Code:
days = ("Mon", "Tue", "Wed")
print(days[-1:-2])
Python Coding April 19, 2024 Python Coding Challenge No comments
days = ("Mon", "Tue", "Wed")
print(days[-1:-2])
Python Coding April 18, 2024 Data Science No comments
Why Take a Meta Data Analyst Professional Certificate?
Collect, clean, sort, evaluate, and visualize data
Apply the Obtain, Sort, Explore, Model, Interpret (OSEMN) framework to guide the data analysis process
Learn to use statistical analysis, including hypothesis testing, regression analysis, and more, to make data-driven decisions
Develop an understanding of the foundational principles underpinning effective data management and usability of data assets within organizational context
Aquire the confidence to add the following skills to add to your resume:
Data analysis
Python Programming
Statistics
Data management
Data-driven decision making
Data visualization
Linear Regression
Hypothesis testing
Data Management
Tableau
Collect, clean, sort, evaluate, and visualize data
Apply the OSEMN, framework to guide the data analysis process, ensuring a comprehensive and structured approach to deriving actionable insights
Use statistical analysis, including hypothesis testing, regression analysis, and more, to make data-driven decisions
Develop an understanding of the foundational principles of effective data management and usability of data assets within organizational context
Prepare for a career in the high-growth field of data analytics. In this program, you’ll build in-demand technical skills like Python, Statistics, and SQL in spreadsheets to get job-ready in 5 months or less, no prior experience needed.
Data analysis involves collecting, processing, and analyzing data to extract insights that can inform decision-making and strategy across an organization.
In this program, you’ll learn basic data analysis principles, how data informs decisions, and how to apply the OSEMN framework to approach common analytics questions. You’ll also learn how to use essential tools like SQL, Python, and Tableau to collect, connect, visualize, and analyze relevant data.
You’ll learn how to apply common statistical methods to writing hypotheses through project scenarios to gain practical experience with designing experiments and analyzing results.
When you complete this full program, you’ll have a portfolio of hands-on projects and a Professional Certificate from Meta to showcase your expertise.
Applied Learning Project
Throughout the program, you’ll get to practice your new data analysis skills through hands-on projects including:
Identifying data sources
Using spreadsheets to clean and filter data
Using Python to sort and explore data
Using Tableau to visualize results
Using statistical analyses
By the end, you’ll have a professional portfolio that you can show to prospective employers or utilize for your own business.
Python Coding April 18, 2024 Python Coding Challenge No comments
a = 'A'
print(int(a, 16))
Let's break it down step by step:
a = 'A': This line assigns the character 'A' to the variable a. In Python, characters are represented by strings containing a single character.
int(a, 16): This line converts the string 'A' to an integer using base 16 (hexadecimal) representation. In hexadecimal, 'A' represents the decimal number 10.
So, when you execute print(int(a, 16)), it will output:
10
Python Coding April 17, 2024 Python Coding Challenge No comments
x = [1, 2, 3]
y = [4, 5, 6]
z = [x, y]
print(z[1][1])
Python Coding April 17, 2024 Python Coding Challenge No comments
x = [1, 2, 3]
y = x.copy()
x[0] = 4
print(y)
Python Coding April 16, 2024 Python No comments
import periodictable
# Function to get information about an element
def get_element_info(symbol):
# Check if the symbol is valid
if not periodictable.elements.symbol(symbol):
print("Invalid element symbol.")
return
# Access information about the specified element
element = periodictable.elements.symbol(symbol)
# Print information about the element
print(f"Element: {element.name}")
print(f"Symbol: {element.symbol}")
print(f"Atomic Number: {element.number}")
print(f"Atomic Weight: {element.mass}")
print(f"Density: {element.density}")
# Prompt the user to input an element symbol
element_symbol = input("Enter the symbol of the element: ")
# Call the function to get information about the specified element
get_element_info(element_symbol)
#clcoding.com
Python Coding April 16, 2024 Data Science No comments
A data analyst sits between business intelligence and data science. They provide vital information to business stakeholders.
Data Management in SQL (PostgreSQL)
Data Analysis in SQL (PostgreSQL)
Exploratory Analysis Theory
Statistical Experimentation Theory
A data scientist is a professional responsible for collecting, analyzing and interpreting extremely large amounts of data.
