concat() for stacking DataFrames in Data Analysis Python - 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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Combine DataFrames along chosen axis

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Return new stacked DataFrame

We start with two or more DataFrames, choose how to stack them (rows or columns), then use concat() to combine them into one DataFrame.

Execution Sample

Data Analysis Python

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.

Execution Table

Step	Action	DataFrames Involved	Axis	Resulting DataFrame Shape	Result Preview
1	Create df1	df1	-	(2, 2)	[[1,3],[2,4]]
2	Create df2	df2	-	(2, 2)	[[5,7],[6,8]]
3	Call pd.concat([df1, df2], axis=0)	df1, df2	0 (rows)	(4, 2)	[[1,3],[2,4],[5,7],[6,8]]
4	Check index after concat	df1, df2	0 (rows)	(4, 2)	Index: [0,1,0,1] (duplicate indices)
5	Call pd.concat([df1, df2], axis=0, ignore_index=True)	df1, df2	0 (rows)	(4, 2)	Index reset to [0,1,2,3]

💡 All DataFrames stacked row-wise; final DataFrame has 4 rows and 2 columns.

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 5
df1	None	A: [1,2], B: [3,4]	A: [1,2], B: [3,4]	A: [1,2], B: [3,4]	A: [1,2], B: [3,4]
df2	None	None	A: [5,6], B: [7,8]	A: [5,6], B: [7,8]	A: [5,6], B: [7,8]
result	None	None	None	A: [1,2,5,6], B: [3,4,7,8], index: [0,1,0,1]	A: [1,2,5,6], B: [3,4,7,8], index: [0,1,2,3]

Key Moments - 3 Insights

Why do the indices repeat after stacking without ignore_index?

Because concat keeps original indices by default, so rows from df2 keep their original index values, causing duplicates as shown in step 4 of execution_table.

What does axis=0 mean in concat()?

Axis=0 means stacking DataFrames vertically, adding rows one below another, as shown in step 3 where rows increase from 2 to 4.

How does ignore_index=True affect the result?

It resets the index to a new continuous range starting at 0, avoiding duplicate indices, as shown in step 5.

Visual Quiz - 3 Questions

Look at the execution_table at step 3, what is the shape of the resulting DataFrame?

A (2, 4)

B (2, 2)

C (4, 2)

D (3, 2)

💡 Hint

Check the 'Resulting DataFrame Shape' column at step 3 in execution_table.

At which step does the index get reset to avoid duplicates?

A Step 5

B Step 4

C Step 3

D Step 2

💡 Hint

Look at the 'Action' and 'Result Preview' columns in execution_table for index changes.

If axis=1 was used instead of axis=0, how would the shape change after concat?

A Rows would double, columns stay same

B Columns would double, rows stay same

C Shape stays the same

D DataFrames cannot be concatenated on axis=1

💡 Hint

Recall axis=1 means stacking side-by-side, increasing columns, not rows.

Concept Snapshot

pd.concat([df1, df2], axis=0) stacks DataFrames vertically (rows).
axis=1 stacks horizontally (columns).
By default, indices are kept, causing duplicates.
Use ignore_index=True to reset indices.
Result is a new combined DataFrame.

Full Transcript

We start with two DataFrames, df1 and df2, each with 2 rows and 2 columns. Using pd.concat with axis=0 stacks them vertically, resulting in a DataFrame with 4 rows and 2 columns. By default, the original row indices are kept, so indices repeat. To avoid this, ignore_index=True resets the index to a continuous range. This process creates a new DataFrame combining the data from both inputs.