Python pandas data exploration and Visualization

 

Data Exploration: Techniques for Exploring and Summarizing Data

Data exploration is an important step in the data analysis process as it helps to understand the structure, distribution and relationships of the data before further analysis. This stage is crucial to identify potential outliers, missing values, trends and patterns in the data. In this article, we will learn about some common techniques for exploring and summarizing data, including descriptive statistics and data visualization.

1. Descriptive Statistics Descriptive statistics summarize the central tendencies and dispersion of the data. The following are some of the commonly used descriptive statistics measures:

• Mean: The average value of the data.
• Median: The middle value of the data.
• Mode: The most frequently occurring value in the data.
• Range: The difference between the highest and the lowest values in the data.
• Variance: The average of the squared differences from the mean.
• Standard Deviation: The square root of the variance.

Example code in Python:

import numpy as np

# Define the data

data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

# Mean

mean = np.mean(data)

print("Mean: ", mean)

 #output -> Mean :5.5

# Median

median = np.median(data)

print("Median: ", median)

 #output -> Median : 5.5

# Mode

from statistics import mode

mode = mode(data)

print("Mode: ", mode)

 #output -> Mode : 1

# Range

range = np.ptp(data)

print("Range: ", range)

 #output -> Range : 9

# Variance

variance = np.var(data)

print("Variance: ", variance)

 #output -> Variance : 8.25

# Standard Deviation

std_dev = np.std(data)

print("Standard Deviation: ", std_dev)

#output -> Standard Deviation : 2.8722813232690143

2. Data Visualization Data visualization is a powerful tool for exploring and summarizing data. It helps to understand the data better and uncover hidden patterns and trends. Some common data visualization techniques are:

• Line Plot: A line plot is used to represent continuous data over time.
• Scatter Plot: A scatter plot is used to visualize the relationship between two variables.
• Histogram: A histogram represents the distribution of the data.
• Box Plot: A box plot represents the distribution of the data and highlights any outliers.

Example code in Python using Matplotlib library:

import matplotlib.pyplot as plt

# Define the data

data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

# Line Plot

plt.plot(data)

plt.title("Line Plot")

plt.show()

# Scatter Plot

x = [1, 2, 3, 4, 5]

y = [2, 4, 6, 8, 10]

plt.scatter(x, y)

plt.title("Scatter Plot")

plt.show()

# Histogram

plt.hist(data, bins=5)

plt.title("Histogram")

plt.show()

You can play with "bins" value.

# Box Plot

plt.boxplot(data)

plt.title("Box Plot")

plt.show()

In conclusion, data exploration is an important step in the data analysis process. Descriptive statistics and data visualization are two important techniques for exploring and summarizing data. 

Amelioration

This article was researched and written with the help of ChatGPT, a language model developed by OpenAI.

Special thanks to ChatGPT for providing valuable information and examples used in this article.

Data Cleaning and Preperation

 

Data Cleaning and Preparation: Essential Techniques for Effective Data Analysis

Data preparation is an important step in the data analysis process. Cleaning and preparing data is crucial because if the data is not accurate, then the analysis and predictions made from it will also be inaccurate. In this article, we will discuss three essential techniques for cleaning and preparing data: handling missing values, handling outliers, and working with duplicate data.

1. Handling Missing Values

Missing values are a common problem in datasets. The missing values can occur due to many reasons, such as data collection errors, incompleteness of the data, or data loss during data transmission. To handle missing values, there are several techniques available, including:

·        Deletion: Deletion is the simplest method to handle missing values. This method involves removing the rows or columns with missing values from the dataset. However, this method may lead to loss of important information, especially if a large number of values are missing.

·        Imputation: Imputation is a process of replacing missing values with estimated values. There are several imputation methods, including mean imputation, median imputation, and mode imputation.