R / Python Programming
1.1 Calculate metrics to effectively report characteristics of data and relationships between
features
● Calculate measures of center (e.g. mean, median, mode) for variables using R or Python.
● Calculate measures of spread (e.g. range, standard deviation, variance) for variables
using R or Python.
● Calculate skewness for variables using R or Python.
● Calculate missingness for variables and explain its influence on reporting characteristics
of data and relationships in R or Python.
● Calculate the correlation between variables using R or Python.
1.2 Create data visualizations in coding language to demonstrate the characteristics of data
● Create and customize bar charts using R or Python.
● Create and customize box plots using R or Python.
● Create and customize line graphs using R or Python.
● Create and customize histograms graph using R or Python.
1.3 Create data visualizations in coding language to represent the relationships between
features
● Create and customize scatterplots using R or Python.
● Create and customize heatmaps using R or Python.
● Create and customize pivot tables using R or Python.
1.4 Identify and reduce the impact of characteristics of data
● Identify when imputation methods should be used and implement them to reduce the
impact of missing data on analysis or modeling using R or Python.
● Describe when a transformation to a variable is required and implement corresponding
transformations using R or Python.
● Describe the differences between types of missingness and identify relevant approaches
to handling types of missingness.
● Identify and handle outliers using R or Python.
2.1 Perform standard data import, joining and aggregation tasks
● Import data from flat files into R or Python.
● Import data from databases into R or Python
● Aggregate numeric, categorical variables and dates by groups using R or Python.
● Combine multiple tables by rows or columns using R or Python.
● Filter data based on different criteria using R or Python.
2.2 Perform standard cleaning tasks to prepare data for analysis
● Match strings in a dataset with specific patterns using R or Python.
● Convert values between data types in R or Python.
● Clean categorical and text data by manipulating strings in R or Python.
● Clean date and time data in R or Python.
2.3 Assess data quality and perform validation tasks
● Identify and replace missing values using R or Python.
● Perform different types of data validation tasks (e.g. consistency, constraints, range
validation, uniqueness) using R or Python.
● Identify and validate data types in a data set using R or Python.
2.4 Collect data from non-standard formats by modifying existing code
● Adapt provided code to import data from an API using R or Python.
● Identify the structure of HTML and JSON data and parse them into a usable format for
data processing and analysis using R or Python
A data engineer collects, stores, and pre-processes data for easy access and use within an organization. Associate certification is available.
Data Management in SQL (PostgreSQL)
Exploratory Analysis Theory
Python Coding April 16, 2024 Python Coding Challenge No comments
x = [1, 2, 3]
y = [4, 5, 6]
z = [x, y]
print(z[0][1])
Let's break down the code step by step:
x = [1, 2, 3]: This line creates a list named x containing the elements 1, 2, and 3.
y = [4, 5, 6]: This line creates another list named y containing the elements 4, 5, and 6.
z = [x, y]: Here, a list named z is created, containing two lists: x and y. So, z becomes [[1, 2, 3], [4, 5, 6]].
print(z[0][1]): This line prints the element at index 1 of the first list in z. Since z[0] refers to [1, 2, 3] and z[0][1] refers to the element at index 1 of that list, the output will be 2.
Python Coding April 15, 2024 Python No comments
from captcha.image import ImageCaptcha
import random
# Specify the image size
image = ImageCaptcha(width=450, height=100)
# Generate random captcha text
def generate_random_captcha_text(length=6):
characters = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789'
captcha_text = ''.join(random.choice(characters) for _ in range(length))
return captcha_text
# Get random captcha text
captcha_text = generate_random_captcha_text()
# Generate the image of the given text
data = image.generate(captcha_text)
# Write the image on the given file and save it
image.write(captcha_text, 'CAPTCHA1.png')
from PIL import Image
Image.open('CAPTCHA1.png')
#clcoding.com
This code snippet demonstrates how to automatically generate an image CAPTCHA using Python. Here's a breakdown of each part:
from captcha.image import ImageCaptcha: This imports the ImageCaptcha class from the captcha.image module. This class allows you to create CAPTCHA images.
