concat() for stacking DataFrames in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of vertical stacking with concat()

What is the output DataFrame after stacking df1 and df2 vertically using pd.concat([df1, df2])?

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])
print(result)

A

   A  B
0  1  3
1  2  4
NaN  5  7
NaN  6  8

B

   A  B
0  1  3
1  2  4
2  5  7
3  6  8

C

   A  B
0  1  3
1  2  4
0  5  7
1  6  8

D

   A  B
0  1  3
1  2  4
0  5  7
2  6  8

💡 Hint

Remember that concat keeps original indices by default when stacking vertically.

When stacking vertically with pd.concat, the default behavior keeps the original row indices from both DataFrames, so the result has duplicate indices 0 and 1.

❓ Predict Output

intermediate

Output of horizontal stacking with concat()

What is the output DataFrame after stacking df1 and df2 horizontally using pd.concat([df1, df2], axis=1)?

Data Analysis Python

import pandas as pd

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

A

   A  B
0  1  3
1  2  4
0  NaN 3

B

   A  B
0  1  3
1  2  4

C

   A  B
0  1  3
1  2  4
2  NaN 4

D

   A  B
0  1  3
1  2  NaN
2  NaN 4

💡 Hint

Horizontal stacking aligns rows by index.

Concatenating along axis=1 stacks columns side by side, matching rows by their index. Since both DataFrames have indices 0 and 1, the result has those rows with columns A and B.

❓ data_output

advanced

Result of concat() with ignore_index=True

What is the output DataFrame after stacking df1 and df2 vertically using pd.concat([df1, df2], ignore_index=True)?

Data Analysis Python

import pandas as pd

df1 = pd.DataFrame({'X': [10, 20], 'Y': [30, 40]})
df2 = pd.DataFrame({'X': [50, 60], 'Y': [70, 80]})
result = pd.concat([df1, df2], ignore_index=True)
print(result)

A

    X   Y
0  10  30
1  20  40
2  NaN 70
3  NaN 80

B

    X   Y
0  10  30
1  20  40
0  50  70
1  60  80

C

    X   Y
0  10  30
1  20  40
2  50  NaN
3  60  NaN

D

    X   Y
0  10  30
1  20  40
2  50  70
3  60  80

💡 Hint

ignore_index resets the row indices to a continuous range.

Using ignore_index=True resets the row indices in the concatenated DataFrame to 0,1,2,... ignoring the original indices.

🔧 Debug

advanced

Identify the error in concat() usage

What error will this code raise when trying to concatenate df1 and df2 with mismatched columns without specifying join?

Data Analysis Python

import pandas as pd

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

A No error, output has columns A and B with NaNs where missing

B ValueError: columns overlap but no join specified

C KeyError: 'B' not found in df1

D TypeError: cannot concatenate DataFrames with different columns

💡 Hint

Check how concat handles different columns by default.

By default, concat stacks rows and includes all columns from both DataFrames, filling missing values with NaN. No error is raised.

🚀 Application

expert

Stacking DataFrames with different columns and resetting index

You have two DataFrames with different columns. Which option correctly stacks them vertically, includes all columns, fills missing values with NaN, and resets the index?

Data Analysis Python

import pandas as pd

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

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

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

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

D pd.concat([df1, df2], join='outer', axis=1)

💡 Hint

Think about vertical stacking with all columns and resetting index.

Option A stacks vertically (default axis=0), includes all columns (default join='outer'), fills missing with NaN, and resets index with ignore_index=True. Other options either stack horizontally or limit columns.

Practice

1. What does the concat() function do when stacking DataFrames with axis=0?

easy

A. It sorts the DataFrames before stacking.

B. It stacks DataFrames horizontally by adding columns.

C. It stacks DataFrames vertically by adding rows.

D. It merges DataFrames based on matching column values.

Solution

1. Step 1: Understand axis parameter in concat()

   The axis=0 means stacking along rows, so new rows are added below existing ones.

2. Step 2: Differentiate vertical vs horizontal stacking

   Vertical stacking adds rows; horizontal stacking (axis=1) adds columns side by side.

