Home assignment 2

Home assignment 2#

Last updated: 2024-11-25 22:40:06

Question 1#

  • Read the DEM of the Carmel in 'carmel.csv' into an ndarray object.

  • Calculate how many cells/pixels are in each of 100-m elevation “bins” starting with -100 and ending with 600 meters, i.e., -100-0, 0-100, …, 500-600.

  • The counts need to include the start point but not the end point, i.e., the first bin is \(-100 ≤ x < 0\), the second bin is \(0 ≤ x < 100\), and so on.

  • Store the results in a dictionary, with the dictionary keys being the bin labels, and dictionary values being the pixel counts.

  • Print the dictionary.

{'-100 - 0': np.int64(380),
 '0 - 100': np.int64(80438),
 '100 - 200': np.int64(40877),
 '200 - 300': np.int64(20841),
 '300 - 400': np.int64(8998),
 '400 - 500': np.int64(5116),
 '500 - 600': np.int64(476)}

Question 2#

  • Read the 'world_cities.csv' file into a DataFrame object

  • Calculate a new column named 'pop_M' (population in millions), by transforming the 'pop' (population) column

  • Remove the original 'pop' column

  • Choose a city that starts with the same English letter as your first name (for example, 'Mexico City' if your first name is Michael). You can do this step manually, e.g., in Excel, and not in your Python code.

  • Define a str variable with your selected city

  • Subset the six biggest (i.e., largest population sizes) cities from the country where your selected city is. (Do not use the country name string in the code; use an expression which returns it.)

  • Print the result

city country lat lon capital pop_M
23660 Mexico City Mexico 19.43 -99.14 1 8.659409
10135 Ecatepec Mexico 19.60 -99.05 0 1.844447
13180 Guadalajara Mexico 20.67 -103.35 0 1.637213
16484 Juarez Mexico 31.74 -106.49 0 1.449006
38138 Tijuana Mexico 32.53 -117.02 0 1.427118
30002 Puebla Mexico 19.05 -98.22 0 1.416551
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