<jupyter_start><jupyter_text>Типы данных в Python (Версия для Python 3)  1. Числовые  int<jupyter_code>x = 5
print(x)
print(type(x))
a = 4 + 5
b = 4 * 5
c = 5 / 4

print(a, b, c)
print(-5 / 4)
print(-(5 / 4))<jupyter_output>-1.25<jupyter_text>long<jupyter_code>x = 5 * 1000000 * 1000000 * 1000000 * 1000000 + 1
print(x)
print(type(x))
y = 5
print(type(y))
y = x
print(type(y))<jupyter_output><class 'int'>
<class 'int'><jupyter_text>float<jupyter_code>y = 5.7
print(y)
print(type(y))
a = 4.2 + 5.1
b = 4.2 * 5.1
c = 5.0 / 4.0

print(a, b, c)
a = 5
b = 4
print(float(a) / float(b))
print(5.0 / 4)
print(5 / 4.0)
print(float(a) / b)<jupyter_output>1.25<jupyter_text>bool<jupyter_code>a = True
b = False

print(a)
print(type(a))

print(b)
print(type(b))
print(a + b)
print(a + a)
print(b + b)
print(int(a), int(b))
print(True and False)
print(True and True)
print(False and False)
print(True or False)
print(True or True)
print(False or False)<jupyter_output>True
True
False<jupyter_text>2. None<jupyter_code>z = None
print(z)
print(type(z))
print(int(z))<jupyter_output><empty_output><jupyter_text>3. Строковые  str<jupyter_code>x = "abc"
print(x)
print(type(x))
a = 'Ivan'
b = "Ivanov"
s = a + " " + b
print(s)
print(a.upper())
print(a.lower())
print(len(a))
print(bool(a))
print(bool(""))
print(int(a))
print(a)
print(a[0])
print(a[1])
print(a[0:3])
print(a[0:4:2])<jupyter_output>Ia<jupyter_text>unicode<jupyter_code>x = u"abc"
print(x)
print(type(x))
x = u'<NAME>'
print(x, type(x))
y = x.encode('utf-8')
print(y, type(y))
z = y.decode('utf-8')
print(z, type(z))
q = y.decode('cp1251')
print(q, type(q))
1
x = u'<NAME>'
print(x, type(x))
y = x.encode('utf-8')
print(y, type(y))
z = y.decode('utf-8')
print(z, type(z))
q = y.decode('cp1251')
print(q, type(q))
print(str(x))
print(y[1:])
print(len(y), type(y))
print(len(x), type(x))
y = u'<NAME>'.encode('utf-8')
print(y.decode('utf-8'))
print(y.decode('cp1251'))
splitted_line = "<NAME>".split(' ')
print(splitted_line)
print(type(splitted_line))
print("<NAME>".split(" "))
print("\x98")
print(u"<NAME>".split(" "))<jupyter_output>['Иванов', 'Иван', 'Иванович']<jupyter_text>3. Массивы  list<jupyter_code>saled_goods_count = [33450, 34010, 33990, 33200]
print(saled_goods_count)
print(type(saled_goods_count))
income = [u'Высокий', u'Средний', u'Высокий']
names = [u'<NAME>', u'<NAME>', u'<NAME>']

print(income)
print(names)
print ("---".join(income))
features = ['<NAME>', 'Medium', 500000, 12, True]
print (features)
print(features[0])
print(features[1])
print(features[3])
print(features[0:5])
print(features[:5])
print(features[1:])
print(features[2:5])
print(features[:-1])
features.append('One more element in list')
print(features)
del features[-2]
print(features)<jupyter_output>['<NAME>', 'Medium', 500000, 12, 'One more element in list']<jupyter_text>tuple<jupyter_code>features_tuple = ('<NAME>', 'Medium', 500000, 12, True)
print(type(features_tuple))
features_tuple[2:5]
features_tuple.append('one more element')<jupyter_output><empty_output><jupyter_text>4. Множества и словари  set<jupyter_code>names = {'Ivan', 'Petr', 'Konstantin'}
print(type(names))
print('Ivan' in names)
print('Mikhail' in names)
names.add('Mikhail')
print(names)
names.add('Mikhail')
print(names)
names.remove('Mikhail')
print(names)
names.add(['Vladimir', 'Vladimirovich'])
names.add(('Vladimir', 'Vladimirovich'))
print(names)
a = range(10000)
b = range(10000)
b = set(b)
print(a[:5])
print(a[-5:])
%%time
print(9999 in a)
%%time
print(9999 in b)<jupyter_output>True
Wall time: 0 ns<jupyter_text>dict<jupyter_code>words_frequencies = dict()
words_frequencies['I'] = 1
words_frequencies['am'] = 1
words_frequencies['I'] += 1

print(words_frequencies)
print(words_frequencies['I'])
words_frequencies = {'I': 2, 'am': 1}
print(words_frequencies)
yet_another_dict = {'abc': 3.4, 5: 7.8, u'123': None}
print(yet_another_dict)
yet_another_dict[(1,2,5)] = [4, 5, 7]
print(yet_another_dict)
yet_another_dict[[1,2,7]] = [4, 5]<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.21127819548872181, 'fraction_numerical': 0.0518796992481203, 'mean_word_length': 7.280082987551867, 'pattern_counts': {'":': 0, '<': 51, '<?xml version=': 0, '>': 51, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '11', 'dir': 'jupyter-structured-clean-dedup', 'id': '498893', 'license': 'MIT', 'n_tokens_mistral': 1935, 'n_tokens_neox': 1813, 'n_words': 333, 'path': 'MathematicsAndPython/materials/notebooks/1-2.TypesInPython.ipynb', 'repo_name': 'SmirnovAlexander/MachineLearningDataAnalysis', 'size': '4120'}