Given a DataFrame df with a 'Salary' column, write code to increase salary by 5% and store it in a new column 'UpdatedSalary'. **Hint:** Multiply the Salary column by 1.05 to increase by 5%. **Choose the correct code:** - (a) `df['UpdatedSalary'] = df['Salary'] * 5` - (b) `df['UpdatedSalary'] = df['Salary'] * 1.05` - (c) `df['UpdatedSalary'] = df['Salary'] + 0.05` - (d) `df['UpdatedSalary'] = df['Salary'].apply(lambda x: x * 5)`

Given a DataFrame with columns: Name, Score1, Score2 Write Python code to: 1. Select only rows where Score1 > 40 AND Score2 > 50 2. Create a new column AverageScore = mean of Score1 and Score2 3. return dataframe with only the `[['Name', 'AverageScore']]` **Hint:** Filter first using boolean indexing, then add the new column, then select specific columns.

You have a DataFrame with an 'Age' column containing missing values and outliers (Age > 100). Write Python code to: 1. Replace missing values with the median age 2. Remove rows where Age > 100 3. Return the cleaned DataFrame **Hint:** Use `.fillna()` with median, then filter with boolean indexing.

**Return:** A DataFrame where the specified column has been converted to datetime **Choose the correct code:** - (a) `df_copy[col_name] = df_copy[col_name].astype(datetime)` - (b) `df_copy[col_name] = pd.to_datetime(df_copy[col_name])` - (c) `df_copy[col_name].convert_to_datetime()` - (d) `df_copy[col_name] = datetime.strptime(df_copy[col_name], '%Y-%m-%d')`

**Return:** A pandas Series with column names as index and count of NaN as values **Hint:** Use `.isnull().sum()` to count missing values in each column.

**Return:** A pandas DataFrame with descriptive statistics (using .describe())

You have a DataFrame with duplicate rows. The command `drop_duplicates` on subset of columns named `['Name', 'Team']` is to be used.

**Return:** A DataFrame with all rows containing NaN removed **Hint:** Use `.dropna()` to remove rows with missing values, then `.reset_index(drop=True)` to renumber rows.

Write a Python command to fill all missing values in the column 'College' with the text 'Unknown'.

**Return:** A DataFrame where NaN values in numeric columns are replaced by column mean **Hint:** Get numeric columns using `.select_dtypes()`, then use `.fillna()` with the column mean.

**Return:** A DataFrame containing only rows where column > threshold **Hint:** Use boolean indexing `df[df[col_name] > threshold]` and `.reset_index(drop=True)` to reset row indices. **Choose the correct code:** - (a) `return df.filter(column=col_name, value=threshold)` - (b) `return df.loc[df[col_name] > threshold]` - (c) `return df[df[col_name] > threshold].reset_index(drop=True)` - (d) `return df.query(f'{col_name} > {threshold}')`

Write Python code to select rows where 'Profit' is between 30 and 55 (inclusive). **Hint:** Use boolean indexing with AND operator `&`.

**Return:** A tuple of (number of rows, number of columns, list of column names) **Choose the correct code:** - (a) `return (df.size, df.ndim, df.columns)` - (b) `return (df.shape[0], df.shape[1], list(df.columns))` - (c) `return df.info()` - (d) `return (len(df), len(df.index), df.to_list())`

**Return:** DataFrame containing first n rows **Choose the correct code:** - (a) `return df.iloc[:n]` - (b) `return df.head(n)` - (c) `return df.nlargest(n, axis=0)` - (d) `return df[:n:1]`

Write a Python command to show summary statistics (mean, median, std, min, max, etc.) for the entire DataFrame.

**Return:** A DataFrame with grouped results (group column and mean) **Hint:** Use `.groupby(group_col)[agg_col].mean()` and `.reset_index()` to convert to DataFrame.

You have a DataFrame with columns: Name, Team, Salary, Profit Write Python code to: 1. Group the data by Team 2. aggregate average salary and total profit for each team 3. return the result **Hint:** Use `.groupby()` with `.agg()` for multiple aggregations. **Choose the correct code:** - (a) `df.groupby('Team').agg({'Salary': 'mean', 'Profit': 'sum'})` - (b) `df.groupby('Team')[['Salary', 'Profit']].agg(['mean', 'sum'])` - (c) `df.group('Team').apply(lambda x: {'avg_salary': x['Salary'].mean(), 'total_profit': x['Profit'].sum()})`

**Return:** A pandas DataFrame from the CSV string **Choose the correct line:** - (a) `return pd.read_excel(StringIO(csv_string))` - (b) `return pd.read_csv(StringIO(csv_string))` - (c) `return pd.DataFrame(csv_string.split('\n'))` - (d) `return csv_string.to_dataframe()`

**Return:** A merged DataFrame (inner join on the specified key) **Choose the correct code:** - (a) `return left.join(right, on=on)` - (b) `return pd.concat([left, right])` - (c) `return pd.merge(left, right, on=on, how='inner')` - (d) `return left.combine(right)`

**Return:** A pandas Series for the specified column

Given a DataFrame df with a 'Salary' column, write code to increase salary by 5% and store it in a new column 'UpdatedSalary'. **Hint:** Multiply the Salary column by 1.05 to increase by 5%. **Choose the correct code:** - (a) `df['UpdatedSalary'] = df['Salary'] * 5` - (b) `df['UpdatedSalary'] = df['Salary'] * 1.05` - (c) `df['UpdatedSalary'] = df['Salary'] + 0.05` - (d) `df['UpdatedSalary'] = df['Salary'].apply(lambda x: x * 5)`

Given a DataFrame with columns: Name, Score1, Score2 Write Python code to: 1. Select only rows where Score1 > 40 AND Score2 > 50 2. Create a new column AverageScore = mean of Score1 and Score2 3. return dataframe with only the `[['Name', 'AverageScore']]` **Hint:** Filter first using boolean indexing, then add the new column, then select specific columns.

You have a DataFrame with an 'Age' column containing missing values and outliers (Age > 100). Write Python code to: 1. Replace missing values with the median age 2. Remove rows where Age > 100 3. Return the cleaned DataFrame **Hint:** Use `.fillna()` with median, then filter with boolean indexing.

**Return:** A DataFrame where the specified column has been converted to datetime **Choose the correct code:** - (a) `df_copy[col_name] = df_copy[col_name].astype(datetime)` - (b) `df_copy[col_name] = pd.to_datetime(df_copy[col_name])` - (c) `df_copy[col_name].convert_to_datetime()` - (d) `df_copy[col_name] = datetime.strptime(df_copy[col_name], '%Y-%m-%d')`

**Return:** A pandas Series with column names as index and count of NaN as values **Hint:** Use `.isnull().sum()` to count missing values in each column.

**Return:** A pandas DataFrame with descriptive statistics (using .describe())

You have a DataFrame with duplicate rows. The command `drop_duplicates` on subset of columns named `['Name', 'Team']` is to be used.

**Return:** A DataFrame with all rows containing NaN removed **Hint:** Use `.dropna()` to remove rows with missing values, then `.reset_index(drop=True)` to renumber rows.

Write a Python command to fill all missing values in the column 'College' with the text 'Unknown'.

**Return:** A DataFrame where NaN values in numeric columns are replaced by column mean **Hint:** Get numeric columns using `.select_dtypes()`, then use `.fillna()` with the column mean.

**Return:** A DataFrame containing only rows where column > threshold **Hint:** Use boolean indexing `df[df[col_name] > threshold]` and `.reset_index(drop=True)` to reset row indices. **Choose the correct code:** - (a) `return df.filter(column=col_name, value=threshold)` - (b) `return df.loc[df[col_name] > threshold]` - (c) `return df[df[col_name] > threshold].reset_index(drop=True)` - (d) `return df.query(f'{col_name} > {threshold}')`

Write Python code to select rows where 'Profit' is between 30 and 55 (inclusive). **Hint:** Use boolean indexing with AND operator `&`.

**Return:** A tuple of (number of rows, number of columns, list of column names) **Choose the correct code:** - (a) `return (df.size, df.ndim, df.columns)` - (b) `return (df.shape[0], df.shape[1], list(df.columns))` - (c) `return df.info()` - (d) `return (len(df), len(df.index), df.to_list())`

**Return:** DataFrame containing first n rows **Choose the correct code:** - (a) `return df.iloc[:n]` - (b) `return df.head(n)` - (c) `return df.nlargest(n, axis=0)` - (d) `return df[:n:1]`

Write a Python command to show summary statistics (mean, median, std, min, max, etc.) for the entire DataFrame.

**Return:** A DataFrame with grouped results (group column and mean) **Hint:** Use `.groupby(group_col)[agg_col].mean()` and `.reset_index()` to convert to DataFrame.

You have a DataFrame with columns: Name, Team, Salary, Profit Write Python code to: 1. Group the data by Team 2. aggregate average salary and total profit for each team 3. return the result **Hint:** Use `.groupby()` with `.agg()` for multiple aggregations. **Choose the correct code:** - (a) `df.groupby('Team').agg({'Salary': 'mean', 'Profit': 'sum'})` - (b) `df.groupby('Team')[['Salary', 'Profit']].agg(['mean', 'sum'])` - (c) `df.group('Team').apply(lambda x: {'avg_salary': x['Salary'].mean(), 'total_profit': x['Profit'].sum()})`

**Return:** A pandas DataFrame from the CSV string **Choose the correct line:** - (a) `return pd.read_excel(StringIO(csv_string))` - (b) `return pd.read_csv(StringIO(csv_string))` - (c) `return pd.DataFrame(csv_string.split('\n'))` - (d) `return csv_string.to_dataframe()`

**Return:** A merged DataFrame (inner join on the specified key) **Choose the correct code:** - (a) `return left.join(right, on=on)` - (b) `return pd.concat([left, right])` - (c) `return pd.merge(left, right, on=on, how='inner')` - (d) `return left.combine(right)`

**Return:** A pandas Series for the specified column

Given a DataFrame df with a 'Salary' column, write code to increase salary by 5% and store it in a new column 'UpdatedSalary'. **Hint:** Multiply the Salary column by 1.05 to increase by 5%. **Choose the correct code:** - (a) `df['UpdatedSalary'] = df['Salary'] * 5` - (b) `df['UpdatedSalary'] = df['Salary'] * 1.05` - (c) `df['UpdatedSalary'] = df['Salary'] + 0.05` - (d) `df['UpdatedSalary'] = df['Salary'].apply(lambda x: x * 5)`

Given a DataFrame with columns: Name, Score1, Score2 Write Python code to: 1. Select only rows where Score1 > 40 AND Score2 > 50 2. Create a new column AverageScore = mean of Score1 and Score2 3. return dataframe with only the `[['Name', 'AverageScore']]` **Hint:** Filter first using boolean indexing, then add the new column, then select specific columns.

