🐼 Let's Explore Pandas in Python – A Fun Guide for School Students!

 

👋 Introduction

Have you ever used Excel to create tables and analyze data? What if we told you Python can do all of that — and more — using a library called Pandas? 📊🐼

Pandas is like a super-smart assistant that helps Python handle data easily. It's perfect for students who want to play with numbers, lists, tables, and real-world data like marksheets, cricket scores, or class attendance.

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🧠 What is Pandas?

Pandas is a Python library that helps you store, analyze, and manipulate data just like Excel but with code!

🧪 Fun Fact:

The name Pandas comes from "Panel Data," a term used in statistics.

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✅ Why Should School Students Learn Pandas?

• Helps you work with tables of data easily

• Perfect for Science projects, maths stats, or IT assignments

• Gives you a real-world skill used in data science and AI!

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🛠️ How to Install Pandas?

Before using Pandas, install it by running:

pip install pandas

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📦 Importing Pandas in Your Code

python
import pandas as pd

Here, pd is just a nickname we use for Pandas to make writing code faster.

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📋 Pandas Data Structures

Pandas has two main data structures:

1. Series – Like a single column (like a list with labels)

2. DataFrame – Like an Excel sheet (table with rows and columns)

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🔢 1. Pandas Series

A Series is like a list, but each item has a label (called an index).

✨ Example:

python

import pandas as pd

marks = pd.Series([85, 90, 78, 92], index=["Math", "Science", "English", "History"])
print(marks)

🖨️ Output:

Math       85
Science    90
English    78
History    92
dtype: int64

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📊 2. Pandas DataFrame

A DataFrame is like a table. It's the most used tool in Pandas.

✨ Example:

python

import pandas as pd

data = {
    "Name": ["Aarav", "Diya", "Kabir"],
    "Math": [89, 76, 93],
    "Science": [90, 85, 88]
}

df = pd.DataFrame(data)
print(df)

🖨️ Output:

    Name  Math  Science
0  Aarav    89       90
1   Diya    76       85
2  Kabir    93       88

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🔍 Exploring the DataFrame

✅ View first few rows:

python

print(df.head())  # Shows top 5 rows

✅ Get column names:

python

print(df.columns)

✅ Get statistics:

python

print(df.describe())

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🎯 Real-Life Example – Class Marks

Let’s create a student marksheet!

python

import pandas as pd

marksheet = {
    "Student": ["Anaya", "Rohan", "Priya", "Arjun"],
    "English": [88, 76, 90, 85],
    "Math": [92, 81, 95, 87],
    "Science": [89, 78, 88, 91]
}

df = pd.DataFrame(marksheet)

print("🏫 Class Marksheet:")
print(df)

# Calculate Average Marks
df["Average"] = (df["English"] + df["Math"] + df["Science"]) / 3
print("\n📊 With Average Marks:")
print(df)

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✏️ Editing the Data

➕ Add a new column:

python

df["Grade"] = ["A", "B", "A+", "A"]

➖ Remove a column:

python

df.drop("Grade", axis=1, inplace=True)

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📁 Reading from a CSV File

If your data is stored in a file like students.csv, you can read it like this:

python

df = pd.read_csv("students.csv")
print(df)

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💾 Writing Data to a File

You can also save your data to a file:

python

df.to_csv("output.csv", index=False)

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🧪 Try it Yourself – Challenge Time! 🎯

1. Create a DataFrame for your daily study schedule (Subjects, Time in minutes)

2. Calculate the total study time in a day

3. Save it in a CSV file

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📊 Let's Make Graphs with Pandas!

Do you enjoy pictures more than numbers? Pandas lets you create beautiful charts and graphs to see patterns in your data clearly!

To create charts, we’ll use another Python library called matplotlib.

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🛠️ Install Matplotlib

Before using it, install the library:

pip install matplotlib

Then import it in your Python code:

python

import matplotlib.pyplot as plt

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📘 Example 1: Line Graph – Student Marks Over Subjects

python

import pandas as pd
import matplotlib.pyplot as plt

data = {
    "Subjects": ["Math", "Science", "English", "History"],
    "Anaya": [92, 89, 88, 85],
    "Rohan": [81, 78, 76, 80]
}

df = pd.DataFrame(data)

# Plotting Line Graph
plt.plot(df["Subjects"], df["Anaya"], label="Anaya", marker='o')
plt.plot(df["Subjects"], df["Rohan"], label="Rohan", marker='s')

plt.title("📈 Marks Comparison")
plt.xlabel("Subjects")
plt.ylabel("Marks")
plt.legend()
plt.grid(True)
plt.show()

🎨 This shows how marks change across subjects for each student.

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📘 Example 2: Bar Graph – Student Average Marks

python

import pandas as pd
import matplotlib.pyplot as plt

data = {
    "Student": ["Anaya", "Rohan", "Priya", "Arjun"],
    "Average Marks": [89.6, 78.3, 91.0, 87.6]
}

df = pd.DataFrame(data)

# Bar Chart
plt.bar(df["Student"], df["Average Marks"], color='skyblue')
plt.title("📊 Average Marks of Students")
plt.xlabel("Student")
plt.ylabel("Average Marks")
plt.ylim(0, 100)
plt.show()

🎨 This bar chart gives a quick view of how each student performed overall.

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📘 Example 3: Pie Chart – Time Spent on Subjects

python

import pandas as pd
import matplotlib.pyplot as plt

data = {
    "Subject": ["Math", "Science", "English", "History"],
    "Time Spent (mins)": [60, 45, 30, 15]
}

df = pd.DataFrame(data)

# Pie Chart
plt.pie(df["Time Spent (mins)"], labels=df["Subject"], autopct='%1.1f%%', startangle=140)
plt.title("🕒 Study Time Distribution")
plt.show()

🎨 Great for understanding how you divide time between subjects.

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💡 Tips for Better Charts

• Always label axes!

• Add a title so people know what it shows.

• Use different colors for better clarity.

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🧪 Try it Yourself – Challenge Time! 🎯

Make a graph showing your weekly screen time for activities like:

• YouTube

• Games

• Study

• Social Media

Use a bar or pie chart to show where your time goes!

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🎓 Final Thoughts

Pandas is an amazing tool to help you become a data expert while still in school! Whether it's your marks, hobbies, sports scores, or class surveys — Pandas makes everything easier to analyze and visualize.

Learning Pandas and visualizing data using graphs and charts makes you think like a data scientist — solving real-world problems with numbers and pictures.

Whether it's school marks, study hours, or cricket scores — you can turn boring tables into colorful charts in seconds!