NumPy and Pandas Libraries: Two Marks Important Questions and Answers

Python for Data Science

Chapter 5: NumPy and Pandas Libraries

Two Marks Questions with Answers

1. What is numPy ? Enlist its usefulness.

Answer: The NumPy is a python library which is used to perform numerical calculations. It is normally used for working with arrays

NumPy is generally used for

1. Data science

2. Machine learning

3. Scientific computing

4. Image and signal processing.

2. If I want to use numpy functionality in my Python program, how can I do so?

Answer: For using the numpy functionality in a Python program, we have to import the numpy library. The code for importing it is (r)inglenil.qa xalom vod

import numpy as np

 After this import statement, we can use NumPy functions and objects by calling them with np.

3. What is the purpose of copyto function ?

Answer: When we want to copy one array to another array then the copyto function is used.

For example ‒

import numpy as np

a = np.array(11,22,33])

b = np.array([10,20,30])

np.copyto(a,b)

print(a) //Output: [10 20 30]

4. How to compute the determinant of a matrix using Python ?

Answer: We can find the determinant of a matrix using the function linalg.det() which is supported by numpy.

Syntax: numpy.linalg.det(matrix)

For example ‒

import numpy as np

x = np.matrix("6,2,1;4,‒2,4;2,9,8")

 print(x)

print("The determinant of the matrix is...")

det_mat = np.linalg.det(x)

print(det_mat)

5. Can we find inverse of a matrix using Python library? If yes, how?

Answer: In order to find the inverse of a matrix in Python, there is a function available in numpy

library. It is ‒

numpy.linalg.inv()

Syntax

numpy.linalg.inv(matrix)

The matrix can be stored in some variable x as follows‒

x = numpy.matrix ("1,2,3,4,5,6,7,2,9")

The complete Python program can be written as follows ‒

Python program

import numpy as np

x = np.matrix ("1,2,3;4,5,6;7,2,9")

print(x)

print("The inverse of the matrix is...")

inv matrix = np.linalg.inv(x)

print(inv_matrix)

6. Write a Python program to get a random value from the given array.

Answer: We can obtain a random value from the given array. The choice() function is used to choose this random number.

Python code

from numpy import random

num = random.choice([2,3,5,8,13,21])

print("The random number from the array is ...")

print(num)

7. What is Pandas? Enlist its features.

Answer: Pandas is an open‒source library provided by Python. It is used in data science, data analysis and machine learning activities. Pandas is a data manipulation package in Python for tabular data. It has functions for analyzing, cleaning, exploring and manipulating data.

Features of Pandas

1) It represents the data in tabular form.

2) It can perform quick and efficient data manipulation and analysis.

3) It can load data from different file formats into in‒memory data objects.

4) It can merge and join two datasets easily.

5) It provides time‒series functionality.

6) It can pivot and reshape data sets.

8. What is series and dataframes in Pandas ?

Answer: The Pandas series is like a column in a table. It is basically a one dimensional array holding data of any type. Using the pd.Series function, the series can be created. A DataFrame is a multi‒ dimensional data structure in which data is arranged in the form of rows and columns. Series is like a column and DataFrame is the whole table.

9. What is purpose of loc and iloc functions?

Answer: The loc and iloc are two functions in Pandas that are used to slice a dataset in a Pandas DataFrame.

The loc method is used to select the data from the dataframe. Using this function we can pass the condition for selection of data. We can select a single row using the index label.

   print(df.loc['s4'])

10. What is the use of fillna function ?

Answer: For filling the missing data by some value we use the function fillna.

11. What is hierarchical indexing ?

Answer: Hierarchical indexing is also known as multiple indexing. The multiple index is an array of tuples where each tuple is unique. We can create a MultiIndex from array of arrays using from_arrays() method.