Learn the fundamentals of data science with Python by analyzing real datasets and solving problems using pandas
- Learn how to apply data retrieval, transformation, visualization, and modeling techniques using pandas
- Become highly efficient in unlocking deeper insights from your data, including databases, web data, and more
- Build your experience and confidence with hands-on exercises and activities
The Pandas Workshop will teach you how to be more productive with data and generate real business insights to inform your decision-making. You will be guided through real-world data science problems and shown how to apply key techniques in the context of realistic examples and exercises. Engaging activities will then challenge you to apply your new skills in a way that prepares you for real data science projects.
You’ll see how experienced data scientists tackle a wide range of problems using data analysis with pandas. Unlike other Python books, which focus on theory and spend too long on dry, technical explanations, this workshop is designed to quickly get you to write clean code and build your understanding through hands-on practice. As you work through this Python pandas book, you’ll tackle various real-world scenarios, such as using an air quality dataset to understand the pattern of nitrogen dioxide emissions in a city, as well as analyzing transportation data to improve bus transportation services.
By the end of this data analytics book, you’ll have the knowledge, skills, and confidence you need to solve your own challenging data science problems with pandas.
What you will learn
- Access and load data from different sources using pandas
- Work with a range of data types and structures to understand your data
- Perform data transformation to prepare it for analysis
- Use Matplotlib for data visualization to create a variety of plots
- Create data models to find relationships and test hypotheses
- Manipulate time-series data to perform date-time calculations
- Optimize your code to ensure more efficient business data analysis