Creating new columns in Pandas - Quick Revision & Summary

Recall & Review

beginner

How do you add a new column to a pandas DataFrame with a fixed value?

You can add a new column by assigning a value to a new column name like this:
df['new_column'] = 10
This sets the value 10 for all rows in the new column.

beginner

What is the syntax to create a new column based on existing columns in pandas?

You can create a new column by performing operations on existing columns:
df['new_col'] = df['col1'] + df['col2']
This adds values from 'col1' and 'col2' row-wise.

intermediate

How can you create a new column using a function applied to each row in pandas?

Use the apply() method with axis=1 to apply a function row-wise:
df['new_col'] = df.apply(lambda row: row['col1'] * 2, axis=1)

beginner

What happens if you assign a list of values to a new column in pandas?

If the list length matches the number of rows, pandas assigns each list item to the corresponding row in the new column.
Example:
df['new_col'] = [1, 2, 3] for a DataFrame with 3 rows.

intermediate

How do you create a new column conditionally in pandas?

Use np.where() or boolean indexing:
df['new_col'] = np.where(df['col'] > 5, 'High', 'Low')
This sets 'High' if value > 5, else 'Low'.

Which of the following adds a new column 'age_plus_10' that adds 10 to the 'age' column?

A df['age_plus_10'] = df['age'] - 10

B df['age_plus_10'] = 10

C df['age_plus_10'] = df['age'] * 10

D df['age_plus_10'] = df['age'] + 10

Adding 10 to the 'age' column creates a new column with each age increased by 10.

What does this code do?
df['new_col'] = df.apply(lambda row: row['a'] * 2, axis=1)

A Creates a new column doubling values in column 'a' for each row

B Creates a new column with the sum of all columns

C Deletes column 'a'

D Filters rows where 'a' is doubled

The apply function with axis=1 applies the lambda to each row, doubling the 'a' value.

If you assign a list shorter than the DataFrame rows to a new column, what happens?

A Pandas raises an error

B Pandas fills missing rows with NaN

C Pandas repeats the list values

D Pandas ignores the assignment

The list length must match the number of rows; otherwise, pandas raises a ValueError.

How to create a new column 'status' that is 'Adult' if 'age' >= 18, else 'Minor'?

A df['status'] = df['age'] >= 18

B df['status'] = np.where(df['age'] >= 18, 'Adult', 'Minor')

C df['status'] = 'Adult' if df['age'] >= 18 else 'Minor'

D df['status'] = df['age'] + 18

np.where applies the condition element-wise to create the new column.

Which method is best to create a new column based on multiple existing columns with complex logic?

A Using df['new_col'] = df['col1'] + df['col2']

B Assigning a fixed value

C Using df.apply() with a custom function

D Using df.drop()

df.apply() allows applying complex logic row-wise using a custom function.

Explain how to add a new column to a pandas DataFrame with values based on existing columns.

Think about using df['new_col'] = df['col1'] + df['col2'] or apply with lambda.

Describe how to create a new column conditionally in pandas.

Use np.where(condition, value_if_true, value_if_false).

Practice

1. What is the correct way to add a new column named age + 10 to a DataFrame df that adds 10 to the existing age column?

easy

A. df['age + 10'] == df['age'] + 10

B. df['age + 10'] = df['age'] + 10

C. df.add_column('age + 10', df['age'] + 10)

D. df.age + 10 = df.age + 10

Solution

1. Step 1: Understand how to create a new column in pandas

   In pandas, new columns are created by assigning a value to df['new_column_name'].

2. Step 2: Apply the addition operation to the existing column

   Adding 10 to the age column is done by df['age'] + 10. Assigning this to df['age + 10'] creates the new column.

3. Final Answer:

   df['age + 10'] = df['age'] + 10 -> Option B

4. Quick Check:

   New column assignment = df['age + 10'] = df['age'] + 10 [OK]

Hint: Use df['new_col'] = expression to add columns [OK]

Common Mistakes:

• Using double equals (==) instead of single equals (=) for assignment
• Trying to use a method like add_column which doesn't exist
• Using dot notation for new columns with spaces or special characters

2. Which of the following is the correct syntax to create a new column full_name by combining first_name and last_name columns with a space in between in DataFrame df?

easy

A. df['full_name'] = df['first_name'] + ' ' + df['last_name']

B. df['full_name'] = df['first_name'].append(' ').append(df['last_name'])

C. df['full_name'] = df['first_name'].join(' ', df['last_name'])

D. df['full_name'] = df['first_name'] & ' ' & df['last_name']

Solution

1. Step 1: Combine string columns with + operator

   In pandas, string columns can be combined using the + operator. To add a space, include ' ' between them.

