Сдал лабу, это пипец

LabWork01/AnalysCustomers.py

@@ -10,14 +10,29 @@ def analysCustomersDataFrame(df: pandas.DataFrame):
     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_two = minMaxMean_two.iloc[:, 1:4]
     minMaxMean_three = table_three.agg({'Daily_Customer_Count': ['min', 'max', 'mean']}).round(2).reset_index()
+    minMaxMean_three.columns = minMaxMean_three.columns.droplevel()
+    minMaxMean_three = minMaxMean_three.iloc[:, 1:4]
 
-    totalTable = pd.merge(minMaxMean_one, minMaxMean_two, left_index=True, right_index=True)
+    df1 = pd.DataFrame()
+    df1['small'] = minMaxMean_one['min']
+    df1['average'] = minMaxMean_two['min']
+    df1['big'] = minMaxMean_three['min']
 
-    totalTable = pd.merge(totalTable, minMaxMean_three, left_index=True, right_index=True)
+    df2 = pd.DataFrame()
+    df2['small'] = minMaxMean_one['max']
+    df2['average'] = minMaxMean_two['max']
+    df2['big'] = minMaxMean_three['max']
 
-    # for data in roundedListShops.items():
+    df3 = pd.DataFrame()
-    #     roundedListShops.loc[data[0], 'Store_Area'] = (data[1].astype("Int64")/100)*100
+    df3['small'] = minMaxMean_one['mean']
+    df3['average'] = minMaxMean_two['mean']
+    df3['big'] = minMaxMean_three['mean']
+
+    totalTable = [df1, df2, df3]
 
     return totalTable

LabWork01/AnalysSales.py

@@ -10,14 +10,29 @@ def analysSalesDataFrame(df: pandas.DataFrame):
     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]
 
-    totalTable = pd.merge(minMaxMean_one, minMaxMean_two, left_index=True, right_index=True)
+    df1 = pd.DataFrame()
+    df1['small'] = minMaxMean_one['min']
+    df1['average'] = minMaxMean_two['min']
+    df1['big'] = minMaxMean_three['min']
 
-    totalTable = pd.merge(totalTable, minMaxMean_three, left_index=True, right_index=True)
+    df2 = pd.DataFrame()
+    df2['small'] = minMaxMean_one['max']
+    df2['average'] = minMaxMean_two['max']
+    df2['big'] = minMaxMean_three['max']
 
-    # for data in roundedListShops.items():
+    df3 = pd.DataFrame()
-    #     roundedListShops.loc[data[0], 'Store_Area'] = (data[1].astype("Int64")/100)*100
+    df3['small'] = minMaxMean_one['mean']
+    df3['average'] = minMaxMean_two['mean']
+    df3['big'] = minMaxMean_three['mean']
+
+    totalTable = [df1, df2, df3]
 
     return totalTable

LabWork01/AnalysSalesCustomers.py

@@ -10,14 +10,29 @@ def analysSalesCustomersDataFrame(df: pandas.DataFrame):
     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]
 
-    totalTable = pd.merge(minMaxMean_one, minMaxMean_two, left_index=True, right_index=True)
+    df1 = pd.DataFrame()
+    df1['small'] = minMaxMean_one['min']
+    df1['average'] = minMaxMean_two['min']
+    df1['big'] = minMaxMean_three['min']
 
-    totalTable = pd.merge(totalTable, minMaxMean_three, left_index=True, right_index=True)
+    df2 = pd.DataFrame()
+    df2['small'] = minMaxMean_one['max']
+    df2['average'] = minMaxMean_two['max']
+    df2['big'] = minMaxMean_three['max']
 
-    # for data in roundedListShops.items():
+    df3 = pd.DataFrame()
-    #     roundedListShops.loc[data[0], 'Store_Area'] = (data[1].astype("Int64")/100)*100
+    df3['small'] = minMaxMean_one['mean']
+    df3['average'] = minMaxMean_two['mean']
+    df3['big'] = minMaxMean_three['mean']
+
+    totalTable = [df1, df2, df3]
 
     return totalTable

LabWork01/DataFrameAnalys.py

@@ -2,7 +2,6 @@ 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'))
@@ -12,14 +11,29 @@ def analysItemsDataFrame(df: pandas.DataFrame):
     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]
 
-    totalTable = pd.merge(minMaxMean_one, minMaxMean_two, left_index=True, right_index=True)
+    df1 = pd.DataFrame()
+    df1['small'] = minMaxMean_one['min']
+    df1['average'] = minMaxMean_two['min']
+    df1['big'] = minMaxMean_three['min']
 
-    totalTable = pd.merge(totalTable, minMaxMean_three, left_index=True, right_index=True)
+    df2 = pd.DataFrame()
+    df2['small'] = minMaxMean_one['max']
+    df2['average'] = minMaxMean_two['max']
+    df2['big'] = minMaxMean_three['max']
 