You have a DataFrame with an 'Age' column containing missing values and outliers (Age > 100). Write Python code to: 1. Replace missing values with the median age 2. Remove rows where Age > 100 3. Return the cleaned DataFrame **Hint:** Use `.fillna()` with median, then filter with boolean indexing.

**Return:** A DataFrame where the specified column has been converted to datetime **Choose the correct code:** - (a) `df_copy[col_name] = df_copy[col_name].astype(datetime)` - (b) `df_copy[col_name] = pd.to_datetime(df_copy[col_name])` - (c) `df_copy[col_name].convert_to_datetime()` - (d) `df_copy[col_name] = datetime.strptime(df_copy[col_name], '%Y-%m-%d')`

**Return:** A pandas Series with column names as index and count of NaN as values **Hint:** Use `.isnull().sum()` to count missing values in each column.

**Return:** A pandas DataFrame with descriptive statistics (using .describe())

You have a DataFrame with duplicate rows. The command `drop_duplicates` on subset of columns named `['Name', 'Team']` is to be used.

**Return:** A DataFrame with all rows containing NaN removed **Hint:** Use `.dropna()` to remove rows with missing values, then `.reset_index(drop=True)` to renumber rows.

Write a Python command to fill all missing values in the column 'College' with the text 'Unknown'.

**Return:** A DataFrame where NaN values in numeric columns are replaced by column mean **Hint:** Get numeric columns using `.select_dtypes()`, then use `.fillna()` with the column mean.

**Return:** A DataFrame containing only rows where column > threshold **Hint:** Use boolean indexing `df[df[col_name] > threshold]` and `.reset_index(drop=True)` to reset row indices. **Choose the correct code:** - (a) `return df.filter(column=col_name, value=threshold)` - (b) `return df.loc[df[col_name] > threshold]` - (c) `return df[df[col_name] > threshold].reset_index(drop=True)` - (d) `return df.query(f'{col_name} > {threshold}')`

Write Python code to select rows where 'Profit' is between 30 and 55 (inclusive). **Hint:** Use boolean indexing with AND operator `&`.

**Return:** A tuple of (number of rows, number of columns, list of column names) **Choose the correct code:** - (a) `return (df.size, df.ndim, df.columns)` - (b) `return (df.shape[0], df.shape[1], list(df.columns))` - (c) `return df.info()` - (d) `return (len(df), len(df.index), df.to_list())`

**Return:** DataFrame containing first n rows **Choose the correct code:** - (a) `return df.iloc[:n]` - (b) `return df.head(n)` - (c) `return df.nlargest(n, axis=0)` - (d) `return df[:n:1]`

Write a Python command to show summary statistics (mean, median, std, min, max, etc.) for the entire DataFrame.

**Return:** A DataFrame with grouped results (group column and mean) **Hint:** Use `.groupby(group_col)[agg_col].mean()` and `.reset_index()` to convert to DataFrame.

You have a DataFrame with columns: Name, Team, Salary, Profit Write Python code to: 1. Group the data by Team 2. aggregate average salary and total profit for each team 3. return the result **Hint:** Use `.groupby()` with `.agg()` for multiple aggregations. **Choose the correct code:** - (a) `df.groupby('Team').agg({'Salary': 'mean', 'Profit': 'sum'})` - (b) `df.groupby('Team')[['Salary', 'Profit']].agg(['mean', 'sum'])` - (c) `df.group('Team').apply(lambda x: {'avg_salary': x['Salary'].mean(), 'total_profit': x['Profit'].sum()})`

**Return:** A pandas DataFrame from the CSV string **Choose the correct line:** - (a) `return pd.read_excel(StringIO(csv_string))` - (b) `return pd.read_csv(StringIO(csv_string))` - (c) `return pd.DataFrame(csv_string.split('\n'))` - (d) `return csv_string.to_dataframe()`

**Return:** A merged DataFrame (inner join on the specified key) **Choose the correct code:** - (a) `return left.join(right, on=on)` - (b) `return pd.concat([left, right])` - (c) `return pd.merge(left, right, on=on, how='inner')` - (d) `return left.combine(right)`

**Return:** A pandas Series for the specified column

Given a DataFrame df with a 'Salary' column, write code to increase salary by 5% and store it in a new column 'UpdatedSalary'. **Hint:** Multiply the Salary column by 1.05 to increase by 5%. **Choose the correct code:** - (a) `df['UpdatedSalary'] = df['Salary'] * 5` - (b) `df['UpdatedSalary'] = df['Salary'] * 1.05` - (c) `df['UpdatedSalary'] = df['Salary'] + 0.05` - (d) `df['UpdatedSalary'] = df['Salary'].apply(lambda x: x * 5)`

Given a DataFrame with columns: Name, Score1, Score2 Write Python code to: 1. Select only rows where Score1 > 40 AND Score2 > 50 2. Create a new column AverageScore = mean of Score1 and Score2 3. return dataframe with only the `[['Name', 'AverageScore']]` **Hint:** Filter first using boolean indexing, then add the new column, then select specific columns.

You have a DataFrame with an 'Age' column containing missing values and outliers (Age > 100). Write Python code to: 1. Replace missing values with the median age 2. Remove rows where Age > 100 3. Return the cleaned DataFrame **Hint:** Use `.fillna()` with median, then filter with boolean indexing.

**Return:** A DataFrame where the specified column has been converted to datetime **Choose the correct code:** - (a) `df_copy[col_name] = df_copy[col_name].astype(datetime)` - (b) `df_copy[col_name] = pd.to_datetime(df_copy[col_name])` - (c) `df_copy[col_name].convert_to_datetime()` - (d) `df_copy[col_name] = datetime.strptime(df_copy[col_name], '%Y-%m-%d')`

**Return:** A pandas Series with column names as index and count of NaN as values **Hint:** Use `.isnull().sum()` to count missing values in each column.

**Return:** A pandas DataFrame with descriptive statistics (using .describe())

You have a DataFrame with duplicate rows. The command `drop_duplicates` on subset of columns named `['Name', 'Team']` is to be used.

**Return:** A DataFrame with all rows containing NaN removed **Hint:** Use `.dropna()` to remove rows with missing values, then `.reset_index(drop=True)` to renumber rows.

Write a Python command to fill all missing values in the column 'College' with the text 'Unknown'.

**Return:** A DataFrame where NaN values in numeric columns are replaced by column mean **Hint:** Get numeric columns using `.select_dtypes()`, then use `.fillna()` with the column mean.

**Return:** A DataFrame containing only rows where column > threshold **Hint:** Use boolean indexing `df[df[col_name] > threshold]` and `.reset_index(drop=True)` to reset row indices. **Choose the correct code:** - (a) `return df.filter(column=col_name, value=threshold)` - (b) `return df.loc[df[col_name] > threshold]` - (c) `return df[df[col_name] > threshold].reset_index(drop=True)` - (d) `return df.query(f'{col_name} > {threshold}')`

Write Python code to select rows where 'Profit' is between 30 and 55 (inclusive). **Hint:** Use boolean indexing with AND operator `&`.

**Return:** A tuple of (number of rows, number of columns, list of column names) **Choose the correct code:** - (a) `return (df.size, df.ndim, df.columns)` - (b) `return (df.shape[0], df.shape[1], list(df.columns))` - (c) `return df.info()` - (d) `return (len(df), len(df.index), df.to_list())`

**Return:** DataFrame containing first n rows **Choose the correct code:** - (a) `return df.iloc[:n]` - (b) `return df.head(n)` - (c) `return df.nlargest(n, axis=0)` - (d) `return df[:n:1]`

Write a Python command to show summary statistics (mean, median, std, min, max, etc.) for the entire DataFrame.

**Return:** A DataFrame with grouped results (group column and mean) **Hint:** Use `.groupby(group_col)[agg_col].mean()` and `.reset_index()` to convert to DataFrame.

You have a DataFrame with columns: Name, Team, Salary, Profit Write Python code to: 1. Group the data by Team 2. aggregate average salary and total profit for each team 3. return the result **Hint:** Use `.groupby()` with `.agg()` for multiple aggregations. **Choose the correct code:** - (a) `df.groupby('Team').agg({'Salary': 'mean', 'Profit': 'sum'})` - (b) `df.groupby('Team')[['Salary', 'Profit']].agg(['mean', 'sum'])` - (c) `df.group('Team').apply(lambda x: {'avg_salary': x['Salary'].mean(), 'total_profit': x['Profit'].sum()})`

**Return:** A pandas DataFrame from the CSV string **Choose the correct line:** - (a) `return pd.read_excel(StringIO(csv_string))` - (b) `return pd.read_csv(StringIO(csv_string))` - (c) `return pd.DataFrame(csv_string.split('\n'))` - (d) `return csv_string.to_dataframe()`

**Return:** A merged DataFrame (inner join on the specified key) **Choose the correct code:** - (a) `return left.join(right, on=on)` - (b) `return pd.concat([left, right])` - (c) `return pd.merge(left, right, on=on, how='inner')` - (d) `return left.combine(right)`

**Return:** A pandas Series for the specified column

Given a DataFrame df with a 'Salary' column, write code to increase salary by 5% and store it in a new column 'UpdatedSalary'. **Hint:** Multiply the Salary column by 1.05 to increase by 5%. **Choose the correct code:** - (a) `df['UpdatedSalary'] = df['Salary'] * 5` - (b) `df['UpdatedSalary'] = df['Salary'] * 1.05` - (c) `df['UpdatedSalary'] = df['Salary'] + 0.05` - (d) `df['UpdatedSalary'] = df['Salary'].apply(lambda x: x * 5)`

Given a DataFrame with columns: Name, Score1, Score2 Write Python code to: 1. Select only rows where Score1 > 40 AND Score2 > 50 2. Create a new column AverageScore = mean of Score1 and Score2 3. return dataframe with only the `[['Name', 'AverageScore']]` **Hint:** Filter first using boolean indexing, then add the new column, then select specific columns.

You have a DataFrame with an 'Age' column containing missing values and outliers (Age > 100). Write Python code to: 1. Replace missing values with the median age 2. Remove rows where Age > 100 3. Return the cleaned DataFrame **Hint:** Use `.fillna()` with median, then filter with boolean indexing.

**Return:** A DataFrame where the specified column has been converted to datetime **Choose the correct code:** - (a) `df_copy[col_name] = df_copy[col_name].astype(datetime)` - (b) `df_copy[col_name] = pd.to_datetime(df_copy[col_name])` - (c) `df_copy[col_name].convert_to_datetime()` - (d) `df_copy[col_name] = datetime.strptime(df_copy[col_name], '%Y-%m-%d')`

**Return:** A pandas Series with column names as index and count of NaN as values **Hint:** Use `.isnull().sum()` to count missing values in each column.

