append equivalent with concat in Pandas - Step-by-Step Execution

Concept Flow - append equivalent with concat

Start with DataFrame A

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Have DataFrame B to add

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Use pd.concat([A, B

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Combine rows of A and B

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New DataFrame with rows from A and B

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End

We start with two data tables (DataFrames). We use concat to join them by stacking rows, creating a new combined table.

Execution Sample

Pandas

import pandas as pd

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

result = pd.concat([df1, df2], ignore_index=True)
print(result)

This code stacks two DataFrames df1 and df2 vertically, resetting the index.

Execution Table

Step	Action	DataFrames Involved	Resulting DataFrame Rows	Index Reset
1	Create df1	df1	Rows: 2 (values 1, 2)	Index: 0,1
2	Create df2	df2	Rows: 2 (values 3, 4)	Index: 0,1
3	Call pd.concat([df1, df2], ignore_index=True)	df1 and df2	Rows: 4 (1,2,3,4)	Index reset to 0,1,2,3
4	Print result	result	Rows: 4 (1,2,3,4)	Index: 0,1,2,3

💡 All rows from df1 and df2 combined; index reset because ignore_index=True

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
df1	None	DataFrame with 2 rows (1,2)	Same	Same	Same
df2	None	None	DataFrame with 2 rows (3,4)	Same	Same
result	None	None	None	DataFrame with 4 rows (1,2,3,4)	Same

Key Moments - 2 Insights

Why do we use ignore_index=True in pd.concat?

Without ignore_index=True, the combined DataFrame keeps original indexes, which can cause duplicate indexes. The execution_table step 3 shows index reset to 0,1,2,3 to avoid this.

Is pd.concat([df1, df2]) exactly the same as df1.append(df2)?

Yes, pd.concat([df1, df2]) is the modern equivalent of df1.append(df2). The execution_table step 3 shows concat combining rows just like append would.

Visual Quiz - 3 Questions

Look at the execution_table at step 3, how many rows does the resulting DataFrame have?

A 2

B 4

C 3

D 5

💡 Hint

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

According to variable_tracker, what is the value of 'result' after step 3?

A None

B DataFrame with 2 rows

C DataFrame with 4 rows

D DataFrame with 3 rows

💡 Hint

Look at the 'result' row under 'After Step 3' in variable_tracker.

If we remove ignore_index=True from pd.concat, what happens to the index in the result?

A Index keeps original values, possibly duplicated

B Index resets to 0,1,2,3

C Index becomes empty

D Code throws an error

💡 Hint

Refer to key_moments explanation about ignore_index and execution_table step 3.

Concept Snapshot

pd.concat([df1, df2]) stacks DataFrames vertically.
Use ignore_index=True to reset index.
Equivalent to df1.append(df2).
Returns a new DataFrame combining all rows.
Useful for merging data tables by rows.

Full Transcript

We start with two DataFrames, df1 and df2, each with two rows. Using pd.concat([df1, df2], ignore_index=True), we combine them into one DataFrame with four rows. The index is reset to avoid duplicates. This method is the modern replacement for df1.append(df2). The execution table shows each step: creating df1 and df2, concatenating them, and printing the result. The variable tracker shows how df1 and df2 remain unchanged, and result holds the combined DataFrame. Key moments clarify why ignore_index is important and confirm concat is like append. The quiz tests understanding of row counts, variable states, and index behavior.