Data handling in Python refers to the process of manipulating and working with data in various formats such as integers, floats, strings, lists, dictionaries, and more. Python provides built-in data structures and functions that allow developers to handle data efficiently and effectively. Data handling is a crucial aspect of advanced analytics and data science as it enables professionals to process, analyze and visualize large sets of data.
Understanding data handling and functions in Python is essential for professionals in various fields, especially in data science and analytics. Also, it is also critical for code reusability and maintainability, making it easier for developers to write clean, efficient, and maintainable code. The Indian Institute of Management Kozhikode (IIM Kozhikode) offers several courses that cover data handling and functions in Python as a part of their curriculum.
Basic Data Types and Handling
A. Variables
In Python, variables are used to store data values that can be later referenced and manipulated. A variable is created when a value is assigned to it using the assignment operator (=). Variable names can be made up of letters, numbers, and underscores, but cannot start with a number.
B. Integers and Floats
Integers and floats are numeric data types in Python. Integers are whole numbers, while floats are numbers with decimals. Numeric data types can be used in mathematical operations such as addition, subtraction, multiplication, and division.
C. Strings
Strings are sequences of characters enclosed in quotes, either single (”) or double (“”). Strings can be concatenated using the + operator and sliced using indexing and slicing operations. They are also immutable, meaning that once a string is created, it cannot be modified.
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D. Lists
Lists are collections of items enclosed in square brackets ([]). They can contain any data type, including other lists. Lists are mutable, meaning that they can be modified after creation by adding, removing, or modifying elements.
E. Tuples
Tuples are similar to lists, but they are immutable, meaning that once created, their elements cannot be modified. They are enclosed in parentheses ().
F. Dictionaries
Dictionaries are collections of key-value pairs enclosed in curly braces ({}). They are used to store data in a structured format and can be used to represent real-world objects. Dictionaries are mutable and can be modified by adding, removing, or modifying key-value pairs.
G. Sets
Sets are collections of unique items enclosed in curly braces ({}). They are useful for performing operations on unique elements, such as finding the intersection or difference between sets.
H. Typecasting
Typecasting is the process of converting a variable of one data type to another. Python provides built-in functions such as int(), float(), str(), and list() for type casting variables.
Functions in Python
A. Defining Functions
Functions in Python are defined using the def keyword followed by the function name and parameters, if any. The body of the function is indented and contains the code that needs to be executed when the function is called.Â
B. Arguments and Parameters
Functions can take inputs (arguments) that are passed when it is called. Parameters are the variables in the function that receive the arguments.
C. Return Statements
Functions can return outputs using the return statement. The return statement terminates the function and returns a value to the caller.
D. Lambda Functions
Lambda functions are anonymous functions that can be defined in a single line. They are useful for short, one-off functions that do not need to be defined separately.
E. Recursive Functions
Recursive functions are functions that call themselves, either directly or indirectly. They are useful for solving problems that can be broken down into smaller sub-problems.
F. Built-in Functions
Python provides several built-in functions that can be used without the need for defining them. Examples include print(), len(), range(), and sum(). These functions are useful for performing common operations quickly and efficiently.
Control Structures
A. If Statements
‘If statements’ are used for conditional execution of code. They check if a certain condition is true, and if it is, execute a block of code. If the condition is false, the code inside the if statement is not executed.
B. While Loops
‘While loops’ are used to repeatedly execute a block of code while a certain condition is true. The loop continues until the condition becomes false.
C. For Loops
‘For loops’ are used to iterate over a sequence of items, such as a list or a string. The loop executes once for each item in the sequence.
D. Break and Continue Statements
‘Break and continue statements’ are used inside loops to modify the control flow. The break statement terminates the loop immediately, while the continue statement skips the rest of the loop iteration and moves on to the next one.
File Handling
A. Opening and Closing Files
To work with files in Python, you must first open the file. The open() function takes two arguments, the name of the file and the mode in which to open it (read, write, append, etc.). Once you are finished with the file, you should close it using the close() method.
B. Reading Files
To read the contents of a file, you can use the read() method. This method returns the entire contents of the file as a single string. You can also read the contents of a file line by line using the readline() method.
C. Writing to Files
To write to a file, you can use the write() method. This method writes a string to the file. If the file does not exist, it will be created. If it does exist, the contents of the file will be overwritten.
D. Appending to Files
To append to a file, you can use the append() method. This method appends a string to the end of the file without overwriting the existing contents. If the file does not exist, it will be created.
Modules and Libraries
A. Built-in Modules
Python comes with a number of built-in modules that provide additional functionality. These modules can be imported and used in your code. Some examples of built-in modules include math, random, datetime, and os.Â
B. Importing Modules
To use a module in your code, you must first import it using the import statement. You can import the entire module or specific functions or classes within the module. For example, to import the random module, you would use the following statement: import random.
C. Third-party Libraries
In addition to the built-in modules, there are also many third-party libraries available for Python. These libraries provide additional functionality that is not included in the standard library. Some popular third-party libraries include NumPy, Pandas, Matplotlib, and Scikit-learn.
D. Installing Libraries
To use third-party libraries in your code, you must first install them. This can be done using a package manager such as pip. For example, to install the NumPy library, you would use the following command: pip install numpy.Â
Thus, Data handling and functions are fundamental concepts in Python that are essential for advanced analytics and data science. Understanding these concepts will help you to create more complex programs. Python is a powerful language with many applications, and there is always more to learn.To get an in-depth understanding of it, you can also check out Professional Certificate Programme in Advanced Analytics & Business Intelligence offered by IIM Kozhikode is designed for working professionals who want to enhance their skills in data analytics and business intelligence. The curriculum is focused on providing practical knowledge and skills that can be applied in real-world business scenarios. Apart from that, there are also many resources available online, including tutorials, courses, and forums.
Remember, practice is key to mastering Python, so be sure to work on projects and exercises to solidify your knowledge.
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