NCERT Solutions Class 12 Informatics Practices Chapter 2 2026-27

Around 8 to 10 marks of the Class 12 Informatics Practices theory paper come from Data Handling using Pandas Series. The NCERT Solutions on this page answer every Series question in the back exercise, according to the latest 2026-27 CBSE syllabus. Each answer uses the same series names that NCERT uses, so the code you write matches the book line for line.

• Exercise questions 1, 4, 5, 6, 7 and 8 are pure Series work; the rest belong to the DataFrame chapter.
• Unit 1 Data Handling using Pandas and Data Visualisation carries 25 of the 70 theory marks.
• Every command here runs in Python, so you can type it and check the output yourself.

Every solution on this page is written by Collegedunia subject experts on the 2026-27 NCERT print, tested in Python with pandas, and checked against the CBSE Class 12 Informatics Practices marking scheme.

Data Handling using Pandas Series Exercise Questions and What Each One Tests

The back exercise mixes short theory with command writing. Questions 1, 3 and 4 are one-line theory. Questions 5 to 8 hand you five named series and ask you to build them, then operate on them. Use the map below to decide what to revise first.

Tip: Question 5 builds the five series that Questions 6, 7 and 8 depend on. Write those five statements once on a fresh page and keep them in front of you while solving the rest.

Python Libraries for Data Handling: NumPy, Pandas and Matplotlib Compared

A Python library is a set of ready-made modules you can import instead of writing long programs yourself. NCERT names three of them at the start of the chapter. The install command is short and CBSE has asked for it directly: pip install pandas.

Pandas has three data structures named in the book: Series, DataFrame and Panel. Only the first two are studied in Class 12. The four differences NCERT lists between Pandas and NumPy are worth learning as points, because they answer a two-mark question on their own.

• A NumPy array needs one data type throughout; a Pandas DataFrame can hold float, int, string and datetime together.
• Pandas has a simpler interface for file loading, plotting, selection, joining and GROUP BY.
• Column names in a DataFrame make it easy to keep track of what each column holds.
• Pandas suits tabular data; NumPy suits numeric array work.

Pandas Series Chapter 2 Explained in Video

Source: Magnet Brains on YouTube

What a Pandas Series Is and How It Differs from a List, 1-D Array and Dictionary

This is Question 1 of the exercise and the most common two-part question from the chapter. Start with the definition, then give the three differences as separate points.

Definition. A Series is a one-dimensional array that holds a sequence of values of any data type. Every value carries a data label with it, and that label is called its index. By default the index runs from 0 to N minus 1. Think of a Series as one column of a spreadsheet.

Two more short answers sit right next to this one. Question 3 asks how DataFrames relate to Series: a DataFrame is two-dimensional, and each of its columns is a Series, with every column sharing the same row index. Question 4 asks about size: the size of a Series is the count of values in it, while the size of a DataFrame is the number of rows multiplied by the number of columns.

Four Ways to Create a Pandas Series for the Class 12 Board Paper

NCERT shows four sources for a Series, and Question 5 asks you to use all of them in one go. Import the library first with import pandas as pd. The alias pd is a convention, not a rule, but the marking scheme expects it.

One error carries a mark of its own. If the index list and the value list are of different lengths, Python raises a ValueError. Passing four values with three labels prints the message Length of passed values is 4, index implies 3. Write that message in full if the question asks what happens.

Indexing and Slicing a Series: Positional Index Against Labelled Index

There are two ways to reach the values inside a Series: indexing and slicing. Both accept either a position or a label, and the two behave differently at the end of a slice. Questions 7 and 8 are built entirely on this difference.

• A positional index is an integer that counts from 0, so seriesNum[2] gives the third value.
• A labelled index is any label you set yourself, so seriesCapCntry['India'] gives NewDelhi.
• Pass a list to pull out several values at once: seriesCapCntry[['UK','USA']] prints both rows.
• Replace all labels at once by assigning a new list to .index, for example seriesCapCntry.index=[10,20,30,40].

The slicing rule is the single most tested line in this chapter. A positional slice leaves out the value at the end position, but a labelled slice includes the value at the end label. So seriesCapCntry[1:3] returns two rows, while seriesCapCntry['USA':'France'] returns three.

Question 8(b) is the reverse-order slice and Question 7(e) asks for the alphabets e to p from EngAlph. Both are one-line answers once you know which bracket form to pick.

Attributes and Methods of a Series Every Class 12 Student Must Memorise

An attribute is a property you read off the series without brackets. A method is a function you call with brackets. Question 7 walks through most of this table, so learn which of the two each name is.

Two of these are asked as a pair almost every year. size counts every value, while count() skips the NaN entries. On a series holding 12, NaN and 10, size gives 3 and count() gives 2. Question 7(f) and 7(g) then ask for the first ten and the last ten alphabets, which is simply EngAlph.head(10) and EngAlph.tail(10).

