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Python for Data Science

The 9 Best Ways to Use Python for Data Science

The ecosystem for data science is dominated by Python. I believe that the relative simplicity of learning and the abundance of data science resources to pick from are the two key factors behind this supremacy. Python is a multifaceted language that is not simply used for data science. Some cases for Python include creating websites, mobile apps, and video games.

You don’t need to be an expert in Python if you solely use it for data science projects. However, there are several fundamentals and characteristics that I believe you should possess. This article does not focus on libraries specifically. They might be regarded as the fundamental Python for data science. Even if you simply use scikit-learn, pandas, and matplotlib, you should be well-versed in the fundamentals of Python. These libraries rely on your knowledge of Python’s fundamentals. With a few examples, I will quickly describe each issue. For the majority of the topics, I will also include a link to a more in-depth article.

The Fundamentals of Python

  1. Functions

In Python data science, functions serve as building pieces. They return a value with zero or more parameters. We use the def keyword to build a function.

Here is a simple function that multiplies two numbers.

def multiply(a, b):
  return a * bmultiply(5, 4)

Here is another example that evaluates a word based on its length.

def is_long(word):
  if len(word) > 8:
    return f”{word} is a long word.”is_long(“artificial”)
‘artificial is a long word.’

A single job should be completed by each function of python data science. The goal of employing functions is defeated when a function is created that completes a number of tasks. Additionally, we ought to provide functions evocative names so that even without looking at the code, we can understand what they perform.

  1. Positional and keyword arguments

The parameters of a function in python for data science are specified when it is defined. The values for the necessary arguments must be passed to a function when it is called. Arguments are another name for parameter values.

Think about the multiplication function that was developed in the preceding phase. When we call the function, we pass its two parameters their respective values.

  • Keyword arguments are declared with a name and a default value, while positional arguments are simply declared with a name.

When invoking a function, positional parameters must have values given. If not, we encounter a problem. Without a value specified, a keyword argument uses its default value.

Let’s redefine the multiply function with keyword arguments so we can see the difference.

def multiply(a=1, b=1):
  return a * bprint(multiply(5, 4))

  1. *args and **kwargs

In Python for data science, functions serve as building pieces. They return a value with zero or more parameters. When it comes to how parameters are provided to functions, Python is rather flexible. Argument handling is made simpler and cleaner by the use of *args and **kwargs* allow a function to take any number of positional arguments.

Here is a simple example:

def addition(*args):
  result = 0
  for i in args:
      result += i
  return resultprint(addition(1,4))

  • **kwargs allow a function to take any number of keyword arguments.

By default, **kwargs is an empty dictionary. Each undefined keyword argument is stored as a key-value pair in the **kwargs dictionary.

Here is a simple example:

def arg_printer(a, b, option=True, **kwargs):
  print(a, b)
  print(kwargs)arg_printer(3, 4, param1=5, param2=6)
3 4
{‘param1’: 5, ‘param2’: 6}

  1. Classes

The foundation of the object-oriented programming (OOP) paradigm is the concept of objects belonging to a certain type of python for data sciences. The type sort of gives us an explanation of the thing. In Python, everything is an object of a certain type, such as an integer, a list, a dictionary, a function, and so on. Classes help us specify different types of objects.

Classes include the following data:

Methods (also known as procedural attributes): How we communicate with instances of a class. Data attributes: What is required to construct an instance of a class.

  1. Lists

Python for data sciences has a built-in data structure called a list. It is shown as a group of data points enclosed in square brackets. Any data type, or a combination of data types, can be stored in lists.

One of the reasons lists are so widely used is that they are changeable. As a result, we may delete and add objects. Another option is to update a list’s items.

Here are a few examples on how to create and modify a list.

words = [‘data’,’science’] #create a listprint(words[0]) #access an item
‘data’words.append(‘machine’) #add an itemprint(len(words)) #length of list
[‘data’, ‘science’, ‘machine’]

  1. List comprehension

Creating lists based on other iterables like lists, tuples, sets, and so forth is essentially what list comprehension entails in python for data sciences. It may also be thought of as a more aesthetically pleasing representation of the for and if loops. Comparatively speaking, list comprehensions are quicker than for loops.


(image by author)

Here is a simple list comprehension that creates a list from another list based on a given condition.

a = [4,6,7,3,2]b = [x for x in a if x > 5]
[6, 7]

The following list comprehension applies a function to the items in another list.

words = [‘data’,’science’,’machine’,’learning’]b = [len(word) for word in words]
[4, 7, 7, 8]

11 Examples to Master Python List Comprehensions

How to efficiently use list comprehensions.

  1. Dictionaries

A dictionary in python for data sciences is a collection of key-value pairs that is not ordered. Every entry has a value and a key. You may think of a dictionary as a list with a unique index.

The keys need to be distinct and unchangeable. As a result, we may utilise tuples, strings, or numbers (int or float) as keys. Values may take on any form.

Think of a situation where we need to save student grades. They can be kept in a list or a dictionary.


(image by author)

One way to create a dictionary is writing key-value pairs in curly braces.

grades = {‘John’:’A’, ‘Emily’:’A+’, ‘Betty’:’B’, ‘Mike’:’C’, ‘Ashley’:’A’}

We can access a value in a dictionary using its key.


12 Examples to Master Python Dictionaries

A comprehensive practical guide for learning dictionaries

  1. Sets

An unordered grouping of unique hashable objects is referred to as a set. The official Python (for data sciences) manual gives this definition of a set. Let’s do it.

  • Unordered collection: It has one or more elements but no order. The components of a set are not arranged in any particular sequence. As a result, unlike lists, it does not support indexing or slicing.
  • Distinct hashable objects: Each element in a collection is distinct. Hashable refers to immutable. Sets are variable, but their constituent parts must be immutable.

By placing items separated by commas in curly braces, we may make a set.

a = {1, 4, ‘foo’}print(type(a))
<class ‘set’>

Sets do not contain repeated elements so even if we try to add same elements more than once, the resulting set will contain unique elements.

a = {1, 4, ‘foo’, 4, ‘foo’}print(a)
{1, 4, ‘foo’}

12 Examples to Master Python Sets

A comprehensive practical guide for learning sets

  1. Tuples

A tuple in Python for data sciences is a group of values that are enclosed in parentheses, separated by commas. Tuples, as opposed to lists, are immutable. Tuples’ immutability might be regarded one of its distinguishing characteristics.

Values included in parentheses and separated by commas make form a tuple.

a = (3, 4)print(type(a))
<class ‘tuple’>

We can also create tuples without using the parenthesis. A sequence of values separated by comma will create a tuple.

a = 3, 4, 5, 6print(type(a))
<class ‘tuple’>

One of the most common use cases of tuples is with functions that return multiple values.

import numpy as npdef count_sum(arr):
  count = len(arr)
  sum = arr.sum()
  return count, sumarr = np.random.randint(10, size=8)
a = count_sum(arr)print(a)
(8, 39)print(type(a))
<class ‘tuple’>

About the Author

Ahsan Azam is the author who specializes in avionics as well as research writing. The author has a keen attention to detail and is focused on providing interesting content to the readers.

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