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x = 1
y = 2
x + y
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x
add_numbers is a function that takes two numbers and adds them together.
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def add_numbers(x, y):
return x + y
add_numbers(1, 2)
add_numbers updated to take an optional 3rd parameter. Using print allows printing of multiple expressions within a single cell.
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def add_numbers(x,y,z=None):
if (z==None):
return x+y
else:
return x+y+z
print(add_numbers(1, 2))
print(add_numbers(1, 2, 3))
add_numbers updated to take an optional flag parameter.
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def add_numbers(x, y, z=None, flag=False):
if (flag):
print('Flag is true!')
if (z==None):
return x + y
else:
return x + y + z
print(add_numbers(1, 2, flag=True))
Assign function add_numbers to variable a.
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def add_numbers(x,y):
return x+y
a = add_numbers
a(1,2)
Use type to return the object's type.
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type('This is a string')
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type(None)
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type(1)
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type(1.0)
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type(add_numbers)
Tuples are an immutable data structure (cannot be altered).
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x = (1, 'a', 2, 'b')
type(x)
Lists are a mutable data structure.
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x = [1, 'a', 2, 'b']
type(x)
Use append to append an object to a list.
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x.append(3.3)
print(x)
This is an example of how to loop through each item in the list.
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for item in x:
print(item)
Or using the indexing operator:
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i=0
while( i != len(x) ):
print(x[i])
i = i + 1
Use + to concatenate lists.
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[1,2] + [3,4]
Use * to repeat lists.
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[1]*3
Use the in operator to check if something is inside a list.
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1 in [1, 2, 3]
Now let's look at strings. Use bracket notation to slice a string.
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x = 'This is a string'
print(x[0]) #first character
print(x[0:1]) #first character, but we have explicitly set the end character
print(x[0:2]) #first two characters
This will return the last element of the string.
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x[-1]
This will return the slice starting from the 4th element from the end and stopping before the 2nd element from the end.
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x[-4:-2]
This is a slice from the beginning of the string and stopping before the 3rd element.
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x[:3]
And this is a slice starting from the 3rd element of the string and going all the way to the end.
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x[3:]
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firstname = 'Christopher'
lastname = 'Brooks'
print(firstname + ' ' + lastname)
print(firstname*3)
print('Chris' in firstname)
split returns a list of all the words in a string, or a list split on a specific character.
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firstname = 'Christopher Arthur Hansen Brooks'.split(' ')[0] # [0] selects the first element of the list
lastname = 'Christopher Arthur Hansen Brooks'.split(' ')[-1] # [-1] selects the last element of the list
print(firstname)
print(lastname)
Make sure you convert objects to strings before concatenating.
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'Chris' + 2
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'Chris' + str(2)
Dictionaries associate keys with values.
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x = {'Christopher Brooks': 'brooksch@umich.edu', 'Bill Gates': 'billg@microsoft.com'}
x['Christopher Brooks'] # Retrieve a value by using the indexing operator
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x['Kevyn Collins-Thompson'] = None
x['Kevyn Collins-Thompson']
Iterate over all of the keys:
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for name in x:
print(x[name])
Iterate over all of the values:
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for email in x.values():
print(email)
Iterate over all of the items in the list:
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for name, email in x.items():
print(name)
print(email)
You can unpack a sequence into different variables:
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x = ('Christopher', 'Brooks', 'brooksch@umich.edu')
fname, lname, email = x
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fname
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lname
Make sure the number of values you are unpacking matches the number of variables being assigned.
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x = ('Christopher', 'Brooks', 'brooksch@umich.edu', 'Ann Arbor')
fname, lname, email = x
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print('Chris' + 2)
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print('Chris' + str(2))
Python has a built in method for convenient string formatting.
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sales_record = {
'price': 3.24,
'num_items': 4,
'person': 'Chris'}
sales_statement = '{} bought {} item(s) at a price of {} each for a total of {}'
print(sales_statement.format(sales_record['person'],
sales_record['num_items'],
sales_record['price'],
sales_record['num_items']*sales_record['price']))
Let's import our datafile mpg.csv, which contains fuel economy data for 234 cars.
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import csv
%precision 2
with open('mpg.csv') as csvfile:
mpg = list(csv.DictReader(csvfile))
mpg[:3] # The first three dictionaries in our list.
csv.Dictreader has read in each row of our csv file as a dictionary. len shows that our list is comprised of 234 dictionaries.
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len(mpg)
keys gives us the column names of our csv.
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mpg[0].keys()
This is how to find the average cty fuel economy across all cars. All values in the dictionaries are strings, so we need to convert to float.