Here's an example of how to perform mean imputation in Python using Pandas:

import pandas as pd

import numpy as np

df = pd.read_csv("data.csv")

df.fillna(df.mean(), inplace=True)

2. Handling Outliers

Outliers are extreme values that deviate significantly from the other values in the dataset. Outliers can have a significant impact on the results of the analysis and predictions. To handle outliers, there are several techniques available, including:

·        Z-Score: Z-score is a statistical method that measures the number of standard deviations away from the mean. Any value with a Z-score greater than 3 or less than -3 is considered an outlier.

·        Interquartile Range (IQR): IQR is a statistical measure that separates the upper and lower 25% of the data. Any value outside of the IQR range is considered an outlier.

Here's an example of how to detect and remove outliers in Python using Z-Score:

import pandas as pd

import numpy as np

df = pd.read_csv("data.csv")

z_score = np.abs(zscore(df))

df = df[(z_score < 3).all(axis=1)]

3. Working with Duplicate Data

Duplicate data is a common problem in datasets. Duplicate data can lead to inaccurate results and conclusions. To handle duplicate data, there are several techniques available, including:

·        Drop Duplicates: Drop Duplicates is a method that involves removing all duplicate rows from the dataset.

·        Merge Duplicates: Merge Duplicates is a method that involves combining all the information from duplicate rows into a single row.

Here's an example of how to drop duplicates in Python using Pandas:

import pandas as pd

df = pd.read_csv("data.csv")

df.drop_duplicates(inplace=True)

In conclusion, data cleaning and preparation are critical steps in the data analysis process. By handling missing values, outliers, and duplicate data, you can ensure that the data is accurate and ready for analysis. With these techniques, you can make informed decisions and accurate predictions based on your data.

 

Data input and Output in Python Pandas

 

Data Input and Output is an essential aspect of working with pandas. The library provides several functions and methods to read and write data to and from different file formats. In this article, we will discuss how to read and write data to and from CSV, Excel, JSON, and SQL file formats using pandas.

CSV

Comma Separated Values (CSV) is one of the most widely used file formats for storing data. pandas provides the read_csv and to_csv functions to read and write CSV files, respectively.

To read a CSV file, use the read_csv() function and pass the file path as an argument. The function returns a DataFrame object.

import pandas as pd

df = pd.read_csv('data.csv')

The read_csv() function also accepts several optional parameters to customize the reading process. For example, you can use the header parameter to specify the row number to use as the column names, or use the names parameter to provide a list of column names.

To write a DataFrame to a CSV file, use the to_csv() function and pass the file path as an argument.

df.to_csv('data_modified.csv', index=False)

The to_csv() function also accepts several optional parameters to customize the writing process. For example, you can use the sep parameter to specify the separator character to use between fields, or use the index parameter to specify whether to write the row index to the file.

Excel

Excel is another widely used file format for storing data. pandas provides the read_excel and to_excel functions to read and write Excel files, respectively.

To read an Excel file, use the read_excel() function and pass the file path as an argument. The function returns a DataFrame object.

df = pd.read_excel('data.xlsx')

The read_excel() function also accepts several optional parameters to customize the reading process. For example, you can use the sheet_name parameter to specify the sheet to read from the Excel file, or use the usecols parameter to specify the columns to read.

To write a DataFrame to an Excel file, use the to_excel() function and pass the file path as an argument.

df.to_excel('data_modified.xlsx', index=False)

The to_excel() function also accepts several optional parameters to customize the writing process. For example, you can use the engine parameter to specify the engine to use when writing to the file, or use the columns parameter to specify the columns to write.

JSON

JavaScript Object Notation (JSON) is a lightweight data interchange format. pandas provides the read_json and to_json functions to read and write JSON files, respectively.

To read a JSON file, use the read_json() function and pass the file path as an argument. The function returns a DataFrame object.

df = pd.read_json('data.json')

 

Python Pandas series

 

The Pandas library in Python is a powerful tool for data manipulation and analysis. It provides data structures such as Series and DataFrame that allow you to work with and manipulate data in a flexible and efficient way.

