Carefully observe the following code: import pandas as pd dic = 'pid' : [101, 102, 103, 104, 105], 'pname' : [

Question

Q. Carefully observe the following code: import pandas as pd dic = {'pid' : [101, 102, 103, 104, 105], 'pname' : ['Shyam', 'Roushan', 'Archit', 'Medha', 'Lalit'], 'sports' : ['Cricket', 'Tennis', 'Football', 'Cricket', 'Cricket'], 'points' : [45000, 20000, 15000, 53000, 60000]} player = pd.DataFrame(dic) print(player) Write Python statements for the following:

(i) In the data frame player created above, set the row labels as 'Player 1', 'Player 2', 'Player 3', 'Player 4', 'Player 5'.

(ii) Rename the column 'points' to 'net point' in the Data Frame player.

CBSECBSE Class XII Board 2023Subjective· 2mImportance★★★★★

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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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Let's break this down step by step.

The code given creates a DataFrame player from a dictionary. By default, the row labels (index) are integers 0, 1, 2, 3, 4. The columns are pid, pname, sports, points.

Setting custom row labels To change the row labels to the strings given, you reassign the index attribute of the DataFrame. The statement is: player.index = ['Player 1', 'Player 2', 'Player 3', 'Player 4', 'Player 5'] This directly replaces the default integer index with your list. The number of new labels must match the number of rows (5 in this case).
Renaming a column To rename the column 'points' to 'net point', use the rename() method. You pass a dictionary to the columns parameter, mapping old names to new names. The statement is: player.rename(columns = {'points': 'net point'}, inplace = True) …

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This question tests your ability to modify a Pandas DataFrame's row labels and column names after creation — two fundamental data-wrangling operations.

Let's first understand what the given code does. It creates a dictionary dic with four keys — pid, pname, sports, and points — each mapped to a list of five values. When you pass this dictionary to pd.DataFrame(), Pandas automatically uses the dictionary keys as column names and assigns default integer row labels (0, 1, 2, 3, 4). The resulting DataFrame player looks like this:

   pid    pname    sports  points
0  101    Shyam   Cricket   45000
1  102  Roushan    Tennis   20000
2  103   Archit  Football   15000
3  104    Medha   Cricket   53000
4  105    Lalit   Cricket   60000

Now, the question asks you to write Python statements for two specific modifications.

Part (i): Setting custom row labels

The default row labels (0 through 4) are fine for many purposes, but sometimes you want more meaningful identifiers. Here, you need to replace them with 'Player 1', 'Player 2', and so on. The Pandas attribute that holds row labels is called the index. You can assign a new index using the .index attribute directly.

The statement is:

player.index = ['Player 1', 'Player 2', 'Player 3', 'Player 4', 'Player 5']

This overwrites the existing index with your custom list. After this, the DataFrame will display with those labels on the left.

Note

The length of the new index list must exactly match the number of rows in the DataFrame — here, five rows, so five labels. If the lengths differ, Pandas will raise an error.

Part (ii): Renaming the 'points' column to 'net point'

Column names are stored in the .columns attribute, but you should not directly assign to .columns for a single rename — that would require you to list every column name. Instead, Pandas provides the .rename() method, which lets you change specific column names using a dictionary mapping old names to new names.

The statement is:

player.rename(columns={'points': 'net point'}, inplace=True)
``` …

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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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