Combine Google Trends with other Time Series Data

What can the popularity of search terms tell us about the world? Google Trends gives us access to the popularity of Google Search terms. Let's investigate:

• How search volume for "Bitcoin" relates to the price of Bitcoin

• How search volume for a hot stock like Telsa relates to that stock's price and

• How searches for "Unemployment Benefits" vary with the actual unemployment rate in the United States

What you'll learn today

• How to make time-series data comparable by resampling and converting to the same periodicity (e.g., from daily data to monthly data).

• Fine-tuning the styling of Matplotlib charts by using limits, labels, linestyles, markers, colours, and the chart's resolution.

• Using grids to help visually identify seasonality in a time series.

• Finding the number of missing and NaN values and how to locate NaN values in a DataFrame.

• How to work with Locators to better style the time axis on a chart

• Review the concepts learned in the previous three days and apply them to new datasets

Download and add the Notebook to Google Drive

Download the .zip file from this lesson and extract it. Add the .ipynb file into your Google Drive and open it as a Google Colaboratory notebook.

Add the Data to the Notebook

The .zip file also includes 5 .csv files. This is the data for the project. Add these to the notebook.

and let's get this party started!