Assignment: Pandas Groupby with Hurricane Data

Assignment: Pandas Groupby with Hurricane Data#

Import Numpy, Pandas and Matplotlib and set the display options.

Use the following code to load a CSV file of the NOAA IBTrACS hurricane dataset:

import pandas as pd
url = 'https://www.ncei.noaa.gov/data/international-best-track-archive-for-climate-stewardship-ibtracs/v04r00/access/csv/ibtracs.ALL.list.v04r00.csv'
df = pd.read_csv(url, parse_dates=['ISO_TIME'], usecols=range(12),
                 skiprows=[1], na_values=[' ', 'NOT_NAMED'],
                 keep_default_na=False, dtype={'NAME': str})
df.tail()

	SID	SEASON	NUMBER	BASIN	SUBBASIN	NAME	ISO_TIME	NATURE	LAT	LON	WMO_WIND	WMO_PRES
716160	2024147N19089	2024	27	NI	BB	REMAL	2024-05-27 06:00:00	NR	23.0325	89.3509	NaN	NaN
716161	2024147N19089	2024	27	NI	BB	REMAL	2024-05-27 09:00:00	NR	23.3337	89.6178	NaN	NaN
716162	2024147N19089	2024	27	NI	BB	REMAL	2024-05-27 12:00:00	NR	23.6263	89.8799	NaN	NaN
716163	2024147N19089	2024	27	NI	BB	REMAL	2024-05-27 15:00:00	NR	23.9143	90.1400	NaN	NaN
716164	2024147N19089	2024	27	NI	BB	REMAL	2024-05-27 18:00:00	NR	24.2000	90.4000	NaN	NaN

Basin Key: (NI - North Indian, SI - South Indian, WP - Western Pacific, SP - Southern Pacific, EP - Eastern Pacific, NA - North Atlantic)

How many rows does this dataset have?

How many North Atlantic hurricanes are in this dataset?

1) Get the unique values of the `BASIN`, `SUBBASIN`, and `NATURE` columns#

2) Rename the `WMO_WIND` and `WMO_PRES` columns to `WIND` and `PRES`#

3) Get the 10 largest rows in the dataset by `WIND`#

You will notice some names are repeated.

4) Group the data on `SID` and get the 10 largest hurricanes by `WIND`#

5) Make a bar chart of the wind speed of the 20 strongest-wind hurricanes#

Use the name on the x-axis.

6) Plot the count of all datapoints by Basin#

as a bar chart

7) Plot the count of unique hurricanes by Basin#

as a bar chart.

8) Make a `hexbin` of the location of datapoints in Latitude and Longitude#

https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.hexbin.html

9) Find Hurricane Katrina (from 2005) and plot its track as a scatter plot#

First find the SID of this hurricane.

Next get this hurricane’s group and plot its position as a scatter plot. Use wind speed to color the points.

10) Make time the index on your dataframe#

11) Plot the count of all datapoints per year as a timeseries#

You should use resample https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.resample.html

Which years stand out as having anomalous hurricane activity?

Assignment: Pandas Groupby with Hurricane Data

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