diff --git a/lab1.ipynb b/lab1.ipynb
new file mode 100644
index 0000000..ae6c6d7
--- /dev/null
+++ b/lab1.ipynb
@@ -0,0 +1,198 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Начинаем работу...\n",
+    "\n",
+    "Выгрузка данных будет проводиться с помощью Pandas из cvs файла (Данные по продажам домов). Выгрузим-ка данные из cvs файла в датафрейм:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['id', 'date', 'price', 'bedrooms', 'bathrooms', 'sqft_living',\n",
+      "       'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade',\n",
+      "       'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode',\n",
+      "       'lat', 'long', 'sqft_living15', 'sqft_lot15'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Загрузка данных\n",
+    "df = pd.read_csv(\".//static//csv//kc_house_data.csv\")\n",
+    "\n",
+    "# Вывод столбцов\n",
+    "print(df.columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Ураа мы справились с выводом данных**\n",
+    "\n",
+    "Помимо вывода, подсоединили дополнительные библиотеки, которые помогут построить графики :)\n",
+    "\n",
+    "Приступим к построению диаграмм..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function matplotlib.pyplot.show(close=None, block=None)>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1. Диаграмма распределения цен (гистограмма)\n",
+    "plt.figure(figsize=(10,6))\n",
+    "sns.histplot(df['price'], bins=50, kde=True)\n",
+    "plt.title('Распределение цен на недвижимость')\n",
+    "plt.xlabel('Цена')\n",
+    "plt.ylabel('Частота')\n",
+    "plt.show"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Диаграмма №1 (Гистограмма)\n",
+    "\n",
+    "Данная круговая диаграмма отображает распределение цен на недвижимость. Bins позволяет установить интервальность исследования, так на графике заданы 50 интервалов, для более детального отображения распределения цен. Это позволяет сделать вывод о том, что большинство объектов недвижимости находится в более низком ценовом сегменте и дорогая недвижимость встречается реже."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 2. Связь между площадью жилья и ценой\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.scatter(x='sqft_living', y='price', data=df)\n",
+    "plt.title('Связь между площадью жилья и ценой')\n",
+    "plt.xlabel('Площадь жилья (кв. футы)')\n",
+    "plt.ylabel('Цена')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Диаграмма №2 (Точечная диаграмма)\n",
+    "\n",
+    "Данная точечная диаграмма отображает связь между площадью жилья и ценой. Массовое скопление точек в нижней части графика сообщает о том, что большинство объектов недвижимости находятся в доступном ценовом сегменте с умеренной жилой площадью. Площадь влияет на цену недвижимости (с увеличением жилой площади возрастает и цена). Таким образом, наблюдается прямолинейная, положительная корреляция между ценой и площадью жилья."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 3. Круговая диаграмма, показыващая состояние домов\n",
+    "plt.figure(figsize=(8, 8))\n",
+    "df['condition'].value_counts().plot.pie(autopct='%1.1f%%', startangle=90, cmap='Accent', wedgeprops={'edgecolor' : 'black'})\n",
+    "plt.title('Доля домов по их техническому состоянию')\n",
+    "plt.ylabel('')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Диаграмма №3 (Круговая диаграмма)\n",
+    "\n",
+    "Данная круговая диаграмма позволяет отслеживать в каких состояниях объекты недвижимости находятся. Значения варьируются от 1 до 5, где 1-2 - это плохое и ужасное состояния, 3 - среднее, а 4-5 хорошее и отличное. Преобладающее большинство недвижимости находится в удовлетворительном состоянии (где потребовался бы небольшой ремонт). В плохом и ужасном состоянии доля недвижимости состовляет < 1%, что является очень хорошим показателем.  \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "# Урааа, всё вроде получилось, теперь будем пушиться :)\n",
+    "P.S. Markdown и правда прикольная и нужная вещь. Однако, почему по началу работы проект не видел, две установленные библиотечки, а после того как пересоздали полностью весь проект, всё прошло без особых проблем..."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "mai",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/mai/data/Stores.csv b/mai/data/Stores.csv
new file mode 100644
index 0000000..d5cd92c
--- /dev/null
+++ b/mai/data/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
+3,1340,1609,720,54010
+4,1451,1748,620,53730
+5,1770,2111,450,46620
+6,1442,1733,760,45260
+7,1542,1858,1030,72240
+8,1261,1507,1020,37720
+9,1090,1321,680,46310
+10,1030,1235,1130,44150
+11,1187,1439,1090,71280
+12,1751,2098,720,57620
+13,1746,2064,1050,60470
+14,1615,1931,1160,59130
+15,1469,1756,770,66360
+16,1644,1950,790,78870
+17,1578,1907,1440,77250
+18,1703,2045,670,38170
+19,1438,1731,1030,63540
+20,1940,2340,980,40190
+21,1421,1700,370,43460
+22,1458,1746,690,68890
+23,1719,2065,950,52780
+24,1449,1752,620,50680
+25,1234,1488,840,41880
+26,1732,2073,820,70050
+27,1475,1777,1100,25820
+28,1390,1648,980,60530
+29,1642,1943,710,78100
+30,1715,2071,650,84860
+31,1439,1746,990,80140
+32,1250,1508,990,14920
+33,1331,1608,880,60460
+34,1784,2163,620,74560
+35,1375,1648,1020,72430
+36,1871,2230,700,45460
+37,1442,1744,610,41570
+38,1174,1411,1080,62870
+39,1839,2204,1010,55170
+40,1270,1516,10,45480
+41,1435,1725,1250,49550
+42,965,1152,600,48140
+43,1665,2001,730,67640
+44,1780,2117,780,39730
+45,1009,1194,520,35800
+46,1227,1471,870,49270
+47,1769,2087,690,66510
+48,1660,1982,910,62530
+49,1472,1776,1260,59980
+50,1408,1688,1040,76350
+51,1514,1820,910,81820
+52,1565,1880,1300,57830
+53,1074,1288,320,70450
+54,1864,2240,530,67000
+55,1570,1898,980,64090
+56,1417,1701,740,48670
+57,1734,2060,1240,66210
+58,1470,1763,1080,83660
+59,1761,2104,1080,70770
+60,1756,2070,460,53870
+61,1704,2045,300,71300
+62,2011,2391,530,46100
+63,1472,1748,600,49100
+64,1310,1561,860,65920
+65,1544,1821,590,58660
+66,1707,2052,920,69130
+67,1881,2262,570,49080
+68,1416,1681,290,72710
+69,1631,1941,650,33430
+70,1318,1576,710,42430
+71,1692,2019,850,56650
+72,1152,1380,530,33580
+73,891,1073,630,67370
+74,1468,1749,700,71780
+75,1539,1833,650,84840
+76,1635,1956,720,82070
+77,1267,1520,450,26770
+78,1250,1475,1390,65560
+79,1720,2044,960,38660
+80,1462,1761,600,65660
+81,1431,1711,620,40700
+82,1539,1858,1020,88910
+83,1441,1723,330,57860
+84,1572,1884,1410,42670
+85,1287,1525,1200,90180
+86,1468,1760,280,51280
+87,1931,2342,940,97260
+88,1252,1506,850,39650
+89,1238,1468,960,45720
+90,1479,1758,420,42060
+91,1590,1912,830,65350
+92,2169,2617,600,67080
+93,1838,2205,400,54030
+94,1385,1655,760,56360
+95,1921,2305,1470,77120
+96,1975,2385,500,50810
+97,1853,2235,1120,60960
+98,1816,2171,1160,61180
+99,1785,2147,820,63660
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diff --git a/mai/data/Stores_updated.csv b/mai/data/Stores_updated.csv
new file mode 100644
index 0000000..b120a14
--- /dev/null
+++ b/mai/data/Stores_updated.csv
@@ -0,0 +1,897 @@
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diff --git a/mai/data/dollar.csv b/mai/data/dollar.csv
deleted file mode 100644
index 3f87c92..0000000
--- a/mai/data/dollar.csv
+++ /dev/null
@@ -1,244 +0,0 @@
-"my_date","my_value","bullet","bulletClass","label"
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diff --git a/mai/data/titanic.csv b/mai/data/titanic.csv
deleted file mode 100644
index 5cc466e..0000000
--- a/mai/data/titanic.csv
+++ /dev/null
@@ -1,892 +0,0 @@
-PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
-1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
-2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
-3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
-4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
-5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
-6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
-7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
-8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
-9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
-10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
-11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
-12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
-13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
-14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S
-15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S
-16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S
-17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q
-18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S
-19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S
-20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C
-21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S
-22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S
-23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q
-24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S
-25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S
-26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S
-27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C
-28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S
-29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q
-30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S
-31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C
-32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C
-33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q
-34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S
-35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C
-36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S
-37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C
-38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S
-39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S
-40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C
-41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S
-42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S
-43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C
-44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C
-45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q
-46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S
-47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q
-48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q
-49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C
-50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S
-51,0,3,"Panula, Master. Juha Niilo",male,7,4,1,3101295,39.6875,,S
-52,0,3,"Nosworthy, Mr. Richard Cater",male,21,0,0,A/4. 39886,7.8,,S
-53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,1,0,PC 17572,76.7292,D33,C
-54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,1,0,2926,26,,S
-55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65,0,1,113509,61.9792,B30,C
-56,1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S
-57,1,2,"Rugg, Miss. Emily",female,21,0,0,C.A. 31026,10.5,,S
-58,0,3,"Novel, Mr. Mansouer",male,28.5,0,0,2697,7.2292,,C
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-721,1,2,"Harper, Miss. Annie Jessie ""Nina""",female,6,0,1,248727,33,,S
-722,0,3,"Jensen, Mr. Svend Lauritz",male,17,1,0,350048,7.0542,,S
-723,0,2,"Gillespie, Mr. William Henry",male,34,0,0,12233,13,,S
-724,0,2,"Hodges, Mr. Henry Price",male,50,0,0,250643,13,,S
-725,1,1,"Chambers, Mr. Norman Campbell",male,27,1,0,113806,53.1,E8,S
-726,0,3,"Oreskovic, Mr. Luka",male,20,0,0,315094,8.6625,,S
-727,1,2,"Renouf, Mrs. Peter Henry (Lillian Jefferys)",female,30,3,0,31027,21,,S
-728,1,3,"Mannion, Miss. Margareth",female,,0,0,36866,7.7375,,Q
-729,0,2,"Bryhl, Mr. Kurt Arnold Gottfrid",male,25,1,0,236853,26,,S
-730,0,3,"Ilmakangas, Miss. Pieta Sofia",female,25,1,0,STON/O2. 3101271,7.925,,S
-731,1,1,"Allen, Miss. Elisabeth Walton",female,29,0,0,24160,211.3375,B5,S
-732,0,3,"Hassan, Mr. Houssein G N",male,11,0,0,2699,18.7875,,C
-733,0,2,"Knight, Mr. Robert J",male,,0,0,239855,0,,S
-734,0,2,"Berriman, Mr. William John",male,23,0,0,28425,13,,S
-735,0,2,"Troupiansky, Mr. Moses Aaron",male,23,0,0,233639,13,,S
-736,0,3,"Williams, Mr. Leslie",male,28.5,0,0,54636,16.1,,S
-737,0,3,"Ford, Mrs. Edward (Margaret Ann Watson)",female,48,1,3,W./C. 6608,34.375,,S
-738,1,1,"Lesurer, Mr. Gustave J",male,35,0,0,PC 17755,512.3292,B101,C
-739,0,3,"Ivanoff, Mr. Kanio",male,,0,0,349201,7.8958,,S
-740,0,3,"Nankoff, Mr. Minko",male,,0,0,349218,7.8958,,S
-741,1,1,"Hawksford, Mr. Walter James",male,,0,0,16988,30,D45,S
-742,0,1,"Cavendish, Mr. Tyrell William",male,36,1,0,19877,78.85,C46,S
-743,1,1,"Ryerson, Miss. Susan Parker ""Suzette""",female,21,2,2,PC 17608,262.375,B57 B59 B63 B66,C
-744,0,3,"McNamee, Mr. Neal",male,24,1,0,376566,16.1,,S
-745,1,3,"Stranden, Mr. Juho",male,31,0,0,STON/O 2. 3101288,7.925,,S
-746,0,1,"Crosby, Capt. Edward Gifford",male,70,1,1,WE/P 5735,71,B22,S
-747,0,3,"Abbott, Mr. Rossmore Edward",male,16,1,1,C.A. 2673,20.25,,S
-748,1,2,"Sinkkonen, Miss. Anna",female,30,0,0,250648,13,,S
-749,0,1,"Marvin, Mr. Daniel Warner",male,19,1,0,113773,53.1,D30,S
-750,0,3,"Connaghton, Mr. Michael",male,31,0,0,335097,7.75,,Q
-751,1,2,"Wells, Miss. Joan",female,4,1,1,29103,23,,S
-752,1,3,"Moor, Master. Meier",male,6,0,1,392096,12.475,E121,S
-753,0,3,"Vande Velde, Mr. Johannes Joseph",male,33,0,0,345780,9.5,,S
-754,0,3,"Jonkoff, Mr. Lalio",male,23,0,0,349204,7.8958,,S
-755,1,2,"Herman, Mrs. Samuel (Jane Laver)",female,48,1,2,220845,65,,S
-756,1,2,"Hamalainen, Master. Viljo",male,0.67,1,1,250649,14.5,,S
-757,0,3,"Carlsson, Mr. August Sigfrid",male,28,0,0,350042,7.7958,,S
-758,0,2,"Bailey, Mr. Percy Andrew",male,18,0,0,29108,11.5,,S
-759,0,3,"Theobald, Mr. Thomas Leonard",male,34,0,0,363294,8.05,,S
-760,1,1,"Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards)",female,33,0,0,110152,86.5,B77,S
-761,0,3,"Garfirth, Mr. John",male,,0,0,358585,14.5,,S
-762,0,3,"Nirva, Mr. Iisakki Antino Aijo",male,41,0,0,SOTON/O2 3101272,7.125,,S
-763,1,3,"Barah, Mr. Hanna Assi",male,20,0,0,2663,7.2292,,C
-764,1,1,"Carter, Mrs. William Ernest (Lucile Polk)",female,36,1,2,113760,120,B96 B98,S
-765,0,3,"Eklund, Mr. Hans Linus",male,16,0,0,347074,7.775,,S
-766,1,1,"Hogeboom, Mrs. John C (Anna Andrews)",female,51,1,0,13502,77.9583,D11,S
-767,0,1,"Brewe, Dr. Arthur Jackson",male,,0,0,112379,39.6,,C
-768,0,3,"Mangan, Miss. Mary",female,30.5,0,0,364850,7.75,,Q
-769,0,3,"Moran, Mr. Daniel J",male,,1,0,371110,24.15,,Q
-770,0,3,"Gronnestad, Mr. Daniel Danielsen",male,32,0,0,8471,8.3625,,S
-771,0,3,"Lievens, Mr. Rene Aime",male,24,0,0,345781,9.5,,S
-772,0,3,"Jensen, Mr. Niels Peder",male,48,0,0,350047,7.8542,,S
-773,0,2,"Mack, Mrs. (Mary)",female,57,0,0,S.O./P.P. 3,10.5,E77,S
-774,0,3,"Elias, Mr. Dibo",male,,0,0,2674,7.225,,C
-775,1,2,"Hocking, Mrs. Elizabeth (Eliza Needs)",female,54,1,3,29105,23,,S
-776,0,3,"Myhrman, Mr. Pehr Fabian Oliver Malkolm",male,18,0,0,347078,7.75,,S
-777,0,3,"Tobin, Mr. Roger",male,,0,0,383121,7.75,F38,Q
-778,1,3,"Emanuel, Miss. Virginia Ethel",female,5,0,0,364516,12.475,,S
-779,0,3,"Kilgannon, Mr. Thomas J",male,,0,0,36865,7.7375,,Q
-780,1,1,"Robert, Mrs. Edward Scott (Elisabeth Walton McMillan)",female,43,0,1,24160,211.3375,B3,S
-781,1,3,"Ayoub, Miss. Banoura",female,13,0,0,2687,7.2292,,C
-782,1,1,"Dick, Mrs. Albert Adrian (Vera Gillespie)",female,17,1,0,17474,57,B20,S
-783,0,1,"Long, Mr. Milton Clyde",male,29,0,0,113501,30,D6,S
-784,0,3,"Johnston, Mr. Andrew G",male,,1,2,W./C. 6607,23.45,,S
-785,0,3,"Ali, Mr. William",male,25,0,0,SOTON/O.Q. 3101312,7.05,,S
-786,0,3,"Harmer, Mr. Abraham (David Lishin)",male,25,0,0,374887,7.25,,S
-787,1,3,"Sjoblom, Miss. Anna Sofia",female,18,0,0,3101265,7.4958,,S
-788,0,3,"Rice, Master. George Hugh",male,8,4,1,382652,29.125,,Q
-789,1,3,"Dean, Master. Bertram Vere",male,1,1,2,C.A. 2315,20.575,,S
-790,0,1,"Guggenheim, Mr. Benjamin",male,46,0,0,PC 17593,79.2,B82 B84,C
-791,0,3,"Keane, Mr. Andrew ""Andy""",male,,0,0,12460,7.75,,Q
-792,0,2,"Gaskell, Mr. Alfred",male,16,0,0,239865,26,,S
-793,0,3,"Sage, Miss. Stella Anna",female,,8,2,CA. 2343,69.55,,S
-794,0,1,"Hoyt, Mr. William Fisher",male,,0,0,PC 17600,30.6958,,C
-795,0,3,"Dantcheff, Mr. Ristiu",male,25,0,0,349203,7.8958,,S
-796,0,2,"Otter, Mr. Richard",male,39,0,0,28213,13,,S
-797,1,1,"Leader, Dr. Alice (Farnham)",female,49,0,0,17465,25.9292,D17,S
-798,1,3,"Osman, Mrs. Mara",female,31,0,0,349244,8.6833,,S
-799,0,3,"Ibrahim Shawah, Mr. Yousseff",male,30,0,0,2685,7.2292,,C
-800,0,3,"Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert)",female,30,1,1,345773,24.15,,S
-801,0,2,"Ponesell, Mr. Martin",male,34,0,0,250647,13,,S
-802,1,2,"Collyer, Mrs. Harvey (Charlotte Annie Tate)",female,31,1,1,C.A. 31921,26.25,,S
-803,1,1,"Carter, Master. William Thornton II",male,11,1,2,113760,120,B96 B98,S
-804,1,3,"Thomas, Master. Assad Alexander",male,0.42,0,1,2625,8.5167,,C
-805,1,3,"Hedman, Mr. Oskar Arvid",male,27,0,0,347089,6.975,,S
-806,0,3,"Johansson, Mr. Karl Johan",male,31,0,0,347063,7.775,,S
-807,0,1,"Andrews, Mr. Thomas Jr",male,39,0,0,112050,0,A36,S
-808,0,3,"Pettersson, Miss. Ellen Natalia",female,18,0,0,347087,7.775,,S
-809,0,2,"Meyer, Mr. August",male,39,0,0,248723,13,,S
-810,1,1,"Chambers, Mrs. Norman Campbell (Bertha Griggs)",female,33,1,0,113806,53.1,E8,S
-811,0,3,"Alexander, Mr. William",male,26,0,0,3474,7.8875,,S
-812,0,3,"Lester, Mr. James",male,39,0,0,A/4 48871,24.15,,S
-813,0,2,"Slemen, Mr. Richard James",male,35,0,0,28206,10.5,,S
-814,0,3,"Andersson, Miss. Ebba Iris Alfrida",female,6,4,2,347082,31.275,,S
-815,0,3,"Tomlin, Mr. Ernest Portage",male,30.5,0,0,364499,8.05,,S
-816,0,1,"Fry, Mr. Richard",male,,0,0,112058,0,B102,S
-817,0,3,"Heininen, Miss. Wendla Maria",female,23,0,0,STON/O2. 3101290,7.925,,S
-818,0,2,"Mallet, Mr. Albert",male,31,1,1,S.C./PARIS 2079,37.0042,,C
-819,0,3,"Holm, Mr. John Fredrik Alexander",male,43,0,0,C 7075,6.45,,S
-820,0,3,"Skoog, Master. Karl Thorsten",male,10,3,2,347088,27.9,,S
-821,1,1,"Hays, Mrs. Charles Melville (Clara Jennings Gregg)",female,52,1,1,12749,93.5,B69,S
-822,1,3,"Lulic, Mr. Nikola",male,27,0,0,315098,8.6625,,S
-823,0,1,"Reuchlin, Jonkheer. John George",male,38,0,0,19972,0,,S
-824,1,3,"Moor, Mrs. (Beila)",female,27,0,1,392096,12.475,E121,S
-825,0,3,"Panula, Master. Urho Abraham",male,2,4,1,3101295,39.6875,,S
-826,0,3,"Flynn, Mr. John",male,,0,0,368323,6.95,,Q
-827,0,3,"Lam, Mr. Len",male,,0,0,1601,56.4958,,S
-828,1,2,"Mallet, Master. Andre",male,1,0,2,S.C./PARIS 2079,37.0042,,C
-829,1,3,"McCormack, Mr. Thomas Joseph",male,,0,0,367228,7.75,,Q
-830,1,1,"Stone, Mrs. George Nelson (Martha Evelyn)",female,62,0,0,113572,80,B28,
-831,1,3,"Yasbeck, Mrs. Antoni (Selini Alexander)",female,15,1,0,2659,14.4542,,C
-832,1,2,"Richards, Master. George Sibley",male,0.83,1,1,29106,18.75,,S
-833,0,3,"Saad, Mr. Amin",male,,0,0,2671,7.2292,,C
-834,0,3,"Augustsson, Mr. Albert",male,23,0,0,347468,7.8542,,S
-835,0,3,"Allum, Mr. Owen George",male,18,0,0,2223,8.3,,S
-836,1,1,"Compton, Miss. Sara Rebecca",female,39,1,1,PC 17756,83.1583,E49,C
-837,0,3,"Pasic, Mr. Jakob",male,21,0,0,315097,8.6625,,S
-838,0,3,"Sirota, Mr. Maurice",male,,0,0,392092,8.05,,S
-839,1,3,"Chip, Mr. Chang",male,32,0,0,1601,56.4958,,S
-840,1,1,"Marechal, Mr. Pierre",male,,0,0,11774,29.7,C47,C
-841,0,3,"Alhomaki, Mr. Ilmari Rudolf",male,20,0,0,SOTON/O2 3101287,7.925,,S
-842,0,2,"Mudd, Mr. Thomas Charles",male,16,0,0,S.O./P.P. 3,10.5,,S
-843,1,1,"Serepeca, Miss. Augusta",female,30,0,0,113798,31,,C
-844,0,3,"Lemberopolous, Mr. Peter L",male,34.5,0,0,2683,6.4375,,C
-845,0,3,"Culumovic, Mr. Jeso",male,17,0,0,315090,8.6625,,S
-846,0,3,"Abbing, Mr. Anthony",male,42,0,0,C.A. 5547,7.55,,S
