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https://pypi.org/project/python-chess/ [ official documentation link for python chess game that we posted yesterday ] Thanks for watching [ Keep support me to get 10k subscribers

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‼️New video about creating a GUI timer using python { without tkinter } @8:00 pm

import PySimpleGUI as sg
import time

# Basic timer in PSG

def Timer():
sg.theme('DarkAmber')

gui= [[sg.Text(size=(24, 10), font=('Bahnschrift ', 20),
justification='center', key='text')],
[sg.Button('Pause', key='-RUN-PAUSE-'),
sg.Button('Reset'),
sg.Exit(button_color=('white', 'firebrick4'))]]
window = sg.Window('AK Timer', gui,
no_titlebar=False, auto_size_buttons=True)

#logic started
i = 0
paused = False
start_time = int(round(time.time() * 100))

while True:
# This is the code that reads and updates your window
button, values = window.read(timeout=10)
window['text'].update('{:02d}:{:02d}.{:02d}'.format(
(i // 100) // 60, (i // 100) % 60, i % 100))

#conditions

if values is None or button == 'Exit':
break

if button == 'Reset':
i = 0

elif button == '-RUN-PAUSE-':
paused = not paused
window['-RUN-PAUSE-'].update('Run' if paused else 'Pause')

if not paused:
i += 1

window.close()

Timer()

Code for the previous video [ Like share comment & subscribe for more updates ]

‼️ Code for Calendar GUI using python

import PySimpleGUI as sg

sg.theme('DarKAmber')
layout = [[sg.CalendarButton('Click here to see the date', target='-IN4-', format='%m-%d', default_date_m_d_y=(1,None,2020), )],
[sg.Button('Date Popup'), sg.Exit()]]

window = sg.Window('AK calendar',layout,no_titlebar=False)

while True:
event, values = window.read()
print(event, values)
if event in (sg.WIN_CLOSED, 'Exit'):
break
elif event == 'Date Popup':
sg.popup('You chose:', sg.popup_get_date())
window.close()

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‼️New video is about [ Dictionary attack explained in python ] Today Night @8:00 pm Subscribe for instant notification👍

import wordlist
generator=wordlist.Generator('1234567890')
for each in generator.generate('1,10') #10 different permutations
print(each)

‼️Code for previous video [ Subscribe & share ]

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline


dataset=pd.read_csv('A:\data\studentscores.csv')

dataset.shape
(25,2)

dataset.head()
dataset.describe()


dataset.plot(x='Hours',y='Scores',style="*")
plt.title('Student mark prediction')
plt.xlabel('Hours')
plt.ylabel('Percentage marks')
plt.show()

X=dataset.iloc[:, :-1].values
Y=dataset.iloc[:,1].values


from sklearn.model_selection import train_test_split
X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.2,random_state=0)

from sklearn.linear_model import LinearRegression
regressor=LinearRegression()
regressor.fit(X_train,Y_train)

print(regressor.intercept_)

print(regressor.coef_)

y_pred=regressor.predict(X_test)
df=pd.DataFrame({'Actual':Y_test,'Predicted':y_pred})
df