Code:
def test(a, b = 5):
print(a, b)
test(-3)
Python Coding April 26, 2024 Python Coding Challenge No comments
def test(a, b = 5):
print(a, b)
test(-3)
Python Coding April 26, 2024 Data Science No comments
Python Coding April 26, 2024 Course, Coursera, Data Science No comments
There are 6 modules in this course
Welcome to Practical Time Series Analysis!
Many of us are "accidental" data analysts. We trained in the sciences, business, or engineering and then found ourselves confronted with data for which we have no formal analytic training. This course is designed for people with some technical competencies who would like more than a "cookbook" approach, but who still need to concentrate on the routine sorts of presentation and analysis that deepen the understanding of our professional topics.
In practical Time Series Analysis we look at data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, the price of agricultural products, and more. We look at several mathematical models that might be used to describe the processes which generate these types of data. We also look at graphical representations that provide insights into our data. Finally, we also learn how to make forecasts that say intelligent things about what we might expect in the future.
Please take a few minutes to explore the course site. You will find video lectures with supporting written materials as well as quizzes to help emphasize important points. The language for the course is R, a free implementation of the S language. It is a professional environment and fairly easy to learn.
You can discuss material from the course with your fellow learners. Please take a moment to introduce yourself!
Time Series Analysis can take effort to learn- we have tried to present those ideas that are "mission critical" in a way where you understand enough of the math to fell satisfied while also being immediately productive. We hope you enjoy the class!
Python Coding April 26, 2024 Course, Coursera No comments
There are 5 modules in this course
This course will provide you with a basic, intuitive and practical introduction into Probability Theory. You will be able to learn how to apply Probability Theory in different scenarios and you will earn a "toolbox" of methods to deal with uncertainty in your daily life.
The course is split in 5 modules. In each module you will first have an easy introduction into the topic, which will serve as a basis to further develop your knowledge about the topic and acquire the "tools" to deal with uncertainty. Additionally, you will have the opportunity to complete 5 exercise sessions to reflect about the content learned in each module and start applying your earned knowledge right away.
The topics covered are: "Probability", "Conditional Probability", "Applications", "Random Variables", and "Normal Distribution".
You will see how the modules are taught in a lively way, focusing on having an entertaining and useful learning experience! We are looking forward to see you online!
Python Coding April 26, 2024 Python Coding Challenge No comments
print(bool(""))
Answer: False
Explanation: An empty string is considered to be False in a boolean context.
print(1 + "2")
Answer: TypeError: unsupported operand type(s) for +: 'int' and 'str'
Explanation: You cannot add an integer and a string in Python.
print(2 * "2")
Answer: '22'
Explanation: In Python, you can multiply a string by an integer, which will result in the string being repeated that many times.
print(0 == False)
Answer: True
Explanation: In Python, both 0 and False are considered to be False in a boolean context.
print(len("Hello, World!"))
Answer: 13
Explanation: The len() function returns the length of a string, which is the number of characters it contains.
print(1 in [1, 2, 3])
Answer: True
Explanation: The in keyword can be used to check if a value is present in a list.
print({1, 2, 3} & {2, 3, 4})
Answer: {2, 3}
Explanation: The & operator can be used to find the intersection of two sets.
print(1 > 2 > 3)
Answer: False
Explanation: In Python, the > operator has a higher precedence than the and operator, so the expression is evaluated as (1 > 2) and (2 > 3), which is False.
print(1 is 1.0)
Answer: False
Explanation: In Python, the is keyword checks if two variables refer to the same object, not if they have the same value.
print(1 is not 1.0)
Answer: True
Explanation: The is not keyword checks if two variables do not refer to the same object.
Python Coding April 26, 2024 Books, Python No comments
Python Coding April 25, 2024 Python Coding Challenge No comments
What is the output of following Python code?
x = [1, 2, 3]
y = x[:-1]
print(y)
let's go through each part of the code:
Creating the list x:
x = [1, 2, 3]
Here, a list named x is created with three elements: 1, 2, and 3.
Slicing x to create a new list y:
y = x[:-1]
This line uses slicing to create a new list y from x. The slicing expression x[:-1] means to select all elements from x starting from the first element (index 0) up to, but not including, the last element (index -1). In Python, negative indices refer to elements from the end of the list. So, x[:-1] selects all elements of x except for the last one.
Printing the list y:
print(y)
This line prints the list y.
After executing this code, the output of print(y) would be [1, 2]. This is because the last element (3) of x is excluded when creating y using slicing.
