# 1. Create a NumPy array of the integers 1 through 7.
>>> import numpy as np
>>> a = np.array([1, 2, 3, 4, 5, 6, 7])
# 2. Set the first and last elements in the array to zero.
>>> a[0] = 0
>>> a[-1] = 1
>>> a
array([0, 2, 3, 4, 5, 6, 0])
# 3. Use `.size` to determine the length of the array.
>>> a.size
7
# 4. Sum all of the integers in the array.
>>> import numpy as np
>>>
>>> np.sum(a)
20
# 5. Using the results from parts 3 and 4, find the average of the array.
>>> float(np.sum(a)) / a.size
2.857142857142857
# 1. Create a 1D NumPy array of 10,000 elements, all initially set to 0.0.
>>> import numpy as np
>>>
>>> a = np.zeros(10000)
# 2. Use a `while` loop and set every 101st element equal to 1.0. (indices 0, 101, 202, 303, ...).
>>> i = 0
>>> while i <= a.size:
... a[i] = 1.0
... i += 101
# 3. Change the shape of the array to be 100 x 100.
>>> b = a.reshape(100, 100)
# 4. Use a `for` loop to set the 1st element of every row equal to the sum of that row.
>>> for row in range(100):
... b[row][0] = np.sum(b[row])
# 5. Create a new array from the first elements in each of your rows.
>>> lst = []
>>> for row in b:
... lst.append(row[0])
...
>>> lst
>>> c = np.array(lst)
# 6. Take the product of all the elements in your new array.
>>> import numpy as np
>>> np.prod(c)
1.0
# 1. Use `arange` to create an array of decimals from zero to 26. (HINT: dtype=float)
>>> import numpy as np
>>> np.arange(27, dtype=float)
# 2. Reshape that array to be a 3 x 3 x 3 multi-dimensional array.
>>> a = np.arange(27, dtype=float).reshape(3, 3, 3)
>>> a
array([[[ 0., 1., 2.],
[ 3., 4., 5.],
[ 6., 7., 8.]],
[[ 9., 10., 11.],
[ 12., 13., 14.],
[ 15., 16., 17.]],
[[ 18., 19., 20.],
[ 21., 22., 23.],
[ 24., 25., 26.]]])
# 3. Print the first and last element in the array, using three indexes.
>>> a[0][0][0]
0.0
>>> a[2][2][2]
26.0
# 4. Using three `for` loops, divide every number by the `sum` of all 3 elements in its row.
>>> for i in range(3):
... for j in range(3):
... total = np.sum(a[i][j])
... for k in range(3):
... a[i][j][k] /= total
# 5. Calculate the `sum` of all the elements in the array.
>>> import numpy as np
>>> np.sum(a)
9.0
# 6. Create a new array, where every element is the square root of the old one.
>>> import numpy as np
>>> s = np.sqrt(a)
# 7. Calculate the product of all the elements in your new array.
>>> import numpy as np
>>> np.prod(s)
0.0
>>> # Why zero?
... # Because a[0][0][0] and s[0][0][0] were both 0.0.
# 1. Use `np.ones` to create an array, `a`, which is five elements long.
>>> a = np.ones(5)
# 2. Use `np.arange` to create an array, `b`, which is the integers from 2 to 6 (five elements long).
>>> b = np.arange(2, 7)
# 3. Use `np.arange` to create an array, `c`, which is the even integers 2 to 12.
>>> c = np.arange(2, 12, 2)
# 4. Use `np.arange` to create an array, `d`, which is the float values of the integers from 1 to 5.
>>> d = np.arange(1, 6, dtype=float)
# 5. Create a new array: x = c + a - b.
>>> x = c + a - b
# 6. What is the value of d - x?
>>> d - x
array([ 0., 0., 0., 0., 0.])
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