**Return:** A pandas DataFrame with descriptive statistics (using .describe())

You have a DataFrame with duplicate rows. The command `drop_duplicates` on subset of columns named `['Name', 'Team']` is to be used.

**Return:** A DataFrame with all rows containing NaN removed **Hint:** Use `.dropna()` to remove rows with missing values, then `.reset_index(drop=True)` to renumber rows.

Write a Python command to fill all missing values in the column 'College' with the text 'Unknown'.

**Return:** A DataFrame where NaN values in numeric columns are replaced by column mean **Hint:** Get numeric columns using `.select_dtypes()`, then use `.fillna()` with the column mean.

**Return:** A DataFrame containing only rows where column > threshold **Hint:** Use boolean indexing `df[df[col_name] > threshold]` and `.reset_index(drop=True)` to reset row indices. **Choose the correct code:** - (a) `return df.filter(column=col_name, value=threshold)` - (b) `return df.loc[df[col_name] > threshold]` - (c) `return df[df[col_name] > threshold].reset_index(drop=True)` - (d) `return df.query(f'{col_name} > {threshold}')`

Write Python code to select rows where 'Profit' is between 30 and 55 (inclusive). **Hint:** Use boolean indexing with AND operator `&`.

**Return:** A tuple of (number of rows, number of columns, list of column names) **Choose the correct code:** - (a) `return (df.size, df.ndim, df.columns)` - (b) `return (df.shape[0], df.shape[1], list(df.columns))` - (c) `return df.info()` - (d) `return (len(df), len(df.index), df.to_list())`

**Return:** DataFrame containing first n rows **Choose the correct code:** - (a) `return df.iloc[:n]` - (b) `return df.head(n)` - (c) `return df.nlargest(n, axis=0)` - (d) `return df[:n:1]`

Write a Python command to show summary statistics (mean, median, std, min, max, etc.) for the entire DataFrame.

**Return:** A DataFrame with grouped results (group column and mean) **Hint:** Use `.groupby(group_col)[agg_col].mean()` and `.reset_index()` to convert to DataFrame.

You have a DataFrame with columns: Name, Team, Salary, Profit Write Python code to: 1. Group the data by Team 2. aggregate average salary and total profit for each team 3. return the result **Hint:** Use `.groupby()` with `.agg()` for multiple aggregations. **Choose the correct code:** - (a) `df.groupby('Team').agg({'Salary': 'mean', 'Profit': 'sum'})` - (b) `df.groupby('Team')[['Salary', 'Profit']].agg(['mean', 'sum'])` - (c) `df.group('Team').apply(lambda x: {'avg_salary': x['Salary'].mean(), 'total_profit': x['Profit'].sum()})`

**Return:** A pandas DataFrame from the CSV string **Choose the correct line:** - (a) `return pd.read_excel(StringIO(csv_string))` - (b) `return pd.read_csv(StringIO(csv_string))` - (c) `return pd.DataFrame(csv_string.split('\n'))` - (d) `return csv_string.to_dataframe()`

**Return:** A merged DataFrame (inner join on the specified key) **Choose the correct code:** - (a) `return left.join(right, on=on)` - (b) `return pd.concat([left, right])` - (c) `return pd.merge(left, right, on=on, how='inner')` - (d) `return left.combine(right)`

**Return:** A pandas Series for the specified column

Given a DataFrame df with a 'Salary' column, write code to increase salary by 5% and store it in a new column 'UpdatedSalary'. **Hint:** Multiply the Salary column by 1.05 to increase by 5%. **Choose the correct code:** - (a) `df['UpdatedSalary'] = df['Salary'] * 5` - (b) `df['UpdatedSalary'] = df['Salary'] * 1.05` - (c) `df['UpdatedSalary'] = df['Salary'] + 0.05` - (d) `df['UpdatedSalary'] = df['Salary'].apply(lambda x: x * 5)`

Given a DataFrame with columns: Name, Score1, Score2 Write Python code to: 1. Select only rows where Score1 > 40 AND Score2 > 50 2. Create a new column AverageScore = mean of Score1 and Score2 3. return dataframe with only the `[['Name', 'AverageScore']]` **Hint:** Filter first using boolean indexing, then add the new column, then select specific columns.

You have a DataFrame with an 'Age' column containing missing values and outliers (Age > 100). Write Python code to: 1. Replace missing values with the median age 2. Remove rows where Age > 100 3. Return the cleaned DataFrame **Hint:** Use `.fillna()` with median, then filter with boolean indexing.

**Return:** A DataFrame where the specified column has been converted to datetime **Choose the correct code:** - (a) `df_copy[col_name] = df_copy[col_name].astype(datetime)` - (b) `df_copy[col_name] = pd.to_datetime(df_copy[col_name])` - (c) `df_copy[col_name].convert_to_datetime()` - (d) `df_copy[col_name] = datetime.strptime(df_copy[col_name], '%Y-%m-%d')`

**Return:** A pandas Series with column names as index and count of NaN as values **Hint:** Use `.isnull().sum()` to count missing values in each column.

**Return:** A pandas DataFrame with descriptive statistics (using .describe())

You have a DataFrame with duplicate rows. The command `drop_duplicates` on subset of columns named `['Name', 'Team']` is to be used.

**Return:** A DataFrame with all rows containing NaN removed **Hint:** Use `.dropna()` to remove rows with missing values, then `.reset_index(drop=True)` to renumber rows.

Write a Python command to fill all missing values in the column 'College' with the text 'Unknown'.

**Return:** A DataFrame where NaN values in numeric columns are replaced by column mean **Hint:** Get numeric columns using `.select_dtypes()`, then use `.fillna()` with the column mean.

**Return:** A DataFrame containing only rows where column > threshold **Hint:** Use boolean indexing `df[df[col_name] > threshold]` and `.reset_index(drop=True)` to reset row indices. **Choose the correct code:** - (a) `return df.filter(column=col_name, value=threshold)` - (b) `return df.loc[df[col_name] > threshold]` - (c) `return df[df[col_name] > threshold].reset_index(drop=True)` - (d) `return df.query(f'{col_name} > {threshold}')`

Write Python code to select rows where 'Profit' is between 30 and 55 (inclusive). **Hint:** Use boolean indexing with AND operator `&`.

**Return:** A tuple of (number of rows, number of columns, list of column names) **Choose the correct code:** - (a) `return (df.size, df.ndim, df.columns)` - (b) `return (df.shape[0], df.shape[1], list(df.columns))` - (c) `return df.info()` - (d) `return (len(df), len(df.index), df.to_list())`

**Return:** DataFrame containing first n rows **Choose the correct code:** - (a) `return df.iloc[:n]` - (b) `return df.head(n)` - (c) `return df.nlargest(n, axis=0)` - (d) `return df[:n:1]`

Write a Python command to show summary statistics (mean, median, std, min, max, etc.) for the entire DataFrame.

**Return:** A DataFrame with grouped results (group column and mean) **Hint:** Use `.groupby(group_col)[agg_col].mean()` and `.reset_index()` to convert to DataFrame.

You have a DataFrame with columns: Name, Team, Salary, Profit Write Python code to: 1. Group the data by Team 2. aggregate average salary and total profit for each team 3. return the result **Hint:** Use `.groupby()` with `.agg()` for multiple aggregations. **Choose the correct code:** - (a) `df.groupby('Team').agg({'Salary': 'mean', 'Profit': 'sum'})` - (b) `df.groupby('Team')[['Salary', 'Profit']].agg(['mean', 'sum'])` - (c) `df.group('Team').apply(lambda x: {'avg_salary': x['Salary'].mean(), 'total_profit': x['Profit'].sum()})`

**Return:** A pandas DataFrame from the CSV string **Choose the correct line:** - (a) `return pd.read_excel(StringIO(csv_string))` - (b) `return pd.read_csv(StringIO(csv_string))` - (c) `return pd.DataFrame(csv_string.split('\n'))` - (d) `return csv_string.to_dataframe()`

**Return:** A merged DataFrame (inner join on the specified key) **Choose the correct code:** - (a) `return left.join(right, on=on)` - (b) `return pd.concat([left, right])` - (c) `return pd.merge(left, right, on=on, how='inner')` - (d) `return left.combine(right)`

**Return:** A pandas Series for the specified column

Given a DataFrame df with a 'Salary' column, write code to increase salary by 5% and store it in a new column 'UpdatedSalary'. **Hint:** Multiply the Salary column by 1.05 to increase by 5%. **Choose the correct code:** - (a) `df['UpdatedSalary'] = df['Salary'] * 5` - (b) `df['UpdatedSalary'] = df['Salary'] * 1.05` - (c) `df['UpdatedSalary'] = df['Salary'] + 0.05` - (d) `df['UpdatedSalary'] = df['Salary'].apply(lambda x: x * 5)`

Given a DataFrame with columns: Name, Score1, Score2 Write Python code to: 1. Select only rows where Score1 > 40 AND Score2 > 50 2. Create a new column AverageScore = mean of Score1 and Score2 3. return dataframe with only the `[['Name', 'AverageScore']]` **Hint:** Filter first using boolean indexing, then add the new column, then select specific columns.

You have a DataFrame with an 'Age' column containing missing values and outliers (Age > 100). Write Python code to: 1. Replace missing values with the median age 2. Remove rows where Age > 100 3. Return the cleaned DataFrame **Hint:** Use `.fillna()` with median, then filter with boolean indexing.

**Return:** A DataFrame where the specified column has been converted to datetime **Choose the correct code:** - (a) `df_copy[col_name] = df_copy[col_name].astype(datetime)` - (b) `df_copy[col_name] = pd.to_datetime(df_copy[col_name])` - (c) `df_copy[col_name].convert_to_datetime()` - (d) `df_copy[col_name] = datetime.strptime(df_copy[col_name], '%Y-%m-%d')`

**Return:** A pandas Series with column names as index and count of NaN as values **Hint:** Use `.isnull().sum()` to count missing values in each column.

**Return:** A pandas DataFrame with descriptive statistics (using .describe())

You have a DataFrame with duplicate rows. The command `drop_duplicates` on subset of columns named `['Name', 'Team']` is to be used.

**Return:** A DataFrame with all rows containing NaN removed **Hint:** Use `.dropna()` to remove rows with missing values, then `.reset_index(drop=True)` to renumber rows.

Write a Python command to fill all missing values in the column 'College' with the text 'Unknown'.

**Return:** A DataFrame where NaN values in numeric columns are replaced by column mean **Hint:** Get numeric columns using `.select_dtypes()`, then use `.fillna()` with the column mean.

