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4. Which of the following methods can be used to set the column 'RollNumber' as the index of a DataFrame named
'df'?
a. df.set_index('RollNumber')
b. df.index('RollNumber')
c. df.set_index(['RollNumber'])
d. df.index = 'RollNumber'
5. Which of the following method is used for label-based indexing in Pandas DataFrame to access specific rows and columns?
a. df.loc[] b. df.iloc[] c. df.idx[] d. df.get()
6. Which of the following is the correct way to access the value in the second row and third column of a DataFrame using
integer-based indexing?
a. df.get(2, 3) b. df.loc[1, 2] c. df.iloc[1, 2] d. df.slice(1, 2, 2, 3)
7. Which of the following statement will correctly create a new DataFrame df2 containing only rows where column A is True
in df?
a. df2 = df[df['A'] == True]
b. df2 = df[df['A']]
c. df2 = df[df['A'].bool()]
d. df2 = df[df['A'].isin([True])]
8. Consider the following Pandas Series representing color names:
s = pd.Series(['Red', 'Green', 'Blue'])
Which of the following statements can be used create a DataFrame with two columns: Color containing the color names
and Code containing the corresponding color codes ['R', 'G', 'B']?
a. df = pd.DataFrame({'Color': s, 'Code': ['R', 'G', 'B']})
b. df = pd.DataFrame(s, columns=['Color'])
c. df = pd.DataFrame({'Color': s, 'Code': s.str[0]})
d. df = pd.DataFrame.from_dict({'Color': s, 'Code': ['R', 'G', 'B']})
9. Which of the following statements can be used to set the column names of a DataFrame df to ['Name', 'Age',
'City']?
a. df.columns = ['Name', 'Age', 'City']
b. df.set_columns(['Name', 'Age', 'City'])
c. df.rename_columns(['Name', 'Age', 'City'])
d. df.rename(columns={'Name': 'Name', 'Age': 'Age', 'City': 'City'})
10. How can you create a new DataFrame df containing only rows where the Revenue is greater than 5000 in df?
a. df2 = df[df['Revenue'] > 5000]
b. df2 = df[df['Revenue']]
c. df2 = df[df['Revenue'].bool()]
d. df2 = df[df['Revenue'].isin([5000])]
B. State whether the following statements are True or False:
1. Given a DataFrame named 'df', The slice df.loc[2:5] will yields rows from index 2 to 5 (inclusive). _________
2. To slice rows and columns in a DataFrame using positional integer-based indexing, we can use the
loc attribute. _________
3. The index attribute of the DataFrame can be used to set column labels in a DataFrame. _________
4. The default row and column indexes also known as row and column labels begin with 0. _________
5. The keyword usecols of pd.read_csv is used to specify the list of irrelevant columns. _________
6. Boolean indexing is used to filter a DataFrame based on a boolean condition. _________
7. The keyword argument inplace should be set to False if we want the methods associated with the
DataFrame to modify the original dataframe directly. _________
Data Handling using Pandas DataFrame 67