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sum(float(d['cty']) for d in mpg) / len(mpg)
Similarly this is how to find the average hwy fuel economy across all cars.
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sum(float(d['hwy']) for d in mpg) / len(mpg)
Use set to return the unique values for the number of cylinders the cars in our dataset have.
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cylinders = set(d['cyl'] for d in mpg)
cylinders
Here's a more complex example where we are grouping the cars by number of cylinder, and finding the average cty mpg for each group.
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CtyMpgByCyl = []
for c in cylinders: # iterate over all the cylinder levels
summpg = 0
cyltypecount = 0
for d in mpg: # iterate over all dictionaries
if d['cyl'] == c: # if the cylinder level type matches,
summpg += float(d['cty']) # add the cty mpg
cyltypecount += 1 # increment the count
CtyMpgByCyl.append((c, summpg / cyltypecount)) # append the tuple ('cylinder', 'avg mpg')
CtyMpgByCyl.sort(key=lambda x: x[0])
CtyMpgByCyl
Use set to return the unique values for the class types in our dataset.
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vehicleclass = set(d['class'] for d in mpg) # what are the class types
vehicleclass
And here's an example of how to find the average hwy mpg for each class of vehicle in our dataset.
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HwyMpgByClass = []
for t in vehicleclass: # iterate over all the vehicle classes
summpg = 0
vclasscount = 0
for d in mpg: # iterate over all dictionaries
if d['class'] == t: # if the cylinder amount type matches,
summpg += float(d['hwy']) # add the hwy mpg
vclasscount += 1 # increment the count
HwyMpgByClass.append((t, summpg / vclasscount)) # append the tuple ('class', 'avg mpg')
HwyMpgByClass.sort(key=lambda x: x[1])
HwyMpgByClass
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import datetime as dt
import time as tm
time returns the current time in seconds since the Epoch. (January 1st, 1970)
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tm.time()
Convert the timestamp to datetime.
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dtnow = dt.datetime.fromtimestamp(tm.time())
dtnow
Handy datetime attributes:
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dtnow.year, dtnow.month, dtnow.day, dtnow.hour, dtnow.minute, dtnow.second # get year, month, day, etc.from a datetime
timedelta is a duration expressing the difference between two dates.
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delta = dt.timedelta(days = 100) # create a timedelta of 100 days
delta
date.today returns the current local date.
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today = dt.date.today()
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today - delta # the date 100 days ago
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today > today-delta # compare dates
An example of a class in python:
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class Person:
department = 'School of Information' #a class variable
def set_name(self, new_name): #a method
self.name = new_name
def set_location(self, new_location):
self.location = new_location
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person = Person()
person.set_name('Christopher Brooks')
person.set_location('Ann Arbor, MI, USA')
print('{} live in {} and works in the department {}'.format(person.name, person.location, person.department))
Here's an example of mapping the min function between two lists.
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store1 = [10.00, 11.00, 12.34, 2.34]
store2 = [9.00, 11.10, 12.34, 2.01]
cheapest = map(min, store1, store2)
cheapest
Now let's iterate through the map object to see the values.
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for item in cheapest:
print(item)
Here's an example of lambda that takes in three parameters and adds the first two.
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my_function = lambda a, b, c : a + b
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my_function(1, 2, 3)
Let's iterate from 0 to 999 and return the even numbers.
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my_list = []
for number in range(0, 1000):
if number % 2 == 0:
my_list.append(number)
my_list
Now the same thing but with list comprehension.
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my_list = [number for number in range(0,1000) if number % 2 == 0]
my_list
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import numpy as np
Create a list and convert it to a numpy array
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mylist = [1, 2, 3]
x = np.array(mylist)
x
Or just pass in a list directly
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y = np.array([4, 5, 6])
y
Pass in a list of lists to create a multidimensional array.
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m = np.array([[7, 8, 9], [10, 11, 12]])
m
Use the shape method to find the dimensions of the array. (rows, columns)
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m.shape
arange returns evenly spaced values within a given interval.
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n = np.arange(0, 30, 2) # start at 0 count up by 2, stop before 30
n
reshape returns an array with the same data with a new shape.
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n = n.reshape(3, 5) # reshape array to be 3x5
n
linspace returns evenly spaced numbers over a specified interval.
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o = np.linspace(0, 4, 9) # return 9 evenly spaced values from 0 to 4
o
resize changes the shape and size of array in-place.
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o.resize(3, 3)
o
ones returns a new array of given shape and type, filled with ones.
