from collections import ChainMap
a = {'x': 1, 'z': 3 }
b = {'y': 2, 'z': 4 }
c = ChainMap(a,b)
c['z'] = 10
c['w'] = 40
del c['x']
print(sum(a.values()), sum(b.values()))

from collections import ChainMap
a = {'x': 1}
b = {'x': 2}
c = {'x': 3}
d = ChainMap(a, b, c)
del d['x']
del d['x']
print(d['x'])

from collections import ChainMap
values = ChainMap()
values['x'] = 1
values = values.new_child()
values['x'] = 2
values = values.parents
print(values['x'])

from collections import ChainMap
a = {'x': 1, 'z': 3 }
b = {'y': 2, 'z': 4 }
c = ChainMap(a, b)
a['x'] = 10
c['y'] = 20
c['z'] = 30 
print(c['x'], b['y'], a['z'], b['z'])

import re
line = "a+b"
x = re.split(r"(\+)", line)
y = re.split(r"\+", line)
z = re.split(r"(?:\+)", line)
p = set(x) - set(y) | set(z)
print(len(p))

from fnmatch import fnmatch
n = "foo2022.txt"
x = fnmatch(n, "foo[0-9]*")
y = fnmatch(n, "foo[0-9]+")
print(x, y)

from fnmatch import fnmatchcase
l = [f'{b}{a}' for a in range(20) for b in ['x', 'y']]
r = [x for x in l if fnmatchcase(x, 'x*0')]
print(*r)

from sklearn.linear_model import LinearRegression
import numpy as np
y = np.array([0, 2, 4])
n = len(y)
X = np.arange(n).reshape((n, 1))
model = LinearRegression()
model.fit(X, y)
p = model.predict([[3], [4]])
print(*(round(x) for x in p))

import re
d = re.compile('(\d+)/(\d+)')
text = '05/06 and 07/08'
x = d.match(text).group(0)
y = d.match(text).groups()[0]
print(x == y)