Beautiful Python Code

XRP is a cryptocurrency that powers the Ripple network, which is a system for cross-border payments. Predicting XRP price can be done using Python and machine learning techniques, such as regression or neural networks. Here is an example of a code snippet that uses a neural network to predict XRP price based on historical data: # Import libraries import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.preprocessing import MinMaxScaler from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, LSTM # Load data df = pd.read_csv('XRP-USD.csv') df = df.dropna() df = df[['Close']] # Scale data scaler = MinMaxScaler(feature_range=(0,1)) df = scaler.fit_transform(df) # Split data into train and test sets train_size = int(len(df) * 0.8) test_size = len(df) - train_size train_data, test_data = df[0:train_size,:], df[train_size:len(df),:] # Create x_train and y_train data sets x_train = [] y_train = [] for i in range(6...

Cryptocurrency price prediction is the task of forecasting the future prices of a digital currency based on historical data and various factors. There are various methods and libraries in Python that can perform this task, such as machine learning, time series analysis, AutoTS, etc. One example is using a machine learning algorithm to predict the future prices of Dogecoin, a popular cryptocurrency . Here is a code snippet that shows how to use it: # Importing libraries import pandas as pd import numpy as np from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split # Reading data from csv file data = pd.read_csv("DOGE-USD.csv") # Selecting features and target X = data[["Open", "High", "Low", "Volume"]] y = data["Close"] # Splitting data into train and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) # Creating and fitting a linear regression model m...

Language detection is the task of identifying the natural language of a given text. There are various modules and libraries in Python that can perform this task, such as langdetect, textblob, langrid, etc. One example is using the langdetect library, which is a port of Google’s language-detection library that supports 55 languages. Here is a code snippet that shows how to use it: # Importing langdetect module from langdetect import detect # Text for language detection text = "Bonjour le monde" # Detecting the language language = detect(text) # Printing the language print(language) # The output of this code is: fr This means that the text is in French (fr) according to the ISO 639-1 codes. Another example is using the textblob library, which is a natural language processing toolkit that also supports language detection. Here is a code snippet that shows how to use it: # Importing textblob module from textblob import TextBlob # Text for language detection text = "Hola mu...

The isalpha() method is a string method that returns True if all the characters in the string are alphabetic letters (a-z). For example: name = "Alice" print(name.isalpha()) # prints True The method returns False if the string contains any non-alphabetic characters, such as spaces, numbers, punctuation marks, symbols, etc. For example: name = "Alice 123" print(name.isalpha()) # prints False If you have any questions about this code, you can drop a line in comment .

Data analysis on Instagram post reach is a useful way to understand how your posts perform on the social media platform and what factors influence their engagement. You can use Python to scrape, analyze and visualize Instagram data using various libraries and tools. Here are some examples of data analysis on Instagram post reach using Python: Using pandas, numpy, matplotlib, seaborn and plotly.express libraries to analyze the reach of an Instagram account based on followers count, hashtags used, caption length and time of posting. # Import libraries import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import plotly.express as px # Read the dataset data = pd.read_csv("instagram_reach.csv") # Explore the dataset data.head()       # Plot the correlation matrix sns.heatmap(data.corr(), annot=True) plt.show() # Plot a scatter plot of followers vs likes px.scatter(data, x="Followers", y="Likes") Using sklearn librar...

Scraping stock prices from Yahoo Finance is a common task for data analysis or web scraping projects. There are different ways to do it using Python, such as using requests and BeautifulSoup libraries, using yfinance library , or using Scrapingdog API. Here is an example of using requests and BeautifulSoup libraries to scrape stock prices from Yahoo Finance: # Import libraries import requests from bs4 import BeautifulSoup   # Define the URL of the page to scrape url = "https://finance.yahoo.com/quote/AAPL?p=AAPL"   # Make a GET request to the URL response = requests.get(url)   # Parse the HTML content with BeautifulSoup soup = BeautifulSoup(response.text, "html.parser") # Find the span element that contains the stock price price = soup.find("span", class_="Trsdu(0.3s) Fw(b) Fz(36px) Mb(-4px) D(ib)") # Print the stock price print(price.text) If you have any questions about this code, you can drop a line in comment .

Python collections are data structures that store multiple items of the same or different types. There are four built-in collection types in Python: list, tuple, set and dictionary. Additionally, there is a module called collections that provides more specialized collection types such as Counter, OrderedDict, defaultdict and namedtuple. Here are some examples of using Python collections: List: A list is a collection that is ordered and changeable. The Python lists are defined with square brackets. # Create a list of fruits fruits = ["apple", "banana", "cherry"] # Print the list print(fruits) # Output: ['apple', 'banana', 'cherry'] Tuple: A tuple is a collection that is ordered and unchangeable. The Python tuples are written with round brackets. # Create a tuple of colors colors = ("red", "green", "blue") # Print the tuple print(colors) # Output: ('red', 'green', 'blue') Set: A...