import random: This imports the random module, which is used to generate random characters for the CAPTCHA text.
image = ImageCaptcha(width=450, height=100): This initializes an instance of the ImageCaptcha class with the specified width and height for the CAPTCHA image.
generate_random_captcha_text(length=6): This is a function that generates random CAPTCHA text. It takes an optional parameter length, which specifies the length of the CAPTCHA text. By default, it generates a text of length 6.
captcha_text = generate_random_captcha_text(): This calls the generate_random_captcha_text function to generate random CAPTCHA text and assigns it to the variable captcha_text.
data = image.generate(captcha_text): This generates the CAPTCHA image using the generated text. It returns the image data.
image.write(captcha_text, 'CAPTCHA1.png'): This writes the generated CAPTCHA image to a file named "CAPTCHA1.png" with the text embedded in the image.
from PIL import Image: This imports the Image class from the Python Imaging Library (PIL) module, which is used to open and display the generated CAPTCHA image.
Image.open('CAPTCHA1.png'): This opens the generated CAPTCHA image named "CAPTCHA1.png" using the PIL library.
Overall, this code generates a random CAPTCHA text, creates an image of the text using the ImageCaptcha class, saves the image to a file, and then displays the image using PIL.
Python Coding April 15, 2024 Machine Learning No comments
Python Coding April 14, 2024 Python Coding Challenge No comments
What is the output of following Python Code?
s = 'clcoding'
print(s[1:6][1:3])
Python Coding April 14, 2024 Python Coding Challenge No comments
s = 'coder'
print(s[::0])
Python Coding April 14, 2024 Data Science No comments
Don't simply show your data - tell a story with it!
Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory but made accessible through numerous real-world examples - ready for immediate application to your next graph or presentation.
Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to:
Understand the importance of context and audience
Determine the appropriate type of graph for your situation
Recognize and eliminate the clutter clouding your information
Direct your audience's attention to the most important parts of your data
Think like a designer and utilize concepts of design in data visualization
Leverage the power of storytelling to help your message resonate with your audience
Together, the lessons in this book will help you turn your data into high-impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data - Storytelling with Data will give you the skills and power to tell it!
Gain a competitive edge in today’s data-driven world and build a rich career as a data professional that drives business success and innovation…
Today, data is everywhere… and it has become the essential building block of this modern society.
And that’s why now is the perfect time to pursue a career in data.
But what does it take to become a competent data professional?
This book is your ultimate guide to understanding the fundamentals of data analytics, helping you unlock the expertise of efficiently solving real-world data-related problems.
Here is just a fraction of what you will discover:
A beginner-friendly 5-step framework to kickstart your journey into analyzing and processing data
How to get started with the fundamental concepts, theories, and models for accurately analyzing data
Everything you ever needed to know about data mining and machine learning principles
Why business run on a data-driven culture, and how you can leverage it using real-time business intelligence analytics
Strategies and techniques to build a problem-solving mindset that can overcome any complex and unique dataset
How to create compelling and dynamic visualizations that help generate insights and make data-driven decisions
The 4 pillars of a new digital world that will transform the landscape of analyzing data
And much more.
Believe it or not, you can be terrible in math or statistics and still pursue a career in data.
And this book is here to guide you throughout this journey, so that crunching data becomes second nature to you.
Ready to master the fundamentals and build a successful career in data analytics? Click the “Add to Cart” button right now.
PLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.
Harvard Business Review called data science “the sexiest job of the 21st century,” so it's no surprise that data science jobs have grown up to 20 times in the last three years. With demand outpacing supply, companies are willing to pay top dollar for talented data professionals. However, to stand out in one of these positions, having foundational knowledge of interpreting data is essential. You can be a spreadsheet guru, but without the ability to turn raw data into valuable insights, the data will render useless. That leads us to data analytics and visualization, the ability to examine data sets, draw meaningful conclusions and trends, and present those findings to the decision-maker effectively.
Mastering this skill will undoubtedly lead to better and faster business decisions. The three audiobooks in this series will cover the foundational knowledge of data analytics, data visualization, and presenting data, so you can master this essential skill in no time. This series includes:
Everything data analytics: a beginner's guide to data literacy and understanding the processes that turns data into insights.
