Question 1

What is a Series and how is it different from a 1-D array, a list and a dictionary?

Accepted Answer

A Series is a one-dimensional labeled array capable of holding any data type (integers, strings, floating point numbers, Python objects, etc.). It is a core data structure in the pandas library. Unlike a 1-D array which only has integer-based indexing, a Series has an associated index that labels each element, allowing for more flexible data access. Compared to a list, which is a basic Python data structure without labels, a Series provides labeled indexing and many built-in methods for data manipulation. Unlike a dictionary, which is an unordered collection of key-value pairs, a Series maintains order and supports vectorized operations. — A Series differs from a 1-D array by having labeled indices, from a list by providing labels and vectorized operations, and from a dictionary by maintaining order and supporting array-like operations.

Question 2

What is a DataFrame and how is it different from a 2-D array?

Accepted Answer

A DataFrame is a two-dimensional labeled data structure with columns of potentially different types. It is similar to a spreadsheet or SQL table. Unlike a 2-D array which can only hold elements of the same data type, a DataFrame can hold heterogeneous data types across columns. Additionally, DataFrames have labeled rows and columns, allowing for more intuitive data access and manipulation. — DataFrames differ from 2-D arrays by supporting heterogeneous data types and labeled axes (rows and columns), making them more versatile for data analysis.

Question 3

How are DataFrames related to Series?

Accepted Answer

A DataFrame is essentially a collection of Series objects that share the same index. Each column in a DataFrame is a Series. Thus, a DataFrame can be thought of as a dictionary of Series objects, where each Series represents a column. — Understanding that DataFrames are composed of multiple Series helps in manipulating and accessing data efficiently.

Question 4

What do you understand by the size of (i) a Series, (ii) a DataFrame?

Accepted Answer

The size of a Series is the number of elements it contains. For a DataFrame, the size is the total number of elements, which is the product of the number of rows and columns. — Size helps in understanding the amount of data contained in the Series or DataFrame.

Question 5

Create the following Series and do the specified operations:
a) EngAlph, having 26 elements with the alphabets as values and default index values.
b) Vowels, having 5 elements with index labels ‘a’, ‘e’, ‘i’, ‘o’ and ‘u’ and all the five values set to zero. Check if it is an empty series.
c) Friends, from a dictionary having roll numbers of five of your friends as data and their first name as keys.
d) MTseries, an empty Series. Check if it is an empty series.
e) MonthDays, from a numpy array having the number of days in the 12 months of a year. The labels should be the month numbers from 1 to 12.

Accepted Answer

a) EngAlph = pd.Series(list('abcdefghijklmnopqrstuvwxyz'))

b) Vowels = pd.Series([0,0,0,0,0], index=['a','e','i','o','u'])
Check empty: Vowels.empty  # Returns False

c) Friends = pd.Series({ 'Alice': 101, 'Bob': 102, 'Charlie': 103, 'David': 104, 'Eva': 105 })

d) MTseries = pd.Series([])
Check empty: MTseries.empty  # Returns True

Note: Creating empty Series without dtype will raise warning in recent pandas versions, so use pd.Series(dtype='float64')

e) import numpy as np
MonthDays = pd.Series(np.array([31,28,31,30,31,30,31,31,30,31,30,31]), index=range(1,13)) — Each Series is created using pandas.Series with appropriate data and index. Empty Series can be checked using the .empty attribute.

Question 6

Using the Series created in Question 5, write commands for the following:
a) Set all the values of Vowels to 10 and display the Series.
b) Divide all values of Vowels by 2 and display the Series.
c) Create another series Vowels1 having 5 elements with index labels ‘a’, ‘e’, ‘i’, ‘o’ and ‘u’ having values [2,5,6,3,8] respectively.
d) Add Vowels and Vowels1 and assign the result to Vowels3.
e) Subtract, Multiply and Divide Vowels by Vowels1.
f) Alter the labels of Vowels1 to [‘A’, ‘E’, ‘I’, ‘O’, ‘U’].

