Finish commit.

# Conflicts:
#	LabWork01/LoadDB.py

.gitignore

@@ -77,3 +77,5 @@ fabric.properties
 # Android studio 3.1+ serialized cache file
 .idea/caches/build_file_checksums.ser
 
+# чтобы не закидывало jpg
+*.jpg

.idea/csv-editor.xml

@@ -0,0 +1,16 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="CsvFileAttributes">
+    <option name="attributeMap">
+      <map>
+        <entry key="\res\Stores.csv">
+          <value>
+            <Attribute>
+              <option name="separator" value="," />
+            </Attribute>
+          </value>
+        </entry>
+      </map>
+    </option>
+  </component>
+</project>

.idea/misc.xml

@@ -1,5 +1,8 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.11" />
+  </component>
   <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11" project-jdk-type="Python SDK" />
   <component name="PyCharmProfessionalAdvertiser">
     <option name="shown" value="true" />

LabWork01/AnalysCustomers.py

@@ -0,0 +1,36 @@
+import pandas
+import pandas as pd
+
+def analysCustomersDataFrame(df: pandas.DataFrame):
+
+    df['Size'] = df['Store_Area'].apply(lambda x: 'Small' if x <= 1100 else ('Average' if 1100 < x <= 1800 else 'Big'))
+
+    table_one = df.query("Size == 'Small'").groupby('Size')
+    table_two = df.query("Size == 'Average'").groupby('Size')
+    table_three = df.query("Size == 'Big'").groupby('Size')
+
+    minMaxMean_one = table_one.agg({'Daily_Customer_Count': ['min', 'max', 'mean']}).round(2).reset_index()
+    minMaxMean_one.columns = minMaxMean_one.columns.droplevel()
+    minMaxMean_two = table_two.agg({'Daily_Customer_Count': ['min', 'max', 'mean']}).round(2).reset_index()
+    minMaxMean_two.columns = minMaxMean_two.columns.droplevel()
+    minMaxMean_three = table_three.agg({'Daily_Customer_Count': ['min', 'max', 'mean']}).round(2).reset_index()
+    minMaxMean_three.columns = minMaxMean_three.columns.droplevel()
+
+    df1 = pd.DataFrame()
+    df1['small'] = minMaxMean_one['min']
+    df1['average'] = minMaxMean_two['min']
+    df1['big'] = minMaxMean_three['min']
+
+    df2 = pd.DataFrame()
+    df2['small'] = minMaxMean_one['max']
+    df2['average'] = minMaxMean_two['max']
+    df2['big'] = minMaxMean_three['max']
+
+    df3 = pd.DataFrame()
+    df3['small'] = minMaxMean_one['mean']
+    df3['average'] = minMaxMean_two['mean']
+    df3['big'] = minMaxMean_three['mean']
+
+    totalTable = [df1, df2, df3]
+
+    return totalTable

LabWork01/AnalysSales.py

@@ -0,0 +1,38 @@
+import pandas
+import pandas as pd
+
+def analysSalesDataFrame(df: pandas.DataFrame):
+
+    df['Size'] = df['Store_Area'].apply(lambda x: 'Small' if x <= 1100 else ('Average' if 1100 < x <= 1800 else 'Big'))
+
+    table_one = df.query("Size == 'Small'").groupby('Size')
+    table_two = df.query("Size == 'Average'").groupby('Size')
+    table_three = df.query("Size == 'Big'").groupby('Size')
+
+    minMaxMean_one = table_one.agg({'Store_Sales': ['min', 'max', 'mean']}).round(2).reset_index()
+    minMaxMean_one.columns = minMaxMean_one.columns.droplevel()
+    minMaxMean_two = table_two.agg({'Store_Sales': ['min', 'max', 'mean']}).round(2).reset_index()
+    minMaxMean_two.columns = minMaxMean_two.columns.droplevel()
+    minMaxMean_two = minMaxMean_two.iloc[:, 1:4]
+    minMaxMean_three = table_three.agg({'Store_Sales': ['min', 'max', 'mean']}).round(2).reset_index()
+    minMaxMean_three.columns = minMaxMean_three.columns.droplevel()
+    minMaxMean_three = minMaxMean_three.iloc[:, 1:4]
+
+    df1 = pd.DataFrame()
+    df1['small'] = minMaxMean_one['min']
+    df1['average'] = minMaxMean_two['min']
+    df1['big'] = minMaxMean_three['min']
+
+    df2 = pd.DataFrame()
+    df2['small'] = minMaxMean_one['max']
+    df2['average'] = minMaxMean_two['max']
+    df2['big'] = minMaxMean_three['max']
+
+    df3 = pd.DataFrame()
+    df3['small'] = minMaxMean_one['mean']
+    df3['average'] = minMaxMean_two['mean']
+    df3['big'] = minMaxMean_three['mean']
+
+    totalTable = [df1, df2, df3]
+
+    return totalTable

LabWork01/AnalysSalesCustomers.py

@@ -0,0 +1,38 @@
+import pandas
+import pandas as pd
+
+def analysSalesCustomersDataFrame(df: pandas.DataFrame):
+
+    df['SaleCustomer'] = df['Daily_Customer_Count'].apply(lambda x: 'Small' if x <= 200 else ('Average' if 200 < x <= 800 else 'Big'))
+
+    table_one = df.query("SaleCustomer == 'Small'").groupby('SaleCustomer')
+    table_two = df.query("SaleCustomer == 'Average'").groupby('SaleCustomer')
+    table_three = df.query("SaleCustomer == 'Big'").groupby('SaleCustomer')
+
+    minMaxMean_one = table_one.agg({'Store_Sales': ['min', 'max', 'mean']}).round(2).reset_index()
+    minMaxMean_one.columns = minMaxMean_one.columns.droplevel()
+    minMaxMean_two = table_two.agg({'Store_Sales': ['min', 'max', 'mean']}).round(2).reset_index()
+    minMaxMean_two.columns = minMaxMean_two.columns.droplevel()
+    minMaxMean_two = minMaxMean_two.iloc[:, 1:4]
+    minMaxMean_three = table_three.agg({'Store_Sales': ['min', 'max', 'mean']}).round(2).reset_index()
+    minMaxMean_three.columns = minMaxMean_three.columns.droplevel()
+    minMaxMean_three = minMaxMean_three.iloc[:, 1:4]
+
+    df1 = pd.DataFrame()
+    df1['small'] = minMaxMean_one['min']
+    df1['average'] = minMaxMean_two['min']
+    df1['big'] = minMaxMean_three['min']
+
+    df2 = pd.DataFrame()
+    df2['small'] = minMaxMean_one['max']
+    df2['average'] = minMaxMean_two['max']
+    df2['big'] = minMaxMean_three['max']
+
+    df3 = pd.DataFrame()
+    df3['small'] = minMaxMean_one['mean']
+    df3['average'] = minMaxMean_two['mean']
+    df3['big'] = minMaxMean_three['mean']
+
+    totalTable = [df1, df2, df3]
+
+    return totalTable

