#### 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:
```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 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))`?
```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
[[0 3 1 2 4]
[7 6 8 9 5]]
```
