This is a Python challenge. Create pull requests (PR's) to this repository to solve it. Upon PR submission, the GitHub action robots will check your code and report back how well you did. You can then add more commits to your PR until all tests come back green, which means you win!
The exercises are meant to test your knowledge of some important features of the Python programming language and the NumPy and Pandas libraries. When it's not immediately obvious to you how to solve an exercise using only a few lines of code, it is likely you can learn a new Python trick by checking the links below the exercise.
- Fork this repository
- Create a new branch (name it whatever you like)
- Add a new file
gizmo.pyto the repository - Create a pull-request from your new branch to the master branch of this repository
When gizmo.py exists, there should be gizmo python module that you can import.
For example:
>>> import gizmoLearn how to fork, branch and submit a pull request
Learn how to create a Python module
Add a hello() function to the gizmo module. The function should take two parameters: name and country. The country parameter is optional and should default to 'Finland'. When called, the function uses the print() function to write the text Hello {name}, how are things in {country}? to the screen, where {name} and {country} should be the name and country given as parameters.
For example:
>>> import gizmo
>>> gizmo.hello('Gizmo', 'Germany')
Hello Gizmo, how are things in Germany?
>>> gizmo.hello('Gizmo')
Hello Gizmo, how are things in Finland?Learn how to define functions in Python
Add a spell() function to the gizmo module that uses the print() function
to spell out the word given as a parameter to the function, with dots between
the letters. Use a for-loop to implement this (no split/join).
For example:
>>> import gizmo
>>> gizmo.spell('hello')
h.e.l.l.oLearn how to write a for loop in Python
Reading a bunch of files with only small differences in their filenames is a
very common occurrence in any research code. Add a function relative_path()
to the gizmo module that returns a list of files, including their relative
path, following the following pattern:
'./subjects/mock_recording_{subject_identifier}.rec'
where {subject_identifier} is any string. Subject identifiers will be passed
to the function as a list of strings.
For example:
>>> import gizmo
>>> subject_identifiers = ['subject1', 'subject2']
>>> names = gizmo.relative_path(subject_identifiers)
>>> print(names)
['./subjects/mock_recording_subject1.rec', './subjects/mock_recording_subject2.rec']Get started with string formatting
If you want to go into more detail you can also check the python documentation here.
Add a class called Gizmo to your gizmo.py module.
For example:
>>> from gizmo import Gizmo
>>> g = Gizmo()Learn how to create a Python class
Modify your class such that when you create a new instance of Gizmo, you can give it a name.
The name is a string.
Afterwards, the name should be available as the .name attribute.
For example:
>>> from gizmo import Gizmo
>>> g = Gizmo('Ariel')
>>> g.name
'Ariel'Modify your class such that it has a .speak() method. Calling this method
will make the Gizmo object print its name using the print() function.
For example:
>>> from gizmo import Gizmo
>>> g = Gizmo('Ariel')
>>> g.speak()
ArielLearn how to add methods to your class
Modify the gizmo module such that it has a multiplication_table function.
This function should return a two-dimensional NumPy array (i.e. a matrix) that
contains the multiplication table from 1 to 12
that you had to learn in elementary school. A quick way to generate this table is to take the outer product of two vectors with numbers from 1 to 12.
For example:
>>> import gizmo
>>> gizmo.multiplication_table()
array([[ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
[ 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24],
[ 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36],
[ 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 48],
[ 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60],
[ 6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 66, 72],
[ 7, 14, 21, 28, 35, 42, 49, 56, 63, 70, 77, 84],
[ 8, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96],
[ 9, 18, 27, 36, 45, 54, 63, 72, 81, 90, 99, 108],
[ 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120],
[ 11, 22, 33, 44, 55, 66, 77, 88, 99, 110, 121, 132],
[ 12, 24, 36, 48, 60, 72, 84, 96, 108, 120, 132, 144]])Learn about NumPy arrays
Learn about the outer product
Modify the multiplication_table function so that it takes a parameter called
zero_out_multiples. When this parameter is set to an integer number, then the
multiplication table that is returned by the function will have all multiples
of the given number set to zero. Use NumPy's boolean indexing to accomplish
this. The default value of the zero_out_multiples parameter should be
None, meaning that no numbers will be set to zero.
>>> gizmo.multiplication_table(zero_out_multiples=5)
array([[ 1, 2, 3, 4, 0, 6, 7, 8, 9, 0, 11, 12],
[ 2, 4, 6, 8, 0, 12, 14, 16, 18, 0, 22, 24],
[ 3, 6, 9, 12, 0, 18, 21, 24, 27, 0, 33, 36],
[ 4, 8, 12, 16, 0, 24, 28, 32, 36, 0, 44, 48],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 6, 12, 18, 24, 0, 36, 42, 48, 54, 0, 66, 72],
[ 7, 14, 21, 28, 0, 42, 49, 56, 63, 0, 77, 84],
[ 8, 16, 24, 32, 0, 48, 56, 64, 72, 0, 88, 96],
[ 9, 18, 27, 36, 0, 54, 63, 72, 81, 0, 99, 108],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 11, 22, 33, 44, 0, 66, 77, 88, 99, 0, 121, 132],
[ 12, 24, 36, 48, 0, 72, 84, 96, 108, 0, 132, 144]])Learn about the modulo (%) operator
Learn about NumPy array boolean indexing
Add a docstring to the multiplication_table function. Use the "numpydoc" style
for this documentation. The documentation should contain:
- Short summary: a one-line description of what the function does.
