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Articles by Prasad Naik
Page 2 of 4
Draw a filled polygon using the OpenCV function fillPoly()
In this tutorial, we will learn how to draw a filled polygon using OpenCV's fillPoly() function. This function fills a polygon defined by a set of vertices with a specified color. Syntax cv2.fillPoly(image, pts, color) Parameters The fillPoly() function accepts the following parameters ? image ? The input image on which to draw the polygon pts ? Array of polygon vertices (points) color ? Fill color of the polygon in BGR format Algorithm Step 1: Import cv2 and numpy Step 2: Define the polygon vertices (endpoints) Step 3: ...
Read MoreHow to create a simple screen using Tkinter?
Tkinter is Python's standard GUI library for creating desktop applications. We will create a simple screen using the Tkinter library to demonstrate the basic setup. Algorithm Step 1: Import tkinter. Step 2: Create an object of the tkinter class. Step 3: Display the screen using mainloop(). Example Code Here's how to create a basic Tkinter window ? import tkinter as tk # Create the main window window = tk.Tk() # Set window title window.title("Simple Tkinter Screen") # Set window size window.geometry("400x300") # Start the event loop window.mainloop() ...
Read MorePython program to display various datetime formats
The datetime module in Python provides powerful classes for manipulating dates and times. The strftime() method allows you to format dates and times in various ways using format codes. Basic Date Formatting Let's start with common date formats using format codes ? import datetime today = datetime.date.today() print("Day of the week:", today.strftime("%A")) print("Week number:", today.strftime("%W")) print("Day of the year:", today.strftime("%j")) Day of the week: Monday Week number: 47 Day of the year: 332 Various Date and Time Formats Here are more formatting options with complete date and time ...
Read MoreHow to use regular expressions (Regex) to filter valid emails in a Pandas series?
A regular expression is a sequence of characters that define a search pattern. In this program, we will use these regular expressions to filter valid and invalid emails in a Pandas series. We will define a Pandas series with different emails and check which email is valid using Python's re library for regex operations. Email Validation Regex Pattern The regex pattern for email validation contains several components ? ^: Anchor for the start of the string [a-z0-9]: Character class to match lowercase letters and digits [\._]?: Optional dot or underscore character @: Required @ symbol ...
Read MorePandas program to convert a string of date into time
In this program, we will convert date strings like "24 August 2020" into datetime format using Pandas. The to_datetime() function automatically parses various date string formats and converts them to standardized datetime objects. Algorithm Step 1: Define a Pandas series containing date strings. Step 2: Convert these date strings into datetime format using to_datetime(). Step 3: Print the results. Example Let's convert different date string formats to datetime ? import pandas as pd series = pd.Series(["24 August 2020", "25 December 2020 20:05"]) print("Original Series:") print(series) datetime_series = pd.to_datetime(series) print("DateTime Format:") ...
Read MoreFinding the multiples of a number in a given list using NumPy
Finding multiples of a number in a list is a common task in data analysis. NumPy provides efficient methods to identify multiples using vectorized operations and built-in functions like argwhere() and modulo operations. Using Basic Loop Method The traditional approach uses a loop to check each element ? import numpy as np listnum = np.arange(1, 20) multiples = [] n = 5 print("NumList:", listnum) for num in listnum: if num % n == 0: multiples.append(num) ...
Read MoreHow to calculate the frequency of each item in a Pandas series?
In this program, we will calculate the frequency of each element in a Pandas series. The function value_counts() in the pandas library helps us to find the frequency of elements. Algorithm Step 1: Define a Pandas series. Step 2: Print the frequency of each item using the value_counts() function. Example Code import pandas as pd series = pd.Series([10, 10, 20, 30, 40, 30, 50, 10, 60, 50, 50]) print("Series:", series) frequency = series.value_counts() print("Frequency of elements:", frequency) Output Series: 0 10 1 ...
Read MoreHow to get the nth percentile of a Pandas series?
A percentile is a statistical measure that indicates the value below which a certain percentage of observations fall. In Pandas, you can calculate the nth percentile of a series using the quantile() method. Syntax series.quantile(q) Parameters: q − The quantile value between 0 and 1 (percentile/100) Example Here's how to calculate the 50th percentile (median) of a Pandas series ? import pandas as pd # Create a Pandas series series = pd.Series([10, 20, 30, 40, 50]) print("Series:") print(series) # Calculate 50th percentile percentile_50 = series.quantile(0.5) print(f"The 50th ...
Read MoreComparing two Pandas series and printing the the difference
In this article, we will compare two Pandas series and print the differences between them. By difference, we mean the index positions where elements did not match, along with the actual values from both series. What is Series Comparison? Pandas provides the compare() method to identify differences between two series. This method returns a DataFrame showing only the positions where values differ, with columns representing each series. Basic Example Let's start with a simple comparison between two series ? import pandas as pd s1 = pd.Series([10, 20, 30, 40, 50, 60]) s2 = ...
Read MorePrint the standard deviation of Pandas series
In this program, we will find the standard deviation of a Pandas series. Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. Syntax Series.std(axis=None, skipna=True, level=None, ddof=1, numeric_only=None) Parameters The std() method accepts several parameters: ddof − Delta Degrees of Freedom (default is 1) skipna − Exclude NaN values (default is True) axis − Not applicable for Series Example Let's calculate the standard deviation of a Pandas series using the std() function: ...
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