US Tropical Storm Data: 1851-1965, 1983-2019
Explore methods to clean and preprocess US tropical storm data by removing irrelevant rows and footnotes. Learn to convert dates for analysis and focus on maximum wind speeds to categorize storm intensities. Understand how to segment data into meaningful subsets and visualize storm patterns over time using Python in a Jupyter environment.
Cleaning up the hurricane data
The rows representing years without events should, again, be removed:
df = df[(df.Date.str.contains("None")) == False]
This dataset’s Date column has characters pointing to five
footnotes: $, *, #, %, and &. The footnotes contain essential
information, but those characters will give us grief if we remove them. These commands will get that done, replacing
all such strings in the Date ...