concat() for stacking DataFrames in Pandas - Mini Project: Build & Apply

Stacking DataFrames using concat()

📖 Scenario: You work in a small store that tracks sales in two separate lists for two days. You want to combine these lists into one to see all sales together.

🎯 Goal: Combine two sales DataFrames into one using concat() to stack them vertically.

📋 What You'll Learn

Create two DataFrames named sales_day1 and sales_day2 with given data

Create a variable all_sales that stacks the two DataFrames vertically using pd.concat()

Print the combined DataFrame all_sales

💡 Why This Matters

🌍 Real World

Combining sales data from multiple days or stores helps businesses analyze total sales easily.

💼 Career

Data scientists often combine data from different sources to prepare it for analysis or reporting.

1

Create two sales DataFrames

Create a DataFrame called sales_day1 with columns 'Product' and 'Quantity' and these rows: 'Apple', 10 and 'Banana', 5. Also create a DataFrame called sales_day2 with the same columns and these rows: 'Orange', 7 and 'Banana', 3.

Pandas

import pandas as pd
# Create sales_day1 DataFrame
# Create sales_day2 DataFrame

Hint

Use pd.DataFrame() with a dictionary where keys are column names and values are lists of column values.

2

Set concat axis for stacking

Create a variable called axis and set it to 0 to stack DataFrames vertically using pd.concat().

Pandas

import pandas as pd
sales_day1 = pd.DataFrame({'Product': ['Apple', 'Banana'], 'Quantity': [10, 5]})
sales_day2 = pd.DataFrame({'Product': ['Orange', 'Banana'], 'Quantity': [7, 3]})
# Set axis variable for vertical stacking

Hint

Use axis = 0 because stacking means adding rows.

3

Stack the DataFrames using concat()

Use pd.concat() with the list [sales_day1, sales_day2] and the variable axis to create a new DataFrame called all_sales that stacks the two DataFrames vertically.

Pandas

import pandas as pd
sales_day1 = pd.DataFrame({'Product': ['Apple', 'Banana'], 'Quantity': [10, 5]})
sales_day2 = pd.DataFrame({'Product': ['Orange', 'Banana'], 'Quantity': [7, 3]})
axis = 0
# Use pd.concat() to stack sales_day1 and sales_day2 into all_sales

Hint

Pass a list of DataFrames to pd.concat() and set axis=0 to stack rows.

4

Print the combined DataFrame

Print the DataFrame all_sales to see the stacked sales data.

Pandas

import pandas as pd
sales_day1 = pd.DataFrame({'Product': ['Apple', 'Banana'], 'Quantity': [10, 5]})
sales_day2 = pd.DataFrame({'Product': ['Orange', 'Banana'], 'Quantity': [7, 3]})
axis = 0
all_sales = pd.concat([sales_day1, sales_day2], axis=axis)
# Print all_sales DataFrame

Hint

Use print(all_sales) to display the combined DataFrame.

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