A Series is a one-dimensional array-like object that can hold any data type. It is similar to a column in a spreadsheet or a dataset in R. Each Series has a name, called the index, which is used to identify the elements in the Series.

Here is an example of creating a Series in Pandas:

import pandas as pd

data = [1, 2, 3, 4, 5]

index = ['a', 'b', 'c', 'd', 'e']

s = pd.Series(data, index=index)

print(s)

This will output:

a    1

b    2

c    3

d    4

e    5

dtype: int64

In the example above, we created a Series called "s" with the data [1, 2, 3, 4, 5] and the index ['a', 'b', 'c', 'd', 'e']. The elements in the Series can be accessed by their index, just like in a dictionary. For example, to access the element at index 'c', we can use the following code:

print(s['c'])

This will output:

3

We can also perform mathematical operations on the elements of a Series, like adding or multiplying them. For example:

s2 = s * 2

print(s2)

This will output:

a     2

b     4

c     6

d     8

e    10

dtype: int64

In addition to these basic operations, the Pandas library provides a wide range of methods for working with Series, such as sorting, filtering, and aggregating data. These methods allow you to easily manipulate and analyze your data, making Pandas a valuable tool for data science and machine learning tasks.

In summary, Pandas Series is a powerful data structure that allows you to work with and manipulate data in a flexible and efficient way. With the wide range of methods provided by the Pandas library, you can easily sort, filter, and aggregate your data, making it a valuable tool for data science and machine learning tasks.

Amelioration

This article was researched and written with the help of ChatGPT, a language model developed by OpenAI.

Special thanks to ChatGPT for providing valuable information and examples used in this article.

 

Python Pandas Introduction

 

Python Pandas is a powerful library for data manipulation and analysis. It provides a wide range of data structures and operations for manipulating numerical tables and time series data.

The most important data structure in Pandas is the DataFrame, which is a table with labeled rows and columns. DataFrames can be created from a variety of data sources such as CSV files, Excel sheets, SQL databases, and even Python lists and dictionaries. They can also be easily exported to a variety of formats, such as CSV, Excel, and JSON.

One of the key features of Pandas is its ability to handle missing data. It provides a variety of methods for filling in missing values, such as forward filling, backward filling, and interpolation. This makes it easy to work with incomplete datasets.

Another powerful feature of Pandas is its ability to perform groupby operations. This allows you to group rows in a DataFrame based on the values in one or more columns, and then apply a variety of aggregation functions to each group, such as sum, mean, and count.

Pandas also provides a wide range of tools for data manipulation and cleaning, such as filtering, sorting, and reshaping data. It also supports advanced features such as merging, joining, and concatenating DataFrames.

In addition to DataFrames, Pandas also provides a Series object, which is a one-dimensional array-like object with a labeled index. Series can be used for a variety of tasks such as data cleaning and transformation, and can also be easily converted to and from a DataFrame.

Overall, Pandas is a powerful and flexible tool for data manipulation and analysis, and is widely used in data science and machine learning projects.

To get started with Pandas, you will need to install it first. You can do this by running "pip install pandas" in your command line or terminal. Once it's installed, you can start using it by importing it in your code like this: "import pandas as pd".

A simple example of how to use pandas is by loading a csv file into a dataframe and then printing first 5 rows.

import pandas as pd

df = pd.read_csv("your_file.csv")

print(df.head())

This is just an introduction to the capabilities of Python Pandas library, but the possibilities are endless and it's a fundamental tool for data manipulation and analysis.

Amelioration

This article was researched and written with the help of ChatGPT, a language model developed by OpenAI.

Special thanks to ChatGPT for providing valuable information and examples used in this article.

Python Numpy Operations and Slicing

Python's NumPy library is a powerful tool for performing mathematical operations on arrays and matrices. In this article, we'll take a look at some of the most commonly used mathematical operations in NumPy, as well as how to slice and access elements in a NumPy array.

