Completed LabWork4.

.idea/csv-editor.xml

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+<?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

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+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

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+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

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+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

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+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

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+import pandas as pd
+
+def createDataFrame():
+    df = pd.read_csv('../res/Stores.csv')
+
+    return df

LabWork01/LabWork3/AddData.py

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+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

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+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

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+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

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+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

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+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

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+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/LoadDB.py

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+import os
+import secrets
+from flask import Flask, redirect, url_for, 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
+
+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("/")
+def home():
+    return render_template('main_page.html', context=[], main_img=[], 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=[], 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,
+                           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)
+
+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 +1,129 @@
+<!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="/findURL" method="get">
+            <div class="mb-3">
+                <button type="submit" class="btn btn-primary mb-3">Запуск фильтра</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 }}">
+        {% 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>

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 @@
+Store_ID,Store_Area,Items_Available,Daily_Customer_Count,Store_Sales
+1,1659,1961,530,66490
+2,1461,1752,210,39820
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