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: <br>df['new_column'] = 10<br>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:<br>df['new_col'] = df['col1'] + df['col2']<br>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:<br>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.<br>Example:<br>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:<br>df['new_col'] = np.where(df['col'] > 5, 'High', 'Low')<br>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? <br>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).