missing values

↓

Decide axis: rows or columns

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Remove rows/columns with missing values

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Return cleaned DataFrame

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End

The process checks for missing values in rows or columns, removes those containing missing data, and returns a cleaned DataFrame.

Execution Sample

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
  'A': [1, 2, None],
  'B': [4, None, 6]
})

clean_df = df.dropna()

This code creates a DataFrame with missing values and removes rows that contain any missing values.

Execution Table

Step	DataFrame State	Action	Resulting DataFrame
1	{'A': [1, 2, None], 'B': [4, None, 6]}	Initial DataFrame with missing values	Same as input
2	Check row 0: A=1, B=4	No missing values in row 0	Row 0 kept
3	Check row 1: A=2, B=None	Missing value in B at row 1	Row 1 dropped
4	Check row 2: A=None, B=6	Missing value in A at row 2	Row 2 dropped
5	Final DataFrame after dropna()	Rows with missing values removed	{'A': [1.0], 'B': [4.0]}

💡 All rows with any missing values are removed, leaving only complete rows.

Variable Tracker

Variable	Start	After Step 2	After Step 3	After Step 4	Final
df	{'A': [1, 2, None], 'B': [4, None, 6]}	Same	Same	Same	Same
clean_df	Undefined	Undefined	Undefined	Undefined	{'A': [1.0], 'B': [4.0]}

Key Moments - 3 Insights

Why does dropna() remove entire rows instead of just the missing values?

dropna() removes rows (or columns) that contain any missing values to keep data complete, as shown in execution_table rows 3 and 4 where rows with None are dropped entirely.

What happens if no axis is specified in dropna()?

By default, dropna() removes rows with missing values (axis=0), as demonstrated in the example where rows with missing data are dropped.

Can dropna() remove columns instead of rows?

Yes, by setting axis=1, dropna() removes columns with missing values. This is not shown here but works similarly to row removal.

Visual Quiz - 3 Questions

Look at the execution table, what is the state of the DataFrame after step 3?

A Only row 0 remains

B Rows 0 and 1 remain

C Rows 1 and 2 remain

D All rows remain

💡 Hint

Refer to execution_table row 3 where row 1 is dropped due to missing value.

At which step does the DataFrame lose row 2?

A Step 2

B Step 4

C Step 3

D Step 5

💡 Hint

Check execution_table row 4 where row 2 is dropped for missing value in column A.

If dropna(axis=1) was used instead, what would happen to the DataFrame?

A Rows with missing values would be dropped

B Nothing would change

C Columns with missing values would be dropped

D All data would be removed

💡 Hint

dropna with axis=1 removes columns containing missing values, not rows.

Concept Snapshot

dropna() removes missing data from DataFrames.
By default, it drops rows with any missing values.
Use axis=1 to drop columns instead.
Returns a new DataFrame without missing data.
Helps clean data for analysis.

Full Transcript

This visual execution shows how dropna() works in pandas. We start with a DataFrame containing missing values. Step by step, each row is checked for missing data. Rows with any missing values are removed. The final DataFrame contains only complete rows. Variables track the original and cleaned DataFrames. Key moments clarify why entire rows are dropped and how axis affects behavior. The quiz tests understanding of these steps and outcomes.