public

ytursyna

public

History

eslopfer 4aaa6ccedb docs(numpy): fix audits format		2 years ago
..
README.md	docs(numpy): fix audits format	2 years ago

README.md

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?

<class 'int'>
<class 'float'>
<class 'str'>
<class 'dict'>
<class 'list'>
<class 'tuple'>
<class 'set'>
<class 'bool'>

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([0, 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:

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 solution `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))`?

    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))`?

    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?

    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?

    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:

        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?

[[ 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:

    np.where(np.isnan(grades[:, 0]), grades[:, 1],     grades[:, 0])

Add this vector as third column of the array. Here are two ways:

    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?

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?

    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?

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?

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?

 [[0 3 1 2 4]
  [7 6 8 9 5]]

README.md

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?

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([0, 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:

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 solution 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))?

For question 4, is the solution np.random.randint(1,18,(4,2,5))?

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?

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?

For question 2, is the output the following?

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.

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?

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?

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?

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?

Is the output for the bad wines the following?

Exercise 9: Football tournament

Is the output the following?

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?

Does the second cell contain `print("Buy the dip ?")` and return `Buy the dip ?` in the output section?

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`

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)`?

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([0, 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:

For question 1, is the solution `np.random.seed(888)`?

For question 2, is the solution `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))`?

For question 4, is the solution `np.random.randint(1,18,(4,2,5))`?

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)`?

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 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`.