LabWork01/DataFrameAnalys.py

@@ -0,0 +1,39 @@
+import pandas
+import pandas as pd
+
+#или как analysDataFrame(df)->pandas.DataFrame:
+def analysItemsDataFrame(df: pandas.DataFrame):
+
+    df['Size'] = df['Store_Area'].apply(lambda x: 'Small' if x <= 1100 else ('Average' if 1100 < x <= 1800 else 'Big'))
+
+    table_one = df.query("Size == 'Small'").groupby('Size')
+    table_two = df.query("Size == 'Average'").groupby('Size')
+    table_three = df.query("Size == 'Big'").groupby('Size')
+
+    minMaxMean_one = table_one.agg({'Items_Available': ['min', 'max', 'mean']}).round(2).reset_index()
+    minMaxMean_one.columns = minMaxMean_one.columns.droplevel()
+    minMaxMean_two = table_two.agg({'Items_Available': ['min', 'max', 'mean']}).round(2).reset_index()
+    minMaxMean_two.columns = minMaxMean_two.columns.droplevel()
+    minMaxMean_two = minMaxMean_two.iloc[:, 1:4]
+    minMaxMean_three = table_three.agg({'Items_Available': ['min', 'max', 'mean']}).round(2).reset_index()
+    minMaxMean_three.columns = minMaxMean_three.columns.droplevel()
+    minMaxMean_three = minMaxMean_three.iloc[:, 1:4]
+
+    df1 = pd.DataFrame()
+    df1['small'] = minMaxMean_one['min']
+    df1['average'] = minMaxMean_two['min']
+    df1['big'] = minMaxMean_three['min']
+
+    df2 = pd.DataFrame()
+    df2['small'] = minMaxMean_one['max']
+    df2['average'] = minMaxMean_two['max']
+    df2['big'] = minMaxMean_three['max']
+
+    df3 = pd.DataFrame()
+    df3['small'] = minMaxMean_one['mean']
+    df3['average'] = minMaxMean_two['mean']
+    df3['big'] = minMaxMean_three['mean']
+
+    totalTable = [df1, df2, df3]
+
+    return totalTable

LabWork01/FuncLoad.py

@@ -0,0 +1,6 @@
+import pandas as pd
+
+def createDataFrame() -> pd.DataFrame:
+    df = pd.read_csv('res/Stores.csv')
+
+    return df

LabWork01/LabWork3/AddData.py

@@ -0,0 +1,14 @@
+import pandas
+
+# создаём усовершенствованную таблицу
+def addData(df: pandas.DataFrame):
+
+    # ищем 10% от кол-ва строк (-1 из-за строки заголовков)
+    additionForTable = int(len(df) / 10) - 1
+
+    # добавили 10% значений
+    for i in range(additionForTable):
+        df.loc[len(df.index)] = [df['Store_ID'].max().round(2) + 1, df['Store_Area'].mean().round(2), df['Items_Available'].mean().round(2),
+                                 df['Daily_Customer_Count'].mean().round(2), df['Store_Sales'].mean().round(2)]
+
+    return df

LabWork01/LabWork3/CreateGraphics.py

@@ -0,0 +1,21 @@
+import os
+import pandas as pd
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+
+def createGraphics(df: list[pd.DataFrame], nameFile):
+    # для сохранения диаграммы в конкретной папке
+    script_dir = os.path.dirname(__file__)
+    results_dir = os.path.join(script_dir, '../static/')
+
+    if not os.path.isdir(results_dir):
+        os.makedirs(results_dir)
+
+    for i in range(len(df)):
+        dataFrame = df[i]
+        dataFrame.plot.bar()
+        plt.savefig(results_dir + nameFile + str(i) + '.jpg')
+        plt.close()
+
+    return True

LabWork01/LabWork3/CustomGraphics.py

@@ -0,0 +1,47 @@
+import os
+import numpy as np
+import pandas as pd
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+
+def createCusGraphics(df: pd.DataFrame, newDf: pd.DataFrame):
+
+    # для сохранения диаграммы в конкретной папке
+    script_dir = os.path.dirname(__file__)
+    results_dir = os.path.join(script_dir, '../static/')
+
+    if not os.path.isdir(results_dir):
+        os.makedirs(results_dir)
+    df.rename(columns={'small': 'small_1', 'average': 'average_1', 'big': 'big_1'}, inplace=True)
+    newDf.rename(columns={'small': 'small_2', 'average': 'average_2', 'big': 'big_2'}, inplace=True)
+
+    # Объединяем две группы в один DataFrame
+    merged_df = pd.concat([df, newDf], axis=1, ignore_index=False)
+    # Создаем массив с индексами для каждой компании
+    index = np.arange(len(merged_df))
+    print(merged_df)
+    # Задаем ширину каждого столбца
+    bar_width = 0.2
+    # Построение столбчатой гистограммы с увеличенным расстоянием между столбцами
+    plt.bar(index, merged_df['small_1'], bar_width, label='Min 1', color='b')
+    plt.bar(index + bar_width, merged_df['average_1'], bar_width, label='Mean 1', color='g')
+    plt.bar(index + 2 * bar_width, merged_df['big_1'], bar_width, label='Max 1', color='r')
+    plt.bar(index + 3 * bar_width, merged_df['small_2'], bar_width, label='Min 2', color='c')
+    plt.bar(index + 4 * bar_width, merged_df['average_2'], bar_width, label='Mean 2', color='m')
+    plt.bar(index + 5 * bar_width, merged_df['big_2'], bar_width, label='Max 2', color='y')
+
+    # Устанавливаем подписи на оси x
+    plt.xlabel('Company')
+    plt.ylabel('Valuation')
+    plt.title('Company Valuation Comparison')
+    plt.xticks(index + 2.5 * bar_width, merged_df.index)
+
+    # Добавляем легенду
+    plt.legend()
+
+    plt.savefig(results_dir + 'CustomJPG' + str(0) + '.jpg')
+
+    plt.close()
+
+    return True

LabWork01/LabWork3/DeletePng.py

@@ -0,0 +1,13 @@
+import os
+import glob
+
+# функция для уудаления всех предыдущих png с графиками
+def deleteAllPng():
+    script_dir = os.path.dirname(__file__)
+    results_dir = os.path.join(script_dir, 'images/*')
+
+    files = glob.glob(results_dir)
+    for f in files:
+        os.remove(f)
+
+    return True