**Return:** A DataFrame containing only rows where column > threshold **Hint:** Use boolean indexing `df[df[col_name] > threshold]` and `.reset_index(drop=True)` to reset row indices. **Choose the correct code:** - (a) `return df.filter(column=col_name, value=threshold)` - (b) `return df.loc[df[col_name] > threshold]` - (c) `return df[df[col_name] > threshold].reset_index(drop=True)` - (d) `return df.query(f'{col_name} > {threshold}')`

Write Python code to select rows where 'Profit' is between 30 and 55 (inclusive). **Hint:** Use boolean indexing with AND operator `&`.

**Return:** A tuple of (number of rows, number of columns, list of column names) **Choose the correct code:** - (a) `return (df.size, df.ndim, df.columns)` - (b) `return (df.shape[0], df.shape[1], list(df.columns))` - (c) `return df.info()` - (d) `return (len(df), len(df.index), df.to_list())`

**Return:** DataFrame containing first n rows **Choose the correct code:** - (a) `return df.iloc[:n]` - (b) `return df.head(n)` - (c) `return df.nlargest(n, axis=0)` - (d) `return df[:n:1]`

Write a Python command to show summary statistics (mean, median, std, min, max, etc.) for the entire DataFrame.

**Return:** A DataFrame with grouped results (group column and mean) **Hint:** Use `.groupby(group_col)[agg_col].mean()` and `.reset_index()` to convert to DataFrame.

You have a DataFrame with columns: Name, Team, Salary, Profit Write Python code to: 1. Group the data by Team 2. aggregate average salary and total profit for each team 3. return the result **Hint:** Use `.groupby()` with `.agg()` for multiple aggregations. **Choose the correct code:** - (a) `df.groupby('Team').agg({'Salary': 'mean', 'Profit': 'sum'})` - (b) `df.groupby('Team')[['Salary', 'Profit']].agg(['mean', 'sum'])` - (c) `df.group('Team').apply(lambda x: {'avg_salary': x['Salary'].mean(), 'total_profit': x['Profit'].sum()})`

**Return:** A pandas DataFrame from the CSV string **Choose the correct line:** - (a) `return pd.read_excel(StringIO(csv_string))` - (b) `return pd.read_csv(StringIO(csv_string))` - (c) `return pd.DataFrame(csv_string.split('\n'))` - (d) `return csv_string.to_dataframe()`

**Return:** A merged DataFrame (inner join on the specified key) **Choose the correct code:** - (a) `return left.join(right, on=on)` - (b) `return pd.concat([left, right])` - (c) `return pd.merge(left, right, on=on, how='inner')` - (d) `return left.combine(right)`

**Return:** A pandas Series for the specified column

Given a DataFrame df with a 'Salary' column, write code to increase salary by 5% and store it in a new column 'UpdatedSalary'. **Hint:** Multiply the Salary column by 1.05 to increase by 5%. **Choose the correct code:** - (a) `df['UpdatedSalary'] = df['Salary'] * 5` - (b) `df['UpdatedSalary'] = df['Salary'] * 1.05` - (c) `df['UpdatedSalary'] = df['Salary'] + 0.05` - (d) `df['UpdatedSalary'] = df['Salary'].apply(lambda x: x * 5)`

Given a DataFrame with columns: Name, Score1, Score2 Write Python code to: 1. Select only rows where Score1 > 40 AND Score2 > 50 2. Create a new column AverageScore = mean of Score1 and Score2 3. return dataframe with only the `[['Name', 'AverageScore']]` **Hint:** Filter first using boolean indexing, then add the new column, then select specific columns.

You have a DataFrame with an 'Age' column containing missing values and outliers (Age > 100). Write Python code to: 1. Replace missing values with the median age 2. Remove rows where Age > 100 3. Return the cleaned DataFrame **Hint:** Use `.fillna()` with median, then filter with boolean indexing.

**Return:** A DataFrame where the specified column has been converted to datetime **Choose the correct code:** - (a) `df_copy[col_name] = df_copy[col_name].astype(datetime)` - (b) `df_copy[col_name] = pd.to_datetime(df_copy[col_name])` - (c) `df_copy[col_name].convert_to_datetime()` - (d) `df_copy[col_name] = datetime.strptime(df_copy[col_name], '%Y-%m-%d')`

**Return:** A pandas Series with column names as index and count of NaN as values **Hint:** Use `.isnull().sum()` to count missing values in each column.

**Return:** A pandas DataFrame with descriptive statistics (using .describe())

You have a DataFrame with duplicate rows. The command `drop_duplicates` on subset of columns named `['Name', 'Team']` is to be used.

**Return:** A DataFrame with all rows containing NaN removed **Hint:** Use `.dropna()` to remove rows with missing values, then `.reset_index(drop=True)` to renumber rows.

Write a Python command to fill all missing values in the column 'College' with the text 'Unknown'.

**Return:** A DataFrame where NaN values in numeric columns are replaced by column mean **Hint:** Get numeric columns using `.select_dtypes()`, then use `.fillna()` with the column mean.

**Return:** A DataFrame containing only rows where column > threshold **Hint:** Use boolean indexing `df[df[col_name] > threshold]` and `.reset_index(drop=True)` to reset row indices. **Choose the correct code:** - (a) `return df.filter(column=col_name, value=threshold)` - (b) `return df.loc[df[col_name] > threshold]` - (c) `return df[df[col_name] > threshold].reset_index(drop=True)` - (d) `return df.query(f'{col_name} > {threshold}')`

Write Python code to select rows where 'Profit' is between 30 and 55 (inclusive). **Hint:** Use boolean indexing with AND operator `&`.

**Return:** A tuple of (number of rows, number of columns, list of column names) **Choose the correct code:** - (a) `return (df.size, df.ndim, df.columns)` - (b) `return (df.shape[0], df.shape[1], list(df.columns))` - (c) `return df.info()` - (d) `return (len(df), len(df.index), df.to_list())`

**Return:** DataFrame containing first n rows **Choose the correct code:** - (a) `return df.iloc[:n]` - (b) `return df.head(n)` - (c) `return df.nlargest(n, axis=0)` - (d) `return df[:n:1]`

Write a Python command to show summary statistics (mean, median, std, min, max, etc.) for the entire DataFrame.

**Return:** A DataFrame with grouped results (group column and mean) **Hint:** Use `.groupby(group_col)[agg_col].mean()` and `.reset_index()` to convert to DataFrame.

You have a DataFrame with columns: Name, Team, Salary, Profit Write Python code to: 1. Group the data by Team 2. aggregate average salary and total profit for each team 3. return the result **Hint:** Use `.groupby()` with `.agg()` for multiple aggregations. **Choose the correct code:** - (a) `df.groupby('Team').agg({'Salary': 'mean', 'Profit': 'sum'})` - (b) `df.groupby('Team')[['Salary', 'Profit']].agg(['mean', 'sum'])` - (c) `df.group('Team').apply(lambda x: {'avg_salary': x['Salary'].mean(), 'total_profit': x['Profit'].sum()})`

**Return:** A pandas DataFrame from the CSV string **Choose the correct line:** - (a) `return pd.read_excel(StringIO(csv_string))` - (b) `return pd.read_csv(StringIO(csv_string))` - (c) `return pd.DataFrame(csv_string.split('\n'))` - (d) `return csv_string.to_dataframe()`

**Return:** A merged DataFrame (inner join on the specified key) **Choose the correct code:** - (a) `return left.join(right, on=on)` - (b) `return pd.concat([left, right])` - (c) `return pd.merge(left, right, on=on, how='inner')` - (d) `return left.combine(right)`

**Return:** A pandas Series for the specified column

Given a DataFrame df with a 'Salary' column, write code to increase salary by 5% and store it in a new column 'UpdatedSalary'. **Hint:** Multiply the Salary column by 1.05 to increase by 5%. **Choose the correct code:** - (a) `df['UpdatedSalary'] = df['Salary'] * 5` - (b) `df['UpdatedSalary'] = df['Salary'] * 1.05` - (c) `df['UpdatedSalary'] = df['Salary'] + 0.05` - (d) `df['UpdatedSalary'] = df['Salary'].apply(lambda x: x * 5)`

Given a DataFrame with columns: Name, Score1, Score2 Write Python code to: 1. Select only rows where Score1 > 40 AND Score2 > 50 2. Create a new column AverageScore = mean of Score1 and Score2 3. return dataframe with only the `[['Name', 'AverageScore']]` **Hint:** Filter first using boolean indexing, then add the new column, then select specific columns.

You have a DataFrame with an 'Age' column containing missing values and outliers (Age > 100). Write Python code to: 1. Replace missing values with the median age 2. Remove rows where Age > 100 3. Return the cleaned DataFrame **Hint:** Use `.fillna()` with median, then filter with boolean indexing.

**Return:** A DataFrame where the specified column has been converted to datetime **Choose the correct code:** - (a) `df_copy[col_name] = df_copy[col_name].astype(datetime)` - (b) `df_copy[col_name] = pd.to_datetime(df_copy[col_name])` - (c) `df_copy[col_name].convert_to_datetime()` - (d) `df_copy[col_name] = datetime.strptime(df_copy[col_name], '%Y-%m-%d')`

**Return:** A pandas Series with column names as index and count of NaN as values **Hint:** Use `.isnull().sum()` to count missing values in each column.

**Return:** A pandas DataFrame with descriptive statistics (using .describe())

You have a DataFrame with duplicate rows. The command `drop_duplicates` on subset of columns named `['Name', 'Team']` is to be used.

**Return:** A DataFrame with all rows containing NaN removed **Hint:** Use `.dropna()` to remove rows with missing values, then `.reset_index(drop=True)` to renumber rows.

Write a Python command to fill all missing values in the column 'College' with the text 'Unknown'.

**Return:** A DataFrame where NaN values in numeric columns are replaced by column mean **Hint:** Get numeric columns using `.select_dtypes()`, then use `.fillna()` with the column mean.

**Return:** A DataFrame containing only rows where column > threshold **Hint:** Use boolean indexing `df[df[col_name] > threshold]` and `.reset_index(drop=True)` to reset row indices. **Choose the correct code:** - (a) `return df.filter(column=col_name, value=threshold)` - (b) `return df.loc[df[col_name] > threshold]` - (c) `return df[df[col_name] > threshold].reset_index(drop=True)` - (d) `return df.query(f'{col_name} > {threshold}')`

Write Python code to select rows where 'Profit' is between 30 and 55 (inclusive). **Hint:** Use boolean indexing with AND operator `&`.

**Return:** A tuple of (number of rows, number of columns, list of column names) **Choose the correct code:** - (a) `return (df.size, df.ndim, df.columns)` - (b) `return (df.shape[0], df.shape[1], list(df.columns))` - (c) `return df.info()` - (d) `return (len(df), len(df.index), df.to_list())`

**Return:** DataFrame containing first n rows **Choose the correct code:** - (a) `return df.iloc[:n]` - (b) `return df.head(n)` - (c) `return df.nlargest(n, axis=0)` - (d) `return df[:n:1]`

Write a Python command to show summary statistics (mean, median, std, min, max, etc.) for the entire DataFrame.