-847,0,3,"Sage, Mr. Douglas Bullen",male,,8,2,CA. 2343,69.55,,S
-848,0,3,"Markoff, Mr. Marin",male,35,0,0,349213,7.8958,,C
-849,0,2,"Harper, Rev. John",male,28,0,1,248727,33,,S
-850,1,1,"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)",female,,1,0,17453,89.1042,C92,C
-851,0,3,"Andersson, Master. Sigvard Harald Elias",male,4,4,2,347082,31.275,,S
-852,0,3,"Svensson, Mr. Johan",male,74,0,0,347060,7.775,,S
-853,0,3,"Boulos, Miss. Nourelain",female,9,1,1,2678,15.2458,,C
-854,1,1,"Lines, Miss. Mary Conover",female,16,0,1,PC 17592,39.4,D28,S
-855,0,2,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",female,44,1,0,244252,26,,S
-856,1,3,"Aks, Mrs. Sam (Leah Rosen)",female,18,0,1,392091,9.35,,S
-857,1,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45,1,1,36928,164.8667,,S
-858,1,1,"Daly, Mr. Peter Denis ",male,51,0,0,113055,26.55,E17,S
-859,1,3,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24,0,3,2666,19.2583,,C
-860,0,3,"Razi, Mr. Raihed",male,,0,0,2629,7.2292,,C
-861,0,3,"Hansen, Mr. Claus Peter",male,41,2,0,350026,14.1083,,S
-862,0,2,"Giles, Mr. Frederick Edward",male,21,1,0,28134,11.5,,S
-863,1,1,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",female,48,0,0,17466,25.9292,D17,S
-864,0,3,"Sage, Miss. Dorothy Edith ""Dolly""",female,,8,2,CA. 2343,69.55,,S
-865,0,2,"Gill, Mr. John William",male,24,0,0,233866,13,,S
-866,1,2,"Bystrom, Mrs. (Karolina)",female,42,0,0,236852,13,,S
-867,1,2,"Duran y More, Miss. Asuncion",female,27,1,0,SC/PARIS 2149,13.8583,,C
-868,0,1,"Roebling, Mr. Washington Augustus II",male,31,0,0,PC 17590,50.4958,A24,S
-869,0,3,"van Melkebeke, Mr. Philemon",male,,0,0,345777,9.5,,S
-870,1,3,"Johnson, Master. Harold Theodor",male,4,1,1,347742,11.1333,,S
-871,0,3,"Balkic, Mr. Cerin",male,26,0,0,349248,7.8958,,S
-872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,1,11751,52.5542,D35,S
-873,0,1,"Carlsson, Mr. Frans Olof",male,33,0,0,695,5,B51 B53 B55,S
-874,0,3,"Vander Cruyssen, Mr. Victor",male,47,0,0,345765,9,,S
-875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,1,0,P/PP 3381,24,,C
-876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C
-877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S
-878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S
-879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S
-880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C
-881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S
-882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S
-883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S
-884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S
-885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S
-886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q
-887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S
-888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S
-889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S
-890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C
-891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q
diff --git a/mai/lec1.ipynb b/mai/lec1.ipynb
index 0cc9cd8..d26f2fd 100644
--- a/mai/lec1.ipynb
+++ b/mai/lec1.ipynb
@@ -4,131 +4,56 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Работа с NumPy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "matrix = \n",
-      " [[4 5 0]\n",
-      " [9 9 9]] \n",
-      "\n",
-      "tmatrix = \n",
-      " [[4 9]\n",
-      " [5 9]\n",
-      " [0 9]] \n",
-      "\n",
-      "vector = \n",
-      " [4 5 0 9 9 9] \n",
-      "\n",
-      "tvector = \n",
-      " [[4]\n",
-      " [5]\n",
-      " [0]\n",
-      " [9]\n",
-      " [9]\n",
-      " [9]] \n",
-      "\n",
-      "list_matrix = \n",
-      " [array([4, 5, 0]), array([9, 9, 9])] \n",
-      "\n",
-      "matrix as str = \n",
-      " [[4 5 0]\n",
-      " [9 9 9]] \n",
-      "\n",
-      "matrix type is <class 'numpy.ndarray'> \n",
-      "\n",
-      "vector type is <class 'numpy.ndarray'> \n",
-      "\n",
-      "list_matrix type is <class 'list'> \n",
-      "\n",
-      "str_matrix type is <class 'str'> \n",
-      "\n",
-      "formatted_vector = \n",
-      " 4; 5; 0; 9; 9; 9 \n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
+    "# Лабораторная работа 1\n",
     "\n",
-    "matrix = np.array([[4, 5, 0], [9, 9, 9]])\n",
-    "print(\"matrix = \\n\", matrix, \"\\n\")\n",
+    "Вариант - 9\n",
     "\n",
-    "tmatrix = matrix.T\n",
-    "print(\"tmatrix = \\n\", tmatrix, \"\\n\")\n",
+    "Датасет - магазины\n",
     "\n",
-    "vector = np.ravel(matrix)\n",
-    "print(\"vector = \\n\", vector, \"\\n\")\n",
-    "\n",
-    "tvector = np.reshape(vector, (6, 1))\n",
-    "print(\"tvector = \\n\", tvector, \"\\n\")\n",
-    "\n",
-    "list_matrix = list(matrix)\n",
-    "print(\"list_matrix = \\n\", list_matrix, \"\\n\")\n",
-    "\n",
-    "str_matrix = str(matrix)\n",
-    "print(\"matrix as str = \\n\", str_matrix, \"\\n\")\n",
-    "\n",
-    "print(\"matrix type is\", type(matrix), \"\\n\")\n",
-    "\n",
-    "print(\"vector type is\", type(vector), \"\\n\")\n",
-    "\n",
-    "print(\"list_matrix type is\", type(list_matrix), \"\\n\")\n",
-    "\n",
-    "print(\"str_matrix type is\", type(str_matrix), \"\\n\")\n",
-    "\n",
-    "formatted_vector = \"; \".join(map(str, vector))\n",
-    "print(\"formatted_vector = \\n\", formatted_vector, \"\\n\")"
+    "Поля \n",
+    "1. ID магазина\n",
+    "2. площадь магазина \n",
+    "3. количество разных товаров \n",
+    "4. количество покупателей\n",
+    "5. объем продаж"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Работа с Pandas DataFrame\n",
-    "\n",
-    "https://pandas.pydata.org/docs/user_guide/10min.html"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Работа с данными - чтение и запись CSV"
+    "Загрузка и сохранение данных"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd\n",
     "\n",
-    "df = pd.read_csv(\"data/titanic.csv\", index_col=\"PassengerId\")\n",
-    "\n",
-    "df.to_csv(\"test.csv\")"
+    "df = pd.read_csv(\"data/Stores.csv\")\n",
+    "df.to_csv(\"data/Stores_updated.csv\", index=False)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Работа с данными - основные команды"
+    "Получение сведений о датафрейме с данными"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. Общая информация о датафрейме"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
@@ -136,534 +61,1939 @@
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "Index: 891 entries, 1 to 891\n",
-      "Data columns (total 11 columns):\n",
-      " #   Column    Non-Null Count  Dtype  \n",
-      "---  ------    --------------  -----  \n",
-      " 0   Survived  891 non-null    int64  \n",
-      " 1   Pclass    891 non-null    int64  \n",
-      " 2   Name      891 non-null    object \n",
-      " 3   Sex       891 non-null    object \n",
-      " 4   Age       714 non-null    float64\n",
-      " 5   SibSp     891 non-null    int64  \n",
-      " 6   Parch     891 non-null    int64  \n",
-      " 7   Ticket    891 non-null    object \n",
-      " 8   Fare      891 non-null    float64\n",
-      " 9   Cabin     204 non-null    object \n",
-      " 10  Embarked  889 non-null    object \n",
-      "dtypes: float64(2), int64(4), object(5)\n",
-      "memory usage: 83.5+ KB\n",
-      "          count       mean        std   min      25%      50%   75%       max\n",
-      "Survived  891.0   0.383838   0.486592  0.00   0.0000   0.0000   1.0    1.0000\n",
-      "Pclass    891.0   2.308642   0.836071  1.00   2.0000   3.0000   3.0    3.0000\n",
-      "Age       714.0  29.699118  14.526497  0.42  20.1250  28.0000  38.0   80.0000\n",
-      "SibSp     891.0   0.523008   1.102743  0.00   0.0000   0.0000   1.0    8.0000\n",
-      "Parch     891.0   0.381594   0.806057  0.00   0.0000   0.0000   0.0    6.0000\n",
-      "Fare      891.0  32.204208  49.693429  0.00   7.9104  14.4542  31.0  512.3292\n",
-      "             Survived  Pclass     Sex   Age  SibSp  Parch     Fare Cabin\n",
-      "PassengerId                                                             \n",
-      "1                   0       3    male  22.0      1      0   7.2500   NaN\n",
-      "2                   1       1  female  38.0      1      0  71.2833   C85\n",
-      "3                   1       3  female  26.0      0      0   7.9250   NaN\n",
-      "4                   1       1  female  35.0      1      0  53.1000  C123\n",
-      "5                   0       3    male  35.0      0      0   8.0500   NaN\n",
-      "             Survived  Pclass     Sex   Age  SibSp  Parch   Fare Cabin\n",
-      "PassengerId                                                           \n",
-      "887                 0       2    male  27.0      0      0  13.00   NaN\n",
-      "888                 1       1  female  19.0      0      0  30.00   B42\n",
-      "889                 0       3  female   NaN      1      2  23.45   NaN\n",
-      "890                 1       1    male  26.0      0      0  30.00  C148\n",
-      "891                 0       3    male  32.0      0      0   7.75   NaN\n",
-      "             Survived  Pclass     Sex   Age  SibSp  Parch     Fare Cabin\n",
-      "PassengerId                                                             \n",
-      "804                 1       3    male  0.42      0      1   8.5167   NaN\n",
-      "756                 1       2    male  0.67      1      1  14.5000   NaN\n",
-      "470                 1       3  female  0.75      2      1  19.2583   NaN\n",
-      "645                 1       3  female  0.75      2      1  19.2583   NaN\n",
-      "79                  1       2    male  0.83      0      2  29.0000   NaN\n",
-      "             Survived  Pclass     Sex  Age  SibSp  Parch     Fare Cabin\n",
-      "PassengerId                                                            \n",
-      "860                 0       3    male  NaN      0      0   7.2292   NaN\n",
-      "864                 0       3  female  NaN      8      2  69.5500   NaN\n",
-      "869                 0       3    male  NaN      0      0   9.5000   NaN\n",
-      "879                 0       3    male  NaN      0      0   7.8958   NaN\n",
-      "889                 0       3  female  NaN      1      2  23.4500   NaN\n"
+      "RangeIndex: 896 entries, 0 to 895\n",
+      "Data columns (total 5 columns):\n",
+      " #   Column                Non-Null Count  Dtype\n",
+      "---  ------                --------------  -----\n",
+      " 0   Store ID              896 non-null    int64\n",
+      " 1   Store_Area            896 non-null    int64\n",
+      " 2   Items_Available       896 non-null    int64\n",
+      " 3   Daily_Customer_Count  896 non-null    int64\n",
+      " 4   Store_Sales           896 non-null    int64\n",
+      "dtypes: int64(5)\n",
+      "memory usage: 35.1 KB\n"
      ]
     }
    ],
    "source": [
-    "df.info()\n",
-    "\n",
-    "print(df.describe().transpose())\n",
-    "\n",
-    "cleared_df = df.drop([\"Name\", \"Ticket\", \"Embarked\"], axis=1)\n",
-    "print(cleared_df.head())\n",
-    "print(cleared_df.tail())\n",
-    "\n",
-    "sorted_df = cleared_df.sort_values(by=\"Age\")\n",
-    "print(sorted_df.head())\n",
-    "print(sorted_df.tail())"
+    "df.info()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Работа с данными - работа с элементами"
+    "2. Статистическая информация"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "PassengerId\n",
-      "1      22.0\n",
-      "2      38.0\n",
-      "3      26.0\n",
-      "4      35.0\n",
-      "5      35.0\n",
-      "       ... \n",
-      "887    27.0\n",
-      "888    19.0\n",
-      "889     NaN\n",
-      "890    26.0\n",
-      "891    32.0\n",
-      "Name: Age, Length: 891, dtype: float64\n",
-      "Survived                    0\n",
-      "Pclass                      2\n",
-      "Name        Kantor, Mr. Sinai\n",
-      "Sex                      male\n",
-      "Age                      34.0\n",
-      "SibSp                       1\n",
-      "Parch                       0\n",
-      "Ticket                 244367\n",
-      "Fare                     26.0\n",
-      "Cabin                     NaN\n",
-      "Embarked                    S\n",
-      "Name: 100, dtype: object\n",
-      "Kantor, Mr. Sinai\n",
-      "              Age                                    Name\n",
-      "PassengerId                                              \n",
-      "100          34.0                       Kantor, Mr. Sinai\n",
-      "101          28.0                 Petranec, Miss. Matilda\n",
-      "102           NaN        Petroff, Mr. Pastcho (\"Pentcho\")\n",
-      "103          21.0               White, Mr. Richard Frasar\n",
-      "104          33.0              Johansson, Mr. Gustaf Joel\n",
-      "...           ...                                     ...\n",
-      "196          58.0                    Lurette, Miss. Elise\n",
-      "197           NaN                     Mernagh, Mr. Robert\n",
-      "198          42.0        Olsen, Mr. Karl Siegwart Andreas\n",
-      "199           NaN        Madigan, Miss. Margaret \"Maggie\"\n",
-      "200          24.0  Yrois, Miss. Henriette (\"Mrs Harbeck\")\n",
-      "\n",
-      "[101 rows x 2 columns]\n",
-      "             Survived  Pclass  \\\n",
-      "PassengerId                     \n",
-      "1                   0       3   \n",
-      "2                   1       1   \n",
-      "3                   1       3   \n",
-      "\n",
-      "                                                          Name     Sex   Age  \\\n",
-      "PassengerId                                                                    \n",
-      "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
-      "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
-      "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
-      "\n",
-      "             SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
-      "PassengerId                                                          \n",
-      "1                1      0         A/5 21171   7.2500   NaN        S  \n",
-      "2                1      0          PC 17599  71.2833   C85        C  \n",
-      "3                0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
-      "Survived                          0\n",
-      "Pclass                            3\n",
-      "Name        Braund, Mr. Owen Harris\n",
-      "Sex                            male\n",
-      "Age                            22.0\n",
-      "SibSp                             1\n",
-      "Parch                             0\n",
-      "Ticket                    A/5 21171\n",
-      "Fare                           7.25\n",
-      "Cabin                           NaN\n",
-      "Embarked                          S\n",
-      "Name: 1, dtype: object\n",
-      "             Survived  Pclass\n",
-      "PassengerId                  \n",
-      "4                   1       1\n",
-      "5                   0       3\n",
-      "             Survived  Pclass\n",
-      "PassengerId                  \n",
-      "4                   1       1\n",
-      "5                   0       3\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(df[\"Age\"])\n",
-    "\n",
-    "print(df.loc[100])\n",
-    "\n",
-    "print(df.loc[100, \"Name\"])\n",
-    "\n",
-    "print(df.loc[100:200, [\"Age\", \"Name\"]])\n",
-    "\n",
-    "print(df[0:3])\n",
-    "\n",
-    "print(df.iloc[0])\n",
-    "\n",
-    "print(df.iloc[3:5, 0:2])\n",
-    "\n",
-    "print(df.iloc[[3, 4], [0, 1]])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Работа с данными - отбор и группировка"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['male' 'female']\n",
-      "male count = 577\n",
-      "female count = 314\n",
-      "Total count =  891\n",
-      "   Pclass  Survived  Count\n",
-      "0       1         0     80\n",
-      "1       1         1    136\n",
-      "2       2         0     97\n",
-      "3       2         1     87\n",
-      "4       3         0    372\n",
-      "5       3         1    119\n"
-     ]
-    }
-   ],
-   "source": [
-    "s_values = df[\"Sex\"].unique()\n",
-    "print(s_values)\n",
-    "\n",
-    "s_total = 0\n",
-    "for s_value in s_values:\n",
-    "    count = df[df[\"Sex\"] == s_value].shape[0]\n",
-    "    s_total += count\n",
-    "    print(s_value, \"count =\", count)\n",
-    "print(\"Total count = \", s_total)\n",
-    "\n",
-    "print(df.groupby([\"Pclass\", \"Survived\"]).size().reset_index(name=\"Count\")) # type: ignore"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Визуализация - Исходные данные"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "             Pclass  Survived   Age\n",
-      "PassengerId                        \n",
-      "1                 3         0  22.0\n",
-      "2                 1         1  38.0\n",
-      "3                 3         1  26.0\n",
-      "4                 1         1  35.0\n",
-      "5                 3         0  35.0\n",
-      "...             ...       ...   ...\n",
-      "886               3         0  39.0\n",
-      "887               2         0  27.0\n",
-      "888               1         1  19.0\n",
-      "890               1         1  26.0\n",
-      "891               3         0  32.0\n",
-      "\n",
-      "[714 rows x 3 columns]\n"
-     ]
-    }
-   ],
-   "source": [
-    "data = df[[\"Pclass\", \"Survived\", \"Age\"]].copy()\n",
-    "data.dropna(subset=[\"Age\"], inplace=True)\n",
-    "print(data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Визуализация - Сводка пяти чисел\n",
-    "\n",
-    "<img src=\"assets/quantile.png\" width=\"400\" style=\"background-color: white\">"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "         Age                               \n",
-      "         min    q1    q2 median    q3   max\n",
-      "Pclass                                     \n",
-      "1       0.92  27.0  37.0   37.0  49.0  80.0\n",
-      "2       0.67  23.0  29.0   29.0  36.0  70.0\n",
-      "3       0.42  18.0  24.0   24.0  32.0  74.0\n",
-      "           Age               \n",
-      "       low_iqr   iqr high_iqr\n",
-      "Pclass                       \n",
-      "1          0.0  22.0     82.0\n",
-      "2          3.5  13.0     55.5\n",
-      "3          0.0  14.0     53.0\n"
-     ]
-    },
     {
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Store ID</th>\n",
+       "      <th>Store_Area</th>\n",
+       "      <th>Items_Available</th>\n",
+       "      <th>Daily_Customer_Count</th>\n",
+       "      <th>Store_Sales</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>896.000000</td>\n",
+       "      <td>896.000000</td>\n",
+       "      <td>896.000000</td>\n",
+       "      <td>896.000000</td>\n",
+       "      <td>896.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>448.500000</td>\n",
+       "      <td>1485.409598</td>\n",
+       "      <td>1782.035714</td>\n",
+       "      <td>786.350446</td>\n",
+       "      <td>59351.305804</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>258.797218</td>\n",
+       "      <td>250.237011</td>\n",
+       "      <td>299.872053</td>\n",
+       "      <td>265.389281</td>\n",
+       "      <td>17190.741895</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>775.000000</td>\n",
+       "      <td>932.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>14920.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>224.750000</td>\n",