LabWork01/LabWork4/BloomFilter.py

@@ -0,0 +1,22 @@
+import mmh3
+from bitarray import bitarray
+
+class BloomFilter(object):
+
+    def __init__(self, size, hash_count):
+        self.size = size
+        self.hash_count = hash_count
+        self.bit_array = bitarray(size)
+        self.bit_array.setall(0)
+
+    def add(self, item):
+        for seed in range(self.hash_count):
+            index = mmh3.hash(item, seed) % self.size
+            self.bit_array[index] = 1
+
+    def contains(self, item):
+        for seed in range(self.hash_count):
+            index = mmh3.hash(item, seed) % self.size
+            if self.bit_array[index] == 0:
+                return False
+        return True

LabWork01/LabWork4/SiteSearch.py

@@ -0,0 +1,31 @@
+from LabWork01.LabWork4.BloomFilter import BloomFilter
+
+class SiteSearch:
+    def __init__(self):
+        self.filter: BloomFilter = BloomFilter(100000, 5)
+        self.keyword_urls: dict[str, list[str]] = {}
+
+    # для всех слов-ассоциаций проводим добавление в фильтр Блума и назначаем к каждому нужную ссылку на ресурс
+    def add(self, url: str, keywords: list[str]) -> None:
+        for keyword in keywords:
+            lowercase_string = keyword.lower()
+            self.filter.add(lowercase_string)
+            if lowercase_string not in self.keyword_urls:
+                self.keyword_urls[lowercase_string] = []
+            self.keyword_urls[lowercase_string].append(url)
+
+    # проводим поиск по слову-ключу в словаре
+    def find_url(self, keyword: str) -> list[str]:
+        lowercase_string = keyword.lower()
+        if self.filter.contains(lowercase_string):
+            return self.keyword_urls.get(lowercase_string)
+        else:
+            return []
+
+    # проверка на содержание слова-ключа в словаре
+    def contains(self, keyword: str) -> list[str]:
+        lowercase_string = keyword.lower()
+        if self.filter.contains(lowercase_string):
+            return True
+        else:
+            return False

LabWork01/LabWork5/Сreate_plot.py

@@ -0,0 +1,69 @@
+import os
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.metrics import r2_score
+
+def create_plot_jpg(df: pd.DataFrame, nameFile):
+    # для сохранения диаграммы в конкретной папке
+    script_dir = os.path.dirname(__file__)
+    results_dir = os.path.join(script_dir, '../static/')
+
+    if not os.path.isdir(results_dir):
+        os.makedirs(results_dir)
+
+    # набор атрибутов - независимых переменных - площадь
+    X = df["Store_Area"].array
+
+    # набор меток - зависимых переменных, значение которых требуется предсказать - выручка
+    Y = df["Store_Sales"].array
+
+    n = df.shape[0]
+
+    # делим датафрейм на набор тренировочных данных и данных для тестов, test_size содержит определние соотношения этих наборов
+    n_test = int(n * 0.01)
+    n_train = n - n_test
+    X_train, Y_train = X[:n_train], Y[:n_train]
+    X_test, Y_test = X[n_train:], Y[n_train:]
+
+    sumY_train = sum(Y_train)
+    sumX_train = sum(X_train)
+
+    sumXY_train = sum(X_train * Y_train)
+    sumXX_train = sum(X_train * X_train)
+
+    b1 = (sumXY_train - (sumY_train * sumX_train) / n_train) / (sumXX_train - sumX_train * sumX_train / n_train)
+    b0 = (sumY_train - b1 * sumX_train) / n_train
+
+    # Построение модели на обучающем наборе
+    plt.scatter(X_train, Y_train, alpha=0.8)
+    plt.axline(xy1=(0, b0), slope=b1, color='r', label=f'$y = {b1:.5f}x {b0:+.5f}$')
+
+    # Оценка производительности модели на тестовом наборе
+    Y_pred = b0 + b1 * X_test
+    first_half = sum((Y_pred - Y_test.mean()) ** 2)
+    second_half = sum((Y_test - Y_pred) ** 2) + first_half
+
+    plt.scatter(X_test, Y_test, alpha=0.8, color='g')
+    plt.legend()
+    plt.savefig(results_dir + nameFile + '.jpg')
+
+    r2 = r_squared(Y_test, Y_pred)
+    listMessages = [f"Коэффициент по странной формуле (по википедии): {first_half/second_half}",
+                    f"Истинный коэффициент (по википедии): {r2}",
+                    f"Подсчёт по библиотеке: {r2_score(Y_test, Y_pred)}"]
+
+    return listMessages
+
+def r_squared(y_true, y_pred):
+    # Вычисляем среднее значение целевой переменной
+    mean_y_true = np.mean(y_true)
+
+    # Вычисляем сумму квадратов отклонений от среднего
+    ss_total = np.sum((y_true - mean_y_true) ** 2)
+
+    # Вычисляем сумму квадратов остатков
+    ss_residual = np.sum((y_true - y_pred) ** 2)
+
+    # Вычисляем коэффициент детерминации
+    return 1 - (ss_residual / ss_total)

LabWork01/LabWork6/ConvertorDataFrame.py

@@ -0,0 +1,43 @@
+import pandas as pd
+
+def CovertorDataFrame():
+
+    df = pd.read_csv("res/Stores.csv")
+
+    # кол-во строчек для считывания
+    countMainRows = 35
+
+    # получаем указанное кол-во строчек
+    mainDF = df.head(countMainRows)
+
+    mainDF['TextStoreArea'] = mainDF['Store_Area'].apply(
+        lambda x: 'Small_Area' if x <= 1100 else ('Average_Area' if 1100 < x <= 1700 else 'Big_Area'))
+
+    mainDF['TextStoreSales'] = mainDF['Store_Sales'].apply(
+        lambda x: 'Small_Sales' if x <= 50000 else ('Average_Sales' if 50000 < x <= 70000 else 'Big_Sales'))
+
+    mainDF['TextDailyCustomerCount'] = mainDF['Daily_Customer_Count'].apply(
+        lambda x: 'Small_Customer' if x <= 400 else ('Average_Customer' if 400 < x <= 900 else 'Big_Customer'))
+
+    # using dictionary to convert specific columns
+    convert_dict = {'Store_ID': str,
+                    'Store_Area': str,
+                    'Items_Available': str,
+                    'Daily_Customer_Count': str,
+                    'Store_Sales': str
+                    }
+
+    newMainDF = mainDF.astype(convert_dict)
+
+    # генеральная выборка
+    newDfGeneral = newMainDF.iloc[0:25]
+
+    # выборка для проверки
+    newDfSupport = newMainDF.iloc[25:35]
+
+    return [newDfGeneral[['TextDailyCustomerCount', 'TextStoreArea', 'TextStoreSales']],
+            newDfSupport[['TextDailyCustomerCount', 'TextStoreArea', 'TextStoreSales']]]
+
+# [['Store_Area', 'Store_Sales', 'Daily_Customer_Count', 'TextStoreArea']]
+
+# [['Store_ID', 'Store_Area', 'TextStoreArea', 'Items_Available', 'Daily_Customer_Count', 'Store_Sales']]

