Home assignment 2

Home assignment 2#

Last updated: 2026-02-12 16:07:33

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

  • 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.

  • Subset the cities which are in the same \(1 \times 1\) degrees “cell” of longitude and latitude. For example, if your chosen city is Mexico City which is at (-99.14, 19.43) (lon, lat), then you need to subset the cities with longitude between -100 and -99, and latitude between 19 and 20.

  • Sort the resulting table by population (column 'pop'), from biggest city to smallest

  • Print the result

city country pop lat lon capital
23660 Mexico City Mexico 8659409 19.43 -99.14 1
10135 Ecatepec Mexico 1844447 19.60 -99.05 0
25921 Nezahualcoyotl Mexico 1230816 19.41 -99.03 0
25496 Naucalpan Mexico 847600 19.48 -99.23 0
38320 Tlalnepantla Mexico 715026 19.54 -99.19 0
... ... ... ... ... ... ...
8506 Cuautlalpan Mexico 8349 19.69 -99.29 0
33002 San Juan Tilapa Mexico 8188 19.22 -99.66 0
38672 Totocuitlapilco Mexico 8134 19.23 -99.58 0
16288 Jilotzingo Mexico 8050 19.87 -99.07 0
7383 Chiconautla Mexico 8010 19.62 -99.01 0

77 rows × 6 columns

  • Hint: you can use the numpy methods .floor and .ceil to get the nearest whole number below and above the given value, respectively.

  • Note: do not use any longitude or latitude values in your code! The only specific value in the code should be the city name you chose, all other values need to be calculated.

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