Data Types in Python for Data Science Applications

Python is a multipurpose programming language that finds applications across various domains. Its simplicity and flexibility make it a popular choice for web development, where frameworks like Django and Flask enable the creation of dynamic websites.

In Data Science and machine learning, Python's robust libraries, such as NumPy, pandas, and scikit-learn, empower data analysts and scientists to analyze data, build predictive models, and extract meaningful insights.

Additionally, Python's adaptability extends to scientific computing, automation and scripting, game development, desktop applications, web scraping, database management, education, Internet of Things (IoT) projects, and natural language processing (NLP). Its usage spans diverse fields, including cybersecurity, cloud computing, finance, healthcare, and artificial intelligence (AI), making Python an indispensable tool for developers, researchers, and professionals worldwide.

In detail, we shall discuss a few crucial Data Types in Python for Data Science Applications.

Numeric Data Types

Numeric data types in programming represent numbers and are used to perform mathematical operations and calculations. These data types are crucial for handling numerical values in various applications. Common numeric data types are:

• Integers (int)- It is used for programming tasks like counting and indexing.
• Floating-point numbers (float)- It is suitable for mathematical calculations involving fractions or real-world measurements.
• Complex numbers (complex)- It is used in scientific and engineering applications where calculations involve imaginary numbers (e.g., square roots of negative values).

# Examples of numeric data types

Integers (int):

x = 5 y = -10 z = 0

Floating-Point Numbers (float)

pi = 3.14159temperature = 98.6

Complex Numbers

z1 = 3 + 4jz2 = -2.5 - 1.8j

Strings

Strings are a fundamental data type in programming used to represent text or sequences of characters. They are typically enclosed within single quotes (' '), double quotes (" "), or triple quotes for multiline strings (""" """ or ''' '''). It is primarily utilized in the data science.

Single-Quoted String:

single_quoted_string = 'This is a single-quoted string.'

Double-Quoted String:

double_quoted_string = "This is a double-quoted string."

Multiline String (Triple Quotes):

multiline_string = """This is a multiline string."""

Lists and Tuples

Lists and tuples are two commonly used data structures in programming for storing collections of items. In data science, it is used to store data.

Lists:

• Lists are ordered collections of items that can be of different data types.
• They are defined using square brackets [], and commas separate items.
• Lists are mutable
• After creation, we can change their contents (add, remove, or modify items).

Example:

my_list = [1, 2, 3, "orange", "apple", True]

Tuples:

• They are defined using parentheses (), and commas separate items.
• Tuples are immutable
• We cannot change the content once it has been created.

For example:

my_tuple = (1, 2, 3, "apple", "banana", True)

Key Difference:

Lists are defined with square brackets [ ], and tuples are defined with parentheses ( ).

Sets and Dictionaries

The two fundamental data structures used in programming are sets and dictionaries, each with unique properties and applications.

Sets:

• A set is an unordered collection of unique elements.
• Sets are defined using curly braces {} or the set() constructor, and elements are separated by commas.
• Sets do not allow duplicate values
• Sets are mutable

Example:

my_set = {1, 2, 3, 4, 5}

Dictionaries:

• A dictionary is a collection of key-value pairs.
• Each key-value pair in a dictionary is separated by a colon (:), and pairs are separated by commas. Dictionaries are defined using curly braces {} or the dict() constructor.
• Keys must be unique, but values can be duplicated.
• Dictionaries are mutable

Example:

my_dict = {"name": "Alice", "age": 30, "city": "New York"}

Key Differences:

Sets are unordered, so they don't have an index or key associated with each element. In contrast, dictionaries use keys to access values.

Booleans

Booleans are a fundamental data type in programming representing one of two values: True or False. Booleans are primarily used for logical operations, conditional statements, and making decisions within a program.

# Example of a boolean

print (type (True))print (type (False))

Python offers a rich array of data types in data science that provide flexibility and efficiency for various tasks. These data types, from numeric types like integers and floating-point numbers to specialized structures like lists, dictionaries, and sets, empower data scientists to manipulate, analyze, and visualize data effectively.

Python's versatility and extensive ecosystem make it a top choice for professionals in data science, facilitating the extraction of valuable insights from diverse datasets and driving informed decision-making.