2. Step 2: Assign the combined string to a new column

   Assign the result to df['full_name'] to create the new column.

3. Final Answer:

   df['full_name'] = df['first_name'] + ' ' + df['last_name'] -> Option A

4. Quick Check:

   String concat with + = df['full_name'] = df['first_name'] + ' ' + df['last_name'] [OK]

Hint: Use + to join string columns with spaces [OK]

Common Mistakes:

• Using append or join methods which don't work on pandas Series
• Using & operator which is for bitwise operations
• Forgetting to add space between names

3. Given the DataFrame df below:

   price  quantity
0    100         2
1    150         3
2    200         1

What will be the output of this code?

df['total'] = df['price'] * df['quantity']
df['total']

medium

A. [200, 450, 200]

B. [100, 150, 200]

C. [2, 3, 1]

D. [300, 450, 600]

Solution

1. Step 1: Multiply price and quantity element-wise

   Multiply each row's price by quantity: 100*2=200, 150*3=450, 200*1=200.

2. Step 2: Assign result to new column and output it

   The new column 'total' will have values [200, 450, 200]. Printing df['total'] shows this list.

3. Final Answer:

   [200, 450, 200] -> Option A

4. Quick Check:

   Element-wise multiply = [200, 450, 200] [OK]

Hint: Multiply columns element-wise for new column [OK]

Common Mistakes:

• Adding instead of multiplying columns
• Confusing row-wise with column-wise operations
• Expecting a scalar instead of a Series output

4. What is wrong with this code that tries to create a new column discounted_price by subtracting 10 from price in DataFrame df?

df.discounted_price = df['price'] - 10

medium

A. It will fail if the column name has spaces or special characters.

B. It will cause a syntax error because of dot notation.

C. It will create a new variable, not a DataFrame column.

D. It will create the column correctly.

Solution

1. Step 1: Understand dot notation for DataFrame columns

   Dot notation df.col = value works to create new columns only if 'col' is a valid Python identifier (no spaces, no special characters).

2. Step 2: Check the column name 'discounted_price'

   'discounted_price' consists of letters and underscores, which is valid. Therefore, the code creates the column correctly.

3. Final Answer:

   It will create the column correctly. -> Option D

4. Quick Check:

   Dot notation works for valid identifiers [OK]

Hint: Use df['col'] if column name has spaces or special chars [OK]

Common Mistakes:

• Thinking dot notation fails for valid column names
• Confusing DataFrame column assignment with local variable creation
• Believing underscores invalidate column names for dot notation

5. You have a DataFrame df with columns score and max_score. You want to create a new column percentage that shows the score as a percentage of max_score, but if max_score is zero, set percentage to None. Which code correctly does this?

hard

A. df['percentage'] = df['score'] / df['max_score'].replace(0, None) * 100

B. df.loc[df['max_score'] == 0, 'percentage'] = None
df['percentage'] = df['score'] / df['max_score'] * 100

C. df['percentage'] = df.apply(lambda row: row['score'] / row['max_score'] * 100 if row['max_score'] != 0 else None, axis=1)

D. df['percentage'] = df['score'] / df['max_score'] * 100
df['percentage'][df['max_score'] == 0] = None

Solution

1. Step 1: Use row-wise function to handle zero max_score

   Using df.apply with a lambda lets us check each row and apply the condition: if max_score is zero, return None; else calculate percentage.

2. Step 2: Assign the result to new column 'percentage'

   The lambda returns the correct value per row, and axis=1 ensures row-wise operation.

3. Final Answer:

   df['percentage'] = df.apply(lambda row: row['score'] / row['max_score'] * 100 if row['max_score'] != 0 else None, axis=1) -> Option C

4. Quick Check:

   Use apply with condition for safe division [OK]

Hint: Use apply with lambda for row-wise conditional columns [OK]

Common Mistakes:

• Replacing zero with None in Series causes errors
• Using chained indexing which raises warnings
• Not handling division by zero properly