In this challenge, you are given a table of closing stock prices for 10 different stocks with data going back as far as 1999. For each stock, find the date where it had its largest one-day percentage loss.
Begin working this challenge now in a Jupyter Notebook thanks to Binder (mybinder.org). The data is found in the
stocks10.csv file with the ticker symbol as a column name.
The Dunder Data Challenges Github repository also contains all of the challenges.
Can you return a Series that has the ticker symbols in the index and the date where the largest percentage price drop happened as the values? There is a nice, fast solution that uses just a minimal amount of code without any loops.
Can you return a DataFrame with the ticker symbol as the columns with a row for the date and another row for the percentage price drop?