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np.ones((3, 2))
zeros returns a new array of given shape and type, filled with zeros.
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np.zeros((2, 3))
eye returns a 2-D array with ones on the diagonal and zeros elsewhere.
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np.eye(3)
diag extracts a diagonal or constructs a diagonal array.
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np.diag(y)
Create an array using repeating list (or see np.tile)
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np.array([1, 2, 3] * 3)
Repeat elements of an array using repeat.
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np.repeat([1, 2, 3], 3)
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p = np.ones([2, 3], int)
p
Use vstack to stack arrays in sequence vertically (row wise).
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np.vstack([p, 2*p])
Use hstack to stack arrays in sequence horizontally (column wise).
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np.hstack([p, 2*p])
Use +, -, *, / and ** to perform element wise addition, subtraction, multiplication, division and power.
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print(x + y) # elementwise addition [1 2 3] + [4 5 6] = [5 7 9]
print(x - y) # elementwise subtraction [1 2 3] - [4 5 6] = [-3 -3 -3]
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print(x * y) # elementwise multiplication [1 2 3] * [4 5 6] = [4 10 18]
print(x / y) # elementwise divison [1 2 3] / [4 5 6] = [0.25 0.4 0.5]
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print(x**2) # elementwise power [1 2 3] ^2 = [1 4 9]
Dot Product:
$ \begin{bmatrix}x_1 \ x_2 \ x_3\end{bmatrix} \cdot \begin{bmatrix}y_1 \\ y_2 \\ y_3\end{bmatrix} = x_1 y_1 + x_2 y_2 + x_3 y_3$
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x.dot(y) # dot product 1*4 + 2*5 + 3*6
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z = np.array([y, y**2])
print(len(z)) # number of rows of array
Let's look at transposing arrays. Transposing permutes the dimensions of the array.
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z = np.array([y, y**2])
z
The shape of array z is (2,3) before transposing.
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z.shape
Use .T to get the transpose.
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z.T
The number of rows has swapped with the number of columns.
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z.T.shape
Use .dtype to see the data type of the elements in the array.
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z.dtype
Use .astype to cast to a specific type.
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z = z.astype('f')
z.dtype
Numpy has many built in math functions that can be performed on arrays.
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a = np.array([-4, -2, 1, 3, 5])
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a.sum()
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a.max()
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a.min()
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a.mean()
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a.std()
argmax and argmin return the index of the maximum and minimum values in the array.
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a.argmax()
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a.argmin()
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s = np.arange(13)**2
s
Use bracket notation to get the value at a specific index. Remember that indexing starts at 0.
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s[0], s[4], s[-1]
Use : to indicate a range. array[start:stop]
Leaving start or stop empty will default to the beginning/end of the array.
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s[1:5]
Use negatives to count from the back.
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s[-4:]
A second : can be used to indicate step-size. array[start:stop:stepsize]
Here we are starting 5th element from the end, and counting backwards by 2 until the beginning of the array is reached.
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s[-5::-2]
Let's look at a multidimensional array.
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r = np.arange(36)
r.resize((6, 6))
r
Use bracket notation to slice: array[row, column]
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r[2, 2]
And use : to select a range of rows or columns
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r[3, 3:6]
Here we are selecting all the rows up to (and not including) row 2, and all the columns up to (and not including) the last column.
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r[:2, :-1]
This is a slice of the last row, and only every other element.
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r[-1, ::2]
We can also perform conditional indexing. Here we are selecting values from the array that are greater than 30. (Also see np.where)
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r[r > 30]
Here we are assigning all values in the array that are greater than 30 to the value of 30.
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r[r > 30] = 30
r
Be careful with copying and modifying arrays in NumPy!
r2 is a slice of r
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r2 = r[:3,:3]
r2
Set this slice's values to zero ([:] selects the entire array)
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r2[:] = 0
r2
r has also been changed!
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r
To avoid this, use r.copy to create a copy that will not affect the original array
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r_copy = r.copy()
r_copy
Now when r_copy is modified, r will not be changed.
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r_copy[:] = 10
print(r_copy, '\n')
print(r)
Let's create a new 4 by 3 array of random numbers 0-9.
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test = np.random.randint(0, 10, (4,3))
test
Iterate by row:
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for row in test:
print(row)
Iterate by index:
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for i in range(len(test)):
print(test[i])
Iterate by row and index:
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for i, row in enumerate(test):
print('row', i, 'is', row)
Use zip to iterate over multiple iterables.
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test2 = test**2
test2
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for i, j in zip(test, test2):
print(i,'+',j,'=',i+j)