Exercise 1¶

Note

Please complete this exercise by the end of day on Thursday the 5th of November 2020 (day before the next practical session).

Start your assignment

You can start working on your copy of Exercise 1 by accepting the GitHub Classroom assignment.

You can also take a look at the open course copy of Exercise 1 in the course GitHub repository (does not require logging in). Note that you should not try to make changes to this copy of the exercise, but rather only to the copy available via GitHub Classroom.

Note

We will continue to use git and GitHub when working with the exercises. You can find instructions for using git and the Jupyter Lab git plugin in the Geo-Python course website.

Pair programming (optional!)

Students attending the course in Helsinki can continue working in pairs. See more information in Slack, and in week 2: Why are we working in pairs?. However, each student should submit their own copy of the exercise.

Hints¶

Useful materials from Geo-Python¶

Assert statements¶

Assertions are a way to assert, or ensure, that the values being used in your scripts are going to be suitable for what the code does. It is common to use assert statements with functions as they are a good way to ensure the correct functionality of a function and guide the user to use function as intended. Read more about assertions from Geo-Python week 6 good coding practices.

One good example how to use assertions inside a function is to ensure that the values passed into the function are of correct type. It is also common to test value ranges with assert, such as test that values are positive. Consider following example that combines these two checks:

# A function for summing positive values
def sum_positive_values(value1, value2):
    """Sums positive values together."""

    # Check that the input values are of correct type (i.e. integers or floats)
    # We can check if the type of the input value can be found from a list of "correct" data types
    # --------------------------------------------------------------------------------------------

    # value1 -parameter
    assert type(value1) in [int, float], "Input value for 'value1' needs to be integer or floating point number! Found: %s" % type(value1)

    # value2 -parameter
    assert type(value2) in [int, float], "Input value for 'value2' needs to be integer or floating point number! Found: %s" % type(value2)

    # Check that the input values are positive
    # ----------------------------------------
    assert value1 > 0, "'value1' needs to be higher than 0! Found: %s" % value1
    assert value2 > 0, "'value2' needs to be higher than 0! Found: %s" % value2

    # If all the tests were passed, do the calculation and return the output
    return value1 + value2

This example demonstrates how it is possible to check and control that the input values are appropriate for the function, and guide the user how to use the function correctly with informative error messages.

Alternatives for iterrows (Problem 3)¶

It is possible to solve problem 3 using iterrows() following this example:

#-----------------------------------------

# OPTION 1: Iterate over dataframe rows:
for idx, row in df.iterrows():

    # create a point based on x and y column values on this row:
    point = Point(row['x'], row['y'])

    # ..continue

However, there are other faster (and shorter) solutions for this. Check out the following examples:

#-----------------------------------------

# OPTION 2: apply a function

# Define a function for creating points from row values
def create_point(row):
    '''Returns a shapely point object based on values in x and y columns'''

    point = Point(row['x'], row['y'])

    return point

# Apply the function to each row
point_series = df.apply(create_point, axis=1)

#-----------------------------------------


# OPTION 3: apply a lambda function
# see: https://docs.python.org/3.5/tutorial/controlflow.html#lambda-expressions

point_series = df.apply(lambda row: Point(row['x'], row['y']), axis=1)

#-----------------------------------------

# OPTION 4: zip and for-loop

geom = []
for x, y in zip(df['x'], df['y']):
    geom.append(Point(x, y))

Iterating multiple lists simultaneously¶

In Python a function called zip() makes it easy to iterate over multiple lists at the same time. Consider following example:

# Create lists
In [1]: dog_list = ['Blackie', 'Musti', 'Svarte']

In [2]: age_list = [4.5, 2, 15]

# Iterate over the lists using zip() to print an informative message
In [3]: for dog, age in zip(dog_list, age_list):
   ...:     print(dog, 'is', age, 'years old.')
   ...: 
Blackie is 4.5 years old.
Musti is 2 years old.
Svarte is 15 years old.

This example demonstrates how it was possible to take two lists (could be even more lists) and access the values from them using the same index number.

Note

This approach assumes that the length of the lists are identical. If not, you will most probably get IndexError because the list index is out of range.