Unit 5 · Advanced Topics
Practice Programs using NumPy, Pandas and Matplotlib
This chapter provides hands-on practice programs combining NumPy for numerical computing, Pandas for data manipulation, and Matplotlib for visualization — the complete Python data science toolkit.
Practice Programs
Program 1: NumPy Array Operations
Problem: Create a NumPy array, perform slicing, reshaping, and mathematical operations.
import numpy as np # Create array arr = np.array([10, 20, 30, 40, 50, 60]) print("Original:", arr) # Slicing print("First 3:", arr[:3]) print("Last 2:", arr[-2:]) # Reshape to 2x3 matrix matrix = arr.reshape(2, 3) print("\nReshaped (2x3):\n", matrix) # Mathematical operations print("\nSum:", np.sum(arr)) print("Mean:", np.mean(arr)) print("Max:", np.max(arr)) print("Std Dev:", round(np.std(arr), 2)) # Element-wise operations print("\nDoubled:", arr * 2) print("Squared:", arr ** 2)
Expected Output
Original: [10 20 30 40 50 60] First 3: [10 20 30] Last 2: [50 60] Reshaped (2x3): [[10 20 30] [40 50 60]] Sum: 210 Mean: 35.0 Max: 60 Std Dev: 17.08 Doubled: [ 20 40 60 80 100 120] Squared: [ 100 400 900 1600 2500 3600]
Explanation: This program demonstrates array creation, slicing to extract sub-arrays, reshape to change dimensions, statistical functions (sum, mean, max, std), and element-wise arithmetic without loops.
Program 2: Student Marks DataFrame
Problem: Create a student DataFrame, filter top scorers, sort by marks, and compute class average.
import pandas as pd # Create DataFrame data = { "Name": ["Amit", "Priya", "Rahul", "Sneha", "Vikram"], "Math": [85, 92, 78, 95, 88], "Science": [80, 88, 72, 91, 85], "English": [75, 90, 85, 88, 80] } df = pd.DataFrame(data) # Add Total and Average columns df["Total"] = df["Math"] + df["Science"] + df["English"] df["Average"] = df["Total"] / 3 print("=== Student Records ===") print(df.to_string(index=False)) # Top 3 students by average print("\n=== Top 3 Students ===") top3 = df.nlargest(3, "Average") print(top3[["Name", "Average"]].to_string(index=False)) # Class statistics print("\n=== Class Statistics ===") for subj in ["Math", "Science", "English"]: print(f"{subj}: Avg={df[subj].mean():.1f}, Max={df[subj].max()}, Min={df[subj].min()}")
Expected Output
=== Student Records === Name Math Science English Total Average Amit 85 80 75 240 80.000000 Priya 92 88 90 270 90.000000 Rahul 78 72 85 235 78.333333 Sneha 95 91 88 274 91.333333 Vikram 88 85 80 253 84.333333 === Top 3 Students === Name Average Sneha 91.333333 Priya 90.000000 Vikram 84.333333 === Class Statistics === Math: Avg=87.6, Max=95, Min=78 Science: Avg=83.2, Max=91, Min=72 English: Avg=83.6, Max=90, Min=75
Program 3: CSV Data Analysis with Pandas
Problem: Read a CSV file, display summary statistics, filter data, and export filtered results.
import pandas as pd # Assume CSV has columns: Name, Age, Department, Salary df = pd.read_csv("employees.csv") # Basic inspection print("Shape:", df.shape) print("\nInfo:") df.info() # Statistical summary print("\n=== Statistics ===") print(df.describe()) # Filter: IT department employees it_employees = df[df["Department"] == "IT"] print("\n=== IT Department ===") print(it_employees) # Department-wise average salary print("\n=== Dept-wise Avg Salary ===") print(df.groupby("Department")["Salary"].mean()) # Export filtered data it_employees.to_csv("it_employees.csv", index=False) print("\nIT employees exported successfully!")
