#### Exercise 0: Environment and libraries ##### The exercise is validated if all questions of the exercise are validated ##### Install the virtual environment with `requirements.txt` ##### Activate the virtual environment. If you used `conda`, run `conda activate ex00` ###### Does the shell specify the name `ex00` of the environment on the left? ##### Run `python --version` ###### Does it print `Python 3.8.x`? x could be any number from 0 to 9 ###### Does `import jupyter` and `import numpy` run without any error? ###### Have you used the following command `jupyter notebook --port 8891`? ###### Is there a file named `Notebook_ex00.ipynb` in the working directory? ###### Is the following markdown code executed in a markdown cell in the first cell? ``` # H1 TITLE ## H2 TITLE ``` ###### Does the second cell contain `print("Buy the dip ?")` and return `Buy the dip ?` in the output section? --- --- #### Exercise 1: Your first NumPy array ##### Add cell and run `type(your_numpy_array)` ###### Is the your_numpy_array an NumPy array? It can be checked with that should be equal to `numpy.ndarray`. ##### Run all the cells of the notebook or `python main.py` ###### Are the types printed are as follows? ``` ``` ##### Delete all the cells you added for the audit and restart the notebook --- --- #### Exercise 2: Zeros ##### The exercise is validated if all questions of the exercise are validated ###### For question 1, does the solution use `np.zeros` and is the shape of the array `(300,)`? ###### For question 2, does the solution use `reshape` and is the shape of the array `(3, 100)`? --- --- #### Exercise 3: Slicing ##### The exercise is validated if all questions of the exercise are validated ###### For question 1, is validated if the solution doesn't involve a for loop or writing all integers from 1 to 100 and if the array is: `np.array([1,...,100])`. The list from 1 to 100 can be generated with an iterator: `range`. Were the previous requirements fulfilled? ###### For question 2, is the solution `integers[::2]`? ###### For question 3, is the solution `integers[::-2]`? ###### For question 4, is the array `np.array([1,0,3,4,0,...,0,99,100])`? There are at least two ways to get this results without for loop. The first one uses `integers[1::3] = 0` and the second involves creating a boolean array that indexes the array: ```python mask = (integers+1)%3 == 0 integers[mask] = 0 ``` --- --- #### Exercise 4: Random ##### The exercise is validated if all questions of the exercise are validated ##### For this exercise, as the results may change depending on the version of the package or the OS, I give the code to correct the exercise. If the code is correct and the output is not the same as mine, it is accepted. ###### For question 1, is the solution `np.random.seed(888)`? ###### For question 2, is the output of the solution the same as `np.random.randn(100)`? The value of the first element is `0.17620087373662233`. ###### For question 3, is the solution `np.random.randint(1,11,(8,8))`? ```console Given the NumPy version and the seed, you should have this output: array([[ 7, 4, 8, 10, 2, 1, 1, 10], [ 4, 1, 7, 4, 3, 5, 2, 8], [ 3, 9, 7, 4, 9, 6, 10, 5], [ 7, 10, 3, 10, 2, 1, 3, 7], [ 3, 2, 3, 2, 10, 9, 5, 4], [ 4, 1, 9, 7, 1, 4, 3, 5], [ 3, 2, 10, 8, 6, 3, 9, 4], [ 4, 4, 9, 2, 8, 5, 9, 5]]) ``` ###### For question 4, is the solution `np.random.randint(1,18,(4,2,5))`? ```console Given the NumPy version and the seed, you should have this output: array([[[14, 16, 8, 15, 14], [17, 13, 1, 4, 17]], [[ 7, 15, 2, 8, 3], [ 9, 4, 13, 9, 15]], [[ 5, 11, 11, 14, 10], [ 2, 1, 15, 3, 3]], [[ 3, 10, 5, 16, 13], [17, 12, 9, 7, 16]]]) ``` --- --- #### Exercise 5: Split, concatenate, reshape arrays ##### The exercise is validated if all questions of the exercise are validated ###### For question 1, is the generated array based on an iterator as `range` or `np.arange`? Check that 50 is part of the array. ###### For question 2, is the generated array based on an iterator as `range` or `np.arange`? Check that 100 is part of the array. ###### For question 