Itertools is a Python module that provides a collection of functions for creating and working with iterators. Iterators are objects that can be iterated over, such as lists, tuples, dictionaries, sets, etc. Itertools offers various tools for manipulating and combining iterators in efficient ways. Some of the functions in Itertools are: itertools.product(): This function takes any number of iterables as arguments and returns an iterator over tuples in the Cartesian product. For example: # Import itertools import itertools # Define two iterables A = [1, 2] B = ["a", "b"] # Get the Cartesian product of A and B product = itertools.product(A, B) # Print out the product for p in product:     print(p) # This will output: (1, 'a') (1, 'b') (2, 'a') (2, 'b') itertools.islice(): This function takes an iterable and returns an iterator that returns selected elements from the iterable based on start, stop and step parameters. For example: # Im...

PyForest is a Python library that automates imports for data science and machine learning projects. PyForest allows you to use your favorite Python commands without writing import statements. PyForest will automatically import the necessary modules for you and add the import statements to the first cell of your Jupyter notebook. PyForest comes with a set of pre-defined imports for common data science and machine learning tasks. For example, it already includes pandas as pd, numpy as np, seaborn as sns, matplotlib.pyplot as plt, OneHotEncoder from sklearn and many more. You can see an overview of all currently available imports here:  https://github.com/8080labs/pyforest/blob/master/src/pyforest/_imports.py To use PyForest in your Jupyter notebook, you need to install it first using pip:   pip install pyforest Then you need to install its Jupyter extension: jupyter nbextension enable --py pyforest --sys-prefix After that, you can use PyForest in your notebook like this: ...

Resource handling is the process of acquiring, using and releasing resources such as files, sockets, locks, databases, etc. in your program. Resource handling can be tricky because you need to ensure that: You acquire the resources properly and handle any errors or exceptions that may occur. You use the resources efficiently and avoid wasting memory or CPU time. You release the resources timely and correctly and avoid resource leaks or corruption. Context managers are a feature of Python that simplify resource handling by providing a mechanism for setup and teardown of resources easily. Context managers allow you to control what to do when objects are created or destroyed. To use context managers in Python, you need to use the with statement that evaluates an expression that returns an object that performs context management. The object must have two special methods: enter() and exit() that define what to do before and after the execution of a block of code. For example: # Open a fil...

Python is a flexible and dynamic programming language that allows you to modify or extend its features without changing the original code. Here are some common ways to add new functionalities smartly in Python: You can use setattr() to dynamically add methods or attributes to a class or an object. For example: class Foo:     def __init__(self, v):         self.v = v def my_new_method(self):     print("self.v =", self.v) # Add a new method to the Foo class setattr(Foo, "print_v", my_new_method) # Call the new method on an instance of Foo Foo(5).print_v() This will print self.v = 5. You can use decorators to wrap a function or a class with another function that adds some extra functionality. For example: def average(func):     # A decorator function that calculates the average of two numbers     def wrapper(*args, **kwargs):         # Call the original functio...

Debugging is the process of finding and fixing errors in your code. Print statements are a common way to debug by printing out values or messages to the console. However, print statements have some limitations and drawbacks: They can clutter up your code and make it less readable. They can affect the performance of your program by slowing it down or changing its behavior. They can be hard to manage when you have multiple print statements in different modules or functions. They can be lost or overwritten when you run your program in different environments or platforms. Logging is a better alternative to print statements for debugging because it allows you to: Control the level of detail and format of your output by using different log levels (such as DEBUG, INFO, WARNING, ERROR, CRITICAL) and handlers (such as StreamHandler, FileHandler, etc.). Filter out unwanted messages by using loggers (such as root logger, named logger) and filters. Record useful information such as timestamps, mo...

Python is a popular programming language that has many features and libraries for working with sequences. A sequence is an ordered collection of items that can be accessed by index or iterated over. There are different ways to generate sequences in Python depending on your requirements. Here are some common methods: You can use range() to create a sequence of numbers with a fixed step size. For example: range(1, 10, 2) will generate [1, 3, 5, 7, 9] You can use list comprehension to create a sequence based on an expression or a condition. For example: [x**2 for x in range(1, 6)] will generate [1, 4, 9, 16, 25] You can use generators and yield to create a sequence lazily without storing it in memory. For example: def fib():     # A generator function that yields the Fibonacci sequence     a = 0     b = 1     while True:         yield a         a, b = b, a + b...

