concat() for stacking DataFrames in Pandas - Step-by-Step Execution

Concept Flow - concat() for stacking DataFrames

Start with DataFrames

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Choose axis: 0 for rows, 1 for columns

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Call pd.concat() with list of DataFrames

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pandas stacks DataFrames along chosen axis

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Return new combined DataFrame

This flow shows how pandas concat() stacks DataFrames by choosing an axis and combining them into one.

Execution Sample

Pandas

import pandas as pd

df1 = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df2 = pd.DataFrame({'A': [5, 6], 'B': [7, 8]})

result = pd.concat([df1, df2], axis=0)

This code stacks two DataFrames vertically (row-wise) using concat with axis=0.

Execution Table

Step	Action	Input DataFrames	Axis	Resulting DataFrame	Notes
1	Create df1	{'A': [1, 2], 'B': [3, 4]}	N/A	A B 0 1 3 1 2 4	First DataFrame created
2	Create df2	{'A': [5, 6], 'B': [7, 8]}	N/A	A B 0 5 7 1 6 8	Second DataFrame created
3	Call pd.concat([df1, df2], axis=0)	[df1, df2]	0 (rows)	A B 0 1 3 1 2 4 0 5 7 1 6 8	DataFrames stacked vertically, index repeated
4	Call pd.concat([df1, df2], axis=1)	[df1, df2]	1 (columns)	A B A B 0 1 3 5 7 1 2 4 6 8	DataFrames stacked horizontally, columns duplicated
5	Call pd.concat([df1, df2], axis=0, ignore_index=True)	[df1, df2]	0 (rows)	A B 0 1 3 1 2 4 2 5 7 3 6 8	Index reset to avoid duplicates
6	Exit	N/A	N/A	N/A	No more stacking, concat finished

💡 All DataFrames stacked; concat() returns combined DataFrame

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	After Step 5	Final
df1	undefined	A B 0 1 3 1 2 4	A B 0 1 3 1 2 4	A B 0 1 3 1 2 4	A B 0 1 3 1 2 4	A B 0 1 3 1 2 4	A B 0 1 3 1 2 4
df2	undefined	undefined	A B 0 5 7 1 6 8	A B 0 5 7 1 6 8	A B 0 5 7 1 6 8	A B 0 5 7 1 6 8	A B 0 5 7 1 6 8
result	undefined	undefined	undefined	A B 0 1 3 1 2 4 0 5 7 1 6 8	A B A B 0 1 3 5 7 1 2 4 6 8	A B 0 1 3 1 2 4 2 5 7 3 6 8	A B 0 1 3 1 2 4 2 5 7 3 6 8

Key Moments - 3 Insights

Why do the row indexes repeat after stacking with axis=0?

Because concat() keeps original indexes by default, so rows from both DataFrames keep their own indexes (see execution_table step 3). Use ignore_index=True to reset.

What happens when stacking with axis=1?

DataFrames are joined side-by-side, adding columns. If columns have same names, they appear duplicated (see execution_table step 4).

How does ignore_index=True affect the result?

It resets the row index to a continuous sequence, avoiding duplicate indexes (see execution_table step 5).

Visual Quiz - 3 Questions

Look at the execution table at step 3. What is the index of the third row in the resulting DataFrame?

A 0

B 1

C 2

D 3

💡 Hint

Check the 'Resulting DataFrame' column at step 3; indexes repeat from original DataFrames.

At which step does concat() stack DataFrames horizontally?

A Step 2

B Step 3

C Step 4

D Step 5

💡 Hint

Look for axis=1 in the 'Axis' column of the execution table.

If we remove ignore_index=True in step 5, what will happen to the index?

A It will reset to 0,1,2,3

B It will keep duplicate indexes from original DataFrames

C It will create a multi-level index

D It will drop the index column

💡 Hint

Compare step 3 and step 5 results in the execution table to see the effect of ignore_index.

Concept Snapshot

pd.concat([df1, df2], axis=0) stacks DataFrames vertically (rows).
axis=1 stacks horizontally (columns).
By default, indexes are kept; use ignore_index=True to reset.
Result is a new combined DataFrame.
Useful for combining data from multiple sources.

Full Transcript

This visual execution shows how pandas concat() stacks DataFrames. First, two DataFrames df1 and df2 are created. Then, concat() is called with axis=0 to stack rows, resulting in a DataFrame with repeated indexes. Next, concat() with axis=1 stacks columns side-by-side, duplicating column names. Using ignore_index=True resets the row index to avoid duplicates. Variables df1, df2, and result change as concat() runs. Key points include index behavior and axis choice. The quiz tests understanding of index values, stacking direction, and ignore_index effect.