Program 4: Line Plot — Temperature Data
Problem: Plot weekly temperature data for two cities with proper labels and legend.
import matplotlib.pyplot as plt days = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"] delhi = [35, 37, 39, 38, 36, 34, 33] mumbai = [30, 31, 33, 32, 31, 30, 29] plt.figure(figsize=(10, 6)) plt.plot(days, delhi, "ro-", linewidth=2, markersize=8, label="Delhi") plt.plot(days, mumbai, "bs--", linewidth=2, markersize=8, label="Mumbai") plt.title("Weekly Temperature Comparison", fontsize=16) plt.xlabel("Day", fontsize=12) plt.ylabel("Temperature (°C)", fontsize=12) plt.legend(fontsize=12) plt.grid(True, alpha=0.3) plt.tight_layout() plt.savefig("temperature_chart.png", dpi=150) plt.show()
Explanation: Two line plots on the same axes for comparison. Markers (o, s) differentiate cities. tight_layout() prevents label clipping. savefig() exports the plot.
Program 5: Bar Chart — Student Scores
Problem: Create a grouped bar chart comparing marks of students in three subjects.
import matplotlib.pyplot as plt import numpy as np names = ["Amit", "Priya", "Rahul", "Sneha"] math = [85, 92, 78, 95] science = [80, 88, 72, 91] english = [75, 90, 85, 88] x = np.arange(len(names)) width = 0.25 plt.figure(figsize=(10, 6)) plt.bar(x - width, math, width, label="Math", color="skyblue") plt.bar(x, science, width, label="Science", color="salmon") plt.bar(x + width, english, width, label="English", color="lightgreen") plt.title("Student Marks Comparison", fontsize=16) plt.xlabel("Students") plt.ylabel("Marks") plt.xticks(x, names) plt.legend() plt.ylim(0, 100) plt.tight_layout() plt.show()
Program 6: Scatter Plot — Correlation
Problem: Plot the relationship between hours studied and marks obtained using a scatter plot.
import matplotlib.pyplot as plt import numpy as np np.random.seed(42) hours = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) marks = np.array([15, 25, 40, 50, 62, 70, 78, 85, 90, 98]) # Trend line z = np.polyfit(hours, marks, 1) p = np.poly1d(z) plt.figure(figsize=(8, 6)) plt.scatter(hours, marks, color="crimson", s=120, edgecolor="black", zorder=5) plt.plot(hours, p(hours), "b--", linewidth=2, label="Trend Line") plt.title("Hours Studied vs Marks", fontsize=16) plt.xlabel("Hours Studied") plt.ylabel("Marks Obtained") plt.grid(True, alpha=0.3) plt.legend() plt.tight_layout() plt.show()
Program 7: Histogram — Marks Distribution
Problem: Generate and visualize the distribution of student marks using a histogram.
import matplotlib.pyplot as plt import numpy as np # Generate 200 student marks between 20 and 100 np.random.seed(10) marks = np.random.normal(loc=65, scale=15, size=200) marks = np.clip(marks, 20, 100) plt.figure(figsize=(10, 6)) plt.hist(marks, bins=10, color="steelblue", edgecolor="black", alpha=0.8) plt.axvline(np.mean(marks), color="red", linestyle="--", linewidth=2, label=f"Mean: {np.mean(marks):.1f}") plt.axvline(np.median(marks), color="green", linestyle="-.", linewidth=2, label=f"Median: {np.median(marks):.1f}") plt.title("Marks Distribution of 200 Students", fontsize=16) plt.xlabel("Marks") plt.ylabel("Number of Students") plt.legend() plt.grid(axis="y", alpha=0.3) plt.tight_layout() plt.show()
Explanation: np.random.normal() generates marks following a normal distribution centered at 65. The histogram shows frequency per bin. Red dashed line shows the mean; green dash-dot line shows the median.
University Exam Tip
University practical exams often ask to write programs using Pandas (read CSV, filter, sort, groupby) and Matplotlib (plot bar chart, histogram, pie chart). Practice all 7 programs above — they cover the most commonly asked patterns. Always include comments and expected output in your answer.
Key Points
NumPy: Use array(), reshape(), sum(), mean(), std() for numerical operations.
Pandas: Use DataFrame(), read_csv(), groupby(), nlargest() for data manipulation.
Matplotlib: Use plot(), bar(), hist(), scatter(), pie() for visualization.
Always use plt.title(), plt.xlabel(), plt.ylabel(), plt.legend() for proper charts.
Combine all three libraries: NumPy for data generation, Pandas for processing, Matplotlib for plotting.
Practice writing code by hand — university exams are typically pen-and-paper based.
Summary
These 7 practice programs cover the most important use cases of NumPy, Pandas, and Matplotlib. Mastering these patterns will prepare you for university practical exams, competitive coding, and real-world data analysis tasks.
