Now we'll visualize our data effectively by plotting both the closing prices and identifying key data points. First, let's incorporate the low price data alongside our existing analysis, removing any unnecessary print statements that clutter our code.
To identify the lowest price point in our dataset, we'll use lowest_apple_price = apple_prices["3. low"].min(). This function mirrors our previous approach for finding maximum values, but targets the minimum value within the "3. low" column. This gives us the absolute lowest price recorded during our time period—a critical data point for understanding Apple's historical valuation range.
Next, we need to locate the specific row containing this minimum price: low_date = apple_prices[apple_prices["3. low"] == lowest_apple_price]. This boolean indexing technique filters our DataFrame to return only the row(s) where the low price equals our calculated minimum.
Finally, we'll extract the actual date from this filtered result: low_date = low_date.index[0]. The index[0] method retrieves the first (and typically only) date value from our filtered DataFrame, giving us the precise timestamp when Apple reached this low point.
With our data points identified, we can now create a comprehensive visualization. Using matplotlib's pyplot with the clean BMH style, we'll plot the date values along the X-axis against the closing prices on the Y-axis. The pyplot.show() command renders our graph, revealing Apple's price trajectory over time.
The resulting chart tells a compelling story: Apple's stock prices bottomed out in the early 2000s—a period when the company was still recovering from near-bankruptcy in the late 1990s. From there, we see the remarkable upward trajectory that coincided with the iPod launch, iPhone revolution, and subsequent product innovations that transformed Apple into one of the world's most valuable companies.
Now for an advanced visualization challenge that will enhance your data analysis skills. Use pyplot.scatter() to overlay both the highest and lowest price points directly onto your line graph. While you might be able to visually estimate the peak—likely that notable spike around 2012 when Apple became the world's most valuable company—the exact location of the minimum price point requires our calculated precision.
The low point appears to fall somewhere in the early portion of our timeline, but visual estimation isn't sufficient for accurate analysis. This is precisely why we calculated these values programmatically rather than relying on visual approximation.
You now have all the necessary variables at your disposal: low_date and lowest_apple_price for the minimum point, plus high_price and high_date for the maximum. Your task is to plot these as distinct scatter points using pyplot.scatter(), where each point represents an X,Y coordinate pair of date and price. This technique allows you to highlight specific data points of interest while maintaining the overall trend visualization of the line plot.
Take time to implement this solution independently—the ability to combine multiple visualization techniques is essential for creating compelling, informative data presentations. We'll review the implementation approach and best practices in the following segment.