Why combining DataFrames matters in Pandas - See It in Action

Why combining DataFrames matters

📖 Scenario: Imagine you work in a small bookstore. You have two lists of books: one list shows books currently in stock, and another list shows books that were sold last month. You want to combine these lists to understand your inventory better.

🎯 Goal: You will create two small tables (DataFrames) for books in stock and books sold, then combine them to see all books together. This helps you learn why combining data is important in real life.

📋 What You'll Learn

Create two DataFrames named stock and sold with exact book data

Create a variable combined_books that combines stock and sold vertically

Print the combined_books DataFrame to see the result

💡 Why This Matters

🌍 Real World

Combining data from different sources is common in business to get a full picture, like merging sales and inventory data.

💼 Career

Data scientists often combine multiple datasets to prepare data for analysis and decision-making.

1

Create the initial DataFrames

Create a DataFrame called stock with these exact entries: columns 'Book' and 'Quantity', and rows 'Python Basics', 10 and 'Data Science 101', 5. Also create a DataFrame called sold with columns 'Book' and 'Quantity', and rows 'Python Basics', 3 and 'Machine Learning', 7. Use pandas and the exact variable names.

Pandas

import pandas as pd
# Create the stock DataFrame and the sold DataFrame with the exact data
# Your code here

Hint

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

2

Set up the combination method

Create a variable called combine_method and set it to the string 'vertical'. This will help us remember how we want to combine the DataFrames.

Pandas

import pandas as pd

stock = pd.DataFrame({
    'Book': ['Python Basics', 'Data Science 101'],
    'Quantity': [10, 5]
})

sold = pd.DataFrame({
    'Book': ['Python Basics', 'Machine Learning'],
    'Quantity': [3, 7]
})

# Create the combine_method variable and set it to 'vertical'
# Your code here

Hint

Just create a variable with the exact name and value.

3

Combine the DataFrames vertically

Use pd.concat to combine the stock and sold DataFrames vertically (one below the other). Store the result in a variable called combined_books. Use the exact variable names.

Pandas

import pandas as pd

stock = pd.DataFrame({
    'Book': ['Python Basics', 'Data Science 101'],
    'Quantity': [10, 5]
})

sold = pd.DataFrame({
    'Book': ['Python Basics', 'Machine Learning'],
    'Quantity': [3, 7]
})

combine_method = 'vertical'

# Combine stock and sold DataFrames vertically using pd.concat
# Your code here

Hint

Use pd.concat with a list of DataFrames and ignore_index=True to reset the row numbers.

4

Print the combined DataFrame

Print the combined_books DataFrame to see all books from stock and sold combined.

Pandas

import pandas as pd

stock = pd.DataFrame({
    'Book': ['Python Basics', 'Data Science 101'],
    'Quantity': [10, 5]
})

sold = pd.DataFrame({
    'Book': ['Python Basics', 'Machine Learning'],
    'Quantity': [3, 7]
})

combine_method = 'vertical'

combined_books = pd.concat([stock, sold], ignore_index=True)

# Print the combined_books DataFrame
# Your code here

Hint

Use print(combined_books) to show the combined table.

Practice

1. Why do we combine DataFrames in pandas?
pd.concat and pd.merge are common methods. What is the main reason to combine DataFrames?

easy

A. To get a complete view by joining or stacking data

B. To delete duplicate rows automatically

C. To convert DataFrames into lists

D. To change the data types of columns

Solution

1. Step 1: Understand the purpose of combining DataFrames

   Combining DataFrames helps to bring together data from different sources or parts to analyze them as one.

2. Step 2: Identify the main reason

   Using pd.concat stacks data vertically or horizontally, and pd.merge joins data based on matching columns, both to get a fuller dataset.

3. Final Answer:

   To get a complete view by joining or stacking data -> Option A

4. Quick Check:

   Combining DataFrames = Complete view [OK]

Hint: Combining means joining or stacking data for full info [OK]

Common Mistakes:

• Thinking combining deletes duplicates automatically
• Confusing combining with data type changes
• Believing combining converts DataFrames to lists

2. Which of the following is the correct syntax to stack two DataFrames df1 and df2 vertically using pandas?

easy

A. pd.merge(df1, df2, axis=0)

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

C. df1.append(df2, axis=1)

D. pd.concat(df1, df2, axis=1)

Solution

1. Step 1: Recall the function to stack DataFrames vertically

   pd.concat stacks DataFrames; axis=0 means stacking rows vertically.

