Python - Numpy Library

Question 1

What are numpy arrays and how are they different than lists?

Answer 1

Numpy arrays are grids that contain values of the same type.

On a structural level, they are composed of nothing but pointers.

Question 2

What are the 4 components of a numpy array?

Answer 2

1 - Data pointer - the memory address of the first byte in an array
ex) my_array.data

2 - Data-type pointer (dtype) - describes the kind of elements in the array
ex) my_array.dtype

3 - Shape - indicates the shape of an array (row, col)
ex) my_array.shape

4 - Stride - number of bytes that should be skipped in memory to get to next element; a stride of (10,1) means you need to proceed 10 bytes to get to next row and one byte to locate next column
ex) my_array.strides

Question 3

Make an array of
1 2 3
4 5 6

Answer 3

my_arrray = np.array ( [ [ 1, 2, 3 ], [ 4, 5, 6 ] ] )

*notice the nested list-like arrangement

Question 4

Create a 3x4 array of ones

Answer 4

np. ones ( (3,4) )

* notice how the input to the function is in tuple-format

Question 5

Create a 2x3x4 array of zeros of type int16

Answer 5

np.zeros ( (2,3,4) , dtype=np.int16 )

Question 6

Create a 2x2 array with random values

Answer 6

np.random.random ( (2,2) )

Question 7

Create an empty 3x2 array

Answer 7

np.empty ( (3,2) )

Question 8

Create a full 2x2 array of 7s

Answer 8

np.full ( (2,2), 7 )

Question 9

Create an array of evenly-spaced values starting from 4 going no further than 20, and incrementing by 3 each time

Answer 9

np.arange ( 4, 20, 3 )
_____
[ 4 7 10 13 16 19 ]

*notice the arguments are not in tuple-format, just regular old argument format

Question 10

Create an array of evenly-spaced values starting at 0, ending at 2, and having 9 values total

Answer 10

np.linspace ( 0, 2, 9 )
_____
[ 0. , 0.25, 0.5 , 0.75, 1. , 1.25, 1.5 , 1.75, 2. ]

*notice how this is different than arange since you can specify the exact ending and how many values you want, where in arange you specify the max endpoint and the step/increment size

Question 11

Create an identity matrix

(this is a matrix where all elements in the principal diagonal are ones and all other elements are zero…when you multiply a matrix with an identity matrix you get the same matrix unchanged)

Answer 11

np. eye()
- or-
np. identity()

Question 12

How would you load the following text file (data.txt) into your program as 3 different variables x, y, z:
\_\_\_\_\_
# This is your data in the text file
# Value1  Value2  Value3
# 0.2536  0.1008  0.3857
# 0.4839  0.4536  0.3561
# 0.1292  0.6875  0.5929
# 0.1781  0.3049  0.8928
# 0.6253  0.3486  0.8791

Answer 12

x, y, z = np.loadtxt (‘data.txt’,
skiprows=1,
unpack=True)

____
Where first arg is file name,
second arg is skipping first row,
third arg returns cols as separate arrays
(there is also a delimiter arg too in case for commas, etc)

Question 13

How would you load the following text file (data.txt) into your program as 3 different variables x, y, z (but now handling MISSING VALUES):
\_\_\_\_\_
# Your data in the text file
# Value1  Value2  Value3
# 0.4839  0.4536  0.3561
# 0.1292  0.6875  MISSING
# 0.1781  0.3049  0.8928
# MISSING 0.5801  0.2038
# 0.5993  0.4357  0.7410

Answer 13

my_array2 = np.genfromtxt(‘data2.txt’,
skip_header=1,
filling_values=-999)

____
By default, this function converts character strings that happen to be in numeric columns into NAN values. You can specify that instead you convert these to a number like -999.

If by any chance, you have values that don’t get converted to nan by genfromtxt(), there’s always the missing_values argument that allows you to specify what the missing values of your data exactly are.

Question 14

How would you save the following array to a text file?
_____
import numpy as np
x = np.arange(0.0,5.0,1.0)

Answer 14

import numpy as np
x = np.arange(0.0,5.0,1.0)
np.savetxt('test.out', x, delimiter=',')
\_\_\_\_\_
*Remember that np.arange() creates a NumPy array of evenly-spaced values. The third value that you pass to this function is the step value.

Question 15

What 3 functions allow you to save numpy arrays to text files as…
1 - binary file .npy
2 - uncompressed .npz archive
3 - compressed .npz archive

Answer 15

1 - save()
2 - savez()
3 - savez_compressed()

Question 16

What is the attribute to print the number of array dimensions?

Answer 16

print( my_array.ndim )

Question 17

What is the attribute to print the number of array elements?

Answer 17

print( my_array.size )

Question 18

What is the attribute to print the length of one array in bytes?

Answer 18

print( my_array.itemsize )

Question 19

How do you change the data type of the array elements?

Answer 19

my_array.astype( float )

Question 20

At a high-level, what is Broadcasting?

Answer 20

It’s a mechanism that allows NumPy to work with arrays of different shapes when you’re performing arithmetic operations.

To put it in a more practical context, you often have an array that’s somewhat larger and another one that’s slightly smaller. Ideally, you want to use the smaller array multiple times to perform an operation (such as a sum, multiplication, etc.) on the larger array.

However, to make sure that the broadcasting is successful, the dimensions of your arrays need to be compatible. Two dimensions are compatible when they are equal.

Question 21

If the dimensions of the arrays are not compatible/equal, what kind of error do you get?

Answer 21

ValueError

Question 22

You can either just use +, -, *, / or % to add, subtract, multiply, divide or calculate the remainder of two (or more) arrays.