+       "      <td>1316.750000</td>\n",
+       "      <td>1575.500000</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>46530.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>448.500000</td>\n",
+       "      <td>1477.000000</td>\n",
+       "      <td>1773.500000</td>\n",
+       "      <td>780.000000</td>\n",
+       "      <td>58605.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>672.250000</td>\n",
+       "      <td>1653.500000</td>\n",
+       "      <td>1982.750000</td>\n",
+       "      <td>970.000000</td>\n",
+       "      <td>71872.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>896.000000</td>\n",
+       "      <td>2229.000000</td>\n",
+       "      <td>2667.000000</td>\n",
+       "      <td>1560.000000</td>\n",
+       "      <td>116320.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "<Axes: title={'center': 'Age'}, xlabel='Pclass'>"
+       "        Store ID    Store_Area  Items_Available  Daily_Customer_Count  \\\n",
+       "count  896.000000   896.000000       896.000000            896.000000   \n",
+       "mean   448.500000  1485.409598      1782.035714            786.350446   \n",
+       "std    258.797218   250.237011       299.872053            265.389281   \n",
+       "min      1.000000   775.000000       932.000000             10.000000   \n",
+       "25%    224.750000  1316.750000      1575.500000            600.000000   \n",
+       "50%    448.500000  1477.000000      1773.500000            780.000000   \n",
+       "75%    672.250000  1653.500000      1982.750000            970.000000   \n",
+       "max    896.000000  2229.000000      2667.000000           1560.000000   \n",
+       "\n",
+       "         Store_Sales  \n",
+       "count     896.000000  \n",
+       "mean    59351.305804  \n",
+       "std     17190.741895  \n",
+       "min     14920.000000  \n",
+       "25%     46530.000000  \n",
+       "50%     58605.000000  \n",
+       "75%     71872.500000  \n",
+       "max    116320.000000  "
       ]
      },
-     "execution_count": 7,
+     "execution_count": 123,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "def q1(x):\n",
-    "    return x.quantile(0.25)\n",
-    "\n",
-    "\n",
-    "# median = quantile(0.5)\n",
-    "def q2(x):\n",
-    "    return x.quantile(0.5)\n",
-    "\n",
-    "\n",
-    "def q3(x):\n",
-    "    return x.quantile(0.75)\n",
-    "\n",
-    "\n",
-    "def iqr(x):\n",
-    "    return q3(x) - q1(x)\n",
-    "\n",
-    "\n",
-    "def low_iqr(x):\n",
-    "    return max(0, q1(x) - 1.5 * iqr(x))\n",
-    "\n",
-    "\n",
-    "def high_iqr(x):\n",
-    "    return q3(x) + 1.5 * iqr(x)\n",
-    "\n",
-    "\n",
-    "quantiles = data[[\"Pclass\", \"Age\"]].groupby([\"Pclass\"]).aggregate([\"min\", q1, q2, \"median\", q3, \"max\"])\n",
-    "print(quantiles)\n",
-    "\n",
-    "iqrs = data[[\"Pclass\", \"Age\"]].groupby([\"Pclass\"]).aggregate([low_iqr, iqr, high_iqr])\n",
-    "print(iqrs)\n",
-    "\n",
-    "data.boxplot(column=\"Age\", by=\"Pclass\")"
+    "df.describe()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Визуализация - Гистограмма"
+    "Получение сведений о колонках датафрейма"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. Названия колонок"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<Axes: ylabel='Frequency'>"
+       "Index(['Store ID ', 'Store_Area', 'Items_Available', 'Daily_Customer_Count',\n",
+       "       'Store_Sales'],\n",
+       "      dtype='object')"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 124,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "data.plot.hist(column=[\"Age\"], bins=80)"
+    "df.columns"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Визуализация - Точечная диаграмма"
+    "Вывод отдельных строк и столбцов"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. Столбец \"Store_Area\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Store_Area</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1659</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1461</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1340</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1451</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1770</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>891</th>\n",
+       "      <td>1582</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>892</th>\n",
+       "      <td>1387</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>893</th>\n",
+       "      <td>1200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>894</th>\n",
+       "      <td>1299</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>895</th>\n",
+       "      <td>1174</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>896 rows × 1 columns</p>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "<Axes: xlabel='Pclass', ylabel='Age'>"
+       "     Store_Area\n",
+       "0          1659\n",
+       "1          1461\n",
+       "2          1340\n",
+       "3          1451\n",
+       "4          1770\n",
+       "..          ...\n",
+       "891        1582\n",
+       "892        1387\n",
+       "893        1200\n",
+       "894        1299\n",
+       "895        1174\n",
+       "\n",
+       "[896 rows x 1 columns]"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 125,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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IAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsGAVolatWuX1vjfeeMO6GAAAgIrCKkTddNNNGjlypH766Sdn27Fjx3Trrbdq9OjRpVYcAABAeWX9SdSSJUvUvn17bd++XcuXL1fr1q2Vl5en9PT0Ui4RAACg/LEKUZ06dVJ6erpat26tdu3a6fbbb9eIESO0evVqNWrUqLRrBAAAKHesTyzfuXOn0tLS1LBhQwUFBWnHjh0qKCgozdoAAADKLasQ9fLLLyslJUU33HCDtm7dqvXr12vTpk1KTEzUunXrSrtGAACAcscqRE2dOlVLly7V9OnTVaVKFbVu3Vrr16/XHXfcoeuuu66USwQAACh/gmwe9O233yomJsalLTg4WJMmTdItt9xSKoUBAACUZ1afRMXExCgnJ0dvv/22xowZoxMnTkiSNm7cqISEhFItEAAAoDyy+iRqy5Yt6tGjhyIjI7V3714NHjxYNWvW1IcffqisrCzNmzevtOsEAAAoV6w+iRoxYoQGDRqkjIwMValSxdl+880364svvii14gAAAMorq0+i0tLS9Oabb7q1N2jQQIcOHbrsogAAAMo7q0+iQkNDlZeX59a+c+dO1apV67KLAgAAKO+sQtRtt92m559/3vm38xwOh7KysjRq1CjdeeedpVogAABAeWQVoiZPnqxTp06pdu3aOn36tLp166amTZsqPDxcEyZMKO0aAQAAyh2rc6IiIyO1YsUKrVmzRlu2bNGpU6d09dVXq3v37qVdHwAAQLnk0ydR69at00cffeS83aVLF1WrVk2vvfaa+vXrp4ceekhnzpwp9SIBAADKG59C1PPPP69t27Y5b3/77bcaPHiwbrjhBo0ePVp///vfNXHixFIvEgAAoLzxKUSlp6e7/Mpu/vz56tChg9566y09/vjjmjZtmhYuXFjqRQIAAJQ3PoWo7Oxs1alTx3k7NTVVvXr1ct5u37699u/fX3rVAQAAlFM+hag6deooMzNTknT27Flt3LhRHTt2dN5/8uRJBQcHl26FAAAA5ZBPIermm2/W6NGj9eWXX2rMmDEKCwtT165dnfdv2bJFTZs2LfUiAQAAyhufLnHwwgsv6I477lC3bt0UHh6uuXPnKiQkxHn/7Nmz1bNnz1IvEgAAoLxxGGOMrw/Kzc1VeHi4AgMDXdpPnDih8PBwl2D1W5WXl6fIyEjl5uYqIiLC3+UAAIAS8OX92/pim57UrFnTZjgAAIAKx+rPvgAAAPzWEaIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIkDRo0SH369PF3GQAAoAIJ8ncB8N2eo6f0deZx7Tx8Ul98d0y5P57V9S3raNLdbZx9ZqzM0Nrdx9S1WS09en2C1zZJen7ZNn2155i6JNTS2FtaXfK5950oUOPoaoqPqeZsT91xROnf56hdXA11bVar2HZvdXgbe8H6LK3LPK7OTWN0d3JssX298VSHtzG6T1qlfdkFio+uphVPXOdsf2juBn2zP1vtG9XUzAHJkqSOE1bo8MmzqhcRqq+e6uHse8XYf+j0OaOwIIe2v3izs71o//cGd9S+EwV69N00nT5nFB4coK0v9HL2TXrun8o+fU41qwZp4/gbne03/yVVu47lq3ntcE37fTvtO1Ggx+dvUvbpc6pdLVjzH+7knNfNU1IvWceFuru+vFI/5P6o2KiqSh31O49zeX1AsvN1HLkw3W2M5Bc+1bH8n9zqGPJumrPmj4Zd63GOix/trH0nCjTp438r83iBrqwXocWPdva4Xd64L7nYsT2N2zi6mt79aq/Hff2JBen6eu9xpTSJcR5H/d9cpy0HctW2YZTefbCjx/2gX4c45+tRWGjc9rGi26rovD3t097qKNp2S2I953McyD7tcQxP+6m3Y9yXNcFTbcWN4Ym3Oooei8YY57/X7zle4mPflzXI2+vva7sn3uoojXXMF0XraBBV1fk8WcfzPdbni7Ksu6LV4TDGGL88czkyaNAg5eTkaOnSpaU2Zl5eniIjI5Wbm6uIiIhSGTOn4KwefX+jvtp93Gufodc10YzVe0o03hM9EjT5s11u7bPuS1b3VnXcnvuxD9L1RcZRZ9u1zWrpyZuaa8Cs9cou+MnZXiMsWK//vp0e+etGl/bw0ECdOnPe/fkGJGvuv/a5jf1ItyYaMHu9zhX+sosGBTiU2CBCG/fnuvSd3i9JkWHBbmPvO56vPq+udakjqmqwmtUO14Z92S5jNIwM0V/TfnAbo1N8lL7KzHFr96Rm1QCdOF3o1h4dFqDjBe7tnkRVlXJOu7fH16yizBM/lmgMb2LCAnSshHXUCgvU0QL37VUa4qOqKDOnZHNp17C6Nn5/skzqeKBTnGZ9lVWivr1a1dbH24+UqG9oUIDOnHN/ne9p10Afph9w26eH/a6px2OxpIICHBrctbFeT80sUf8/90jQ/3h4vie6J2jyypLVMfS6eM1Y7f58CwZ31DVNo13aPt16UA+9t9Gt77R72mrxph9cjn1vggKkxAZR2rg/x9l2bbNaevLG5how28Ma1L+dHnn/4jUoQKd/KtT5wqLjOvQ/d1+lPy/61m27TL4rUU8s3uLWvmxIZ7VqEOlSn6e1pkZYsP7fXVfpkfc2uY1xVf0Ibfq+ZOuYLzzV4U2NsGAtG9JFsdFhJRrb2/tAadTti7Kuw5f37woXoq677jpdddVVCgwM1Ny5cxUSEqIXX3xRv//97zV06FAtXrxYderU0fTp09WrVy+dP39eDz30kD7//HMdOnRIcXFxevTRRzVs2DDnmBeHqMLCQr3yyit68803dejQITVv3lzPPPOM7rrrrhLXWRYh6r5Z60u02JSGvS/3dnvutbuO6XyR3SXQ4ZDDIZfFwVagw+E29vkS7pqBDoc6J8Ro3gMd3O5Lev7TEi0mvjwfgOJdvH40Hr3ca9/LOfZKcw3yRVCAQ7teutmlraRrjTfFrWO+8LWOGmHB2jSuZ4n6ensfKI26fVHWdfjy/l0hz4maO3euYmJitH79ev3pT3/SI488orvvvludOnXSxo0b1bNnTw0YMEAFBQUqLCxUw4YNtWjRIm3fvl3jxo3TU089pYULF3odf+LEiZo3b55mzpypbdu2acSIEbr33nuVmprq9TFnzpxRXl6ey09p2nP01K8WoCTpxY+2uz33xQvdeWNKbfHyNLYvj/0i46gyj+W7tKfuOFLixYQABZSe11b98mnW88u2Fdv3co690lyDfHGu0GhR2n7nbV/WGm+8rWO+sKkju+AnfVmC95bi3gcut25flJc6LqiQIapNmzYaO3asmjVrpjFjxqhKlSqKiYnR4MGD1axZM40bN07Hjx/Xli1bFBwcrOeee07JycmKj49X//799Yc//MFriDpz5oxeeuklzZ49WzfeeKOaNGmiQYMG6d5779Ubb7zhtaaJEycqMjLS+RMbW/zvzX2170RBqY53KWt2/XJQ/drPbWvvcdeDJ/37HP8UAvzGFX1T/mrPMT9WUnbW7v5lXqW51ly8jvnCto6NWdmX7HOp94HLqdsX5aWOCyrkieWJiYnOfwcGBio6OlpXXXWVs61OnZ/P5zly5OfzF1599VXNnj1bWVlZOn36tM6ePau2bdt6HHvXrl0qKCjQDTfc4NJ+9uxZJSUlea1pzJgxevzxx5238/LySjVINapZst9Zl5YuCb+ccPhrP7etxtGuJxa2bRjln0KA37iiJyx3ahKj7w6d8mM1ZaNz0xjnv0tzrbl4HfOFbR3t4mpcss+l3gcup25flJc6LqiQn0QFB7ueOOZwOFzaHA6HpJ/PbZo/f77+/Oc/64EHHtCnn36q9PR0/eEPf9DZs2c9jn3q1M8H+/Lly5Wenu782b59uxYvXuy1ptDQUEVERLj8lKYmtcJ1reU3KWwU/ebMhecO/M/rekGgw6GgAMfFD7XiaWxfHntts1pu387o1qK2apTwJENfng9A8Yp+S2/cbVcW2/dyjr3SXIN8ERTgcPmWni9rjTfe1jFf2NRRIyy4RN/SK+594HLr9kV5qeOCChmifLF27Vp16tRJjz76qJKSkpSQkKDdu3d77d+qVSuFhoYqKytLCQkJLj+l/Ss6X03vl6ROF33r5WJ/ur5piccb2bO5x/ZZ9yV7fO7OCTEubZ0TYrRsSGe3g7ZGWLAWDO7o1l491PMHn7PuS/Y49oLBHd0WyKAAh9rFRrr1nd7P86eEy4Z0casjqmqw2jdy/Z9X54QY3dfR8/bt0vTS/0u7oFa1QJ/aPYkO83xYNo2uWuIxvPGljjrhZfdBtS9zSY4t3f+QFDW4S+MS9+3dus6lO/1HaJDnbdg3uYHHfdrbsVhSQQGOUjn2fanD2/MtGNzRrc3TmiJJ0/q2dTv2vQkKkNrFRrm0+b4GBSjwok0TFODQtL5tPW4Xb+3LhnTWxTytNTXCgjXrvmSPYyQ1LPk65gtPdXhz4dt5JeXtfaA06vZFealDqqDfzmvbtq2mTJnibGvcuLGGDx+u4cOHO9scDoeWLFmirKwsPfPMM1q4cKHi4+P17rvvatq0aYqPj1d6erok92/njR07VjNnztTkyZPVpUsX5ebmau3atYqIiNDAgQNLVGdZfDvvgsxj+fp6z3FlHD6p1TuOKve0+3WiXlu1S19mHHW5dounNunnk8jX7DpaoutEZR7L197j+W7X5fgy46g2ZmW7XXvEU7u3OryNvShtv9buPuZyfRVvfb3xVIe3MW6YvFqZx/PdrhP18Ltp2rDvhMv1dzq99JkO5p1xu05Uq7H/UIGH6zMV7f/+QynaezxfQ95NU4GH60S1e+6fOuHhOlG3TP1CO4+cUvPa4Zre/2rtPZ6vJ+Zv0on/XCdqwSOdnfPqPSX1knVcqLvbK59rf85pt+tEFZ3LGwPbO1/HUYs2u43R4YVPdeQ/14kqWsef3v/GWXPR6yUVneP/DemivcfzNfmT77T7WL7bdaKKbpc3B7YvdmxP4zaOrqb3/7XP474+ctFmrdtzzOUaSAPe/pfSv89xu05U0f2gf8dGztdDkts+VnRbFZ23p33aWx1F225rW9/5HIdyf/Q4hqf91Nsx7sua4Km24sbwxFsdRY9FSc5/p+09UeJj35c1yNvr72u7J97qKI11zBdF62hYI8z5PN9nF3iszxdlWXd5qKPSX+LAlxDVq1cvPfzww1qyZIkcDof69eunyMhIffzxx15DlDFG06ZN0+uvv649e/YoKipK7dq101NPPaVrr/1lISxOWYYoAABQNip1iKooCFEAAFQ8lf46UQAAAP5GiAIAALBAiAIAALBAiAIAALBAiAIAALBAiAIAALBAiAIAALBAiAIAALBAiAIAALBQdn9h9DfuwoXg8/Ly/FwJAAAoqQvv2yX5gy6EqDJy8uRJSVJsbPF/rBIAAJQ/J0+eVGRkZLF9+Nt5ZaSwsFAHDhxQ9erV5XA4Lnu8vLw8xcbGav/+/ZX2b/Exx4qvss9PYo6VQWWfn8QcL4cxRidPnlT9+vUVEFD8WU98ElVGAgIC1LBhw1IfNyIiotIeEBcwx4qvss9PYo6VQWWfn8QcbV3qE6gLOLEcAADAAiEKAADAAiGqgggNDdX48eMVGhrq71LKDHOs+Cr7/CTmWBlU9vlJzPHXwonlAAAAFvgkCgAAwAIhCgAAwAIhCgAAwAIhCgAAwAIhqoJ49dVX1bhxY1WpUkXXXHON1q9f7++SrH3xxRe69dZbVb9+fTkcDi1dutTlfmOMxo0bp3r16qlq1arq0aOHMjIy/FOshYkTJ6p9+/aqXr26ateurT59+mjHjh0ufX788UcNGTJE0dHRCg8P15133qnDhw/7qWLfvf7660pMTHRe5C4lJUUff/yx8/6KPr+Lvfzyy3I4HBo+fLizraLP8dlnn5XD4XD5admypfP+ij6/C3744Qfde++9io6OVtWqVXXVVVcpLS3NeX9FXm8aN27stg0dDoeGDBkiqXJsw/Pnz+uZZ55RfHy8qlatqqZNm+qFF15w+bt2ft2GBuXe/PnzTUhIiJk9e7bZtm2bGTx4sImKijKHDx/2d2lW/vGPf5inn37afPjhh0aSWbJkicv9L7/8somMjDRLly41mzdvNrfddpuJj483p0+f9k/BPrrxxhvNO++8Y7Zu3WrS09PNzTffbOLi4sypU6ecfR5++GETGxtrVq5cadLS0kzHjh1Np06d/Fi1b5YtW2aWL19udu7caXbs2GGeeuopExwcbLZu3WqMqfjzK2r9+vWmcePGJjEx0QwbNszZXtHnOH78eHPllVeagwcPOn+OHj3qvL+iz88YY06cOGEaNWpkBg0aZL7++muzZ88e889//tPs2rXL2acirzdHjhxx2X4rVqwwksyqVauMMZVjG06YMMFER0ebjz76yGRmZppFixaZ8PBwM3XqVGcff25DQlQF0KFDBzNkyBDn7fPnz5v69eubiRMn+rGq0nFxiCosLDR169Y1kyZNcrbl5OSY0NBQ88EHH/ihwst35MgRI8mkpqYaY36eT3BwsFm0aJGzz7///W8jyaxbt85fZV62GjVqmLfffrtSze/kyZOmWbNmZsWKFaZbt27OEFUZ5jh+/HjTpk0bj/dVhvkZY8yoUaNMly5dvN5f2dabYcOGmaZNm5rCwsJKsw179+5t7r//fpe2O+64w/Tv398Y4/9tyK/zyrmzZ8/qm2++UY8ePZxtAQEB6tGjh9atW+fHyspGZmamDh065DLfyMhIXXPNNRV2vrm5uZKkmjVrSpK++eYb/fTTTy5zbNmypeLi4irkHM+fP6/58+crPz9fKSkplWp+Q4YMUe/evV3mIlWebZiRkaH69eurSZMm6t+/v7KysiRVnvktW7ZMycnJuvvuu1W7dm0lJSXprbfect5fmdabs2fP6r333tP9998vh8NRabZhp06dtHLlSu3cuVOStHnzZq1Zs0a9evWS5P9tyB8gLueOHTum8+fPq06dOi7tderU0XfffeenqsrOoUOHJMnjfC/cV5EUFhZq+PDh6ty5s1q3bi3p5zmGhIQoKirKpW9Fm+O3336rlJQU/fjjjwoPD9eSJUvUqlUrpaenV4r5zZ8/Xxs3btSGDRvc7qsM2/Caa67RnDlz1KJFCx08eFDPPfecunbtqq1bt1aK+UnSnj179Prrr+vxxx/XU089pQ0bNuixxx5TSEiIBg4cWKnWm6VLlyonJ0eDBg2SVDn2UUkaPXq08vLy1LJlSwUGBur8+fOaMGGC+vfvL8n/7xmEKKAMDRkyRFu3btWaNWv8XUqpa9GihdLT05Wbm6vFixdr4MCBSk1N9XdZpWL//v0aNmyYVqxYoSpVqvi7nDJx4X/ykpSYmKhrrrlGjRo10sKFC1W1alU/VlZ6CgsLlZycrJdeekmSlJSUpK1bt2rmzJkaOHCgn6srXbNmzVKvXr1Uv359f5dSqhYuXKj3339ff/3rX3XllVcqPT1dw4cPV/369cvFNuTXeeVcTEyMAgMD3b5RcfjwYdWtW9dPVZWdC3OqDPMdOnSoPvroI61atUoNGzZ0ttetW1dnz55VTk6OS/+KNseQkBAlJCTo6quv1sSJE9WmTRtNnTq1Uszvm2++0ZEjR9SuXTsFBQUpKChIqampmjZtmoKCglSnTp0KP8eLRUVFqXnz5tq1a1el2IaSVK9ePbVq1cql7YorrnD+2rKyrDf79u3TZ599pgcffNDZVlm24ciRIzV69Gj993//t6666ioNGDBAI0aM0MSJEyX5fxsSosq5kJAQXX311Vq5cqWzrbCwUCtXrlRKSoofKysb8fHxqlu3rst88/Ly9PXXX1eY+RpjNHToUC1ZskSff/654uPjXe6/+uqrFRwc7DLHHTt2KCsrq8LM0ZPCwkKdOXOmUsyve/fu+vbbb5Wenu78SU5OVv/+/Z3/ruhzvNipU6e0e/du1atXr1JsQ0nq3Lmz2+VFdu7cqUaNGkmqHOuNJL3zzjuqXbu2evfu7WyrLNuwoKBAAQGuUSUwMFCFhYWSysE2LPNT13HZ5s+fb0JDQ82cOXPM9u3bzUMPPWSioqLMoUOH/F2alZMnT5pNmzaZTZs2GUnmf//3f82mTZvMvn37jDE/f101KirK/O1vfzNbtmwx//Vf/1VhvnJsjDGPPPKIiYyMNKtXr3b5+nFBQYGzz8MPP2zi4uLM559/btLS0kxKSopJSUnxY9W+GT16tElNTTWZmZlmy5YtZvTo0cbhcJhPP/3UGFPx5+dJ0W/nGVPx5/jEE0+Y1atXm8zMTLN27VrTo0cPExMTY44cOWKMqfjzM+bny1MEBQWZCRMmmIyMDPP++++bsLAw89577zn7VPT15vz58yYuLs6MGjXK7b7KsA0HDhxoGjRo4LzEwYcffmhiYmLMk08+6ezjz21IiKogpk+fbuLi4kxISIjp0KGD+de//uXvkqytWrXKSHL7GThwoDHm56+sPvPMM6ZOnTomNDTUdO/e3ezYscO/RfvA09wkmXfeecfZ5/Tp0+bRRx81NWrUMGFhYeb22283Bw8e9F/RPrr//vtNo0aNTEhIiKlVq5bp3r27M0AZU/Hn58nFIaqiz7Fv376mXr16JiQkxDRo0MD07dvX5fpJFX1+F/z97383rVu3NqGhoaZly5bmzTffdLm/oq83//znP40kjzVXhm2Yl5dnhg0bZuLi4kyVKlVMkyZNzNNPP23OnDnj7OPPbegwpshlPwEAAFAinBMFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAEWsW7dOgYGBLn/MFQA84c++AEARDz74oMLDwzVr1izt2LFD9evX93dJAMopPokCgP84deqUFixYoEceeUS9e/fWnDlzXO5ftmyZmjVrpipVquj666/X3Llz5XA4lJOT4+yzZs0ade3aVVWrVlVsbKwee+wx5efn/7oTAfCrIEQBwH8sXLhQLVu2VIsWLXTvvfdq9uzZuvBhfWZmpu666y716dNHmzdv1h//+Ec9/fTTLo/fvXu3brrpJt15553asmWLFixYoDVr1mjo0KH+mA6AMsav8wDgPzp37qx77rlHw4YN07lz51SvXj0tWrRI1113nUaPHq3ly5fr22+/dfYfO3asJkyYoOzsbEVFRenBBx9UYGCg3njjDWefNWvWqFu3bsrPz1eVKlX8MS0AZYRPogBA0o4dO7R+/Xr169dPkhQUFKS+fftq1qxZzvvbt2/v8pgOHTq43N68ebPmzJmj8PBw58+NN96owsJCZWZm/joTAfCrCfJ3AQBQHsyaNUvnzp1zOZHcGKPQ0FDNmDGjRGOcOnVKf/zjH/XYY4+53RcXF1dqtQIoHwhRAH7zzp07p3nz5mny5Mnq2bOny319+vTRBx98oBYtWugf//iHy30bNmxwud2uXTtt375dCQkJZV4zAP/jnCgAv3lLly5V3759deTIEUVGRrrcN2rUKH3++edauHChWrRooREjRuiBBx5Qenq6nnjiCX3//ffKyclRZGSktmzZoo4dO+r+++/Xgw8+qGrVqmn79u1asWJFiT/NAlBxcE4UgN+8WbNmqUePHm4BSpLuvPNOpaWl6eTJk1q8eLE+/PBDJSYm6vXXX3d+Oy80NFSSlJiYqNTUVO3cuVNdu3ZVUlKSxo0bx7WmgEqKT6IAwNKECRM0c+ZM7d+/39+lAPADzokCgBJ67bXX1L59e0VHR2vt2rWaNGkS14ACfsMIUQBQQhkZGXrxxRd14sQJxcXF6YknntCYMWP8XRYAP+HXeQAAABY4sRwAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMDC/wfGGnH4UTTIJwAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "df.plot.scatter(x=\"Age\", y=\"Sex\")\n",
-    "\n",
-    "df.plot.scatter(x=\"Pclass\", y=\"Age\")"
+    "df[[\"Store_Area\"]]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Визуализация - Столбчатая диаграмма"
+    "2. Несколько столбцокв"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Store ID</th>\n",
+       "      <th>Store_Sales</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>66490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>39820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>54010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>53730</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>46620</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>891</th>\n",
+       "      <td>892</td>\n",
+       "      <td>66390</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>892</th>\n",
+       "      <td>893</td>\n",
+       "      <td>82080</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>893</th>\n",
+       "      <td>894</td>\n",
+       "      <td>76440</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>894</th>\n",
+       "      <td>895</td>\n",
+       "      <td>96610</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>895</th>\n",
+       "      <td>896</td>\n",
+       "      <td>54340</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>896 rows × 2 columns</p>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x1ac1cd1c6e0>"
+       "     Store ID   Store_Sales\n",
+       "0            1        66490\n",
+       "1            2        39820\n",
+       "2            3        54010\n",
+       "3            4        53730\n",
+       "4            5        46620\n",
+       "..         ...          ...\n",
+       "891        892        66390\n",
+       "892        893        82080\n",
+       "893        894        76440\n",
+       "894        895        96610\n",
+       "895        896        54340\n",
+       "\n",
+       "[896 rows x 2 columns]"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 126,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "plot = data.groupby([\"Pclass\", \"Survived\"]).size().unstack().plot.bar(color=[\"pink\", \"green\"])\n",
-    "plot.legend([\"Not survived\", \"Survived\"])"
+    "df[[\"Store ID \", \"Store_Sales\"]]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Визуализация - Временные ряды"
+    "3. Первая строка"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 243 entries, 0 to 242\n",
-      "Data columns (total 6 columns):\n",
-      " #   Column       Non-Null Count  Dtype         \n",
-      "---  ------       --------------  -----         \n",
-      " 0   my_date      243 non-null    object        \n",
-      " 1   my_value     243 non-null    float64       \n",
-      " 2   bullet       2 non-null      object        \n",
-      " 3   bulletClass  2 non-null      object        \n",
-      " 4   label        2 non-null      object        \n",
-      " 5   date         243 non-null    datetime64[ns]\n",
-      "dtypes: datetime64[ns](1), float64(1), object(4)\n",
-      "memory usage: 11.5+ KB\n",
-      "        my_date  my_value bullet bulletClass label       date\n",
-      "0    28.03.2023   76.5662    NaN         NaN   NaN 2023-03-28\n",
-      "1    31.03.2023   77.0863    NaN         NaN   NaN 2023-03-31\n",
-      "2    01.04.2023   77.3233    NaN         NaN   NaN 2023-04-01\n",
-      "3    04.04.2023   77.9510    NaN         NaN   NaN 2023-04-04\n",
-      "4    05.04.2023   79.3563    NaN         NaN   NaN 2023-04-05\n",
-      "..          ...       ...    ...         ...   ...        ...\n",
-      "238  20.03.2024   92.2243    NaN         NaN   NaN 2024-03-20\n",
-      "239  21.03.2024   92.6861    NaN         NaN   NaN 2024-03-21\n",
-      "240  22.03.2024   91.9499    NaN         NaN   NaN 2024-03-22\n",
-      "241  23.03.2024   92.6118    NaN         NaN   NaN 2024-03-23\n",
-      "242  26.03.2024   92.7761    NaN         NaN   NaN 2024-03-26\n",
-      "\n",
-      "[243 rows x 6 columns]\n"
-     ]
-    },
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Store ID</th>\n",
+       "      <th>Store_Area</th>\n",
+       "      <th>Items_Available</th>\n",
