Python Cheat Sheet.pdf
677.7 KB
This cheat sheet includes basic python required for data analysis excluding pandas, numpy & other libraries
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Advanced Concepts in Operating Systems (Indian Edition).pdf
331.2 MB
Advanced Concepts in Operating Systems
Mukesh Singhal, 2008 (scanned)
Mukesh Singhal, 2008 (scanned)
HTML Tags List.pdf
115.1 KB
HTML Tags List ๐
Do not forget to React โค๏ธ to this Message for More Content Like this
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Do not forget to React โค๏ธ to this Message for More Content Like this
Thanks For Joining All โค๏ธ๐
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๐ป ๐จ๐ป๐ฑ๐ฒ๐ฟ๐๐๐ฎ๐ป๐ฑ ๐๐ถ๐ด ๐ข ๐ป๐ผ๐๐ฎ๐๐ถ๐ผ๐ป!
O(1) - Constant Time: Simple tasks that take the same amount of time no matter how much data you have, like finding an item in a list by its position.
O(log n) - Logarithmic Time: Tasks that take less time as the data grows, like finding an item in a sorted list by repeatedly dividing it in half.
O(n) - Linear Time: Tasks that take more time as the data grows, like counting all items in a list by checking each one.
O(n log n) - Linearithmic Time: Tasks that get a bit slower as the data grows, like sorting a list using efficient methods such as merge sort or quick sort.
O(nยฒ) - Quadratic Time: Tasks that get noticeably slower as the data grows, like sorting a list using simpler methods like bubble sort or finding all pairs in a list.
O(2^n) - Exponential Time: Tasks that get much slower as the data grows, like finding all subsets of a set or solving complex problems like the traveling salesman using a basic approach.
O(n!) - Factorial Time: Tasks that get extremely slow as the data grows, like solving problems that involve checking every possible arrangement of items.
O(1) - Constant Time: Simple tasks that take the same amount of time no matter how much data you have, like finding an item in a list by its position.
O(log n) - Logarithmic Time: Tasks that take less time as the data grows, like finding an item in a sorted list by repeatedly dividing it in half.
O(n) - Linear Time: Tasks that take more time as the data grows, like counting all items in a list by checking each one.
O(n log n) - Linearithmic Time: Tasks that get a bit slower as the data grows, like sorting a list using efficient methods such as merge sort or quick sort.
O(nยฒ) - Quadratic Time: Tasks that get noticeably slower as the data grows, like sorting a list using simpler methods like bubble sort or finding all pairs in a list.
O(2^n) - Exponential Time: Tasks that get much slower as the data grows, like finding all subsets of a set or solving complex problems like the traveling salesman using a basic approach.
O(n!) - Factorial Time: Tasks that get extremely slow as the data grows, like solving problems that involve checking every possible arrangement of items.
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Expert Python Programming.pdf
4.3 MB
Expert Python Programming (2021)
100 likes = new books
100 likes = new books
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Voice Recorder in Python
pip install sounddevice
Join us for more -
https://t.iss.one/pythonfreebootcamp
pip install sounddevice
import sounddevice
from scipy.io.wavfile import write
#sample_rate
fs=44100
#Ask to enter the recording time
second = int(input("Enter the Recording Time in second: "))
print("Recordingโฆ\n")
record_voice = sounddevice.rec(int(second * fs),samplerate=fs,channels=2)
sounddevice.wait()
write("MyRecording.wav",fs,record_voice)
print("Recording is done Please check you folder to listen recording")Join us for more -
https://t.iss.one/pythonfreebootcamp
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