-    # for data in roundedListShops.items():
+    df3 = pd.DataFrame()
-    #     roundedListShops.loc[data[0], 'Store_Area'] = (data[1].astype("Int64")/100)*100
+    df3['small'] = minMaxMean_one['mean']
+    df3['average'] = minMaxMean_two['mean']
+    df3['big'] = minMaxMean_three['mean']
+
+    totalTable = [df1, df2, df3]
 
     return totalTable

LabWork01/LabWork3/CreateGraphics.py

@@ -1,11 +1,10 @@
 import os
-import glob
 import pandas as pd
 import matplotlib
 matplotlib.use('Agg')
 import matplotlib.pyplot as plt
 
-def createGraphics(df: pd.DataFrame):
+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/')
@@ -13,34 +12,13 @@ def createGraphics(df: pd.DataFrame):
     if not os.path.isdir(results_dir):
         os.makedirs(results_dir)
 
-    # ax = plt.gca()
+    for i in range(len(df)):
-    #
+        dataFrame = df[i]
-    # df.plot(kind='line', x='Store_Area', y='Items_Available', color='red', ax=ax)
+        dataFrame.plot.bar()
-    # df.plot(kind='line', x='Store_Area', y='Daily_Customer_Count', color='yellow', ax=ax)
+        plt.xlabel('Здесь чё-то')
-
+        plt.ylabel('Тоже чё-то')
-    df.plot(x="Items_Available", y="Store_Area", kind="bar")
+        plt.title('Диаграмма про чё-то')
-
+        plt.savefig(results_dir + nameFile + str(i) + '.jpg')
-    plt.savefig(results_dir + 'file' + str(1) + '.jpg')
+        plt.close()
-
-    plt.close()
-
-    df.boxplot(column=['Store_Area', 'Items_Available', 'Daily_Customer_Count'])
-
-    plt.savefig(results_dir + 'file' + str(2) + '.jpg')
-
-    plt.close()
-
-    df.boxplot(column=['Store_Sales'])
-
-    plt.savefig(results_dir + 'file' + str(3) + '.jpg')
-
-    plt.close()
-
-    # # сразу загружаем все созданные png файлы
-    # results_dir = os.path.join(script_dir, 'static/*')
-    #
-    # files = glob.glob(results_dir)
-    #
-    # return files
 
     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/LoadDB.py

@@ -1,4 +1,7 @@
+import os
+
 from flask import Flask, redirect, url_for, request, render_template
+from matplotlib import pyplot as plt
 
 from LabWork01.AnalysCustomers import analysCustomersDataFrame
 from LabWork01.AnalysSales import analysSalesDataFrame
@@ -7,6 +10,7 @@ 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
 
 
@@ -23,17 +27,7 @@ countNull = listShops.isnull().sum()
 
 @app.route("/")
 def home():
-    additionListShops = addData(createDataFrame())
+    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)
-
-    # удаляем текущие диаграммы
-    deleteAllPng()
-
-    # создаём новые диаграммы
-    createGraphics(additionListShops)
-
-    image_names = ['file1.jpg', 'file2.jpg', 'file3.jpg']
-
-    return render_template('main_page.html', context=[], image_names=image_names, tableAnalys=[], titles=[''], listTypes=listTypes, countNull=countNull, firstRow=1, secondRow=4, firstColumn=1, secondColumn=4)
 
 @app.route("/showDiapason", methods=['GET','POST'])
 def numtext():
@@ -71,7 +65,7 @@ def numtext():
         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, image_names=[], listTypes=listTypes, countNull=countNull,
+        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']))
 
@@ -81,20 +75,70 @@ def numtext():
 @app.route("/analysis", methods=['GET', 'POST'])
 def analysis():
     firstAnalys = analysItemsDataFrame(listShops)
-
     secondAnalys = analysCustomersDataFrame(listShops)
-
     thirdAnalys = analysSalesDataFrame(listShops)
-
     fourthAnalys = analysSalesCustomersDataFrame(listShops)
 
-    print(firstAnalys)
+    # удаляем все текущие диаграммы
+    deleteAllPng()
 
-    return render_template('main_page.html', context=[], image_names=[],
+    # начинаем создавать диаграммы
-                           tableAnalysOne=[firstAnalys.to_html()],
+    createGraphics(firstAnalys, 'firstAn')
-                           tableAnalysTwo=[secondAnalys.to_html()],
+    createGraphics(secondAnalys, 'secondAn')
-                           tableAnalysThree=[thirdAnalys.to_html()],
+    createGraphics(thirdAnalys, 'thirdAn')
-                           tableAnalysFour=[fourthAnalys.to_html()],
+    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)

LabWork01/templates/main_page.html

@@ -53,7 +53,26 @@
         </table>
     </div>
     <div>
-        {% for image_name in image_names %}
+        <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>