To start, we'll need to import the NumPy library:

import numpy as np

One of the most basic mathematical operations that can be performed on a NumPy array is addition. For example, to add two arrays together, we can use the + operator:

a = np.array([1, 2, 3])

b = np.array([4, 5, 6])

c = a + b

print(c)

# Output: [5 7 9]

Subtraction, multiplication, and division can also be performed using the -, *, and / operators, respectively. Additionally, NumPy provides a number of useful mathematical functions, such as np.sin, np.cos, and np.exp, that can be applied to an entire array at once. For example:

a = np.array([1, 2, 3])

b = np.sin(a)

print(b)

# Output: [0.84147098 0.90929743 0.14112001]

Another important feature of NumPy is its ability to perform element-wise operations on arrays. For example, if we have two arrays of the same shape, we can multiply them element-wise using the * operator:

a = np.array([1, 2, 3])

b = np.array([4, 5, 6])

c = a * b

print(c)

# Output: [ 4 10 18]

In addition to mathematical operations, NumPy also provides a number of ways to slice and access elements in an array. For example, we can use the [] operator to access a specific element in an array:

a = np.array([1, 2, 3, 4, 5])

print(a[2])

# Output: 3

We can also use slicing to access a range of elements in an array:

a = np.array([1, 2, 3, 4, 5])

print(a[1:3])

# Output: [2 3]

And we can use the : operator to select all elements along a specific axis:

a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

print(a[:, 1])

# Output: [2 5 8]

These are just a few examples of the many mathematical operations and slicing capabilities that are available in NumPy. By leveraging the power of this library, we can perform complex mathematical computations on large arrays and matrices with ease.

Amelioration

This article was researched and written with the help of ChatGPT, a language model developed by OpenAI.

Special thanks to ChatGPT for providing valuable information and examples used in this article.

 

 

Python Numpy

 

Python NumPy is a powerful library for numerical computations in Python. It provides a high-performance array object and a set of routines for performing operations on those arrays. In this article, we will explore some of the key features of NumPy and how to use them with example code.

First, let's start by creating a simple array using the numpy array function. Here's an example:

import numpy as np

# Create a 1-dimensional array

arr = np.array([1, 2, 3, 4, 5])

print(arr)

# Output: [1 2 3 4 5]

As you can see, we have created a 1-dimensional array of integers using the numpy array function. This function can also be used to create arrays of other data types, such as floating point numbers or complex numbers.

NumPy also provides a wide range of mathematical functions that can be used to perform operations on arrays. For example, we can use the sum function to calculate the sum of all elements in an array:

# Calculate the sum of all elements

s = np.sum(arr)

print(s)

# Output: 15

Another useful function is the mean function, which calculates the average of all elements in an array:

# Calculate the mean of all elements

m = np.mean(arr)

print(m)

# Output: 3.0

We can also use NumPy to perform more advanced operations on arrays, such as matrix multiplication. The dot function can be used to perform matrix multiplication:

# Create a 2-dimensional array

matrix_a = np.array([[1, 2], [3, 4]])

matrix_b = np.array([[5, 6], [7, 8]])

# Perform matrix multiplication

result = np.dot(matrix_a, matrix_b)

print(result)

# Output: [[19 22], [43 50]]

In addition to these basic operations, NumPy also provides a wide range of other functions for working with arrays, such as sorting, reshaping, and indexing. These functions are well-documented in the NumPy documentation and are easy to use.

In conclusion, NumPy is a powerful library for numerical computations in Python. Its high-performance arrays and powerful mathematical functions make it easy to manipulate and analyze large datasets, while its seamless integration with other libraries makes it a great choice for building data analysis pipelines.  

Amelioration

This article was researched and written with the help of ChatGPT, a language model developed by OpenAI.

