Adding and removing columns in Data Analysis Python - Step-by-Step Execution

Concept Flow - Adding and removing columns

Start with DataFrame

↓

Add Column

↓

Check DataFrame

↓

Remove Column

↓

Check DataFrame

↓

End

Start with a DataFrame, add a new column, check the result, then remove a column and check again.

Execution Sample

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df['C'] = df['A'] + df['B']
df = df.drop('B', axis=1)
print(df)

Create a DataFrame, add a new column 'C' as sum of 'A' and 'B', then remove column 'B' and print the result.

Execution Table

Step	Action	DataFrame Columns	DataFrame Content	Output
1	Create DataFrame	['A', 'B']	[[1, 3], [2, 4]]	Initial DataFrame with columns A and B
2	Add column 'C' as A+B	['A', 'B', 'C']	[[1, 3, 4], [2, 4, 6]]	Column C added with sums
3	Remove column 'B'	['A', 'C']	[[1, 4], [2, 6]]	Column B removed
4	Print DataFrame	['A', 'C']	[[1, 4], [2, 6]]	Printed final DataFrame

💡 All steps completed; DataFrame now has columns A and C only.

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
df.columns.tolist()	N/A	['A', 'B']	['A', 'B', 'C']	['A', 'C']	['A', 'C']
df.values.tolist()	N/A	[[1, 3], [2, 4]]	[[1, 3, 4], [2, 4, 6]]	[[1, 4], [2, 6]]	[[1, 4], [2, 6]]

Key Moments - 2 Insights

Why does the new column 'C' appear after step 2?

Because we assigned df['C'] = df['A'] + df['B'], which creates a new column 'C' with the sum of columns 'A' and 'B' as shown in execution_table row 2.

Why does the DataFrame no longer have column 'B' after step 3?

Because we used df.drop('B', axis=1) which removes column 'B' from the DataFrame, as shown in execution_table row 3.

Visual Quiz - 3 Questions

Look at the execution table, what columns does the DataFrame have after step 2?

A ['A', 'B', 'C']

B ['A', 'C']

C ['A', 'B']

D ['B', 'C']

💡 Hint

Check the 'DataFrame Columns' column in execution_table row 2.

At which step is column 'B' removed from the DataFrame?

A Step 1

B Step 2

C Step 3

D Step 4

💡 Hint

Look at the 'Action' and 'DataFrame Columns' in execution_table rows.

If we did not specify axis=1 in df.drop('B', axis=1), what would happen?

A Column 'B' would still be removed

B An error would occur

C Row 'B' would be removed instead

D Nothing would change

💡 Hint

Refer to pandas drop method requirements for axis parameter.

Concept Snapshot

Adding and removing columns in pandas:
- Add column: df['new_col'] = values
- Remove column: df = df.drop('col_name', axis=1)
- axis=1 means column, axis=0 means row
- Changes update DataFrame structure
- Use print(df) to see changes

Full Transcript

We start with a DataFrame with columns A and B. Then we add a new column C by summing columns A and B. This adds column C to the DataFrame. Next, we remove column B using the drop method with axis=1 to specify column removal. Finally, we print the DataFrame which now has columns A and C only. This shows how to add and remove columns step-by-step.

Practice

1. What is the correct way to add a new column named 'age' with all values set to 30 in a pandas DataFrame df?

easy

A. df.append('age', 30)

B. df.add_column('age', 30)

C. df['age'] = 30

D. df.insert_column('age', 30)

Solution

1. Step 1: Understand how to add columns in pandas

   In pandas, you add a new column by assigning a value or list to df['column_name'].

2. Step 2: Apply the correct syntax to add a column named 'age'

   Using df['age'] = 30 adds a new column 'age' with all values set to 30.

3. Final Answer:

   df['age'] = 30 -> Option C

4. Quick Check:

   Adding column = df['new_col'] = value [OK]

Hint: Use df['col_name'] = value to add columns fast [OK]

Common Mistakes:

• Using non-existent methods like add_column or insert_column
• Trying to use append which is for rows, not columns
• Forgetting to use square brackets for column name

2. Which of the following is the correct syntax to remove a column named 'salary' from a pandas DataFrame df?

easy

A. df.drop('salary', axis=1)

B. df.remove('salary')

C. df.delete_column('salary')

D. df.drop_column('salary')

Solution

1. Step 1: Recall pandas method to remove columns

   The pandas method to remove columns is drop() with axis=1 to specify columns.

