concat() for stacking DataFrames in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the concat() function do in pandas?

The concat() function joins multiple DataFrames together either vertically (stacking rows) or horizontally (stacking columns).

beginner

How do you stack two DataFrames vertically using concat()?

Use pd.concat([df1, df2]) to stack rows of df2 below df1. By default, it stacks vertically (axis=0).

beginner

What parameter controls whether concat() stacks DataFrames horizontally or vertically?

The axis parameter: axis=0 stacks vertically (rows), axis=1 stacks horizontally (columns).

intermediate

What happens if the DataFrames have different columns when stacked vertically with concat()?

Missing columns in each DataFrame become NaN in the result, so all columns from all DataFrames appear.

intermediate

How can you reset the index after stacking DataFrames with concat()?

Use ignore_index=True inside concat() to reset the index in the combined DataFrame.

What is the default axis value for pd.concat() when stacking DataFrames?

A 0 (vertical stacking)

B 1 (horizontal stacking)

C -1 (reverse stacking)

D None (no stacking)

The default axis=0 stacks DataFrames vertically by rows.

If two DataFrames have different columns, what does concat() do when stacking vertically?

A Fills missing columns with NaN

B Duplicates columns

C Raises an error

D Drops columns not common to both

Missing columns are filled with NaN so all columns appear in the result.

How do you stack DataFrames side-by-side (horizontally) using concat()?

A Set <code>axis=0</code>

B Use <code>join='outer'</code>

C Use <code>ignore_index=True</code>

D Set <code>axis=1</code>

Setting axis=1 stacks DataFrames horizontally by columns.

What does ignore_index=True do in concat()?

A Keeps original indexes

B Resets index in the combined DataFrame

C Drops duplicate rows

D Sorts the DataFrame

It resets the index so the new DataFrame has a continuous index starting at 0.

Which pandas function is used to stack DataFrames?

A groupby()

B merge()

C concat()

D pivot()

concat() is used to stack DataFrames either vertically or horizontally.

Explain how to stack two DataFrames vertically using concat() and how to handle their indexes.

Think about stacking rows and index resetting.

Describe what happens when you stack DataFrames with different columns using concat().

Consider how pandas handles missing data in combined DataFrames.

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