+       "      <th>Daily_Customer_Count</th>\n",
+       "      <th>Store_Sales</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1659</td>\n",
+       "      <td>1961</td>\n",
+       "      <td>530</td>\n",
+       "      <td>66490</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Store ID   Store_Area  Items_Available  Daily_Customer_Count  Store_Sales\n",
+       "0          1        1659             1961                   530        66490"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.iloc[[0]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "4. Вывод по условию"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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",
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+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Store ID</th>\n",
+       "      <th>Store_Area</th>\n",
+       "      <th>Items_Available</th>\n",
+       "      <th>Daily_Customer_Count</th>\n",
+       "      <th>Store_Sales</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1659</td>\n",
+       "      <td>1961</td>\n",
+       "      <td>530</td>\n",
+       "      <td>66490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1461</td>\n",
+       "      <td>1752</td>\n",
+       "      <td>210</td>\n",
+       "      <td>39820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>1609</td>\n",
+       "      <td>720</td>\n",
+       "      <td>54010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1451</td>\n",
+       "      <td>1748</td>\n",
+       "      <td>620</td>\n",
+       "      <td>53730</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1770</td>\n",
+       "      <td>2111</td>\n",
+       "      <td>450</td>\n",
+       "      <td>46620</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>891</th>\n",
+       "      <td>892</td>\n",
+       "      <td>1582</td>\n",
+       "      <td>1910</td>\n",
+       "      <td>1080</td>\n",
+       "      <td>66390</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>892</th>\n",
+       "      <td>893</td>\n",
+       "      <td>1387</td>\n",
+       "      <td>1663</td>\n",
+       "      <td>850</td>\n",
+       "      <td>82080</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>893</th>\n",
+       "      <td>894</td>\n",
+       "      <td>1200</td>\n",
+       "      <td>1436</td>\n",
+       "      <td>1060</td>\n",
+       "      <td>76440</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>894</th>\n",
+       "      <td>895</td>\n",
+       "      <td>1299</td>\n",
+       "      <td>1560</td>\n",
+       "      <td>770</td>\n",
+       "      <td>96610</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>895</th>\n",
+       "      <td>896</td>\n",
+       "      <td>1174</td>\n",
+       "      <td>1429</td>\n",
+       "      <td>1110</td>\n",
+       "      <td>54340</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>896 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Store ID   Store_Area  Items_Available  Daily_Customer_Count  Store_Sales\n",
+       "0            1        1659             1961                   530        66490\n",
+       "1            2        1461             1752                   210        39820\n",
+       "2            3        1340             1609                   720        54010\n",
+       "3            4        1451             1748                   620        53730\n",
+       "4            5        1770             2111                   450        46620\n",
+       "..         ...         ...              ...                   ...          ...\n",
+       "891        892        1582             1910                  1080        66390\n",
+       "892        893        1387             1663                   850        82080\n",
+       "893        894        1200             1436                  1060        76440\n",
+       "894        895        1299             1560                   770        96610\n",
+       "895        896        1174             1429                  1110        54340\n",
+       "\n",
+       "[896 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df[\"Store_Sales\"] > 2000]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Группировка и агрегация данных"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. Средняя продажа по каждому магазину"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Store_Sales</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Store ID</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>66490.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>39820.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>54010.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>53730.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>46620.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>892</th>\n",
+       "      <td>66390.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>893</th>\n",
+       "      <td>82080.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>894</th>\n",
+       "      <td>76440.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>895</th>\n",
+       "      <td>96610.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>896</th>\n",
+       "      <td>54340.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>896 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Store_Sales\n",
+       "Store ID              \n",
+       "1              66490.0\n",
+       "2              39820.0\n",
+       "3              54010.0\n",
+       "4              53730.0\n",
+       "5              46620.0\n",
+       "...                ...\n",
+       "892            66390.0\n",
+       "893            82080.0\n",
+       "894            76440.0\n",
+       "895            96610.0\n",
+       "896            54340.0\n",
+       "\n",
+       "[896 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby([\"Store ID \"])[[\"Store_Sales\"]].mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2. Для каждого магазина общая сумма продаж и среднее количество посетителей"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Store_Sales</th>\n",
+       "      <th>Daily_Customer_Count</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Store ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>66490</td>\n",
+       "      <td>530.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>39820</td>\n",
+       "      <td>210.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>54010</td>\n",
+       "      <td>720.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>53730</td>\n",
+       "      <td>620.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>46620</td>\n",
+       "      <td>450.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>892</th>\n",
+       "      <td>66390</td>\n",
+       "      <td>1080.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>893</th>\n",
+       "      <td>82080</td>\n",
+       "      <td>850.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>894</th>\n",
+       "      <td>76440</td>\n",
+       "      <td>1060.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>895</th>\n",
+       "      <td>96610</td>\n",
+       "      <td>770.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>896</th>\n",
+       "      <td>54340</td>\n",
+       "      <td>1110.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>896 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Store_Sales  Daily_Customer_Count\n",
+       "Store ID                                    \n",
+       "1                66490                 530.0\n",
+       "2                39820                 210.0\n",
+       "3                54010                 720.0\n",
+       "4                53730                 620.0\n",
+       "5                46620                 450.0\n",
+       "...                ...                   ...\n",
+       "892              66390                1080.0\n",
+       "893              82080                 850.0\n",
+       "894              76440                1060.0\n",
+       "895              96610                 770.0\n",
+       "896              54340                1110.0\n",
+       "\n",
+       "[896 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby(\"Store ID \").agg({\"Store_Sales\": \"sum\", \"Daily_Customer_Count\": \"mean\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Сортировка данных"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. Сортировка по продажам по убыванию"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Store ID</th>\n",
+       "      <th>Store_Area</th>\n",
+       "      <th>Items_Available</th>\n",
+       "      <th>Daily_Customer_Count</th>\n",
+       "      <th>Store_Sales</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>649</th>\n",
+       "      <td>650</td>\n",
+       "      <td>1989</td>\n",
+       "      <td>2414</td>\n",
+       "      <td>860</td>\n",
+       "      <td>116320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>868</th>\n",
+       "      <td>869</td>\n",
+       "      <td>1775</td>\n",
+       "      <td>2104</td>\n",
+       "      <td>980</td>\n",
+       "      <td>105150</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>432</th>\n",
+       "      <td>433</td>\n",
+       "      <td>1365</td>\n",
+       "      <td>1638</td>\n",
+       "      <td>680</td>\n",
+       "      <td>102920</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>408</th>\n",
+       "      <td>409</td>\n",
+       "      <td>1303</td>\n",
+       "      <td>1587</td>\n",
+       "      <td>1310</td>\n",
+       "      <td>102310</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>758</th>\n",
+       "      <td>759</td>\n",
+       "      <td>1486</td>\n",
+       "      <td>1758</td>\n",
+       "      <td>820</td>\n",
+       "      <td>101820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>352</th>\n",
+       "      <td>353</td>\n",
+       "      <td>1397</td>\n",
+       "      <td>1686</td>\n",
+       "      <td>850</td>\n",
+       "      <td>21300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>593</th>\n",
+       "      <td>594</td>\n",
+       "      <td>1624</td>\n",
+       "      <td>1946</td>\n",
+       "      <td>870</td>\n",
+       "      <td>20270</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>775</th>\n",
+       "      <td>776</td>\n",
+       "      <td>1537</td>\n",
+       "      <td>1877</td>\n",
+       "      <td>660</td>\n",
+       "      <td>17670</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>852</th>\n",
+       "      <td>853</td>\n",
+       "      <td>1477</td>\n",
+       "      <td>1790</td>\n",
+       "      <td>880</td>\n",
+       "      <td>16370</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>32</td>\n",
+       "      <td>1250</td>\n",
+       "      <td>1508</td>\n",
+       "      <td>990</td>\n",
+       "      <td>14920</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>896 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Store ID   Store_Area  Items_Available  Daily_Customer_Count  Store_Sales\n",
+       "649        650        1989             2414                   860       116320\n",
+       "868        869        1775             2104                   980       105150\n",
+       "432        433        1365             1638                   680       102920\n",
+       "408        409        1303             1587                  1310       102310\n",
+       "758        759        1486             1758                   820       101820\n",
+       "..         ...         ...              ...                   ...          ...\n",
+       "352        353        1397             1686                   850        21300\n",
+       "593        594        1624             1946                   870        20270\n",
+       "775        776        1537             1877                   660        17670\n",
+       "852        853        1477             1790                   880        16370\n",
+       "31          32        1250             1508                   990        14920\n",
+       "\n",
+       "[896 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.sort_values(\"Store_Sales\", ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2. Сортировка по нескольким столбцам"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Store ID</th>\n",
+       "      <th>Store_Area</th>\n",
+       "      <th>Items_Available</th>\n",
+       "      <th>Daily_Customer_Count</th>\n",
+       "      <th>Store_Sales</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>158</th>\n",
+       "      <td>159</td>\n",
+       "      <td>775</td>\n",
+       "      <td>932</td>\n",
+       "      <td>1090</td>\n",
+       "      <td>42530</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>865</th>\n",
+       "      <td>866</td>\n",
+       "      <td>780</td>\n",
+       "      <td>951</td>\n",
+       "      <td>790</td>\n",
+       "      <td>25600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>744</th>\n",
+       "      <td>745</td>\n",
+       "      <td>854</td>\n",
+       "      <td>1018</td>\n",
+       "      <td>660</td>\n",
+       "      <td>77740</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>549</th>\n",
+       "      <td>550</td>\n",
+       "      <td>869</td>\n",
+       "      <td>1050</td>\n",
+       "      <td>850</td>\n",
+       "      <td>52540</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>73</td>\n",
+       "      <td>891</td>\n",
+       "      <td>1073</td>\n",
+       "      <td>630</td>\n",
+       "      <td>67370</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>398</th>\n",
+       "      <td>399</td>\n",
+       "      <td>2063</td>\n",
+       "      <td>2493</td>\n",
+       "      <td>810</td>\n",
+       "      <td>51480</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>849</th>\n",
+       "      <td>850</td>\n",
+       "      <td>2067</td>\n",
+       "      <td>2492</td>\n",
+       "      <td>790</td>\n",
+       "      <td>70230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>91</th>\n",
+       "      <td>92</td>\n",
+       "      <td>2169</td>\n",
+       "      <td>2617</td>\n",
+       "      <td>600</td>\n",
+       "      <td>67080</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>540</th>\n",
+       "      <td>541</td>\n",
+       "      <td>2214</td>\n",
+       "      <td>2647</td>\n",
+       "      <td>740</td>\n",
+       "      <td>65900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>466</th>\n",
+       "      <td>467</td>\n",
+       "      <td>2229</td>\n",
+       "      <td>2667</td>\n",
+       "      <td>660</td>\n",
+       "      <td>87410</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>896 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Store ID   Store_Area  Items_Available  Daily_Customer_Count  Store_Sales\n",
+       "158        159         775              932                  1090        42530\n",
+       "865        866         780              951                   790        25600\n",
+       "744        745         854             1018                   660        77740\n",
+       "549        550         869             1050                   850        52540\n",
+       "72          73         891             1073                   630        67370\n",
+       "..         ...         ...              ...                   ...          ...\n",
+       "398        399        2063             2493                   810        51480\n",
+       "849        850        2067             2492                   790        70230\n",
+       "91          92        2169             2617                   600        67080\n",
+       "540        541        2214             2647                   740        65900\n",
+       "466        467        2229             2667                   660        87410\n",
+       "\n",
+       "[896 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.sort_values([\"Store_Area\", \"Store_Sales\"], ascending=[True, False])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Удаление строк/столбцов"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. Удаление столбца"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Store ID</th>\n",
+       "      <th>Items_Available</th>\n",
+       "      <th>Daily_Customer_Count</th>\n",
+       "      <th>Store_Sales</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1961</td>\n",
+       "      <td>530</td>\n",
+       "      <td>66490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1752</td>\n",
+       "      <td>210</td>\n",
+       "      <td>39820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1609</td>\n",
+       "      <td>720</td>\n",
+       "      <td>54010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1748</td>\n",
+       "      <td>620</td>\n",
+       "      <td>53730</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2111</td>\n",
+       "      <td>450</td>\n",
+       "      <td>46620</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>891</th>\n",
+       "      <td>892</td>\n",
+       "      <td>1910</td>\n",
+       "      <td>1080</td>\n",
+       "      <td>66390</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>892</th>\n",
+       "      <td>893</td>\n",
+       "      <td>1663</td>\n",
+       "      <td>850</td>\n",
+       "      <td>82080</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>893</th>\n",
+       "      <td>894</td>\n",
+       "      <td>1436</td>\n",
+       "      <td>1060</td>\n",
+       "      <td>76440</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>894</th>\n",
+       "      <td>895</td>\n",
+       "      <td>1560</td>\n",
+       "      <td>770</td>\n",
+       "      <td>96610</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>895</th>\n",
+       "      <td>896</td>\n",
+       "      <td>1429</td>\n",
+       "      <td>1110</td>\n",
+       "      <td>54340</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>896 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Store ID   Items_Available  Daily_Customer_Count  Store_Sales\n",
+       "0            1             1961                   530        66490\n",
+       "1            2             1752                   210        39820\n",
+       "2            3             1609                   720        54010\n",
+       "3            4             1748                   620        53730\n",
+       "4            5             2111                   450        46620\n",
+       "..         ...              ...                   ...          ...\n",
+       "891        892             1910                  1080        66390\n",
+       "892        893             1663                   850        82080\n",
+       "893        894             1436                  1060        76440\n",
+       "894        895             1560                   770        96610\n",
+       "895        896             1429                  1110        54340\n",
+       "\n",
+       "[896 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.drop(\"Store_Area\", axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Удаление строки"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Store ID</th>\n",
+       "      <th>Store_Area</th>\n",
+       "      <th>Items_Available</th>\n",
+       "      <th>Daily_Customer_Count</th>\n",
+       "      <th>Store_Sales</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1461</td>\n",
