Q.Conduct a class census by preparing a questionnaire. The questionnaire should contain a minimum of five questions. Questions should relate to students, their family members, their class performance, their health etc. Each student is required to fill up the questionnaire. Compile the information in numerical terms (in terms of percentage). Present the information through a bar, scatter–diagram. (NCERT Geography class IX, Page 60)

To conduct a class census and present the information, we first design a questionnaire, then compile the data into numerical terms (percentages), and finally visualize it using a bar chart and a scatter diagram.

1. Questionnaire Design

Here is a sample questionnaire with six questions covering various aspects of students' lives:

1. What is your gender? (Options: Male, Female, Other)
2. How many siblings do you have? (Numerical: 0, 1, 2, 3, ...)
3. What is your average percentage score in the last three exams? (Numerical: e.g., 75.5, 82.0, 68.3)
4. On average, how many hours do you spend studying per day? (Numerical: e.g., 1, 2.5, 4)
5. Do you participate in any regular sports or physical activity? (Options: Yes, No)
6. On average, how many hours do you sleep per night? (Numerical: e.g., 6, 7.5, 8)

2. Data Compilation in Numerical Terms (Percentages)

After collecting responses from all students, the data needs to be compiled. For categorical questions, we count the frequency of each option and express it as a percentage of the total number of students. For numerical questions, we can calculate averages, ranges, or group data into bins and then find the percentage of students falling into each bin.

For example, if there are 40 students in the class:

• Gender: If 20 are Male, 18 are Female, and 2 are Other, the percentages are: Male: $(20/40) \times 100 = 50\%$, Female: $(18/40) \times 100 = 45\%$, Other: $(2/40) \times 100 = 5\%$.
• Sports Participation: If 28 students say 'Yes' and 12 say 'No', the percentages are: Yes: $(28/40) \times 100 = 70\%$, No: $(12/40) \times 100 = 30\%$.
• Average Percentage Score: For numerical data, we might group scores (e.g., 0-50%, 51-70%, 71-90%, 91-100%) and then calculate the percentage of students in each group.

3. Data Presentation through Bar and Scatter Diagrams

We will use Python with the pandas library for data handling and matplotlib.pyplot for visualization. First, let's create some hypothetical data for a class of 30 students based on our questionnaire.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Set a seed for reproducibility
np.random.seed(42)

# Generate hypothetical data for 30 students
num_students = 30
data = {
    'Gender': np.random.choice(['Male', 'Female', 'Other'], num_students, p=[0.5, 0.45, 0.05]),
    'Siblings': np.random.randint(0, 4, num_students),
    'Avg_Score': np.random.uniform(55, 95, num_students).round(1),
    'Study_Hours_Per_Day': np.random.uniform(1, 5, num_students).round(1),
    'Sports_Activity': np.random.choice(['Yes', 'No'], num_students, p=[0.7, 0.3]),
    'Sleep_Hours_Per_Night': np.random.uniform(6, 9, num_students).round(1)
}
df = pd.DataFrame(data)

print("Sample of the compiled data:")
print(df.head())

Sample of the compiled data:
   Gender  Siblings  Avg_Score  Study_Hours_Per_Day Sports_Activity  Sleep_Hours_Per_Night
0    Male         0       79.4                  4.0             Yes                    8.0
1  Female         1       84.0                  4.2             Yes                    7.5
2    Male         1       78.4                  4.6             Yes                    7.6
3    Male         0       68.8                  3.8             Yes                    7.9
4    Male         0       77.6                  3.7             Yes                    7.1

(a) Bar Diagram

A bar diagram is suitable for displaying the distribution of categorical data or numerical data grouped into categories. We will use it to show the percentage distribution of students based on their 'Sports Activity'.

# Calculate percentages for 'Sports_Activity'
sports_activity_counts = df['Sports_Activity'].value_counts()
sports_activity_percentages = (sports_activity_counts / num_students) * 100

# Create a bar chart
plt.figure(figsize=(8, 6))
sports_activity_percentages.plot(kind='bar', color=['skyblue', 'lightcoral'])
plt.title('Percentage of Students Participating in Sports Activity')
plt.xlabel('Participation in Sports Activity')
plt.ylabel('Percentage of Students (%)')
plt.xticks(rotation=0) # Keep labels horizontal
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()

Description of the Bar Diagram: …

To conduct a class census and present the findings visually, we first need to define what information we want to collect. This involves designing a questionnaire with relevant questions. Once data is collected, it needs to be processed, typically by converting raw counts into percentages for easier comparison and understanding. Finally, data visualization tools are used to create graphical representations that highlight patterns and insights.