Accepted Answer

a) Vowels[:] = 10
print(Vowels)

b) Vowels = Vowels / 2
print(Vowels)

c) Vowels1 = pd.Series([2,5,6,3,8], index=['a','e','i','o','u'])

d) Vowels3 = Vowels + Vowels1

print(Vowels3)

e) Subtract: Vowels - Vowels1
Multiply: Vowels * Vowels1
Divide: Vowels / Vowels1

f) Vowels1.index = ['A', 'E', 'I', 'O', 'U']
print(Vowels1) — These operations demonstrate element-wise arithmetic and index manipulation on Series objects.

Question 7

Using the Series created in Question 5, write commands for the following:
a) Find the dimensions, size and values of the Series EngAlph, Vowels, Friends, MTseries, MonthDays.
b) Rename the Series MTseries as SeriesEmpty.
c) Name the index of the Series MonthDays as monthno and that of Series Friends as Fname.
d) Display the 3rd and 2nd value of the Series Friends, in that order.
e) Display the alphabets 'e' to 'p' from the Series EngAlph.
f) Display the first 10 values in the Series EngAlph.
g) Display the last 10 values in the Series EngAlph.
h) Display the MTseries.

Accepted Answer

a) For each Series:
- Dimensions: .ndim
- Size: .size
- Values: .values

Example:
EngAlph.ndim, EngAlph.size, EngAlph.values

b) SeriesEmpty = MTseries.rename('SeriesEmpty')

c) MonthDays.index.name = 'monthno'
Friends.index.name = 'Fname'

d) Friends.iloc[[2,1]]  # 3rd and 2nd values in that order

e) EngAlph['e':'p']  # Slicing from 'e' to 'p'

f) EngAlph.head(10)

g) EngAlph.tail(10)

h) print(MTseries) — These commands demonstrate accessing metadata, renaming, indexing, slicing, and displaying Series data.

Question 8

Using the Series created in Question 5, write commands for the following:
a) Display the names of the months 3 through 7 from the Series MonthDays.
b) Display the Series MonthDays in reverse order.

Accepted Answer

a) MonthDays.loc[3:7]

b) MonthDays[::-1] — Using .loc for label-based slicing and Python slicing syntax for reversing the Series.

Question 9

Create the following DataFrame Sales containing year wise sales figures for five sales persons in INR. Use the years as column labels, and sales person names as row labels.

2014 2015 2016 2017
Madhu 100.5 12000 20000 50000
Kusum 150.8 18000 50000 60000
Kinshuk 200.9 22000 70000 70000
Ankit 30000 30000 100000 80000
Shruti 40000 45000 125000 90000

Accepted Answer

Create the DataFrame using pandas as:

import pandas as pd

sales_data = {
    '2014': [100.5, 150.8, 200.9, 30000, 40000],
    '2015': [12000, 18000, 22000, 30000, 45000],
    '2016': [20000, 50000, 70000, 100000, 125000],
    '2017': [50000, 60000, 70000, 80000, 90000]
}

Sales = pd.DataFrame(sales_data, index=['Madhu', 'Kusum', 'Kinshuk', 'Ankit', 'Shruti']) — DataFrame is created by passing a dictionary of lists with years as keys and sales data as values, and row labels as sales person names.

Question 10

Use the DataFrame created in Question 9 above to do the following:
a) Display the row labels of Sales.
b) Display the column labels of Sales.
c) Display the data types of each column of Sales.
d) Display the dimensions, shape, size and values of Sales.
e) Display the last two rows of Sales.
f) Display the first two columns of Sales.
g) Create a dictionary using the following data. Use this dictionary to create a DataFrame Sales2.

2018
Madhu 160000
Kusum 110000
Kinshuk 500000
Ankit 340000
Shruti 900000

h) Check if Sales2 is empty or it contains data.

Accepted Answer

a) Sales.index

b) Sales.columns

c) Sales.dtypes

d) Sales.ndim, Sales.shape, Sales.size, Sales.values

e) Sales.tail(2)

f) Sales.iloc[:, :2]

g) sales2_data = {'2018': [160000, 110000, 500000, 340000, 900000]}
Sales2 = pd.DataFrame(sales2_data, index=['Madhu', 'Kusum', 'Kinshuk', 'Ankit', 'Shruti'])

h) Sales2.empty  # Returns False if data present — These commands demonstrate accessing DataFrame metadata, slicing, and creating a new DataFrame from a dictionary.

