Let's take a look at how Python can be an effective platform for analyzing and visualizing data.

In this talk we'll use Jupyter Notebooks, Pandas, and Plotly to visualize several datasets to find trends in our data.

We'll cover loading and transforming data in Pandas and NumPy, including cleaning missing values and performing feature engineering. We'll also look into the built-in aggregation and statistical techniques in Pandas DataFrames.

Next we'll take our data and see how Plotly can generate compelling scatter plots, box plots, histograms, tree maps, and more with just a bit of code.

By the time we're done you'll see why I often prefer data visualization in Python over dedicated tools like Tableau or Power BI.