2. Step 2: Use correct syntax to drop 'salary' column

   Using df.drop('salary', axis=1) removes the 'salary' column from the DataFrame.

3. Final Answer:

   df.drop('salary', axis=1) -> Option A

4. Quick Check:

   Remove column = df.drop('col', axis=1) [OK]

Hint: Use df.drop('col', axis=1) to remove columns [OK]

Common Mistakes:

• Using methods like remove or delete_column which don't exist
• Forgetting axis=1 to specify column removal
• Using drop without parentheses or wrong parameters

3. Given the DataFrame df below:

   name  score
0  Amy     90
1  Bob     80
2  Cara    85

What will be the output of this code?

df['passed'] = df['score'] >= 85
print(df)

medium

A. name score passed
0 Amy 90 False
1 Bob 80 True
2 Cara 85 False

B. name score passed
0 Amy 90 True
1 Bob 80 False
2 Cara 85 True

C. name score
0 Amy 90
1 Bob 80
2 Cara 85

D. name score passed
0 Amy 90 90
1 Bob 80 80
2 Cara 85 85

Solution

1. Step 1: Understand the condition applied to 'score'

   The expression df['score'] >= 85 creates a boolean series: True where score is 85 or more, False otherwise.

2. Step 2: Assign boolean series to new column 'passed'

   Adding df['passed'] = ... adds this boolean series as a new column.

3. Final Answer:

   DataFrame with 'passed' column showing True for scores >= 85 -> Option B

4. Quick Check:

   Boolean condition adds True/False column [OK]

Hint: Compare column with condition to add True/False column [OK]

Common Mistakes:

• Expecting numeric values instead of booleans in new column
• Not using >= operator correctly
• Printing df before adding the new column

4. What is wrong with this code snippet to remove the column 'height' from DataFrame df?

df.drop('height')

medium

A. Missing axis=1 to specify column removal

B. The column name should be in a list like ['height']

C. The method drop does not exist in pandas

D. You must assign the result back to df like df = df.drop('height')

Solution

1. Step 1: Check the drop method parameters

   By default, drop() removes rows. To remove columns, you must specify axis=1.

2. Step 2: Identify the missing axis parameter

   The code misses axis=1, so it tries to remove a row named 'height', causing an error if no such row exists.

3. Final Answer:

   Missing axis=1 to specify column removal -> Option A

4. Quick Check:

   drop columns = drop('col', axis=1) [OK]

Hint: Always add axis=1 to drop columns, not rows [OK]

Common Mistakes:

• Forgetting axis=1 causes wrong row removal or error
• Thinking drop modifies df inplace without assignment
• Using wrong data type for column name

5. You have a DataFrame df with columns ['A', 'B', 'C']. You want to create a new column 'D' which is the sum of 'A' and 'B', then remove column 'C'. Which code correctly does this in one step?

hard

A. df['D'] = df['A'] + df['B']
df.remove('C')

B. df['D'] = df['A'] + df['B']
df.drop('C', axis=1, inplace=True)

C. df['D'] = df['A'] + df['B']
df = df.drop('C', axis=1)

D. df = df.assign(D=df['A'] + df['B']).drop('C', axis=1)

Solution

1. Step 1: Create new column 'D' as sum of 'A' and 'B'

   Using assign() allows adding a new column in a chainable way without modifying df inplace.

2. Step 2: Remove column 'C' using drop() chained after assign()

   Chaining drop('C', axis=1) removes column 'C' and returns the modified DataFrame.

3. Step 3: Assign the result back to df

   Assigning the chained operations back to df updates the DataFrame correctly in one step.

4. Final Answer:

   df = df.assign(D=df['A'] + df['B']).drop('C', axis=1) -> Option D

5. Quick Check:

   Use assign + drop chained and assign back [OK]

Hint: Chain assign and drop, then assign back to df [OK]

Common Mistakes:

• Using drop without axis=1 or inplace causing no change
• Using non-existent remove method
• Not assigning chained result back to df