Kavyanjali, a chemical analyst, needs to arrange data of few elements in the form of two series containing sym

Question

Q. Kavyanjali, a chemical analyst, needs to arrange data of few elements in the form of two series containing symbols and their atomic numbers respectively. Thereafter, the data of these two series has to be arranged and displayed in the form of Data Frame as shown below: Symbol Atomic Number Hydrogen H 1 Helium He 2 Lithium Li 3 Beryllium Be 4 Help her in writing suitable Python code to complete the task.

CBSECBSE Class XII Board 2023Subjective· 3mImportance★★★★★

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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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Kavyanjali needs two Python lists: one holding element symbols and the other their atomic numbers. She can then pass both lists into a dictionary where each key is a column name and each value is the corresponding list. The pandas DataFrame constructor accepts this dictionary and builds the table in one step.

Here's the code:

import pandas as pd

symbols = ['H', 'He', 'Li', 'Be']
atomic_numbers = [1, 2, 3, 4]

df = pd.DataFrame({'Symbol': symbols, 'Atomic Number': atomic_numbers}, 
                  index=['Hydrogen', 'Helium', 'Lithium', 'Beryllium'])

print(df)
``` …

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Kavyanjali needs to create a pandas DataFrame with two columns—element symbols and atomic numbers—by first storing the data in separate lists, then combining them into a dictionary, and finally converting that dictionary into a DataFrame for display.

The task at hand is straightforward but foundational: organizing chemical data into a structured table that Python can manipulate and display. Kavyanjali has raw information—element names, their symbols, and atomic numbers—and needs to transform it into a DataFrame, which is essentially a two-dimensional labeled data structure, much like a spreadsheet or SQL table. The pandas library in Python is the standard tool for this job.

She begins by recognizing that the data naturally falls into two parallel sequences: one holding the chemical symbols (H, He, Li, Be) and the other holding the corresponding atomic numbers (1, 2, 3, 4). The most intuitive approach is to store these as two Python lists. Once the lists are ready, she can pair them together—symbols as one column, atomic numbers as another—and feed them into a DataFrame constructor.

The pandas library offers multiple ways to build a DataFrame, but the cleanest method here is to use a dictionary. Each key in the dictionary becomes a column name, and the associated list becomes the column's data. The DataFrame constructor then reads this dictionary and arranges everything into rows and columns, automatically aligning the data by position.

Here is the Python code that accomplishes this:

import pandas as pd

# Step 1: Create two lists for symbols and atomic numbers
symbols = ['H', 'He', 'Li', 'Be']
atomic_numbers = [1, 2, 3, 4]

# Step 2: Combine them into a dictionary
data = {
    'Symbol': symbols,
    'Atomic Number': atomic_numbers
}

# Step 3: Create the DataFrame
df = pd.DataFrame(data)

# Step 4: Display the DataFrame
print(df)

When this code runs, the output will be:

  Symbol  Atomic Number
0      H              1
1     He              2
2     Li              3
3     Be              4
``` …

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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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