1. Designing the Questionnaire

The goal is to gather diverse information about students, their families, academic performance, and health. The questions should be clear, unambiguous, and designed to elicit quantifiable responses. Here's a sample questionnaire with more than five questions, covering the specified areas:

Class Census Questionnaire

Please provide accurate information. Your responses will be kept confidential and used only for statistical analysis.

1. Student Information:
   • What is your current age (in years)? _____
2. Family Information:
   • How many members are there in your immediate family (parents, siblings, and yourself)? _____
3. Class Performance:
   • What was your percentage score in the last Mathematics unit test? _____ %
4. Health Information:
   • On average, how many hours do you sleep per night? _____ hours
5. Extracurricular Activities:
   • Which of the following extracurricular activities do you participate in most frequently? (Please tick one)
     • Sports
     • Music/Dance
     • Art/Craft
     • Debating/Quizzing
     • None
6. Study Habits:
   • On average, how many hours do you spend studying daily outside of school hours? _____ hours

2. Data Collection and Compilation

After distributing the questionnaire, each student fills it out. The responses are then compiled into a structured format, such as a spreadsheet or a database. For demonstration purposes, we will simulate data for 15 students using a Pandas DataFrame in Python.

The next step is to compile this information numerically, specifically in terms of percentages. This involves calculating the proportion of students falling into different categories or ranges for each question.

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Set a style for the plots for better aesthetics
sns.set_style("whitegrid")

# Simulate data for 15 students based on the questionnaire
data = {
    'Student_ID': range(1, 16),
    'Age': [16, 17, 16, 17, 16, 18, 17, 16, 17, 16, 18, 17, 16, 17, 16],
    'Family_Members': [4, 5, 3, 4, 4, 6, 3, 5, 4, 3, 5, 4, 4, 3, 5],
    'Math_Score': [75, 88, 62, 91, 78, 55, 85, 70, 95, 68, 80, 72, 83, 60, 90],
    'Sleep_Hours': [7, 6, 8, 7, 7, 5, 8, 6, 7, 8, 6, 7, 7, 6, 8],
    'Extracurricular': ['Sports', 'Music/Dance', 'None', 'Debating/Quizzing', 'Sports',
                        'None', 'Art/Craft', 'Sports', 'Music/Dance', 'Debating/Quizzing',
                        'Art/Craft', 'Sports', 'Music/Dance', 'None', 'Debating/Quizzing'],
    'Study_Hours_Daily': [3, 4, 2, 5, 3, 1, 4, 2, 5, 3, 4, 2, 3, 1, 4]
}

df = pd.DataFrame(data)

print("Simulated Class Census Data:")
print(df)
print("\n" + "="*50 + "\n")

# Compile information in numerical terms (percentages)
print("Compiled Information (Percentages):")

# Percentage of students in each extracurricular activity
extracurricular_counts = df['Extracurricular'].value_counts()
extracurricular_percentages = (extracurricular_counts / len(df)) * 100
print("\nPercentage of students in each Extracurricular Activity:")
print(extracurricular_percentages.to_string()) # .to_string() for better display
print("\n" + "="*50 + "\n")

# Percentage of students by Age group (example for numerical data)
age_bins = [15, 16, 17, 18, 19] # Define age groups
age_labels = ['16 years', '17 years', '18 years']
df['Age_Group'] = pd.cut(df['Age'], bins=age_bins, labels=age_labels, right=False)
age_group_counts = df['Age_Group'].value_counts().sort_index()
age_group_percentages = (age_group_counts / len(df)) * 100
print("Percentage of students by Age Group:")
print(age_group_percentages.to_string())
print("\n" + "="*50 + "\n")

# Percentage of students by Sleep Hours (example for numerical data)
sleep_bins = [4, 6, 7, 8, 9] # Define sleep hour groups
sleep_labels = ['< 6 hours', '6-7 hours', '7-8 hours', '> 8 hours']
df['Sleep_Group'] = pd.cut(df['Sleep_Hours'], bins=sleep_bins, labels=sleep_labels, right=False)
sleep_group_counts = df['Sleep_Group'].value_counts().sort_index()
sleep_group_percentages = (sleep_group_counts / len(df)) * 100
print("Percentage of students by Sleep Hours Group:")
print(sleep_group_percentages.to_string())
print("\n" + "="*50 + "\n")

