Programming in Python

Data visualization is the process of translating raw data and numbers into visual graphics, like charts, graphs, or maps. This makes it much easier for people to quickly understand patterns, trends, and complex information.

Businesses generate massive amounts of data daily. Visualization is critical because it turns that overwhelming data into clear, understandable insights. This allows professionals to spot market trends, identify problems, and make smarter, faster business decisions.

Three popular libraries are Matplotlib, Seaborn, and Plotly.

Matplotlib is a “low-level” library with a Matlab-like interface. This characteristic means it doesn’t make many assumptions for you; it gives you total freedom to customize every single pixel of your graph, but requires you to write more lines of code to achieve it.

A bar chart is typically used to represent categorical data. It is perfect for comparing different distinct groups or categories against each other (e.g., total sales across different months, or populations of different cities).

Seaborn is built directly on top of the Matplotlib library. It primarily focuses on creating beautiful, informative statistical visualizations, such as complex line plots, bar plots, and heatmaps that show patterns and relationships.

The key distinguishing feature of Plotly is interactivity. While Matplotlib mostly generates static (unmoving) images, Plotly creates dynamic graphs that allow the user to zoom in, click on categories, and hover over data points to see exact numbers directly in their web browser.

The plt.xlabel() function is used to add a descriptive text label to the horizontal X-axis of a graph, while plt.ylabel() adds a descriptive text label to the vertical Y-axis, helping viewers understand what the graph is measuring.

A pie plot (or circular chart) is best used to represent parts of a whole. It is ideal for showing percentages or proportional distributions (e.g., showing the market share of different smartphone brands adding up to 100%).

The color argument automatically groups and color-codes the data points on the graph based on a specific category or column in your dataset (e.g., color='Gender' will automatically color male and female data points differently and generate a legend).

In the process of data analysis, analysts must comb through thousands or even millions of rows of raw data. Human brains are not naturally equipped to find mathematical patterns in giant spreadsheets. Data visualization acts as a translation tool, converting complex numerical datasets into visual context (shapes, colors, lines). Its importance lies in three areas:

Matplotlib is a foundational, low-level plotting library.

Plotly is a modern, high-level, open-source library.

# 1. Import the library
import matplotlib.pyplot as plt

# Example Data
months = ['Jan', 'Feb', 'Mar']
sales = [150, 200, 180]

# --- A. Line Chart ---
# Uses plt.plot() to draw a line connecting the data points
plt.plot(months, sales)
plt.title("Monthly Sales Trend")
plt.show()

# --- B. Bar Graph ---
# Uses plt.bar() to create rectangular bars for categorical comparison
plt.bar(months, sales)
plt.title("Monthly Sales Comparison")
plt.show()

# --- C. Pie Chart ---
# Uses plt.pie() to show parts of a whole (percentages)
# 'labels' assigns names to the slices, 'autopct' displays the percentages
plt.pie(sales, labels=months, autopct='%1.1f%%')
plt.title("Sales Distribution")
plt.show()

To accomplish this, we first import pandas and plotly.express. We then read the dataset into a DataFrame and pass that DataFrame into the specific Plotly Express functions (px.line, px.bar, px.pie).

import pandas as pd
import plotly.express as px

# Step 1: Assume the data is loaded into a Pandas DataFrame
# (In a real scenario, this would be: df = pd.read_csv("sales.csv"))
data = {
    'Month': ['Jan', 'Feb', 'Mar', 'Jan', 'Feb', 'Mar'],
    'Category': ['Tech', 'Tech', 'Tech', 'Home', 'Home', 'Home'],
    'Sales': [5000, 6000, 5500, 3000, 3200, 3100]
}
df = pd.DataFrame(data)

# a) Line Chart showing total sales trend over the months
# 'color' splits the lines by category
fig_line = px.line(df, x='Month', y='Sales', color='Category', title="Sales Trend")
fig_line.show()

# b) Bar Graph comparing total sales for each product category
fig_bar = px.bar(df, x='Category', y='Sales', title="Category Sales Comparison")
fig_bar.show()

# c) Pie Plot showing the percentage contribution of each category
fig_pie = px.pie(df, names='Category', values='Sales', title="Category Contribution")
fig_pie.show()

a = float(input("Enter first number: "))
b = float(input("Enter second number: "))
print("Sum:", a + b)
print("Difference:", a - b)
print("Product:", a * b)
print("Quotient:", a / b)

name = input("Enter your name: ")
print("Hello there,", name, "! Welcome to Python.")

num = int(input("Enter an integer: "))
if num > 0:
    print("The number is positive.")
elif num < 0:
    print("The number is negative.")
else:
    print("The number is exactly zero.")

x = int(input("Enter X: "))
y = int(input("Enter Y: "))
# Python allows elegant simultaneous swapping!
x, y = y, x 
print("After swapping: X =", x, "and Y =", y)

sentence = input("Enter a sentence: ").lower()
vowel_count = 0
for char in sentence:
    if char in 'aeiou':
        vowel_count += 1
print("Number of vowels:", vowel_count)

file = open("data.txt", "w")
file.write("Hello, this is a test file.\n")
file.close()
print("File created successfully.")

file = open("data.txt", "a") # Open in Append mode
file.write("This is an appended line.\n")
file.close()
print("Line appended successfully.")

file = open("data.txt", "r")
content = file.read()
print("--- File Contents ---")
print(content)
file.close()

import csv

data = [
    ["Name", "Marks"],
    ["Aarosh", 85],
    ["Subigya", 92],
    ["Binay", 78]
]

with open("students.csv", "w", newline='') as file:
    writer = csv.writer(file)
    writer.writerows(data)
print("students.csv created successfully.")

import csv

with open("students.csv", "r") as file:
    reader = csv.reader(file)
    for row in reader:
        print(row)

import pandas as pd
# Using the file we created in question 9
df = pd.read_csv("students.csv")
print(df.head()) # .head() prints the first 5 rows by default

import pandas as pd

my_dict = {
    'Item': ['Laptop', 'Mouse', 'Keyboard'],
    'Stock': [15, 100, 45]
}
df = pd.DataFrame(my_dict)
df.to_csv("inventory.csv", index=False)
print("Dictionary saved to inventory.csv")

import pandas as pd
import matplotlib.pyplot as plt

# Using the dictionary from Q12 as a mock dataset
data = {'Product': ['Laptop', 'Mouse', 'Keyboard'], 'Sales': [5000, 1500, 2500]}
df = pd.DataFrame(data)

plt.pie(df['Sales'], labels=df['Product'], autopct='%1.1f%%')
plt.title("Product Sales Distribution")
plt.show()

def get_sum(num1, num2):
    return num1 + num2

print("Sum is:", get_sum(10, 20))

import math

def circle_area(radius):
    return math.pi * (radius ** 2)

r = float(input("Enter radius: "))
print("Area of circle:", circle_area(r))

def is_prime(n):
    if n <= 1:
        return False
    for i in range(2, int(n**0.5) + 1):
        if n % i == 0:
            return False
    return True

num = int(input("Enter a number to check if prime: "))
if is_prime(num):
    print("It is a prime number.")
else:
    print("It is not a prime number.")

def find_max(number_list):
    # Python has a built-in max() function!
    return max(number_list)

my_list = [4, 99, 23, 1, 85]
print("The maximum number is:", find_max(my_list))

try:
    file = open("missing_file.txt", "r")
    print(file.read())
    file.close()
except FileNotFoundError:
    print("Error: The file you are looking for does not exist on this computer!")