LabWork01/LabWork6/ReservCopy/CopyTree.py

@@ -0,0 +1,117 @@
+import math
+import pandas as pd
+from functools import reduce
+
+from LabWork01.LabWork6.ConvertorDataFrame import CovertorDataFrame
+
+# Дата сет
+# data = [
+#     ["красный", "квадрат", "красный квадрат"],
+#     ["красный", "прямоугольник", "красный прямоугольник"],
+#     ["красный", "круг", "красный круг"],
+#     ["красный", "треугольник", "красный треугольник"],
+#
+#     ["зеленый", "квадрат", "зеленый квадрат"],
+#     ["зеленый", "треугольник", "зеленый треугольник"],
+#     ["зеленый", "круг", "зеленый круг"],
+# ]
+# df0 = pd.DataFrame(data)
+# df0.columns = ["цвет", "форма", "результат"]
+
+df0 = CovertorDataFrame()[0]
+
+# Лямбда-выражение для распределения значений, аргумент - pandas.Series,
+# возвращаемое значение - массив с количеством каждого из значений
+# Из вводных данных s с помощью value_counts() находим частоту каждого из значений,
+# и пока в нашем словаре есть элементы, будет работать цикл, запускаемый items().
+# Чтобы выходные данные не менялись с каждым запуском цикла, мы используем sorted,
+# который меняет порядок от большего к меньшему
+# В итоге, генерируется массив, содержащий строку из следующих компонентов: ключ (k) и значение (v).
+cstr = lambda s: [k + ":" + str(v) for k, v in sorted(s.value_counts().items())]
+
+# Структура данных Decision Tree
+tree = {
+    # name: Название этого нода (узла)
+    "name": "decision tree " + df0.columns[-1] + " " + str(cstr(df0.iloc[:, -1])),
+    # df: Данные, связанные с этим нодом (узлом)
+    "df": df0,
+    # edges: Список ребер (ветвей), выходящих из этого узла,
+    # или пустой массив, если ниже нет листового узла.
+    "edges": [],
+}
+
+# Генерацию дерева, у узлов которого могут быть ветви, сохраняем в open
+open = [tree]
+
+# Лямба-выражение для вычесления энтропии.
+# Аргумент - pandas.Series、возвращаемое значение - число энтропии
+entropy = lambda s: -reduce(lambda x, y: x + y, map(lambda x: (x / len(s)) * math.log2(x / len(s)), s.value_counts()))
+
+# Зацикливаем, пока open не станет пустым
+while (len(open) != 0):
+    # Вытаскиваем из массива open первый элемент,
+    # и вытаскиваем данные, хранящиеся в этом узле
+    n = open.pop(0)
+    df_n = n["df"]
+
+    # В случае, если энтропия этого узла равна 0, мы больше не можем вырастить из него новые ветви
+    # поэтому прекращаем ветвление от этого узла
+    if 0 == entropy(df_n.iloc[:, -1]):
+        continue
+    # Создаем переменную, в которую будем сохранять список значений атрибута с возможностью разветвления
+    attrs = {}
+    # Исследуем все атрибуты, кроме последнего столбца класса атрибутов
+    for attr in df_n.columns[:-1]:
+        # Создаем переменную, которая хранит значение энтропии при ветвлении с этим атрибутом,
+        # данные после разветвления и значение атрибута, который разветвляется.
+        attrs[attr] = {"entropy": 0, "dfs": [], "values": []}
+        # Исследуем все возможные значения этого атрибута.
+        # Кроме того, sorted предназначен для предотвращения изменения порядка массива,
+        # из которого были удалены повторяющиеся значения атрибутов, при каждом его выполнении.
+        for value in sorted(set(df_n[attr])):
+            # Фильтруем данные по значению атрибута
+            df_m = df_n.query(attr + "=='" + value + "'")
+            # Высчитываем энтропию, данные и значения сохрнаяем
+            attrs[attr]["entropy"] += entropy(df_m.iloc[:, -1]) * df_m.shape[0] / df_n.shape[0]
+            attrs[attr]["dfs"] += [df_m]
+            attrs[attr]["values"] += [value]
+            pass
+        pass
+    # Если не осталось ни одного атрибута, значение которого можно разделить,
+    # прерываем исследование этого узла.
+    if len(attrs) == 0:
+        continue
+    # Получаем атрибут с наименьшим значением энтропии
+    attr = min(attrs, key=lambda x: attrs[x]["entropy"])
+    # Добавляем каждое значение разветвленного атрибута
+    # и данные, полученные после разветвления, в наше дерево и в open.
+    for d, v in zip(attrs[attr]["dfs"], attrs[attr]["values"]):
+        m = {"name": attr + "=" + v, "edges": [], "df": d.drop(columns=attr)}
+        n["edges"].append(m)
+        open.append(m)
+    pass
+
+# Выводим дата сет
+print(df0, "\n-------------")
+
+
+# Метод преобразования дерева в символы, аргуметы - tree:структура данных древа,
+# indent:присоединяймый к дочернему узлу indent,
+# Возвращаемое значение - символьное представление древа.
+# Этот метод вызывается рекурсивно для преобразования всех данных в дереве в символы.
+def tstr(tree, indent=""):
+    # Создаем символьное представление этого узла.
+    # Если этот узел является листовым узлом (количество элементов в массиве ребер равно 0),
+    # частотное распределение последнего столбца данных df, связанных с деревом, преобразуется в символы.
+    s = indent + tree["name"] + str(cstr(tree["df"].iloc[:, -1]) if len(tree["edges"]) == 0 else "") + "\n"
+    # Зацикливаем все ветви этого узла.
+    for e in tree["edges"]:
+        # Добавляем символьное представление дочернего узла к символьному представлению родительского узла.
+        # Добавляем еще больше символов к indent этого узла.
+        s += tstr(e, indent + "  ")
+        pass
+    return s
+
+
+# Выводим древо в его символьном представлении.
+print(tstr(tree))

