From the course: Advanced Python: Practical Database Examples

Introduction to pandas - Python Tutorial

From the course: Advanced Python: Practical Database Examples

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Introduction to pandas

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- [Instructor] As a Python developer, sometimes you might not be developing a consumer product. Instead, you'll use Python to analyze and manipulate data in order to discover new information about that product. Rather than serving up information from a database to a customer, you might want to work with data in memory to find new trends that inform how you build a given application or modify your workflow. You'll often see this type of work in finance, advertising, web analytics, and economic domains. So far, we've created a database and a Python web API that interacts with it. In this chapter, we're going to look at interacting with databases for the purposes of data manipulation and analysis. We can do this with a tool called Pandas. Pandas is a library that gives us lots of tools for doing practical, real world data analysis and Python. It features robust built-in data structures that can help you organize your data and memory and gain meaningful insight from that data. Along with these analysis tools, Pandas also has a tight integration with SQLAlchemy for working with databases so you can easily access the data you want to work with. When you're analyzing data with Python, likely, you won't be making a traditional application. Instead, you'll be writing code to make diagrams, plots and calculations and analyzing the visualizations they return. This means we won't be working in sublime but rather in a tool called Jupiter Notebook. Jupyter Notebooks allow you to create and share pages that contain code, visualizations and equations on the fly. Making computing interactive which is very useful if you're analyzing data. Writing Python code with Pandas in a Jupyter Notebook will set us up for success when analyzing data in Python.

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