Consider the following Python code : import pandas as pd S1=pd.Series(['Rubina','Jaya','Vaibhav'],index=[10,16

Question

Q. Consider the following Python code : import pandas as pd S1=pd.Series(['Rubina','Jaya','Vaibhav'],index=[10,16,18]) S2=pd.Series(, index=[10,16,18]) S3=pd.Series([56,67,86],) xiia={'Name':,'Subject':S2,'Marks':S3} df=pd.DataFrame() print(df) Complete the above Python code to display the following output : Name Subject Marks 10 Rubina IP 56 16 Jaya HSc 67 18 Vaibhav IP 86

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Concept understanding — DataFrame Creation

DataFrame Creation: A First Look

Think of a spreadsheet you might use to track your monthly expenses. You have rows — one for each day or each purchase — and columns with labels like "Date", "Item", "Category", "Amount", and "Payment Method". That grid of labelled rows and columns is exactly what a DataFrame is in computing. It is a two-dimensional, table-like structure for storing and working with data.

The word "DataFrame" comes from two ideas: data (the information itself) and frame (the structure that holds it together). You can imagine it as a digital filing cabinet where every drawer (column) has a name, every folder (row) has a number, and every cell contains a single piece of information — a date, a word, a number, or even a missing entry.

The Core Idea

A DataFrame is not just a random collection of numbers and text. It is an organised table where:

Each column represents a specific attribute or variable — for example, "Product Name", "Quantity Sold", "Price per Unit".
Each row represents a single observation or record — one sale, one customer, one transaction.
Every column has a label (its name), and every row has an index (its position or label).

This organisation is what makes a DataFrame powerful. You can ask questions like "Show me all rows where the category is 'Groceries'" or "What is the average amount spent in March?" — and the structure gives you a clear, fast way to get answers.

How a DataFrame Is Created

Creating a DataFrame means taking raw data and arranging it into this labelled table. The data can come from many places:

A list of dictionaries, where each dictionary is one row and the keys become column names.
A dictionary of lists, where each key is a column name and the list holds the values for that column.
A CSV file (a comma-separated values file) that you read from your computer.
Data entered manually, row by row.

Note

The key point is that creation always involves two decisions: what the column names will be, and what data goes under each column. The row index is usually assigned automatically (0, 1, 2, …) unless you specify your own labels.

Why It Matters

For a commerce or humanities student, the DataFrame is the single most useful tool for working with real-world data. Think of a small business tracking its inventory: the owner needs to see product names, stock levels, reorder dates, and supplier details all in one place. A DataFrame lets them do that, and then filter, sort, or summarise the information without rewriting everything by hand.

In academic work — analysing survey responses, comparing economic indicators across years, or studying historical records — the DataFrame turns messy, scattered data into a clean, queryable table. You can add new columns (like "Profit" calculated from "Revenue" minus "Cost"), remove rows that are incomplete, or merge two tables on a common column (like matching customer IDs from a sales table with names from a customer table).

Important

A DataFrame is not the same as a simple list or a plain table in a word processor. It is a structured object that understands its own columns, rows, and data types. This understanding is what allows you to perform operations — like finding the total sales for a region — with a single command, rather than manually scanning hundreds of rows. …

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The code creates a DataFrame from three Series. The first Series S1 holds the names with indices 10, 16, 18. The second Series S2 must hold the subjects corresponding to those same indices — looking at the output, the subjects are IP, HSc, IP. So S2 should be a list ['IP','HSc','IP'] with the same index [10,16,18]. The third Series S3 holds marks [56,67,86] — its index must match the others, so the missing argument is index=[10,16,18].

The dictionary xiia uses the key 'Name' to refer to S1, 'Subject' to S2, and 'Marks' to S3. Finally, the DataFrame is created from this dictionary, so the missing argument in pd.DataFrame(______) is xiia.

First blank (for S2): ['IP','HSc','IP'] …

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The code builds a DataFrame from three Series, where the missing parts supply the second Series’ data, the third Series’ index, the dictionary key for the Name column, and the dictionary itself as the DataFrame argument.

Let’s walk through what this code is trying to do. You have three Series — S1, S2, and S3 — each with the same index [10, 16, 18]. The first Series, S1, is already given: it holds the names 'Rubina', 'Jaya', and 'Vaibhav'. The output shows that the DataFrame will have three columns: Name, Subject, and Marks. The index of the DataFrame is the same [10, 16, 18], which matches the indices of the Series.

Now look at the output row by row. For index 10, the Name is Rubina, Subject is IP, and Marks is 56. For index 16, Name is Jaya, Subject is HSc, and Marks is 67. For index 18, Name is Vaibhav, Subject is IP, and Marks is 86.

So the missing pieces are:

S2 — the Subject Series. It must contain ['IP', 'HSc', 'IP'] in that order, because that’s what appears in the output for indices 10, 16, and 18 respectively. So the blank in S2=pd.Series(______, index=[10,16,18]) should be filled with ['IP','HSc','IP'].
S3 — the Marks Series. It already has the data [56, 67, 86], but its index is missing. The output shows that the marks align with the same index [10, 16, 18]. So the blank in S3=pd.Series([56,67,86],______) should be index=[10,16,18].
The dictionary xiia — it maps column names to the Series. The key for the Name column is missing. Since S1 holds the names, the blank in 'Name':______ should be S1. The other two keys ('Subject' and 'Marks') are already correctly assigned to S2 and S3.
The DataFrame constructor — pd.DataFrame(______). The dictionary xiia is already built, so this blank should simply be xiia. …

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Same / Similar Concept — real previous-year questions on the same or a closely similar concept, not this exact question.

CBSE 2025Set 90/1/11 mark
$Q. State whether the following statement is True or False: In Python, we cannot create an empty DataFrame.$
›Reveal solutionSolution
Python's pandas library allows you to create an empty DataFrame with no rows or columns using pd.DataFrame(). The statement is False.

Why DataFrames can be empty

A DataFrame in pandas is a two-dimensional labeled data structure, essentially a table with rows and columns. Like any container in programming, it doesn't need to start with content. You can initialize an empty DataFrame and populate it later as data becomes available—this is common when building up results iteratively or when you need a structure ready before data arrives.

The flexibility to create empty DataFrames is actually quite useful in practice. You might start with an empty frame and add columns one by one, or append rows as you process data in a loop.

Creating an empty DataFrame

Here are the standard ways to create an empty DataFrame in pandas:

Completely empty (no rows, no columns):

import pandas as pd df = pd.DataFrame()

This gives you a DataFrame with shape (0, 0).

Empty with predefined columns:

df = pd.DataFrame(columns=['Name', 'Age', 'City'])

This creates a DataFrame with three columns but zero rows—shape (0, 3). The structure is ready; you just need to add data.

Empty with a specific index:

df = pd.DataFrame(index=range(5)) ``` …
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