Python Coding April 25, 2024 Python Coding Challenge No comments
x = [1, 2, 3]
x.insert(1, 4)
print(x)
Python Coding April 24, 2024 Python Coding Challenge No comments
x = [1, 2, 3]
y = x[:-1]
x[-1] = 4
print('*' * len(y))
Python Coding April 24, 2024 Python No comments
Python Coding April 24, 2024 Python Coding Challenge No comments
Python Coding April 23, 2024 Python Coding Challenge No comments
x = [1, 2, 3]
y = x[:-1]
x[-1] = 4
print(y)
Python Coding April 23, 2024 Python No comments
You can create a tuple by enclosing a sequence of elements within parentheses ().
tuple1 = (1, 2, 3)
tuple2 = ('a', 'b', 'c')
mixed_tuple = (1, 'hello', 3.14)
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You can access elements of a tuple using indexing, just like with lists.
tuple1 = (1, 2, 3)
print(tuple1[0])
print(tuple1[1])
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1
2
Once a tuple is created, you cannot modify its elements.
tuple1 = (1, 2, 3)
# This will raise an error
tuple1[0] = 4
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You can unpack a tuple into individual variables.
tuple1 = ('apple', 'banana', 'cherry')
a, b, c = tuple1
print(a)
print(b)
print(c)
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apple
banana
cherry
Functions in Python can return tuples, allowing you to return multiple values.
def get_coordinates():
x = 10
y = 20
return x, y
x_coord, y_coord = get_coordinates()
print("x coordinate:", x_coord)
print("y coordinate:", y_coord)
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x coordinate: 10
y coordinate: 20
You can iterate over the elements of a tuple using a loop.
tuple1 = ('apple', 'banana', 'cherry')
for fruit in tuple1:
print(fruit)
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apple
banana
cherry
Python Coding April 23, 2024 Python No comments
def selection_sort(arr):
n = len(arr)
for i in range(n):
min_idx = i
for j in range(i+1, n):
if arr[j] < arr[min_idx]:
min_idx = j
arr[i], arr[min_idx] = arr[min_idx], arr[i]
return arr
# Example usage:
arr = [64, 34, 25, 12, 22, 11, 90]
print("Original array:", arr)
sorted_arr = selection_sort(arr)
print("Sorted array:", sorted_arr)
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def quick_sort(arr):
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quick_sort(left) + middle + quick_sort(right)
# Example usage:
arr = [64, 34, 25, 12, 22, 11, 90]
print("Original array:", arr)
sorted_arr = quick_sort(arr)
print("Sorted array:", sorted_arr)
#clcoding.com
The quick_sort function implements the quicksort algorithm, a popular sorting algorithm known for its efficiency. Here's how it works:
Base Case:
If the length of the input array arr is 0 or 1, it is already sorted, so we return the array as is.
Pivot Selection:
Choose a pivot element from the array. In this implementation, the pivot is selected as the element at the middle index (len(arr) // 2).
Partitioning:
Partition the array into three sub-arrays:
left: Contains elements less than the pivot.
middle: Contains elements equal to the pivot.
right: Contains elements greater than the pivot.
Recursion:
Recursively apply the quicksort algorithm to the left and right sub-arrays.
Combine:
Concatenate the sorted left, middle, and right sub-arrays to form the sorted array.
Now, let's walk through the provided example:
arr = [64, 34, 25, 12, 22, 11, 90]
We start with the original array [64, 34, 25, 12, 22, 11, 90].
sorted_arr = quick_sort(arr)
We call the quick_sort function with the array arr and store the sorted array in sorted_arr.
print("Sorted array:", sorted_arr)
We print the sorted array.
Output:
Original array: [64, 34, 25, 12, 22, 11, 90]
Sorted array: [11, 12, 22, 25, 34, 64, 90]
The original array is [64, 34, 25, 12, 22, 11, 90].
After sorting, the array becomes [11, 12, 22, 25, 34, 64, 90], which is printed as the sorted array.
def bubble_sort(arr):
n = len(arr)
for i in range(n):
for j in range(0, n-i-1):
if arr[j] > arr[j+1]:
arr[j], arr[j+1] = arr[j+1], arr[j]
return arr
# Example usage:
arr = [64, 34, 25, 12, 22, 11, 90]
print("Original array:", arr)
sorted_arr = bubble_sort(arr)
print("Sorted array:", sorted_arr)
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def insertion_sort(arr):
for i in range(1, len(arr)):
key = arr[i]
j = i - 1
while j >= 0 and key < arr[j]:
arr[j + 1] = arr[j]
j -= 1
arr[j + 1] = key
return arr
# Example usage:
arr = [64, 34, 25, 12, 22, 11, 90]
print("Original array:", arr)
sorted_arr = insertion_sort(arr)
print("Sorted array:", sorted_arr)
#clcoding.com
Python Coding April 22, 2024 Python Coding Challenge No comments
x = [1, 2, 3]
y = x[:]
x[0] = 4
print(y)
Python Coding April 21, 2024 Python Coding Challenge No comments
x = {"name": "John", "age": 30}
y = x.copy()
x["name"] = "Jane"
print(y["name"])
Python Coding April 21, 2024 Python No comments
import pubchempy as pcp
# Take name as input
chemical_name = input("Enter chemical name: ")
try:
# Search PubChem for the compound by its name
compound = pcp.get_compounds(chemical_name, 'name')[0]
# Display information about the compound
print(f"IUPAC Name: {compound.iupac_name}")
print(f"Common Name: {compound.synonyms[0]}")
print(f"Molecular Weight: {compound.molecular_weight}")
print(f"Formula: {compound.molecular_formula}")
# You can access more properties as needed
except IndexError:
print(f"No information found for {chemical_name}.")
