Q.Consider the DataFrame Scholarship shown below : (3 + 1 + 1 = 5) Name Course Amount 0 Ananya B.Sc. 62000 1 Karan B.Tech 67000 2 Simran MCA 64000 3 Rahul BCA 70000 4 Priya BA 69000 Answer the following questions : I. Write Python code to create and display the given DataFrame using the Dictionary of Series method. II. Rename row indexes to ['a', 'b', 'c', 'd', 'e'] III. Write Python statement to remove the last row of the given DataFrame

A DataFrame can be built from a dictionary where each key is a column name and each value is a pandas Series holding that column's data. When you pass such a dictionary to pd.DataFrame(), pandas aligns the Series by their indexes and constructs the table.

Part I: You create three Series—one each for Name, Course, and Amount—with indexes 0 through 4, then bundle them into a dictionary and pass it to the DataFrame constructor:

import pandas as pd

s1 = pd.Series(['Ananya', 'Karan', 'Simran', 'Rahul', 'Priya'], index=[0,1,2,3,4])
s2 = pd.Series(['B.Sc.', 'B.Tech', 'MCA', 'BCA', 'BA'], index=[0,1,2,3,4])
s3 = pd.Series([62000, 67000, 64000, 70000, 69000], index=[0,1,2,3,4])

df = pd.DataFrame({'Name': s1, 'Course': s2, 'Amount': s3})
print(df)

Part II: To replace the row labels, assign a new list to the index attribute:

df.index = ['a', 'b', 'c', 'd', 'e']
``` …

Creating the DataFrame from a Dictionary of Series

When you build a DataFrame in pandas, you have several construction methods at your disposal. The dictionary-of-Series approach treats each column as a separate Series object, then bundles them together under column names. Each Series holds the values for one column, and pandas aligns them by their indexes to form rows.

For the Scholarship DataFrame, you need three columns: Name, Course, and Amount. Each becomes a Series. The beauty of this method is that it makes the structure explicit—you see exactly what data belongs to which column, and pandas handles the row alignment automatically.

Here's the code:

import pandas as pd

# Create Series for each column
name_series = pd.Series(['Ananya', 'Karan', 'Simran', 'Rahul', 'Priya'])
course_series = pd.Series(['B.Sc.', 'B.Tech', 'MCA', 'BCA', 'BA'])
amount_series = pd.Series([62000, 67000, 64000, 70000, 69000])

# Build DataFrame from dictionary of Series
scholarship = pd.DataFrame({
    'Name': name_series,
    'Course': course_series,
    'Amount': amount_series
})

print(scholarship)

The dictionary keys become column headers, and the Series values populate the rows. By default, pandas assigns integer indexes starting from 0, which matches the original DataFrame shown in the question.

Renaming Row Indexes

Row indexes in a DataFrame are stored in the index attribute. When you want to replace the default numeric indexes with custom labels, you assign a new list to this attribute. The list must have exactly as many elements as there are rows, otherwise pandas will raise an error.

For this DataFrame with five rows, renaming to ['a', 'b', 'c', 'd', 'e'] is straightforward:

scholarship.index = ['a', 'b', 'c', 'd', 'e']
print(scholarship)

After this operation, the DataFrame will display with alphabetic row labels instead of 0, 1, 2, 3, 4. The data itself remains unchanged—only the way you reference rows has shifted. You can now select row 'a' instead of row 0. …