If you’re a Class 12 student pursuing Information Practices (IP) under the CBSE curriculum, practical exams are a vital component. This blog provides a step-by-step solution for each practical topic covered in the syllabus. Let’s dive into how you can master these practicals through Python, Pandas, NumPy, Matplotlib, and SQL for database management.
1. Data Handling using Pandas & Data Visualization
a. Reading and Writing CSV Files
One of the first and most basic operations in data handling is reading and writing CSV files. Using the Pandas library, we can easily perform this task.
import pandas as pd
# Reading CSV file
df = pd.read_csv('data.csv')
print(df.head())
# Writing DataFrame to CSV
data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35], 'City': ['New York', 'Los Angeles', 'Chicago']}
df = pd.DataFrame(data)
df.to_csv('output.csv', index=False)This code reads a CSV file and displays the first five rows. It also demonstrates how to create a new DataFrame and save it into a CSV file.
b. DataFrame Operations
A DataFrame is central to data manipulation in Pandas. Here’s how you can perform basic operations like indexing, filtering, and sorting.
data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'], 'Age': [25, 30, 35, 40], 'Salary': [50000, 60000, 70000, 80000]}
df = pd.DataFrame(data)
# Select 'Name' column
print(df['Name'])
# Filter rows where age is greater than 30
print(df[df['Age'] > 30])
# Sort by 'Salary'
df_sorted = df.sort_values(by='Salary')
print(df_sorted)This code filters data where the age is greater than 30 and sorts the data based on salaries.
c. Handling Missing Data
Real-world data often contains missing values. You can handle this using the following Pandas functions:
import numpy as np
data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, np.nan, 35], 'Salary': [50000, 60000, np.nan]}
df = pd.DataFrame(data)
# Filling missing values with 0
df_filled = df.fillna(0)
print(df_filled)
# Dropping rows with missing values
df_dropped = df.dropna()
print(df_dropped)This code fills missing values with zeros or drops rows containing missing values, providing clean data for analysis.
d. Data Visualization using Matplotlib
Visualization is essential for understanding data. Using Matplotlib, you can create various types of graphs, including bar charts, line charts, histograms, and scatter plots.
import matplotlib.pyplot as plt
# Bar Graph
plt.bar(['A', 'B', 'C'], [10, 20, 15])
plt.title('Bar Graph Example')
plt.xlabel('Category')
plt.ylabel('Values')
plt.show()This creates a simple bar graph. You can easily modify it to plot other kinds of graphs like histograms or line charts, offering great flexibility in how you visualize your data.
2. Database Management
a. SQL Queries
In the database management section, you will learn to write and execute SQL queries to manage relational databases. Here’s a sample SQL script for table creation, data insertion, and basic queries.
CREATE TABLE Employees (
ID INT PRIMARY KEY,
Name VARCHAR(100),
Age INT,
Salary INT
);
-- Insert data into the table
INSERT INTO Employees (ID, Name, Age, Salary)
VALUES (1, 'Alice', 25, 50000), (2, 'Bob', 30, 60000);
-- Update salary
UPDATE Employees SET Salary = 65000 WHERE ID = 2;
-- Delete data where age is less than 30
DELETE FROM Employees WHERE Age < 30;
-- Fetch records with salary greater than 55000
SELECT * FROM Employees WHERE Salary > 55000;b. Connecting MySQL with Python
You can integrate Python with SQL databases using the mysql-connector package to run SQL queries directly from your Python code.
import mysql.connector
# Connecting to MySQL database
conn = mysql.connector.connect(
host="localhost",
user="root",
password="password",
database="school"
)
cursor = conn.cursor()
# Fetch data from a MySQL table
query = "SELECT * FROM students"
cursor.execute(query)
data = cursor.fetchall()
# Convert data to pandas DataFrame
df = pd.DataFrame(data, columns=['ID', 'Name', 'Age', 'Marks'])
print(df)
cursor.close()
conn.close()This code demonstrates how to connect to a MySQL database, fetch data, and load it into a Pandas DataFrame for further analysis.
3. Data Aggregation and Grouping
Aggregation and grouping are crucial for summarizing data. For example, you can group data by specific columns and apply aggregation functions like sum(), mean(), etc.
df = pd.DataFrame({'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
'Department': ['HR', 'Finance', 'HR', 'IT', 'Finance'],
'Salary': [50000, 60000, 55000, 70000, 62000]})
# Group by Department and find the total salary
grouped_data = df.groupby('Department').agg({'Salary': 'sum'})
print(grouped_data)This example groups the data by department and sums the salaries for each group, a useful feature in data analytics.
4. Data Analysis and Visualization: Case Study
Let’s take a simple case study of analyzing COVID-19 data. This project involves data cleaning, analysis, and visualization.
import pandas as pd
import matplotlib.pyplot as plt
# Load dataset
df = pd.read_csv('covid_data.csv')
# Data cleaning: removing missing values
df_cleaned = df.dropna()
# Analysis: calculating total cases by country
total_cases_by_country = df_cleaned.groupby('Country')['TotalCases'].sum()
# Data visualization: Bar plot for total cases
total_cases_by_country.plot(kind='bar')
plt.title('Total COVID-19 Cases by Country')
plt.xlabel('Country')
plt.ylabel('Total Cases')
plt.show()This example showcases how to load a dataset, clean it, analyze it by grouping, and visualize the data using bar charts.
5. Python Programs
a. Linear Search
def linear_search(arr, x):
for i in range(len(arr)):
if arr[i] == x:
return i
return -1
arr = [10, 20, 30, 40, 50]
x = 30
result = linear_search(arr, x)
print("Element found at index:", result)b. Binary Search
def binary_search(arr, x):
low, high = 0, len(arr) - 1
while low <= high:
mid = (low + high) // 2
if arr[mid] == x:
return mid
elif arr[mid] < x:
low = mid + 1
else:
high = mid - 1
return -1
arr = [10, 20, 30, 40, 50]
x = 30
result = binary_search(arr, x)
print("Element found at index:", result)6. Numpy-based Practical
a. Numpy Array Operations
import numpy as np
# Creating 1D and 2D arrays
arr_1d = np.array([1, 2, 3, 4, 5])
arr_2d = np.array([[1, 2, 3], [4, 5, 6]])
# Basic operations on arrays
print("1D Array:", arr_1d)
print("2D Array:", arr_2d)
print("Reshaped Array:", arr_2d.reshape(3, 2))
# Matrix multiplication
arr_2d_2 = np.array([[7, 8, 9], [10, 11, 12]])
matrix_product = np.dot(arr_2d, arr_2d_2.T)
print("Matrix Product:\n", matrix_product)b. Statistical Functions using Numpy
data = np.array([22, 23, 25, 27, 29, 30])
# Mean
print("Mean:", np.mean(data))
# Median
print("Median:", np.median(data))
# Variance
print("Variance:", np.var(data))
# Standard Deviation
print("Standard Deviation:", np.std(data))Conclusion
This comprehensive guide covers all practicals outlined in the CBSE Class 12 IP curriculum. Mastering these hands-on exercises will equip you with the necessary skills to excel in your practical exams. From working with Pandas and NumPy to visualizing data using Matplotlib and managing databases with SQL, this guide serves as your roadmap to acing your IP practicals.
Happy coding, and best of luck with your exams!