Question 11

Use the DataFrame created in Question 9 above to do the following:
a) Append the DataFrame Sales2 to the DataFrame Sales.
b) Change the DataFrame Sales such that it becomes its transpose.
c) Display the sales made by all sales persons in the year 2017.
d) Display the sales made by Madhu and Ankit in the year 2017 and 2018.
e) Display the sales made by Shruti 2016.
f) Add data to Sales for salesman Sumeet where the sales made are [196.2, 37800, 52000, 78438, 38852] in the years [2014, 2015, 2016, 2017, 2018] respectively.
g) Delete the data for the year 2014 from the DataFrame Sales.
h) Delete the data for sales man Kinshuk from the DataFrame Sales.
i) Change the name of the salesperson Ankit to Vivaan and Madhu to Shailesh.
j) Update the sale made by Shailesh in 2018 to 100000.
k) Write the values of DataFrame Sales to a comma separated file SalesFigures.csv on the disk. Do not write the row labels and column labels.
l) Read the data in the file SalesFigures.csv into a DataFrame SalesRetrieved and Display it. Now update the row labels and column labels of SalesRetrieved to be the same as that of Sales.

Accepted Answer

a) Sales = Sales.append(Sales2)

b) Sales = Sales.T

c) Sales['2017']

d) Sales.loc[['Madhu', 'Ankit'], ['2017', '2018']]

e) Sales.loc['Shruti', '2016']

f) Sales.loc['Sumeet'] = [196.2, 37800, 52000, 78438, 38852]

Note: If '2018' column does not exist, it will be added automatically.

g) Sales = Sales.drop('2014', axis=1)

h) Sales = Sales.drop('Kinshuk', axis=0)

i) Sales = Sales.rename(index={'Ankit': 'Vivaan', 'Madhu': 'Shailesh'})

j) Sales.loc['Shailesh', '2018'] = 100000

k) Sales.values.tofile('SalesFigures.csv', sep=',', format='%s')

Alternatively, use:
Sales.to_csv('SalesFigures.csv', index=False, header=False)

l) SalesRetrieved = pd.read_csv('SalesFigures.csv', header=None)
SalesRetrieved.columns = Sales.columns
SalesRetrieved.index = Sales.index
print(SalesRetrieved) — These operations cover appending DataFrames, transposing, selecting data, adding and deleting rows/columns, renaming indices, updating values, and file I/O operations.

Question 12

What is the primary purpose of data handling in Informatics Practices?

Accepted Answer

To organize and present data meaningfully for analysis and decision-making — Data handling involves collecting, organizing, and presenting data in a meaningful way to facilitate analysis and decision-making, which is essential in Informatics Practices.

Question 13

Which of the following is NOT a typical step in the data handling process flow?

Accepted Answer

Data Encryption — The typical steps in data handling are data collection, data entry, data processing, data analysis, and data presentation. Data encryption is a security process, not a core step in data handling.

Question 14

Data can be classified into qualitative and quantitative types. Which of the following is an example of qualitative data?

Accepted Answer

Colors of cars in a parking lot — Qualitative data describes qualities or categories such as colors, names, or labels, whereas quantitative data involves numerical measurements.

Question 15

Explain the importance of data visualization in data handling.

Accepted Answer

Data visualization is important because it represents data graphically, making it easier to identify patterns, trends, and insights that might be missed in raw data. For example, a pie chart can show the market share of different companies clearly. — Data visualization helps in quick comprehension and better interpretation of data by presenting it in graphical forms such as charts and graphs, which reveal patterns and trends effectively.

Data Handling Using

Data Handling Using — Study Notes

2.1 INTRODUCTION TO PYTHON LIBRARIES

2.1.2 Data Structure in Pandas

2.2 SERIES

Practice Questions — Data Handling Using

All 7 Chapters in Informatics Practices