In Python, mutable objects are those that can be changed after they are created, such as lists or dictionaries. Immutable objects are those that cannot be changed after they are created, such as strings or tuples. To cast a mutable object to an immutable one, you can use built-in functions that return a new immutable object with the same content as the original mutable object. For example, you can use tuple() to cast a list to a tuple: my_list = [1, 2, 3] # mutable my_tuple = tuple(my_list) # immutable print(my_tuple) # (1, 2, 3) # Mutable List lst = [1,2,3,4,5] lst[0] = 6 print(lst) # output [6,2,3,4,5] # Converting It to Immutable lst = [1,2,3,4,5] lst2 = tuple(lst) lst[0] = 6 # output TypeError: 'tuple' object does not support item assignment If you have any questions about this code, you can drop a line in comment .

A for else statement in Python is a loop that executes a block of code for each item in an iterable object, and then executes another block of code if the loop finishes normally without encountering a break statement. For example, you can use a for else statement to search for a person with a certain name in a list of people, and print a message if no such person is found: people = [{'name': 'John', 'age': 25}, {'name': 'Jane', 'age': 22}, {'name': 'Peter', 'age': 30}, {'name': 'Jenifer', 'age': 28}] for person in people:     if person['name'] == 'Alice':         print('Found Alice!')         break else:     print('Alice not found.')  for x in range(9):   print(x) else:   print("Finally finished!") If you have any questions about this code, you can drop a line in comment .

Python allows you to write expressions that can be evaluated in one line of code. An expression is a combination of values, variables, operators, and functions that produces a result. For example: # A simple expression x = 2 + 3 * 4 # Output: x = 14 # A complex expression y = (x ** 2 + math.sqrt(x)) / (x - 1) # Output: y = 15.64575131106459 You can also use conditional expressions to write one-line if-else statements. For example: # A conditional expression z = "Even" if x % 2 == 0 else "Odd" # Output: z = "Even" You can also use list comprehensions to create lists with one-line expressions. For example: # A list comprehension w = [i * i for i in range(10)] # Output: w = [0, 1, 4, 9, 16, 25, 36, 49, 64, 81] If you have any questions about this code, you can drop a line in comment .

Python has two different division operators: / and //. The single forward slash / operator is known as float division, which returns a floating point value. The double forward slash // operator is known as integer division, which returns the quotient without the remainder. For example: # Float division 5 / 2 = 2.5 # Integer division 5 // 2 = 2 In Python 2.x, there is no difference for integers unless you perform a from __future__ import division, which causes Python 2.x to adopt the Python 3.x behavior The divmod() function in Python takes two numbers and returns a tuple containing their quotient and remainder. For example, divmod(8, 3) returns (2, 2) , which means 8 divided by 3 is 2 with a remainder of 2. Here are some examples of using divmod() with different types of arguments: # Integer arguments print(divmod(9, 3)) # Output: (3, 0) print(divmod(5, 5)) # Output: (1, 0) print(divmod(3, 8)) # Output: (0, 3) # Float arguments print(divmod(8.0, 3)) # Output: (2.0, 2.0) print(divmod(...

There are different ways to print readable results in Python depending on what kind of data you have. For example: If you have a complex data structure such as a nested dictionary or list, you can use pprint module to format it nicely with indentation and line breaks. For example: import pprint data = {"name": "Alice", "age": 25, "hobbies": ["reading", "coding", "gaming"]} pprint.pprint(data) # Output: {'age': 25,  'hobbies': ['reading', 'coding', 'gaming'],  'name': 'Alice'} If you have a numerical data such as a matrix or an array, you can use numpy module to print it with precision and alignment. For example: import numpy as np matrix = np.array([[1.2345, 2.3456], [3.4567, 4.5678]]) np.set_printoptions(precision=2) print(matrix) # Output: [[1.23 2.35] [3.46 4.57]] If you have a text data such as a string or a paragraph, you can use f-strings to format it w...

A static method is a method that does not depend on an instance or a class. You can define a static method by using @staticmethod decorator before the method definition. For example: class A:     @staticmethod     def add(a, b):         return a + b result = A.add(12, 12) # call static method by class name print(result) A class method takes cls as the first parameter while a static method needs no specific parameters. A class method can access or modify the class state while a static method can’t access or modify it. For example: class A:     count = 0 # class variable       @classmethod     def increment(cls): # class method         cls.count += 1 # access class variable     @staticmethod     def add(a, b): # static method         return a + b # no access to class varia...

