People are still crazy about Python after twenty-five years by@harunshimanto

People are still crazy about Python after twenty-five years

August 6th 2018 7,925 reads
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Harun Ur Rashid

…which I find hard to believe. Here are 10 ways to make python a dangerous tool for data science.

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Python has numerous applications — web development, desktop GUIs, software development, business applications and scientific/numeric computing. In this series we will be focusing on how to use numeric computing in Python for data science and ML.

This is not a comprehensive Python tutorial but instead is intended to highlight the parts of the language that will be most important to us(some of which are often not the focus of Python tutorials).

In this tutorial, we will be looking at the following basic features of Python :

  1. Python function

2. Data types and sequences

3. Date and time

4. Lambda

5. Map

6. Filter

7. Reduce

8. Zip

9. For loop

10. List comprehension

1. Python function

A function is a block of code which only runs when it is called. You can pass data, known as parameters into a function. Let’s write a function to multiply two numbers.




_#multiply two numbers using a python function_def multiply(x,y):z = x*yreturn z


#call the function to multiply the numbers 2 and 3multiply(4,3)

Output : 12

2. Python data types and sequences

Python has built-in data types to store numeric and character data. Let us take a look at a few common types.

type(' My name is Shimanto')

Output : str

type(5)

Output : int

type(5.0)

Output : float

type(None) #None signifies 'no value' or 'empty value'

Output : NoneType

type(multiply) #multiply is a function we created previously

Output : function

Now, let’s take a look at how we can store a list of numbers and characters, and how to perform few basic manipulations.

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i. Tuples : They are immutable data structures which cannot be altered unlike lists


a = (1,2,3,4)type(a)

Output : tuple

ii. Lists : They are mutable objects


b = [1,2,3,4]type(b)

Output : list

Let’s append a number to the list b created above.


b.append(2.5) #append to list using this functionprint(b)

Output : [1, 2, 3, 4, 2.5]

Loop through the list and print the numbers


for number in b: #looping through listprint(number)

Output :





12342.5

Now, let’s concatanate two lists

[1,2,3] + [5,'bc','de'] #concatenate lists

Output : [1, 2, 3, 5, ‘bc’, ‘de’]

Create a list with repeating numbers.

[1,2]*3 #repeat lists

Output : [1, 2, 1, 2, 1, 2]

Check if an object you are searching for is in the list.

3 in b #in operator to check if required object is in list

Output : True

Unpack a list into separate variables.



a,b = ('bc','def')print(a)print(b)


Output : bcdef

iii. Strings : A string stores character objects

x = 'My name is Shimanto'

Access characters from string :

x[0] #Access first letter

Output : ‘M’

x[0:2] #Accesses two letters

Output : ‘My’

x[:-1] #Accesses everything except last letter

Output : ‘My name is shimant’

x[10:] #returns all the characters from 10th position till end

Output : ‘ Shimanto’

Now, let’s concatenate two strings.


first = 'Harun'last = 'Shimanto'


Name = first + ' ' + last _#string concatenation_print(Name)

Output : Harun Shimanto

Show only the first word.

Name.split(' ')[0] #Show the first word

Output : ‘Harun’

Now, show only the second word in the string

Name.split(' ')[1] #Show the second word

Output : ‘Shimanto’

For concatenating numeric data to string, convert the number to a string first


#for concatenation convert objects to strings'Harun' + str(2)

Output : Harun2

iv. Dictionary : A dictionary is a collection which is not ordered, but is indexed — and they have keys and values.


c = {"Name" : "Harun", "Height" : 175}type(c)

Output : dict

Print data contained within a dictionary

print(c)

Output : {‘Name’: ‘Harun’, ‘Height’: 175}

Access dictionary values based on keys

c['Name'] #Access Name

Output : ‘Harun’

c['Height']

Output : 175

Print all the keys in the dictionary



#print all the keysfor i in c:print(i)


Output : NameHeight

Print all the values in the dictionary


for i in c.values():print(i)


Output : Harun175

Iterate over all the items in the dictionary



for name, height in c.items():print(name)print(height)




Output : NameHarunHeight175

3. Python Date and Time

The following modules helps us in manipulating date and time variables in simple ways.


import datetime as dtimport time as tm

Print the current time in seconds (starting from January 1, 1970)

tm.time() #print current time in seconds from January 1, 1970

Output : 1533370235.0210752



_#convert timestamp to datetime_dtnow = dt.datetime.fromtimestamp(tm.time())dtnow.year

Output : 2018

Get today’s date


today = dt.date.today()today

Output : datetime.date(2018, 8, 4)

Subtract 100 days from today’s date


delta = dt.timedelta(days=100)today - delta

Output : datetime.date(2018, 4, 26)

4. Map function

Map function returns a list of the results after applying the given function to each item of a given sequence. For example, let’s find the minimum value between two pairs of lists.


a = [1,2,3,5]b = [8,9,10,11]

c = map(min,a,b) #Find the minimum between two pairs of lists


for item in c:print(item) #print the minimum of the pairs




Output : 1235

5. Lambda function

Lambda function is used for creating small, one-time and anonymous function objects in Python.

function = lambda a,b,c : a+b+c #function to add three numbersfunction(5,6,8) #call the function

Output : 19

6. Filter function

Filter offers an easy way to filter out all the elements of a list. Filter (syntax : filter(function,list)) needs a function as its first argument, for which lambdacan be used. As an example, let’s filter out only the numbers greater than 5 from a list


x = [0,2,3,4,5,7,8,9,10] #create a listx2 = filter(lambda a : a>5, x) #filter using filter function

print(list(x2))

Output : [7,8,9,10]

7. Reduce function

Reduce is a function for performing some computation on a list and returning the result. It applies a rolling computation to sequential pairs of values in a list. As an example, let’s calculate the product of all the numbers in a list.



from functools import reduce #import reduce functiony = [6,7,8,9,10] #create listreduce(lambda a,b : a*b,y) #use reduce

Output : 30240

8. Zip function

Zip function returns a list of tuples, where the i-th tuple contains the i-th element from each of the sequences. Let’s look at an example.


a = [1,2,3,4] #create two listsb = [5,6,7,8]


c = zip(a,b) #Use the zip functionprint(list(c))

Output : [(1,5), (2,6), (3,7), (4,8)]

If the sequences used in the zip function is unequal, the returned list is truncated in length to the length of the shortest sequence.


a = [1,2] #create two listsb = [5,6,7,8]


c = zip(a,b) #Use the zip functionprint(c)

Output : [(1,5), (2,6)]

9. For loop

For loops are usually used when you have a block of code which you want to repeat a fixed number of times.

Let us use a for loop to print the list of even numbers from 1 to 50.

#return even numbers from 1 to 50






even=[]for i in range(50):if i%2 ==0:even.append(i)else:None

print(even) #print the list

Output : [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48]

10. List comprehension

List comprehension provides an easier way to create lists. Continuing the same example, let’s create a list of even numbers from 1 to 50 using list comprehension.


even = [i for i in range(50) if i%2==0]print(even)

Output : [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48]

The features we looked at help in understanding the basic features of Python which are used for numerical computing. Apart from these in-built functions, there are other libraries such as Numpy and Pandas (which we look at in the upcoming articles) which are used extensively in data science and Machine learning.

Resources :

  1. Python 3.7.0 documentation
  2. Applied Data Science with Python Specialization.

Connect on LinkedIn and, check out Github (below) for the complete notebook.


harunshimanto/Python-The-Dangerous-Tool-For-ML-Data-Science_GitHub is where people build software. More than 28 million people use GitHub to discover, fork, and contribute to over…_github.com

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Thanks to everyone.

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