Скачать или смотреть Student Marks Analysis Using Pandas and Matplotlib | Python Data Analysis Tutorial 📊🐍

In this tutorial, we’ll show you *how to analyze student marks using Python with Pandas and Matplotlib**. This is a practical guide for beginners and data enthusiasts who want to learn **data analysis and visualization* using Python. By the end of this tutorial, you’ll be able to *load datasets, clean data, perform analysis, and visualize student performance* in a clear and insightful way.

We’ll cover the entire workflow of a typical data analysis project: from importing data, calculating statistics, generating summaries, to creating *graphs and plots* that help interpret student marks effectively. This tutorial is perfect for **students, teachers, and beginners in data science**.

---

🛠️ *What You’ll Learn in This Tutorial:*

How to *load student marks data* using Pandas (`CSV` or `Excel`)
Understanding **Pandas DataFrame operations**: head, info, describe, sorting, filtering
Calculating *average, highest, and lowest marks*
Using *groupby* to analyze marks by subjects or classes
Creating visualizations with **Matplotlib**:

Bar charts for subject-wise marks
Pie charts for grade distribution
Line plots for trends in scores
Histograms for marks distribution
Customizing plots with titles, labels, colors, and legends
Saving plots for reports or presentations

---

📌 *Example Code Snippet:*

```python
import pandas as pd
import matplotlib.pyplot as plt

Load data
data = pd.read_csv('student_marks.csv')

Display basic information
print(data.head())
print(data.describe())

Calculate average marks
data['Average'] = data[['Math', 'Science', 'English']].mean(axis=1)

Bar chart for average marks
plt.bar(data['Student'], data['Average'], color='skyblue')
plt.title('Average Marks of Students')
plt.xlabel('Student')
plt.ylabel('Average Marks')
plt.show()

Histogram of Math marks
plt.hist(data['Math'], bins=10, color='green', edgecolor='black')
plt.title('Distribution of Math Marks')
plt.xlabel('Marks')
plt.ylabel('Number of Students')
plt.show()
```

---

💡 *Pro Tips:*

Always check for *missing or incorrect data* before analysis.
Use *Pandas groupby and aggregation* to get insights by class, subject, or grade.
Customize *Matplotlib plots* for better readability in reports or presentations.
Combine *Pandas and Matplotlib* for powerful data analysis and visualization.

---

📢 If this tutorial helped you, don’t forget to *like, share, and subscribe* for more **Python, Pandas, Matplotlib, and data science tutorials**.

\#Pandas #Matplotlib #PythonDataAnalysis #StudentMarksAnalysis #DataScience #PythonTutorial #DataVisualization #PythonForBeginners #StudentPerformance #DataAnalytics