concat() for stacking DataFrames in Data Analysis Python - Mini Project: Build & Apply

Stacking DataFrames using concat()

📖 Scenario: You work in a small store and keep sales records for two different months separately. Now, you want to combine these records to see all sales together.

🎯 Goal: Learn how to stack two sales records DataFrames vertically using concat() to get a combined sales record.

📋 What You'll Learn

Create two DataFrames named sales_jan and sales_feb with given sales data

Create a list named sales_list containing both DataFrames

Use pd.concat() with sales_list to stack the DataFrames vertically into all_sales

Print the all_sales DataFrame to see the combined sales

💡 Why This Matters

🌍 Real World

Combining monthly or weekly sales data into one table for easier analysis and reporting.

💼 Career

Data analysts often need to merge data from multiple sources or time periods to get a complete picture.

1

Create two sales DataFrames

Create a DataFrame called sales_jan with columns 'Product' and 'Quantity' and these rows: 'Apple', 10, 'Banana', 15. Also create a DataFrame called sales_feb with the same columns and these rows: 'Apple', 7, 'Orange', 12. Use pd.DataFrame() and pass a list of dictionaries.

Data Analysis Python

import pandas as pd
# Create sales_jan DataFrame
# Create sales_feb DataFrame

Hint

Use pd.DataFrame with a list of dictionaries. Each dictionary is a row with keys as column names.

2

Create a list of the two DataFrames

Create a list called sales_list that contains the two DataFrames sales_jan and sales_feb in that order.

Data Analysis Python

import pandas as pd
sales_jan = pd.DataFrame([
    {'Product': 'Apple', 'Quantity': 10},
    {'Product': 'Banana', 'Quantity': 15}
])
sales_feb = pd.DataFrame([
    {'Product': 'Apple', 'Quantity': 7},
    {'Product': 'Orange', 'Quantity': 12}
])
# Create sales_list containing sales_jan and sales_feb

Hint

Use square brackets to create a list with the two DataFrames inside.

3

Stack the DataFrames vertically using concat()

Use pd.concat() with the list sales_list to stack the two DataFrames vertically. Assign the result to a new variable called all_sales.

Data Analysis Python

import pandas as pd
sales_jan = pd.DataFrame([
    {'Product': 'Apple', 'Quantity': 10},
    {'Product': 'Banana', 'Quantity': 15}
])
sales_feb = pd.DataFrame([
    {'Product': 'Apple', 'Quantity': 7},
    {'Product': 'Orange', 'Quantity': 12}
])
sales_list = [sales_jan, sales_feb]
# Use pd.concat() to stack sales_list vertically into all_sales

Hint

Call pd.concat() with the list of DataFrames as the argument.

4

Print the combined DataFrame

Print the DataFrame all_sales to see the stacked sales records from January and February.

Data Analysis Python

import pandas as pd
sales_jan = pd.DataFrame([
    {'Product': 'Apple', 'Quantity': 10},
    {'Product': 'Banana', 'Quantity': 15}
])
sales_feb = pd.DataFrame([
    {'Product': 'Apple', 'Quantity': 7},
    {'Product': 'Orange', 'Quantity': 12}
])
sales_list = [sales_jan, sales_feb]
all_sales = pd.concat(sales_list)
# Print all_sales

Hint

Use print(all_sales) to display the combined DataFrame.

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