Beginner's guide to data visualization: how to understand, design, and optimize over 40 different charts.
How to win with your data visualizations: the five part guide for junior analysts to create effective data visualizations and engaging data stories.
These three audiobooks cover an extensive amount of information, such as:
Overview of the data collection, management, and storage processes.
Fundamentals of cleaning data.
Essential machine learning algorithms required for analysis such as regression, clustering, classification, and more....
The fundamentals of data visualization.
An in-depth view of over 40 plus charts and when to use them.
A comprehensive data visualization design guide.
Walkthrough on how to present data effectively.
And so much more!
Python Coding April 13, 2024 Python Coding Challenge No comments
def fun(a, b):
if a == 1:
return b
else:
return fun(a - 1, a * b)
print(fun(4, 2))
Python Coding April 13, 2024 Python No comments
from rembg import remove
from PIL import Image
input_path = 'p22.jpg'
output_path = 'p22.png'
inp = Image.open(input_path)
output = remove(inp)
output.save(output_path)
#clcoding.com
Python Coding April 13, 2024 Python No comments
from better_profanity import profanity
text = "What the hell is going on?"
censored_text = profanity.censor(text)
print(censored_text)
#clcoding.com
from better_profanity import profanity
text = "I can't believe he said that!"
has_profanity = profanity.contains_profanity(text)
print(has_profanity)
#clcoding.com
Python Coding April 12, 2024 Python Coding Challenge No comments
def fun(x, y):
if x == 0:
return y
else:
return fun(x - 1, x * y)
print(fun(3, 5))
Python Coding April 11, 2024 Python No comments
import turtle
t = turtle.Turtle()
t.shapesize(0.2, 0.2)
s = turtle.Screen()
s.bgcolor('black')
t.fillcolor("yellow")
t.begin_fill()
t.left(50)
t.forward(240)
t.circle(90, 200)
t.left(221)
t.circle(90, 200)
t.forward(260)
t.end_fill()
turtle.done()
#clcoding.com
Python Coding April 11, 2024 Python No comments
import turtle
t = turtle.Turtle()
#clcoding.com
s = turtle.Screen()
colors=['orange', 'red', 'magenta', 'blue', 'magenta',
'yellow', 'green', 'cyan', 'purple']
s.bgcolor('black')
t.pensize('2')
t.speed(0)
for x in range (360):
t.pencolor(colors[x%6])
t.width(x//100+1)
t.forward(x)
t.right(59)
turtle.hideturtle()
#clcoding.com
Python Coding April 11, 2024 Python Coding Challenge No comments
Let's break down the code:
list1 = [0, 1, 2, 3]
list2 = list1[1::-1]
print(list2)
list1 = [0, 1, 2, 3]: This line initializes a list list1 with elements [0, 1, 2, 3].
list2 = list1[1::-1]: Here, list1[1::-1] is using list slicing to create a new list list2. Let's break down the slicing expression:
1: This is the start index of the slice. It starts from index 1, which is the second element in list1.
::-1: This specifies the step value for the slice. In this case, -1 means to step backward through the list.
So, list1[1::-1] starts from index 1 (the second element) and goes backward to the beginning of the list.
When slicing backward ([::-1]), it reverses the order of elements. So, list2 will contain elements from index 1 (inclusive) to the beginning of the list (inclusive), in reverse order.
print(list2): This line prints the contents of list2.
Now, let's evaluate list2 based on the slicing operation:
list1[1::-1] starts from index 1, which is 1, and includes the element at that index.
The step -1 means it goes backward.
So, it goes from index 1 (1) to the beginning of the list (0) in reverse order.
As a result, list2 will contain [1, 0].
