Posts

Featured Post

The Quick and Easy Way to Analyze Numpy Arrays

Image
The quickest and easiest way to analyze NumPy arrays is by using the numpy.array() method. This method allows you to quickly and easily analyze the values contained in a numpy array. This method can also be used to find the sum, mean, standard deviation, max, min, and other useful analysis of the value contained within a numpy array. Sum You can find the sum of Numpy arrays using the np.sum() function.  For example:  import numpy as np  a = np.array([1,2,3,4,5])  b = np.array([6,7,8,9,10])  result = np.sum([a,b])  print(result)  # Output will be 55 Mean You can find the mean of a Numpy array using the np.mean() function. This function takes in an array as an argument and returns the mean of all the values in the array.  For example, the mean of a Numpy array of [1,2,3,4,5] would be  result = np.mean([1,2,3,4,5])  print(result)  #Output: 3.0 Standard Deviation To find the standard deviation of a Numpy array, you can use the NumPy std() function. This function takes in an array as a par

How to Delete an Item from a Set in Python: Best Example

Image
Set is a built-in data type in Python. Furthermore, it is an unordered collection without duplicate items. Here are the two methods that explain to delete an item from a Set. Methods to delete an item from a Set discard remove Discrd Vs. Remove discard() will not raise an error if the item to remove does not exist. The remove() will raise an error if the item does not exist. Explanation to discard and remove methods Python program: #Prints all the Set items food = {"pasta", "burger", "hot dog", "pizza"} print(food) # Prints the Set items without pasta food.discard("pasta") print(food) # Prints the Set items without burger and pasta food.remove("burger") print(food) # The next two lines try to remove an item that isn't in the set! food.discard("pasta")  # this will not report an error food.remove("pasta")   # this will report an error The output: {'pasta', 'burger', 'pizza', '

How to Access Dictionary Key-Value Data in Python

Image
Use for-loop to read dictionary data in python. Here's an example of reading dictionary data. It's helpful to use in real projects. Python program to read dictionary data yearly_revenue = {    2017 : 1000000,    2018 : 1200000,    2019 : 1250000,    2020 : 1100000,    2021 : 1300000,  } total_income = 0 for year_id in yearly_revenue.keys() :   total_income+=yearly_revenue[year_id]   print(year_id, yearly_revenue[year_id]) print(total_income) print(total_income/len(yearly_revenue)) Output 2017 1000000 2018 1200000 2019 1250000 2020 1100000 2021 1300000 5850000 1170000.0 ** Process exited - Return Code: 0 ** Press Enter to exit the terminal Explanation The input is dictionary data. The total revenue sums up for each year. Notably, the critical point is using the dictionary keys method. References Python in-depth and sample programs

How to Decode Python Exception Messages Like a Pro

Image
While developing python programs, you might see exception messages from python. Here's an explanation to understand each part of the message. Here're tips on how to understand python exceptions. You can find two kinds of exceptions. These are StandardError and StopIteration errors. Here is a chart that shows the types of python errors. Python exceptions class Python exceptions are basically three parts. Reading an error message produced by  Python is not very difficult . The error type, the error description, and the traceback. Understand the python exception message The Error Type There are so many in-built exception types in python. Here is the command to get all the exception types: [x for x in dir(__builtins__) if 'Error' in x] The Error description The text message right after the error type gives us a description of what exactly the problem was. These descriptions are sometimes very accurate, sometimes not. Sample error Traceback (most recent call last):       Fil

Python Tuples: An Overview with Code Examples

Image
Tuple in python is one of the streaming datasets. The other streaming datasets are List and Dictionary. Operations that you can perform on it are shown here for your reference. Writing tuple is easy. It has values of comma separated, and enclosed with parenthesis '()'. The values in the tuple are immutable, which means you cannot replace with new values. #1. How to create a tuple Code: Tuple example my_tuple=(1,2,3,4,5) print(my_tuple) Output: (1, 2, 3, 4, 5) ** Process exited - Return Code: 0 ** Press Enter to exit terminal #2. How to read tuple values Code: print(my_tuple[0]) Output: 1 ** Process exited - Return Code: 0 ** Press Enter to exit terminal #3. How to add two tuples Code: a=(1,6,7,8) c=(3,4,5,6,7,8) d=print(a+c) Output: (1, 6, 7, 8, 3, 4, 5, 6, 7, 8) ** Process exited - Return Code: 0 ** Press Enter to exit terminal #4.  How to count tuple values Here the count is not counting values; count the repetition of a given value. Code: sample=(1, 6, 7, 8, 3, 4, 5, 6, 7, 8

How to Use Relational Operators in Python

Image
If you are working with numeric values in Python, you may find it helpful to use comparison operators to compare them. In this article, we'll explore eight different comparison operators that you can use, and provide examples of how each one works. So if you're looking to compare numeric values in Python, read on to learn more! Python comparison operators list < <= > >= == != Is is not Sample python program Assign 23 to a and 11 to b. Then, apply all the comparison operators. The output is self-explanatory. Bookmark this article to refresh when you are in doubt. Example a = 23 b = 11 print("Is a greater than b?", a > b) #greater than print("Is a less than b?", a < b) #less than print("Is a greater or equal to b?", a >= b) #greater or equal print("Is a less or equal to b?", a <= b) #less or equal print("Is a equal to b (option 1)?", a == b) #test for equality print("Is a equal to b (option 2)?",

Python Program: JSON to CSV Conversion

Image
JavaScript object notion is also called JSON file, it's data you can write to a CSV file. Here's a sample python logic for your ready reference.  You can write a simple python program by importing the JSON, and CSV packages. This is your first step. It is helpful to use all the JSON methods in your python logic. That means the required package is JSON. So far, so good. In the next step, I'll show you how to write a Python program. You'll also find each term explained. What is JSON File JSON is key value pair file. The popular use of JSON file is to transmit data between heterogeneous applications. Python supports JSON file. What is CSV File The CSV is comma separated file. It is popularly used to send and receive data. How to Write JSON file data to a CSV file Here the JSON data that has written to CSV file. It's simple method and you can use for CSV file conversion use. import csv, json json_string = '[{"value1": 1, "value2": 2,"value3

Numpy Array Vs. List: What's the Difference

Image
Here are the differences between List and NumPy Array. Both store data, but technically these are not the same. You'll find here where they differ from each other. Python Lists Here is all about Python lists: Lists can have data of different data types. For instance, data = [3, 3.2, 4.6, 6, 6.8, 9, “hello”, ‘a’] Operations such as subtraction, multiplying, and division allow doing through loops Storage space required is more, as each element is considered an object in Python Execution time is high for large datasets Lists are inbuilt data types How to create array types in Python NumPy Arrays Here is all about NumPy Arrays: Numpy arrays are containers for storing only homogeneous data types. For example: data= [3.2, 4.6, 6.8]; data=[3, 6, 9]; data=[‘hello’, ‘a’] Numpy is designed to do all mathematical operations in parallel and is also simpler than Python Numpy storage space is very much less compared to the list due to the practice of homogeneous data type Execution time is