Modules and Packages in Python

0    False
1    False
2     True
3    False
Name: Age, dtype: bool

1. import pandas as pd — The pandas library is imported and renamed as pd.

2. A dictionary named data is created. It contains three keys: Name, Age, and City. Each key has a list of values.

3. df = pd.DataFrame(data) — The DataFrame() function of the Pandas library is used to convert the dictionary into a DataFrame.

4. print(df['Age'] > 29) — The Age column is selected from the DataFrame. Each value in the Age column is compared with 29. The condition returns True if the age is greater than 29, otherwise False.

True

Reason — A module can be imported and used in another module or script to access its functions and variables using the import statement.

False

Reason — A package is a collection of modules, not a sub-part of a module.

True

Reason — A package can be defined as the collection of modules. It contains more than one module.

True

Reason — Modules in Python help in code reusability by minimising the development of redundant codes.

False

Reason — In Python, we can give another name to a library/module while importing it using the as keyword. This new name is called an alias. An alias is just a short/alternate name used only inside that program to make code easier to write and read (it does not change the actual library name).

True

Reason — The Pandas library provides functions for data analysis, data handling and data filtering.

False

Reason — The Matplotlib library is used to create static, animated, and interactive visualisations and provides functions for different plots, not for complex mathematical operations.

the collection of related modules.

Reason — A package can be defined as the collection of modules. It contains more than one module.

the collection of Python code in a file.

Reason — A module is a simple Python file that can contain functions, variables, and other objects.

import

Reason — The import statement can be used to add a module in a program. We can access the definitions inside it by using the dot operator.

graph plotting

Reason — The Matplotlib library is used to create static, animated, and interactive visualisations and provides many functions for creating various types of plots.

SciPy

Reason — The SciPy library includes modules for optimisation, integration, interpolation, linear algebra, and more.

1. Packages and modules are used to organise Python project.
2. Pandas is a Python library for data handling.
3. NumPy library supports mathematical operations.

import numpy as np
data = [40, 36, 25, 35, 25, 89]
mean = np.mean(data)
standard_deviation = np.std(data)
print("Mean:", mean)
print("Standard Deviation:", standard_deviation)

Mean: 41.666666666666664
Standard Deviation: 21.891144835805694

import numpy as np
from scipy.linalg import solve
coefficients = np.array([[1, 2], [2, 2]])
constants = np.array([1, -1])
solution = solve(coefficients, constants)
X = solution[0]
Y = solution[1]
print("X =", X)
print("Y =", Y)

X = -2.0
Y = 1.5

import pandas as pd
data = {
    'Name': ['Jai', 'Anny', 'Akshat', 'Priyanshi'],
    'Roll No': [10, 11, 12, 13],
    'Address': ['New Delhi', 'Mumbai', 'Los Angeles', 'Chicago']
}
df = pd.DataFrame(data)
print(df)

       Name  Roll No      Address
0        Jai       10    New Delhi
1       Anny       11       Mumbai
2     Akshat       12  Los Angeles
3  Priyanshi       13      Chicago

import matplotlib.pyplot as plt
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]
plt.plot(x, y)
plt.xlabel('x')
plt.ylabel('y')
plt.title('Line Plot')
plt.show()

Output

Modules help in code reusability by allowing a program or part of a program to be used again without rewriting it.

One of the advantages of using modules is:

• Reusability: It minimises the development of redundant codes.
• Simplicity: It is simple to reuse a code than to write a program again from the beginning.

When related code such as functions and variables is stored in a module, it can be imported and reused in different programs, which saves time and effort.

Example:

The math module contains predefined mathematical functions and variables. It can be reused in a program as shown below:

import math
print("The value of pi is", math.pi)

Here, the value of pi is reused from the math module instead of writing the code again, which shows how modules help in code reusability.

# days.py
def calculate(total_days):
    months = total_days // 30
    remaining_days = total_days % 30
    return months, remaining_days

# calculate.py
from days import calculate

total_days = int(input("Enter the total number of days: "))
months, remaining_days = calculate(total_days)
print("Total number of months:", months)
print("Remaining days:", remaining_days)

Enter the total number of days: 100
Total number of months:  3
Remaining days:  10

The Matplotlib library provides various functions to create and customise plots. Four important functions are explained below:

1. plot(): This function is used to plot data on a graph. It is used to draw line plots by specifying values for the x-axis and y-axis.

2. xlabel(): This function is used to assign a label to the x-axis of the graph.

3. ylabel(): This function is used to assign a label to the y-axis of the graph.

4. title(): This function is used to assign a title to the graph, which describes what the graph represents.

import numpy as np
from scipy.linalg import solve
coefficients = np.array([[2, 1], [1, 3]])
constants = np.array([5, 8])
solution = solve(coefficients, constants)
x = solution[0]
y = solution[1]
print("x =", x)
print("y =", y)

x = 1.3999999999999997
y = 2.2

The Matplotlib library is used to create different types of plots for data visualisation.

Line chart:

A line chart is used to show trends or changes in data over a period of time. The plot() function is used to plot data points and join them with lines.

Bar chart:

A bar chart is used to compare different categories of data. The bar() function is used to represent data using rectangular bars.

Pie chart:

A pie chart is used to represent data in the form of percentages or proportions. The pie() function divides the data into slices to show each part’s contribution to the whole.

The Pandas library is used to read data from .csv files, and the Matplotlib library is used to plot the data.

# data.csv

X,Y
1,2
2,4
3,6
4,8
5,10

Program

import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_csv("data.csv")
plt.plot(data['X'], data['Y'])
plt.xlabel('X')
plt.ylabel('Y')
plt.title('Plot using Pandas and Matplotlib')
plt.show()

Output