LabWork01/LabWork6/SecondTree.py

@@ -0,0 +1,78 @@
+import pandas as pd # 0 tabs
+import numpy as np # 0 tabs
+
+class DecisionTree: # 0 tabs
+    def __init__(self, max_depth=None): # 1 tab
+        self.max_depth = max_depth # 2 tabs
+        self.tree = {} # 2 tabs
+
+    def entropy(self, y): # 1 tab
+        classes, counts = np.unique(y, return_counts=True) # 2 tabs
+        probabilities = counts / len(y) # 2 tabs
+        entropy = sum(-p * np.log2(p) for p in probabilities) # 2 tabs
+        return entropy # 2 tabs
+
+    def information_gain(self, X, y, feature, threshold): # 1 tab
+        left_indices = X[:, feature] < threshold # 2 tabs
+        right_indices = ~left_indices # 2 tabs
+        left_entropy = self.entropy(y[left_indices]) # 2 tabs
+        right_entropy = self.entropy(y[right_indices]) # 2 tabs
+        left_weight = np.sum(left_indices) / len(y) # 2 tabs
+        right_weight = 1 - left_weight # 2 tabs
+        gain = self.entropy(y) - (left_weight * left_entropy + right_weight * right_entropy) # 2 tabs
+        return gain # 2 tabs
+
+    def best_split(self, X, y): # 1 tab
+        best_feature = None # 2 tabs
+        best_threshold = None # 2 tabs
+        best_gain = 0 # 2 tabs
+        for feature in range(X.shape[1]): # 2 tabs
+            thresholds = np.unique(X[:, feature]) # 3 tabs
+            for threshold in thresholds: # 3 tabs
+                gain = self.information_gain(X, y, feature, threshold) # 4 tabs
+                if gain > best_gain: # 4 tabs
+                    best_gain = gain # 5 tabs
+                    best_feature = feature # 5 tabs
+                    best_threshold = threshold # 5 tabs
+        return best_feature, best_threshold # 2 tabs
+
+    def build_tree(self, X, y, depth=0): # 1 tab
+        if len(np.unique(y)) == 1 or (self.max_depth is not None and depth >= self.max_depth): # 2 tabs
+            return {'class': np.argmax(np.bincount(y))} # 3 tabs
+        best_feature, best_threshold = self.best_split(X, y) # 2 tabs
+        left_indices = X[:, best_feature] < best_threshold # 2 tabs
+        right_indices = ~left_indices # 2 tabs
+        tree = {'feature': best_feature, 'threshold': best_threshold} # 2 tabs
+        tree['left'] = self.build_tree(X[left_indices], y[left_indices], depth + 1) # 2 tabs
+        tree['right'] = self.build_tree(X[right_indices], y[right_indices], depth + 1) # 2 tabs
+        return tree # 2 tabs
+
+    def fit(self, X, y): # 1 tab
+        self.tree = self.build_tree(X, y) # 2 tabs
+
+    def predict_instance(self, tree, x): # 1 tab
+        if 'class' in tree: # 2 tabs
+            return tree['class'] # 3 tabs
+        if x[tree['feature']] < tree['threshold']: # 2 tabs
+            return self.predict_instance(tree['left'], x) # 3 tabs
+        else: # 2 tabs
+            return self.predict_instance(tree['right'], x) # 3 tabs
+
+    def predict(self, X): # 1 tab
+        return [self.predict_instance(self.tree, x) for x in X] # 2 tabs
+
+# Пример использования
+data = { # 0 tabs
+    'feature1': [1, 2, 3, 4, 5], # 1 tab
+    'feature2': [0, 0, 1, 1, 0], # 1 tab
+    'target': [0, 0, 1, 1, 1] # 1 tab
+}
+
+df = pd.DataFrame(data) # 0 tabs
+X = df[['feature1', 'feature2']].values # 1 tab
+y = df['target'].values # 1 tab
+
+model = DecisionTree(max_depth=3) # 0 tabs
+model.fit(X, y) # 1 tab
+predictions = model.predict(X) # 1 tab
+print(predictions) # 0 tabs

LabWork01/LabWork6/Tree.py

@@ -0,0 +1,103 @@
+import math
+from functools import reduce
+
+from LabWork01.LabWork6.ConvertorDataFrame import CovertorDataFrame
+
+# дата-сет
+dfMain = CovertorDataFrame()[0]
+dfTest = CovertorDataFrame()[1]
+
+cstr = lambda s: [k + ":" + str(v) for k, v in sorted(s.value_counts().items())]
+
+# Структура данных Decision Tree
+tree = {
+    # name: Название этого нода (узла)
+    "name": "decision tree " + dfMain.columns[-1] + " " + str(cstr(dfMain.iloc[:, -1])),
+    # df: Данные, связанные с этим нодом (узлом)
+    "df": dfMain,
+    # edges: Список ребер (ветвей), выходящих из этого узла, или пустой массив, если ниже нет листового узла.
+    "edges": [],
+}
+
+# Генерацию дерева, у узлов которого могут быть ветви, сохраняем в open
+open = [tree]
+
+# Лямба-выражение для вычесления энтропии.
+# Аргумент - pandas.Series、возвращаемое значение - число энтропии
+entropy = lambda s: -reduce(lambda x, y: x + y, map(lambda x: (x / len(s)) * math.log2(x / len(s)), s.value_counts()))
+
+# Зацикливаем, пока open не станет пустым
+while (len(open) != 0):
+
+    n = open.pop(0)
+    df_n = n["df"]
+
+    if 0 == entropy(df_n.iloc[:, -1]):
+        continue
+
+    attrs = {}
+
+    for attr in df_n.columns[:-1]:
+
+        attrs[attr] = {"entropy": 0, "dfs": [], "values": []}
+
+        for value in sorted(set(df_n[attr])):
+            df_m = df_n.query(attr + "=='" + value + "'")
+
+            attrs[attr]["entropy"] += entropy(df_m.iloc[:, -1]) * df_m.shape[0] / df_n.shape[0]
+            attrs[attr]["dfs"] += [df_m]
+            attrs[attr]["values"] += [value]
+            pass
+        pass
+
+    if len(attrs) == 0:
+        continue
+
+    attr = min(attrs, key=lambda x: attrs[x]["entropy"])
+
+    for d, v in zip(attrs[attr]["dfs"], attrs[attr]["values"]):
+        m = {"name": attr + "=" + v, "edges": [], "df": d.drop(columns=attr)}
+        n["edges"].append(m)
+        open.append(m)
+    pass
+
+# Выводим дата сет
+print(dfMain, "\n-------------")
+
+# оценка тестовых данных
+def predict_bp(nodes, target) -> int:
+    overlap = None
+    for node in nodes:
+        check: bool = node["value"] == target[node["attr"]]
+        if check:
+            overlap = node
+            break
+    if overlap is None:
+        overlap = nodes[-1]
+    if len(overlap["edges"]) == 0:
+        return int(overlap["df"]["StoreSales"].mean())
+    else:
+        return predict_bp(overlap["edges"], target)
+
+def predict_str(count: int):
+    predictions = []
+    for i in range(count):
+        row = dfTest.iloc[i]
+        prediction = f"{ {'Age': row['Age'], 'BMI': row['BMI']} }" + \
+                     f"<br/>predict {predict_bp(tree['edges'], {'Age': row['Age'], 'BMI': row['BMI']})} / fact {row['BloodPressure']}"
+        predictions.append(prediction)
+    return '<br/>'.join(predictions)
+
+def tstr(tree, indent=""):
+    s = indent + tree["name"] + str(cstr(tree["df"].iloc[:, -1]) if len(tree["edges"]) == 0 else "") + "\n"
+    # Зацикливаем все ветви этого узла.
+    for e in tree["edges"]:
+        s += tstr(e, "\t" + indent + "  ")
+        pass
+    return s
+
+def getStringTree():
+    return tstr(tree)
+
+# Выводим древо в его символьном представлении.
+print(tstr(tree))