Mathematical Operations on Series and Why NaN Appears in the Output

You can add, subtract, multiply and divide two series with the normal operators. Pandas does not work position by position here. It matches the labels first, then applies the operation. Any label that is missing on one side produces NaN, which stands for a missing value.

Take the two series NCERT uses. seriesA holds 1 to 5 against labels a to e. seriesB holds 10, 20, -10, -50 and 100 against labels z, y, a, c and e. Only a, c and e are common, so only those three give a number.

To stop NaN from appearing, call the method form instead of the operator and pass fill_value. seriesA.add(seriesB, fill_value=0) treats every missing entry as 0, so label b returns 2.0 instead of NaN. The same parameter works with sub(), mul() and div(). Question 6(e) asks you to subtract, multiply and divide Vowels by Vowels1, and Question 6(f) then changes the labels of Vowels1 to capitals, which breaks the alignment on purpose.

• Vowels + Vowels1 uses the operator and leaves NaN wherever a label is missing.
• Vowels.add(Vowels1, fill_value=0) replaces the missing entries with 0 before adding.
• Vowels1.index = ['A','E','I','O','U'] renames all five labels in one statement.
• After that rename, adding the two series gives ten rows and every one of them is NaN.

Pandas Series Against NumPy ndarray: The Four Differences NCERT Lists

The chapter closes with a comparison table, and CBSE has lifted questions straight from it. Learn it as four clean points rather than as prose.

One extra line from the same section is worth adding to a five-mark answer. Pandas allows non-unique index values, and an error is raised only when you attempt an operation that cannot handle duplicates.

Data Handling using Pandas Series Weightage Compared Across Class 12 Informatics Practices Chapters

The Informatics Practices theory paper is 70 marks split across four units. Series is the first of the three chapters inside Unit 1, and it feeds the DataFrame chapter that follows it. The table below shows where it sits against the rest of the book.

The chapter also feeds the practical file and the viva, where an examiner can ask you to build a series on the spot. Time spent here pays twice.

Common Mistakes Students Make in the Data Handling using Pandas Series Chapter

Also Check: Read your statement backwards from the closing bracket. Counting brackets first catches most careless errors in under ten seconds.

Student Feedback on the Data Handling using Pandas Series Chapter

Solved Example from Exercise Question 5 with Step-by-Step Working

Question 5 asks for five series in one go. Here is the full working, written the way it should appear in your answer sheet, with the import lines counted as part of the answer.

1. Import both libraries. import pandas as pd and import numpy as np
2. EngAlph with 26 alphabets and default labels. EngAlph = pd.Series(list('abcdefghijklmnopqrstuvwxyz'))
3. Vowels with five zeros. Vowels = pd.Series([0,0,0,0,0], index=['a','e','i','o','u']), then Vowels.empty returns False because the series does hold values.
4. Friends from a dictionary. Friends = pd.Series({'Reema':11,'Kabir':12,'Neha':13,'Aman':14,'Diya':15}), so the names become the labels.
5. MTseries, an empty series. MTseries = pd.Series(), then MTseries.empty returns True.
6. MonthDays from a NumPy array. MonthDays = pd.Series(np.array([31,28,31,30,31,30,31,31,30,31,30,31]), index=range(1,13))
7. Check the build. print(MonthDays.size) gives 12, which confirms the labels and the values are of equal length.

Each statement carries its own mark, so write every one on a fresh line even when two of them look almost the same. The examiner marks step by step, not answer by answer.

How to Use the Data Handling using Pandas Series Solutions Page Most Effectively

Reading pandas code is not the same as writing it. Split your revision into three short blocks so you type more than you read.

• Block 1, 30 minutes: learn the attribute and method table by heart. Cover the third column and test yourself.
• Block 2, 45 minutes: build the five series from Question 5 in Python and print each one.
• Block 3, 45 minutes: solve Questions 6, 7 and 8 on paper without looking, then check them here.

Repeat Block 3 one week later. Students who redo the slicing questions a second time make far fewer end-label errors in the board paper.

All Data Handling using Pandas Series Exercise Questions with Step-by-Step Solutions

Every Series question from the NCERT back exercise is answered in full on the question bank page, with the statement, its printed output and a short note on why each bracket form is used.

Related Class 12 Informatics Practices Resources for Data Handling using Pandas Series

Pair the solutions with the notes and the handwritten notes. The notes explain the theory, the handwritten notes are for the last hour before the paper, and the book PDF gives you the original tables and outputs to check against.

NCERT Solutions for Class 12 Informatics Practices: All Chapters

Solutions for every chapter of the Class 12 Informatics Practices book are listed below. The highlighted row is the chapter you are on.

Data Handling using Pandas Series Class 12 Informatics Practices NCERT Solutions FAQs