Special thanks to ChatGPT for providing valuable information and examples used in this article.

Reading and Writing Files in Python

In Python, reading and writing files is relatively simple with the built-in open() function. The open() function takes in two arguments: the file path, and the mode in which the file should be opened. The mode can be 'r' for reading, 'w' for writing, 'a' for appending, and 'x' for exclusive creation. If no mode is specified, the default mode is 'r'.

Here is an example of how to read a file in Python:

# Open the file for reading

file = open('example.txt', 'r')

# Read the contents of the file

contents = file.read()

# Print the contents of the file

print(contents)

# Close the file

file.close()

Here is an example of how to write to a file in Python:

# Open the file for writing

file = open('example.txt', 'w')

# Write to the file

file.write('This is some text')

# Close the file

file.close()

It is also possible to append to a file using the 'a' mode:

# Open the file for appending

file = open('example.txt', 'a')

# Append to the file

file.write('This is some more text')

# Close the file

file.close()

It is important to close the file after you are done reading from or writing to it. This is to ensure that any changes made to the file are saved and to release any resources being used by the file.

You can also use the with statement to open a file in python, this will automatically close the file after the indented block of code is executed.

with open('example.txt', 'r') as file:

    contents = file.read()

    print(contents)

It is also possible to read or write to a file using a variety of other methods. The most common one being .readlines() and .writelines() these allow you to read or write multiple lines at a time.

# Open the file for reading

file = open('example.txt', 'r')

# Read the contents of the file

contents = file.readlines()

# Print the contents of the file

print(contents)

# Close the file

file.close()

# Open the file for writing

file = open('example.txt', 'w')

# Write to the file

file.writelines(["This is some text", "This is some more text"])

# Close the file

file.close()

Amelioration

This article was researched and written with the help of ChatGPT, a language model developed by OpenAI.

Special thanks to ChatGPT for providing valuable information and examples used in this article.

 

 

Python exceptions

 

Python exceptions are events that occur during the execution of a program that disrupts the normal flow of instructions. When an exception occurs, it is said to be "raised."

For example, imagine you have a program that prompts the user for a number, and then divides 10 by that number. If the user enters 0, the program will raise a ZeroDivisionError, because you cannot divide by zero.

Here's some sample code that demonstrates how this might look:

while True:

    try:

        x = int(input("Enter a number: "))

        print(10 / x)

        break

    except ZeroDivisionError:

        print("You can't divide by zero! Try again.")

    except ValueError:

        print("You must enter a number! Try again.")

In this example, we use a "try-except" block to handle the possibility of a ZeroDivisionError. The code within the "try" block is the code that may raise the exception. The code within the "except" block is the code that is executed if the exception is raised. In this case, if the user enters a zero, the program will print a message saying "You can't divide by zero! Try again."

We also have another except block to handle the ValueError which will be raised when user inputs non numeric values.

It's also possible to use the "finally" block which will be executed regardless of whether an exception was raised or not.

try:

    x = int(input("Enter a number: "))

    print(10 / x)

except ZeroDivisionError:

    print("You can't divide by zero! Try again.")

finally:

    print("This code will always run.")

In this example, the "finally" block will always run, whether an exception was raised or not.

You can also raise an exception manually using the raise statement.

x = int(input("Enter a number: "))

if x < 0:

    raise ValueError("x should be positive")

In this example, the program raises a ValueError if the user enters a negative number.

Using try-except-finally block and raising exceptions manually can help to handle errors and make your code more robust.

Amelioration

This article was researched and written with the help of ChatGPT, a language model developed by OpenAI.

Special thanks to ChatGPT for providing valuable information and examples used in this article.

 

Object Oriented Programming in Python

 

Object-oriented programming (OOP) is a programming paradigm that uses objects and their interactions to design applications and computer programs. Python, like many other modern programming languages, supports OOP. In this article, we will discuss the basics of OOP in Python and provide an example of how to implement it in a program.

