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Python 3 jupyter notebook 1. Simulate a dataset with a log normal distribution, a mean of 1, a standard deviation of .1 and a size
Python 3 jupyter notebook
1.
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Simulate a dataset with a log normal distribution, a mean of 1, a standard deviation of .1 and a size (N) of 100
- Plot the data using a linear X-scale
- Calculate the mean, median, mode, skewness, and kurtosis for the distribution
- Plot the mean, median, and mode as vertical lines
- Plot the skewness and kurtosis as notes on the plot
- Include a legend
2.
- Plot your simulated distribution using a log X-scale (apply np.log() to your dataset)
- Calculate the mean, median, mode, skewness, and kurtosis
- Plot the mean, median, and mode as vertical lines
- Plot the skewness and kurtosis as notes on the plot
- Include a legend
3.
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Make a heat map of volcano locations in the Seattle region
- We searched for data between 32-49N and 124-110W on the NAVDAT database: http://www.navdat.org/NavdatSearch/Search.cfm as excel spreadsheet. We translated it to WUS_navdat.txt in the Datasets folder.
- Read in this datafile as a Pandas DataFrame.
- Filter the data for ages within the last 10,000
- Filter the data to be between 40 and 50 degrees latitude and -124 and -110 longitude
- make NumPy arrays for the latitude and longitude values
- make a matplotlib figure (plt.figure()) with height and width both 10
- a heat map is really a 2d histogram in color and there is a handy function in matplotlib that makes a plot called plt.hist2d(). Look at the help message for that function.
- call plt.hist2d() with your longitude, latitude arrays as x and y and 25 bins.
- label your x and y axes Longitude and Latitude respectively.
- make a big red star (markersize=25) at the location of Seattle (47.61N,122.33W).
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give your plot the title "Volcano density near Seattle"
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Make the same figure, but this time use the seaborn function sns.kdeplot(). Use the argument shade=True to fill in your contours.
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