Working with DataFrames

Question 1

How do you apply a function to a specific column in a DataFrame?

Answer 1

Use df[‘column’].apply(func).

df[‘A_squared’] = df[‘A’].apply(lambda x: x**2)
print(df)

Question 2

How do you apply a function across multiple columns?

Answer 2

Use .apply() with axis=1.

df[‘Sum’] = df.apply(lambda row: row[‘A’] + row[‘B’], axis=1)
print(df)

Question 3

How do you use .map() to transform a column using a dictionary?

Answer 3

Use df[‘column’].map(mapping_dict).

mapping = {1: ‘One’, 2: ‘Two’}
df[‘Mapped_A’] = df[‘A’].map(mapping)
print(df)

Question 4

How do you replace values in a column using .replace()?

Answer 4

Use df[‘column’].replace({old_value: new_value}).

df[‘A’] = df[‘A’].replace({1: 100, 2: 200})
print(df)

Question 5

How do you use .transform() to apply a function to a column while maintaining its shape?

Answer 5

Use df[‘column’].transform(func).

df[‘Normalized_A’] = df[‘A’].transform(lambda x: (x - x.mean()) / x.std())
print(df)

Question 6

How do you calculate row-wise means for selected columns?

Answer 6

Use .mean(axis=1).

df[‘Row_Mean’] = df[[‘A’, ‘B’]].mean(axis=1)
print(df)

Question 7

How do you filter rows based on conditions across multiple columns?

Answer 7

Use boolean indexing with conditions.

filtered_df = df[(df[‘A’] > 1) & (df[‘B’] < 5)]
print(filtered_df)

Question 8

How do you compute the rank of values in a column?

Answer 8

Use df[‘column’].rank().

df[‘Rank’] = df[‘A’].rank(ascending=False)
print(df)

Question 9

How do you rename index labels in a DataFrame?

Answer 9

Use df.rename(index={old_label: new_label}).

df.rename(index={0: ‘Row_0’, 1: ‘Row_1’}, inplace=True)
print(df)

Question 10

How do you reset the index of a DataFrame?

Answer 10

Use df.reset_index().

df.reset_index(drop=True, inplace=True)
print(df)

Question 11

How do you set a column as the index of a DataFrame?

Answer 11

Use df.set_index(‘column_name’).

df.set_index(‘A’, inplace=True)
print(df)

Question 12

How do you filter rows where a column’s value is in a list?

Answer 12

Use .isin().

filtered_df = df[df[‘A’].isin([1, 2])]
print(filtered_df)

Question 13

How do you drop rows based on a condition?

Answer 13

Use df[~condition] or df.drop().

df = df[~(df[‘A’] > 2)]
print(df)

Question 14

How do you find the maximum value in a column and its corresponding row?

Answer 14

Use df[‘column’].max() and .idxmax().

max_val = df[‘A’].max()
max_row = df.loc[df[‘A’].idxmax()]
print(max_val, max_row)

Question 15

How do you append a new row to a DataFrame?

Answer 15

Use df.append(row, ignore_index=True).

new_row = {‘A’: 10, ‘B’: 20}
df = df.append(new_row, ignore_index=True)
print(df)