Informatics Practices · Class 12 Optional

Ch 2Data Handling using Pandas – I — Class 12 Informatics Practices, concept-first.

A Python library is a collection of built-in modules that let you perform many actions without writing detailed programs from scratch. Each library contains a large number of modules that you can import and use as needed. For scientific and analytical work, three libraries stand out: NumPy, Pandas, and Matplotlib.

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Pandas Series Creation

Imagine you have a messy desk with loose notes, receipts, and to-do lists scattered everywhere. Now imagine you take a single strip of paper, write one item on each line in a fixed order, and label the strip "Monday's Ex…

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Chapter contents

The NCERT structure, section by section. Open a section to see its questions, then read the concept-first solution.

2.1

Introduction to Python Libraries

A Python library is a collection of built-in modules that let you perform many actions without writing detailed programs from scratch.

2.1.1

Installing Pandas

Installing Pandas is very similar to installing NumPy. Both libraries are external to Python, meaning they are not part of the standard library that comes with a fresh Python installation.

2.1.2

Data Structure in Pandas

A data structure is simply a way to organise data so that it can be stored, accessed, and changed efficiently. You have already seen one such structure in NumPy — the ndarray.

2.2

Series

A Series is the most basic data structure in Pandas. Think of it as a single column of data, like one column in an Excel spreadsheet.

2.2.1

Creation of Series

There are different ways in which a Series can be created in Pandas. Whichever way we choose, the first step is always the same: to create or use a Series, we must first import the Pandas library.

(A)

Creation of Series from Scalar Values

2 Q

The simplest way to build a Series is directly from scalar values — a plain Python list of data items passed to pd.Series():

+Activities1 question

Activity 2.1Create a series having names of any five famous monuments of India and assign their States as index values.Preview

+Think & Reflect1 question

Q1While importing Pandas, is it mandatory to always use pd as an alias name? What would happen if we give any other name?Preview

(B)

Creation of Series from NumPy Arrays

A Series can also be created from a one-dimensional (1D) NumPy array. NumPy is imported alongside Pandas (conventionally with the alias np), the array is built with np.array(), and the array object is…

(C)

Creation of Series from Dictionary

Recall that a Python dictionary stores key: value pairs, and a value can be quickly retrieved when its key is known.

2.2.2

Accessing Elements of a Series

Once a Series has been created, we need ways to get at its contents. There are two common ways of accessing the elements of a Series: indexing and slicing.

(A)

Indexing

Indexing is the mechanism for accessing individual elements of a Series, and it works very much like indexing on NumPy arrays. Pandas supports two kinds of indexes:

+Activities1 question

Activity 2.2Write the statement to get NewDelhi as output using positional index. [Context: In the textbook, seriesCapCntry = pd.Series(['NewDelhi', 'Wa…Preview

(B)

Slicing

Sometimes we need only a part of a Series rather than a single element or the whole thing. Extracting a contiguous portion is called slicing, and it works like slicing on NumPy arrays: we specify star…

2.2.3

Attributes of Series

A Series object in Pandas is more than just a labelled array — it carries several built-in properties, called attributes, that let you inspect or label the data directly.

2.2.4

Methods of Series

A Pandas Series comes with built-in methods that let you quickly inspect, summarise, and manipulate your data. These methods save you from writing loops or manual slicing.

+Activities1 question

Activity 2.3Consider the following code: >>>import pandas as pd >>>import numpy as np >>>s2=pd.Series([12,np.nan,10]) >>>print(s2) Find output of the ab…Preview

2.2.5

Mathematical Operations on Series

In Class XI we saw that basic mathematical operations — addition, subtraction, multiplication, division, and so on — applied to two NumPy arrays act on each corresponding pair of elements.

(A)

Addition of two Series

Two Series can be added in two ways.

(B)

Subtraction of two Series

Subtraction, like addition, can be done in two different ways.

+Activities1 question

Activity 2.4Draw two tables for subtraction similar to tables 2.2 and 2.3 showing the changes in the series elements and corresponding output without re…Preview

(C)

Multiplication of two Series

Multiplication of two Series follows the same two-way pattern.

+Activities1 question

Activity 2.5Draw two tables for multiplication similar to Tables 2.2 and 2.3 showing the changes in the series elements and corresponding output without…Preview

(D)

Division of two Series

Division of two Series, once again, can be done in two different ways.

+Activities1 question

Activity 2.6Draw two tables for division similar to tables 2.2 and 2.3 showing the changes in the series elements and corresponding output without repla…Preview

2.3

DataFrame

A DataFrame is what you reach for when a single Series — one column of data — is not enough. Real-world data is tabular: a mark sheet with student names, subjects, and marks; a restaurant menu with it…

2.3.1

Creation of DataFrame

There are a number of ways to create a DataFrame, and this section lists some of them. Each of the lettered sub-sections that follow demonstrates one creation method with working code and its output.

(A)

Creation of an empty DataFrame

The simplest DataFrame of all is an empty one — no columns, no rows. It is created by calling the DataFrame() constructor with no arguments:

(B)

Creation of DataFrame from NumPy ndarrays

Pandas can build a DataFrame directly from NumPy ndarrays. To try this, first import NumPy and create three arrays:

+Think & Reflect1 question

Q2What would happen if we pass 3 columns or 5 columns instead of 4 in the above code? What is the reason? [Context: The referenced textbook co…Preview

(C)

Creation of DataFrame from List of Dictionaries

A DataFrame can also be created from a list of dictionaries. Each dictionary in the list becomes one row of the DataFrame:

(D)

Creation of DataFrame from Dictionary of Lists

DataFrames can also be created from a dictionary of lists. Here each key of the dictionary names a column, and the list stored against that key supplies the values going down that column.