LabWork01/LoadDB.py

@@ -0,0 +1,224 @@
+import os
+import secrets
+from flask import Flask, request, render_template, session
+from matplotlib import pyplot as plt
+
+from LabWork01.AnalysCustomers import analysCustomersDataFrame
+from LabWork01.AnalysSales import analysSalesDataFrame
+from LabWork01.AnalysSalesCustomers import analysSalesCustomersDataFrame
+from LabWork01.DataFrameAnalys import analysItemsDataFrame
+from LabWork01.FuncLoad import createDataFrame
+from LabWork01.LabWork3.AddData import addData
+from LabWork01.LabWork3.CreateGraphics import createGraphics
+from LabWork01.LabWork3.CustomGraphics import createCusGraphics
+from LabWork01.LabWork3.DeletePng import deleteAllPng
+from LabWork01.LabWork4.SiteSearch import SiteSearch
+from LabWork01.LabWork5.Сreate_plot import create_plot_jpg
+from LabWork01.LabWork6.Tree import getStringTree
+
+app = Flask(__name__)
+
+# Для работы session
+secret = secrets.token_urlsafe(32)
+app.secret_key = secret
+
+#сразу загружаем весь док, чтобы потом просто прыгать по нему
+listShops = createDataFrame()
+
+#список типов данных по столбцам
+listTypes = listShops.dtypes.to_list()
+
+#формируем записи о кол-ве пустых ячеек в каждом столбце
+countNull = listShops.isnull().sum()
+
+# для фильтра Блума
+search_engine = SiteSearch()
+search_engine.add("https://www.kaggle.com/datasets/ankanhore545/100-highest-valued-unicorns", ["Company", "Valuation", "Country", "State", "City", "Industries", "Founded Year", "Name of Founders", "Total Funding", "Number of Employees"])
+search_engine.add("https://www.kaggle.com/datasets/ilyaryabov/tesla-insider-trading", ["Insider Trading", "Relationship", "Date", "Transaction", "Cost", "Shares", "Value", "Shares Total", "SEC Form 4"])
+search_engine.add("https://www.kaggle.com/datasets/sameepvani/nasa-nearest-earth-objects", ["NASA", "est_diameter_min", "est_diameter_max", "relative_velocity", "miss_distance", "orbiting_body", "sentry_object", "absolute_magnitude", "hazardous"])
+search_engine.add("https://www.kaggle.com/datasets/surajjha101/stores-area-and-sales-data", ["Store", "Area", "Available", "Daily", "Customer", "Sales"])
+search_engine.add("https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database", ["Health", "Diabetes", "India"])
+search_engine.add("https://www.kaggle.com/datasets/mirichoi0218/insurance", ["age", "sex", "bmi"])
+search_engine.add("https://www.kaggle.com/datasets/muhammedtausif/world-population-by-countries", ["Country", "Population", "Continent", "Capital", "Yearly Change", "Land Area", "Fertility","Density"])
+search_engine.add("https://www.kaggle.com/datasets/deepcontractor/car-price-prediction-challenge", ["car", "price", "manufacturer"])
+search_engine.add("https://www.kaggle.com/datasets/surajjha101/forbes-billionaires-data-preprocessed", ["Name", "Networth", "Source"])
+search_engine.add("https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset", [ "heart_disease" , "bmi", "stroke" ])
+
+
+@app.route("/", methods=['GET','POST'])
+def home():
+    return render_template('main_page.html', context=[], main_img=[], messages=[], image_names=[], tableAnalys=[], titles=[''], listTypes=listTypes, countNull=countNull, firstRow=1, secondRow=4, firstColumn=1, secondColumn=4)
+
+@app.route("/showDiapason", methods=['GET','POST'])
+def numtext():
+    data = request.args
+
+    #получаем срез и таблицы по введёным параметрам
+    newListShops = listShops.iloc[int(data['firstRow'])-1:int(data['secondRow']), int(data['firstColumn']):int(data['secondColumn'])+1]
+
+    _range = range(int(data['firstColumn']), int(data['secondColumn'])+1)
+
+    #список списков для шаблона
+    totalList = []
+
+    print(countNull[1])
+
+    #формирование 4-х списков для шаблонизатора
+    if 1 in _range:
+        listStoreArea = newListShops['Store_Area'].to_list()
+
+        totalList.append(listStoreArea)
+
+    if 2 in _range:
+        listItemsAvailable = newListShops['Items_Available'].to_list()
+
+        totalList.append(listItemsAvailable)
+
+    if 3 in _range:
+        listDailyCustomerCount = newListShops['Daily_Customer_Count'].to_list()
+
+        totalList.append(listDailyCustomerCount)
+
+    if 4 in _range:
+        listStoreSales = newListShops['Store_Sales'].to_list()
+
+        totalList.append(listStoreSales)
+
+    if int(data['firstRow']) and int(data['secondRow']) and int(data['firstColumn']) and int(data['secondColumn']):
+        return render_template('main_page.html', context=totalList, main_img=[], messages=[], image_names=[], listTypes=listTypes, countNull=countNull,
+                               firstColumn=int(data['firstColumn']), secondColumn=int(data['secondColumn']),
+                               firstRow=int(data['firstRow']), secondRow=int(data['secondRow']))
+
+    return home()
+
+#функция для проведения анализа данных
+@app.route("/analysis", methods=['GET', 'POST'])
+def analysis():
+    firstAnalys = analysItemsDataFrame(listShops)
+    secondAnalys = analysCustomersDataFrame(listShops)
+    thirdAnalys = analysSalesDataFrame(listShops)
+    fourthAnalys = analysSalesCustomersDataFrame(listShops)
+
+    # удаляем все текущие диаграммы
+    deleteAllPng()
+
+    # начинаем создавать диаграммы
+    createGraphics(firstAnalys, 'firstAn')
+    createGraphics(secondAnalys, 'secondAn')
+    createGraphics(thirdAnalys, 'thirdAn')
+    createGraphics(fourthAnalys, 'fourthAn')
+
+    # дополняем новыми значениями
+    additionListShops = addData(createDataFrame())
+
+    # получаем новые данные
+    newfirstAnalys = analysItemsDataFrame(additionListShops)
+    secondAnalys = analysCustomersDataFrame(additionListShops)
+    thirdAnalys = analysSalesDataFrame(additionListShops)
+    fourthAnalys = analysSalesCustomersDataFrame(additionListShops)
+
+    # создаём новые диаграммы
+    createGraphics(newfirstAnalys, 'addFirstAn')
+    createGraphics(secondAnalys, 'addSecondAn')
+    createGraphics(thirdAnalys, 'addThirdAn')
+    createGraphics(fourthAnalys, 'addFourthAn')
+
+    createCusGraphics(firstAnalys[0], newfirstAnalys[0])
+
+    image_names_start = ['firstAn0.jpg', 'firstAn1.jpg', 'firstAn2.jpg',
+                         'secondAn0.jpg', 'secondAn1.jpg', 'secondAn2.jpg',
+                         'thirdAn0.jpg', 'thirdAn1.jpg', 'thirdAn2.jpg',
+                         'fourthAn0.jpg', 'fourthAn1.jpg', 'fourthAn2.jpg']
+
+    image_names_addition = ['addFirstAn0.jpg', 'addFirstAn1.jpg', 'addFirstAn2.jpg',
+                         'addSecondAn0.jpg', 'addSecondAn1.jpg', 'addSecondAn2.jpg',
+                         'addThirdAn0.jpg', 'addThirdAn1.jpg', 'addThirdAn2.jpg',
+                         'addFourthAn0.jpg', 'addFourthAn1.jpg', 'addFourthAn2.jpg']
+
+    main_img = ['CustomJPG0.jpg']
+
+    result = listShops[['Store_Sales']]
+    # Строим boxplot с использованием Seaborn
+    plt.title('Valuation Boxplot by Store Sales')
+    plt.xlabel('Store Sales')
+
+    plt.boxplot(result['Store_Sales'])
+
+    script_dir = os.path.dirname(__file__)
+    results_dir = os.path.join(script_dir, 'static/')
+
+    plt.savefig(results_dir + 'NewCustomJPG' + str(0) + '.jpg')
+
+    newCustomJpg = ['NewCustomJPG0.jpg']
+
+
+    return render_template('main_page.html', context=[], image_names_start=image_names_start,
+                           image_names_addition=image_names_addition,
+                           tableAnalysOne=[],
+                           tableAnalysTwo=[],
+                           tableAnalysThree=[],
+                           tableAnalysFour=[],
+                           main_img=newCustomJpg,
+                           messages=[],
+                           titles=[''],
+                           listTypes=listTypes, countNull=countNull, firstRow=1,
+                           secondRow=4, firstColumn=1, secondColumn=4)
+
+@app.route('/findURL', methods=['GET'])
+def get_page_findURL():
+    return render_template('findURL.html', find=False, notFind=False)
+
+@app.route('/findURL', methods=['POST'])
+def findURL():
+    word = request.form["word"]
+    if (search_engine.contains(word)):
+        session["new_word"] = word
+        return render_template('findURL.html', find=True, notFind=False)
+    return render_template('findURL.html', find=False, notFind=True)
+
+@app.route('/showFindLinks', methods=['POST'])
+def get_page_showFindURL():
+    word = session.get("new_word", None)
+
+    links = search_engine.find_url(word)
+    word_links = []
+    for item in links:
+        word_links.append({item, word})
+
+    return render_template('showLinks.html', links=links)
+
+# 5-я лабораторная
+@app.route('/createPlotImage',  methods=['GET', 'POST'])
+def get_plot_image():
+
+    # 99%
+    # main_df = listShops.loc[listShops['Store_ID'] <= listShops.shape[0]*0.9]
+
+    # 1%
+    # support_df = listShops.loc[listShops['Store_ID'] > listShops.shape[0]*0.9]
+
+    messages = create_plot_jpg(listShops, "myPlot")
+
+    myPlotJpg = ['myPlot.jpg']
+
+    return render_template('main_page.html', context=[], image_names_start=[],
+                           image_names_addition=[],
+                           tableAnalysOne=[],
+                           tableAnalysTwo=[],
+                           tableAnalysThree=[],
+                           tableAnalysFour=[],
+                           main_img=myPlotJpg,
+                           messages=messages,
+                           titles=[''],
+                           listTypes=listTypes, countNull=countNull, firstRow=1,
+                           secondRow=4, firstColumn=1, secondColumn=4)
+
+@app.route('/createTree', methods=['GET'])
+def get_data_Tree():
+    DataTree = getStringTree().split('\n')
+
+    return render_template('tree_page.html', DataTree=DataTree)
+
+if __name__=="__main__":
+    app.run(debug=True)
+

