Lesson 6 Numpy Flashcards
Import numpy and check what version you have
import numpy as np
numpy.__version__
Create an array from this list my_list = [1,2,3]
np.array(my_list)
Create an array from the matrix my_matrix = [[1,2,3],[4,5,6],[7,8,9]]
np.array(my_matrix)
my_arr = np.array([1, 2, 3, 4, 5, 6, 7, 8])
Add the values 1,2 to the array above.
np.append(my_arr, [1,2])
my_arr = np.array([1, 2, 3, 4, 5, 6, 7, 8])
Delete index 1 from my array
np.delete(my_arr, 1)
Arrange the numbers in ascending order: another_arr = np.array([2, 1, 5, 3, 7, 4, 6, 8])
np.sort(another_arr)
Return evenly spaced values within a given interval. Do this for the values from 0-9
np.arange(0,10)
Return evenly spaced values within a given interval. Do this for the values from 0-10 every 2 values
np.arange(0,11,2)
Generate a matrix of zeros - one row, 3 columns
np.zeros(3)
Generate a matrix of zeros - 10 rows, 10 cols
np.zeros((10,10))
Generate a matrix of ones, 5 rows, 5 cols
np.ones((5,5))
Return evenly spaced numbers over a specified interval. Do this for the numbers 0-10 with 3 numbers.
np.linspace(0,10,3)
Return evenly spaced numbers over a specified interval. Do this for the numbers 0-20 with 50 numbers.
np.linspace(0,20,50)
Create an identity matrix for 8 rows.
np.eye(8)
Return 3 random numbers (0-1) in an array.
np.random.rand(3)
Return random numbers (0-1) in a matrix with 4 rows and 4 cols.
np.random.rand(4,4)
Return a random sample from the “standard normal” distribution. Do this for 3 numbers.
np.random.randn(3)
Return a random sample from the “standard normal” distribution. Do this for 3 numbers.
np.random.randn(3)
Return random integers from 1-99
np.random.randint(1,100)
Return random integers from 1-99 as an array for 10 numbers.
np.random.randint(1,100,10)
Return an array containing the same data with a new shape. Do this for the array arr = np.arange(30). Reshape the array into 5 rows and 6 cols.
arr.reshape(5,6)
Return an array containing the same data with a new shape. Do this for the array arr = np.arange(30). Reshape the array into 5 rows and 2 cols.
rand_arr.reshape(5,2)
find the index for the max and min value in the array rand_arr = np.random.randint(0,100,10)
rand_arr.argmax()
rand_arr.argmin()
Find the max and min value for the array rand_arr = np.random.randint(0,100,10)
rand_arr.max()
rand_arr.min()
Tell me how to find out the number of elements in a dimension
arr.shape
How do I find out the number of axes or dimensions in an array.
arr.ndim
How do I find out the total number of elements of the array.
arr.size
Tell me the datatype of the object in the array.
arr.dtype
Specify the datatype to be an integer when making the following matrix. arr_2 = np.ones(2) and then check the datatype.
arr_2 = np.ones(2, dtype=int)
arr_2.dtype
x = np.array([1, 2, 3, 4, 5, 6]) add a new axis
x2 = x[np.newaxis]
x2.shape
For the array x = np.array([1, 2, 3, 4, 5, 6]), convert the 1D array to a col vector by inserting an axis along the first dimension. Then show the number of elements in each row and col.
col_vector = x[np.newaxis, :]
col_vector.shape
For the array x = np.array([1, 2, 3, 4, 5, 6]), convert the 1D array to a row vector by inserting an axis along the first dimension. Then show the number of elements in each row and col.
row_vector = x[:, np.newaxis]
row_vector.shape
Select the 8th index in this array new_arr = np.arange(0,110,10)
new_arr[8]
Select the numbers 10,20,40,40 in this array by splicing new_arr = np.arange(0,110,10)
new_arr[1:5]
new_arr = np.arange(0,110,10) convert everything from 0-40 to 100.
new_arr[0:5]=100
Multiply everything in this array by 2 new_arr = np.arange(0,110,10)
new_arr * 2
Change everything in the array new_arr = np.arange(0,110,10) to 88
Show Slice again
arr_slice[:]=88
arr_slice
Create a copy of the array.
new_arr_copy = new_arr.copy()
a = np.array([[1,1], [2,2]])
b = np.array([[3,3], [4,4]])
Combine the two arrays vertically
np.vstack((a, b))
Combine the two arrays horizontally
a = np.array([[1,1], [2,2]])
b = np.array([[3,3], [4,4]])
np.hstack((a, b))
c = np.arange(1, 25)
Make a new array and split it 3 ways
np.hsplit(c,3)
np.hsplit(c,3) split your array after the second column here three ways.
np.hsplit(c,(2,3))
Add, subtract and multiply these two arrays arr_1 = np.array([2,4])
arr_2 = np.ones(2)
arr_1 + arr_2
arr_1 - arr_2
arr_1 * arr_2
Add all of the elements in the array arr_3 = np.array([1, 2, 3, 4, 5, 6, 7, 8])
arr_3.sum()
Add the rows in a 2d array arr_4 = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
arr_4.sum(axis=0)
Add the columns in a 2d array. arr_4 = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
arr_4.sum(axis=1)
Find the unique elements in an array easily mixed_array = np.array([5,5,11,11,2,3,4,8,14,14,15,5])
mixed_array = np.array([5,5,11,11,2,3,4,8,14,14,15,5])
unique_vals = np.unique(mixed_array)
Find the index positions of unique values in an array mixed_array = np.array([5,5,11,11,2,3,4,8,14,14,15,5])
unique_vals_idx = np.unique(mixed_array, return_index=True)
Count the number of unique values in an array. mixed_array = np.array([5,5,11,11,2,3,4,8,14,14,15,5])
unique_vals_idx = np.unique(mixed_array, return_counts=True)