**Return:** A DataFrame with grouped results (group column and mean) **Hint:** Use `.groupby(group_col)[agg_col].mean()` and `.reset_index()` to convert to DataFrame.

You have a DataFrame with columns: Name, Team, Salary, Profit Write Python code to: 1. Group the data by Team 2. aggregate average salary and total profit for each team 3. return the result **Hint:** Use `.groupby()` with `.agg()` for multiple aggregations. **Choose the correct code:** - (a) `df.groupby('Team').agg({'Salary': 'mean', 'Profit': 'sum'})` - (b) `df.groupby('Team')[['Salary', 'Profit']].agg(['mean', 'sum'])` - (c) `df.group('Team').apply(lambda x: {'avg_salary': x['Salary'].mean(), 'total_profit': x['Profit'].sum()})`

**Return:** A pandas DataFrame from the CSV string **Choose the correct line:** - (a) `return pd.read_excel(StringIO(csv_string))` - (b) `return pd.read_csv(StringIO(csv_string))` - (c) `return pd.DataFrame(csv_string.split('\n'))` - (d) `return csv_string.to_dataframe()`

**Return:** A merged DataFrame (inner join on the specified key) **Choose the correct code:** - (a) `return left.join(right, on=on)` - (b) `return pd.concat([left, right])` - (c) `return pd.merge(left, right, on=on, how='inner')` - (d) `return left.combine(right)`

**Return:** A pandas Series for the specified column

Given a DataFrame df with a 'Salary' column, write code to increase salary by 5% and store it in a new column 'UpdatedSalary'. **Hint:** Multiply the Salary column by 1.05 to increase by 5%. **Choose the correct code:** - (a) `df['UpdatedSalary'] = df['Salary'] * 5` - (b) `df['UpdatedSalary'] = df['Salary'] * 1.05` - (c) `df['UpdatedSalary'] = df['Salary'] + 0.05` - (d) `df['UpdatedSalary'] = df['Salary'].apply(lambda x: x * 5)`

Given a DataFrame with columns: Name, Score1, Score2 Write Python code to: 1. Select only rows where Score1 > 40 AND Score2 > 50 2. Create a new column AverageScore = mean of Score1 and Score2 3. return dataframe with only the `[['Name', 'AverageScore']]` **Hint:** Filter first using boolean indexing, then add the new column, then select specific columns.

You have a DataFrame with an 'Age' column containing missing values and outliers (Age > 100). Write Python code to: 1. Replace missing values with the median age 2. Remove rows where Age > 100 3. Return the cleaned DataFrame **Hint:** Use `.fillna()` with median, then filter with boolean indexing.

**Return:** A DataFrame where the specified column has been converted to datetime **Choose the correct code:** - (a) `df_copy[col_name] = df_copy[col_name].astype(datetime)` - (b) `df_copy[col_name] = pd.to_datetime(df_copy[col_name])` - (c) `df_copy[col_name].convert_to_datetime()` - (d) `df_copy[col_name] = datetime.strptime(df_copy[col_name], '%Y-%m-%d')`

**Return:** A pandas Series with column names as index and count of NaN as values **Hint:** Use `.isnull().sum()` to count missing values in each column.

**Return:** A pandas DataFrame with descriptive statistics (using .describe())

You have a DataFrame with duplicate rows. The command `drop_duplicates` on subset of columns named `['Name', 'Team']` is to be used.

**Return:** A DataFrame with all rows containing NaN removed **Hint:** Use `.dropna()` to remove rows with missing values, then `.reset_index(drop=True)` to renumber rows.

Write a Python command to fill all missing values in the column 'College' with the text 'Unknown'.

**Return:** A DataFrame where NaN values in numeric columns are replaced by column mean **Hint:** Get numeric columns using `.select_dtypes()`, then use `.fillna()` with the column mean.

**Return:** A DataFrame containing only rows where column > threshold **Hint:** Use boolean indexing `df[df[col_name] > threshold]` and `.reset_index(drop=True)` to reset row indices. **Choose the correct code:** - (a) `return df.filter(column=col_name, value=threshold)` - (b) `return df.loc[df[col_name] > threshold]` - (c) `return df[df[col_name] > threshold].reset_index(drop=True)` - (d) `return df.query(f'{col_name} > {threshold}')`

Write Python code to select rows where 'Profit' is between 30 and 55 (inclusive). **Hint:** Use boolean indexing with AND operator `&`.

**Return:** A tuple of (number of rows, number of columns, list of column names) **Choose the correct code:** - (a) `return (df.size, df.ndim, df.columns)` - (b) `return (df.shape[0], df.shape[1], list(df.columns))` - (c) `return df.info()` - (d) `return (len(df), len(df.index), df.to_list())`

**Return:** DataFrame containing first n rows **Choose the correct code:** - (a) `return df.iloc[:n]` - (b) `return df.head(n)` - (c) `return df.nlargest(n, axis=0)` - (d) `return df[:n:1]`

Write a Python command to show summary statistics (mean, median, std, min, max, etc.) for the entire DataFrame.

**Return:** A DataFrame with grouped results (group column and mean) **Hint:** Use `.groupby(group_col)[agg_col].mean()` and `.reset_index()` to convert to DataFrame.

You have a DataFrame with columns: Name, Team, Salary, Profit Write Python code to: 1. Group the data by Team 2. aggregate average salary and total profit for each team 3. return the result **Hint:** Use `.groupby()` with `.agg()` for multiple aggregations. **Choose the correct code:** - (a) `df.groupby('Team').agg({'Salary': 'mean', 'Profit': 'sum'})` - (b) `df.groupby('Team')[['Salary', 'Profit']].agg(['mean', 'sum'])` - (c) `df.group('Team').apply(lambda x: {'avg_salary': x['Salary'].mean(), 'total_profit': x['Profit'].sum()})`

**Return:** A pandas DataFrame from the CSV string **Choose the correct line:** - (a) `return pd.read_excel(StringIO(csv_string))` - (b) `return pd.read_csv(StringIO(csv_string))` - (c) `return pd.DataFrame(csv_string.split('\n'))` - (d) `return csv_string.to_dataframe()`

**Return:** A merged DataFrame (inner join on the specified key) **Choose the correct code:** - (a) `return left.join(right, on=on)` - (b) `return pd.concat([left, right])` - (c) `return pd.merge(left, right, on=on, how='inner')` - (d) `return left.combine(right)`

**Return:** A pandas Series for the specified column

Given a DataFrame df with a 'Salary' column, write code to increase salary by 5% and store it in a new column 'UpdatedSalary'. **Hint:** Multiply the Salary column by 1.05 to increase by 5%. **Choose the correct code:** - (a) `df['UpdatedSalary'] = df['Salary'] * 5` - (b) `df['UpdatedSalary'] = df['Salary'] * 1.05` - (c) `df['UpdatedSalary'] = df['Salary'] + 0.05` - (d) `df['UpdatedSalary'] = df['Salary'].apply(lambda x: x * 5)`

Given a DataFrame with columns: Name, Score1, Score2 Write Python code to: 1. Select only rows where Score1 > 40 AND Score2 > 50 2. Create a new column AverageScore = mean of Score1 and Score2 3. return dataframe with only the `[['Name', 'AverageScore']]` **Hint:** Filter first using boolean indexing, then add the new column, then select specific columns.

You have a DataFrame with an 'Age' column containing missing values and outliers (Age > 100). Write Python code to: 1. Replace missing values with the median age 2. Remove rows where Age > 100 3. Return the cleaned DataFrame **Hint:** Use `.fillna()` with median, then filter with boolean indexing.

**Return:** A DataFrame where the specified column has been converted to datetime **Choose the correct code:** - (a) `df_copy[col_name] = df_copy[col_name].astype(datetime)` - (b) `df_copy[col_name] = pd.to_datetime(df_copy[col_name])` - (c) `df_copy[col_name].convert_to_datetime()` - (d) `df_copy[col_name] = datetime.strptime(df_copy[col_name], '%Y-%m-%d')`

**Return:** A pandas Series with column names as index and count of NaN as values **Hint:** Use `.isnull().sum()` to count missing values in each column.

**Return:** A pandas DataFrame with descriptive statistics (using .describe())

You have a DataFrame with duplicate rows. The command `drop_duplicates` on subset of columns named `['Name', 'Team']` is to be used.

**Return:** A DataFrame with all rows containing NaN removed **Hint:** Use `.dropna()` to remove rows with missing values, then `.reset_index(drop=True)` to renumber rows.

Write a Python command to fill all missing values in the column 'College' with the text 'Unknown'.

**Return:** A DataFrame where NaN values in numeric columns are replaced by column mean **Hint:** Get numeric columns using `.select_dtypes()`, then use `.fillna()` with the column mean.

**Return:** A DataFrame containing only rows where column > threshold **Hint:** Use boolean indexing `df[df[col_name] > threshold]` and `.reset_index(drop=True)` to reset row indices. **Choose the correct code:** - (a) `return df.filter(column=col_name, value=threshold)` - (b) `return df.loc[df[col_name] > threshold]` - (c) `return df[df[col_name] > threshold].reset_index(drop=True)` - (d) `return df.query(f'{col_name} > {threshold}')`

Write Python code to select rows where 'Profit' is between 30 and 55 (inclusive). **Hint:** Use boolean indexing with AND operator `&`.

**Return:** A tuple of (number of rows, number of columns, list of column names) **Choose the correct code:** - (a) `return (df.size, df.ndim, df.columns)` - (b) `return (df.shape[0], df.shape[1], list(df.columns))` - (c) `return df.info()` - (d) `return (len(df), len(df.index), df.to_list())`

**Return:** DataFrame containing first n rows **Choose the correct code:** - (a) `return df.iloc[:n]` - (b) `return df.head(n)` - (c) `return df.nlargest(n, axis=0)` - (d) `return df[:n:1]`

Write a Python command to show summary statistics (mean, median, std, min, max, etc.) for the entire DataFrame.

**Return:** A DataFrame with grouped results (group column and mean) **Hint:** Use `.groupby(group_col)[agg_col].mean()` and `.reset_index()` to convert to DataFrame.

You have a DataFrame with columns: Name, Team, Salary, Profit Write Python code to: 1. Group the data by Team 2. aggregate average salary and total profit for each team 3. return the result **Hint:** Use `.groupby()` with `.agg()` for multiple aggregations. **Choose the correct code:** - (a) `df.groupby('Team').agg({'Salary': 'mean', 'Profit': 'sum'})` - (b) `df.groupby('Team')[['Salary', 'Profit']].agg(['mean', 'sum'])` - (c) `df.group('Team').apply(lambda x: {'avg_salary': x['Salary'].mean(), 'total_profit': x['Profit'].sum()})`

**Return:** A pandas DataFrame from the CSV string **Choose the correct line:** - (a) `return pd.read_excel(StringIO(csv_string))` - (b) `return pd.read_csv(StringIO(csv_string))` - (c) `return pd.DataFrame(csv_string.split('\n'))` - (d) `return csv_string.to_dataframe()`

**Return:** A merged DataFrame (inner join on the specified key) **Choose the correct code:** - (a) `return left.join(right, on=on)` - (b) `return pd.concat([left, right])` - (c) `return pd.merge(left, right, on=on, how='inner')` - (d) `return left.combine(right)`

**Return:** A pandas Series for the specified column

Given a DataFrame df with a 'Salary' column, write code to increase salary by 5% and store it in a new column 'UpdatedSalary'. **Hint:** Multiply the Salary column by 1.05 to increase by 5%. **Choose the correct code:** - (a) `df['UpdatedSalary'] = df['Salary'] * 5` - (b) `df['UpdatedSalary'] = df['Salary'] * 1.05` - (c) `df['UpdatedSalary'] = df['Salary'] + 0.05` - (d) `df['UpdatedSalary'] = df['Salary'].apply(lambda x: x * 5)`

Given a DataFrame with columns: Name, Score1, Score2 Write Python code to: 1. Select only rows where Score1 > 40 AND Score2 > 50 2. Create a new column AverageScore = mean of Score1 and Score2 3. return dataframe with only the `[['Name', 'AverageScore']]` **Hint:** Filter first using boolean indexing, then add the new column, then select specific columns.

You have a DataFrame with an 'Age' column containing missing values and outliers (Age > 100). Write Python code to: 1. Replace missing values with the median age 2. Remove rows where Age > 100 3. Return the cleaned DataFrame **Hint:** Use `.fillna()` with median, then filter with boolean indexing.

**Return:** A DataFrame where the specified column has been converted to datetime **Choose the correct code:** - (a) `df_copy[col_name] = df_copy[col_name].astype(datetime)` - (b) `df_copy[col_name] = pd.to_datetime(df_copy[col_name])` - (c) `df_copy[col_name].convert_to_datetime()` - (d) `df_copy[col_name] = datetime.strptime(df_copy[col_name], '%Y-%m-%d')`

**Return:** A pandas Series with column names as index and count of NaN as values **Hint:** Use `.isnull().sum()` to count missing values in each column.

**Return:** A pandas DataFrame with descriptive statistics (using .describe())

You have a DataFrame with duplicate rows. The command `drop_duplicates` on subset of columns named `['Name', 'Team']` is to be used.

**Return:** A DataFrame with all rows containing NaN removed **Hint:** Use `.dropna()` to remove rows with missing values, then `.reset_index(drop=True)` to renumber rows.

Write a Python command to fill all missing values in the column 'College' with the text 'Unknown'.

**Return:** A DataFrame where NaN values in numeric columns are replaced by column mean **Hint:** Get numeric columns using `.select_dtypes()`, then use `.fillna()` with the column mean.

**Return:** A DataFrame containing only rows where column > threshold **Hint:** Use boolean indexing `df[df[col_name] > threshold]` and `.reset_index(drop=True)` to reset row indices. **Choose the correct code:** - (a) `return df.filter(column=col_name, value=threshold)` - (b) `return df.loc[df[col_name] > threshold]` - (c) `return df[df[col_name] > threshold].reset_index(drop=True)` - (d) `return df.query(f'{col_name} > {threshold}')`

Write Python code to select rows where 'Profit' is between 30 and 55 (inclusive). **Hint:** Use boolean indexing with AND operator `&`.

**Return:** A tuple of (number of rows, number of columns, list of column names) **Choose the correct code:** - (a) `return (df.size, df.ndim, df.columns)` - (b) `return (df.shape[0], df.shape[1], list(df.columns))` - (c) `return df.info()` - (d) `return (len(df), len(df.index), df.to_list())`

**Return:** DataFrame containing first n rows **Choose the correct code:** - (a) `return df.iloc[:n]` - (b) `return df.head(n)` - (c) `return df.nlargest(n, axis=0)` - (d) `return df[:n:1]`

Write a Python command to show summary statistics (mean, median, std, min, max, etc.) for the entire DataFrame.

**Return:** A DataFrame with grouped results (group column and mean) **Hint:** Use `.groupby(group_col)[agg_col].mean()` and `.reset_index()` to convert to DataFrame.

You have a DataFrame with columns: Name, Team, Salary, Profit Write Python code to: 1. Group the data by Team 2. aggregate average salary and total profit for each team 3. return the result **Hint:** Use `.groupby()` with `.agg()` for multiple aggregations. **Choose the correct code:** - (a) `df.groupby('Team').agg({'Salary': 'mean', 'Profit': 'sum'})` - (b) `df.groupby('Team')[['Salary', 'Profit']].agg(['mean', 'sum'])` - (c) `df.group('Team').apply(lambda x: {'avg_salary': x['Salary'].mean(), 'total_profit': x['Profit'].sum()})`

**Return:** A pandas DataFrame from the CSV string **Choose the correct line:** - (a) `return pd.read_excel(StringIO(csv_string))` - (b) `return pd.read_csv(StringIO(csv_string))` - (c) `return pd.DataFrame(csv_string.split('\n'))` - (d) `return csv_string.to_dataframe()`

**Return:** A merged DataFrame (inner join on the specified key) **Choose the correct code:** - (a) `return left.join(right, on=on)` - (b) `return pd.concat([left, right])` - (c) `return pd.merge(left, right, on=on, how='inner')` - (d) `return left.combine(right)`

**Return:** A pandas Series for the specified column

Student did not customize name/roll_number.

Given a DataFrame df with a 'Salary' column, write code to increase salary by 5% and store it in a new column 'UpdatedSalary'. **Hint:** Multiply the Salary column by 1.05 to increase by 5%. **Choose the correct code:** - (a) `df['UpdatedSalary'] = df['Salary'] * 5` - (b) `df['UpdatedSalary'] = df['Salary'] * 1.05` - (c) `df['UpdatedSalary'] = df['Salary'] + 0.05` - (d) `df['UpdatedSalary'] = df['Salary'].apply(lambda x: x * 5)`

Given a DataFrame with columns: Name, Score1, Score2 Write Python code to: 1. Select only rows where Score1 > 40 AND Score2 > 50 2. Create a new column AverageScore = mean of Score1 and Score2 3. return dataframe with only the `[['Name', 'AverageScore']]` **Hint:** Filter first using boolean indexing, then add the new column, then select specific columns.

You have a DataFrame with an 'Age' column containing missing values and outliers (Age > 100). Write Python code to: 1. Replace missing values with the median age 2. Remove rows where Age > 100 3. Return the cleaned DataFrame **Hint:** Use `.fillna()` with median, then filter with boolean indexing.

**Return:** A DataFrame where the specified column has been converted to datetime **Choose the correct code:** - (a) `df_copy[col_name] = df_copy[col_name].astype(datetime)` - (b) `df_copy[col_name] = pd.to_datetime(df_copy[col_name])` - (c) `df_copy[col_name].convert_to_datetime()` - (d) `df_copy[col_name] = datetime.strptime(df_copy[col_name], '%Y-%m-%d')`

**Return:** A pandas Series with column names as index and count of NaN as values **Hint:** Use `.isnull().sum()` to count missing values in each column.

**Return:** A pandas DataFrame with descriptive statistics (using .describe())

You have a DataFrame with duplicate rows. The command `drop_duplicates` on subset of columns named `['Name', 'Team']` is to be used.

**Return:** A DataFrame with all rows containing NaN removed **Hint:** Use `.dropna()` to remove rows with missing values, then `.reset_index(drop=True)` to renumber rows.

Write a Python command to fill all missing values in the column 'College' with the text 'Unknown'.

**Return:** A DataFrame where NaN values in numeric columns are replaced by column mean **Hint:** Get numeric columns using `.select_dtypes()`, then use `.fillna()` with the column mean.

**Return:** A DataFrame containing only rows where column > threshold **Hint:** Use boolean indexing `df[df[col_name] > threshold]` and `.reset_index(drop=True)` to reset row indices. **Choose the correct code:** - (a) `return df.filter(column=col_name, value=threshold)` - (b) `return df.loc[df[col_name] > threshold]` - (c) `return df[df[col_name] > threshold].reset_index(drop=True)` - (d) `return df.query(f'{col_name} > {threshold}')`

Write Python code to select rows where 'Profit' is between 30 and 55 (inclusive). **Hint:** Use boolean indexing with AND operator `&`.

**Return:** A tuple of (number of rows, number of columns, list of column names) **Choose the correct code:** - (a) `return (df.size, df.ndim, df.columns)` - (b) `return (df.shape[0], df.shape[1], list(df.columns))` - (c) `return df.info()` - (d) `return (len(df), len(df.index), df.to_list())`

**Return:** DataFrame containing first n rows **Choose the correct code:** - (a) `return df.iloc[:n]` - (b) `return df.head(n)` - (c) `return df.nlargest(n, axis=0)` - (d) `return df[:n:1]`

Write a Python command to show summary statistics (mean, median, std, min, max, etc.) for the entire DataFrame.

**Return:** A DataFrame with grouped results (group column and mean) **Hint:** Use `.groupby(group_col)[agg_col].mean()` and `.reset_index()` to convert to DataFrame.

You have a DataFrame with columns: Name, Team, Salary, Profit Write Python code to: 1. Group the data by Team 2. aggregate average salary and total profit for each team 3. return the result **Hint:** Use `.groupby()` with `.agg()` for multiple aggregations. **Choose the correct code:** - (a) `df.groupby('Team').agg({'Salary': 'mean', 'Profit': 'sum'})` - (b) `df.groupby('Team')[['Salary', 'Profit']].agg(['mean', 'sum'])` - (c) `df.group('Team').apply(lambda x: {'avg_salary': x['Salary'].mean(), 'total_profit': x['Profit'].sum()})`

**Return:** A pandas DataFrame from the CSV string **Choose the correct line:** - (a) `return pd.read_excel(StringIO(csv_string))` - (b) `return pd.read_csv(StringIO(csv_string))` - (c) `return pd.DataFrame(csv_string.split('\n'))` - (d) `return csv_string.to_dataframe()`

**Return:** A merged DataFrame (inner join on the specified key) **Choose the correct code:** - (a) `return left.join(right, on=on)` - (b) `return pd.concat([left, right])` - (c) `return pd.merge(left, right, on=on, how='inner')` - (d) `return left.combine(right)`

**Return:** A pandas Series for the specified column

Given a DataFrame df with a 'Salary' column, write code to increase salary by 5% and store it in a new column 'UpdatedSalary'. **Hint:** Multiply the Salary column by 1.05 to increase by 5%. **Choose the correct code:** - (a) `df['UpdatedSalary'] = df['Salary'] * 5` - (b) `df['UpdatedSalary'] = df['Salary'] * 1.05` - (c) `df['UpdatedSalary'] = df['Salary'] + 0.05` - (d) `df['UpdatedSalary'] = df['Salary'].apply(lambda x: x * 5)`

Given a DataFrame with columns: Name, Score1, Score2 Write Python code to: 1. Select only rows where Score1 > 40 AND Score2 > 50 2. Create a new column AverageScore = mean of Score1 and Score2 3. return dataframe with only the `[['Name', 'AverageScore']]` **Hint:** Filter first using boolean indexing, then add the new column, then select specific columns.