+       "      <td>1752</td>\n",
+       "      <td>210</td>\n",
+       "      <td>39820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>1609</td>\n",
+       "      <td>720</td>\n",
+       "      <td>54010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1451</td>\n",
+       "      <td>1748</td>\n",
+       "      <td>620</td>\n",
+       "      <td>53730</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1770</td>\n",
+       "      <td>2111</td>\n",
+       "      <td>450</td>\n",
+       "      <td>46620</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>1442</td>\n",
+       "      <td>1733</td>\n",
+       "      <td>760</td>\n",
+       "      <td>45260</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>891</th>\n",
+       "      <td>892</td>\n",
+       "      <td>1582</td>\n",
+       "      <td>1910</td>\n",
+       "      <td>1080</td>\n",
+       "      <td>66390</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>892</th>\n",
+       "      <td>893</td>\n",
+       "      <td>1387</td>\n",
+       "      <td>1663</td>\n",
+       "      <td>850</td>\n",
+       "      <td>82080</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>893</th>\n",
+       "      <td>894</td>\n",
+       "      <td>1200</td>\n",
+       "      <td>1436</td>\n",
+       "      <td>1060</td>\n",
+       "      <td>76440</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>894</th>\n",
+       "      <td>895</td>\n",
+       "      <td>1299</td>\n",
+       "      <td>1560</td>\n",
+       "      <td>770</td>\n",
+       "      <td>96610</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>895</th>\n",
+       "      <td>896</td>\n",
+       "      <td>1174</td>\n",
+       "      <td>1429</td>\n",
+       "      <td>1110</td>\n",
+       "      <td>54340</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>895 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Store ID   Store_Area  Items_Available  Daily_Customer_Count  Store_Sales\n",
+       "1            2        1461             1752                   210        39820\n",
+       "2            3        1340             1609                   720        54010\n",
+       "3            4        1451             1748                   620        53730\n",
+       "4            5        1770             2111                   450        46620\n",
+       "5            6        1442             1733                   760        45260\n",
+       "..         ...         ...              ...                   ...          ...\n",
+       "891        892        1582             1910                  1080        66390\n",
+       "892        893        1387             1663                   850        82080\n",
+       "893        894        1200             1436                  1060        76440\n",
+       "894        895        1299             1560                   770        96610\n",
+       "895        896        1174             1429                  1110        54340\n",
+       "\n",
+       "[895 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.drop(0, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Создание новых столбцов"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. Создание нового столбца с доходом на одного клиента"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Revenue_per_Customer</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>125.452830</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>189.619048</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>75.013889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>86.661290</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>103.600000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>891</th>\n",
+       "      <td>61.472222</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>892</th>\n",
+       "      <td>96.564706</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>893</th>\n",
+       "      <td>72.113208</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>894</th>\n",
+       "      <td>125.467532</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>895</th>\n",
+       "      <td>48.954955</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>896 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Revenue_per_Customer\n",
+       "0              125.452830\n",
+       "1              189.619048\n",
+       "2               75.013889\n",
+       "3               86.661290\n",
+       "4              103.600000\n",
+       "..                    ...\n",
+       "891             61.472222\n",
+       "892             96.564706\n",
+       "893             72.113208\n",
+       "894            125.467532\n",
+       "895             48.954955\n",
+       "\n",
+       "[896 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Revenue_per_Customer\"] = df[\"Store_Sales\"] / df[\"Daily_Customer_Count\"]\n",
+    "df[[\"Revenue_per_Customer\"]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Удаление строк с пустыми значениями"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. Удаление строк с NaN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Store ID</th>\n",
+       "      <th>Store_Area</th>\n",
+       "      <th>Items_Available</th>\n",
+       "      <th>Daily_Customer_Count</th>\n",
+       "      <th>Store_Sales</th>\n",
+       "      <th>Revenue_per_Customer</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1659</td>\n",
+       "      <td>1961</td>\n",
+       "      <td>530</td>\n",
+       "      <td>66490</td>\n",
+       "      <td>125.452830</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1461</td>\n",
+       "      <td>1752</td>\n",
+       "      <td>210</td>\n",
+       "      <td>39820</td>\n",
+       "      <td>189.619048</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>1609</td>\n",
+       "      <td>720</td>\n",
+       "      <td>54010</td>\n",
+       "      <td>75.013889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1451</td>\n",
+       "      <td>1748</td>\n",
+       "      <td>620</td>\n",
+       "      <td>53730</td>\n",
+       "      <td>86.661290</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1770</td>\n",
+       "      <td>2111</td>\n",
+       "      <td>450</td>\n",
+       "      <td>46620</td>\n",
+       "      <td>103.600000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>891</th>\n",
+       "      <td>892</td>\n",
+       "      <td>1582</td>\n",
+       "      <td>1910</td>\n",
+       "      <td>1080</td>\n",
+       "      <td>66390</td>\n",
+       "      <td>61.472222</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>892</th>\n",
+       "      <td>893</td>\n",
+       "      <td>1387</td>\n",
+       "      <td>1663</td>\n",
+       "      <td>850</td>\n",
+       "      <td>82080</td>\n",
+       "      <td>96.564706</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>893</th>\n",
+       "      <td>894</td>\n",
+       "      <td>1200</td>\n",
+       "      <td>1436</td>\n",
+       "      <td>1060</td>\n",
+       "      <td>76440</td>\n",
+       "      <td>72.113208</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>894</th>\n",
+       "      <td>895</td>\n",
+       "      <td>1299</td>\n",
+       "      <td>1560</td>\n",
+       "      <td>770</td>\n",
+       "      <td>96610</td>\n",
+       "      <td>125.467532</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>895</th>\n",
+       "      <td>896</td>\n",
+       "      <td>1174</td>\n",
+       "      <td>1429</td>\n",
+       "      <td>1110</td>\n",
+       "      <td>54340</td>\n",
+       "      <td>48.954955</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>896 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Store ID   Store_Area  Items_Available  Daily_Customer_Count  \\\n",
+       "0            1        1659             1961                   530   \n",
+       "1            2        1461             1752                   210   \n",
+       "2            3        1340             1609                   720   \n",
+       "3            4        1451             1748                   620   \n",
+       "4            5        1770             2111                   450   \n",
+       "..         ...         ...              ...                   ...   \n",
+       "891        892        1582             1910                  1080   \n",
+       "892        893        1387             1663                   850   \n",
+       "893        894        1200             1436                  1060   \n",
+       "894        895        1299             1560                   770   \n",
+       "895        896        1174             1429                  1110   \n",
+       "\n",
+       "     Store_Sales  Revenue_per_Customer  \n",
+       "0          66490            125.452830  \n",
+       "1          39820            189.619048  \n",
+       "2          54010             75.013889  \n",
+       "3          53730             86.661290  \n",
+       "4          46620            103.600000  \n",
+       "..           ...                   ...  \n",
+       "891        66390             61.472222  \n",
+       "892        82080             96.564706  \n",
+       "893        76440             72.113208  \n",
+       "894        96610            125.467532  \n",
+       "895        54340             48.954955  \n",
+       "\n",
+       "[896 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dropna()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2. Заполнить пустые значения для определённого столбца"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.fillna({\"Store_Sales\": df[\"Store_Sales\"].mean()}, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Заполнение пустых значений"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. Заполнение средним значением"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.fillna(df.mean(), inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Визуализация данных с Pandas и Matplotlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. Линейная диаграмма (plot). Продажи по магазинам"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -673,19 +2003,229 @@
     }
    ],
    "source": [
-    "from datetime import datetime\n",
-    "import matplotlib.dates as md\n",
+    "import matplotlib.pyplot as plt\n",
+    "df.plot(x=\"Store ID \", y=\"Store_Sales\", kind=\"line\")\n",
     "\n",
-    "ts = pd.read_csv(\"data/dollar.csv\")\n",
-    "ts[\"date\"] = ts.apply(lambda row: datetime.strptime(row[\"my_date\"], \"%d.%m.%Y\"), axis=1)\n",
-    "ts.info()\n",
+    "plt.xlabel(\"Store ID\") \n",
+    "plt.ylabel(\"Store Sales\")\n",
+    "plt.title(\"Store Sales by Store ID\")\n",
     "\n",
-    "print(ts)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2. Столбчатая диаграмма (bar). Средние продажи"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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wwJYtW3D8+HHMnz+/UOdILV68GC1btoSvry+GDBmC6tWrIzY2FpGRkXjw4AEuXrxYqPX5t3PnzuGXX36BTqdDUlISzpw5g99++018rQsTSolMQileIUlERrB7925h8ODBgre3t6BSqQS5XC7UqFFDGDVqlBAbG2vQ9vr160Lr1q3FLhhe7ILg3LlzQlBQkKBSqQRLS0uhbdu2wokTJwym13cL8bIuEw4dOiQEBQUJNjY2glKpFLy8vISBAwcKZ8+eLXAdjh8/LowYMUKoV6+eYGNjI5ibmwtubm7CwIEDDbqq0Ld96623BAsLC8HV1VXsBgOAcOjQIbHdv7uF0Fu+fLnQuHFjwcLCQrC2thZ8fX2FTz/9VHj06JG4Hfr27Su4ubkJCoVCcHJyEjp37vzKdRCE591CBAcHC3v27BHq168vKBQKwdvbW9i8efNLp/Hx8RGkUqnw4MGDV85fEATh0qVLwoQJEwQ/Pz/B3t5ekMlkQuXKlYVevXoJ586dM2ibmpoqfPjhh4Ktra0AwGB7xMbGCoMGDRIcHBwEuVwu+Pr6CqtWrTKY/lVdNdy6dUsYMGCA4OLiIpibmwtVqlQROnfuLGzZsuWV64GXdAuhf8hkMsHe3l7w9/cXJk2aJNy9e7dQ24fIVPBeikREJqRRo0awt7fHgQMHSrsUIipGPIeLiMhEnD17FhcuXMCAAQNKuxQiKmbcw0VEVMquXLmCqKgozJkzB0+ePMHt27fFeyASUfnAPVxERKVsy5YtGDRoELKzs/Hrr78ybBGVQ9zDRURERGRk3MNFREREZGTsh6sE6XQ6PHr0CNbW1i/t8ZmIiIhMiyAISElJgaura5Fut/UiBq4S9OjRo1feEoWIiIhM0/3791G1atXXmpaBqwTpe2u+f/8+b7ZKRERURmi1WlSrVq1Qd114GQauEqQ/jKhWqxm4iIiIypg3OR2oVE+aP3r0KN577z24urpCIpFg27Zt4rjs7Gx89tln8PX1hZWVFVxdXTFgwAA8evTIYB4JCQno168f1Go1bG1tERoaitTUVIM2ly5dQqtWraBUKlGtWrV87wW3efNmeHt7Q6lUwtfXV7xJrp4gCAgLC0PlypVhYWGBwMBA3Lx5s/g2BhEREZVbpRq40tLS0KBBAyxevDjPuPT0dJw7dw6TJ0/GuXPn8Pvvv+PGjRvo0qWLQbt+/fohOjoa+/btw86dO3H06FEMHTpUHK/VatGhQwe4u7sjKioK3333HaZMmYLly5eLbU6cOIG+ffsiNDQU58+fR7du3dCtWzdcuXJFbDN79mwsXLgQS5cuxalTp2BlZYWgoCBkZGQYYcsQERFRuVJ6t3E0BEDYunVrgW1Onz4tABBvWnr16tU8N87dvXu3IJFIhIcPHwqCIAg//vijYGdnJ2RmZoptPvvsM6F27dri8969ewvBwcEGy/L39xeGDRsmCIIg6HQ6wcXFxeCGrUlJSYJCoRB+/fXXQq9jcnKyAEBITk4u9DRERERUuorj73eZOocrOTkZEokEtra2AIDIyEjY2tqiSZMmYpvAwEBIpVKcOnUK3bt3R2RkJFq3bg25XC62CQoKwqxZs5CYmAg7OztERkZi/PjxBssKCgoSD3HGxMRAo9EgMDBQHG9jYwN/f39ERkaiT58++dabmZmJzMxM8blWqy3Ueubm5iI7O7tQbal8kcvlr33JMRERma4yE7gyMjLw2WefoW/fvuIJ5xqNBk5OTgbtZDIZ7O3todFoxDaenp4GbZydncVxdnZ20Gg04rAX27w4jxeny69NfmbOnImpU6cWeh0FQYBGo0FSUlKhp6HyRSqVwtPT0+AHAhERlX1lInBlZ2ejd+/eEAQBS5YsKe1yCm3SpEkGe870l5W+jD5sOTk5wdLSkp2jVjD6jnEfP34MNzc3vv5EROWIyQcufdi6e/cuDh48aNCdgouLC+Li4gza5+TkICEhAS4uLmKb2NhYgzb6569q8+J4/bDKlSsbtGnYsOFLa1coFFAoFIVaz9zcXDFsVapUqVDTUPnj6OiIR48eIScnB+bm5qVdDhERFROTPllEH7Zu3ryJ/fv35wkiAQEBSEpKQlRUlDjs4MGD0Ol08Pf3F9scPXrU4Jyoffv2oXbt2rCzsxPbHDhwwGDe+/btQ0BAAADA09MTLi4uBm20Wi1OnToltimOdQUAS0vLYpkflU36Q4m5ubmlXAkRERWnUg1cqampuHDhAi5cuADg+cnpFy5cwL1795CdnY2ePXvi7NmzWLduHXJzc6HRaKDRaJCVlQUAqFOnDjp27IghQ4bg9OnTOH78OEaOHIk+ffrA1dUVAPDhhx9CLpcjNDQU0dHR2LhxIxYsWGBwqG/MmDGIiIjAnDlzcP36dUyZMgVnz57FyJEjATzv6Gzs2LH4+uuvsX37dly+fBkDBgyAq6srunXrVqzbhIeRKja+/kRE5VSxXTP5Gg4dOiQAyPMICQkRYmJi8h0HQDh06JA4j6dPnwp9+/YVVCqVoFarhUGDBgkpKSkGy7l48aLQsmVLQaFQCFWqVBG+/fbbPLVs2rRJqFWrliCXywUfHx9h165dBuN1Op0wefJkwdnZWVAoFEK7du2EGzduFGl9C7qs9NmzZ8LVq1eFZ8+eFWmeVL7wfUBEZHqKo1sIiSAIQqkkvQpIq9XCxsYGycnJeW7tk5GRgZiYGHh6ekKpVJZShVTa+D4gIjI9Bf39LiyTP2m+ovOYuKtEl3fn2+ASXV5ZNHDgQCQlJRncioqIiKggJn3SPJUN8fHx+OSTT+Dm5gaFQgEXFxcEBQXh+PHjAJDnPpml6eLFi+jSpQucnJygVCrh4eGBDz74IM/VrkRERMWJe7jojfXo0QNZWVlYvXo1qlevjtjYWBw4cABPnz4t1uVkZ2e/UVcJ8fHxaNeuHTp37ow9e/bA1tYWd+7cwfbt25GWllaMlRIRERniHi56I0lJSTh27BhmzZqFtm3bwt3dHc2aNcOkSZPQpUsXeHh4AAC6d+8OiUQiPgeAJUuWwMvLC3K5HLVr18batWsN5i2RSLBkyRJ06dIFVlZW+OabbwAAf/zxB/z8/KBUKlG9enVMnToVOTk5r6z1+PHjSE5OxooVK9CoUSN4enqibdu2mDdvnng3gtzcXISGhsLT0xMWFhaoXbs2FixYUOB8dTodZs6cKU7ToEEDbNmyRRyfmJiIfv36wdHRERYWFqhZsyZWrVpVmM1LRETlBAMXvRGVSgWVSoVt27YZ3DdS78yZMwCAVatW4fHjx+LzrVu3YsyYMfjvf/+LK1euYNiwYRg0aBAOHTpkMP2UKVPQvXt3XL58GYMHD8axY8cwYMAAjBkzBlevXsWyZcsQHh4uhrGCuLi4ICcnB1u3bsXLrhXR6XSoWrUqNm/ejKtXryIsLAyff/45Nm3a9NL5zpw5E2vWrMHSpUsRHR2NcePGoX///jhy5AgAYPLkybh69Sp2796Na9euYcmSJXBwcHhlvURUckr6fFmqeHiVYgl6nasUy8JJ87/99huGDBmCZ8+ewc/PD2+//Tb69OmD+vXrA3i+p2rr1q0GfZa1aNECPj4+WL58uTisd+/eSEtLw65du8Tpxo4di3nz5oltAgMD0a5dO0yaNEkc9ssvv+DTTz/Fo0ePXlnrF198gdmzZ0OtVqNZs2Z45513MGDAgDz3yXzRyJEjodFoxL1WL540n5mZCXt7e+zfv9+gE9yPP/4Y6enpWL9+Pbp06QIHBwesXLnylfXxKkWi0uExcRcvGqKXKo6rFLmHi95Yjx498OjRI2zfvh0dO3bE4cOH4efnh/Dw8JdOc+3aNbRo0cJgWIsWLXDt2jWDYU2aNDF4fvHiRUybNk3cs6ZSqTBkyBA8fvwY6enpr6z1m2++gUajwdKlS+Hj44OlS5fC29sbly9fFtssXrwYjRs3hqOjI1QqFZYvX4579+7lO79//vkH6enpaN++vUFNa9aswa1btwAAn3zyCTZs2ICGDRvi008/xYkTJ15ZJxERlS8MXFQslEol2rdvj8mTJ+PEiRMYOHAgvvrqqzeer5WVlcHz1NRUTJ06VbxDwYULF3D58mXcvHmz0HuEKlWqhF69euH777/HtWvX4Orqiu+//x4AsGHDBvzvf/9DaGgo9u7diwsXLmDQoEHi3Q3+LTU1FQCwa9cug5quXr0q7hHr1KkT7t69i3HjxuHRo0do164d/ve//73uJiEiojKIVymSUdStW1fsCsLc3DzPvQHr1KmD48ePIyQkRBx2/Phx1K1bt8D5+vn54caNG6hRo0ax1CmXy+Hl5SVepXj8+HE0b94cw4cPF9vo91Tlp27dulAoFLh37x7efvvtl7ZzdHRESEgIQkJC0KpVK0yYMEEMeUREVP4xcNEbefr0KXr16oXBgwejfv36sLa2xtmzZzF79mx07doVAODh4YEDBw6gRYsWUCgUsLOzw4QJE9C7d280atQIgYGB2LFjB37//Xfs37+/wOWFhYWhc+fOcHNzQ8+ePSGVSnHx4kVcuXIFX3/9dYHT7ty5Exs2bECfPn1Qq1YtCIKAHTt24M8//xSvGqxZsybWrFmDPXv2wNPTE2vXrsWZM2fEqxj/zdraGv/73/8wbtw46HQ6tGzZEsnJyTh+/DjUajVCQkIQFhaGxo0bw8fHB5mZmdi5cyfq1KnzGlubiIjKrGK5yRAVSnm8l2JGRoYwceJEwc/PT7CxsREsLS2F2rVrC19++aWQnp4uCIIgbN++XahRo4Ygk8kEd3d3cdoff/xRqF69umBubi7UqlVLWLNmjcG8AQhbt27Ns8yIiAihefPmgoWFhaBWq4VmzZoJy5cvf2Wtt27dEoYMGSLUqlVLsLCwEGxtbYWmTZsKq1atMlifgQMHCjY2NoKtra3wySefCBMnThQaNGggtgkJCRG6du0qPtfpdML8+fOF2rVrC+bm5oKjo6MQFBQkHDlyRBAEQZg+fbpQp04dwcLCQrC3txe6du0q3L59O98ay+r7gKisc/9sZ2mXQCaM91IsY3gvRXoVvg+ISgevUqSC8CpFIiIiojKAgYvKjXXr1hl0zfDiw8fHp7TLIyKiCownzVO50aVLF/j7++c77k3uwUhERPSmGLio3LC2toa1tXVpl0FERJQHDymaGF7DULHx9SciKp8YuEyE/pBXYW5PQ+WXvkd7MzOzUq6EiIiKEw8pmggzMzPY2toiLi4OAGBpaQmJRFLKVVFJ0ul0iI+Ph6WlJWQyfjSJiMoTfqubEBcXFwAQQxdVPFKpFG5ubgzbRETlDAOXCZFIJKhcuTKcnJyQnZ1d2uVQKZDL5ZBKeaSfiKi8YeAyQWZmZjyHh4iIqBzhT2kiIiIiI2PgIiIiIjIyBi4iIiIiI2PgIiIiIjIyBi4iIiIiI2PgIiIiIjIyBi4iIiIiI2PgIiIiIjIyBi4iIiIiI2PgIiIiIjIyBi4iIiIiI2PgIiIiIjIyBi4iIiIiI2PgIiIiIjIyBi4iIiIiI2PgIiIiIjIyBi4iIiIiI2PgIiIiIjIyBi4iIiIiI2PgIiIiIjIyBi4iIiIiI2PgIiIiIjIyBi4iIiIiI2PgIiIiIjIyBi4iIiIiI2PgIiIiIjIyBi4iInotHhN3lXYJRGUGAxcRERGRkZVq4Dp69Cjee+89uLq6QiKRYNu2bQbjBUFAWFgYKleuDAsLCwQGBuLmzZsGbRISEtCvXz+o1WrY2toiNDQUqampBm0uXbqEVq1aQalUolq1apg9e3aeWjZv3gxvb28olUr4+vrizz//LHItRERERPkp1cCVlpaGBg0aYPHixfmOnz17NhYuXIilS5fi1KlTsLKyQlBQEDIyMsQ2/fr1Q3R0NPbt24edO3fi6NGjGDp0qDheq9WiQ4cOcHd3R1RUFL777jtMmTIFy5cvF9ucOHECffv2RWhoKM6fP49u3bqhW7duuHLlSpFqISIiItNjEoe/BRMBQNi6dav4XKfTCS4uLsJ3330nDktKShIUCoXw66+/CoIgCFevXhUACGfOnBHb7N69W5BIJMLDhw8FQRCEH3/8UbCzsxMyMzPFNp999plQu3Zt8Xnv3r2F4OBgg3r8/f2FYcOGFbqW/GRkZAjJycni4/79+wIAITk5uSibhojIJLl/trO0Syg25WldKK83fX2Tk5Pf+O+3yZ7DFRMTA41Gg8DAQHGYjY0N/P39ERkZCQCIjIyEra0tmjRpIrYJDAyEVCrFqVOnxDatW7eGXC4X2wQFBeHGjRtITEwU27y4HH0b/XIKU0t+Zs6cCRsbG/FRrVq1190cREREVIaZbODSaDQAAGdnZ4Phzs7O4jiNRgMnJyeD8TKZDPb29gZt8pvHi8t4WZsXx7+qlvxMmjQJycnJ4uP+/fuvWGsiIiIqj2SlXUB5plAooFAoSrsMIiIiKmUmu4fLxcUFABAbG2swPDY2Vhzn4uKCuLg4g/E5OTlISEgwaJPfPF5cxsvavDj+VbUQERERvYzJBi5PT0+4uLjgwIED4jCtVotTp04hICAAABAQEICkpCRERUWJbQ4ePAidTgd/f3+xzdGjR5GdnS222bdvH2rXrg07OzuxzYvL0bfRL6cwtRARERG9TKkGrtTUVFy4cAEXLlwA8Pzk9AsXLuDevXuQSCQYO3Ysvv76a2zfvh2XL1/GgAED4Orqim7dugEA6tSpg44dO2LIkCE4ffo0jh8/jpEjR6JPnz5wdXUFAHz44YeQy+UIDQ1FdHQ0Nm7ciAULFmD8+PFiHWPGjEFERATmzJmD69evY8qUKTh79ixGjhwJAIWqhYiIiOil3ug6yTd06NAhAUCeR0hIiCAIz7tjmDx5suDs7CwoFAqhXbt2wo0bNwzm8fTpU6Fv376CSqUS1Gq1MGjQICElJcWgzcWLF4WWLVsKCoVCqFKlivDtt9/mqWXTpk1CrVq1BLlcLvj4+Ai7du0yGF+YWl6lOC4rJSIyFeWpK4XytC6Ulyl0CyERBEEoxbxXoWi1WtjY2CA5ORlqtbq0yyEieiMeE3fhzrfBpV1GsShP60J5venrWxx/v032HC4iIiKi8oKBi4iIiMjIGLiIiIiIjIyBi4hMlknccJaIqBgwcBEREREZGQMXERERkZExcBEREREZGQMXERERkZExcBEREREZGQMXERERkZExcBERERkJuzYhPQYuIiIiIiNj4CIiIiIyMgYuIiIiIiNj4CIiIiqjeI5Y2cHARURERGWeqYdPBi4iIiIiI2PgIiIiIjIyBi4iIiIiI2PgIiIiIjIyBi4iIiIiI2PgIiIiIjIyBi4iIiIiI2PgIiIiIjIyBi4iIiIiI2PgIiIiIjIyBi4iIiIiI2PgIiIiIjIyBi4iIioy/Y2CTf2GweURt3nZxMBFREQVDkMLlTQGLiIiIiIjY+AionKBeyyIyJS/Bxi4iIiIiIyMgYuIiMo8U96zQQQwcBEREREZHQMXERERkZExcBFRmcJDR0Svj5+f0sPARURERGRkDFxERERERsbARURERGRkDFxERGTSSuq8I57fRMbEwEVERK/EMEL0Zhi4iKhcY1AgIlPAwEVUShgEiIgqDgYuIiIiIiNj4CIiIiIyMgYuIiIyGTzUTuUVAxcRGRX/gBIZFz9jZQMDFxERUQXAYFa6TDpw5ebmYvLkyfD09ISFhQW8vLwwffp0CIIgthEEAWFhYahcuTIsLCwQGBiImzdvGswnISEB/fr1g1qthq2tLUJDQ5GammrQ5tKlS2jVqhWUSiWqVauG2bNn56ln8+bN8Pb2hlKphK+vL/7880/jrDhRGcQvcyIqSEX/jjDpwDVr1iwsWbIEixYtwrVr1zBr1izMnj0bP/zwg9hm9uzZWLhwIZYuXYpTp07BysoKQUFByMjIENv069cP0dHR2LdvH3bu3ImjR49i6NCh4nitVosOHTrA3d0dUVFR+O677zBlyhQsX75cbHPixAn07dsXoaGhOH/+PLp164Zu3brhypUrJbMxyrGK/iEkIqLyz6QD14kTJ9C1a1cEBwfDw8MDPXv2RIcOHXD69GkAz/duzZ8/H19++SW6du2K+vXrY82aNXj06BG2bdsGALh27RoiIiKwYsUK+Pv7o2XLlvjhhx+wYcMGPHr0CACwbt06ZGVlYeXKlfDx8UGfPn0wevRozJ07V6xlwYIF6NixIyZMmIA6depg+vTp8PPzw6JFi0p8u5RlDFdERGUXv8Nfn0kHrubNm+PAgQP4+++/AQAXL17EX3/9hU6dOgEAYmJioNFoEBgYKE5jY2MDf39/REZGAgAiIyNha2uLJk2aiG0CAwMhlUpx6tQpsU3r1q0hl8vFNkFBQbhx4wYSExPFNi8uR99Gv5z8ZGZmQqvVGjyoZPBLwTTwdSAiek5W2gUUZOLEidBqtfD29oaZmRlyc3PxzTffoF+/fgAAjUYDAHB2djaYztnZWRyn0Wjg5ORkMF4mk8He3t6gjaenZ5556MfZ2dlBo9EUuJz8zJw5E1OnTi3qahMREVE5Y9J7uDZt2oR169Zh/fr1OHfuHFavXo3vv/8eq1evLu3SCmXSpElITk4WH/fv3y/tkoiIiKgUmHTgmjBhAiZOnIg+ffrA19cXH330EcaNG4eZM2cCAFxcXAAAsbGxBtPFxsaK41xcXBAXF2cwPicnBwkJCQZt8pvHi8t4WRv9+PwoFAqo1WqDR0XCw0lERFTSTPVvj0kHrvT0dEilhiWamZlBp9MBADw9PeHi4oIDBw6I47VaLU6dOoWAgAAAQEBAAJKSkhAVFSW2OXjwIHQ6Hfz9/cU2R48eRXZ2tthm3759qF27Nuzs7MQ2Ly5H30a/nOJiqm8UIjIOfuaJKgaTDlzvvfcevvnmG+zatQt37tzB1q1bMXfuXHTv3h0AIJFIMHbsWHz99dfYvn07Ll++jAEDBsDV1RXdunUDANSpUwcdO3bEkCFDcPr0aRw/fhwjR45Enz594OrqCgD48MMPIZfLERoaiujoaGzcuBELFizA+PHjxVrGjBmDiIgIzJkzB9evX8eUKVNw9uxZjBw5ssS3CxEREZUtJn3S/A8//IDJkydj+PDhiIuLg6urK4YNG4awsDCxzaeffoq0tDQMHToUSUlJaNmyJSIiIqBUKsU269atw8iRI9GuXTtIpVL06NEDCxcuFMfb2Nhg7969GDFiBBo3bgwHBweEhYUZ9NXVvHlzrF+/Hl9++SU+//xz1KxZE9u2bUO9evVKZmOUAR4Td+HOt8GlXQYREZHJMenAZW1tjfnz52P+/PkvbSORSDBt2jRMmzbtpW3s7e2xfv36ApdVv359HDt2rMA2vXr1Qq9evQpsQ0TGx3BPRGWNSR9SJCIi4+I5ZEQlg4GLiIiIyMgYuIiIiIiMjIGLiIiIyMgYuMik8fySklUc25uvGRUW3ytUFhTX+5SBi4iIiMjIGLiI6JW4J4KI6M0wcBERUYXCHxBUGhi4iCoo/tEhIio5DFxlFP9YEpU9/NwSVVwMXERUoTD0EFFpYOAiKmb8g05EZV1+32P8bnszDFxEJYBfVEQE8LuguJWl7fnGgUur1WLbtm24du1acdRDREREVO4UOXD17t0bixYtAgA8e/YMTZo0Qe/evVG/fn389ttvxV4gERFRaShLe0/I9BU5cB09ehStWrUCAGzduhWCICApKQkLFy7E119/XewFEhUGvxiJyNTwe4leVOTAlZycDHt7ewBAREQEevToAUtLSwQHB+PmzZvFXmB5xg9j2fKmrxdfbyKiiqvIgatatWqIjIxEWloaIiIi0KFDBwBAYmIilEplsRdIREREVNYVOXCNHTsW/fr1Q9WqVVG5cmW0adMGwPNDjb6+vsVdH5Uw7oUhIiIqfkUOXMOHD0dkZCRWrlyJ48ePQyp9Povq1avzHK4ygqGqeHF7EhHRq8heZ6ImTZqgfv36iImJgZeXF2QyGYKDg4u7NiIiIqJyoch7uNLT0xEaGgpLS0v4+Pjg3r17AIBRo0bh22+/LfYCiYiobODeXgL4PniZIgeuSZMm4eLFizh8+LDBSfKBgYHYuHFjsRZHRETGwz+MlB++L4yjyIFr27ZtWLRoEVq2bAmJRCIO9/Hxwa1bt4q1OCIioqJgWCBTVeTAFR8fDycnpzzD09LSDAIYERERET1X5MDVpEkT7Nr1f78g9CFrxYoVCAgIKL7KiIyEv4DpTfD9U3TcZq/GbVT+FfkqxRkzZqBTp064evUqcnJysGDBAly9ehUnTpzAkSNHjFEjEVGZ4jFxF+58yyu3iej/FHkPV8uWLXHhwgXk5OTA19cXe/fuhZOTEyIjI9G4cWNj1EgVBH/hERFRefVa/XB5eXnhp59+Ku5aiIiIiMqlQgUurVZb6Bmq1erXLoaIiIioPCpU4LK1tX3lFYiCIEAikSA3N7dYCqOi43kjxYfbkoiIilOhAtehQ4eMXQcRUblTEYN7RVxnosIoVOB6++23jV0HGZExvgD5pUpERFR4r3XSPPD8nor37t1DVlaWwfD69eu/cVFERERE5UmRA1d8fDwGDRqE3bt35zue53ARERFReVCc3RUVuR+usWPHIikpCadOnYKFhQUiIiKwevVq1KxZE9u3by+2wqh8Y59bz3E7kDHwfVV88tuW3L70Ooq8h+vgwYP4448/0KRJE0ilUri7u6N9+/ZQq9WYOXMmgoN5Xg8RUWnjeZZEpqXIe7jS0tLEm1fb2dkhPj4eAODr64tz584Vb3VERCWAeyzoTfD9Q4VR5MBVu3Zt3LhxAwDQoEEDLFu2DA8fPsTSpUtRuXLlYi+QiKii4R9wovKnyIFrzJgxePz4MQDgq6++wu7du+Hm5oaFCxdixowZxV4gkbGV9z9u5X39iIjKgiKfw9W/f3/x/40bN8bdu3dx/fp1uLm5wcHBoViLIyIiIioPXrsfLj25XI5atWpBpVIVRz1ERERE5U6hDynu2LED4eHhBsO++eYbqFQq2NraokOHDkhMTCzu+qiM4GEr4+L2JSIq2woduObOnYu0tDTx+YkTJxAWFobJkydj06ZNuH//PqZPn26UIomIiIjKskIHrujoaDRv3lx8vmXLFrRv3x5ffPEF3n//fcyZMwc7duwwSpFERETlVUXYg10R1vFVCh24UlJSUKlSJfH5X3/9hXbt2onPfXx88OjRo+KtjsoFftCIShc/g0Slr9CBq0qVKrh27RoAIDU1FRcvXjTY4/X06VNYWloWf4VEREQmhiGWiqrQgatXr14YO3Ys1q5diyFDhsDFxQVvvfWWOP7s2bOoXbu2UYokovKHf7DI2PgeI1NS6G4hwsLC8PDhQ4wePRouLi745ZdfYGZmJo7/9ddf8d577xmlSCIiIqKyrNCBy8LCAmvWrHnp+EOHDhVLQURERETlTZFv7VPSHj58iP79+6NSpUqwsLCAr68vzp49K44XBAFhYWGoXLkyLCwsEBgYiJs3bxrMIyEhAf369YNarYatrS1CQ0ORmppq0ObSpUto1aoVlEolqlWrhtmzZ+epZfPmzfD29oZSqYSvry/+/PNP46w0EZkkHqIiotdl0oErMTERLVq0gLm5OXbv3o2rV69izpw5sLOzE9vMnj0bCxcuxNKlS3Hq1ClYWVkhKCgIGRkZYpt+/fohOjoa+/btw86dO3H06FEMHTpUHK/VatGhQwe4u7sjKioK3333HaZMmYLly5eLbU6cOIG+ffsiNDQU58+fR7du3dCtWzdcuXKlZDZGBcQ/blRUfM+8OW5D08DXofx541v7GNOsWbNQrVo1rFq1Shzm6ekp/l8QBMyfPx9ffvklunbtCgBYs2YNnJ2dsW3bNvTp0wfXrl1DREQEzpw5gyZNmgAAfvjhB7z77rv4/vvv4erqinXr1iErKwsrV66EXC6Hj48PLly4gLlz54rBbMGCBejYsSMmTJgAAJg+fTr27duHRYsWYenSpSW1SYhMhsfEXbjzbXBpl0FEJYAB8M2Z9B6u7du3o0mTJujVqxecnJzQqFEj/PTTT+L4mJgYaDQaBAYGisNsbGzg7++PyMhIAEBkZCRsbW3FsAUAgYGBkEqlOHXqlNimdevWkMvlYpugoCDcuHFDvF1RZGSkwXL0bfTLyU9mZia0Wq3Bg4iIiEpfSYfINwpcLx62M4bbt29jyZIlqFmzJvbs2YNPPvkEo0ePxurVqwEAGo0GAODs7GwwnbOzszhOo9HAycnJYLxMJoO9vb1Bm/zm8eIyXtZGPz4/M2fOhI2NjfioVq1akdafiIjKDu4FooIUOXDpdDpMnz4dVapUgUqlwu3btwEAkydPxs8//1ysxel0Ovj5+WHGjBlo1KgRhg4diiFDhpSZQ3iTJk1CcnKy+Lh//35pl0REVKYwxBQOt5PpK3Lg+vrrrxEeHo7Zs2cbHIKrV68eVqxYUazFVa5cGXXr1jUYVqdOHdy7dw8A4OLiAgCIjY01aBMbGyuOc3FxQVxcnMH4nJwcJCQkGLTJbx4vLuNlbfTj86NQKKBWqw0eBeEHhoiIqHDK2t/MIgeuNWvWYPny5ejXr59Bx6cNGjTA9evXi7W4Fi1a4MaNGwbD/v77b7i7uwN4fgK9i4sLDhw4II7XarU4deoUAgICAAABAQFISkpCVFSU2ObgwYPQ6XTw9/cX2xw9ehTZ2dlim3379qF27driFZEBAQEGy9G30S+HiIiI6GWKHLgePnyIGjVq5Bmu0+kMAktxGDduHE6ePIkZM2bgn3/+wfr167F8+XKMGDECACCRSDB27Fh8/fXX2L59Oy5fvowBAwbA1dUV3bp1A/B8j1jHjh0xZMgQnD59GsePH8fIkSPRp08fuLq6AgA+/PBDyOVyhIaGIjo6Ghs3bsSCBQswfvx4sZYxY8YgIiICc+bMwfXr1zFlyhScPXsWI0eOLNZ1Lkll7dcBvRm+3lQR8X1PpqLIgatu3bo4duxYnuFbtmxBo0aNiqUovaZNm2Lr1q349ddfUa9ePUyfPh3z589Hv379xDaffvopRo0ahaFDh6Jp06ZITU1FREQElEql2GbdunXw9vZGu3bt8O6776Jly5YGfWzZ2Nhg7969iImJQePGjfHf//4XYWFhBn11NW/eXAx8DRo0wJYtW7Bt2zbUq1evWNeZiOhNMGBQeVNe3tNF7ocrLCwMISEhePjwIXQ6HX7//XfcuHEDa9aswc6dO4u9wM6dO6Nz584vHS+RSDBt2jRMmzbtpW3s7e2xfv36ApdTv379fIPki3r16oVevXoVXLCJKY43KvtbIiIiejNF3sPVtWtX7NixA/v374eVlRXCwsJw7do17NixA+3btzdGjURERERlWpH2cOXk5GDGjBkYPHgw9u3bZ6yaiIiIiMqVIu3hkslkmD17NnJycoxVD1UA5eV4PBERUWEV+ZBiu3btcOTIEWPUQkRU7vEHB5VnfH+/XJFPmu/UqRMmTpyIy5cvo3HjxrCysjIY36VLl2Irjqgi4kUKRFRW8Puq8IocuIYPHw4AmDt3bp5xEokEubm5b14VERER0f9XHoLda91L8WUPhi2isqEs7fYvS7VSxVWR36cVed2LosiBi4iIiIiK5rUC15EjR/Dee++hRo0aqFGjBrp06fLKTkOJiIiIKqoiB65ffvkFgYGBsLS0xOjRozF69GhYWFigXbt2r+zNnYioIKZ0aMKUaqGyh+8f+rcinzT/zTffYPbs2Rg3bpw4bPTo0Zg7dy6mT5+ODz/8sFgLJCIiIuMpDyekG1u9r/a88TyKvIfr9u3beO+99/IM79KlC2JiYt64ICKi8oB7OIiKV1n/TBU5cFWrVg0HDhzIM3z//v2oVq1asRRFREREVJ4U+ZDif//7X4wePRoXLlxA8+bNAQDHjx9HeHg4FixYUOwFEhEVNx5CKZqyvmeByBQUOXB98skncHFxwZw5c7Bp0yYAQJ06dbBx40Z07dq12AskIiIiKuuKHLgAoHv37ujevXtx10JkUrgXhEwN35NEZVeRz+GqXr06nj59mmd4UlISqlevXixFEZUVPNRCVDB+RoieK3LgunPnTr638MnMzMTDhw+LpSgiIiIqu0w9aJdGfYU+pLh9+3bx/3v27IGNjY34PDc3FwcOHICHh0exFkdE9CZM/UufiCqOQgeubt26AQAkEglCQkIMxpmbm8PDwwNz5swp1uKI/o3nsFB+ymOw4nudqHwp9CFFnU4HnU4HNzc3xMXFic91Oh0yMzNx48YNdO7c2Zi1EhHRC4oaNMtjMCUqK4p8DldMTAwcHByMUQsREdErvSw4MlCSKSt04IqMjMTOnTsNhq1Zswaenp5wcnLC0KFDkZmZWewFEv0bv1SfK0vboSzVSkRkDIUOXNOmTUN0dLT4/PLlywgNDUVgYCAmTpyIHTt2YObMmUYpkoiIyjaGbqroCh24Lly4gHbt2onPN2zYAH9/f/z0008YP348Fi5cKPY8T0RU0TBQkKnge9E0FTpwJSYmwtnZWXx+5MgRdOrUSXzetGlT3L9/v3irowqDXxBERMWH36mmp9CBy9nZGTExMQCArKwsnDt3Dm+99ZY4PiUlBebm5sVfIRkFP4xEREQlp9CB691338XEiRNx7NgxTJo0CZaWlmjVqpU4/tKlS/Dy8jJKkfTmGLCIqKTw+4Yor0J3fDp9+nS8//77ePvtt6FSqbB69WrI5XJx/MqVK9GhQwejFElERERUlhU6cDk4OODo0aNITk6GSqWCmZmZwfjNmzdDpVIVe4FERERExmbsuzsUueNTGxubPGELAOzt7Q32eBGZGh7mICpf+JmmsqTIgYuIildF/KNREdeZSI/v/4qJgYuIqAzjH2+isoGBq5TwS5KIiKjiYOAqZQxexsNtS0REpoKBi6gCYPh8riJvh4q87kSmgIGLqASV9z965X39iEoSP0/lCwNXGcYPI1H5wM8yUfnHwEVEBvjHv+zha0Zk+hi4iIiIiIyMgYuIqARwLxSVN8Xxnq5InwsGLqpQKtKHm4qG7w0iMiYGLiIiIiIjY+AiMkHc20JEVL4wcBEREVGZUhZ/lDJwERERERkZAxcBKJu/FoiIiMoKBq4ywNTCkKnVQ0REZOoYuIjKEYbhksdtTkSFUaYC17fffguJRIKxY8eKwzIyMjBixAhUqlQJKpUKPXr0QGxsrMF09+7dQ3BwMCwtLeHk5IQJEyYgJyfHoM3hw4fh5+cHhUKBGjVqIDw8PM/yFy9eDA8PDyiVSvj7++P06dPGWE0iIiIqZ8pM4Dpz5gyWLVuG+vXrGwwfN24cduzYgc2bN+PIkSN49OgR3n//fXF8bm4ugoODkZWVhRMnTmD16tUIDw9HWFiY2CYmJgbBwcFo27YtLly4gLFjx+Ljjz/Gnj17xDYbN27E+PHj8dVXX+HcuXNo0KABgoKCEBcXZ/yVJ6Ph3gkiovLNVL7ny0TgSk1NRb9+/fDTTz/Bzs5OHJ6cnIyff/4Zc+fOxTvvvIPGjRtj1apVOHHiBE6ePAkA2Lt3L65evYpffvkFDRs2RKdOnTB9+nQsXrwYWVlZAIClS5fC09MTc+bMQZ06dTBy5Ej07NkT8+bNE5c1d+5cDBkyBIMGDULdunWxdOlSWFpaYuXKlSW7MUqYqbxRiYiIyrIyEbhGjBiB4OBgBAYGGgyPiopCdna2wXBvb2+4ubkhMjISABAZGQlfX184OzuLbYKCgqDVahEdHS22+fe8g4KCxHlkZWUhKirKoI1UKkVgYKDYJj+ZmZnQarUGD6KKjiGeiCoiWWkX8CobNmzAuXPncObMmTzjNBoN5HI5bG1tDYY7OztDo9GIbV4MW/rx+nEFtdFqtXj27BkSExORm5ubb5vr16+/tPaZM2di6tSphVtRIiIiKrdMeg/X/fv3MWbMGKxbtw5KpbK0yymySZMmITk5WXzcv3+/tEsiIqpwuFeVTIFJB66oqCjExcXBz88PMpkMMpkMR44cwcKFCyGTyeDs7IysrCwkJSUZTBcbGwsXFxcAgIuLS56rFvXPX9VGrVbDwsICDg4OMDMzy7eNfh75USgUUKvVBg8iIiKqeEw6cLVr1w6XL1/GhQsXxEeTJk3Qr18/8f/m5uY4cOCAOM2NGzdw7949BAQEAAACAgJw+fJlg6sJ9+3bB7Vajbp164ptXpyHvo1+HnK5HI0bNzZoo9PpcODAAbENEVF5wT1CpomvS9lm0udwWVtbo169egbDrKysUKlSJXF4aGgoxo8fD3t7e6jVaowaNQoBAQF46623AAAdOnRA3bp18dFHH2H27NnQaDT48ssvMWLECCgUCgDAf/7zHyxatAiffvopBg8ejIMHD2LTpk3Ytev/3tzjx49HSEgImjRpgmbNmmH+/PlIS0vDoEGDSmhr0OvwmLgLd74NLu0yiN4Y/9hSUfH7z7SYdOAqjHnz5kEqlaJHjx7IzMxEUFAQfvzxR3G8mZkZdu7ciU8++QQBAQGwsrJCSEgIpk2bJrbx9PTErl27MG7cOCxYsABVq1bFihUrEBQUJLb54IMPEB8fj7CwMGg0GjRs2BARERF5TqQnMhZ+eRIRlV1lLnAdPnzY4LlSqcTixYuxePHil07j7u6OP//8s8D5tmnTBufPny+wzciRIzFy5MhC1/q6+IeVTB33thCRMZTnv38mfQ4XERERUXnAwEVlHve2EFFJ4fcNvS4GLqIyjF/+RERlAwMXERERmYzy+kOSgYuMqrx+cIiI6M1UtL8PDFxERERERsbAVc5UtF8MREREZQEDF1EFxoBORFQyGLiIqEAMZUREb46Bi4iIiMjIGLiIiIiIjIyBi6gIysrhtbJS54vKYs1ERIXFwEUi/sErPtyWRET0IgYuIiIiIiNj4CIieonC7qnkHk0iehUGLiIiIiIY98cTAxcRuIeiPOJrSkSmhIGLiIiIyMgYuIiIiIiMjIGLiIioguKh95LDwEVERGUOgwKVNQxcRERU4hiYqKJh4CIiIiIyMgYuIiIiIiNj4CIiIiIyMgYuIiIiIiNj4CIiIiIyMgYuIiIiIiNj4CIiIiIyMgYuIiIiIiNj4CIiIiIyMgYuIiIiIiNj4CIiIiIyMgYuIiIiIiNj4CIiIiIyMgYuIiIiIiNj4CojPCbuKu0SiIiI6DUxcBEREREZGQMXERERkZExcBEREREZGQMXkZHwvDsiItJj4CIiIiIyMgYuIiIiIiNj4CIiIiIyMgYuIiIiIiNj4CIiIiIyMgYuIiIiIiNj4CIiIiIyMgYuonKG/X8REZkeBi4iIqIygj+oyi6TDlwzZ85E06ZNYW1tDScnJ3Tr1g03btwwaJORkYERI0agUqVKUKlU6NGjB2JjYw3a3Lt3D8HBwbC0tISTkxMmTJiAnJwcgzaHDx+Gn58fFAoFatSogfDw8Dz1LF68GB4eHlAqlfD398fp06eLfZ2JiIio/DHpwHXkyBGMGDECJ0+exL59+5CdnY0OHTogLS1NbDNu3Djs2LEDmzdvxpEjR/Do0SO8//774vjc3FwEBwcjKysLJ06cwOrVqxEeHo6wsDCxTUxMDIKDg9G2bVtcuHABY8eOxccff4w9e/aIbTZu3Ijx48fjq6++wrlz59CgQQMEBQUhLi6uZDYGERERlVmy0i6gIBEREQbPw8PD4eTkhKioKLRu3RrJycn4+eefsX79erzzzjsAgFWrVqFOnTo4efIk3nrrLezduxdXr17F/v374ezsjIYNG2L69On47LPPMGXKFMjlcixduhSenp6YM2cOAKBOnTr466+/MG/ePAQFBQEA5s6diyFDhmDQoEEAgKVLl2LXrl1YuXIlJk6cWIJbhYiIiMoak97D9W/JyckAAHt7ewBAVFQUsrOzERgYKLbx9vaGm5sbIiMjAQCRkZHw9fWFs7Oz2CYoKAharRbR0dFimxfnoW+jn0dWVhaioqIM2kilUgQGBopt8pOZmQmtVmvwICKi/PH8JCrPykzg0ul0GDt2LFq0aIF69eoBADQaDeRyOWxtbQ3aOjs7Q6PRiG1eDFv68fpxBbXRarV49uwZnjx5gtzc3Hzb6OeRn5kzZ8LGxkZ8VKtWregrTkRERGVemQlcI0aMwJUrV7Bhw4bSLqXQJk2ahOTkZPFx//790i6JiIiISoFJn8OlN3LkSOzcuRNHjx5F1apVxeEuLi7IyspCUlKSwV6u2NhYuLi4iG3+fTWh/irGF9v8+8rG2NhYqNVqWFhYwMzMDGZmZvm20c8jPwqFAgqFougrTEREROWKSe/hEgQBI0eOxNatW3Hw4EF4enoajG/cuDHMzc1x4MABcdiNGzdw7949BAQEAAACAgJw+fJlg6sJ9+3bB7Vajbp164ptXpyHvo1+HnK5HI0bNzZoo9PpcODAAbENERER0cuY9B6uESNGYP369fjjjz9gbW0tni9lY2MDCwsL2NjYIDQ0FOPHj4e9vT3UajVGjRqFgIAAvPXWWwCADh06oG7duvjoo48we/ZsaDQafPnllxgxYoS49+k///kPFi1ahE8//RSDBw/GwYMHsWnTJuza9X8ncI4fPx4hISFo0qQJmjVrhvnz5yMtLU28apGIiIjoZUw6cC1ZsgQA0KZNG4Phq1atwsCBAwEA8+bNg1QqRY8ePZCZmYmgoCD8+OOPYlszMzPs3LkTn3zyCQICAmBlZYWQkBBMmzZNbOPp6Yldu3Zh3LhxWLBgAapWrYoVK1aIXUIAwAcffID4+HiEhYVBo9GgYcOGiIiIyHMiPREREdG/mXTgEgThlW2USiUWL16MxYsXv7SNu7s7/vzzzwLn06ZNG5w/f77ANiNHjsTIkSNfWRMRERHRi0z6HC4iIiKi8oCBi4iIiMjIGLiIiIiIjIyBi4iIiMjIGLiIiIiIjIyBi4iIiMjIGLiIiIiIjIyBi4iIiMjIGLiIiIiIjIyBi4iIiMjIGLiIiIiIjIyBi4iIiMjIGLiIiIiIjIyBi4iIiMjIGLiIiIiIjIyBi4iIiMjIGLiIiIiIjIyBi4iIiMjIGLiIiIiIjIyBi4iIiMjIGLiIiIiIjIyBi4iIiMjIGLiIiIiIjIyBi4iIiMjIGLiIiIiIjIyBi4iIiMjIGLiIiIiIjIyBi4iIiMjIGLiIiIiIjIyBi4iIiMjIGLiIiIiIjIyBi4iIiMjIGLiIiIiIjIyBi4iIiMjIGLiIiIiIjIyBi4iIiMjIGLiIiIiIjIyBi4iIiMjIGLiIiIiIjIyBi4iIiMjIGLiIiIiIjIyBi4iIiMjIGLiIiIiIjIyBi4iIiMjIGLiIiIiIjIyBi4iIiMjIGLiIiIiIjIyBi4iIiMjIGLiIiIiIjIyBq4gWL14MDw8PKJVK+Pv74/Tp06VdEhEREZk4Bq4i2LhxI8aPH4+vvvoK586dQ4MGDRAUFIS4uLjSLo2IiIhMGANXEcydOxdDhgzBoEGDULduXSxduhSWlpZYuXJlaZdGREREJkxW2gWUFVlZWYiKisKkSZPEYVKpFIGBgYiMjMx3mszMTGRmZorPk5OTAQBarRa6zHRxuFarBYB8h704/E2GvWo5XDaXzWVz2Vw2l81l5z9M/68gCHhtAhXKw4cPBQDCiRMnDIZPmDBBaNasWb7TfPXVVwIAPvjggw8++OCjHDxu3br12jmChxSNaNKkSUhOThYfd+/eLe2SiIiI6DXZ29u/9rQ8pFhIDg4OMDMzQ2xsrMHw2NhYuLi45DuNQqGAQqEoifKIiIjIyKTS199PxT1chSSXy9G4cWMcOHBAHKbT6XDgwAEEBASUYmVERERk6riHqwjGjx+PkJAQNGnSBM2aNcP8+fORlpaGQYMGlXZpREREZMIYuIrggw8+QHx8PMLCwqDRaNCwYUNERETA2dm5UNMrFAp89tlnOHr0KADA398fp06dgk6nE9tIpdI8w0tiGJfNZXPZXDaXzWVz2fkPO3PmDFq1avVGpwlJBOFNrnEkIiIiolfhOVxERERERsbARURERGRkDFxERERERsbARURERGRkDFylhNcqEBERVRwMXKVEoVDg2rVrpV0GERERlQB2C2Fk/fv3x99//40nT57Ax8cHd+7cwc2bN5GZmQm5XA5ra2v4+flh9+7dOHv2LLZu3YqrV6/C1tYWmZmZ0Gg0OHnyJORyOeRyOdzc3BAcHAwHBwfs3LkTPj4+UCgUuH//Pm7fvo1z585BqVRCpVKhSZMmGDZsGNLT07FhwwZERUWhUqVKkMlkkEgkOHPmDIDn/YxUqlQJH3zwAVq2bGnQViqVIikpCbdu3RLXydzcHGq1GlKpFPHx8ZBKpVAqlXBzc8O4ceMwdOjQ0trcxSYnJwfR0dG4ceMGbGxs4OjoCIlEgrNnz2LWrFmQSqWwtbWFm5sbOnXqhEmTJiE7OxvW1taoXbs2+vfvD6VSialTp0KlUsHOzg6+vr7o378/Pv74YwiCAIlEgmbNmqF79+5ISEjI03bgwIEYOnQokpKSIJFIUK1aNdSvXx9qtRpz586FXC6HUqlEzZo10adPH6xYsQK5ubnIzc2Fk5MTVCoVKleujDVr1kAikcDS0hKurq5o1qwZtm7dCisrK9ja2sLLywve3t7o0aMHfH19S3vTv7EXXzuVSgVBEGBlZYXhw4cjOzsbKpUKXl5e8PPzw5o1a5Cbm4tnz55BpVLB19cXvXv3xowZM2BmZgalUon69eu/9DXq378/QkNDkZ2dDbVaLba9c+dOvu+Tzz//XNy73aBBA/Tv3x9ZWVl52nbu3BmTJk1CRkYGJBIJqlatCn9/f1SvXh3z58+HjY0NFAoF6tSpg3HjxpWbu11oNBqcOHECZ8+ehZ2dHTIyMhAXF4fw8HDk5ORAqVTC2dkZ1apVw5MnT3Dp0iVIpVJYWlrCxcUFwcHBCA8PR1ZWFqytrVG1alW0bt0aiYmJ2Lx5M2QyGdRqNdzc3NChQwfMmzcvT9v79+9j+/btkEgksLKygpOTExo0aICtW7ciNzcXAODo6Ah/f39UrlwZ4eHhEAQBcrkcNjY2aNasGbZv347c3FzodDrY2dnBy8sLlpaWeb7LR40ahcGDB5fyVn9zGo0Gp06dwsmTJ+Hh4QGlUonU1FQcOXKk0NuyTp06+PHHH/N8V23ZsgUSiQQSiQROTk6wt7eHl5eXOPzFths2bEBubi4EQYCdnR2srKwgk8lw9epVAM/vGOPg4IBWrVrh2LFjSEtLQ0ZGBpRKJRwdHSGXy3H58mVIpVJIpVI4OTnho48+grm5OebNmwcnJyf07dsXoaGh8PT0LPJ2YuAyohUrVmDIkCGlXUaJe+utt3Dx4kUIggBLS0s4OjrCwcEBZ86cgSAIMDc3h62tLezt7eHj44OIiIjX/pC1bt0aW7ZsyfPBsba2xvnz5wEAMpkMTk5OaNWqFTIzM7F9+3bIZDJYWFgYfMj0y1MoFEhPTy/lrVg6qlWrBoVCYRBKHj58iF9++QUWFhawtbU1aijp3LkzvvjiC/GPVe3atdGpUyfY29vj+++/LzCUVKlSBRKJBJcvXy7tzVji2rRpg/v3779yW3bt2hWBgYEYNGgQZDIZFAoF6tevjy5dumDGjBnIzc2Fvb097O3t4ezsjKCgIPzvf//L03bq1Kl5wr2XlxeWL19u8EOgf//++O2335CSkoLY2FioVCq4ubmhWrVqWL16NXJzc2FlZQUXFxdotVqDH3YVhaOjI3x8fAy2T+3atbF8+XJIJBJYWFigVq1aBf6Iy+/zld9nsXPnzhgxYgTS09PFH1sv+4wPHz4cX3zxRZ4ffDKZTAxGSqUSWVlZSEhIKO3NWGL0HZ8eOnSoyD90GLiMqFKlSkhISIC5uTmys7NLuxwiIiIqBi1atMBff/1VpGkYuIxIIpEAeL6HJScnp5SrISIiojfh4OAAlUqFJ0+eICUlpUjT8l6KJYBhi4iIqOzTarXQarVwcXEp8rS8SrEE2NraAvi/PV5ERERU9piZmSE7OxsjRowo8rQMXEbk7OwMlUolXoXCo7dERERlV3Z2NqpXr45PP/20yNPyHC4j0l922rFjRyQkJKB79+44deoUMjMzS7u0ItF3LzBr1iykpKSgevXqWLt2LVasWAGdTlfa5ZU4MzMz2NjYwNbWFgqFAvHx8UhJSYGLiwukUinkcjlSUlKQmJgInU4HlUqFZ8+ewdLSEqmpqcjKyoK9vT1yc3NhYWGB7OxspKamGrS1srJCZmYmMjIy4ObmhpSUFNja2sLKygp3795FVlYWrKysxCs24+LikJGRgfr16yM2NhbW1tbIzs5GSkoKsrOz4ejoiJycHKSmpiI2Nra0N2GpkEqlMDc3h52dHdzd3XHnzh2kpaWhbt26uHv3LmxsbJCUlISkpCTodDpYWFiI7bVaLVJTU5Gbmwtzc3NYWVlBp9Pl+3rm5uYiIyMDOTk5qFSpEpKTk+Hq6prv+yQtLQ1arRa5ubli2ypVqiAzMxOJiYnw9vaGVquFUqnExYsXK+zFN+bm5mjcuDGkUim8vb0hl8tx8eJFpKWlwdzcHF5eXoiPj8fNmzeRnp4Od3d3ZGdno1KlStDpdLh//z6ePXsGCwsLuLq6QhAEPH78GE+ePEHNmjXFtlKpFLGxsUhPT4eXlxfi4uLQqFEjJCQk4Pr166hfv774+QSAixcvIjk5GdWrV0daWhoaNWqElJQU3Lp1C+3bt0dsbCwkEgnWr18vdoNQ0SiVSrRo0aLAbZmdnY2oqCikp6eL31VVq1bFw4cPkZCQgJYtW+LOnTuwsbFBWloaNBoNMjIyDNo+fvwYycnJ6NChAx49egRLS0uYm5vj3r17SE5OhrOzM8zNzVGjRg2cOXMGjx8/RvPmzfHgwQM4ODhArVbj6dOn0Gg08Pf3h06nQ61atRAQEIDTp0/j5MmT8PPzQ7t27VC1alVUrVq1yNuCgasU5OTkID09XXxxged7w44cOYKzZ89i1KhRSE5Ohr29PRISEpCeno7Lly/DwcEBdnZ2iI+Px6FDhxAfH4+AgABMmTIFOp0uT/j56quvUL16dRw+fBhxcXEICAiAt7c3bGxs0KBBA9y+fRu3b9+GIAjIyspCTEwMbt++jZSUFPTp0wd///03ateujX379mHTpk0FniBoZmaGevXqoX79+jh58mSeL6EXP2RmZmYwMzNDbGys+AVZ1A9Zbm4u6tati5iYGMTFxRl8cCwsLAw+ZLa2tnB2dkZsbCxu374NrVaLSpUqwdXV1eBDVrduXVhaWsLT0xOWlpaIjo4Wt7F+u7Vp0wYyWdk+9fHIkSPYsGEDnJyccPToUbi4uCA6Ohq3b99GdnY2bGxsYGZmBnd3d2i1Wjx58gS5ubmQSqWwt7c3WiipUqUKtFotkpKS4OrqKoaLlJQUpKenQyKRFBhKbGxsYGdnhwcPHiAhIQHt27fHgwcP0KlTJ/Tv3x+VKlUq5S3/ZnQ6HebMmYNDhw7h2bNn8PLyQkZGBs6ePQuNRvPKbalUKpGeng6NRiO+DjqdDmq1GomJicjOzoaTkxPMzMwgCALS0tKQlpYGQRAM2mq1WmRlZRmE+4yMDCQkJECn08HKygpmZmZwcnJCTEwM0tLSUKtWLcTFxcHa2hpSqdTgh4BSqUTVqlWRnZ0tvrd8fX3h7u4ONze3cvHaXbhwAcuWLYNarcbp06fh5eWF9PR0REREIDU11WD7CIJgsC2trKxgbW2NuLg4JCYmFurz9bIfcenp6cjMzESVKlWQkZEBe3t7yGQyaDQa6HQ6WFpaIisrC3K5HFqtFs+ePTP4wWdra4uHDx/m+c6Oi4tDWloaBg4ciMzMTEgkEri4uJSL1644MXCZiISEBKxYsQJHjx7FwYMHYWZmBp1OJ/4Klslk0Ol0r713zMzM7JW/sJRKpRgmsrKykJWVVeTlWFlZ4e+//4arq+tr1WkqTp8+jfnz5+Ovv/5CfHw8MjMzxUPC+o5evb29MWrUKAwYMAAxMTGYMmUK6tWrh6ioKDHEOjs7o3nz5oiLi0NUVBQuXbqElJQUKBQKSCQSxMfHw9bWFomJieL8dTod+vXrhwcPHoghNyMjA9evX0dqaqpBnVKpFFWqVEGdOnWQlJSEzMxM8RfjpUuXCny/mJmZQa1Wo2HDhpg0aRLat29vpK1ZslatWoUffvgB//zzD9LT08X3vUQiEcPhO++8g7Fjx8LT0xMrV67EuXPnIJfL4ePjgyNHjgAA+vbti7i4OFy9ehVHjx6Fubm5GFp0Oh3u3r2LmjVr4sGDB+IPHgcHB7Ru3RpPnz4Vg+KdO3fEgPEia2trtG7dGmq1Grdu3RL3aGo0GqSlpb30Yht9/0eenp547733EBoaipo1axp3o5aA1NRUfPbZZ9i0aROePn2a5xQMiUQCuVwOb29v/Pe//4WtrS3Wrl2Lq1evwsPDA1evXsWzZ88glUrh5+cHnU6HqKgoJCYmQiaTwdzcHDk5OZBIJEhNTRV/JAiCIHZcqg+qmZmZyMnJQU5OTr57FSUSCTw8PKBWq6HRaMT2z549e+X3rEQigY2NDd5++22MGjUK7dq1K9btWNKysrKwZcsWrF69GidPnoRWq83TRv9jrU+fPpg5cyb++usvLFq0CDdu3ADwfJtkZmaiUqVKUCgUEAQBsbGxePz4sThepVLB3Nwcjx8/hlQqhUQigYODA2JjY2FmZgZra2vodDooFApkZWUhOTk533r1PwZ0Op3YIav+M1cQCwsLNGrUCNOmTXvt14yBywTs2bMHnTp1KnfneEmlUqjVavj7+yMkJAQqlQrLli0TP2QKhQLW1ta4c+cOPDw8XutDplarxS/T5OTkl35opFIpnJ2di/whq2gKCiXR0dHw8fHB2bNnxeBXXKEkLi5O3EvzotcNJRWRvpNM/Q+BgQMHIj4+HitXrkRUVJTYLjMzEzVq1EDlypUhCAJu3LiBw4cPIzs7GxKJROwM+MyZMzA3N4dU+vxUX3d3dyQnJ4uH4PR7Ts6cOZOno2B9qPD19UVmZiZkMhkqVaqE69ev486dOxX20OjLmJmZwdLSEjVr1hR/xN26dQtffvkl5HI56tWrhzNnziA5ORn16tWDh4cH5HI51q5di6dPn0IqlUKn0yE3NzffH3HZ2dno0qULJBKJ2AltbGwsnj59avBjEoB42LZ37944dOgQkpKSADz/0Xfv3j08e/asNDaRSXn//fexcePGIh/tYOAqYV988QUOHz6Mu3fv4tmzZ0hPT0dGRkZpl0VEZJLs7e2h1WoZrssg/ZGZ8nKur34vqUKhwGeffYYpU6YUaXoGrhJWUbqGKMwhTDIt7KCXiOjlJBIJ1Go1UlJS4ObmhpiYmKJNz8BVsvQnpALPz3fSn5SqJ5fLX+vcKSIiIjIu/a36FApFkY9OsR+uEubo6AiZTAaZTAa5XA5LS0sAz4+by2QyKJXKUq7w9ZT1K/eIiIheRiKRQCqVwtLSElKpFE5OTkWeB/9KlrAvv/wSERERAAAXFxfI5XJs374dGo0GEokEOTk5sLS0REZGBhQKBerUqYPLly+/9CRTW1tbpKen57tXTB/isrOzC3VCvlqtRkZGBnJzc4t8OLAiH4rSn8yfm5sLiUQifjAL2iYKhaLM9cdWHum7KNF/fv79nEyX/mo0QRDEz6BarUZSUtJLv+/0J5cX1/Kzs7PLzflJJUl/Fb5EIjG4Ij8jI+O1Lx4rymsrkUiKtBypVApfX18olUpYWloiPT0d/fr1K3KNPKRIr61Tp05ieCwM/flr+g+ZIAiwtLTM9zJiYyjqh0xPv/dOf0m5XC6Hr68v2rRpgxYtWkCpVOLevXtie2dnZ3Tt2hUpKSn466+/kJ6ejujoaHF8Tk4OGjVqhIMHD+LYsWNISEhAcnIyzMzMIJFI4OTkhC1btsDHxwe3b9/GH3/8gUOHDuHGjRvQarVQKBRwcXFB/fr14e7ujr59+6J69ep56o6IiMCqVatw+fJl8WpMuVyOBw8eFGlX+IshRP8ampubl7lQou+7qE2bNmjTpo14xaSDgwPu3r0LKysrNGzYED4+Pnj8+DF++uknKBQK1KhRA9HR0UhPT4eVlRX8/PwQHh6OI0eOQKFQID09HVKpFBYWFnj77bcxd+5cpKSk4Nq1a9i/fz/++usvJCYm4tmzZ1CpVHB3d0e9evUwfPhw1KxZM8978s6dO9ixYwe2bNmCmJgY5OTkiD+c4uLiirTOL/5hA57f7UIikZh8SJDL5VAoFOIVcRYWFujcuTNatmyJtm3bwsLCwuC7QyaTwd7eXvzReOrUKbi4uODBgwcAnn/mbGxs4OHhgU2bNuHXX3+FSqUS+0FUqVRo3749xo0bB7lcjmfPniEmJgbR0dF4+vSpOH21atXg7e0NNzc3mJub56k7IyMD8fHx2LNnD86fPy9e+RkVFWVwpeir6Ds01n9u9VcPF/SZK+7zZl93fjKZTPzhqf++HDp0KLp06YJmzZqJr4m+rf5Kc/1rd/z4cYPxlSpVQmpqKpycnLB161asWbMGmZmZsLa2RtWqVfHkyRO0bdtWfO3i4+Nx+fJl3Lp1S7zS3draGmq1GjVr1nzpa5eUlITIyEicO3cOubm5yMrKgpmZGdq1a4fmzZsXy1EcBi56Y0ePHsX27dtx8eJFJCUlQalUws7OTuylt27dugZvcGN+yB48eAC5XI6cnByYmZmhcuXKBX5B5vch069D06ZN0bVr1zJ7mLcgDx48wPLly7F37148evQIqampEAQB5ubm4uX8bdu2RW5uLhwdHVGpUiXcu3fPIJQ8evQIK1asMAglaWlpUKlU8PPzw6pVq3DkyBHxD5i+24J33nkH33//PVJTU3H16lXs378fZ8+exb1798TuBjw8PFC/fn2MGDEi3zB5+/Zt7NixA7/99htiYmKQnZ0NuVyOatWqoVGjRmjevDk6dOgABwcHMWy87KKA/IaXxLDimB54HhaA56HE3d0dnTp1QlBQkEHIt7KygqenJ2xsbAxeOz2FQgEnJyc8ePAArq6u2Lp1K44fPy6GPSsrK9SsWRO1atXC9OnTkZqaavBDIDMzE+bm5nBwcICzszO8vLxe+trpfwj8888/yM7ORnp6OrKzs6FWq9GiRQuMGzcOtWvXznddyzKtVovdu3dj48aNuHjxonjlpT4UVKlSBS1btkSDBg3ETn71P+Re/BG3d+9e/PPPP+JnDgDi4+PRsWNH/PXXX9i9e3eBP+K2bduGc+fO4fr167h27ZrY7YO9vT3q1q2L7t27o1evXvle4PXij7ikpCSYmZmhUqVKsLW1hZ+fH4YNG1YuX7viwsBFRnHq1Cn07NlTfC6TyeDj44OLFy8WOKwobYt7mH64t7c3zpw5Y3DIT61WIyEhQXxevXp1qFQqxMXFicf19b06v2wY8HzXtLW1NSpVqoTbt2+Lwwoz/ZsMe3HZZmZmuH37NmQyGRQKBbRabYF3EaCyQyaTwczMDDKZDLm5uWJIe91h+j1ixTnP/Ibp98K92J9URWBmZoaaNWsiKyvLYK9zjRo18uyJLuywN52+qMP8/Pxw9+5dxMTEiCHS1PegvgmlUolRo0ZhxowZ7IeLTMOmTZvwwQcflHYZRERExcrc3ByTJk3C1KlTizQdAxe9lhc7cE1OThZvgaHT6cpdj/lERFSx6ffAWltbIyUlBZ6enrh582bR5sHARa+jonTgSkREpKe/wvzFixoKi/1w0WvRX4atf6hUKoPnRERE5YlUKoVSqXztfrgYuOi1vNiB64uduOqfm5mZlXaJVIGp1erSLoGoQqhInV7rdDrx6s/u3bsXeXoeUqTXsmjRIoM+uPSduOovY87MzERCQgJkMhmSkpLEK+dSU1OhUqkgk8mg1WrzDHuxLYBiHVaYZScmJiI5OVnsIkF/VU5Fu4JKKn3+W+zFPpz0//67Xyd9W/1XSXENK+qy9VfTWVtbi6+xTqdDdnY2zM3NYWtrC5VKhSZNmojraWVlhWrVquHWrVti58IlMayo09++fRvXrl1DYmKiePUglQ9OTk54+vSpwbDKlSvj8ePHrzXsTacvyrBHjx5h9OjR2LVrF+7fv1/kW92URVKpFL1798aKFStgZWVVpGkZuIiIiIiMjIcUiYiIiIyMgYuIiIjIyBi4iIiIiIyMgYuIiIjIyBi4iIiIiIyMgYuIyrX4+Hh88skncHNzg0KhgIuLC4KCgnD8+HGxjUQiwbZt20qtxilTpqBhw4YGz/WdCMtkMjg4OKB169aYP3++wU3ViajsqDg9lhFRhdSjRw9kZWVh9erVqF69OmJjY3HgwIE8fR8VB32fX8XBx8cH+/fvh06nw9OnT3H48GF8/fXXWLt2LQ4fPgxra+tiWQ4RlQzu4SKicispKQnHjh3DrFmz0LZtW7i7u6NZs2aYNGkSunTpAgDw8PAAAHTv3h0SiUR8DgBLliyBl5cX5HI5ateujbVr1xrMXyKRYMmSJejSpQusrKzwzTffAAD++OMP+Pn5QalUonr16pg6dSpycnKKVLtMJoOLiwtcXV3h6+uLUaNG4ciRI7hy5QpmzZr1+huFiEoFAxcRlVsqlQoqlQrbtm176aG4M2fOAABWrVqFx48fi8+3bt2KMWPG4L///S+uXLmCYcOGYdCgQTh06JDB9FOmTEH37t1x+fJlDB48GMeOHcOAAQMwZswYXL16FcuWLUN4eLgYxt6Et7c3OnXqhN9///2N50VEJYuBi4jKLZlMhvDwcKxevRq2trZo0aIFPv/8c1y6dEls4+joCACwtbWFi4uL+Pz777/HwIEDMXz4cNSqVQvjx4/H+++/j++//95gGR9++CEGDRqE6tWrw83NDVOnTsXEiRMREhKC6tWro3379pg+fTqWLVtWLOvk7e2NO3fuFMu8iKjkMHARUbnWo0cPPHr0CNu3b0fHjh1x+PBh+Pn5ITw8vMDprl27hhYtWhgMa9GiBa5du2Yw7MV7MwLAxYsXMW3aNHHvmkqlwpAhQ/D48WOkp6e/8fro7xtJRGULT5ononJPqVSiffv2aN++PSZPnoyPP/4YX331FQYOHPjG8/73DWxTU1MxdepUvP/++/nW8aauXbsGT0/PN54PEZUs7uEiogqnbt26SEtLE5+bm5sjNzfXoE2dOnUMuo4AgOPHj6Nu3boFztvPzw83btxAjRo18jyk0jf7yr1+/ToiIiLQo0ePN5oPEZU87uEionLr6dOn6NWrFwYPHoz69evD2toaZ8+exezZs9G1a1exnYeHBw4cOIAWLVpAoVDAzs4OEyZMQO/evdGoUSMEBgZix44d+P3337F///4ClxkWFobOnTvDzc0NPXv2hFQqxcWLF3HlyhV8/fXXha49JycHGo0mT7cQDRs2xIQJE157mxBR6WDgIqJyS6VSwd/fH/PmzcOtW7eQnZ2NatWqYciQIfj888/FdnPmzMH48ePx008/oUqVKrhz5w66deuGBQsW4Pvvv8eYMWPg6emJVatWoU2bNgUuMygoCDt37sS0adMwa9YsmJubw9vbGx9//HGRao+OjkblypVhZmYGGxsb1K1bF5MmTcInn3wChULxOpuDiEqRRBAEobSLICIiIirPeA4XERERkZExcBEREREZGQMXERERkZExcBEREREZGQMXERERkZExcBEREREZGQMXERERkZExcBEREREZGQMXERERkZExcBEREREZGQMXERERkZH9P9MpvlmjmD8aAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot(x=\"Store ID \", y=\"Store_Sales\", kind=\"bar\")\n",
