Pandas ADSC Practice

Given the following DataFrame, how would you sort it by City in ascending order and then by Age in descending order?

• df.sort_values(by='City').sort_values(by='Age', ascending=False)

• df.sort_values(by=['City', 'Age'], ascending=[True, False])

• df.sort(['City', 'Age'], ascending=[True, False])

• df.sort_values(by=['City', 'Age'], ascending=[False, True])

How would you filter the DataFrame to select rows where Age is greater than 25 or City is 'Chicago'?

• df[(df['Age'] > 25) & (df['City'] == 'Chicago')]

• df[(df['Age'] > 25) | (df['City'] == 'Chicago')]

• df.query('Age > 25 and City == "Chicago"')

• df[df['Age'] > 25 and df['City'] == 'Chicago']

Given the following DataFrame, how would you increase each employee's Salary by 10%?

• df['Salary'] *= 1.10

• df['Salary'] = df['Salary'] + 10000

• df['Salary'] = df['Salary'].apply(lambda x: x * 1.10)

• df['Salary'].update(df['Salary'] * 1.10)

How do you drop the Age column from the DataFrame permanently?

• df = df.drop('Age', axis=1)

• df.drop(columns='Age', inplace=True)

• df.remove('Age')

• del df['Age']

Given the following DataFrame, how would you fill missing values in the Age column with the mean age?

• df['Age'].fillna(df['Age'].mean(), inplace=True)

• df.fillna(df.mean(), inplace=True)

• df['Age'] = df['Age'].replace(np.nan, df['Age'].mean())

• df['Age'] = df['Age'].interpolate()

How would you select only the Name and Salary columns from the DataFrame?

• df[['Name', 'Salary']]

• df.loc[:, ['Name', 'Salary']]

• df.iloc[:, [0, 2]]

• All of the above

How would you rename the column Salary to Income?

• df.rename(columns={'Salary': 'Income'}, inplace=True)

• df.columns = ['Name', 'Income']

• df.set_axis(['Name', 'Income'], axis=1, inplace=True)

• df.rename_axis(columns='Income')

How would you add a new column Tax which is 20% of Salary?

• df['Tax'] = df['Salary'] * 0.2

• df.insert(2, 'Tax', df['Salary'] * 0.2)

• df = df.assign(Tax=df['Salary'] * 0.2)

• All of the above

Given the following DataFrame, how would you find the average Salary for each Department?

• df.groupby('Department')['Salary'].mean()

• df.groupby('Department').mean()['Salary']

• df.pivot_table(values='Salary', index='Department', aggfunc='mean')

• df.groupby('Department').sum()

Given two DataFrames df1 and df2, how would you perform an inner join on the Name column?

• pd.merge(df1, df2, on='Name', how='inner')

• df1.merge(df2, on='Name')

• df1.join(df2, on='Name', how='inner')

• Both A and B