(E)

Creation of DataFrame from Series

A DataFrame can be created from one or more Series. Consider the following three Series (note that seriesC deliberately uses a different set of index labels):

(F)

Creation of DataFrame from Dictionary of Series

A dictionary of Series can also be used to create a DataFrame. The textbook's example is ResultSheet, a dictionary of Series holding the marks of 5 students in three subjects.

+Activities1 question

Activity 2.7Use the type function to check the datatypes of ResultSheet and ResultDF. Are they the same? [Context: In the textbook, ResultSheet is a dic…Preview

2.3.2

Operations on Rows and Columns in DataFrames

Once a DataFrame exists, we are not stuck with it as-is. We can perform some basic operations on its rows and columns — selection, deletion, addition, and renaming — and this section works through eac…

(A)

Adding a New Column to a DataFrame

Adding a new column to a DataFrame is as simple as assigning a list of values to a new column label. Consider the DataFrame ResultDF defined earlier (5 students × 3 subjects).

(B)

Adding a New Row to a DataFrame

A new row is added to a DataFrame using the DataFrame.loc[] method. Consider ResultDF, which has three rows for the three subjects — Maths, Science and Hindi:

+Think & Reflect1 question

Q3Can you write a program to count the number of rows and columns in a DataFrame?Preview

(C)

Deleting Rows or Columns from a DataFrame

Rows and columns are deleted from a DataFrame with the DataFrame.drop() method. We need to tell it two things: the names of the labels to be dropped, and the axis from which they are to be dropped — a…

(D)

Renaming Row Labels of a DataFrame

Once a DataFrame has been created, we are not stuck with the row labels it was given — Pandas lets us change the labels of rows (and columns) using the DataFrame.rename() method.

+Think & Reflect1 question

Q4What if in the rename function we pass a value for a row label that does not exist?Preview

(E)

Renaming Column Labels of a DataFrame

The same rename() method that renames row labels also renames column labels — the only change is the axis.

2.3.3

Accessing DataFrames Element through Indexing

Individual data elements in a DataFrame can be accessed using indexing. Pandas offers two ways of indexing DataFrames: label based indexing, where we pick out data by the names of rows and columns, an…

(A)

Label Based Indexing

Pandas provides several methods for label based indexing, and the most important of them for DataFrames is DataFrame.loc[ ].

+Think & Reflect1 question

Q5What would happen if the label or row index passed is not present in the DataFrame?Preview

(B)

Boolean Indexing

Boolean means a binary variable that can represent either of two states — True (indicated by 1) or False (indicated by 0).

2.3.4

Accessing DataFrames Element through Slicing

Slicing is a powerful way to select a subset of rows and/or columns from a DataFrame. Unlike Python lists, slicing in DataFrames is inclusive of the end value — meaning both the start and the stop lab…

+Activities1 question

Activity 2.8(a) Using the DataFrame ResultDF, write the statement to access Marks of Arnab in Maths. (b) Create a DataFrame having 5 rows and write the…Preview

Filtering Rows in DataFrames

Filtering rows in a DataFrame using .loc[] can also be done with a Boolean list — a list of True/False values, one per row, in the same order as the DataFrame's rows.

2.3.5

Joining, Merging and Concatenation of DataFrames

Pandas lets us combine the data of two DataFrames into one. Although this section's title names joining, merging and concatenation together, the textbook (Reprint 2026-27) works these out through a si…

(A)

Joining

The pandas.DataFrame.append() method is used to merge two DataFrames: it appends the rows of the second DataFrame at the end of the first.

+Think & Reflect1 question

Q6How can you check whether a given DataFrame has any missing value or not?Preview

2.3.6

Attributes of DataFrames

python ForestArea = 'Assam' : pd.Series([78438, 2797, 10192, 15116], index = ['GeoArea', 'VeryDense', 'ModeratelyDense', 'OpenForest']), 'Kerala' : pd.Series([38852, 1663, 9407, 9251], index = ['Geo…

2.4

Importing and Exporting Data between CSV Files and DataFrames

We often have data stored in CSV (Comma Separated Values) files — a very common format for spreadsheets and databases.

2.4.1

Importing a CSV file to a DataFrame

A CSV (Comma Separated Values) file stores tabular data as plain text, where each line is a row and values are separated by commas.

+Think & Reflect1 question

Q7What are the other parameters that can be used with read_csv() function? You may explore from https://pandas.pydata.org.Preview

2.4.2

Exporting a DataFrame to a CSV file

After you have created, cleaned, or processed a DataFrame in Python, you often need to save it for later use — either to share with others, to open in a spreadsheet like Excel, or to load into another…

+Think & Reflect1 question

Q8Besides comma, what are the other allowed characters that can be used as a separator while creating a CSV file frmo a DataFrame?Preview

2.5

Pandas Series Vs NumPy ndarray

The fundamental difference between a Pandas Series and a NumPy ndarray is not about speed or memory — it is about identity. A NumPy array is a grid of numbers accessed by their integer position.

Summary

This chapter covered the two core Pandas data structures and how to move data between them and CSV files.

Exercises

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CBSE Sample Papers

Questions from official CBSE sample papers.

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