5 Features of Python that you must know as a Data Scientist

Introduction

Python, as we know, is the magic wand for data scientists. Because of its versatile and flexible nature, it is the most popular programming language among data scientists. With simple command, they can do any kind of task related to data analysis, be it data wrapping or data visualization. If you are aspiring to get into data science career, then there is a better chance of you using Python for machine learning and data science application.

In this article we are going to talk about 10 best features of Python that every data scientist must know to ease their data analysis work. Well, not only data scientists, since Python is used for both data science and AI, these features can prove to be very beneficial for AI professionals as well. So, let’s start.

Top 5 functions of Python

Listed here are the best 5 features you must know that will help you do your data analysis and machine learning tasks easier.

1.Comprehension

Comprehension in Python refers to a function that helps to create complex data structures in an easy to read and understand manner. There are “list comprehension, Nested Comprehension, dictionary and set comprehensions, and Generator comprehensions” that can be used in data science and AI machine learning as and when required. Below mentioned is an example of comprehension function.

Command:

# list comprehension

_list = [x**2 for x in range(1, 11)]

# nested list comprehension to flatten list

matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]

flat_list = [num # append to list

for row in matrix # outer loop

for num in row] # inner loop

print(_list)

print(flat_list)

Output:

[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]

[1, 2, 3, 4, 5, 6, 7, 8, 9]

2.Enumerate

Enumerate, a built-in function, allows iteration through a sequence such as a list or tuple while simultaneously maintaining awareness of the index for each element.

This is beneficial when handling datasets, which enables an effortless access and manipulation of individual elements alongside index tracking.

Here is an example where enumerate is employed to print values if their index is even.

Command:

for idx, value in enumerate(["a", "b", "c", "d"]):

if idx % 2 == 0:

print(value)

Output:

a

c

3. Zip

Zip is a useful function that helps data scientists to work with several lists or tuples together. It lets to go through the lists at the same time.

In the example below, we're using zip to go through two lists, x and y, at once, and perform some tasks simultaneously in parallel.

Command:

x = [1, 2, 3, 4]

y = [5, 6, 7, 8]

# iterate over both arrays simultaneously

for a, b in zip(x, y):

print(a, b, a + b, a * b)

Output:

1 5 6 5

2 6 8 12

3 7 10 21

4 8 12 32

4. Any and All

In Python, any and all are built-in functions used to work with sequences (like lists, tuples, or other iterables) containing boolean values. They help determine whether any or all of the boolean values in the sequence meet a certain condition.

In the following example, we will check the presence of any even numbers and all the odd numbers with this function.

Command:

data = [1, 3, 5, 7]

print(any(x % 2 == 0 for x in data))

print(all(x % 2 == 1 for x in data))

Output:

False

True

5. Filter

The filter is one of the most commonly used machine learning and data science function selects items from an iterable that satisfy a given condition and returns an iterator with those items. It's useful for extracting specific elements. Below is an example for the same:

Command:

numbers = [10, 25, 8, 30, 17]

even_numbers = filter(lambda x: x % 2 == 0, numbers)

Output:

[10, 8, 30]

Sorting, classifying, and filtering data is very important in all data science and AI process after which proper operational functions can be iterated on the data. These are some of the handy functions that will always be used in all your projects. Therefore, it is recommended to learn various applications of these commands and use them appropriately on different kinds of data sets, simple or complex.

Conclusion

Python undoubtedly is the most popular programming language as it offers several functions that makes data analysis and machine learning applications easier. They are easy to use and can be learned easily. Apart from that, the vast community support for python makes it an incredible programming tool as well. And the functions we discussed above are sure to be used in every project you handle. These are the basic and most operational features of Python. If data science and AI is your career ambition, then just master these functions.