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

Concept Flow - append equivalent with concat

Start with DataFrame A

↓

Have DataFrame B to add

↓

Use pd.concat([A, B

↓

Combine rows of A and B

↓

New DataFrame with rows from A and B

↓

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.

Practice

1. What is the main reason to use pandas.concat() instead of DataFrame.append()?

easy

A. concat() is faster for single DataFrame addition only

B. Because append() is deprecated and concat() can join multiple DataFrames at once

C. append() works only for columns, not rows

D. concat() automatically sorts columns alphabetically

Solution

1. Step 1: Understand the deprecation of append()

   The append() method is deprecated in pandas and will be removed in future versions.

2. Step 2: Recognize concat() advantages

   concat() can join multiple DataFrames at once, not just one, making it more flexible.

3. Final Answer:

   Because append() is deprecated and concat() can join multiple DataFrames at once -> Option B

4. Quick Check:

   append() deprecated, concat() multi-join [OK]

Hint: Remember: concat replaces append and handles many DataFrames [OK]

Common Mistakes:

• Thinking append is still recommended
• Believing concat only works for columns
• Assuming append is faster than concat

2. Which of the following is the correct syntax to replace df1.append(df2, ignore_index=True) using concat?

easy

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

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

C. pd.concat(df1, df2, ignore_index=True)

D. pd.concat([df1, df2], axis=1, ignore_index=True)

Solution

1. Step 1: Identify the correct function call

   pd.concat() requires a list of DataFrames as the first argument, so [df1, df2] is correct.

2. Step 2: Use ignore_index=True to reset row numbers

   To mimic append(ignore_index=True), pass ignore_index=True to concat(). The default axis is 0 (rows), so no need to specify.

3. Final Answer:

   pd.concat([df1, df2], ignore_index=True) -> Option A

4. Quick Check:

   List of DataFrames + ignore_index=True = correct concat syntax [OK]

Hint: Use list of DataFrames and ignore_index=True with concat [OK]

Common Mistakes:

• Passing DataFrames without list brackets
• Using axis=1 to add rows (wrong axis)
• Omitting ignore_index when needed

3. Given the code:

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)

What will be the output?

medium

A. A
0 1
1 2
0 3
1 4

B. A
0 3
1 4

C. A
0 1
1 2
2 3
3 4

D. Error because ignore_index is not valid

Solution

1. Step 1: Understand concat with ignore_index=True

   concat joins rows from both DataFrames and resets the index to 0,1,2,...

2. Step 2: Combine rows and reset index

   Rows from df1 (1,2) and df2 (3,4) stack vertically with new index 0 to 3.

3. Final Answer:

   A 0 1 1 2 2 3 3 4 -> Option C

4. Quick Check:

   concat stacks rows and resets index with ignore_index=True [OK]

Hint: concat stacks rows; ignore_index resets numbering [OK]

Common Mistakes:

• Expecting original indexes to remain
• Confusing axis parameter
• Thinking concat drops rows

4. What is wrong with this code snippet?

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)

medium

A. The DataFrames should be passed as a list inside concat

B. ignore_index is not a valid argument for concat

C. concat cannot join DataFrames with same columns

D. The code will run without errors and print combined DataFrame

Solution

1. Step 1: Check concat argument format

   pd.concat() expects a list or iterable of DataFrames as the first argument, but here two DataFrames are passed as separate arguments.

2. Step 2: Identify correct usage

   Correct usage is pd.concat([df1, df2], ignore_index=True) with square brackets around DataFrames.

3. Final Answer:

   The DataFrames should be passed as a list inside concat -> Option A

4. Quick Check:

   concat needs list of DataFrames [OK]

Hint: Always pass DataFrames as a list to concat [OK]

Common Mistakes:

• Passing DataFrames as separate arguments
• Forgetting brackets around DataFrames
• Misusing ignore_index parameter

5. You have three DataFrames:

df1 = pd.DataFrame({'X': [1, 2]})
df2 = pd.DataFrame({'X': [3], 'Y': [5]})
df3 = pd.DataFrame({'X': [4], 'Y': [6]})

You want to combine them vertically using concat so that missing columns fill with NaN and the index resets. Which code achieves this?

hard

A. pd.concat(df1, df2, df3, ignore_index=True)

B. pd.concat([df1, df2, df3], axis=1, ignore_index=True)

C. pd.concat([df1, df2, df3], join='inner', ignore_index=True)

D. pd.concat([df1, df2, df3], ignore_index=True)

Solution

1. Step 1: Combine DataFrames vertically with concat

   Use pd.concat() with a list of DataFrames to stack rows.

2. Step 2: Handle missing columns and reset index

   Default join='outer' fills missing columns with NaN. Use ignore_index=True to reset row numbers.

3. Final Answer:

   pd.concat([df1, df2, df3], ignore_index=True) -> Option D

4. Quick Check:

   concat list, default outer join, ignore_index=True [OK]

Hint: Use concat list with ignore_index=True for clean vertical join [OK]

Common Mistakes:

• Using axis=1 to join rows (actually joins columns)
• Using join='inner' which drops columns
• Passing DataFrames without list brackets