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 (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.

• 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).

  • give your plot the title "Volcano density near Seattle"

  • Make the same figure, but this time use the seaborn function sns.kdeplot(). Use the argument shade=True to fill in your contours.