Preparing to Integrate Data from Additional Sources

Importing or integrating data from additional sources can help add value and context to data storytelling narratives. This activity needs to be done carefully—data storytelling requires us to be clear about which narrative components are objective truth and assumptions. When integrating additional data sources, effective data storytellers take extra care to make these differentiations and be cautious before making broader claims around relationships and causal contexts of events.

Example: Stock and GDPR

In this section, we'll look at two datasets centered around stock and GDPR to identify how we could integrate the data.

Visualizing the Dow Jones dataset

Let's take a look at the Dow Jones dataset from Seaborn, which contains the Dow Jones Industrial Stock Price Index for the United States. Let's start our exploration by plotting the dollars per share on the y-axis and the date on the x-axis, using a line plot with the sns.relplot() function. Note that the date range for this dataset is 1914–1968.

We could alternatively create such a plot with Matplotlib using dowjones.plot.line(x='Date', y='Price').figure.