Sample Datasets and Resources for Practicing Pandas
Tags: pandas, python
Published: 2024-06-10 04:13:27

# Essential Sample Datasets and Resources for Practicing Pandas

Pandas is a powerful Python library for data manipulation and analysis. To master Pandas, it's important to work with real-world datasets and resources. In this article, we'll explore some valuable CSV datasets and resources to help you practice and enhance your Pandas skills.

## Getting Started with Pandas

Before diving into the datasets, make sure you have Pandas installed. If you're using Jupyter Notebook, you can install Pandas with the following command:

```python
!pip install pandas
```

Then, import Pandas in your script or notebook:

```python
import pandas as pd
```

## Essential Datasets for Practice

Here are some publicly available CSV datasets that are perfect for practicing various Pandas operations:

### 1. Titanic Dataset

The Titanic dataset is a classic for data analysis and machine learning. It contains information about the passengers on the Titanic, including whether they survived.

- **Dataset URL**: [Titanic Dataset](https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv)

### 2. Iris Dataset

The Iris dataset includes measurements of iris flowers from three different species. It's commonly used for classification exercises.

- **Dataset URL**: [Iris Dataset](https://raw.githubusercontent.com/uiuc-cse/data-fa14/gh-pages/data/iris.csv)

### 3. Wine Quality Dataset

This dataset contains chemical properties of red and white wines and their quality ratings. It's great for regression tasks.

- **Red Wine Quality**: [Red Wine Quality](https://raw.githubusercontent.com/selva86/datasets/master/winequality-red.csv)
- **White Wine Quality**: [White Wine Quality](https://raw.githubusercontent.com/selva86/datasets/master/winequality-white.csv)

### 4. World Happiness Report

This dataset includes global happiness scores and related data for various countries.

- **Dataset URL**: [World Happiness Report](https://raw.githubusercontent.com/AVu120/World-Happiness-2015-2022/main/world-happiness-report-2022.csv)

### 5. US States Population

Contains population estimates for US states over several years.

- **Dataset URL**: [US States Population](https://raw.githubusercontent.com/jakevdp/data-USstates/master/state-population.csv)

### 6. COVID-19 Dataset

This dataset tracks global COVID-19 cases over time, provided by Johns Hopkins University.

- **Dataset URL**: [COVID-19 Dataset](https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv)

### 7. Air Quality Dataset

The dataset contains historical data on air passengers, suitable for time series analysis.

- **Dataset URL**: [Air Quality](https://raw.githubusercontent.com/jbrownlee/Datasets/master/airline-passengers.csv)

### 8. Student Performance

Includes data on student performance in Portuguese schools.

- **Dataset URL**: [Student Performance](https://raw.githubusercontent.com/jbrownlee/Datasets/master/student-por.csv)

### 9. Global Terrorism Database

A comprehensive dataset on terrorist incidents worldwide.

- **Dataset URL**: [Global Terrorism Database](https://raw.githubusercontent.com/START-UMD/gtd/master/globalterrorismdb_0718dist.csv)

### 10. NYC Property Sales

This dataset includes property sales records in New York City.

- **Dataset URL**: [NYC Property Sales](https://raw.githubusercontent.com/nychealth/coronavirus-data/master/trends/cases-by-day.csv)

## Example: Loading and Exploring a Dataset

Let's load the Titanic dataset and perform some basic operations to get you started:

```python
import pandas as pd

# Load the Titanic dataset
url = 'https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv'
titanic_data = pd.read_csv(url)

# Display the first few rows
print(titanic_data.head())

# Display summary statistics
print(titanic_data.describe())

# Check for missing values
print(titanic_data.isnull().sum())
```

## Additional Resources

### Pandas Documentation

The official Pandas documentation is a comprehensive resource for learning about the library's features and functions.

- **Pandas Documentation**: [pandas.pydata.org](https://pandas.pydata.org/pandas-docs/stable/)

### Books

1. **Python for Data Analysis by Wes McKinney**: This book is written by the creator of Pandas and is an excellent resource for learning data analysis with Pandas.
2. **Pandas Cookbook by Ted Petrou**: A practical guide with examples and recipes for performing data analysis with Pandas.

### Online Courses

1. **DataCamp**: Offers several courses on Pandas and data manipulation.
2. **Coursera**: Courses like "Applied Data Science with Python" cover Pandas extensively.

By practicing with these datasets and utilizing these resources, you'll gain a strong understanding of how to use Pandas for data manipulation and analysis. Happy coding!