Python raises its own built-in exceptions when it detects an error in a program at runtime. You can use a try statement to catch and recover from an exception. You can also use except, else, finally and raise clauses to handle different types of exceptions. An Exception is a condition that occurs during the execution of the program due to some reason and interrupts the execution. To handle exceptions in python we use try and except block. try block contains the code that needs to be executed and except block contains the code that will execute if some error occurred in the try block. # Exception a = 5 b = 0 print(a/b) # ZeroDivisionError: division by zero # Exception Handling try:     a = 5     b = 0     print(a/b) except:     print("Can't Divide Number With 0") # output Can't Divide Number With 0 If you have any questions about this code, you can drop a line in comment .

It is better to use dict.get(‘key’) than dict[‘key’] when accessing values in a dictionary in Python. This is because dict.get(‘key’) returns None if the key does not exist, while dict[‘key’] raises a KeyError exception.  Most Python developers are in a habit of using square brackets when accessing an element from a data structure like Dictionaries. There is no issue with square brackets but when it comes to a value that doesn’t exit it shows an ugly error. Now, to save yourself from that error you can use get method. data_dict = {a:1, b:2,c:3} print(data_dict['d']) # KeyError: 'd' print(data_dict.get('d')) # None If you have any questions about this code, you can drop a line in comment .

To calculate the execution time of a Python program, you can use one of these methods: The time() function from the time module which returns the current system time. You can call it before and after your code and subtract them to get the elapsed time. The clock() function from the time module which returns the CPU execution time. You can use it similarly to time() . The datetime.now() function from the datetime module which returns the current date and time. You can use it similarly to time() . The perf_counter() function from the time module which returns a high-resolution performance counter. You can use it similarly to time() . The timeit() function from the timeit module which measures the execution time of small code snippets by running them multiple times. Here is an example of using each method to measure the execution time of a simple loop: import time import datetime import timeit # Using time() start = time.time() for i in range(1000000):   pass end = time.time() pr...

To apply a function to each element of a list in Python, you can use one of these methods: The map() function which takes a function and one or more iterables as arguments and returns a map object that you can convert to a list using list() . A list comprehension which creates a new list by applying an expression to each element of an existing list. A for loop which iterates over each element of a list and applies a function to it. Here is an example of each method using the upper() function from the string module: from string import upper mylis = ['this is test', 'another test'] # Using map() mapped = map(upper, mylis) print(list(mapped)) # Using list comprehension comp = [upper(x) for x in mylis] print(comp) # Using for loop for i in range(len(mylis)):   mylis[i] = upper(mylis[i]) print(mylis) The output of each method is: ['THIS IS TEST', 'ANOTHER TEST'] ['THIS IS TEST', 'ANOTHER TEST'] ['THIS IS TEST', 'ANOTHER TEST'...

Python one-liner functions are functions that can be written in a single line of code. They are often used to perform simple tasks or expressions without defining a named function. One way to create a one-liner function is to use the lambda keyword which creates an anonymous function that takes some arguments and returns a value. For example: # A lambda function that returns the square of a number square = lambda x: x**2 print(square(5)) # 25 # A lambda function that checks if a number is even is_even = lambda x: x%2 == 0 print(is_even(4)) # True Another way to create a one-liner function is to use the def keyword with a colon and an expression after it. This creates a named function that returns the value of the expression. For example: # A one-liner function that returns the sum of two numbers def add(x, y): return x + y print(add(3, 4)) # 7 # A one-liner function that reverses a string def reverse(s): return s[::-1] print(reverse("hello")) # olleh Python offers a one-l...

One way to take multiple inputs from users in Python is to use the split() method which breaks the given input by a specified separator (such as space, comma, etc.) and returns a list of strings. For example: # Taking multiple inputs separated by space a, b, c = input("Enter three numbers: ").split() # a, b and c are strings # Taking multiple inputs separated by comma x, y, z = input("Enter three words: ").split(",") # x, y and z are strings Another way to take multiple inputs from users in Python is to use list comprehension which creates a list of values based on an expression. For example: # Taking multiple integer inputs separated by space a = [int(x) for x in input("Enter four numbers: ").split()] # a is a list of integers # Taking multiple string inputs separated by comma b = [str(y) for y in input("Enter four words: ").split(",")] # b is a list of strings Python provides a simpler way to take multiple inputs using one...

