Automatic Positional Clicker With Time Intervals

import win32api, win32con, time
def click(x,y):
    win32api.SetCursorPos((x,y))
    win32api.mouse_event(win32con.MOUSEEVENTF_LEFTDOWN,x,y,0,0)
    win32api.mouse_event(win32con.MOUSEEVENTF_LEFTUP,x,y,0,0)
def script():
    while True:
        click(1793,889) #invent
        time.sleep(.2)
        click(1393,872)  #1
        time.sleep(.1)
        click(1393,872)  #1
        time.sleep(.1)
        click(1393,872)  #1
        time.sleep(4)
        click(1395,745) #2
        time.sleep(.1)
        click(1395,745) #2
        time.sleep(6)
        click(1501,782) #3
        time.sleep(.1)
        click(1501,782) #3
        time.sleep(7.5)
        click(1483,885) #4
        time.sleep(.1)
        click(1483,885) #4
        time.sleep(5)
        click(1456,845) #5
        time.sleep(7)
        click(1355,859) #6
        time.sleep(2.8)
        click(1273,822) #7
        time.sleep(7)
        click(1451,766) #8
        time.sleep(.1)
        click(1451,766) #8
        time.sleep(5.5)
        click(1498,870) #9
        time.sleep(.1)
        click(1498,870) #9
        time.sleep(4.2)
        click(1792,854)  #10.0
        time.sleep(5.5)
        click(1767,832) #11.0
        time.sleep(5)
        click(1279,999) #11.0
        time.sleep(5)     
#x, y = win32api.GetCursorPos()
#print(x,y)
script()

Data Scrubbing

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn import ensemble
from sklearn.metrics import mean_absolute_error
df = pd.read_csv("f:/brain/memory/Melbourne_housing_FULL.csv")
del df['Address']
del df['Method']
del df['SellerG']
del df['Date']
del df['Postcode']
del df['Lattitude']
del df['Longtitude']
del df['Regionname']
del df['Propertycount']
df.columns
features_df = pd.get_dummies(df, columns = ['Suburb','CouncilArea','Type'])
del features_df['Price']
x = features_df.values
y = df['Price'].values
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, shuffle = True)
model = ensemble.GradientBoostingRegressor(
    n_estimators = 150,
    learning_rate = 0.1,
    max_depth = 30,
    min_samples_split = 4,
    min_samples_leaf = 6,
    max_features = 0.6,
    loss = 'huber'
)
model.fit(x_train, y_train)
mse = mean_absolute_error(y_train, model.predict(x_train))
print("Training Set Mean Absolute Error: %.2f" % mse)
mse = mean_absolute_erro(y_test, model.predict(x_test))
print("Test Set Mean Absolute Error: %.2f" % mse)

Song_Writer Progress #3 "666"

second = 60
minute = 60*second
modes = "Ionian,Dorian,Phrygian,Lydian,Mixolydian,Aeolian,Locrian"
def main():
    def song_details():
            beat_division = input("what is the x/x beat division:") # a quarter note gets the beat, so four quarter notes a bar
            tempo = int(input("tempo is, in Beats Per Minute:"))
            song_length_sec = int(input("Song Length(in seconds):")) #song length, 165sec is nice
            song_length_min = song_length_sec/60
            print("-----------------------------------------------")
            print("The Beat Division is: "+str(beat_division)+".")
            print("The Tempo is: "+str(tempo)+" Beats Per Minute.")
            print("The Song Length is: "+str(song_length_sec)+" seconds.") 
            
            amount_of_beats = int(tempo*(song_length_min))
            print(amount_of_beats)
            
            
            
    song_details()
    
    
        

main()

Song_Writer Progress #2

#how long the song will be
second = 60
minute = 60*second
class Oedema5_Songs:
    def __init__(self, beat_division, temp, song_length):
            self.beat_division = input("what is the x/x beat division:") # a quarter note gets the beat, so four quarter notes a bar
            self.tempo = input("tempo is, in Beats Per Minute:")
            self.song_length = input("Song Length(in seconds):") #song length
    def song_details(self):  #gets information about the drums
            return '{} {} {}'.format(self.beat_division, self.tempo, self.song_length)
songx1 = Oedema5_Songs(4/4,150,165)

More Object-Orientated Programming

class Employee: 
    
    def __init__(self,first, last, pay):
        self.first = first
        self.last = last
        self.email = first+'.'+last+"@company.com"
        self.pay = pay
        
    def fullname(self):
        return '{} {}'.format(self.first, self.last)
        
emp_1 = Employee("Corey","Schafer",50000)
emp_2 = Employee("test","user",60000)

#print(emp_1)
#print(emp_2)
print(emp_1.email)
print(emp_2.email)
print(emp_1.fullname())

Object-Oriented Programming

import numpy as np
class Coordinate(object):  #class tells python you are making an object. #class name/type(parent object)
    def __init__(self, x, y):  #defines attributes that let us interact with the object
        self.x = x  #these attributes only work with its class
        self.y = y

    def distance(self, dist):  #defines attributes that let us interact with the object
        dist = numpy.linalg.norm(x-y)
        x_diff_sq = (self.x-other.x)**2 #these attributes only work with its class
        y_diff_sq = (self.y-other.y)**2
        return(x_diff_sq + y_diff_sq)**0.5
#self is a parameter that references the instance of this class |  
#methods like __init__ are like a function that only work with this class
c = Coordinate(3,4)
zero = Coordinate (0,0)
print(c.distance(zero))