2. Step 2: Check the syntax

   The correct syntax is pd.concat([df1, df2], axis=0). Other options misuse pd.merge or wrong axis or argument types.

3. Final Answer:

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

4. Quick Check:

   Stack vertically = concat with axis=0 [OK]

Hint: Use pd.concat with a list and axis=0 to stack rows [OK]

Common Mistakes:

• Using pd.merge instead of pd.concat for stacking
• Passing DataFrames without a list to pd.concat
• Using wrong axis value for vertical stacking

3. What will be the output of this code?

import pandas as pd

df1 = pd.DataFrame({'key': ['A', 'B'], 'val1': [1, 2]})
df2 = pd.DataFrame({'key': ['B', 'C'], 'val2': [3, 4]})
result = pd.merge(df1, df2, on='key', how='inner')
print(result)

medium

A. key val1
0 A 1
1 B 2

B. key val1 val2
0 A 1 3
1 B 2 4

C. key val1 val2
0 A 1 NaN
1 B 2 3
2 C NaN 4

D. key val1 val2
0 B 2 3

Solution

1. Step 1: Understand pd.merge with how='inner'

   Inner join keeps only rows with keys present in both DataFrames. Here keys are 'A', 'B' in df1 and 'B', 'C' in df2.

2. Step 2: Find common keys and merge rows

   Only 'B' is common. So result has one row with key 'B', val1=2 from df1, val2=3 from df2.

3. Final Answer:

   key val1 val2 0 B 2 3 -> Option D

4. Quick Check:

   Inner join = common keys only [OK]

Hint: Inner merge keeps only matching keys in both DataFrames [OK]

Common Mistakes:

• Expecting all keys from both DataFrames (confusing with outer join)
• Mixing up val2 values for keys
• Ignoring how='inner' effect

4. The following code throws an error. What is the main issue?

import pandas as pd

df1 = pd.DataFrame({'A': [1, 2]})
df2 = pd.DataFrame({'B': [3, 4]})
result = pd.merge(df1, df2, on='A')
print(result)

medium

A. Column 'A' does not exist in both DataFrames

B. pd.merge cannot merge DataFrames with different columns

C. pd.merge requires axis parameter

D. DataFrames must have same number of rows to merge

Solution

1. Step 1: Check columns in both DataFrames

   df1 has column 'A', but df2 has only column 'B'. So 'A' is missing in df2.

2. Step 2: Understand pd.merge on parameter

   pd.merge requires the 'on' column to be present in both DataFrames to join on it. Missing column causes error.

3. Final Answer:

   Column 'A' does not exist in both DataFrames -> Option A

4. Quick Check:

   Merge 'on' column must be in both DataFrames [OK]

Hint: Merge 'on' column must exist in both DataFrames [OK]

Common Mistakes:

• Thinking axis parameter is needed for merge
• Believing DataFrames must have same row count
• Assuming merge works with different columns without 'on'

5. You have two DataFrames:

df1 = pd.DataFrame({'id': [1, 2, 3], 'score': [90, 80, 70]})
df2 = pd.DataFrame({'id': [2, 3, 4], 'grade': ['B', 'C', 'A']})

You want a DataFrame that includes all ids from both, with scores and grades matched where possible, and NaN where missing. Which code achieves this?

hard

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

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

C. pd.merge(df1, df2, on='id', how='outer')

D. pd.merge(df1, df2, on='id', how='inner')

Solution

1. Step 1: Understand the goal

   We want all ids from both DataFrames, matching scores and grades where ids overlap, and NaN where data is missing.

2. Step 2: Choose the correct merge type

   Using how='outer' in pd.merge keeps all keys from both DataFrames, filling missing values with NaN.

3. Step 3: Check other options

   pd.concat stacks data but does not join on keys; how='inner' drops ids not in both.

4. Final Answer:

   pd.merge(df1, df2, on='id', how='outer') -> Option C

5. Quick Check:

   Outer merge = all keys, fill missing with NaN [OK]

Hint: Use outer merge to keep all keys and fill missing data [OK]

Common Mistakes:

• Using inner merge and losing unmatched ids
• Using concat which stacks instead of joining
• Confusing axis parameter with merge how