concat() for stacking DataFrames in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to stack two DataFrames vertically using concat.

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=[1])
print(result)

Drag options to blanks, or click blank then click option'

A-1

B0

C2

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using axis=1 stacks DataFrames side by side (horizontally).

For vertical stacking, axis must be 0.

To stack DataFrames vertically (one on top of the other), use axis=0 in pd.concat().

2fill in blank

medium

Complete the code to reset the index after stacking DataFrames vertically.

Data Analysis Python

import pandas as pd

df1 = pd.DataFrame({'X': [10, 20]})
df2 = pd.DataFrame({'X': [30, 40]})

stacked = pd.concat([df1, df2], axis=0)
stacked_reset = stacked.[1](drop=True)
print(stacked_reset)

Drag options to blanks, or click blank then click option'

Areset_index

Breindex

Cset_index

Dsort_index

Attempts:

3 left

💡 Hint

Common Mistakes

Using reindex does not reset the index numbering.

For resetting index, use reset_index.

After stacking, the index may be duplicated. Use reset_index(drop=True) to reset it to a clean sequence.

3fill in blank

hard

Fix the error in the code to stack DataFrames horizontally.

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)

Drag options to blanks, or click blank then click option'

A-1

B2

C0

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using axis=0 stacks vertically, not horizontally.

Axis must be 1 for horizontal stacking.

To stack DataFrames side by side (horizontally), use axis=1 in pd.concat().

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

Data Analysis Python

words = ['apple', 'bat', 'car', 'door']
lengths = {word: [1] for word in words if [2]
print(lengths)

Drag options to blanks, or click blank then click option'

Alen(word)

Bword

Clen(word) > 3

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as value instead of its length.

Incorrect condition like comparing word to 3.

The dictionary maps each word to its length using len(word). The condition keeps words with length greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their lengths only if length is greater than 3.

Data Analysis Python

words = ['apple', 'bat', 'car', 'door']
result = { [1]: [2] for word in words if [3] }
print(result)

Drag options to blanks, or click blank then click option'

Aword.upper()

Blen(word)

Clen(word) > 3

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using the original word as key instead of uppercase.

Not filtering words by length.

Using incorrect functions for keys or values.

The dictionary keys are uppercase words using word.upper(), values are lengths with len(word), and the condition filters words longer than 3 characters.

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