…which I find hard to believe. Here are 10 ways to make python a dangerous tool for data science. WordCloud WordCloud 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 : Python function 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 functiondef multiply(x,y):z = x*yreturn z #multiply two numbers using a python function def return #call the function to multiply the numbers 2 and 3multiply(4,3) Output : 12 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 Output : str type(5) Output : int Output : int type(5.0) Output : float Output : float type(None) #None signifies 'no value' or 'empty value' None Output : NoneType Output : NoneType type(multiply) #multiply is a function we created previously Output : function 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. Photo on Unsplash Unsplash i. Tuples : They are immutable data structures which cannot be altered unlike lists a = (1,2,3,4)type(a) Output : tuple Output : tuple ii. Lists : They are mutable objects b = [1,2,3,4]type(b) Output : list Output : list Let’s append a number to the list b created above. b.append(2.5) #append to list using this functionprint(b) #append to list using this function Output : [1, 2, 3, 4, 2.5] Output : [1, 2, 3, 4, 2.5] Loop through the list and print the numbers for number in b: #looping through listprint(number) for in #looping through list Output : Output : Output : 12342.5 12342.5 12342.5 Now, let’s concatanate two lists [1,2,3] + [5,'bc','de'] #concatenate lists Output : [1, 2, 3, 5, ‘bc’, ‘de’] Output : [1, 2, 3, 5, ‘bc’, ‘de’] Create a list with repeating numbers. [1,2]*3 #repeat lists #repeat lists Output : [1, 2, 1, 2, 1, 2] 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 in Output : True Output : True Unpack a list into separate variables. a,b = ('bc','def')print(a)print(b) Output : bcdef 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’ Output : ‘M’ Output : ‘M’ x[0:2] #Accesses two letters Output : ‘My’ Output : ‘My’ Output : ‘My’ x[:-1] #Accesses everything except last letter Output : ‘My name is shimant’ Output : ‘My name is shimant’ Output : ‘My name is shimant’ x[10:] #returns all the characters from 10th position till end Output : ‘ Shimanto’ Output : ‘ Shimanto’ Output : ‘ Shimanto’ Now, let’s concatenate two strings. first = 'Harun'last = 'Shimanto' Name = first + ' ' + last _#string concatenation_print(Name) Output : Harun Shimanto Output : Harun Shimanto Show only the first word. Name.split(' ')[0] #Show the first word #Show the first word Output : ‘Harun’ Output : ‘Harun’ Now, show only the second word in the string Name.split(' ')[1] #Show the second word #Show the second word Output : ‘Shimanto’ Output : ‘Shimanto’ For concatenating numeric data to string, convert the number to a string first #for concatenation convert objects to strings'Harun' + str(2) #for concatenation convert objects to strings Output : Harun2 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 Output : dict Print data contained within a dictionary print(c) Output : {‘Name’: ‘Harun’, ‘Height’: 175} Output : {‘Name’: ‘Harun’, ‘Height’: 175} Output : Access dictionary values based on keys c['Name'] #Access Name #Access Name Output : ‘Harun’ Output : ‘Harun’ c['Height'] Output : 175 Output : 175 Print all the keys in the dictionary #print all the keysfor i in c:print(i) #print all the keys for in Output : NameHeight Output : NameHeight Print all the values in the dictionary for i in c.values():print(i) for in Output : Harun175 Output : Harun175 Iterate over all the items in the dictionary for name, height in c.items():print(name)print(height) for in Output : NameHarunHeight175 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 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 #print current time in seconds from January 1, 1970 Output : 1533370235.0210752 Output : 1533370235.0210752 _#convert timestamp to datetime_dtnow = dt.datetime.fromtimestamp(tm.time())dtnow.year Output : 2018 Output : 2018 Get today’s date today = dt.date.today()today Output : datetime.date(2018, 8, 4) 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) 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 for in Output : 1235 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 lambda Output : 19 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 lambda 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] 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 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. i i 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)] 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] #create two lists c = zip(a,b) #Use the zip functionprint(c) #Use the zip function Output : [(1,5), (2,6)] 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 for in if 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] 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] Output : 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] 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] Output : 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. Numpy Pandas (which we look at in the upcoming articles) Resources : Python 3.7.0 documentation Applied Data Science with Python Specialization. Python 3.7.0 documentation Python 3.7.0 documentation Applied Data Science with Python Specialization. Applied Data Science with Python Specialization. Connect on LinkedIn and, check out Github (below) for the complete notebook. LinkedIn 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 harunshimanto/Python-The-Dangerous-Tool-For-ML-Data-Science 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 You can tell me what you think about this, if you enjoy writing, click on the clap 👏 button. tell me Thanks to everyone.
![Building A Machine Learning Model With PySpark [A Step-by-Step Guide]](https://firebasestorage.googleapis.com/v0/b/hackernoon-app.appspot.com/o/images%2Fk7cO2yZV5YVefM7TWTwAYJmnUUx2-exm3u8q.webp?alt=media&token=7f6ad036-54ac-4da0-b4dc-3c8978147c5f&auto=format&fit=max&w=3840)
![What Are Convolution Neural Networks? [ELI5]](https://hackernoon.imgix.net/images/69s32rn.jpg?auto=format&fit=max&w=3840)