You have a DataFrame with an 'Age' column containing missing values and outliers (Age > 100). Write Python code to: 1. Replace missing values with the median age 2. Remove rows where Age > 100 3. Return the cleaned DataFrame **Hint:** Use `.fillna()` with median, then filter with boolean indexing.

**Return:** A DataFrame where the specified column has been converted to datetime **Choose the correct code:** - (a) `df_copy[col_name] = df_copy[col_name].astype(datetime)` - (b) `df_copy[col_name] = pd.to_datetime(df_copy[col_name])` - (c) `df_copy[col_name].convert_to_datetime()` - (d) `df_copy[col_name] = datetime.strptime(df_copy[col_name], '%Y-%m-%d')`

**Return:** A pandas Series with column names as index and count of NaN as values **Hint:** Use `.isnull().sum()` to count missing values in each column.

**Return:** A pandas DataFrame with descriptive statistics (using .describe())

You have a DataFrame with duplicate rows. The command `drop_duplicates` on subset of columns named `['Name', 'Team']` is to be used.

**Return:** A DataFrame with all rows containing NaN removed **Hint:** Use `.dropna()` to remove rows with missing values, then `.reset_index(drop=True)` to renumber rows.

Write a Python command to fill all missing values in the column 'College' with the text 'Unknown'.

**Return:** A DataFrame where NaN values in numeric columns are replaced by column mean **Hint:** Get numeric columns using `.select_dtypes()`, then use `.fillna()` with the column mean.

**Return:** A DataFrame containing only rows where column > threshold **Hint:** Use boolean indexing `df[df[col_name] > threshold]` and `.reset_index(drop=True)` to reset row indices. **Choose the correct code:** - (a) `return df.filter(column=col_name, value=threshold)` - (b) `return df.loc[df[col_name] > threshold]` - (c) `return df[df[col_name] > threshold].reset_index(drop=True)` - (d) `return df.query(f'{col_name} > {threshold}')`

Write Python code to select rows where 'Profit' is between 30 and 55 (inclusive). **Hint:** Use boolean indexing with AND operator `&`.

**Return:** A tuple of (number of rows, number of columns, list of column names) **Choose the correct code:** - (a) `return (df.size, df.ndim, df.columns)` - (b) `return (df.shape[0], df.shape[1], list(df.columns))` - (c) `return df.info()` - (d) `return (len(df), len(df.index), df.to_list())`

**Return:** DataFrame containing first n rows **Choose the correct code:** - (a) `return df.iloc[:n]` - (b) `return df.head(n)` - (c) `return df.nlargest(n, axis=0)` - (d) `return df[:n:1]`

Write a Python command to show summary statistics (mean, median, std, min, max, etc.) for the entire DataFrame.

**Return:** A DataFrame with grouped results (group column and mean) **Hint:** Use `.groupby(group_col)[agg_col].mean()` and `.reset_index()` to convert to DataFrame.

You have a DataFrame with columns: Name, Team, Salary, Profit Write Python code to: 1. Group the data by Team 2. aggregate average salary and total profit for each team 3. return the result **Hint:** Use `.groupby()` with `.agg()` for multiple aggregations. **Choose the correct code:** - (a) `df.groupby('Team').agg({'Salary': 'mean', 'Profit': 'sum'})` - (b) `df.groupby('Team')[['Salary', 'Profit']].agg(['mean', 'sum'])` - (c) `df.group('Team').apply(lambda x: {'avg_salary': x['Salary'].mean(), 'total_profit': x['Profit'].sum()})`

**Return:** A pandas DataFrame from the CSV string **Choose the correct line:** - (a) `return pd.read_excel(StringIO(csv_string))` - (b) `return pd.read_csv(StringIO(csv_string))` - (c) `return pd.DataFrame(csv_string.split('\n'))` - (d) `return csv_string.to_dataframe()`

**Return:** A merged DataFrame (inner join on the specified key) **Choose the correct code:** - (a) `return left.join(right, on=on)` - (b) `return pd.concat([left, right])` - (c) `return pd.merge(left, right, on=on, how='inner')` - (d) `return left.combine(right)`

**Return:** A pandas Series for the specified column

Given a DataFrame df with a 'Salary' column, write code to increase salary by 5% and store it in a new column 'UpdatedSalary'. **Hint:** Multiply the Salary column by 1.05 to increase by 5%. **Choose the correct code:** - (a) `df['UpdatedSalary'] = df['Salary'] * 5` - (b) `df['UpdatedSalary'] = df['Salary'] * 1.05` - (c) `df['UpdatedSalary'] = df['Salary'] + 0.05` - (d) `df['UpdatedSalary'] = df['Salary'].apply(lambda x: x * 5)`

Given a DataFrame with columns: Name, Score1, Score2 Write Python code to: 1. Select only rows where Score1 > 40 AND Score2 > 50 2. Create a new column AverageScore = mean of Score1 and Score2 3. return dataframe with only the `[['Name', 'AverageScore']]` **Hint:** Filter first using boolean indexing, then add the new column, then select specific columns.

You have a DataFrame with an 'Age' column containing missing values and outliers (Age > 100). Write Python code to: 1. Replace missing values with the median age 2. Remove rows where Age > 100 3. Return the cleaned DataFrame **Hint:** Use `.fillna()` with median, then filter with boolean indexing.

**Return:** A DataFrame where the specified column has been converted to datetime **Choose the correct code:** - (a) `df_copy[col_name] = df_copy[col_name].astype(datetime)` - (b) `df_copy[col_name] = pd.to_datetime(df_copy[col_name])` - (c) `df_copy[col_name].convert_to_datetime()` - (d) `df_copy[col_name] = datetime.strptime(df_copy[col_name], '%Y-%m-%d')`

**Return:** A pandas Series with column names as index and count of NaN as values **Hint:** Use `.isnull().sum()` to count missing values in each column.

**Return:** A pandas DataFrame with descriptive statistics (using .describe())

You have a DataFrame with duplicate rows. The command `drop_duplicates` on subset of columns named `['Name', 'Team']` is to be used.

**Return:** A DataFrame with all rows containing NaN removed **Hint:** Use `.dropna()` to remove rows with missing values, then `.reset_index(drop=True)` to renumber rows.

Write a Python command to fill all missing values in the column 'College' with the text 'Unknown'.

**Return:** A DataFrame where NaN values in numeric columns are replaced by column mean **Hint:** Get numeric columns using `.select_dtypes()`, then use `.fillna()` with the column mean.

**Return:** A DataFrame containing only rows where column > threshold **Hint:** Use boolean indexing `df[df[col_name] > threshold]` and `.reset_index(drop=True)` to reset row indices. **Choose the correct code:** - (a) `return df.filter(column=col_name, value=threshold)` - (b) `return df.loc[df[col_name] > threshold]` - (c) `return df[df[col_name] > threshold].reset_index(drop=True)` - (d) `return df.query(f'{col_name} > {threshold}')`

Write Python code to select rows where 'Profit' is between 30 and 55 (inclusive). **Hint:** Use boolean indexing with AND operator `&`.

**Return:** A tuple of (number of rows, number of columns, list of column names) **Choose the correct code:** - (a) `return (df.size, df.ndim, df.columns)` - (b) `return (df.shape[0], df.shape[1], list(df.columns))` - (c) `return df.info()` - (d) `return (len(df), len(df.index), df.to_list())`

**Return:** DataFrame containing first n rows **Choose the correct code:** - (a) `return df.iloc[:n]` - (b) `return df.head(n)` - (c) `return df.nlargest(n, axis=0)` - (d) `return df[:n:1]`

Write a Python command to show summary statistics (mean, median, std, min, max, etc.) for the entire DataFrame.

**Return:** A DataFrame with grouped results (group column and mean) **Hint:** Use `.groupby(group_col)[agg_col].mean()` and `.reset_index()` to convert to DataFrame.

You have a DataFrame with columns: Name, Team, Salary, Profit Write Python code to: 1. Group the data by Team 2. aggregate average salary and total profit for each team 3. return the result **Hint:** Use `.groupby()` with `.agg()` for multiple aggregations. **Choose the correct code:** - (a) `df.groupby('Team').agg({'Salary': 'mean', 'Profit': 'sum'})` - (b) `df.groupby('Team')[['Salary', 'Profit']].agg(['mean', 'sum'])` - (c) `df.group('Team').apply(lambda x: {'avg_salary': x['Salary'].mean(), 'total_profit': x['Profit'].sum()})`

**Return:** A pandas DataFrame from the CSV string **Choose the correct line:** - (a) `return pd.read_excel(StringIO(csv_string))` - (b) `return pd.read_csv(StringIO(csv_string))` - (c) `return pd.DataFrame(csv_string.split('\n'))` - (d) `return csv_string.to_dataframe()`

**Return:** A merged DataFrame (inner join on the specified key) **Choose the correct code:** - (a) `return left.join(right, on=on)` - (b) `return pd.concat([left, right])` - (c) `return pd.merge(left, right, on=on, how='inner')` - (d) `return left.combine(right)`

**Return:** A pandas Series for the specified column

Given a DataFrame df with a 'Salary' column, write code to increase salary by 5% and store it in a new column 'UpdatedSalary'. **Hint:** Multiply the Salary column by 1.05 to increase by 5%. **Choose the correct code:** - (a) `df['UpdatedSalary'] = df['Salary'] * 5` - (b) `df['UpdatedSalary'] = df['Salary'] * 1.05` - (c) `df['UpdatedSalary'] = df['Salary'] + 0.05` - (d) `df['UpdatedSalary'] = df['Salary'].apply(lambda x: x * 5)`

Given a DataFrame with columns: Name, Score1, Score2 Write Python code to: 1. Select only rows where Score1 > 40 AND Score2 > 50 2. Create a new column AverageScore = mean of Score1 and Score2 3. return dataframe with only the `[['Name', 'AverageScore']]` **Hint:** Filter first using boolean indexing, then add the new column, then select specific columns.

You have a DataFrame with an 'Age' column containing missing values and outliers (Age > 100). Write Python code to: 1. Replace missing values with the median age 2. Remove rows where Age > 100 3. Return the cleaned DataFrame **Hint:** Use `.fillna()` with median, then filter with boolean indexing.