3. Final Answer:

   It stacks DataFrames vertically by adding rows. -> Option C

4. Quick Check:

   axis=0 means vertical stacking [OK]

Hint: axis=0 adds rows, axis=1 adds columns [OK]

Common Mistakes:

• Confusing axis=0 with horizontal stacking
• Thinking concat merges like join
• Assuming concat sorts data automatically

2. Which of the following is the correct syntax to stack two DataFrames df1 and df2 vertically and reset the index?

easy

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

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

C. pd.concat(df1, df2, axis=0, ignore_index=True)

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

Solution

1. Step 1: Check the concat() function syntax

   Correct syntax requires a list of DataFrames as first argument, e.g., [df1, df2].

2. Step 2: Confirm axis and ignore_index usage

   To stack vertically and reset index, use axis=0 and ignore_index=True.

3. Final Answer:

   pd.concat([df1, df2], axis=0, ignore_index=True) -> Option B

4. Quick Check:

   List + axis=0 + ignore_index=True = correct syntax [OK]

Hint: Use list of DataFrames and axis=0 for vertical concat [OK]

Common Mistakes:

• Passing DataFrames without list brackets
• Using axis=1 for vertical stacking
• Forgetting ignore_index to reset row numbers

3. Given these DataFrames:

import pandas as pd
df1 = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df2 = pd.DataFrame({'A': [5], 'B': [6]})
result = pd.concat([df1, df2], axis=0, ignore_index=True)
print(result)

What is the output?

medium

A. A B
0 1 3
1 2 4
2 5 6

B. A B
0 1 3
1 2 4
0 5 6

C. A B
0 1 3
1 2 4

D. Error due to missing axis parameter

Solution

1. Step 1: Understand concat with axis=0 and ignore_index=True

   Rows from df2 are added below df1, and index resets from 0 to 2.

2. Step 2: Combine rows and reset index

   Result has 3 rows with index 0,1,2 and columns A and B filled accordingly.

3. Final Answer:

   A B 0 1 3 1 2 4 2 5 6 -> Option A

4. Quick Check:

   concat vertical + ignore_index resets index [OK]

Hint: ignore_index=True resets row numbers after concat [OK]

Common Mistakes:

• Expecting original index to remain
• Confusing axis=0 with axis=1 output
• Forgetting to pass DataFrames as list

4. What is wrong with this code snippet?

import pandas as pd
df1 = pd.DataFrame({'X': [1, 2]})
df2 = pd.DataFrame({'Y': [3, 4]})
result = pd.concat(df1, df2, axis=0)
print(result)

medium

A. Missing list brackets around DataFrames in concat.

B. axis=0 is invalid for concat.

C. DataFrames must have same columns to concat.

D. ignore_index=True is required.

Solution

1. Step 1: Check concat() argument format

   concat() expects a list of DataFrames as first argument, but here two DataFrames are passed separately.

2. Step 2: Identify correct usage

   Correct call is pd.concat([df1, df2], axis=0) with brackets around DataFrames.

3. Final Answer:

   Missing list brackets around DataFrames in 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
• Assuming axis=0 is invalid
• Thinking ignore_index is mandatory

5. You have two DataFrames:

df1 = pd.DataFrame({'Name': ['Alice', 'Bob'], 'Score': [85, 90]})
df2 = pd.DataFrame({'Name': ['Charlie'], 'Score': [88]})

You want to stack them vertically but keep the original index from both DataFrames. Which code will do this correctly?

hard

A. pd.concat([df1, df2], axis=0, ignore_index=True)

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

C. pd.concat([df1, df2], axis=1)

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

Solution

1. Step 1: Understand index behavior in concat()

   By default, concat keeps original indexes unless ignore_index=True is set.

2. Step 2: Choose correct parameters

   To stack vertically and keep indexes, use axis=0 without ignore_index.

3. Final Answer:

   pd.concat([df1, df2], axis=0) -> Option D

4. Quick Check:

   Default concat keeps original index [OK]

Hint: Omit ignore_index to keep original indexes [OK]

Common Mistakes:

• Using ignore_index=True when original index is needed
• Using axis=1 for vertical stacking
• Passing DataFrames without list brackets