🐼 Pandas 101 — Beginner to Intermediate Tutorial

This guide will walk you through the essentials of using Pandas, the most popular data analysis library in Python — and take it a step further with more powerful filtering and CRUD-style operations.

---

📦 What Is Pandas?

Pandas is an open-source Python library designed for data manipulation and analysis. It makes working with structured data fast, flexible, and intuitive — especially if you're dealing with CSV files, Excel sheets, SQL tables, JSON, or APIs.

The two core data structures in Pandas are:

• Series: A 1D labeled array (like a column)
• DataFrame: A 2D labeled data structure (like a full spreadsheet or SQL table)

---

📊 What Is a DataFrame?

A DataFrame is a two-dimensional, tabular data structure with labeled rows and columns. Think of it as a Python-native spreadsheet — but way more powerful and programmable.

✅ Key Features:

• Label-based indexing
• Column-wise and row-wise operations
• Support for mixed data types
• Fast, vectorized operations (built on NumPy)

---

📌 Example DataFrame

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'Country': ['USA', 'Canada', 'UK']
}

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

Output:

     Name  Age Country
0   Alice   25     USA
1     Bob   30  Canada
2 Charlie   35      UK

---

📥 Loading a DataFrame

df = pd.read_csv('data.csv')        # Load from CSV
df = pd.read_excel('data.xlsx')     # Load from Excel
df = pd.DataFrame(data_dict)        # Load from dictionary

---

🔍 Exploring a DataFrame

df.head()        # First 5 rows
df.tail()        # Last 5 rows
df.info()        # Column types and non-null values
df.describe()    # Summary stats
df.columns       # Column names
df.index         # Row indices

---

🎯 Accessing & Filtering Data

📎 Basic Access

df['Age']                  # Single column
df[['Name', 'Age']]        # Multiple columns
df.iloc[0]                 # First row by position
df.loc[0]                  # First row by label

🧪 Conditional Filtering (Boolean masks)

df[df['Age'] > 30]                          # Age over 30
df[df['Country'] == 'USA']                 # Country match
df[(df['Age'] > 25) & (df['Country'] == 'UK')]  # Multiple conditions

🎯 Advanced Filtering

df[df['Name'].str.startswith('A')]         # Names starting with A
df[df['Country'].isin(['USA', 'UK'])]      # Match multiple values
df[~df['Country'].isin(['Canada'])]        # NOT condition

---

🔁 CRUD Operations in Pandas

✅ Create (Insert Rows)

new_row = pd.DataFrame([{'Name': 'Diana', 'Age': 28, 'Country': 'Germany'}])
df = pd.concat([df, new_row], ignore_index=True)

🕵️ Read (Access Data)

df.loc[df['Name'] == 'Alice']
df.iloc[2]

✏️ Update (Modify Values)

df.loc[df['Name'] == 'Bob', 'Age'] = 31             # Update Bob's age
df['Country'] = df['Country'].str.upper()           # Convert all country names to uppercase

❌ Delete (Drop Data)

df = df.drop(2, axis=0)                             # Drop row with index 2
df = df[df['Name'] != 'Alice']                      # Drop rows where name == Alice
df = df.drop('Age', axis=1)                         # Drop 'Age' column

---

🧹 Cleaning Data

df.isnull().sum()                          # Count missing values
df.dropna(inplace=True)                   # Drop rows with missing values
df.fillna({'Age': 0}, inplace=True)       # Fill missing Age values with 0
df.rename(columns={'Age': 'Years'}, inplace=True)   # Rename column
df['Years'] = df['Years'].astype(float)   # Convert column to float

---

📊 Analyzing Data

df['Country'].value_counts()                       # Count occurrences
df.groupby('Country')['Age'].mean()                # Average age per country
df.sort_values(by='Age', ascending=False)          # Sort by Age descending

---

💾 Saving Data

df.to_csv('cleaned_data.csv', index=False)
df.to_excel('output.xlsx', index=False)