@ -13,7 +13,7 @@ I suggest to use the most recent one.
### Resources
- [Why Should We Use NumPy](https://medium.com/fintechexplained/)why-should-we-use-NumPy-c14a4fb03ee9
- [Why Should We Use NumPy](https://medium.com/fintechexplained/why-should-we-use-NumPy-c14a4fb03ee9)
- [NumPy Documentation](https://numpy.org/doc/)
- [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/)
@ -183,14 +183,14 @@ The goal of this exercise is to learn to access values of n-dimensional arrays e
[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)
[1, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=np.int8)
```
3. Using **broadcasting** create an output matrix based on the following two arrays:
```python
array_1 = np.array([1,2,3,4,5], type=int8)
array_2 = np.array([1,2,3], dtype=int8)
array_1 = np.array([1,2,3,4,5], dytpe=np.int8)
array_2 = np.array([1,2,3], dytpe=np.int8)
```
Expected output:
@ -292,9 +292,9 @@ The goal of this exercise is to perform fundamental data analysis on real data u
The dataset chosen for this task is the [red wine dataset](https://archive.ics.uci.edu/ml/datasets/wine+quality)
1. Load the data using `genfromtxt`, specifying the delimiter as ';', and optimize the numpy array size by reducing the data types. Ensure that the sum of absolute differences between the original and the "memory" optimized dataset is less than `1.10**-3`. Use `np.float32` and verify that the resulting numpy array weighs **76800 bytes**.
1. Load the data using `genfromtxt`, specifying the delimiter as ';', and optimize the numpy array size by reducing the data types. Use `np.float32` and verify that the resulting numpy array weighs **76800 bytes**.
2. Display the 2nd, 7th, and 12th rows as a two-dimensional array.
2. Display the 2nd, 7th, and 12th rows as a two-dimensional array. Exclude `np.nan` values if present.
3. Determine if there is any wine in the dataset with an alcohol percentage greater than 20%. Return True or False.