append equivalent with concat in Pandas - Mini Project: Build & Apply

Append Equivalent with concat in pandas

📖 Scenario: You work in a small store and keep track of daily sales in tables. Sometimes you get new sales data and want to add it to your existing records.

🎯 Goal: Learn how to combine two sales tables using pandas.concat(), which works like append() but is more flexible and recommended.

📋 What You'll Learn

Create two pandas DataFrames with sales data

Create a list containing these DataFrames

Use pandas.concat() to combine the DataFrames

Print the combined DataFrame

💡 Why This Matters

🌍 Real World

Stores and businesses often get new sales data daily and need to combine it with old data to analyze total sales.

💼 Career

Data analysts and scientists frequently combine multiple datasets to prepare data for reports and insights.

1

Create two sales DataFrames

Import pandas as pd. Create a DataFrame called sales_jan with columns 'Product' and 'Quantity' and these rows: 'Apple', 10, 'Banana', 15. Create another DataFrame called sales_feb with the same columns and these rows: 'Apple', 7, 'Orange', 12.

Pandas

# Your code here

Hint

Use pd.DataFrame() with a dictionary for columns and lists for rows.

2

Create a list of DataFrames

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

Pandas

import pandas as pd

sales_jan = pd.DataFrame({
    'Product': ['Apple', 'Banana'],
    'Quantity': [10, 15]
})
sales_feb = pd.DataFrame({
    'Product': ['Apple', 'Orange'],
    'Quantity': [7, 12]
})

# Your code here

Hint

Put the two DataFrames inside square brackets to make a list.

3

Combine DataFrames using concat

Use pd.concat() with the list sales_list to create a new DataFrame called all_sales that stacks the rows from both DataFrames.

Pandas

import pandas as pd

sales_jan = pd.DataFrame({
    'Product': ['Apple', 'Banana'],
    'Quantity': [10, 15]
})
sales_feb = pd.DataFrame({
    'Product': ['Apple', 'Orange'],
    'Quantity': [7, 12]
})

sales_list = [sales_jan, sales_feb]

# Your code here

Hint

Use pd.concat() with the list of DataFrames to combine them.

4

Print the combined DataFrame

Print the DataFrame all_sales to see the combined sales data.

Pandas

import pandas as pd

sales_jan = pd.DataFrame({
    'Product': ['Apple', 'Banana'],
    'Quantity': [10, 15]
})
sales_feb = pd.DataFrame({
    'Product': ['Apple', 'Orange'],
    'Quantity': [7, 12]
})

sales_list = [sales_jan, sales_feb]

all_sales = pd.concat(sales_list)

# Your code here

Hint

Use print(all_sales) to show the combined table.

Practice

1. What is the main reason to use pandas.concat() instead of DataFrame.append()?

easy

A. concat() is faster for single DataFrame addition only

B. Because append() is deprecated and concat() can join multiple DataFrames at once

C. append() works only for columns, not rows

D. concat() automatically sorts columns alphabetically

Solution

1. Step 1: Understand the deprecation of append()

   The append() method is deprecated in pandas and will be removed in future versions.

2. Step 2: Recognize concat() advantages

   concat() can join multiple DataFrames at once, not just one, making it more flexible.

3. Final Answer:

   Because append() is deprecated and concat() can join multiple DataFrames at once -> Option B

4. Quick Check:

   append() deprecated, concat() multi-join [OK]

Hint: Remember: concat replaces append and handles many DataFrames [OK]

Common Mistakes:

• Thinking append is still recommended
• Believing concat only works for columns
• Assuming append is faster than concat

2. Which of the following is the correct syntax to replace df1.append(df2, ignore_index=True) using concat?

easy

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

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

C. pd.concat(df1, df2, ignore_index=True)

D. pd.concat([df1, df2], axis=1, ignore_index=True)

Solution

1. Step 1: Identify the correct function call

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

2. Step 2: Use ignore_index=True to reset row numbers

   To mimic append(ignore_index=True), pass ignore_index=True to concat(). The default axis is 0 (rows), so no need to specify.

3. Final Answer:

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

4. Quick Check:

   List of DataFrames + ignore_index=True = correct concat syntax [OK]

Hint: Use list of DataFrames and ignore_index=True with concat [OK]

Common Mistakes:

• Passing DataFrames without list brackets
• Using axis=1 to add rows (wrong axis)
• Omitting ignore_index when needed

3. Given the code:

import pandas as pd

df1 = pd.DataFrame({'A': [1, 2]})
df2 = pd.DataFrame({'A': [3, 4]})
result = pd.concat([df1, df2], ignore_index=True)
print(result)

What will be the output?

medium

A. A
0 1
1 2
0 3
1 4

B. A
0 3
1 4

C. A
0 1
1 2
2 3
3 4

D. Error because ignore_index is not valid

Solution

1. Step 1: Understand concat with ignore_index=True

   concat joins rows from both DataFrames and resets the index to 0,1,2,...

2. Step 2: Combine rows and reset index

   Rows from df1 (1,2) and df2 (3,4) stack vertically with new index 0 to 3.

3. Final Answer:

   A 0 1 1 2 2 3 3 4 -> Option C

4. Quick Check:

   concat stacks rows and resets index with ignore_index=True [OK]

Hint: concat stacks rows; ignore_index resets numbering [OK]

Common Mistakes:

• Expecting original indexes to remain
• Confusing axis parameter
• Thinking concat drops rows

4. What is wrong with this code snippet?

import pandas as pd

df1 = pd.DataFrame({'A': [1, 2]})
df2 = pd.DataFrame({'A': [3, 4]})
result = pd.concat(df1, df2, ignore_index=True)
print(result)

medium

A. The DataFrames should be passed as a list inside concat

B. ignore_index is not a valid argument for concat

C. concat cannot join DataFrames with same columns

D. The code will run without errors and print combined DataFrame

Solution

1. Step 1: Check 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

   Correct usage is pd.concat([df1, df2], ignore_index=True) with square brackets around DataFrames.

3. Final Answer:

   The DataFrames should be passed as a list inside 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
• Forgetting brackets around DataFrames
• Misusing ignore_index parameter

5. You have three DataFrames:

df1 = pd.DataFrame({'X': [1, 2]})
df2 = pd.DataFrame({'X': [3], 'Y': [5]})
df3 = pd.DataFrame({'X': [4], 'Y': [6]})

You want to combine them vertically using concat so that missing columns fill with NaN and the index resets. Which code achieves this?

hard

A. pd.concat(df1, df2, df3, ignore_index=True)

B. pd.concat([df1, df2, df3], axis=1, ignore_index=True)

C. pd.concat([df1, df2, df3], join='inner', ignore_index=True)

D. pd.concat([df1, df2, df3], ignore_index=True)

Solution

1. Step 1: Combine DataFrames vertically with concat

   Use pd.concat() with a list of DataFrames to stack rows.

2. Step 2: Handle missing columns and reset index

   Default join='outer' fills missing columns with NaN. Use ignore_index=True to reset row numbers.

3. Final Answer:

   pd.concat([df1, df2, df3], ignore_index=True) -> Option D

4. Quick Check:

   concat list, default outer join, ignore_index=True [OK]

Hint: Use concat list with ignore_index=True for clean vertical join [OK]

Common Mistakes:

• Using axis=1 to join rows (actually joins columns)
• Using join='inner' which drops columns
• Passing DataFrames without list brackets