? Pandas Tutorial

? Quick Overview

Pandas is a powerful Python library for data manipulation and analysis. It provides two main data structures: Series for one-dimensional data and DataFrame for tabular, two-dimensional data.

With Pandas you can easily load, clean, transform, and analyze structured data from sources like CSV, Excel, SQL databases, and more.

? Key Concepts

Series: A one-dimensional labeled array (similar to a column in a spreadsheet).
DataFrame: A two-dimensional labeled data structure with rows and columns.
Index: Labels that identify rows (and sometimes columns).
loc: Label-based selection of rows and columns.
iloc: Integer position-based selection of rows and columns.

? Syntax & Theory

To use Pandas, you first install it with pip and then import it in your Python script, usually with the alias pd.

A Series can be created from a Python list, NumPy array, or dictionary. A DataFrame is often created from a dictionary of lists or from external data files (like CSV).

? Series Basics

A Series stores a single column of data with an index. If you don't specify the index, Pandas creates a default integer index starting from 0.

? DataFrame Basics

A DataFrame is like a table: each column is a Series, and each row has an index. You can access columns by name and rows by label or integer position.

? Code Examples

⬇️ Installing Pandas

? View Code Example

# Install pandas library
pip install pandas

? Creating a Series

? View Code Example

import pandas as pd # Import the pandas library

data = [10, 20, 30, 40] # Define a list of numbers
series = pd.Series(data) # Create a Series from the list
print(series) # Display the Series

? Creating a DataFrame

? View Code Example

import pandas as pd # Import the pandas library

data = { # Dictionary to build the DataFrame
    "Name": ["Alice", "Bob", "Charlie"],
    "Age": [25, 30, 35],
    "City": ["New York", "Los Angeles", "Chicago"]
}

df = pd.DataFrame(data) # Create a DataFrame from the dictionary
print(df) # Display the DataFrame

? Accessing Data with loc and iloc

? View Code Example

# Access a single column
print(df["Name"])

# Access multiple columns
print(df[["Name", "Age"]])

# Access rows by label (index)
print(df.loc[0])

# Access rows by integer position
print(df.iloc[0])

? Live Output & Explanation

? Example: Series Output

If you run the Series example, the output will look like this:

0    10
1    20
2    30
3    40
dtype: int64

The left side (0, 1, 2, 3) is the index, and the right side is the value. dtype shows the data type of the values.

? Example: DataFrame Output

For the DataFrame example, the output will look like a table:

      Name  Age         City
0    Alice   25     New York
1      Bob   30  Los Angeles
2  Charlie   35      Chicago

Each row has an index (0, 1, 2) and each column (Name, Age, City) has its own label.

? Use Cases

Reading and analyzing CSV/Excel reports.
Cleaning messy real-world data (missing values, duplicates).
Filtering, grouping, and aggregating data for dashboards.
Preparing datasets for machine learning models.

? Tips & Best Practices

Use DataFrame for tabular data and Series for 1D data.
Use loc for label-based indexing and iloc for integer-based indexing.
Check data types with df.dtypes before performing operations.
Use copy() when needed to avoid unintentionally modifying original data.
Pandas works very well with NumPy arrays for efficient numerical computations.

? Try It Yourself

Create a DataFrame for your favorite books and their authors.
Add columns like rating, year, or genre and print the full table.
Access specific rows and columns using loc and iloc.
Filter the DataFrame to show only rows matching some condition (e.g., rating > 4).
Add a new column based on an existing one (for example, a boolean column like Is_New = Year >= 2020).