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This code snippet performs a similar task to the previous one but takes the name of a chemical compound as input instead of its chemical formula. Here's an explanation of each part:
import pubchempy as pcp: This line imports the PubChemPy library and aliases it as pcp, allowing us to refer to it more conveniently in the code.
chemical_name = input("Enter chemical name: "): This line prompts the user to input the name of the chemical compound they want to retrieve information about. The input is stored in the variable chemical_name.
try:: This starts a try-except block, indicating that we are going to try a piece of code that might raise an exception, and if it does, we'll handle it gracefully.
compound = pcp.get_compounds(chemical_name, 'name')[0]: This line uses the get_compounds function from the PubChemPy library to search for compounds matching the provided chemical name. The function takes two arguments: the chemical name (chemical_name) and a string indicating that we are searching by name ('name'). Since get_compounds returns a list of compounds, we select the first compound using [0] and assign it to the variable compound.
Printing compound information:
print(f"IUPAC Name: {compound.iupac_name}"): This line prints the IUPAC (International Union of Pure and Applied Chemistry) name of the compound.
print(f"Common Name: {compound.synonyms[0]}"): This line prints the common name of the compound, using the first synonym available.
print(f"Molecular Weight: {compound.molecular_weight}"): This line prints the molecular weight of the compound.
print(f"Formula: {compound.molecular_formula}"): This line prints the molecular formula of the compound.
except IndexError:: This block catches the IndexError exception, which occurs if no compound is found for the provided chemical name.
print(f"No information found for {chemical_name}."): If an IndexError occurs, this line prints a message indicating that no information was found for the provided chemical name.
In summary, this code allows the user to input the name of a chemical compound, searches for the corresponding compound in the PubChem database using PubChemPy, and displays information about the compound if found. If no information is found for the provided name, it prints a corresponding message.
import pubchempy as pcp
# Define the chemical formula
chemical_formula = input("Enter chemical Formula : ")
try:
# Search PubChem for the compound by its chemical formula
compound = pcp.get_compounds(chemical_formula, 'formula')[0]
# Display information about the compound
print(f"IUPAC Name: {compound.iupac_name}")
print(f"Common Name: {compound.synonyms[0]}")
print(f"Molecular Weight: {compound.molecular_weight}")
print(f"Formula: {compound.molecular_formula}")
# You can access more properties as needed
except IndexError:
print(f"No information found for {chemical_formula}.")
#clcoding.com
This code snippet aims to retrieve information about a chemical compound using its chemical formula. Here's a breakdown of each part:
import pubchempy as pcp: This line imports the PubChemPy library and aliases it as pcp, allowing us to refer to it more conveniently in the code. PubChemPy is a Python library for accessing chemical information from the PubChem database.
chemical_formula = input("Enter chemical Formula : "): This line prompts the user to input the chemical formula of the compound they want to retrieve information about. The input is stored in the variable chemical_formula.
try:: This starts a try-except block, indicating that we are going to try a piece of code that might raise an exception, and if it does, we'll handle it gracefully.
compound = pcp.get_compounds(chemical_formula, 'formula')[0]: This line uses the get_compounds function from the PubChemPy library to search for compounds matching the provided chemical formula. The function takes two arguments: the chemical formula (chemical_formula) and a string indicating that we are searching by formula ('formula'). Since get_compounds returns a list of compounds, we select the first compound using [0] and assign it to the variable compound.
Printing compound information:
print(f"IUPAC Name: {compound.iupac_name}"): This line prints the IUPAC (International Union of Pure and Applied Chemistry) name of the compound.
print(f"Common Name: {compound.synonyms[0]}"): This line prints the common name of the compound, using the first synonym available.
print(f"Molecular Weight: {compound.molecular_weight}"): This line prints the molecular weight of the compound.
print(f"Formula: {compound.molecular_formula}"): This line prints the molecular formula of the compound.
except IndexError:: This block catches the IndexError exception, which occurs if no compound is found for the provided chemical formula.
print(f"No information found for {chemical_formula}."): If an IndexError occurs, this line prints a message indicating that no information was found for the provided chemical formula.
In summary, this code allows the user to input a chemical formula, searches for the corresponding compound in the PubChem database using PubChemPy, and displays information about the compound if found. If no information is found for the provided formula, it prints a corresponding message.
Python Coding April 20, 2024 Python Coding Challenge No comments
x = {"a": 1, "b": 2}
y = {"b": 3, "c": 4}
z = {**x, **y}
print(z)
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)
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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.
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