Append Data from Excel File to txt File[Update files]

In today's data-driven world, data analysis has become an essential part of many businesses. Data scientists and analysts need to process large amounts of data to gain insights and make informed decisions. Python has become one of the most popular languages in data analysis due to its powerful libraries such as Pandas. In this code, …

Replace / Remove Words from String in Python

When working with text data, it's often necessary to manipulate the text in some way to achieve a desired output. One common task is to replace specific occurrences of a word within a string. In Python, there are several ways to accomplish this, but in this article, we'll focus on using the replace() method. The …

The Ultimate Guide for Data Analytics: Top 30 farmula You Need to Know

SUM: Calculates the sum of a range of cells. (Example: =SUM(A1:A10)) AVERAGE: Calculates the average of a range of cells. (Example: =AVERAGE(A1:A10)) COUNT: Counts the number of cells that contain numbers in a range. (Example: =COUNT(A1:A10)) COUNTIF: Counts the number of cells in a range that meet a certain condition. (Example: =COUNTIF(A1:A10,">50")) SUMIF: Adds the …

Data Preprocessing In Python – Data Analytics

Data preprocessing is a critical step in data analysis, which involves preparing and cleaning raw data to make it suitable for analysis. This step is important because raw data often contains errors, inconsistencies, missing values, and other issues that can adversely affect the quality and accuracy of the results obtained from the analysis.

ChatGPT – Create a simple chatbot in Python [Jupyter]

import randomimport time responses = {"hi": ["Hello!", "Hi there!", "Hey!"],"how are you": ["I'm doing well, thank you for asking.", "I'm good, thanks!", "I'm fine, how are you?"],"what is your name": ["My name is ChatBot.", "I'm ChatBot.", "You can call me ChatBot."],"bye": ["Goodbye!", "Bye!", "See you later!"],"what is database": ["A database is a collection of data …

Display Random values in Python with column headings [Jupyter]

#Display column headings import numpy as np #Draw 25 random numbers from -1 to 1my_data = np.random.uniform(0, 1, 3) #Print headings above the valuesprint("{:<10}{}".format("Index", "Value"))print("-" * 20) #Print index-value pairsfor index, value in enumerate(my_data):print("{:<10}{}".format(index, value))

Display Random values in Python [Jupyter]

Display random values and ignore negative values , display negitive values as a 0 import numpy as np#Draw 25 random numbers from -1 to 1my_data = np.random.uniform(-1, 1, 25) #if number less then 0 or -ive value , the number display as a 0for index, number in enumerate(my_data):if number < 0:my_data[index] = 0 print(my_data)

Display Pi chart and with specific color in Python [Jupyter]

import matplotlib.pyplot as plt #Data for the pie chartlabels = ['Medical Records', 'No of Patients', 'No of Providers', 'No of Insurances']sizes = [30, 20, 40, 10]colors = ['#FFC0CB', '#FFA07A', '#FFD700', '#98FB98'] #Create a pie chartfig1, ax1 = plt.subplots()ax1.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%', startangle=90) #Add a titleax1.set_title('Healthcare Industry') #Show the chartplt.show()

Display Pi chart in Python [Jupyter]

import matplotlib.pyplot as plt #Data for the pie chartlabels = ['Medical Records', 'No of Patients', 'No of Providers', 'No of Insurances']sizes = [30, 20, 40, 10] #Create a pie chartfig1, ax1 = plt.subplots()ax1.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=90) #Add a titleax1.set_title('Healthcare Industry') #Show the chartplt.show()

10 Essential Python Libraries for Data Science in 2023

Python is a popular programming language in the field of data science and data analytics due to its simplicity and versatile applications. Its vast community of developers provides ample support and resources for users to tackle any issues they may face. Here are the 10 essential Python libraries for data science in 2023: NumPy: This …