concat() for stacking DataFrames in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to stack two DataFrames vertically using pandas.

Pandas

import pandas as pd

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

result = pd.[1]([df1, df2])
print(result)

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

Amerge

Bjoin

Cconcat

Dappend

Attempts:

3 left

💡 Hint

Common Mistakes

Using merge instead of concat causes errors or unexpected joins.

Using append is deprecated for stacking multiple DataFrames.

The concat() function stacks DataFrames vertically by default when given a list of DataFrames.

2fill in blank

medium

Complete the code to stack two DataFrames horizontally (side by side) using pandas concat.

Pandas

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

B0

C2

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using axis=0 stacks vertically, not horizontally.

Using axis=2 or -1 causes errors.

Setting axis=1 stacks DataFrames side by side (columns).

3fill in blank

hard

Fix the error in the code to stack DataFrames vertically and reset the index.

Pandas

import pandas as pd

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

result = pd.concat([df1, df2], ignore_index=[1])
print(result)

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

AFalse

BTrue

CNone

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using False keeps original indexes, causing duplicate index values.

Using None or 0 causes errors or unexpected behavior.

Setting ignore_index=True resets the index after stacking.

4fill in blank

hard

Fill both blanks to stack DataFrames vertically and add keys to identify each part.

Pandas

import pandas as pd

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

result = pd.concat([df1, df2], [1]=True, [2]=['first', 'second'])
print(result)

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

Akeys

Bignore_index

Caxis

Dsort

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up 'keys' and 'ignore_index' parameters.

Forgetting to reset index causes confusing output.

Use ignore_index=True to reset index and keys to label each DataFrame part.

5fill in blank

hard

Fill all three blanks to stack DataFrames horizontally, join only common columns, and reset index.

Pandas

import pandas as pd

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

result = pd.concat([df1, df2], axis=[1], join="[2]", ignore_index=[3])
print(result)

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

ATrue

B1

Cinner

Douter

Attempts:

3 left

💡 Hint

Common Mistakes

Using axis=0 stacks vertically, not horizontally.

Using join='outer' keeps all columns, not just common ones.

Not resetting index causes duplicate indexes.

Set axis=1 to stack side by side, join='inner' to keep only common columns, and ignore_index=True to reset index.

Practice

1. What does the concat() function do in pandas?

easy

A. Deletes rows from a DataFrame

B. Joins DataFrames by stacking rows or columns

C. Sorts the DataFrame by index

D. Filters rows based on a condition

Solution

1. Step 1: Understand the purpose of concat()

   concat() is used to combine multiple DataFrames either by stacking rows or columns.

2. Step 2: Compare with other DataFrame operations

   Deleting, sorting, or filtering are different operations and not related to concat().

3. Final Answer:

   Joins DataFrames by stacking rows or columns -> Option B

4. Quick Check:

   concat() stacks DataFrames [OK]

Hint: Remember: concat stacks rows or columns [OK]

Common Mistakes:

• Confusing concat with filtering or sorting
• Thinking concat deletes data
• Assuming concat only merges columns

2. Which of the following is the correct syntax to stack two DataFrames df1 and df2 vertically using concat()?

easy

A. pd.concat(df1 + df2)

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

C. pd.concat(df1, df2)

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

Solution

1. Step 1: Identify axis for vertical stacking

   Vertical stacking means stacking rows, which is done with axis=0.

2. Step 2: Check syntax correctness

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

3. Final Answer:

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

4. Quick Check:

   Vertical stack uses axis=0 [OK]

Hint: Use axis=0 to stack rows vertically [OK]

Common Mistakes:

• Using axis=1 for vertical stacking
• Passing DataFrames without a list
• Trying to add DataFrames with + inside concat

3. Given the following code:

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], ignore_index=True)
print(result)

What is the output?

medium

A. DataFrame with 4 rows, original indexes 0,1,0,1

B. DataFrame with 2 rows, columns stacked horizontally

C. DataFrame with 4 rows, index reset from 0 to 3

D. Error because ignore_index is invalid

Solution

1. Step 1: Understand concat with ignore_index=True

   Concatenating df1 and df2 vertically stacks rows, resulting in 4 rows total. ignore_index=True resets the index to 0,1,2,3.

2. Step 2: Confirm output structure

   The columns remain the same ('A' and 'B'), rows are stacked, and index is reset.

3. Final Answer:

   DataFrame with 4 rows, index reset from 0 to 3 -> Option C

4. Quick Check:

   ignore_index=True resets index [OK]

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

Common Mistakes:

• Forgetting ignore_index resets index
• Thinking columns stack horizontally by default
• Assuming error with ignore_index parameter

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. pd.concat requires a list of DataFrames as first argument

B. axis=0 stacks columns instead of rows

C. DataFrames must have same columns to concat

D. ignore_index=True is missing

Solution

1. Step 1: Check pd.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

   The correct call is pd.concat([df1, df2], axis=0) with a list.

3. Final Answer:

   pd.concat requires a list of DataFrames as first argument -> Option A

4. Quick Check:

   pd.concat([df1, df2]) needs list [OK]

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

Common Mistakes:

• Passing DataFrames as separate arguments
• Confusing axis parameter meaning
• Assuming columns must match exactly

5. You have two DataFrames:

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

You want to stack them vertically and keep all columns, filling missing values with NaN. Which code will do this correctly?

hard

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

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

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

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

Solution

1. Step 1: Understand vertical stacking with different columns

   Stacking vertically with axis=0 combines rows. Since columns differ, missing columns get NaN by default.

2. Step 2: Check parameters for desired behavior

   ignore_index=True resets index. Default join='outer' keeps all columns and fills missing with NaN.

3. Step 3: Evaluate other options

   pd.concat([df1, df2], axis=1) stacks columns horizontally, not vertically. pd.concat([df1, df2], axis=0).fillna(0) fills NaN with 0, not requested. pd.concat([df1, df2], axis=0, join='inner') uses join='inner' which keeps only common columns.

4. Final Answer:

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

5. Quick Check:

   axis=0 + default join='outer' keeps all columns [OK]

Hint: Use axis=0 and default join to keep all columns [OK]

Common Mistakes:

• Using axis=1 to stack rows
• Using join='inner' and losing columns
• Filling NaN when not needed