https://github.com/iankitnegi/datapy

Welcome to DATAPy! This repo contains my Python coding projects for data science, featuring data manipulation, analysis, and visualization. Stay tuned for updates and new content!
https://github.com/iankitnegi/datapy

basic-programming matplotlib numpy pandas python seaborn

Last synced: 13 days ago
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Welcome to DATAPy! This repo contains my Python coding projects for data science, featuring data manipulation, analysis, and visualization. Stay tuned for updates and new content!

• Host: GitHub
• URL: https://github.com/iankitnegi/datapy
• Owner: iankitnegi
• Created: 2024-05-27T04:54:17.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-08-17T17:22:18.000Z (5 months ago)
• Last Synced: 2024-08-17T20:12:28.863Z (5 months ago)
• Topics: basic-programming, matplotlib, numpy, pandas, python, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 1.34 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# DataPy

Welcome to DATAPy! This repo contains my Python learning from basic to data science, featuring basic programming, logic building, data manipulation, analysis, and visualization. Stay tuned for updates and new content!

### **Learning/Quick-Notes:**  

-     #Use of random module

      import random

      r = random.randint(1, 10)

      print(r)

-     #Use of calendar module

      import calendar

      y = int(input("Enter the year: "))

      m = int(input("Enter the month: "))

      c = calendar.month(y, m)

      print(c)

-     #Use of math module

      import math

      print(math.sqrt(25))

-     #Use of format() funn

      print("{0} is Prime number".format(n))

-     #Binary, Octal, Hexadecimal funn: convert decimal -> binary, octal, hexadecimal

      bin(), oct(), hex()

-     #ASCII: Character -> ASCII number {funn}

      ord()

-     # Data Collection

      data = []

      data.extend([10, 20, 30, 40, 50])

      # Data Cleaning

      if 30 in data:

            data.remove(30)

      # Data Transformation

      data.sort()

      data.reverse()

      # Data Summarization

      total = sum(data)

      max_value = max(data)

      min_value = min(data)

      # Preparing for Visualization

      indexed_data = list(enumerate(data))

      print("Cleaned Data:", data)

      print("Total:", total)

      print("Max Value:", max_value)

      print("Min Value:", min_value)

      print("Indexed Data:", indexed_data)