In Python, everything is an object, and each object has a type or class. A class is a blueprint for creating objects (instances) with a certain set of attributes and methods. Attributes are variables that store data, and methods are functions that perform actions.

An example of a class in Python is:

class Dog:

    def __init__(self, name, breed):

        self.name = name

        self.breed = breed

    def bark(self):

        print("Woof woof!")

Here, we have defined a class called Dog. The __init__ method is a special method that is called when an object is created from the class. It is used to initialize the attributes of the object. In this case, we have two attributes, name and breed. The bark method is a function that when called, will print "Woof woof!".

To create an instance of the Dog class, we use the following code:

my_dog = Dog("Fido", "Golden Retriever")

This creates an object called my_dog with the name "Fido" and breed "Golden Retriever". We can access the attributes of the object using the dot notation:

print(my_dog.name) # Output: Fido

print(my_dog.breed) # Output: Golden Retriever

We can also call the methods of the object:

my_dog.bark() # Output: Woof woof!

Inheritance is another important concept in OOP. It allows a new class to be defined that inherits the properties and methods of an existing class. The new class is called a derived class or child class, and the existing class is called the base class or parent class.

For example, let's say we have a base class called Animal and we want to create a derived class called Dog that inherits the properties of the Animals class. We can do this using the following code:

class Animals:

    def __init__(self, name, species):

        self.name = name

        self.species = species

    def make_sound(self):

        pass

class Dog(Animals):

    def __init__(self, name, breed):

        super().__init__(name, species="Dog")

        self.breed = breed

    def make_sound(self):

        print("Woof woof!")

Here, the Dog class inherits the properties and methods of the Animals class. It has its own __init__ method, but it calls the __init__ method of the parent class using the super() function. It also has its own make_sound method that overrides the one inherited from the parent class.

In conclusion, OOP is a powerful programming paradigm that helps to organize and structure code in a logical and efficient manner. Python supports OOP through the use of classes and objects, and it allows for inheritance and polymorph.

Amelioration

This article was researched and written with the help of ChatGPT, a language model developed by OpenAI.

Special thanks to ChatGPT for providing valuable information and examples used in this article.

 

User Input in Python

 

In Python, user input can be taken using the input() function. This function waits for the user to enter a string of text, which is then returned as the output of the function. Here is an example of using the input() function to take in a user's name:

name = input("What is your name? ")

print("Hello, " + name + "!")

In this example, the input() function is used to prompt the user for their name, with the prompt "What is your name? " displayed to the user. The user's response is then stored in the variable name, which is then used in the next line to print a personalized greeting.

It's important to note that the input() function always returns a string, even if the user enters a number. If you want to use the user's input as a number, you'll need to convert it to the appropriate data type. Here is an example of using the input() function to take in a user's age, and converting it to an integer:

age = input("What is your age? ")

age = int(age)

print("You are " + str(age) + " years old.")

In this example, the input() function is used to prompt the user for their age, and the user's response is stored in the variable age. The next line converts the age variable from a string to an integer using the int() function, so that it can be used in mathematical operations. The final line converts age to str to use it in print statement.

It's also important to note that the input() function is not safe to use, as it can easily be exploited by malicious users. For example, a user could enter a string that contains code that can cause security issues. To mitigate this, you should always validate and sanitize user input.

Another way to take input is by using the input().split() function which will take multiple inputs separated by space and return them in the form of list. Here is an example of using input().split() function

name,age,gender = input("Enter your name, age and gender separated by space: ").split()

print("Name: ",name)

print("Age: ",age)

print("Gender: ",gender)

In this example, the input() function is used to prompt the user for their name, age and gender and user's response is splitted by space and stored in variables name,age,gender respectively.

By using the above techniques, you can take user input in Python and use it to customize your program's behavior or make it more interactive. Remember to always validate and sanitize user input to protect your program from malicious attacks.