LabWork01/Shop.py

@@ -0,0 +1,11 @@
+class Shop:
+    def __init__(self, store_area, items_available, daily_customer_count, store_sales):
+        self.store_area = store_area
+        self.items_available = items_available
+        self.daily_customer_count = daily_customer_count
+        self.store_sales = store_sales
+
+    def display_shop(self):
+        print('Площадь: {}. Кол-во товаров: {}. Кол-во посетителей в день: {}. '
+              'Кол-во покупок: {}'.format(self.store_area, self.items_available,
+                                          self.daily_customer_count, self.store_sales))

LabWork01/templates/findUrl.html

@@ -0,0 +1,41 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport"
+          content="width=device-width, user-scalable=no, initial-scale=1.0, maximum-scale=1.0, minimum-scale=1.0">
+    <meta http-equiv="X-UA-Compatible" content="ie=edge">
+    <link rel="stylesheet" type="text/css" href="{{ url_for( 'static', filename='index.css', v=1)}}">
+    <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.0.2/dist/css/bootstrap.min.css" rel="stylesheet"
+          integrity="sha384-EVSTQN3/azprG1Anm3QDgpJLIm9Nao0Yz1ztcQTwFspd3yD65VohhpuuCOmLASjC" crossorigin="anonymous">
+    <title>Document</title>
+</head>
+<body>
+<div class="container">
+    <form action="/findURL" method="post">
+        <div class="mb-5 mt-3">
+            <label class="form-label">Поиск</label>
+            <div class="row">
+               <div class="col" style="width: 100%">
+                    <input type="text" name="word" placeholder="Слово">
+                </div>
+                <div class="col">
+                    <input type=submit value='Вывод диапазона'>
+                </div>
+            </div>
+        </div>
+    </form>
+    <div class="">
+        {% if find: %}
+            <form action='/showFindLinks' method=post>
+                <input type=submit value='Показать результаты'>
+            </form>
+        {% endif %}
+        {% if notFind: %}
+            <h1>Смысла нет</h1>
+        {% endif %}
+    </div>
+</div>
+
+</body>
+</html>