There are several ways to count the occurrence of elements in a Python list. One way is to use the list.count() method which takes an element as an argument and returns the number of times it appears in the list. For example: a = [1, 2, 3, 4, 1, 2, 1] b = a.count(1) # b is 3 c = a.count(5) # c is 0 Another way is to use a loop and a counter variable to iterate over the list and increment the counter whenever the element is found. For example: a = [1, 2, 3, 4, 1, 2, 1] x = 1 count = 0 for ele in a:     if ele == x:         count += 1 # count is 3  A third way is to use a dictionary or a defaultdict to store the elements as keys and their counts as values. For example: from collections import defaultdict a = [1, 2, 3, 4, 1 ,2 ,1] occurrence = defaultdict(lambda:0) for ele in a:     occurrence[ele] += 1 # occurrence is {1:3 ,2:2 ,3:1 ,4:1} It is good to have an idea of the number of times elements occurred in your data...

You can break a long line of Python code into multiple lines using either parentheses () or backslash \. For example: # Using parentheses a = (1 + 2 + 3 + 4 - 5 * 2      + 6 * 3 - 7 * 4) # Using backslash b = 1 + 2 + 3 + 4 - \     5 * 2 + \     6 * 3 - \     7 * 4 Both a and b will have the same value. The preferred way of breaking long lines is using parentheses as they are more readable and less error-prone than backslashes. You can also use parentheses to break long strings, if statements, function calls, etc. One of the biggest reasons a code becomes unreadable is because of the long file address, links, or list elements. url = 'https://medium.com/pythoneers/10-underrated-python-packages-every-data-scientist-should-know-86b4355cc35e' You can change the point of wrap with the help of a backslash \ url = 'https://medium.com/pythoneers/'\        '10-underrated-python-packages-every-'\  ...

Python slicing is a technique to access a range of elements in a list, tuple, string or any other sequence type. You can use the colon (:) operator to specify the start and end index of the slice, as well as an optional step size. For example: a = [1, 2, 3, 4, 5] b = a[1:4] # b is [2, 3, 4] c = a[0:5:2] # c is [1, 3, 5] d = a[::-1] # d is [5, 4, 3, 2 ,1] One advantage of Python slicing is that it allows you to access multiple elements in one line of code without using loops or other methods. Another advantage is that it supports negative indexing and steps which can be useful for reversing or skipping elements. # Checking For Palindrome name = "wow" print(name==name[::-1]) # output True # Retrieving Even Numbers From a Natural Sequence natural_numbers = [1,2,3,4,5,6,7,8,9,10] even_numbers = natural_numbers[1::2] print(even_numbers) # output [2,4,6,8,10] If you have any questions about this code, you can drop a line in comment.

Iterating over multiple lists professionally in Python means using efficient and elegant ways to loop over two or more lists at the same time. There are different methods to achieve this, depending on your needs and preferences. One common method is to use the built-in zip() function, which takes two or more iterables (such as lists) and returns an iterator of tuples, where each tuple contains one element from each iterable. For example, if you have two lists L1 = [1, 2, 3] and L2 = [‘a’, ‘b’, ‘c’], you can write for x, y in zip(L1, L2): to iterate over both lists simultaneously. The zip() function stops when the shortest iterable is exhausted. Another method is to use the itertools module, which provides various functions for working with iterators. For example, you can use itertools.chain() to concatenate multiple iterables into one long iterable, or itertools.cycle() to repeat an iterable indefinitely. You can also use itertools.zip_longest() (or itertools.izip_longest() in ...

Shorter names in Python can refer to different things, such as variable names, function names, module names, or documentation names. Depending on the context, shorter names may have some advantages or disadvantages. For variable names, shorter names can save some typing and space, but they can also reduce readability and clarity. For example, x and y may be fine for simple mathematical expressions, but they may not convey much meaning for complex algorithms or data structures. A good practice is to use descriptive and consistent variable names that follow the PEP 8 style guide. For function names, shorter names can make the code more concise and elegant, but they can also cause confusion or ambiguity if they are not well-defined or documented. For example, len() and sum() are built-in functions that have clear and simple meanings, but func() or do_something() may not be very informative or helpful. A good practice is to use meaningful and consistent function names that follow the P...

The _ operator in Python can have different meanings depending on the context. Here are some common uses of the _ operator: In the interactive interpreter, _ represents the result of the last expression evaluated. For example, if you type 2 + 3 and press enter, you will see 5 as the output. Then, if you type _ * 2 and press enter, you will see 10 as the output, because _ holds the value of 5. In a loop or a list comprehension, _ is often used as a placeholder for a variable that is not needed or used. For example, if you want to create a list of ten zeros, you can write [0 for _ in range(10)]. In internationalization (i18n) and localization (l10n) modules such as gettext, _ is used as an alias for a function that translates a string into another language. For example, if you write print(_(“Hello”)) and have a translation file that maps “Hello” to “Hola”, you will see “Hola” as the output. In some naming conventions, such as PEP 8 style guide for Python code, _ is used as a prefix for p...