Amelioration

This article was researched and written with the help of ChatGPT, a language model developed by OpenAI.

Special thanks to ChatGPT for providing valuable information and examples used in this article.

 

Packages and Modules in Python

 

In Python, modules and packages are used to organize and reuse code. A module is a single file containing Python definitions and statements. A package is a collection of modules that are organized in a directory hierarchy.

To use a module or a package in your Python code, you need to import it. The import statement is used to import modules or packages into a Python script. Here is an example of importing the math module, which provides mathematical functions:

import math

print(math.pi)

print(math.sin(math.pi/2))

You can also use the from keyword to import specific functions or variables from a module or package. This way, you don't need to use the module name as a prefix when calling the imported functions or variables:

from math import pi, sin

print(pi)

print(sin(pi/2))

You can also use the as keyword to give an imported module or package a different name. This can be useful if you want to avoid naming conflicts with other modules or packages:

import math as m

print(m.pi)

print(m.sin(m.pi/2))

You can also use the * wildcard to import all functions and variables from a module or package:

from math import *

print(pi)

print(sin(pi/2))

It's important to note that, using wildcard import is not recommended as it might cause naming conflicts with other modules or packages, and it makes it harder to trace which function or variable came from which module.

In addition, you can also use the pip command to install external packages, which are not part of the Python standard library, such as numpy, pandas, matplotlib, etc.

pip install package_name

In summary, the import statement is used to import modules and packages in Python. You can use the import, from, as, and * keywords to specify how you want to import a module or package. Importing external modules and packages can be done by installing them using pip. It's important to note that, using wildcard import is not recommended as it might cause naming conflicts with other modules or packages and it makes it harder to trace which function or variable came from which module.

Amelioration

This article was researched and written with the help of ChatGPT, a language model developed by OpenAI.

Special thanks to ChatGPT for providing valuable information and examples used in this article.

 

Return values in Python

 

In Python, functions can return values using the return keyword. When a function encounters a return statement, the function terminates and the returned value is passed back to the calling code. Understanding how to use return values in Python functions is an important aspect of writing efficient and effective code.

Here is an example of a simple function that takes two numbers as input and returns their sum:

def add(a, b):

    """This function adds two numbers and return the result"""

    result = a + b

    return result

num1 = 5

num2 = 10

print(add(num1, num2)) # call the function and print the returned value

In this example, the function add takes in two parameters a and b, and returns their sum using the return statement. When the function is called with the arguments 5 and 10, it returns the value 15, which is then printed by the calling code.

Functions can also return multiple values by returning a tuple or a list. For example, the following function add_subtract takes in two numbers, calculates their sum and difference, and returns them as a tuple:

def add_subtract(a, b):

    """This function adds and subtracts two numbers"""

    add = a + b

    subtract = a - b

    return add, subtract

x = 5

y = 10

print(add_subtract(x, y))

In this example, the function add_subtract takes in two parameters a and b and returns the tuple (15, -5) when it is called with the arguments 5 and 10.

You can also use the return statement in a function without any value to return None, which indicates that the function does not return anything.

def print_hello():

    """This function prints 'hello' and return None"""

    print("hello")

    return

print(print_hello())

In this example, the function print_hello does not return any value, it just print 'hello' and return None.

It's important to note that a function does not always have to return a value. In some cases, a function may simply perform a task or calculation and not return anything. For example, the following function print_hello does not return any value, it just print 'hello'

def print_hello():

    """This function prints 'hello' and return None"""

    print("hello")

print_hello()

In summary, the return statement is used to return a value or values from a function in Python. Functions can return a single value, multiple values in a tuple or list, or no value at all. Understanding how to use return values in Python functions is an important aspect of writing efficient and effective code.

Amelioration

This article was researched and written with the help of ChatGPT, a language model developed by OpenAI.

Special thanks to ChatGPT for providing valuable information and examples used in this article.