LabWork01/templates/main_page.html

@@ -0,0 +1,142 @@
+<!DOCTYPE html>
+<html lang="ru">
+<head>
+    <meta charset="UTF-8">
+    <title>Список магазинов</title>
+    <style>
+        .head {
+            background: #fc3;
+            border: 2px solid black;
+            padding: 20px;
+        }
+    </style>
+</head>
+<body>
+    <div>
+        <form action='http://127.0.0.1:5000/showDiapason' method=get>
+            <input type=text size=20 name=firstRow>
+            <input type=text size=20 name=secondRow>
+            <input type=text size=20 name=firstColumn>
+            <input type=text size=20 name=secondColumn>
+            <input type=submit value='Вывод диапазона'>
+        </form>
+        <form action='http://127.0.0.1:5000/analysis' method=get>
+            <input type=submit value='Анализ данных'>
+        </form>
+        <form action='/createPlotImage' method=get>
+            <input type=submit value='Создание регрессии'>
+        </form>
+        <form action="/findURL" method="get">
+            <div class="mb-3">
+                <button type="submit" class="btn btn-primary mb-3">Запуск фильтра</button>
+            </div>
+        </form>
+        <form action="/createTree" method="get">
+            <div class="mb-3">
+                <button type="submit" class="btn btn-primary mb-3">Построить дерево</button>
+            </div>
+        </form>
+    </div>
+    <div>
+        <table>
+            <tr>
+                {{ countNull }}
+            </tr>
+            <tr>
+                {% if firstColumn == 1 %}
+                    <th class="head">Площадь магазина, кв. м, {{ listTypes[1] }}</th>
+                {% endif %}
+                {% if firstColumn <= 2 and secondColumn >= 2 %}
+                    <th class="head">Кол-во продукции, шт, {{ listTypes[2] }}</th>
+                {% endif %}
+                {% if firstColumn <= 3 and secondColumn >= 3 %}
+                    <th class="head">Кол-во посетителей в день, чел., {{ listTypes[3] }}</th>
+                {% endif %}
+                {% if secondColumn == 4 %}
+                    <th class="head">Кол-во проданных товаров, шт., {{ listTypes[4] }}</th>
+                {% endif %}
+            </tr>
+            {% for i in range(secondRow - firstRow + 1) %}
+                <tr>
+                    {% for elem in context %}
+                        <td> {{ elem[i] }} </td>
+                    {% endfor %}
+                </tr>
+            {% endfor %}
+        </table>
+    </div>
+    <div>
+        <h3>
+            Данные до дополнения:
+        </h3>
+        {% for image_name in image_names_start %}
+            <img src="{{ url_for('static', filename=image_name) }}" alt="{{ image_name }}">
+        {% endfor %}
+    </div>
+    <div>
+        <h3>
+            Данные после дополнения:
+        </h3>
+        {% for image_name in image_names_addition %}
+            <img src="{{ url_for('static', filename=image_name) }}" alt="{{ image_name }}">
+        {% endfor %}
+    </div>
+    <div>
+        <h3>
+            По заданию:
+        </h3>
+        {% for image_name in main_img %}
+            <img src="{{ url_for('static', filename=image_name) }}" alt="{{ image_name }}">
+            {% for message in messages %}
+            <div>
+                <p>{{message}}</p>
+            </div>
+            {% endfor %}
+        {% endfor %}
+    </div>
+    <div>
+        <h3>
+            Результаты первого анализа
+        </h3>
+        <h1>
+            {% for table in tableAnalysOne %}
+            <h2>{{ titles[loop.index] }}</h2>
+            {{ table|safe }}
+            {% endfor %}
+        </h1>
+    </div>
+    <div>
+        <h3>
+            Результаты второго анализа
+        </h3>
+        <h1>
+            {% for table in tableAnalysTwo %}
+            <h2>{{ titles[loop.index] }}</h2>
+            {{ table|safe }}
+            {% endfor %}
+        </h1>
+    </div>
+    <div>
+        <h3>
+            Результаты третьего анализа
+        </h3>
+        <h1>
+            {% for table in tableAnalysThree %}
+            <h2>{{ titles[loop.index] }}</h2>
+            {{ table|safe }}
+            {% endfor %}
+        </h1>
+    </div>
+    <div>
+        <h3>
+            Результаты четвёртого анализа
+        </h3>
+        <h1>
+            {% for table in tableAnalysFour %}
+            <h2>{{ titles[loop.index] }}</h2>
+            {{ table|safe }}
+            {% endfor %}
+        </h1>
+    </div>
+</body>
+</html>

LabWork01/templates/showLinks.html

@@ -0,0 +1,19 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>Найденные ссылки</title>
+</head>
+<body>
+    <form action="/findURL" method="get">
+        <div class="mb-3">
+            <button type="submit" class="btn btn-primary mb-3">Вернуться к фильтру</button>
+        </div>
+    </form>
+    <div class="">
+        {% for link in links %}
+            <a href="{{ link }}">{{ link }}</a>
+        {% endfor %}
+    </div>
+</body>
+</html>

LabWork01/templates/tree_page.html

@@ -0,0 +1,38 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport"
+          content="width=device-width, user-scalable=no, initial-scale=1.0, maximum-scale=1.0, minimum-scale=1.0">
+    <meta http-equiv="X-UA-Compatible" content="ie=edge">
+    <link rel="stylesheet" type="text/css" href="{{ url_for( 'static', filename='index.css', v=1)}}">
+    <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.0.2/dist/css/bootstrap.min.css" rel="stylesheet"
+          integrity="sha384-EVSTQN3/azprG1Anm3QDgpJLIm9Nao0Yz1ztcQTwFspd3yD65VohhpuuCOmLASjC" crossorigin="anonymous">
+    <title>Tree</title>
+</head>
+<body>
+<div class="container">
+    <form action="/" method="get">
+        <div class="mb-5 mt-3">
+            <label class="form-label">Вывод дерева</label>
+            <div class="row">
+                <div class="col">
+                    <input type=submit value='Главная'>
+                </div>
+            </div>
+        </div>
+    </form>
+    <div class="">
+        {% for item in DataTree %}
+            {% if item.count("\t") == 1 %}
+                <p  style="margin-left: 50px;"> {{ item }}</p>
+            {% elif item.count("\t") == 2 %}
+                <p  style="margin-left: 100px;"> {{ item }}</p>
+            {% else %}
+                <p>{{ item }}</p>
+            {% endif %}
+        {% endfor %}
+    </div>
+</div>
+</body>
+</html>

Test.py

@@ -15,4 +15,4 @@ def numtext():
     return num2words(data['name'], lang='ru')
 
 if __name__=="__main__":
-    app.run(debug=False)
+    app.run(debug=True)

res/Stores.csv

@@ -0,0 +1,897 @@
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