3, is the array concatenated this way `np.concatenate(array1,array2)`? ###### For question 4, is the result the following? ```console array([[ 1, ... , 10], ... [ 91, ... , 100]]) ``` The easiest way is to use `array.reshape(10,10)`. https://jakevdp.github.io/PythonDataScienceHandbook/ (section: The Basics of NumPy Arrays) --- --- #### Exercise 6: Broadcasting and Slicing ##### The exercise is validated if all questions of the exercise are validated ###### For question 1, is the output the same as the following? `np.ones([9,9], dtype=np.int8)` ###### For question 2, is the output the following? ```console array([[1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 0, 0, 0, 0, 0, 0, 0, 1], [1, 0, 1, 1, 1, 1, 1, 0, 1], [1, 0, 1, 0, 0, 0, 1, 0, 1], [1, 0, 1, 0, 1, 0, 1, 0, 1], [1, 0, 1, 0, 0, 0, 1, 0, 1], [1, 0, 1, 1, 1, 1, 1, 0, 1], [1, 0, 0, 0, 0, 0, 0, 0, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=int8) ``` ##### The solution of question 2 is not accepted if the values of the array have been changed one by one manually. The usage of the for loop is not allowed neither. Here is an example of a possible solution: ```console x[1:8,1:8] = 0 x[2:7,2:7] = 1 x[3:6,3:6] = 0 x[4,4] = 1 ``` --- --- #### Exercise 7: NaN ##### The exercise is validated if all questions of the exercise are validated ###### Without having used a for loop or having filled the array manually, is the output the following? ```console [[ 7. 1. 7.] [nan 2. 2.] [nan 8. 8.] [ 9. 3. 9.] [ 8. 9. 8.] [nan 2. 2.] [ 8. 2. 8.] [nan 6. 6.] [ 9. 2. 9.] [ 8. 5. 8.]] ``` There are two steps in this exercise: - Create the vector that contains the grade of the first exam if available or the second. This can be done using `np.where`: ```python np.where(np.isnan(grades[:, 0]), grades[:, 1], grades[:, 0]) ``` - Add this vector as third column of the array. Here are two ways: ```python np.insert(arr = grades, values = new_vector, axis = 1, obj = 2) np.hstack((grades, new_vector[:, None])) ``` --- --- #### Exercise 8: Wine ##### The exercise is validated if all questions of the exercise are validated ###### Has the text file successfully been loaded in a NumPy array with `genfromtxt('winequality-red.csv', delimiter=',')` and the reduced arrays weights **76800 bytes**? ###### Is the output the following? ```python array([[ 7.4 , 0.7 , 0. , 1.9 , 0.076 , 11. , 34. , 0.9978, 3.51 , 0.56 , 9.4 , 5. ], [ 7.4 , 0.66 , 0. , 1.8 , 0.075 , 13. , 40. , 0.9978, 3.51 , 0.56 , 9.4 , 5. ], [ 6.7 , 0.58 , 0.08 , 1.8 , 0.097 , 15. , 65. , 0.9959, 3.28 , 0.54 , 9.2 , 5. ]]) ``` This slicing gives the answer `my_data[[1,6,11],:]`. ###### Is the answer False? There are many ways to get the answer: find the maximum or check values greater than 20. ###### Is the answer 10.422983114446529? ###### Is the answer the following? ```console pH stats 25 percentile: 3.21 50 percentile: 3.31 75 percentile: 3.4 mean: 3.3111131957473416 min: 2.74 max: 4.01 ``` > *Note: Using `percentile` or `median` may give different results depending on the duplicate values in the column. If you do not have my results please use `percentile`.* ###### Is the answer ~`5.2`? The first step is to get the percentile 20% of the column `sulphates`, then create a boolean array that contains `True` of the value is smaller than the percentile 20%, then select this rows with the column quality and compute the `mean`. ###### Is the output for the best wines the following? ```python array([ 8.56666667, 0.42333333, 0.39111111, 2.57777778, 0.06844444, 13.27777778, 33.44444444, 0.99521222, 3.26722222, 0.76777778, 12.09444444, 8. ]) ``` ###### Is the output for the bad wines the following? ```python array([ 8.36 , 0.8845 , 0.171 , 2.635 , 0.1225 , 11. , 24.9 , 0.997464, 3.398 , 0.57 , 9.955 , 3. ]) ``` This can be done in three steps: Get the max, create a boolean mask that indicates rows with max quality, use this mask to subset the rows with the best quality and compute the mean on the axis 0. --- --- #### Exercise 9: Football tournament ###### Is the output the following? ```console [[2 9 6 0 8] [1 3 5 7 4]] ```