- Extended summary: A longer description with more details about what the function does.
- Parameters: a list of all parameters that the function takes. For each parameter, describe:
- The parameter name
- Its expected type (int, bool, str, array, ...)
- What the parameter does
- Returns: a list of all values returned by the function. For each return value, describe:
- The returned value's name
- Its type (int, bool, str, array, ...)
- What the returned value means
Learn about docstrings
Learn about the numpydoc documentation style
Add a function generate_fibonacci_sequence(n) to your gizmo module that will be a generator that yields the first n numbers of the Fibonacci sequence.
For example:
>>> import gizmo
>>> fib = gizmo.generate_fibonacci_sequence(5)
>>> while(True):
>>> print(next(fib))
0
1
1
2
3
StopIterationAdd a function get_fibonacci_sequence(n) to your gizmo module that takes a single parameter n and returns a NumPy array of the first n Fibonacci numbers. This function should use the generator you build in exercise 11 to generate the numbers, store them in a list and return the list in the form of a NumPy array.
For example:
>>> import gizmo
>>> gizmo.get_fibonacci_sequence(10)
array([ 0, 1, 1, 2, 3, 5, 8, 13, 21, 34])How do I build a NumPy array from a generator?
The Gizmo git repository has a CSV in it called titanic.csv. This file contains information about all the passengers that were on board the Titanic when it sank. Add a function get_titanic() to your gizmo module that uses Pandas to load the CSV file and return it as a DataFrame.
For example:
>>> import gizmo
>>> gizmo.get_titanic()
survived pclass sex age sibsp parch fare embarked class \
0 0 3 male 22.0 1 0 7.2500 S Third
1 1 1 female 38.0 1 0 71.2833 C First
2 1 3 female 26.0 0 0 7.9250 S Third
3 1 1 female 35.0 1 0 53.1000 S First
4 0 3 male 35.0 0 0 8.0500 S Third
.. ... ... ... ... ... ... ... ... ...
886 0 2 male 27.0 0 0 13.0000 S Second
887 1 1 female 19.0 0 0 30.0000 S First
888 0 3 female NaN 1 2 23.4500 S Third
889 1 1 male 26.0 0 0 30.0000 C First
890 0 3 male 32.0 0 0 7.7500 Q Third
who adult_male deck embark_town alive alone
0 man True NaN Southampton no False
1 woman False C Cherbourg yes False
2 woman False NaN Southampton yes True
3 woman False C Southampton yes False
4 man True NaN Southampton no True
.. ... ... ... ... ... ...
886 man True NaN Southampton no True
887 woman False B Southampton yes True
888 woman False NaN Southampton no False
889 man True C Cherbourg yes True
890 man True NaN Queenstown no True
[891 rows x 15 columns]Learn about the Pandas package
Learn about reading CSV data with Pandas
Add a function get_titanic_children() to your gizmo module. This function should read the titanic.csv file in this git repository and return a Pandas DataFrame with all the children, age ≤ 12, that were on board the Titanic when it sunk.
For example:
>>> import gizmo
>>> gizmo.get_titanic_children()
survived pclass sex age sibsp parch fare embarked class \
7 0 3 male 2.00 3 1 21.0750 S Third
10 1 3 female 4.00 1 1 16.7000 S Third
16 0 3 male 2.00 4 1 29.1250 Q Third
24 0 3 female 8.00 3 1 21.0750 S Third
43 1 2 female 3.00 1 2 41.5792 C Second
.. ... ... ... ... ... ... ... ... ...
827 1 2 male 1.00 0 2 37.0042 C Second
831 1 2 male 0.83 1 1 18.7500 S Second
850 0 3 male 4.00 4 2 31.2750 S Third
852 0 3 female 9.00 1 1 15.2458 C Third
869 1 3 male 4.00 1 1 11.1333 S Third
who adult_male deck embark_town alive alone
7 child False NaN Southampton no False
10 child False G Southampton yes False
16 child False NaN Queenstown no False
24 child False NaN Southampton no False
43 child False NaN Cherbourg yes False
.. ... ... ... ... ... ...
827 child False NaN Cherbourg yes False
831 child False NaN Southampton yes False
850 child False NaN Southampton no False
852 child False NaN Cherbourg no False
869 child False NaN Southampton yes False
[69 rows x 15 columns]