{'name': 'Junior',
'age': 11,
'color': 'brown'}
{'name': 'Junior', 'age': 11, 'color': 'brown'}
tracks = {
'Drake': ['Best I Ever Had', "Marvin's Room", 'One Dance', 'Controlla'],
'Lady Gaga': ['Just Dance', 'Paparazzi'],
'DaBaby': ['Rockstar', 'Suge'],
'Olivia Rodrigo': ["Driver's License"]
}
tracks['Lady Gaga']
['Just Dance', 'Paparazzi']
tracks['Drake'].append('One Dance')
tracks['Maroon 5'] = ['Wake Up Call', 'Payphone']
tracks
{'Drake': ['Best I Ever Had',
"Marvin's Room",
'One Dance',
'Controlla',
'One Dance'],
'Lady Gaga': ['Just Dance', 'Paparazzi'],
'DaBaby': ['Rockstar', 'Suge'],
'Olivia Rodrigo': ["Driver's License"],
'Maroon 5': ['Wake Up Call', 'Payphone']}
tracks = {
'Drake': ['Best I Ever Had', "Marvin's Room", 'One Dance', 'Controlla'],
'Lady Gaga': ['Just Dance', 'Paparazzi'],
'DaBaby': ['Rockstar', 'Suge'],
'Olivia Rodrigo': ["Driver's License"]
}
tracks['Lady Gaga'] = tracks['Olivia Rodrigo'] + tracks['DaBaby']
tracks['Drake'] = tracks['Lady Gaga']
tracks['Drake'][1]
'Rockstar'
names = ['bill', 'sarah', 'cal', 'nina', 'joe']
names[1]
'sarah'
me = ['berkeley', 22, 1998, '🇨🇦']
[1, 2, 3] + [4, 5, 6]
[1, 2, 3, 4, 5, 6]
numbers = [5, 4, 9, 12, 18]
new_numbers = []
for num in numbers:
new_numbers.append(num + 5)
new_numbers
[10, 9, 14, 17, 23]
# Necessary to use numpy arrays!
import numpy as np
# Array with 4 numbers
np.array([4, 9, 1, 2])
array([4, 9, 1, 2])
# Array with 3 strings
np.array(['how', 'are', 'you'])
array(['how', 'are', 'you'], dtype='<U3')
# Empty array
np.array([])
array([], dtype=float64)
numbers = [5, 4, 9, 12, 18]
numbers
[5, 4, 9, 12, 18]
type(numbers)
list
numbers_arr = np.array(numbers)
numbers_arr
array([ 5, 4, 9, 12, 18])
type(numbers_arr)
numpy.ndarray
numbers_arr
array([ 5, 4, 9, 12, 18])
numbers_arr * 2
array([10, 8, 18, 24, 36])
numbers_arr - 5
array([ 0, -1, 4, 7, 13])
numbers_arr // 2
array([2, 2, 4, 6, 9])
numbers_arr ** 2 - 1
array([ 24, 15, 80, 143, 323])
c_temps = [17, 18, 22, -4.5, 15, 9, 0, 3, 8]
# Using vanilla lists:
f_temps = []
for c in c_temps:
f = (9 / 5) * c + 32
f_temps.append(f)
f_temps
[62.6, 64.4, 71.6, 23.9, 59.0, 48.2, 32.0, 37.4, 46.4]
# Using arrays: no for loop!
(9 / 5) * np.array(c_temps) + 32
array([62.6, 64.4, 71.6, 23.9, 59. , 48.2, 32. , 37.4, 46.4])
numbers_arr
array([ 5, 4, 9, 12, 18])
np.sqrt(numbers_arr)
array([2.23606798, 2. , 3. , 3.46410162, 4.24264069])
np.log(numbers_arr)
array([1.60943791, 1.38629436, 2.19722458, 2.48490665, 2.89037176])
np.sin(numbers_arr)
array([-0.95892427, -0.7568025 , 0.41211849, -0.53657292, -0.75098725])
np.sqrt(144)
12.0
my_tips = [0.15, 0.16, 0.22, 0.39]
your_tips = [0.25, 0.19, 0.08]
tips = np.array(my_tips + your_tips)
tips_pct = 100 * tips
max(tips_pct)
39.0
a = np.array([1, 2, 3])
b = np.array([-4, 5, 9])
a + b
array([-3, 7, 12])
a - 2 * b
array([ 9, -8, -15])
a**2 + b**2
array([17, 29, 90])
# a and c have different lengths, so you can't add them element-wise
c = np.array([9, 0, 2, 4])
a + c
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) /tmp/ipykernel_77/3333382080.py in <module> 2 c = np.array([9, 0, 2, 4]) 3 ----> 4 a + c ValueError: operands could not be broadcast together with shapes (3,) (4,)
pop_2019 = np.array([100, 55, 23, 91, 121])
pop_2020 = np.array([101, 45, 23, 93, 118])
# Change from 2020 to 2019
pop_2020 - pop_2019
array([ 1, -10, 0, 2, -3])
# Percent change
100 * (pop_2020 - pop_2019) / pop_2019
array([ 1. , -18.18181818, 0. , 2.1978022 ,
-2.47933884])
a = np.array([3, 4, 5])
b = np.array([7, -4.0, 5])
c = (a + b) / (a - b)
/tmp/ipykernel_77/1522059262.py:3: RuntimeWarning: divide by zero encountered in true_divide c = (a + b) / (a - b)
pop_2020 = np.array([101, 45, 23, 93, 118])
pop_2020[0]
101
pop_2020[-2]
93
pop_2020.item(0)
101
pop_2020.item(-2)
93
pop_2020
array([101, 45, 23, 93, 118])
# Sum of all elements
# Equivalent to np.sum(pop_2020)
pop_2020.sum()
380
# Average of all elements
# Equivalent to np.mean(pop_2020)
pop_2020.mean()
76.0
# Product of all elements
# Equivalent to np.prod(pop_2020)
pop_2020.prod()
1147167090
np.arange(10)
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
np.arange(3, 13, 3)
array([ 3, 6, 9, 12])
# Powers of 2
2**np.arange(10)
array([ 1, 2, 4, 8, 16, 32, 64, 128, 256, 512])
# 1^2 + 2^2 + 3^2 + ... + 99^2 + 100^2
np.sum(np.arange(101)**2)
338350
# 2. means 2.0
some_values = [2, 3, 3.5, 4, False]
np.array(some_values)
array([2. , 3. , 3.5, 4. , 0. ])
np.array(some_values).item(0)
2.0
other_values = [9, 8, 'hello', -14.5]
np.array(other_values)
array(['9', '8', 'hello', '-14.5'], dtype='<U21')
pop_2020
array([101, 45, 23, 93, 118])
# Cumulative sum: for each element,
# add all elements so far
np.cumsum(pop_2020)
array([101, 146, 169, 262, 380])
# Difference: takes the differences
# of consecutive differences
np.diff(pop_2020)
array([-56, -22, 70, 25])
# count_nonzero: counts the number of elements
# that are not equal to 0
np.count_nonzero(np.array([1, 2, 3, 0, 0, 4, -5]))
5