**Return:** A DataFrame where the specified column has been converted to datetime **Choose the correct code:** - (a) `df_copy[col_name] = df_copy[col_name].astype(datetime)` - (b) `df_copy[col_name] = pd.to_datetime(df_copy[col_name])` - (c) `df_copy[col_name].convert_to_datetime()` - (d) `df_copy[col_name] = datetime.strptime(df_copy[col_name], '%Y-%m-%d')`

**Return:** A pandas Series with column names as index and count of NaN as values **Hint:** Use `.isnull().sum()` to count missing values in each column.

**Return:** A pandas DataFrame with descriptive statistics (using .describe())

You have a DataFrame with duplicate rows. The command `drop_duplicates` on subset of columns named `['Name', 'Team']` is to be used.

**Return:** A DataFrame with all rows containing NaN removed **Hint:** Use `.dropna()` to remove rows with missing values, then `.reset_index(drop=True)` to renumber rows.

Write a Python command to fill all missing values in the column 'College' with the text 'Unknown'.

**Return:** A DataFrame where NaN values in numeric columns are replaced by column mean **Hint:** Get numeric columns using `.select_dtypes()`, then use `.fillna()` with the column mean.

**Return:** A DataFrame containing only rows where column > threshold **Hint:** Use boolean indexing `df[df[col_name] > threshold]` and `.reset_index(drop=True)` to reset row indices. **Choose the correct code:** - (a) `return df.filter(column=col_name, value=threshold)` - (b) `return df.loc[df[col_name] > threshold]` - (c) `return df[df[col_name] > threshold].reset_index(drop=True)` - (d) `return df.query(f'{col_name} > {threshold}')`

Write Python code to select rows where 'Profit' is between 30 and 55 (inclusive). **Hint:** Use boolean indexing with AND operator `&`.

**Return:** A tuple of (number of rows, number of columns, list of column names) **Choose the correct code:** - (a) `return (df.size, df.ndim, df.columns)` - (b) `return (df.shape[0], df.shape[1], list(df.columns))` - (c) `return df.info()` - (d) `return (len(df), len(df.index), df.to_list())`

**Return:** DataFrame containing first n rows **Choose the correct code:** - (a) `return df.iloc[:n]` - (b) `return df.head(n)` - (c) `return df.nlargest(n, axis=0)` - (d) `return df[:n:1]`

Write a Python command to show summary statistics (mean, median, std, min, max, etc.) for the entire DataFrame.

**Return:** A DataFrame with grouped results (group column and mean) **Hint:** Use `.groupby(group_col)[agg_col].mean()` and `.reset_index()` to convert to DataFrame.

You have a DataFrame with columns: Name, Team, Salary, Profit Write Python code to: 1. Group the data by Team 2. aggregate average salary and total profit for each team 3. return the result **Hint:** Use `.groupby()` with `.agg()` for multiple aggregations. **Choose the correct code:** - (a) `df.groupby('Team').agg({'Salary': 'mean', 'Profit': 'sum'})` - (b) `df.groupby('Team')[['Salary', 'Profit']].agg(['mean', 'sum'])` - (c) `df.group('Team').apply(lambda x: {'avg_salary': x['Salary'].mean(), 'total_profit': x['Profit'].sum()})`

**Return:** A pandas DataFrame from the CSV string **Choose the correct line:** - (a) `return pd.read_excel(StringIO(csv_string))` - (b) `return pd.read_csv(StringIO(csv_string))` - (c) `return pd.DataFrame(csv_string.split('\n'))` - (d) `return csv_string.to_dataframe()`

**Return:** A merged DataFrame (inner join on the specified key) **Choose the correct code:** - (a) `return left.join(right, on=on)` - (b) `return pd.concat([left, right])` - (c) `return pd.merge(left, right, on=on, how='inner')` - (d) `return left.combine(right)`

**Return:** A pandas Series for the specified column

Given a DataFrame df with a 'Salary' column, write code to increase salary by 5% and store it in a new column 'UpdatedSalary'. **Hint:** Multiply the Salary column by 1.05 to increase by 5%. **Choose the correct code:** - (a) `df['UpdatedSalary'] = df['Salary'] * 5` - (b) `df['UpdatedSalary'] = df['Salary'] * 1.05` - (c) `df['UpdatedSalary'] = df['Salary'] + 0.05` - (d) `df['UpdatedSalary'] = df['Salary'].apply(lambda x: x * 5)`

Given a DataFrame with columns: Name, Score1, Score2 Write Python code to: 1. Select only rows where Score1 > 40 AND Score2 > 50 2. Create a new column AverageScore = mean of Score1 and Score2 3. return dataframe with only the `[['Name', 'AverageScore']]` **Hint:** Filter first using boolean indexing, then add the new column, then select specific columns.

You have a DataFrame with an 'Age' column containing missing values and outliers (Age > 100). Write Python code to: 1. Replace missing values with the median age 2. Remove rows where Age > 100 3. Return the cleaned DataFrame **Hint:** Use `.fillna()` with median, then filter with boolean indexing.

**Return:** A DataFrame where the specified column has been converted to datetime **Choose the correct code:** - (a) `df_copy[col_name] = df_copy[col_name].astype(datetime)` - (b) `df_copy[col_name] = pd.to_datetime(df_copy[col_name])` - (c) `df_copy[col_name].convert_to_datetime()` - (d) `df_copy[col_name] = datetime.strptime(df_copy[col_name], '%Y-%m-%d')`

**Return:** A pandas Series with column names as index and count of NaN as values **Hint:** Use `.isnull().sum()` to count missing values in each column.

**Return:** A pandas DataFrame with descriptive statistics (using .describe())

You have a DataFrame with duplicate rows. The command `drop_duplicates` on subset of columns named `['Name', 'Team']` is to be used.

**Return:** A DataFrame with all rows containing NaN removed **Hint:** Use `.dropna()` to remove rows with missing values, then `.reset_index(drop=True)` to renumber rows.

Write a Python command to fill all missing values in the column 'College' with the text 'Unknown'.

**Return:** A DataFrame where NaN values in numeric columns are replaced by column mean **Hint:** Get numeric columns using `.select_dtypes()`, then use `.fillna()` with the column mean.

**Return:** A DataFrame containing only rows where column > threshold **Hint:** Use boolean indexing `df[df[col_name] > threshold]` and `.reset_index(drop=True)` to reset row indices. **Choose the correct code:** - (a) `return df.filter(column=col_name, value=threshold)` - (b) `return df.loc[df[col_name] > threshold]` - (c) `return df[df[col_name] > threshold].reset_index(drop=True)` - (d) `return df.query(f'{col_name} > {threshold}')`

Write Python code to select rows where 'Profit' is between 30 and 55 (inclusive). **Hint:** Use boolean indexing with AND operator `&`.

**Return:** A tuple of (number of rows, number of columns, list of column names) **Choose the correct code:** - (a) `return (df.size, df.ndim, df.columns)` - (b) `return (df.shape[0], df.shape[1], list(df.columns))` - (c) `return df.info()` - (d) `return (len(df), len(df.index), df.to_list())`

**Return:** DataFrame containing first n rows **Choose the correct code:** - (a) `return df.iloc[:n]` - (b) `return df.head(n)` - (c) `return df.nlargest(n, axis=0)` - (d) `return df[:n:1]`

Write a Python command to show summary statistics (mean, median, std, min, max, etc.) for the entire DataFrame.

**Return:** A DataFrame with grouped results (group column and mean) **Hint:** Use `.groupby(group_col)[agg_col].mean()` and `.reset_index()` to convert to DataFrame.

You have a DataFrame with columns: Name, Team, Salary, Profit Write Python code to: 1. Group the data by Team 2. aggregate average salary and total profit for each team 3. return the result **Hint:** Use `.groupby()` with `.agg()` for multiple aggregations. **Choose the correct code:** - (a) `df.groupby('Team').agg({'Salary': 'mean', 'Profit': 'sum'})` - (b) `df.groupby('Team')[['Salary', 'Profit']].agg(['mean', 'sum'])` - (c) `df.group('Team').apply(lambda x: {'avg_salary': x['Salary'].mean(), 'total_profit': x['Profit'].sum()})`

**Return:** A pandas DataFrame from the CSV string **Choose the correct line:** - (a) `return pd.read_excel(StringIO(csv_string))` - (b) `return pd.read_csv(StringIO(csv_string))` - (c) `return pd.DataFrame(csv_string.split('\n'))` - (d) `return csv_string.to_dataframe()`

**Return:** A merged DataFrame (inner join on the specified key) **Choose the correct code:** - (a) `return left.join(right, on=on)` - (b) `return pd.concat([left, right])` - (c) `return pd.merge(left, right, on=on, how='inner')` - (d) `return left.combine(right)`

**Return:** A pandas Series for the specified column

Given a DataFrame df with a 'Salary' column, write code to increase salary by 5% and store it in a new column 'UpdatedSalary'. **Hint:** Multiply the Salary column by 1.05 to increase by 5%. **Choose the correct code:** - (a) `df['UpdatedSalary'] = df['Salary'] * 5` - (b) `df['UpdatedSalary'] = df['Salary'] * 1.05` - (c) `df['UpdatedSalary'] = df['Salary'] + 0.05` - (d) `df['UpdatedSalary'] = df['Salary'].apply(lambda x: x * 5)`

Given a DataFrame with columns: Name, Score1, Score2 Write Python code to: 1. Select only rows where Score1 > 40 AND Score2 > 50 2. Create a new column AverageScore = mean of Score1 and Score2 3. return dataframe with only the `[['Name', 'AverageScore']]` **Hint:** Filter first using boolean indexing, then add the new column, then select specific columns.

You have a DataFrame with an 'Age' column containing missing values and outliers (Age > 100). Write Python code to: 1. Replace missing values with the median age 2. Remove rows where Age > 100 3. Return the cleaned DataFrame **Hint:** Use `.fillna()` with median, then filter with boolean indexing.

**Return:** A DataFrame where the specified column has been converted to datetime **Choose the correct code:** - (a) `df_copy[col_name] = df_copy[col_name].astype(datetime)` - (b) `df_copy[col_name] = pd.to_datetime(df_copy[col_name])` - (c) `df_copy[col_name].convert_to_datetime()` - (d) `df_copy[col_name] = datetime.strptime(df_copy[col_name], '%Y-%m-%d')`

**Return:** A pandas Series with column names as index and count of NaN as values **Hint:** Use `.isnull().sum()` to count missing values in each column.

**Return:** A pandas DataFrame with descriptive statistics (using .describe())

You have a DataFrame with duplicate rows. The command `drop_duplicates` on subset of columns named `['Name', 'Team']` is to be used.