Simulated Class Census Data:
    Student_ID  Age  Family_Members  Math_Score  Sleep_Hours    Extracurricular  Study_Hours_Daily
0            1   16               4          75            7             Sports                  3
1            2   17               5          88            6        Music/Dance                  4
2            3   16               3          62            8               None                  2
3            4   17               4          91            7  Debating/Quizzing                  5
4            5   16               4          78            7             Sports                  3
5            6   18               6          55            5               None                  1
6            7   17               3          85            8        Art/Craft                  4
7            8   16               5          70            6             Sports                  2
8            9   17               4          95            7        Music/Dance                  5
9           10   16               3          68            8  Debating/Quizzing                  3
10          11   18               5          80            6        Art/Craft                  4
11          12   17               4          72            7             Sports                  2
12          13   16               4          83            7        Music/Dance                  3
13          14   17               3          60            6               None                  1
14          15   16               5          90            8  Debating/Quizzing                  4

==================================================

Compiled Information (Percentages):

Percentage of students in each Extracurricular Activity:
Extracurricular
Sports               26.666667
Debating/Quizzing    26.666667
Music/Dance          20.000000
None                 20.000000
Art/Craft            13.333333

==================================================

Percentage of students by Age Group:
Age_Group
16 years    46.666667
17 years    40.000000
18 years    13.333333

==================================================

Percentage of students by Sleep Hours Group:
Sleep_Group
< 6 hours     6.666667
6-7 hours    33.333333
7-8 hours    46.666667
> 8 hours    13.333333

==================================================

Explanation of Key Lines:

• import pandas as pd, import matplotlib.pyplot as plt, import seaborn as sns: These lines import the necessary libraries for data manipulation (Pandas) and plotting (Matplotlib, Seaborn).
• sns.set_style("whitegrid"): This sets a predefined aesthetic style for the plots, making them visually appealing.
• data = {...} and df = pd.DataFrame(data): This creates a dictionary of lists, which is then converted into a Pandas DataFrame. A DataFrame is a tabular data structure, ideal for storing and manipulating census data.
• df['Extracurricular'].value_counts(): This method counts the occurrences of each unique value in the 'Extracurricular' column.
• (extracurricular_counts / len(df)) * 100: This calculates the percentage for each category by dividing its count by the total number of students (len(df)) and multiplying by 100.
• pd.cut(df['Age'], bins=age_bins, labels=age_labels, right=False): This function is used to segment numerical data (like 'Age' or 'Sleep_Hours') into discrete bins or intervals. bins defines the boundaries, labels provides names for these bins, and right=False means the interval includes the left boundary but not the right. This is crucial for converting continuous numerical data into categories suitable for percentage calculation and bar charts.

3. Presenting Information through Diagrams

Data visualization helps in understanding the compiled information quickly. We will use a bar chart for categorical data (Extracurricular Activities) and a scatter diagram for showing the relationship between two numerical variables (e.g., Math Score vs. Study Hours).

# --- Bar Chart: Percentage of students in each Extracurricular Activity ---
plt.figure(figsize=(10, 6)) # Set the figure size for better readability
sns.barplot(x=extracurricular_percentages.index, y=extracurricular_percentages.values, palette='viridis')

# Add labels and title
plt.title('Percentage of Students by Extracurricular Activity', fontsize=16)
plt.xlabel('Extracurricular Activity', fontsize=12)
plt.ylabel('Percentage (%)', fontsize=12)
plt.xticks(rotation=45, ha='right') # Rotate x-axis labels for better fit
plt.ylim(0, 100) # Ensure y-axis goes up to 100%

# Add percentage values on top of bars
for index, value in enumerate(extracurricular_percentages.values):
    plt.text(index, value + 1, f'{value:.1f}%', ha='center', va='bottom')

plt.tight_layout() # Adjust layout to prevent labels from overlapping
plt.show()

# --- Scatter Diagram: Math Score vs. Study Hours Daily ---
plt.figure(figsize=(10, 6))
sns.scatterplot(x='Study_Hours_Daily', y='Math_Score', data=df, hue='Age', size='Family_Members', sizes=(50, 500), palette='coolwarm', legend='full')

# Add labels and title
plt.title('Relationship between Math Score and Daily Study Hours', fontsize=16)
plt.xlabel('Daily Study Hours (excluding school)', fontsize=12)
plt.ylabel('Mathematics Test Score (%)', fontsize=12)

# Add annotations for individual points (optional, for detailed analysis)
# for i, row in df.iterrows():
#     plt.text(row['Study_Hours_Daily'] + 0.1, row['Math_Score'], f"ID:{row['Student_ID']}", fontsize=9)

plt.grid(True, linestyle='--', alpha=0.7) # Add a grid for easier reading
plt.tight_layout()
plt.show()

Description of Plots: …