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Basic numpy exercises¶

For code template see: lab_01_code.py;
For solution see: Basic numpy exercises.

Simple arrays¶

Create an array with variable name a and the following contents (shape (3, 4)):

7 12  0
9  3  4
0  1  3

>>> #- create array "a" with values
>>> #-     2  7 12  0
>>> #-     3  9  3  4
>>> #-     4  0  1  3

What is the array shape?

>>> #- Array shape?

What is the array ndim?

>>> #- Array ndim?

How about the len of the array?

>>> #- Array length

Can you get the ndim and len from the shape?

>>> #- Get ndim and length from the shape

Creating arrays using functions¶

Create a 1D array from 2 through 5 inclusive.

>>> #- 1D array 2 through 5

Make an array with 10 equally spaced elements between 2 and 5 inclusive.

>>> #- 10 equally spaced elementd between 2 and 5

Make an all-ones array shape (4, 4).

>>> #- Shape 4,4 array of 1

Make an identity array shape (6, 6).

>>> #- Identity array shape 6, 6

Make this array with a single Python / numpy command:
```
1  0  0
0  2  0
0  0  3
```

>>> #- Array with top left value == 1 etc

Look at the docstring for np.random.randn. Make a shape (3, 5) array with random numbers from a standard normal distribution (a normal distribution with mean 0 and variance 1).

>>> #- Array of random numbers shape 3, 5

Simple visualizations¶

Make an array x with 100 evenly spaced values between 0 and 2 * pi;

>>> #- x is an array with 100 evenly spaced numbers 0 - 2 pi

Make an array y which contains the cosine of the corresponding value in x - so y[i] = cos(x[i]) (hint: np.lookfor('cosine')).

>>> #- y has cosines of values in x

Plot x against y;

>>> #- plot x against y

Make a 10 by 20 array of mean 0 variance 1 normal random numbers;

>>> #- Shape 10, 20 array of random numbers

Display this array as an image;

>>> #- Display as image

Investigate plt.cm. See if you can work out how to make the displayed image be grayscale instead of color.

>>> #- Grayscale image of array

Indexing and slicing, array creation¶

See discussion at Index ordering and reshape in NumPy and MATLAB.

Create the following array, call this a (you did this before):
```
2  7 12  0
3  9  3  4
4  0  1  3
```

>>> #- Create array "a"

Get the 2nd row of a ([ 3 9 3 4]);

>>> #- 2nd row of a

Get the 3rd column of a ([12 3 1]);

>>> #- 3rd column of a

Create the following arrays (with correct data types):

[[1, 1, 1, 1],
 [1, 1, 1, 1],
 [1, 1, 1, 2],
 [1, 6, 1, 1]]

[[0., 0., 0., 0., 0.],
 [2., 0., 0., 0., 0.],
 [0., 3., 0., 0., 0.],
 [0., 0., 4., 0., 0.],
 [0., 0., 0., 5., 0.],
 [0., 0., 0., 0., 6.]]

Par on course: 3 statements for each

Hint: Individual array elements can be accessed similarly to a list, e.g. a[1] or a[1, 2].

Hint: Examine the docstring for diag.

>>> #- Build given arrays

Skim through the documentation for np.tile, and use this function to construct the array:

[[4, 3, 4, 3, 4, 3],
 [2, 1, 2, 1, 2, 1],
 [4, 3, 4, 3, 4, 3],
 [2, 1, 2, 1, 2, 1]]

>>> #- Use np.tile to construct array

Fancy indexing using boolean arrays¶

Create the following array a (same as before):
```
2  7 12  0
3  9  3  4
4  0  1  3
```

>>> #- Create array a

Use > to make a mask that is true where the elements are greater than 5, like this:

False True  True  False
False True  False False
False False False False

>>> #- Make mask for values greater than 5

Return all the elements in a that are greater than 5.

>>> #- Return all values in a that are greater than 5

Set all the elements greater than 5 to be equal to 5, to get this:
```
2  5  5  0
3  5  3  4
4  0  1  3
```

>>> #- Set all elements greater than 5 to equal 5

Elementwise operations¶

Remember our array a:

7 12  0
9  3  4
0  1  3

Use array slicing to get a new array composed of the even columns (0, 2) of a. Now get array that contains the odd columns (1, 3) of a. Add these two arrays.

>>> #- Add even and odd columns of a

Generate this array:
```
[2**0, 2**1, 2**2, 2**3, 2**4]
```

>>> #- Generate array of powers of 2

Generate an array length 10 such that this is true of the elements (where x[i] is the element of x at index i):
```
x[i] = 2 ** (3 * i) - i
```

>>> #- Generate array

Summary functions¶

Remember our array a:

7 12  0
9  3  4
0  1  3

What are the:

sum of all the values?

>>> #- Sum of values in a

sum of the columns?

>>> #- Sum of the values of the columns in a

sum of the rows?

>>> #- Sum of the values of the rows in a

mean?

>>> #- Mean of all the values in a

min?

>>> #- Minimum of all the values in a

max?

>>> #- Maximum of all the values in a