concat() for stacking DataFrames in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the pandas concat() function do when stacking DataFrames?

It combines multiple DataFrames either vertically (one below another) or horizontally (side by side) into a single DataFrame.

beginner

What parameter in concat() controls whether DataFrames are stacked vertically or horizontally?

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

intermediate

If DataFrames have different columns, what does concat() do by default?

It includes all columns from all DataFrames, filling missing values with NaN where data is absent.

intermediate

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

Use the parameter ignore_index=True in concat() to create a new continuous index in the combined DataFrame.

intermediate

What is the difference between concat() and append() in pandas?

append() is a simpler method to stack DataFrames vertically but is deprecated. concat() is more flexible and recommended for stacking.

Which axis value stacks DataFrames vertically using concat()?

A -1

B 1

C None

D 0

Setting axis=0 stacks DataFrames vertically (one below another).

What happens to columns not shared by all DataFrames when using concat()?

A They are dropped

B They cause an error

C They are included with NaN in missing places

D They are renamed automatically

Columns not present in all DataFrames appear with NaN where data is missing.

How do you combine DataFrames side by side using concat()?

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

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

C Use <code>join='inner'</code>

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

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

Which parameter resets the index after concatenation?

A ignore_index=True

B reset_index=True

C drop_index=True

D index=False

ignore_index=True resets the index to a new continuous range.

Why is concat() preferred over append()?

A Because <code>append()</code> works only on Series

B Because <code>append()</code> is deprecated

C Because <code>concat()</code> is slower

D Because <code>concat()</code> cannot stack horizontally

append() is deprecated; concat() is more flexible and recommended.

Explain how to stack two DataFrames vertically using pandas concat(). Include how to handle index resetting.

Think about stacking rows and making the index continuous.

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

Consider how pandas handles missing data in combined tables.

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