Therefore, the output of print(list2) will be:
[1, 0]
Python Coding April 10, 2024 Python No comments
import math
def fibonacci_closed_form(n):
phi = (1 + math.sqrt(5)) / 2
return round((phi**n - (-1/phi)**n) / math.sqrt(5))
# Define the number of Fibonacci numbers you want to print
num_terms = 5
# Print the Fibonacci sequence
for i in range(num_terms):
print(fibonacci_closed_form(i))
#clcoding.com
Python Coding April 10, 2024 Python Coding Challenge No comments
def fibonacci_tail_recursive(n, a=0, b=1):
if n == 0:
return a
elif n == 1:
return b
else:
return fibonacci_tail_recursive(n - 1, b, a + b)
print(fibonacci_tail_recursive(7))
Let's break down the code step by step:
def fibonacci_tail_recursive(n, a=0, b=1):
This line defines a function called fibonacci_tail_recursive that takes three parameters: n, a, and b. n represents the term of the Fibonacci sequence to compute, while a and b represent the current and next terms of the sequence respectively. By default, a is set to 0 and b is set to 1.
if n == 0:
return a
elif n == 1:
return b
Here, the function checks if the input n is 0 or 1. If n is 0, it returns the current term a (which is 0). If n is 1, it returns the next term b (which is 1). These base cases terminate the recursion.
else:
return fibonacci_tail_recursive(n - 1, b, a + b)
If n is greater than 1, the function makes a recursive call to itself with the parameters (n - 1, b, a + b). This means it calculates the next term by adding the current term a to the next term b, and it decrements n by 1 to move closer to the base cases. This process continues until n reaches 0 or 1, at which point the function returns a or b respectively.
print(fibonacci_tail_recursive(7))
Finally, the function is called with n = 7, which computes the 7th term of the Fibonacci sequence using tail recursion. The result is printed to the console.
This code demonstrates a tail-recursive implementation of the Fibonacci sequence, where the recursive call is the last operation performed by the function. However, it's worth noting that Python's interpreter does not perform tail call optimization by default, so this tail-recursive implementation doesn't gain any performance benefits over a non-tail-recursive version in Python.
Python Coding April 09, 2024 Python Coding Challenge No comments
import pyfiglet
from termcolor import colored
import random
def eid_al_fitr_wishes():
colors = ['red', 'green', 'yellow', 'blue', 'magenta', 'cyan', 'white']
ascii_art = pyfiglet.figlet_format("Eid Mubarak!", font="slant")
print(colored(ascii_art, color=random.choice(colors)))
# Call the function to display Eid-al-Fitr wishes
eid_al_fitr_wishes()
#clcoding.com
Let me break down the code step by step:
import pyfiglet: This line imports the pyfiglet module, which is used for creating ASCII art text. In this script, the figlet_format() function from pyfiglet module is used to generate ASCII art for the text "Eid Mubarak!" with the specified font style ("slant").
from termcolor import colored: This line imports the colored() function from the termcolor module. The colored() function is used to add color to text printed in the terminal. It takes the text and color as arguments and returns the colored text.
import random: This line imports the random module, which provides functions for generating random numbers. It is used in this script to randomly select a color from the list of colors defined later.
def eid_al_fitr_wishes():: This line defines a function named eid_al_fitr_wishes(). This function encapsulates the logic for printing Eid-al-Fitr wishes with ASCII art and colored text.
colors = ['red', 'green', 'yellow', 'blue', 'magenta', 'cyan', 'white']: This line defines a list named colors containing various color names.
ascii_art = pyfiglet.figlet_format("Eid Mubarak!", font="slant"): This line uses the figlet_format() function from the pyfiglet module to generate ASCII art for the text "Eid Mubarak!" with the specified font style ("slant"). The resulting ASCII art is stored in the variable ascii_art.
print(colored(ascii_art, color=random.choice(colors))): This line prints the ASCII art generated earlier with a randomly chosen color from the colors list. The colored() function applies the selected color to the ASCII art before printing it.
eid_al_fitr_wishes(): This line calls the eid_al_fitr_wishes() function, executing the code within it and printing the Eid-al-Fitr wishes with colored ASCII art.
That's a breakdown of the code provided. If you have any further questions or need clarification on any part, feel free to ask!
Free Books Python Programming for Beginnershttps://t.co/uzyTwE2B9O
— Python Coding (@clcoding) September 11, 2023
Top 10 Python Data Science book
— Python Coding (@clcoding) July 9, 2023
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Web Development using Python
— Python Coding (@clcoding) December 2, 2023
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