Or you can use the equivalent functions which are…

Answer 22

np.add()
np.subtract()
np.multiply()
np.divide()
np.remainder()
_____
Other useful functions are:
np.sin()
np.cos()
np.log()
np.dot()

*last one is the dot product

Question 23

Function to take array-wise sum…

Answer 23

a.sum()

Question 24

Function to take array-wise minimum value…

Answer 24

a.min()

Question 25

Function to take max value of an array row…

Answer 25

a.max( axis=0 )

Question 26

Function to take the cumulative sum of elements…

Answer 26

a.cumsum( axis=1 )

Question 27

Function to take the mean of an array…

Answer 27

a.mean()

Question 28

Function to take the median of an array…

Answer 28

a.median()

Question 29

Function to take the correlational coefficient of an array…

Answer 29

a.corrcoef()

Question 30

Function to take standard deviation of array…

Answer 30

np.std( a )

Question 31

Function to check whether all the elements of two arrays are the same…

Answer 31

np.array_equal()

Question 32

Functions to do logical operations on all elements of the array…

Answer 32

np. logical_or()
np. logical_not()
np. logical_and()

Question 33

Select the element of my_array at 1st index

Answer 33

print( my_array [1] )

Question 34

Select the element of my_array at row 1 column 2

Answer 34

print( my_2d_array [1] [2] )
–or–
print( my_2d_array [1,2] )

Question 35

Select items at index 0 and 1

Answer 35

print( my_array [0:2] )

Question 36

Select items at row 0 and 1, column 1

Answer 36

print( my_2d_array [ 0:2, 1 ] )

Question 37

Select items at row 1 in 3D array

Answer 37

print( my_3d_array [ 1 , … ] )
—or—
print( my_3d_array [ 1 , : , : ] )

Question 38

Select all elements of array that are less than 2

Answer 38

print( my_array [my_array<2] )

Question 39

Save a 3D array from old version of the array that only has elements that are greater than or equal to 3

Answer 39

# Specify a condition
bigger_than_3 = (my_3d_array >= 3)

Use the condition to index our 3d array
print(my_3d_array[bigger_than_3])
_____
Note that you can also chain together conditions with logical operators like this:

bigger_than_3 = (my_3d_array > 3) | (my_3d_array == 3)

Question 40

What does the following do?

my_2d_array [ :, [ 0,1,2,0 ] ]

Answer 40

It tells you that you want to keep all the rows of this result, but that you want to change the order of the columns around a bit. You want to display the columns 0, 1, and 2 as they are right now, but you want to repeat column 0 as the last column instead of displaying column number 3

Question 41

How would you transpose a 2D array?

Answer 41

np.transpose ( my_2d_array )
—or—
my_2d_array.T

Question 42

How would you RESIZE an array where if you make it bigger in any one dimension, it just fills in the extra stuff with 0’s.

Resize x to ((6,4))

Answer 42

np. resize(x, (6,4))
- –or—
x. resize((6,4))

Question 43

How would you RESHAPE an array to do following?

Print the size of x to see what’s possible

Reshape x to (2,6)

Flatten x

Print z

Answer 43

# Print the size of `x` to see what's possible
print( x.size )

# Reshape `x` to (2,6)
print( x.reshape( (2,6) ) )

# Flatten `x`
z = x.ravel()

# Print `z`
print(z)

Question 44

Append the folllowing 1D array to your my_array:

[ 7, 8, 9, 10 ]

Answer 44

new_array = np.append( my_array, [7, 8, 9, 10] )

Question 45

How to insert ‘5’ at index 1 of my_array?

Answer 45

np.insert(my_array, 1, 5)

Question 46

How to delete the value at index 1 of my_array?

Answer 46

np.delete( my_array, [1] )

Question 47

Concatentate my_array and x…

Answer 47

np.concatenate( (my_array,x) )

*Notice that you need to pass in a tuple
____
The number of dimensions needs to be the same if you want to concatenate two arrays

Question 48

Stack my_array & my_2d_array row-wise…

Answer 48

np.vstack( (my_array, my_2d_array) )

• Notice that you need to pass in a tuple
• –or—
  np. r_[my_resized_array, my_2d_array]

____
You just have to make sure that, as you’re stacking the arrays row-wise, that the number of columns in both arrays is the same. As such, you could also add an array with shape (2,4) or (3,4) to my_2d_array, as long as the number of columns matches.

Question 49

Stack my_array & my_2d_array horizontally…

Answer 49

np.hstack( (my_resized_array, my_2d_array) )
____
You have to make sure that the number of dimensions is the same and that the number of rows in both arrays is the same. That means that you could stack arrays such as (2,3) or (2,4) to my_2d_array, which itself as a shape of (2,4). Anything is possible as long as you make sure that the number of rows matches. This function is still supported by NumPy, but you should prefer np.concatenate() or np.stack()

Question 50

Stack my_array & my_2d_array column-wise…

Answer 50

np.column_stack( (my_resized_array, my_2d_array) )

• Notice that you need to pass in a tuple
• –or—
  np. c_[my_resized_array, my_2d_array]

_____
You have to make sure that the arrays that you input have the same first dimension.

Question 51

How would you…

Split my_stacked_array horizontally at the 2nd index

Split my_stacked_array vertically at the 2nd index

Answer 51

# Split `my_stacked_array` horizontally at the 2nd index
print( np.hsplit( my_stacked_array, 2) )

# Split `my_stacked_array` vertically at the 2nd index
print( np.vsplit( my_stacked_array, 2 ) )