RT-AI-2

'''
Task 1.3.1

import numpy as np

checkboard_array = np.zeros((8, 8), dtype=int)
checkboard_array[1::2, ::2] = 1
checkboard_array[::2, 1::2] = 1
print(checkboard_array)


Task 1.3.2

import numpy as np

x = np.zeros((5,5))
x += np.arange(5)
print(x)



Task 1.3.3

import numpy as np
x = np.random.random((3,3,3))
print(x)



Task 1.3.4

import numpy as np

x = np.ones((5,5))
x[1:-1, 1:-1] = 0
print(x)



Task  1.3.5

import numpy as np
x = np.random.randint(1,20, 10)
print(x)
x[::-1].sort()
print(x)




Task 1.3.6

import numpy as np
matrix = np.array([np.random.randint(1,10, 5) for _ in range(4)])
print(matrix)
print(f'Matrix shape is: {matrix.shape}',
    f'Matrix ndim is: {matrix.ndim}',
    f'Matrix size is: {matrix.size}', sep='\n')




Task  2.3.1

import pandas as pd

ser_1 = pd.Series([1,3,6,9])
ser_2 = pd.Series([2,5,7,10])

print(sum((ser_1-ser_2)**2)**.5)




Task 2.3.2

import pandas as pd

url = r'https://raw.githubusercontent.com/akmand/datasets/main/airlines.csv'

dataframe = pd.read_csv(url)

print(dataframe.head(10))

Task 2.3.3 <-- продолжение 2.3.2

print(dataframe.tail(3))

print(dataframe.shape)

print(dataframe.describe())

print(dataframe.iloc[2:6])

print(dataframe[dataframe['Airline'] == 'US'].head(2))



#Task 3.3.2

import pandas as pd
from sklearn.preprocessing import MinMaxScaler, StandartScaler

url = r'https://raw.githubusercontent.com/akmand/datasets/master/iris.csv'

dataframe = pd.read_csv(url)


scaler = MinMaxScaler()

std_scaler = StandartScaler()


sepal_length = min_max_scaler.fit_transform(dataframe['sepal_length_cm'].values.reshape(-1, 1))

sepal_width = std_scaler.fit_transform(dataframe['sepal_width_cm'].values.reshape(-1, 1))


print(sepal_width)

print(sepal_length)

'''