Code

import win32clipboard

# set clipboard data
win32clipboard.OpenClipboard()
win32clipboard.EmptyClipboard()
win32clipboard.SetClipboardText('testing 123')
win32clipboard.CloseClipboard()

# get clipboard data
win32clipboard.OpenClipboard()
data = win32clipboard.GetClipboardData()
win32clipboard.CloseClipboard()
print data

import os

file_path = 'mysample.txt'

# check if size of file is 0
if os.stat(file_path).st_size == 0:
    print('File is empty')
else:
    print('File is not empty')

while <expr>:
    <statement(s)>
    <break> #will take you out of the loop
    <statements>
    <continue>
    <statements>

<statements> #break will bring you here


Example:
x = 10
 while x > 0:
    x -= 1
    if x == 5:
        break
    if x == 8:
        continue #Don't print when its 8
    print(x)

print("outside loop")

class myclass:
    def __init__(self, mylst = []):
        print(mylst)

names = ["hello", "python", "devs", "iGnani"] 
myclass(names)

This cheat sheet is a condensed version of machine learning manual, which contains many classical equations and
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In this video on reproducing SQL queries in Python using Pandas library, I am going to show you Pandas SQL examples on how to write pandas code reproducing sql statements.

Using pandas, i will show you how to get sql results in python like 
* grouping and aggregation on multiple columns in pandas, similar to sql groupby clause
* sorting by multiple columns, reproducing sql order by clause
* filter multiple conditions, which involves where conditions with multiple columns

and a lot more...

import psycopg2
con=psycopg2.connect(dbname= 'dbname', host='host', port= 'port', user= 'user', password= 'pwd')

#once the above code executes and connection is established
# create a cursor
cur = con.cursor()

#now you can execute select statements
cur.execute("SELECT * FROM employee;")

#Next you need to instruct Psycopg how to fetch your data
cur.fetchall()

#Finally, don't forget to close your cursor & connection
cur.close() 
conn.close()

 An introduction to machine learning book will get you started with various data science techniques such as decision trees, performance evaluation, among others. It also covers sub-categories such as unsupervised learning, reinforcement learning, and neural networks. Learners can obtain a detailed understanding of various classifiers and algorithms from 17 chapters, thereby making it a good read during the lockdown.

 A proper grasp of statistics is essential for any machine learning enthusiast to succeed in the competitive domain. Consequently, one should focus more on statistics than on the latest fancy techniques. The book — All of Statistics — consists of 24 chapters and covers every topic right from probability to statistical inference and statistical models and methods.