Assignment 4: Pandas¶

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# do imports here

# do imports here

Part I: Basic Pandas¶

1) Use Pandas read_csv function to open the ground-level ozone data at Rutgers site in 2021 ('EPA_AQS_Ozone_Rutgers_2022.csv') as a DataFrame¶

(Don't use any special options). Display the first few rows and the DataFrame info.

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2) Re-read the data in such a way that the Date column is identified as date and Date is used as the index¶

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3) Rename the column 'Daily Max 8-hour Ozone Concentration' as 'ozone'¶

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4) Use describe to get the basic statistics of ozone in 2022¶

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5) Use nlargest to get the 10 days with highest ozone concentration in 2022¶

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6) Make a time series plot of daily ozone concentration in 2022¶

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7) Make a time series plot of monthly average ozone concentration in 2022¶

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8) Read the ozone data in 2021, and merge it with the data in 2022.¶

Remember to rename the ozone column in the new data before merging two dataframes.

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9) Make a time series plot of monthly average ozone concentration from 2021 to 2022¶

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Part II: Advanced Pandas with hurricane data¶

Use the following code to download a csv file of the NOAA IBTrACS hurrican dataset.

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! wget https://www.ncei.noaa.gov/data/international-best-track-archive-for-climate-stewardship-ibtracs/v04r00/access/csv/ibtracs.since1980.list.v04r00.csv

! wget https://www.ncei.noaa.gov/data/international-best-track-archive-for-climate-stewardship-ibtracs/v04r00/access/csv/ibtracs.since1980.list.v04r00.csv

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df=pd.read_csv('ibtracs.since1980.list.v04r00.csv',usecols=range(12), skiprows  = [1], parse_dates=['ISO_TIME'],na_values=[-999, ' '])

df=pd.read_csv('ibtracs.since1980.list.v04r00.csv',usecols=range(12), skiprows = [1], parse_dates=['ISO_TIME'],na_values=[-999, ' '])

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df.head()

df.head()

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

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2) Rename the WMO_WIND column to Wind, and WMO_PRES column to Pressure¶

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3) Get the 10 largest rows in the dataset by Wind¶

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You will notice some names are repeated.

4) Group the data on SID and get the 10 largest hurricanes by maximum Wind¶

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5) Plot the count of all datapoints by Basin¶

as a bar chart

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6) Plot the count of unique hurricanes by Basin¶

as a bar chart. (You will need to call groupby twice.)

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7) Make a hexbin of the location of datapoints in Latitude and Longitude¶

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8) Find Hurricane Sandy (from 2012) and plot its track as a scatter plot¶

Use wind speed to color the points.

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9) Make time the index on your dataframe¶

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10) Plot the count of all datapoints per year as a timeseries¶

You should use resample

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11) Plot all tracks from the West Pacific (BASIN:'WP') in 2005. Color the tracks by hurricane SID.¶

First create a subset dataframe by searching SEASON and BASIN. You will probably have to iterate through a GroupBy object from the subset dataframe.

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12) Create a filtered dataframe that contains only data from the West Pacific ("WP") Basin¶

Use this for the rest of the assignment

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13) Plot the number of datapoints per day¶

Make sure you figure is big enough to actually see the plot

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14) Calculate the climatology of datapoint counts as a function of dayofyear¶

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