     "\n",
-    "plot = ts.plot.line(x=\"date\", y=\"my_value\")\n",
-    "plot.xaxis.set_major_locator(md.DayLocator(interval=10))\n",
-    "plot.xaxis.set_major_formatter(md.DateFormatter(\"%d.%m.%Y\"))\n",
-    "plot.tick_params(axis=\"x\", labelrotation=90)"
+    "plt.xlabel(\"Store ID\") \n",
+    "plt.ylabel(\"Store Sales\")\n",
+    "plt.title(\"Store Sales by Store ID\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "3. Гистограмма (hist). Распределение количества товаров"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df[\"Items_Available\"].plot(kind=\"hist\")\n",
+    "\n",
+    "plt.xlabel(\"Number of Items Available\") \n",
+    "plt.ylabel(\"Frequency\") \n",
+    "plt.title(\"Distribution of Items Available in Stores\") \n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "4. Ящик с усами (box). Продажи"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df[\"Store_Sales\"].plot(kind=\"box\")\n",
+    "\n",
+    "plt.ylabel(\"Store Sales (US $)\") \n",
+    "plt.title(\"Box Plot of Store Sales\") \n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "5. Диаграмма с областями (area). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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d1g2UdrHOmTMHTz75JFq3bo2QkBDs378f33//PcLCwvDOO++IZVu3bo358+dj6NChuOOOOxAREYHHHnsMAwYMwNdff40+ffogJSUFDRo0wM8//4yNGzdi4sSJumKWvvrqK9xzzz1o0aIF+vfvj4YNGyIjIwPJycn4559/sGvXLl3HY8+OHTvwww8/wGq1IjMzE9u2bcMvv/wiXms9wpMQr+HDkYeEEA/w+++/C88//7zQuHFjISIiQggJCRFuvPFG4dVXXxUyMjJkZQ8cOCDcd999YvoC6fD9HTt2CImJiUJERIRQpUoV4cEHHxQ2bdok296WUkEt3cCaNWuExMREISoqSggLCxMaNWok9OnTR9i+fbvmMWzcuFEYOHCg0Lx5cyEqKkoIDg4W6tWrJ/Tp00eW5sFW9q677hLCw8OFOnXqiCkkAAhr1qwRy9mnVLAxbdo0oXXr1kJ4eLhQrVo1oUWLFsKbb74pnDlzRjwPPXv2FOrVqyeEhoYKMTExwqOPPur0GAShNKVC586dhT/++EO49dZbhdDQUKFx48bCggULVLdp1qyZEBAQIPzzzz9O6xcEQdi9e7cwbNgwoVWrVkKNGjWEoKAgoXbt2kL37t2FHTt2yMrm5OQIzzzzjBAdHS0AkJ2PjIwMoW/fvkLNmjWFkJAQoUWLFsL06dNl2ztLc3DkyBHhueeeE+Li4oTg4GDh+uuvFx599FHh559/dnocUEmpYPsXFBQk1KhRQ2jbtq0wfPhw4cSJE7rODyHehHP/EUKIH3H77bejRo0aSEpK8rUphBCDMKaKEEL8hO3btyM1NRXPPfecr00hhLgAPVWEEOJj9u7di5SUFHz22We4cOECjh49Ks7ZRwgpP9BTRQghPubnn39G3759UVRUhB9//JGCipByCj1VhBBCCCEmQE8VIYQQQogJUFQRQgghhJgAk396EavVijNnzqBatWqq00UQQgghxL8QBAFXrlxBnTp1NOfjpKjyImfOnHE6ZxohhBBC/JNTp07hhhtuUF1PUeVFbFM9nDp1irOqE0IIIeWE7Oxs1K1b1/mUTT7M5i6sW7dOePTRR4XatWsLAIRFixaJ6woLC4U333xTaN68uVClShWhdu3awrPPPiucPn1aVsfFixeFZ555RqhWrZoQFRUlPP/888KVK1dkZXbt2iXcc889QmhoqHDDDTcIH3/8sYMtP/30k3DLLbcIoaGhQvPmzYXly5fL1lutVmHEiBFCXFycEBYWJnTo0EH4+++/DR1vVlaWAEDIysoytB0hhBBCfIfe97dPA9Vzc3PRsmVLfPXVVw7r8vLysGPHDowYMQI7duzAwoULcfDgQTz++OOycr169UJaWhpWrlyJZcuWYf369RgwYIC4Pjs7Gx07dkT9+vWRkpKCTz75BO+//z6mTZsmltm0aRN69uyJfv36YefOnejSpQu6dOmCvXv3imXGjx+PyZMnY+rUqdiyZQuqVq2KxMRE5Ofne+DMEEIIIaTc4SWR5xTYeaqU2Lp1qwBAnEhz3759DpO5/v7774LFYhE9Wv/73/+E6tWrCwUFBWKZt956S7jlllvE308//bTQuXNn2b7atm0rvPjii4IglHqp4uLiZJOIZmZmCqGhocKPP/6o+xjpqSKEEELKH+XCU2WUrKwsWCwWREdHAwCSk5MRHR2NNm3aiGUSEhIQEBCALVu2iGXuu+8+hISEiGUSExNx8OBBXL58WSyTkJAg21diYiKSk5MBAMeOHUN6erqsTFRUFNq2bSuWIYQQQkjlptwEqufn5+Ott95Cz549xSDv9PR0xMTEyMoFBQWhRo0aSE9PF8vEx8fLysTGxorrqlevjvT0dHGZtIy0Dul2SmWUKCgoQEFBgfg7Oztb9/ESQgjxLiUlJSgqKvK1GcQHBAcHIzAw0O16yoWoKioqwtNPPw1BEDBlyhRfm6ObcePG4YMPPvC1GYQQQjQQBAHp6enIzMz0tSnEh0RHRyMuLs6tPJJ+L6psgurEiRNYvXq1LBVBXFwczp07JytfXFyMS5cuIS4uTiyTkZEhK2P77ayMdL1tWe3atWVlbrvtNlXbhw8fjqFDh4q/bUMyCSGE+A82QRUTE4MqVaowOXMlQxAE5OXliXpC+p43il+LKpugOnToENasWYPrrrtOtr5du3bIzMxESkoKWrduDQBYvXo1rFYr2rZtK5b573//i6KiIgQHBwMAVq5ciVtuuQXVq1cXyyQlJWHIkCFi3StXrkS7du0AAPHx8YiLi0NSUpIoorKzs7Flyxa8/PLLqvaHhoYiNDTUlHNBCCHEfEpKSkRBZf+OIZWH8PBwAMC5c+cQExPjclegTwPVc3JykJqaitTUVAClAeGpqak4efIkioqK0K1bN2zfvh1z5sxBSUkJ0tPTkZ6ejsLCQgBAkyZN8PDDD6N///7YunUrNm7ciEGDBqFHjx6oU6cOAOCZZ55BSEgI+vXrh7S0NMyfPx+TJk2SeZAGDx6MFStW4LPPPsOBAwfw/vvvY/v27Rg0aBAAwGKxYMiQIRgzZgyWLFmCPXv24LnnnkOdOnXQpUsXr54zQggh5mGLoapSpYqPLSG+xnYPuBVX542hiGqsWbNGAODwr3fv3sKxY8cU1wEQ1qxZI9Zx8eJFoWfPnkJERIQQGRkp9O3bVzP55/XXXy989NFHDrb89NNPws033yyEhIQIzZo1U03+GRsbK4SGhgodOnQQDh48aOh4mVKBEEL8i6tXrwr79u0Trl696mtTiI/Ruhf0vr8tgiAIrksyYoTs7GxERUUhKyuL09QQQogfkJ+fj2PHjiE+Ph5hYWG+Nof4EK17Qe/7269jqgghhBBfcTrzKi7nFnptf9WrhuD66HCv7a880qdPH2RmZmLx4sW+NkURiipCCCHEjtOZV/F/n65FQbHVa/sMDQrA6v88YEhYnT9/HiNHjsTy5cuRkZGB6tWro2XLlhg5ciTat28Pi8WCRYsW+UX8765duzBixAhs3rwZ2dnZiIuLQ9u2bfHFF1845Jwsr1BUEUIIIXZczi30qqACgIJiKy7nFhoSVV27dkVhYSFmzpyJhg0bIiMjA0lJSbh48aKptklH0LvC+fPn0aFDBzz66KP4448/EB0djePHj2PJkiXIzc010VLfUq6mqSGEEEL0UlBcgh0nL8NqrZihw5mZmdiwYQM+/vhjPPjgg6hfvz7uvPNODB8+HI8//jgaNGgAAHjyySdhsVjE3wAwZcoUNGrUCCEhIbjlllswe/ZsWd0WiwVTpkzB448/jqpVq2Ls2LEAgF9//RWtWrVCWFgYGjZsiA8++ADFxcVObd24cSOysrLw7bff4vbbb0d8fDwefPBBfP755+KsJyUlJejXrx/i4+MRHh6OW265BZMmTdKs12q1Yty4ceI2LVu2xM8//yyuv3z5Mnr16oVatWohPDwcN910E6ZPn67n9LoEPVWEEEIqJIPm7sTKfRnod088Rjza1NfmmE5ERAQiIiKwePFi3HXXXQ55Ebdt24aYmBhMnz4dDz/8sJh7adGiRRg8eDAmTpyIhIQELFu2DH379sUNN9yABx98UNz+/fffx0cffYSJEyciKCgIGzZswHPPPYfJkyfj3nvvxZEjRzBgwAAAwHvvvadpa1xcHIqLi7Fo0SJ069ZNMcGq1WrFDTfcgAULFuC6667Dpk2bMGDAANSuXRtPP/20Yr3jxo3DDz/8gKlTp+Kmm27C+vXr8e9//xu1atXC/fffjxEjRmDfvn34/fffUbNmTRw+fBhXr141dJ6NwNF/XoSj/wghxHs0eHu5+PfxjzorllEb8bX3dBYe/eIvj9toz7JX70Hz66N0l//ll1/Qv39/XL16Fa1atcL999+PHj164NZbbwUAxZiq9u3bo1mzZpg2bZq47Omnn0Zubi6WL18ubjdkyBB8/vnnYpmEhAR06NABw4cPF5f98MMPePPNN3HmzBmntv73v//F+PHjERkZiTvvvBP/93//h+eee85hXl0pgwYNQnp6uuh9kgaqFxQUoEaNGli1apWYrBsAXnjhBeTl5WHu3Ll4/PHHUbNmTXz//fdO7TNj9B+7/wghhJBySteuXXHmzBksWbIEDz/8MNauXYtWrVphxowZqtvs378f7du3ly1r37499u/fL1vWpk0b2e9du3Zh1KhRoocsIiIC/fv3x9mzZ5GXl+fU1rFjxyI9PR1Tp05Fs2bNMHXqVDRu3Bh79uwRy3z11Vdo3bo1atWqhYiICEybNg0nT55UrO/w4cPIy8vDQw89JLNp1qxZOHLkCADg5Zdfxrx583DbbbfhzTffxKZNm5za6Q4UVYQQQkg5JiwsDA899BBGjBiBTZs2oU+fPk674/RQtWpV2e+cnBx88MEH4kwoqamp2LNnDw4dOqQ7x9d1112H7t2749NPP8X+/ftRp04dfPrppwCAefPm4T//+Q/69euHP//8E6mpqejbt684i4o9OTk5AIDly5fLbNq3b5/o2erUqRNOnDiB119/HWfOnEGHDh3wn//8x9VT4hTGVBFCCCEViKZNm4p5nIKDg1FSUiJb36RJE2zcuBG9e/cWl23cuBFNm2rHnbVq1QoHDx7EjTfeaIqdISEhaNSokTj6b+PGjbj77rvxyiuviGVsHiclmjZtitDQUJw8eRL333+/arlatWqhd+/e6N27N+69914MGzZMFHJmQ1FFCCGElEMuXryI7t274/nnn8ett96KatWqYfv27Rg/fjyeeOIJAECDBg2QlJSE9u3bIzQ0FNWrV8ewYcPw9NNP4/bbb0dCQgKWLl2KhQsXYtWqVZr7GzlyJB599FHUq1cP3bp1Q0BAAHbt2oW9e/dizJgxmtsuW7YM8+bNQ48ePXDzzTdDEAQsXboUv/32mzga76abbsKsWbPwxx9/ID4+HrNnz8a2bdvE0YH2VKtWDf/5z3/w+uuvw2q14p577kFWVhY2btyIyMhI9O7dGyNHjkTr1q3RrFkzFBQUYNmyZWjSpIkLZ1sfFFWEEEJIOSQiIgJt27bF559/jiNHjqCoqAh169ZF//798c477wAAPvvsMwwdOhTffPMNrr/+ehw/fhxdunTBpEmT8Omnn2Lw4MGIj4/H9OnT8cADD2juLzExEcuWLcOoUaPw8ccfIzg4GI0bN8YLL7zg1NamTZuiSpUqeOONN3Dq1CmEhobipptuwrfffotnn30WAPDiiy9i586d+Ne//gWLxYKePXvilVdewe+//65a7+jRo1GrVi2MGzcOR48eRXR0NFq1aiUef0hICIYPH47jx48jPDwc9957L+bNm6fzDBuHo/+8CEf/EUKI93Bn9F95yahOzINz/xFCCCEe4ProcKz+zwOc+48YgqKKEEIIUeD66HCKHJ3MmTMHL774ouK6+vXrIy0tzcsW+QaKKkIIIYS4xeOPP462bdsqrnNnzsDyBkUVIYQQQtyiWrVqqFatmq/N8DlM/kkIIYQQYgIUVYQQQio9Vqv3RvkR/8SMe4Ddf4QQQiotISEhCAgIwJkzZ1CrVi2EhITAYrH42iziRQRBQGFhIc6fP4+AgACEhIS4XBdFFSGEkEpLQEAA4uPjcfbsWZw5c8bX5hAfUqVKFdSrVw8BAa534lFUEUIIqdSEhISgXr16KC4udpgnj1QOAgMDERQU5LaXkqKKEEJIpcdisSA4OLhSDf8n5sNAdUIIIYQQE6CoIoQQQggxAYoqQgghhBAToKgihBBCCDEBiipCCCGEEBOgqCKEEEIIMQGKKkIIIYQQE6CoIoQQQggxAYoqQgghhBAToKgihBBCCDEBiipCCCGEEBOgqCKEEEIIMQGKKkIIIYQQE6CoIoQQQggxAYoqQgghhBAToKgihBBCiN9gtQqYt/Uk/s644mtTDBPkawMIIYQQQmws3Hkaby/cAwA4/lFnH1tjDHqqCCGEEOI37DqV6WsTXIaiihBCCCHEBCiqCCGEEOI3WCy+tsB1KKoIIYQQQkyAoooQQgghxAQoqgghRAff/3UM7y9JgyAIvjaFEOKnMKUCIYToYNSyfQCAJrWr4V931POxNYQQf4SeKkJIpeS1H3fitR93Gt5u1b4MD1hDCKkIUFQRQiodl3ILsWTXGSzZdQbJRy762hy/Jz0rH0fP5/jaDCLh3JV8/LbnLIpLrL42xXSkg//KW3c7RRUhpNJhlTTU+89mGdrWUp7He7vIXeOS8H+frUNGdr6vTSHXeGTSBrwyZwc+WnHA16Z4FGv50lQUVYSQyof049eCyieSXOWr1Yd9bQK5xoWcQgDAtxuO+dgSz2Klp0o/69evx2OPPYY6derAYrFg8eLFsvWCIGDkyJGoXbs2wsPDkZCQgEOHDsnKXLp0Cb169UJkZCSio6PRr18/5OTI3dS7d+/Gvffei7CwMNStWxfjx493sGXBggVo3LgxwsLC0KJFC/z222+GbSGElEOoqTSRdr/kFRX70BJSGSgstmJm8gnxN0WVAXJzc9GyZUt89dVXiuvHjx+PyZMnY+rUqdiyZQuqVq2KxMRE5OeXuaB79eqFtLQ0rFy5EsuWLcP69esxYMAAcX12djY6duyI+vXrIyUlBZ988gnef/99TJs2TSyzadMm9OzZE/369cPOnTvRpUsXdOnSBXv37jVkCyGkfCCgrKGmptKmRNL/ElQJuz6Jd5mVfFz2u5xpKt+mVOjUqRM6deqkuE4QBEycOBHvvvsunnjiCQDArFmzEBsbi8WLF6NHjx7Yv38/VqxYgW3btqFNmzYAgC+++AKPPPIIPv30U9SpUwdz5sxBYWEhvv/+e4SEhKBZs2ZITU3FhAkTRPE1adIkPPzwwxg2bBgAYPTo0Vi5ciW+/PJLTJ06VZcthJByhLT7jzpBkxLJW80SwJNFPMvxi7my3+VNVPltTNWxY8eQnp6OhIQEcVlUVBTatm2L5ORkAEBycjKio6NFQQUACQkJCAgIwJYtW8Qy9913H0JCQsQyiYmJOHjwIC5fviyWke7HVsa2Hz22KFFQUIDs7GzZP0KI75G205Ux8NwIUk9VoMVvXxmkgmAf48juP5NIT08HAMTGxsqWx8bGiuvS09MRExMjWx8UFIQaNWrIyijVId2HWhnpeme2KDFu3DhERUWJ/+rWrevkqIk/8Nues5ixsWIHf1Z2jLbT0riiyqbBpKKKjqpSTl3Kw74z/Ej2BPbPV/mSVH4sqioCw4cPR1ZWlvjv1KlTvjaJ6OCVOTvw/tJ92Hj4gq9NIV5Aj04oZx/LpiLzVAVSVQHAvePX4JHJG3DyUq7zwsQQARZ6qjxCXFwcACAjQ569OCMjQ1wXFxeHc+fOydYXFxfj0qVLsjJKdUj3oVZGut6ZLUqEhoYiMjJS9o+UH+ZtO+lrE4iHkAaq62mzZd2FlSy0Xe6pqlzH7oyJKzkC3NMI5Sy3qd+Kqvj4eMTFxSEpKUlclp2djS1btqBdu3YAgHbt2iEzMxMpKSlimdWrV8NqtaJt27ZimfXr16OoqEgss3LlStxyyy2oXr26WEa6H1sZ23702EIIKafo0AnSr+XKJivY/adObiFTTJiNvW6np8oAOTk5SE1NRWpqKoDSgPDU1FScPHkSFosFQ4YMwZgxY7BkyRLs2bMHzz33HOrUqYMuXboAAJo0aYKHH34Y/fv3x9atW7Fx40YMGjQIPXr0QJ06dQAAzzzzDEJCQtCvXz+kpaVh/vz5mDRpEoYOHSraMXjwYKxYsQKfffYZDhw4gPfffx/bt2/HoEGDAECXLYSQ8oM8+aex8pWNYqtUUFJVSanM94WnsL/HJqz8G/lFJT6yxjg+Tamwfft2PPjgg+Jvm9Dp3bs3ZsyYgTfffBO5ubkYMGAAMjMzcc8992DFihUICwsTt5kzZw4GDRqEDh06ICAgAF27dsXkyZPF9VFRUfjzzz8xcOBAtG7dGjVr1sTIkSNluazuvvtuzJ07F++++y7eeecd3HTTTVi8eDGaN28ultFjCyGkfGD0XSjAoAqrQEg9Vez9k1NCVWU69t7Q2ZtPIK+wGJ89fZtP7DGKT0XVAw88oDlZosViwahRozBq1CjVMjVq1MDcuXM193Prrbdiw4YNmmW6d++O7t27u2ULqVjw/VFxMTpJq1HPVkWCokoda3mbmK4coHSPLd11ptyIKr+NqSIVk+ISKyb8eRDJRy762hQd8A1SURHkkefGyrvJlLVH0GnSemTmFZpXqQeRJf/kMyGjnMVQlwuU8saVJ+lKUUW8yoKUfzB59WH0/Gazr00hlRiZSNLRYltNzFP18YoD2H/2CgbMSnFe2A+gp0odgZ4q0ynvtxhFFfEq9lMQ+DdsMP2J9X+fR/9Z23Eu2/35NqUiSc9V9sSdcDDjigdqNZ8SCgdVeGo8gJKq0pP2RBAw4c+DWLH3rOkmGcGnMVWkEsJGiLjIc99vBQAcPZ+DpDcecKsuo3mnjMZgVSTknqry7kcwl/I23L88oJQLTc9ZXvf3eUxefRgAcHhsJwQF+sZnRE8VIarwBeKPHDnvvrdT7qly3mRXZo+ETFTxq0hGZQlU33DoPP5MU5+SzUxcbXUzJB7sy3lFGiU9Cz1VhJBKh2EHgwfeneVFshfTU6VKZQhUt1oFPPtdqZd4w5sPom6NKh7dn6u3mGyErg9vU3qqCJFQmbt5KhOCwQzp8kD1yiUsKvOxO6MytBfS0Z8/bD7h8f0pdccbjXv05V1KUUW8SsVvgkh5QN5royOmyhNGlBN9UlwiOfpKICKMUBl6/7w9+lNxKiRdgeplf/tS/FNUESKB74zKgZ44Kll5E1MqlDekL1Wl7q6iEiuu5PsuhsUd3vp5N95fkuby9pUhUN3q7TxlioHqzs+ztAw9VYRIyC8qwf6z2T5xrRt0YJByilWmDhioroW0+0cpMDvx8/Vo8f6fOH+lwJtmuc2ZzKuYv/0UZmw6jr8zsnVtcyA9G49/+Zf4uzKIKtmE2l5QDK42u4ypIpUSPff6c99tRadJGzDbC/339sg8EhW/vfQLSqwCJicdwpaj3suyb9hTJS1v0n1RXjR7iUSBKmmIoxdKR2OOWuq6x8cXFBaXHVfaGX2iqv+s7dj9T5b4uxJoKtkHiDe61VwPVJeM6PXhdaGoIl5Fz72+9fglAMDoZfs8a4wClaCN9Dt+2fEPJqz8G/+a5r0s+/42+i/lxGVczPFPT0+J5KWqJUbPZF31gjXmIT0SpdxISmTmyrs5K0NKBamn0huCQTFQXU9Mlcrf3oaiivgtRSW+bbCE8uJKKOccu+D9LPtGRZUn350bDp1H1ymb0G7cas/txA2kniqt81BczgSGtOtOMThaB3vPZGPj4QsmWeSf+EWgug6kz7QvR2VSVBEioTK484l9LIye0X/GprUxwpoD5wEAhSX+mfVIapaWZ6bExx9BRhEM3gNq9Pp2i9u2rNibjv8s2IX8ohK36zIbb3ermdL9Z5ItrsDkn8RnCIKg2Ufv6heLO/jLCBLiWeRdBT4KVC8nwwiLpTFVGueqnDmqZPbq7f7zVKPw0g+lk2sXlwiY2OM2z+zERUo8NPJ17cFz2H78MoY+dDMCJI290jvBaJ4qxlSRSoORrx7dDZ2J+PJhXLkvQ3dX2LcbjuIPL00b4Wl8IS2MjtqSfwWXM/XgJtJzpSWcytvEy/J7QKftHj7E1QcyPLsDF5BPU2QefaZvw5drDmNS0t+y5Uaa/RV70/Hvb7fgXHa+vPvPh88oRRXxGc5u+0BfuKp8xF+HLqD/rO148NO1TsumnsrEmOX78eLsFNkIJqIf2fBrg+XNCraz1eLvIk2a/FPQ6v4rZ33n0lFtlaipMYz0PFk98An00/Z/XN72pR9S8NfhC+j17Rb5U0RPFSGOVCZRlXrqsvi3syDL9KyyiUNPXsrzmE0VGaOBrJ7UC/6uRZwl/xTX+fuB2CEXs77t/vNnSlzx6BnAvkZXeigOncvxm5gqiiriM5y92IJ8IKr84b3gzAbpeasIutOMU77t+CUMmLUdp3SKTPk+5SexxCpg7+ksmZjwd2+SJ3GW/FMsV+4C1SU/KsBz5Ck83q0raD2NplTpVSiqiFeR93srrS9bGhToA1HlBy9P2xf/4XNXMGXtEVwtlI8IciW/TnnB1aHQ3acm4899Geg6ZZOu8lJxYO9hWbzzNB794i889b+NkjIumaW53/KCXFyqU+48VRJzAyvYc2Qmnp6mxv6ucfVSWP0k7pGj/4jPUGqDpbmpgrwxJ4IdvnovSPdre4clTFgPANh7JgtfPdNKsl7qqSr/LwPpEQiCeyOMzumcKkXQ+JVysrQrdtc/WTh6PgcNa0WYFqjuqZFUnqREQ4DKypUzUVXeRKCvkHuqPND9Z1elq8JNnqfKDYPchJ4q4lWcvfQKisu8MsE+8VT5HvuX9vLdZ2W/pW1ceXkx68Vb519rRFutiFDx7/1nS6cvMaunqDy+yGWiSsPTZi1nYybMSP5ZGZB5Kv05T5XK396Goop4jbQzWViy64z4W+mLv0Aymi04sHLenkZiqiqcqPKS6ND6qlXKd2NWEGx5Ex6A/pgaNcGYkZ2PblM24dfU02aa5Tbyw6pgD5KJePpDwP494Or8gsyoTiodi3bIG1Wl+16aUdgXo/98+TDacNaIyWIcKpiq8sbZz8jOx7krZSMo7Rt16T1gvbbOrAFQ5a2LDJBPP6N1b6qtG7N8P7afuIzB81LNNs0tZM96xXqMTEUmqj1xnhy6/4xjgd2sBz58zBhTRfwKqafKJ6P/JH97IihTz361HAPHLuTiy9WHPW5PRSW/qARtP0ySLXPwVEm/eK/djmY5Ncpj91/21bJJhLXuTTUvnHR7f0KWUd13Zvg9snvWA7evGYHqFot/jNwGKKqIl7iQU4Atxy45LVdQVNYy+8ZT5fVdOqD14n1owjqZ58AfPGvuIm1EPX04l3ILHZY5dv85eqrMEkNWJ9mpnU3d5Av+ybwq/l2xRv9JYqp0qir/ujLeQTr3oyeusIOoMqNOBqqTis5949dgz+ks2TLl0X++FVXutBpns67i0S824Kdtpwxvaz8CTo1iO1dBOXuPOcUXQ6Hthan0FJfFVDkucwXZlB9Kc5z54fX853KZqHKl+88PDwmA8nUmjuhNqeEydpUGuNDuWywWv5lKiqKKeIW8QsfZ15Vu/PIcLzRm+X7sPZ2NN3/Z7VY9FcH75CqePnSl6q0OMVWSdYLt/yZ5qmQ9KY51+uOVP325LKHqlavFquXKs6fKH01fc/Achi3YhbxC9XPuDWQjZb2QZ02t1V+1LwMnLyon97XAf1IqsPuP+AylG9/DKVGc4s4XTl6BOY2fkXarvL3IygPygFeFQHU37hGl6yV/uQvwp06m3IJiXMgp6zJNOnAOGdn5iI0Mcyirdit6+2i2Hb+EGlVD0KhWhGY56XPmi0GZZzKvose0zXimbV3F9X2nbwMAXM4rxLe97/CmaTKknqozmVdRXGJFkIkjsx1jqpTvmBdmbQcAHP+os+J6+QeL76CnivgVvh6R445GccezJg9U129ERdNUvjge+wBrmadKsXl2/TrL0xOU1uPj7whNjp7PdVj2/pI08W/ZSEk/uBlPXMxF96nJ6PDZOqceX6uPPVWf/nkQJy/l4aPfD2qWW3fwvJcsUkZ6nhannsGgH3eauwP7aWpcDVRX+BjyBRRVxGcodsX4eJSzO4+iWfYaaQ98/xozF8UuYauAMcv2GapHmppDVr+Sp0irzDXBZdYL+MetJ9V37GbdnuDohRyHZXslsZFKXaX2ePOQDmWU2Xv4nKPtUuRzGnrfV1Vs4lyJnhQR9mJ5xd50U+svtDsProy6duj+c9Mmd6CoIj5D8QXn53EOWpgVAmakgaxo8VdKh7Nyfwa+/euY7jq+3XAUjUeswBKdySbtz6GSp8qs0/yFNB2Gwv3iD3NPSpEGqduQdgfKvGx+di86ywnm63kYzQwZNTLpcYlVMHStSjysN3MKipFyomxkuGueKovc2+/Da0tRRXyGM0+Vr0eCGX+4zWklK0tMVUFxCUYs3ouk/efEZUpHczHHMQ2CFmOW7wcAvOZiskm5UCj9v+w8e1A8+9vlVPKmXJV4AfV8BJlxuk5dyjM9YNuVmCpvDZ45nekoZrXQm1S2sNiK+8avweNfbnRe2Fa3FwTK8IV7xL9dTf4pvQE5oTIh1zBrOhCX9+/Gtu60t/JuFP1W+Phj2y1mJ5/A7M0nZMt84e2wP4dK8wLK49TL8Uk3iLN7UeYd8JANh89dQcKE9YgKD8au9zrqtscZ8jkN9W1j5v2pNRl6+49WG6pLr1n7z2bjdOZVnM68isPnruDGmGpOt/HGh5uRBLFKudxKY6rK8OV0UPRUEZ/hbPSfL95droobMzEkqsqxqjqTme+wTDEs3ETngNKp1er+U8pTVZlwdth65ltz99StOVAaqJ11tQjFJvZFyZ8z719gxdva1cmEXTA/PUt7cnsb3vBUSQ/cyD0nbm2x+E1MFT1VxHcoiipfe6pc3ysD1d1HscHUKD9/20ncUL2KW/vUnHpFTKlg/pm21egv+XUUceqp8nwMZGhw2bd/oZPh/EaeQVcGH3i8+0+HHZuPXsTinXbzqJrQEpy7ko+fU/7B023qomZEqLjcKx+XBk6rVRAQYLeB/dx/vvzYpKgifoWg+sNc1hw8h8JiKxKbxamWMdp8utPeujpVi3e+Is1j0qpD+HXXacwfcJfu86VWbu/pLLz1yx7llYaQn0O5sBccSpj2jlHymvmZTDbkqfKQDSESEVVQZEWVEHPqlXX/+eK8u9he9Ji22WGZflEo2cbumPvN2I49p7Pwc8o/WP3GA+Jyb7QxemeUANTvM+epULwDu/8qOSVWAYXFnumAdva14CyjursvmI2HL+ChCeuw/bh8zsGiEiv6Tt+GF2enyLJFX9up0p+6cGcCZtdjqvzrJayFIAj4fNXfOHo+F2+riSEDh3PGYDBvqQ3Ol8muhVWhjAedFf52Oc24v9w9XSFBElFlYlsla2t8EIOj2F64eLJcuU723lfbNGK23GSXcwvx3V/HcO6Kvm5Cb6F0rBaL3cADxlQRX9F58ga0GbsS+SaPrDl1KQ+tRq/E6KXq+YWcxbe42573+nYLDp3LQfepybLl0hFNc7aclK3zVaC6lIqa/FM6PF9tdJOSkFZ6+czYeAwbDl0wxS779lduQ+nfnkgUqVSNv11Op14DHQa7e0yhQYHi385GABrZl9XDEwU7w9RYQb37lMYuOdlo0I87MHrZPnz0+wHXDdOJlgfNHiW7AywWr3RF64GiqhIjCAIOpF9B9tViTNtw1NS6p6w7gsyrRfhu4zH1/Sss88QXhv1+pA+f/eSd7mVUd31bKUZEVYkvPrFd5NC5K+LfcVGO05wA+s//+0v3OYwcdBWtCZXF0X86ArJd2HHp//wkE7QSigMHZOvdt7e4xIqv1hzGzpOXFdeHSjxVOW5MBXU5V56ao0Qwft7VnnG17S/lFmLwvJ3YdNjxA8BMh6crt42zTTYevuiSLa5gROwpbm83/M/qk4mHSqGoqsQUSkbSnFCZqNJVwiRfl0ZeFN6YOkL60rR/APwhpsVIDIMv3dxGKZJ4CK2CiqdSaUNTv+gd96DV/Vc2+k/aLW2WLfqWeZuM7HyM+30/Tl7MUxb4kuthRrjNnC0n8ckfB/Hk/zYprpd2/+Xka4sqrVvl2e+3yH67ck3V2iS18zBm2T78mnoGz3y7RbmAWbgkqvzhbjOOYvcfmFKB+AHSl5ze5HE29p/Nxm97zqqurxoqddm7NmWIpx566X4DNFphvafk55R/8PYvu017kI2IKj9zbGgitbXAwDQyZmmqfy7nKaZxcLzNHD0YsvvS5JNudPTfnn+yMGrpPmQZyO1jhBdnp+DrdUfx6BcbFF/W8qBi5wY7u35/Z1zRXB8oeUideaq0rNl7Olv2W5qdwV1xqPbMKmWkt2Fu959xT5s/fZDJvZ/aKH3oWCz+4+Xl6L9KTJEk6LPE4DxUnSZtAABMe641OjZ1HEFXJaTs1lJL7Kb4pe4FT5Us5tii3v2nt837z4JdjvtQSFCnbVPZjosVGui8wmLZObVRngLVpWe+oNiq+FJR7G4y4e2TW1CMez5eo7jOfqSQ0igiT6Q9UKxHR92PffkXACDtTBbmv9jOHGMkpJ7KBABk5xc7f8HpqM/d0yU9T1fyjSWJ1MKV7j81XHkO3RnYYo9L3X/+1HQYTKlQ+v+yZQF2earoqSI+Qdr9p/Qi18NnfyjPsC71VKmNHnGa/NMli5wj3a9DTJUH9mEUpQahzZhVyvu5ZnFxiRUH0rP95mtNCem1LShW81Q5LjPj1aM1gsl+n7KM6koTKptgj1o9RryzW45dcl7ITZyO4DXhZDgXbmUlcguU7xuX6pVeZxc8PfK6dBqloy5XcOUyHLuQ69RL6C1kp8JpbrRS7L2Dsu4/xlQRXyBNpVDk4ozpamJMGlyaka1/6Lvsi89Tnipp95/GOndynbjjQVIKPs8rLEFWnuNXuu2lP+zn3Xh44gZ8qiJy/QHpKVFL4+GT+R7tfyvcgoLKerf2q9T97Wea2Kk5XrBXek5yVUIJnG2nRImJX3Cqz7uGcDJ3pgDjBzD2t/3o+Pl6//gQM3AubOZKz7m9p4qj/4hPKCqxKv5tBtLqzl1RnhBXOU+V9nozkO5Dq/vPrH0Y3lblUhQqXCNbw7LoWoblr9YecX3HHkZ6PZWO5VohB9x5+czcdBzZTrqMHKapUVgnD2o25yZxsffPqzjzHLpyLnILinH0fI5+GyR/O0up4Czea6NkFJ5UVDl77hfvPI2Zm46rrnftI8rE7j+9e1TY5YZD502zw1WMxVSVlpBev9I8VZIPYooq4gsKTRBVaveuNF7hQo5K14vCxt74apIFOGo8Ae40eWZ7qtT34/JuvI70lBSpeqrM5b0laXh5dormtdTq/hMUyng0psrPUBRNkjezK8fwwKdr8X+frcPmI/ryjEmfV1v338Id/2DXtdgvWVmVv230+nYL8q95u+QeSe0DGTI/Fe8tSUOmgrcYUH8OjbYhxS72GOi9DkrlzEyo6ipGUiooe6qAGRLR60vvG0VVJaaouOzGcyaqjl3IRadJG7B01xn5CpV7t0RS3+U8NU+VwjIvuHBlniqNZs+d/buz7ccrlLvwnGWg93eklqZnF+C7v47p2s7dbpKNR7Tz7TicV+k9aHUsY1pMlRdGFrqL0xecC3WevxbfNnzRXn02SP6+WlSMzUcvYuhPu/DEVxs1z9fSXcqjk9df88zIA9V1maJuo0oFWveu0rq8whJHz5Gk3N5rWc8d9u/OXekHt5yRZ1wMVJe8sq7Ypdqgp4r4BCOB6m/+vAv7z2bj1R93yparPczSDy4jX19mTlOjhsxT5bDOpH24YfvOk5m60yr420vYntnJx/Hc91txtbBEl63K3U2encTWIU+V5G9bXJ0s+7ZJ57x8dP85WmQ0pYIaukfySXZxtagEhyTB1fZpJaS2TVuvnND41KXSnHyyuf80DkPPMboyP57aXf3WL7tVt3n0i7+UV+jcvZJ4MZpOx9M4O9+2tVK77d9fvvzY9GtRVVJSghEjRiA+Ph7h4eFo1KgRRo8ebTfsXsDIkSNRu3ZthIeHIyEhAYcOHZLVc+nSJfTq1QuRkZGIjo5Gv379kJMj79PfvXs37r33XoSFhaFu3boYP368gz0LFixA48aNERYWhhYtWuC3337zzIF7CSMxVRdzVbxNKveuVdZgKRdyOvpPx3ORX1SCLUcvothA96U8psrOJpNea+5+KRXrHBPs791/I35Nw/q/z+M/C1J1lVecpsazmsphj1a79sW+jCe7//zs/WZKSgX3bSjbS35hiTwlip000WPPkWtz2+lN36LnGXPlOVS7rx1S0Oio263r4Af3nJFn3HatpEI2wCE2lqJKkY8//hhTpkzBl19+if379+Pjjz/G+PHj8cUXX4hlxo8fj8mTJ2Pq1KnYsmULqlatisTEROTnlyX569WrF9LS0rBy5UosW7YM69evx4ABA8T12dnZ6NixI+rXr4+UlBR88skneP/99zFt2jSxzKZNm9CzZ0/069cPO3fuRJcuXdClSxfs3avPhe2PSEdgOZtU2Whfv54UDUa7swRBwIjFezEr+bi47OUfUvCvaZvxwdI03bZZZZ4q9UB1dx5Ld7+UFEdjOhGh/sy245d1CQZPtYVajbZDoLqdsP9qzWG8s6hsAmh3TIwILcs1ppjd3R/ecBKc3cfuXC/bIBG9MTSA3ANeWonx/f5z2eapKlumdZx6nmXV7j8NA830wOptbxZs/8dxW9OsMAenQl7MUyVpx+1OJbv/VNi0aROeeOIJdO7cGQ0aNEC3bt3QsWNHbN26FUDpyZ04cSLeffddPPHEE7j11lsxa9YsnDlzBosXLwYA7N+/HytWrMC3336Ltm3b4p577sEXX3yBefPm4cyZ0vigOXPmoLCwEN9//z2aNWuGHj164LXXXsOECRNEWyZNmoSHH34Yw4YNQ5MmTTB69Gi0atUKX375pdfPi1lIvVPORJXqEHg1T5WOr0DlL3X17ZKPXsTszScw8tc05F7LrLzmYGn8wezN8omRtVDrYswvKsFyjSzxRjA6JZ/9ser1vFldyHKXcuIyHv/yL2w/7vk8R1L0CAaftIWa3X/AJ38cFOOAAHeFhGQ/gkJ9PtZUaw+ek/1W7I41MPmtFnolhVzkCm4Lb5vX3arzxOvZn2qgugspFVwRW3psPH4hVxbMbcMf4jKNHLPY/WdVF1W+PCK/FlV33303kpKS8PfffwMAdu3ahb/++gudOnUCABw7dgzp6elISEgQt4mKikLbtm2RnJwMAEhOTkZ0dDTatGkjlklISEBAQAC2bNkilrnvvvsQEhIilklMTMTBgwdx+fJlsYx0P7Yytv2UR6SiytkIEPXuQeXbVz5cWf8trlU0+2pZMOKZTMfcV0p5nJztQ/osjl62D5+YlOfJaENlf44UPVU6M5A7o/vUTdj9Txa6TfXuvatH/3nKba/ZvaNhg6DwtnTHRk8EfptFUYkVfaZvky1zao9JAlPvLkpFlWttiw1b26R3CL6eZ9nXwkTP3tVGYSvd495G6WNDDdu5lnX/2TWOzpLWehK/FlVvv/02evTogcaNGyM4OBi33347hgwZgl69egEA0tPTAQCxsbGy7WJjY8V16enpiImJka0PCgpCjRo1ZGWU6pDuQ62Mbb0SBQUFyM7Olv3zJwqK9cdUqeUVUrt19QRuKpXQylMln+vJgt3/ZMrWtxz1p9N9ltZT9rc0T9UvOxxd465itJG1P12K10OhSlcyYfiqvdGzW2XPiHvdJMGBFifdyuq/lbZy5/TJRhEq2OTLd7PSM6s3aFjKzyn/YMTivU5fbEpXVfmcyMWPzJNoV9zInaI3T5U+T5XxC2cfByTiwu0uCAJ2/5OJEYv3IlNltLUa5a/7T/5/QMFTxZgqZX766SfMmTMHc+fOxY4dOzBz5kx8+umnmDlzpq9N08W4ceMQFRUl/qtbt66vTZIh9YY4uwfVRJceUaXe/ee4wr7bUG0m+QAL8PiXGx22txdaSshjqiTL7Q7RnQfTqHCxb5SVYtiUX/C+/8rUgzsTnrobeRISGGBodJdc+CiXP3o+B49/+RdW7JV/VCkJiXNX8sXlyoJNuTva27gSOK+0/j8LdmH25hP4au1hc2ywWy8tY//c6BLu4rb6ttRzTdS8sJ4eZGFDEErbw9mbT+DZ77Yql9HY1tecunRVt3dJ7P7TiqkyyS5X8GtRNWzYMNFb1aJFCzz77LN4/fXXMW7cOABAXFzpRL4ZGRmy7TIyMsR1cXFxOHdOHidQXFyMS5cuycoo1SHdh1oZ23olhg8fjqysLPHfqVOnDB2/pzGS8FMuwJx8ysMuB4xKIWcxVaczr6LNmFVibhb7aQmU0NMFKGuIJdWYObTYqICwb0/yFebGc3a+bKjlsvE1rnuq3NtvSFCA5vWwXyO/vZXF7X8X7cXuf7Lw0g8p4vL9Z7Nx26g/MTmpbPTxpiMXcOfYJDzzzWbHugXHbmxfvuAUj1XpejjZxsbve9S9+KUVOV5YJY+PfUyVrLwLblelQGft7j/ndap5qlyJj3JXh+1Ry2Wlchy+7roESntCJl17bpy1nbZrLvtwdyhjqnmG8GtRlZeXh4AAuYmBgYFicG58fDzi4uKQlJQkrs/OzsaWLVvQrl3p7O3t2rVDZmYmUlLKGr/Vq1fDarWibdu2Ypn169ejqKjshbxy5UrccsstqF69ulhGuh9bGdt+lAgNDUVkZKTsn7cRBAHPfb8VPadtdmiApMHnzh4rtZwuattZdbrWHbYT5H9fzC1Ez2svJK1UCDaKdLSA8uSfZTh0f7jRuhlt6+0bkvwiBVGlNFpSofF46n+bjO3cC1gAXapKMaWCm6+ZwACL5q7t709nL1tBAPIUrs8HS9OQnV+MCSv/Fpd9/9dxAMDma5MfS4+v2Crg7o9WY8eJzLK6Nex0B6tVwNNTk9F/1nbVMspeIiWLyq6H1n3uTAwrdv8pW1a2P7sy7nRl6+3+0yM6Slx4i6ueH/vlOm5/d3SRH2gqAMCcLScMldeaJ9aXHl+/FlWPPfYYxo4di+XLl+P48eNYtGgRJkyYgCeffBJAaazFkCFDMGbMGCxZsgR79uzBc889hzp16qBLly4AgCZNmuDhhx9G//79sXXrVmzcuBGDBg1Cjx49UKdOHQDAM888g5CQEPTr1w9paWmYP38+Jk2ahKFDh4q2DB48GCtWrMBnn32GAwcO4P3338f27dsxaNAgr58XI+QWlmD93+eRfPQifrMb2ebq1DR6RvZpfUWoIQgCvlrj2GVgy5YrmwhZpUXSM2pOrxcpJ78YL81OcWmUnOFAdbvfSqJK6QWiNOmz6rx6PkbX6D8PtYWaMVUOsXvSv5W2E1A1JNBhqWqMjMwOx2UHJcksPcXh8znYevwSVu7LwKFzyvtTOkfKokXFa22Hs7MhmZhEUp/C3rQ8VS7cMLZN9CYa1rMLtYwzrnhZ1T4itL2tep4tZ5LVt+i1QylQ3dHbTFGlyBdffIFu3brhlVdeQZMmTfCf//wHL774IkaPHi2WefPNN/Hqq69iwIABuOOOO5CTk4MVK1YgLCxMLDNnzhw0btwYHTp0wCOPPIJ77rlHloMqKioKf/75J44dO4bWrVvjjTfewMiRI2W5rO6++27MnTsX06ZNQ8uWLfHzzz9j8eLFaN68uXdOhotIG46/Dsvn2pK+fI3chHqC0It1iCr7XSbtP4dzV1TmCbQrr9ZY6UmaqTWhspR1f1/AirR0l0bJTTEYT2LvRbyq5KlSeun5p35SRM8tpugXcbM/xGKxaJ4nh0B1jXW2ZVUl+aZsBAXqaE6dnAOjLwO9qTek1R7JUJ7MWM0rp7deewLEPFQqXWM6u//s9ycPXC/7+5v1R7FqX4bSZvI6JHVB4W8AOHo+B6/+uBMH0rN1XRNXuiH1iHApWrn/BAEICtCuT70dLr2HvBX/BQBr7FJ3lBqib1vb5SjR6DLxpffNsWXwI6pVq4aJEydi4sSJqmUsFgtGjRqFUaNGqZapUaMG5s6dq7mvW2+9FRs2bNAs0717d3Tv3l2zjL8hfdjTs/Jl66Rz/xnB+Ze8Pm+WPWeyHNMkqNWpJoYUUxFo1OMpZm8+idFdWugub99eFhQ5viydBfH6NRbtLjiPIghOk8qq/VaLqZJ6qopLrAgKDEBIoKRbzCogQOElZ3YyzbG/7cd7jzVzWk76uKh1kSuOvHMnD5UL3X82ikqs+GBpGu69qZZmd5/N5JQTlzD2t/36DNPhqeo9fStOXbqK3/ecxdb/ylPpKKEn2bE9env/ikoEFJVYNXMJCgCuiwhBRnbpR2lhsRUhQXKRrzpgSLJfbz2jfe1Sd0hxKuSv/V9p4nOI61yzywz82lNF3Ef6sNvnKXFVXGjdzDbkrlk1t7N8ubMvN5X4chm249X6utTKqK6XDYfO47u/jrnkZs4vcpwHz/5aKMZUKXX/Gc0y6iMscD0ztdsf0BaLIbEiH12mvL6KxFOVW1B6rYIlniqlmCvA/JfW9I3HdZWT6rtXf9yJ3/c6JrnV66ly5smzYXu2VL3BSnnXrtU3b9sp/LD5JF6cneLwEac0+s8mJoyg5ak6dan0A6/YKuh6xvVOK+UqY5fv1xZVgoAqIWX3ZLbCvIpq7bDNdKOeM19hi19zd75GT0FRVcGReqou2eUv0SOOlNAzSk4riFBcLJSKrzHL9mHlvgynX7Z67C2+1vA4c5WLWFx7+J79bitGL9uHhTtPG9ru1KU8tPzgT9mosVKb5HZsPHLRYVtFr4kXNFVOQTG+3XAUpxUSrhpBTzunJLzc7v6DtsfFwfuh8nfZMnlM1ZWC0heYVFTZMv472G6yp0o/ckNe/mGHQwnlkXdO7NVqOXRct6/XHcHe02X5+2z1ncvOd1hW+rf8tyseCdv2euvRsw8903jZn0vdgeoAZm8+oRkraRUEhEo8U3kFxY6FVNthQdseLyF2yzp5G9muh3RwgP029FQRjyEVQFcL5V/PeoSPEtIXuaq4KXHeYAkAFu88jW//Oob+s7Y791RJ/1Zp7G1iSquR0+Px0sv0jccMlZ+z5SQKiq34Iy3DLjZEXm7+Nsf0G3oD1c3m498PYMzy/eg4YZ3i+mMXcvUNENCxL/tjPHUpD3O3upeKxGJx8lXrEKgueYkrum/kMVUXc0o/VoIk3X+XVRIwOmvsPTVqydWs4M620vZU2cooF7qUW4hxvx+QpQCwnR9Z2gY7T5UUIxOp29enJ4zB2TobeuJMHQYXG1QxtvtMre6w4DKhn2k/KTM0YqoM2uNp7eXsdJ+4kIvpG4+JA5iUtvGhpvLvmCriPlJxYX+juTpITI+nSm/Op3TJF6mTOEtdaRpsoqpI8hXjONmmeY+cUZd5VHiw+Hd2fhGiwkN02+SrDNxbjpV6zXILS/BHWjoSm5XlZvs19TQGz0vFIy3i8L9erTXr0fNysi/zwKdrdb2wtCt1cn7tVtmn9bDHKgChQWUvMNu8gMGS9C8XrhQACins3BEp7qDPk+K4TPHU6/wWC7BYkHW1CBsOXVBcrzQ1lnJcl9weaRHbveHKi17vqdZTTu0elQqV0mMr+23AUQULgL4zlJN6AqVto9RTlamQr0/t3rI1lbrPoadUlc4L0n92qZe/xfVRZZs6PMO+C4ugp6qCo5Y9HLAPyHWtTrUnVS6AlMs4uMOdPK263PDXWoiSEvXj1hPwrhd3RNWxC3kSm5xvqzjk3Qtth9TmF2fLuy3fXbQXAPDbnnTkKHU5XONsVj5G/prmdF/2h+i2oAIAJ9ncHeb+k65TOMH23qSLuaWiSvohcSnX0augS1Q6LeEaLnuqnIgczZQKFuDjFQdU1yuNVhNrkzxXGVnyrkDpHm0fUUYeQ7H7T6c41JenSkVUyerRWCkhS8HLBGjHjQmQnwMlUaW1LaD/HHpOU127LjrLSz2cWmlRvA1FVQVHGltkL1pcfWHp8Rjp+DAujY+QCRzlcrblzqYPAcqOt0RDOKkl/3SFQIOiSvoekeYL0jVsW0lUecHRHRkWrLruikRINX/vD5y5nKdaVg+eExUa+7TvOpB2y6ptI7H0yrWgYGkgsbKocm6npwJs9YxOU+np1MSZp+rExVx5ecnxKc41qHDCpaP61EW3/uewrA5Je6LxcaKnmVTzzEubB/vnV+0j0pVm2WqVB/Bn5Svcf2oDhq5tGODgVVPG3Q9RT+D4DPvGDoCiqsJj1VAQsofXwE2oJ6O6LIhQ1e1s38goY1uuNdmyDVtKBa28MfIGw01PlcEnSBpseuJC2QtHlydBaY5lL7QeUk+VM95auMetfXlidnkLtBtZra9ctRFx0jL5RaU/pKJKefSVczx1NfVk/FY6904D1TUEqdJ5l/5WEnq2a6H2VNp3/7kz6k52nTXOvFkxVZ58VK2Qi6rsq3KvsSAIYje1g13X/q85u4QEX8dUKW7j8Nt3qoqiqoJTYtfISdGbUVirTjUxoCejuoM3XOVpFb+g9DRu10SL9nFL/3bv4TPa/Sd98UoTnbo6Ms5TydPHLNuHl39IgSAIiDQgqjKy850X0sATnjcBxmKqnE6obPe74No8jVLBnH8tz5g84FqPcPaQp8rFmCrnxy//VeLEDTxx1d+OC53sz37vslF7Ltz/tq315tLT82zqCZh38FQZVCdt6ldXr9sqr/+Knaj/39ojGPrTLk27pG2ZmfOg6sWdPTrOCemeLe7AQPUKjlZj6nKeKh0Nv7SNUduNfc4ZNbeybbHMU6XW/XfteLW+tOTH7V1RJQ3OlX6pu97F4JnG79u/jgEA5m09aUhUuZvrxhONoQUWzXtdLaFk6TrlJKzSRrzQJqok11ZpCig919hTX9iG06Bcw9lmjgHCZX9bFPKDTV6tPdOAzQa128jeS1jiSjZwoayusnrdayfVUkjJRLXGOj1oWSEIcjvt4xs/+eOg6rbidtKuSl+IEtt1ceEZYPcf8RryoGz1ddn5xeg5bbNhN7ZqTJVGDhG1bdViDJSGZqtZaXt5SBsF++PWG6CqhmwOQo0nSKmhlooqvY26DaWGztON3/YTlx0Cik9dylOdN9LZCE5neKoxNFKvc80t/xjILxbw/pI02TRQSokaH/1Ce8YGo3YaQddzrbhMW8jbr7Vvb4y+IMu6otTijeT12T6ijGkqQbYv+78dyuvyInveU6X5YWDn483JVx804sC1DaUfRNpzZXoWM7r/vBFrqgZFVQVHO1BdXjb56EVMW3fEaZ26uv90CBeH5SqJOG0Pu9xTpVxWKVDdYb9S+w2Kkrd/2Y0HPlnrYJvyfhyX2bqKgNLGPSM7H4XFVn0jjBQabm80Hva23Tt+DV6yGwVow90gVqtqaLh76J2m5tiFXCQfLUu8qha8LV1eUFSCGZuOy8rYxLN0879V5tyT2+K0iEvoC1R3LOM0pYKDp0rywaGw3hlGy5eN/jN+3+n9uNI3+k95uSylgl0ZI7M5OIsLLO3+K/t95Hwuhs5PFZOoRldR9zbbzJKeQm3Ppg/dQCpoTTXlbSiqKjjyrjrnN95fCpm8HerU4THSm1JBukatiSkTVc73K6ZUkIlJO9tkGxt7+OZtO4UTl8pGuAVquGaUMpDLYqqyC9D2wyQkTlyvL6WCYqC6ctl1f5/XnNZCC+n1CgxQnuIl6cA5FCpMxeKfnirBSfLPMl6YKZ+TTEm0CoLcA6N0ntU8ec7w1LyUerrsXcmDZm+v3snKVW2wBaqrbFoaqF62E1dGMNs2l17DrccuqZbX1TWvK6WCe9fW+aCBsvV7Tmdh4c7T+Ne0zQCAOlHhqtspxVRp3i9uHMaV/CK8v0Q5tYo7Z0erG9rbUFRVcLQaHaV12So5UqToEUyujIZxFlMli3VRqd/WHaAdQ+NcnOlFS0TcO34NUk9mypZJu//2nSmdnuPYhVydyT8dl6lt1/v7rRj2s3JgqjPsY43VrvGKtHSHZa7OpWjDE5pCgPYLSbrq6AW7FABKQtZuG6XpQ/RM7K2Ep14GrqZUUHpCZJ9pdqtlHzMW48+XnhguWUyVC8k/BYc/SkVVRpbaIAs9nipvdP9prLMqn7tj1+5n+8mVpfy+Jx3T1h+RncM/FZ5tG+7copNWHXLw6pqBvU3lOqYqOzsbixcvxv79OmcIJ15FaxSe0kNqP2oEcHwhyepUuXn1lLEfBqz2QrZ9QcnnANMWc9L9F5YIsuPSO+pHypX8InyRdEjVNjUG/SifY03q1TBqh9Lwca2G9tfUM84rVUD+YrSo7uOcQjJCi6RFccUF75HGUNCuV7rO/noqe6rkd59SZvDCklIvnlGJ6SlPlb5pVBzLGJ1WxyHvnMHDcSqqIMi6pmx/G0v+6XgNAWDNwXOK5d3JU2W/XynGA9U1PhShL4RAidRTmfjwtwMy4f3mL+qpUawCcDD9iup6LY7ZfbQoYUbXXblKqfD000/jyy+/BABcvXoVbdq0wdNPP41bb70Vv/zyi+kGEvfQG1tkI7fAsUvHvlixilC7kFOA1FOZTvdrw2q1v/mVtwkIUIqpUq7TJlrsXyJ9p5d160i33X78MuZuOenU1lFL9+GzlY7DwbW6/wDgjF0XoJqo0vPSU0yW6IGXsN5g42IFN4482NW9fZuF4KRe6fE5zjqgtlHZCqWuvqJi147DpzFVLuRBc2gbJOfCFa+lszxVgiD3UrvS/Xf68lX0mLZZ97OjK6bKsdkEoJ3805AStGjHf9rHVLmCkZxfnSatd2kfocHqksNMIeTDWWqMi6r169fj3nvvBQAsWrQIgiAgMzMTkydPxpgxY0w3kLhHsYbHSEn4XFWIk7EvJ3N1S1a1GbMKXb7aiLUHz9l5qtx7WJS6/9SqXJDyDxbt/MehAdt+4rL4t3TdirR0vLNoDw6f0w4i3nhYef4yZ54q+4ZOGqgufRfrEaHKyRLNR+Y91PBUKYUN6c3KrLpvT+SpEvTHVOmZJ7I0pqqMIqWYKheHZXpKVOmJqXJlQmV7j4X0nnBl9J9zz5jdQBkXlcSWY5ccJh1We5S7TUl2Wp/SIJJrtYp/OWgqp7XK0TpSK7wbnO2qgJPOmenAtTrNOIxyNfovKysLNWrUAACsWLECXbt2RZUqVdC5c2ccOuTYPUK8y8bDF5D4+XqkXBMRVhWvEqD8YEhf+jbsvwZlOagUbt4PlqTpiltyiI9QeZoCFQLVtb4eX5+/y0meKsdlK/aeVS0PKItNwHhgtpqnSlfuL6WXnge+yKT7CVTINSSWU4gb0pzrTAeCUNodoZb92RWceqqknjm7V53SZvb3raKnytWYKhdGPyp12dujx1OlHLOnvc0bC+Rxe/b3qNEXpO1aqOepEmTtjytz/9nItcvlpFaF1pyWNvSMS3AnpkorthGAwzQ1ruBuPKQeQjViu2yYIYfKVUxV3bp1kZycjNzcXKxYsQIdO3YEAFy+fBlhYWGmG0iM0evbLTiYcQXdpmwCoN2YKr3IlbuY5L+LnUxBc7WoRJZ0VM0C+xeI2se9GFNlIAZJ70vUxnURoZr1qYkqoyOcCtS6/3R1MTiW8cQXmf0k3OrpK5S6/5Tr0cuB9Cvo8tVG3DF2leFttdCMqZL8rZXTrKy8PKt3ocJ10ZNhWwlXHFy3fbDSaRlXAqkB5WuvdVWl+3EhpEpXoLpSt7krgsAh6avhGiR1uTAQwKjNWufGKggei8czEz2iygzKVUzVkCFD0KtXL9xwww2oXbs2HnjgAQCl3YItWrQw2z7iIgKudXtodMPpfQgdu/+kdShsYLHo6v4rjamC03K2F7VsehknD43WO03J5sgw7ckFbNOOuIvMUyU9jy56qjwxrYm0TotF/T7531rHnGayvDwumGaLySvd3rxj06xL1t2pbzvp4lOXHCeR1uMZMosSQXA6PZCreaqM4pid3tzzYBUEWdsi3qsuOFnMfHb0TKhsfy7MTP7pWxmhn9Bg9e4/m/2mdP+Vp5iqV155BcnJyfj++++xceNGBFxLKd2wYUPGVPkZt49eiZX7M8Tf9veqkldKyfNi/zBLv0avFpVgzLJ9DtvkSz07Oh8StTbOFqguNcPZQ6PV/WdmQ6+n+0/qtUiXvPwMe6q8FFMl3U2ASp4qQPlFrTcrs/q+9Xsj9VLq3dBYL/nb3nugJ6bqYm6hQxlbnJXhF6eLL4O8QpVIabFe454UwPg1kHaFWiwWw/dn2TQ1yifuyPlc7D+bLf52R7zaX1t37jc9earcjanSTBNj1V6v5z50M2+vLjQ9VSY2Zr4UmC754tq0aYPOnTvj9OnTKC4u7W/u3Lkz2rdvb6pxxD0y84qwcMfpsgV2d5re9si+Qbbv7bDNEydF2l2m9wZX8z6VearM6f5TOm5Pus7zrp2LjOx8/HO5bDSgdI96XqZKDbezl6Ur3VD2iVONvLdkX+aG92wnnE26JoKTumTTDtl7qhTrcx6/4sqoNMC1mCo9+9MXU6Vwf6mIys9X/o3jCsPj7Wc9MB5T5bzMJkmCYncm/nXs/nO9LlfmVjS0NycC1QrttkCPXjK7Cfw74wrajUvCD5uPi8ucjZYGzPG5lauM6nl5eejXrx+qVKmCZs2a4eTJ0uHor776Kj766CPTDSTmYX+bKd14Sre8fYOt1N1hX4ceUWUtHbIi/61Un5inqgxnD57+CZWv1efiM6gnLiLnWiCx9AvbHk95qnKdeDCUkN4X3288jqW79Oe7cjemSjaFkIntot65zOw9JGreG2f3nze7/wDnLxF9eaqU6lUuOynpEBInOg6rdzv5p2RbPbiS/FPclzc8VbLnwX7/xvah2YMt6I8bdAVXzu+wn3fjbFY+3l1clkFdS/iZmlLBtJqMY1hUDR8+HLt27cLatWtlgekJCQmYP3++qcYRz3EuOx8nnYgjG/bPwbuL9zrdRtr9pxpTZfdVrhZMqzShsrMXtpZIcSXJoRp6Gv/MvFJvbrHGiDA93TNK2ztrmPMKDUysarPFrs7zOfpH4slSKrjgdDEinHXX6eQkyUf/yVG9z5yYpnWttXC1+08pq7sUV5N/al0DpaSn8kB1469iPQH1Upx1F+rZ1oY781bqiol0I9DH+eg/7ShTT8ReOkNp+iZXR6EaxZd5qrSjcxVYvHgx5s+fj7vuukt2EzZr1gxHjjifjJf4DttDKQgC7vwwSfd2Rj0OFrg2pFxtC6WYKqfdf5oxVUrLXHuS9TTDl3JLRYneqXPUWPe3Y8ZnZ8LjqgueKne6VCxuxlQZucZG0LRFskrX6D9Bj6h3cfSfS1spv8CkmDqhsgYO2s7gRTS8v5KyNs0o9k2UO7ebK55AM3N4CdC+J/VlfPe88HK1W9wovsxTZVhUnT9/HjExMQ7Lc3Nz3Z6hnngHo+2P0ZejXje3fcCv2n4sCjFVztAMVFd44Fx91gUdt7wtkFnLfj0idPkex/m4nNktbUw3HbmAkb8qT2Yqq9Okhs+VWvTmIjNqh5aTQLv7z9GGE5fy8M2GY5r7dNVTJbh47tVEldUq4JlvN2PzUfUJg8V9K+3aoDlS79XF3ELVVCTqNti68/S9S2z3tyu3iv19btt3so5J5R3qklSVnpWPtxfuRp+7G8iOw3F/Rveh9VGm/dzqCq30gg7R5akyYT/lKqaqTZs2WL58ufjb1gh9++23aNeunXmWEdMR7P6vFz1fF2lnsiT70bcHB/GllqcKjtPUOPOkOxspY6S8Fnoa/8zcomv7UC/jcl4jZx4Tycv9mW+2OM0cr6dOLTSn5dCBPFDdZTPsKnUWqF72t96UCs64lFcqpI12gbn6hZ2vkLQXANLOZOsSVIDx7j8lnv66LPt46qlM/J3h/H6TUlhixftL0rBWZR4+e2xtkyteFrVr++/vthiuS9q199Yvu7H24Hn0mb5Ndj/ZCwqjFmvHVGmfAT0fSt6QIdqjsu3/cB0fairjnqoPP/wQnTp1wr59+1BcXIxJkyZh37592LRpE9atW+cJG4lZCKWjdq7kq8fZKDkb9YQCdJ78l2p51Vw/0Dfaq2yaGokXw0lHiXaeKgVPlYtNip5B45evOvdUuRrY7Oyl70q97ogZeUZ1V/Zdto1ZX5v295nj+rKVDnP/ubjP/CIrthw17vFw9ZALVPKoGbkGSkW9HYqzbNdZzNh0XHd5m30ueapUPOqudFFJPcJqsaoO9Ro0Wut5cNYl7S+JQb3X/ec7DHuq7rnnHqSmpqK4uBgtWrTAn3/+iZiYGCQnJ6N169aesJGYhBUCJiUdwvcbtbsuHLYz+vDbvYrUtrbv6lDrMQm6lgtNutrpkHadngkRF5/CCzmOOYrssYlYrQbF5SH4zs6DS6LKHU+Ve8k/PeKpghNPhmSV/VyO7gS8TlrlyrRdLnqqVDP+669Db0Z1T5LuJImpPTYPkWuiSnmjIKNzT0HedhWoXAtX4+xsaD0PVmjPb6krpqoidf/5IDDfhmFPFQA0atQI33zzjdm2EE/j4n1mNHBZd0yV/XaqORVs66VeDCc2GEyp4Kqnav0h5YmWpehp+F2egNfJeiMzz9twL6ZK4k10pftP+rdZnirB+YTKu//JxIhf0xwSeboTvBsRFmQ8GNnF926+k0B1PSgG5btdq2exiRlX7jVHz1Hp/0KCAlBscICH9JmRXgtZd7i9B9/QHpyHNDibG9AZ3glUV79Pzdy7L+9bXaIqO1s9v449kZGRLhtDPIvxPnyhNCuyYVGlz1NlFeRzqKl9xYijFqXbOrHB6ITKnnRLW3U0/Erz+unB2ReZKy9p906Fu54qqShzxw45zpJ/9pu5XXESZ3dsqFktFBcNpKMAXO+2UPOOGInpMjN/m6sYbmusjm2D7m0dNFXpgtCgAKcZ6tXsAOTXQnr+7dsYo+dWq7izDwdvdbs5w2spFXx4uLpEVXR0tNORfbYXcEmJ8SHcxDsYudH2/JOF52dsw9CON6NVvepu7Ufvfp15xGTxNk4eTqN5qlyME9eFnlgN12OqtNe70uXgTgPsbkyVnhg7w3XC+XD0ywpTzdjWuUpGVj6SDugLuBb35+IOC1QC1Y11/ynZ4923k9G9iYHqJnT/2X6FuDDpr7S9UfMaOogqA0drsWh7m4qsAi6p3MNK+3a1jBGU7h1viTtfzoSoS1StWbPG03YQP8H2ZfXqjztwPqcAwxfuwW+v3WuoDsfGSvkGzyssxt7TZV5QtQfOVp3shevkodHOU6XQ/efBl4etbq1duB5TJWi++FzpFTJr9J9Lnirp32ZdEsF5kG94cCCuFDgO4HBHVBgVVIC8CzwzrxD7zmTjrobXibna1Cgodv9kKY/+8zIGd1gmZoxb6pD889r/Q4PUJ/1VrUvy/MqeZcll86Sn6osk7fg9PSEcmmldLE4M0Im+mCoz7mW3q3AZXaLq/vvv97QdxAvou1lLy0gfMMOB6jo9VcMX7kXW1aKy/aiJKrEeiafKqYdG4yWqsMyTGYdtpnhk9B+0z8XGwxdwz401DdXplsB0M6WCJ0b/ldalsQ5AWIiyqPJl49xp0gaczcrHfx9pjP73NdIsq+apMoLS+fZ695/B8la3PFXK+3bXU+VOGS20nodTkjlFFfft7o1s0n2gFeag9PHsKr7MU+VSoDpQOgfgyZMnUVgodzneeuutbhtFjLFibzpST2Xi3pucvDx13GdKRSY7+Qqyx2FYusp+pYIKcN4FJ28XXPdUKa1zt8HTtOVa3Z6IebAKgqZ4mbL2CF77vxsRHqL/UXcvTt3NmCqz7LCr01meqvBgZe+Etxtn6SNwNqt0JNwnf/ztVFQVqqRUMLRvhUP19lB8o7vT82yp70tZVYW6IKr07N9hrkGD+3DnUnir2+105lXUjgxT9ax6a05Mv4+pknL+/Hn07dsXv//+u+J6xlR5n3V/n8ePW0/i3BXt4ciu3md/7stwcUvbfvXt2dk8a9L1zgSYPwWq2xpTLeHm6v5L89Nol9lw6AI6NovTXac7L9L9Z7PE+Er3J1Q275ponyNBXVSZZoE+bL1/0vgYZ/P62cpczCnAdRGhsuXup1TQv70ZGN2d7dS40mVkn69vzcHzeLRlHZc8VWofcdLT7zAtjgGTLXDvefDUdRQEAYJQOpXYwh3/YOhPu9CxaSymPddGsbz2/IeC5L9u2uXDmCrDd8+QIUOQmZmJLVu2IDw8HCtWrMDMmTNx0003YcmSJZ6wkTjB9lGwcMdpzXJGbjN3vtDtt9QdqK7a/ef4sA2Zn6pdl8Y+zZxQ2QjasU8uplRw4qkCjDfG7nSFns7MF+9D17wHkr9dtsK+Tu24M0Eo7f5T29abCBBw9HwOWo1eaWi7lfsy0HrMKjw8cb3h+R6PnM/BkHk7FbOfe//lZPBe1RGvqIb9hNBrDp5D7++3muqpki4usRPHZs795wxPeeIHzE5BhwnrUFhsxcRrednUPsI/+v0A1hw8r1qXmRb6fUyVlNWrV+PXX39FmzZtEBAQgPr16+Ohhx5CZGQkxo0bh86dO3vCTqKB7q9RXS5q3UV1s/3EZV3lnLmGpS84razwgLNAdWPl3UXsojDoPdODIDh/oRjvUnHNFhtfrTmMrq1vcEmQyOb+M/GaaHb/AQgPVn6R+iKlwNJdZw1vZ4upOZB+BZ+v+hvvPNIEgL6UCr2+2aKadNPVvFmuYvR8l01TYw57Tmc5D6NQQPVelSx2GDRi0Gh3BK6n2reV1wTUl2sOO02aOnXdEV11mhJT5X4VLmNYkufm5ooTKlevXh3nz5cqzxYtWmDHjh3mWkd0YZ8FWg0jD6VJscqGUHvxiYHeBhp4wykVPBpTZduHehmXY6pgvqfKrK5Qtz1VJl0SwUldgqDe/eftL15BMNZlp4R0ehytumyiVyuLubc9VZl5zmcokFIWqG6enSGBxj1VF3IKFOfvlA28cPNcuhVT5eGvg+MXchHoQiZ6j+FDV5Xhu+eWW27BwYMHAQAtW7bE119/jdOnT2Pq1KmoXbu26QYS55h5K/tS4at3/5ViRBz4VUyV7f8eaNgEwXmDqbU6p6AYC7afkr3M3LVTnKvRhbtJ1l1iopvE2eUNcuFF6gneWLALE1f97ZV9CYLzUYPe9tQl65z82YY73X9qqHUFa7H52CX0nbHNYbnULntvkTGTLW4doyc8jtI2MzDAoimqjLQppqRUcLsG1zHc/Td48GCcPVvqnn7vvffw8MMPY86cOQgJCcGMGTPMto/owFliVhED96ovAv3UXdSly418bRmepsaDbw89KRVcrhtwOj+d1n7f+nk3lu85i8Zx1bBiyH14+5fdWLhTOzZPL6405NLG18yRQs66/9TuF2+PftPMFaQTeVoK9XIlgoB3F+3VXZc/YjtdZrZXrnaVbTh0wcFbJbXrx62n8Pht15etMxrr6Ma18ISnqkhyrEEBFgRrfJjoGx1p+8NNw+D9jwEphkXVv//9b/Hv1q1b48SJEzhw4ADq1auHmjWN90UT99Hf/aenkO/uRrX3iSv5S9ydr9BMbI2hJ7K2O0upUFpGfd3yPaUfSAfSrwAA5m07ZaptRpFuYZaoKo070xBVgtYUSaaY4FVk3hEn3eALUv7RXZc/4glPlTvi5Xa7AQbS2ypZ0i0LGBz9Z3HvGD0RU1VswFPl/dQcvrtxXc5TZSMkJAQ333wzIiIizLCHuIC7MRhSyrqqzKtTL6oTKl/DSBed4TxVPk7+6U7dzurV28CY3RC5Up10G1dHRCqhPU2NoLovXw7NdhW995meYv5+9LZn2czH948011PI2A+g0YzlM1i3q8+nILh/FyttXyzzVAVoBqobaV9dtVWa9N2X963uQIKlS5c6dO+NHTsWERERiI6ORseOHXH58mWz7SM60BsfaOTL352b0tVtf1ZJCeFSTJXmF7rCPjza/SfI/m9u3c5fKHovu9m60qWYKmn3nwldYTY7NE+9oN7t5u+eGiWsArD9+CUMmLUdJy7mqZbT86IzOiG0t7Edgi89E9qY2YXt3e2cIc2fFhgAJzFVnrFBDV92W+sWVRMmTEBubq74e9OmTRg5ciRGjBiBn376CadOncLo0aM9YiTRRndMlQF8MfpPFRc8PVpdbd4e/VfmqfJE3do5mGxl9GB2Q+TK8Uq3KTKxv9RZTJWzeSfLE1ZBQLepyfhzXwbeXrhbs5wzsp2kLvE1tufWHy+Tsy47w/eWiwfpKS+89KOnoNiK81fKBLh9m2OkfTVDIJeLmKq0tDRMmDBB/P3zzz/joYcewn//+18AQFhYGAYPHiwrQ7yDmZrKKgALtpsXV2MGF3ML0Wf6Vs1Z2O3RnpbEcZ1H5/679n9PNG5meqrMss8Vz6K4rQc8Vfb1Oq5TH2nob91/el440iJa+dy8nYPKE4j3mH9dJhFtMa/faHcyqnvKayN9Pu1jMe3bEj02mDvXZzmIqbpy5Qquu+468fdff/2F7t27i7+bNWuGM2fOmGsd0YXeQHW9DPtZ/evWF+QUFGOtRiZeJYymVPDkC6YsmNYDogrOGyz9MVUmGATg6PlcZGTnu3S80i2KTLoozoRnaUyVcgFfZmZWQo89+UVlaRKCAy2qXZv+PrJPD2KeKn9UVYK61hMEwbAn1tUj9JSnSuv5tL/njHy0lvfbUnf33/XXX4/9+/cDAHJycrBr1y7cfffd4vqLFy+iSpUq5ltInOJHKdcA+MdHo7ao8m73nzuTvuqp21mjqdtTZeI5eHXuTpeOd/c/WWX2mOipciYg1Lxi/haro8ce6QtNLakpUEFElQefLTNQO8Wvz0/FD5tPesUGT7VtWp5k+/kqjVwfM6wtFzFV3bt3x5AhQzB79mz0798fcXFxuOuuu8T127dvxy233OIRI4k2ZnuqKgJ5herdHkqPm0fzVF37vycat/yiElwt0k7gqPfYzDwHh89dcfuL0yxPFaDdUJemVCgffWF6Tqne6+jpLNvewCYg/fFQtLzIi1O916vjqdAGLU+bfb4uT46uVqJcxFSNHDkSp0+fxmuvvYa4uDj88MMPCAws+wr68ccf8dhjj3nESKKNv2kqfzDndOZV1XXenlD5amEJnvjyLxw5n+u8sEHWHDyvOUkpcC1BqEorIw2mNbPxzS+yui3SzIyp0o6xKz95qvTFpkj+1lmuvLLndKln0y+7//wEj3X/aYgq++4/XbGAYll3rCqlXGRUDw8Px6xZs1TXr1mzxhSDiHE8MfqvvPPPJXVRpfTQejJQfeuxSyhwmE3Ve5RYraoNa4DFInoszDwFBcUl7osqU5N/OtmXWvefn72s9ZxS3RnV/bXPzACZeUXIKyz2W4GYdbXI1yZ4bvSfRr1FJVbZNfG2V9SX94N/THilwenTp/Hvf/8b1113HcLDw9GiRQts375dXC8IAkaOHInatWsjPDwcCQkJOHTokKyOS5cuoVevXoiMjER0dDT69euHnJwcWZndu3fj3nvvRVhYGOrWrYvx48c72LJgwQI0btwYYWFhaNGiBX777TfPHLRB/GkeS8A/Yqo0PVUKjYEnu/98KaiA0vQSag2gRVbOvHNQokPIOMM8T5WgKZoFqAcN++vLWgu5qFI/AGfz/pUXruQX+13smw1pjKCvMHO6Jylanir7kARdJgi2/7lvry8/hvxaVF2+fBnt27dHcHAwfv/9d+zbtw+fffYZqlevLpYZP348Jk+ejKlTp2LLli2oWrUqEhMTkZ9fNvN6r169kJaWhpUrV2LZsmVYv349BgwYIK7Pzs5Gx44dUb9+faSkpOCTTz7B+++/j2nTpollNm3ahJ49e6Jfv37YuXMnunTpgi5dumDvXu25s7yBxS863PwLrYZEcfSff7bJplBstaqLKsmt4w8Z1aWYFedUGtuivb685KnSlQVdZ/dffmH5iCNzxsWcQr/4kPNXijz0Uaf10XO1SB7TqqcnwMxrWC5iqnzBxx9/jLp162L69Onisvj4ePFvQRAwceJEvPvuu3jiiScAALNmzUJsbCwWL16MHj16YP/+/VixYgW2bduGNm3aAAC++OILPPLII/j0009Rp04dzJkzB4WFhfj+++8REhKCZs2aITU1FRMmTBDF16RJk/Dwww9j2LBhAIDRo0dj5cqV+PLLLzF16lRvnRJF6KkyhuLovwqsqi7mFKoenzT2wWwXvT/FVGl9uQqC+le3v3X/6Tmnerv/yktwvjPO5xT4nfg1HTfaeDMHfEjRun+uFtiP/tN/gcy4luz+U2HJkiVo06YNunfvjpiYGNx+++345ptvxPXHjh1Deno6EhISxGVRUVFo27YtkpOTAQDJycmIjo4WBRUAJCQkICAgAFu2bBHL3HfffQgJCRHLJCYm4uDBg+LUO8nJybL92MrY9qNEQUEBsrOzZf88gf+FVPl3C6eY/LMCt8pfrz/qdF5FwHxh6S8xVYAzcaGep8rfbgsDvSjX/lbfwkzR6ksuXCnw2+4/f2D/2SseqVct/xkA5Nl1/+0/65l3nxq+vB/cElXSLjZPcPToUUyZMgU33XQT/vjjD7z88st47bXXMHPmTABAeno6ACA2Nla2XWxsrLguPT0dMTExsvVBQUGoUaOGrIxSHdJ9qJWxrVdi3LhxiIqKEv/VrVvX0PHrxd8C1f21fbM9aMpz/3nZGC+TfOSS0zJmnwN36zM1xkujroPpV1Tj3vztvtDzstDrqSooqRgxVRdzC/z8M84E3DjAV3/caZ4dErRiqvLtRNVLP+zwiA1q+PJ+MCyqrFYrRo8ejeuvvx4RERE4evQoAGDEiBH47rvvTDXOarWiVatW+PDDD3H77bdjwIAB6N+/v8+72/QyfPhwZGVlif9OnfLM9C/MU6WPHtM2X5uxvXJ5qgDHL0cl/M1TpTZ1jFH05KFSjanys9e1nkukN6Yqr7BiiKrLeUXlJiayInnUtGOqjN9bZSkVTAhUL0/df2PGjMGMGTMwfvx4WXdZ8+bN8e2335pqXO3atdG0aVPZsiZNmuDkydJMtHFxcQCAjIwMWZmMjAxxXVxcHM6dOydbX1xcjEuXLsnKKNUh3YdaGdt6JUJDQxEZGSn75wn8TVP5a7ux5dglfLA0TWWaGj812iSMejjM2ad725vZPaXVVaGF393LBkWVVnl/nyxZL5l5heVGrJQTM3VhxFPlbcqVqJo1axamTZuGXr16yZJ/tmzZEgcOHDDVuPbt2+PgwYOyZX///Tfq168PoDRoPS4uDklJSeL67OxsbNmyBe3atQMAtGvXDpmZmUhJSRHLrF69GlarFW3bthXLrF+/HkVFZTlFVq5ciVtuuUUcadiuXTvZfmxlbPvxJf4WqO7PMVUzNp3AxRzHiZkrQnZpLfQI73cXmzuS1Z9iquwzPOvF3+4KPZ4zaRmt8jn5vs+hZAbZfpALSi+uPhP+dh8COvJUGbS6xCqguMRqyrGWq5QKp0+fxo033uiw3Gq1ykSJGbz++uvYvHkzPvzwQxw+fBhz587FtGnTMHDgQAClsURDhgzBmDFjsGTJEuzZswfPPfcc6tSpgy5dugAo9Ww9/PDD6N+/P7Zu3YqNGzdi0KBB6NGjB+rUqQMAeOaZZxASEoJ+/fohLS0N8+fPx6RJkzB06FDRlsGDB2PFihX47LPPcODAAbz//vvYvn07Bg0aZOoxu4K/pVTwxwZAysmLjpnNK7im0nWPbDpy0dR9uquJzBRVRS7W5W/3heGUChrlc/IrRvdfdr7/Jv+0pyI5xLU+VDLzinD4XI7qejUmrPzbHZNEypWnqmnTptiwYYPD8p9//hm33367KUbZuOOOO7Bo0SL8+OOPaN68OUaPHo2JEyeiV69eYpk333wTr776KgYMGIA77rgDOTk5WLFiBcLCwsQyc+bMQePGjdGhQwc88sgjuOeee2Q5qKKiovDnn3/i2LFjaN26Nd544w2MHDlSlsvq7rvvFkVdy5Yt8fPPP2Px4sVo3ry5qcfsCuz+M4Zynio/N9pNfHF07nbJmJmnylVPlb99Ipg5TU2OxvyY5YmCYvenRPIW+UUl+Gb9UV+bYQpaXeoTVx1ySUD+tP2UOSkVfPjcGs5TNXLkSPTu3RunT5+G1WrFwoULcfDgQcyaNQvLli0z3cBHH30Ujz76qOp6i8WCUaNGYdSoUaplatSogblz52ru59Zbb1UUi1K6d++O7t27axvsAxiobgylrr7y0ii7jA+Oz909mhk472p8lqeyUbuKvpQK+lRVXkHF8FQVFJf4mfRV59sNRzF59WFfm2EKWjFVrhJkUixLufJUPfHEE1i6dClWrVqFqlWrYuTIkdi/fz+WLl2Khx56yBM2Eif4m6by9wZOyWtRQVL2qOILbeCuKDp+Ic8kSwSXu/8uKMTf+RLjGdU1cglVEE9VYZHV773jNlJdnbbGD4/PM6IqwKRpanyHIU9VcXExPvzwQzz//PNYuXKlp2wiBvE7T5UfNgBSlDxVgp95JMzGF7fIP5fV51/Uw4o09RxwhhA8N1WHtzl5yZjQrAwpFQpdCIr2Fa52ifvj0RV64Es0MNBiUkb1chKoHhQUhPHjx6O4uGJ84VQU/E1TFXrgC8ZMShQaA39stMzEF8f38QpzRwO7Q0WYkuX4hVx0nbLJaTmrzqCqqxXFU1VSfjxVrtvpfwdY6IEPFdO6/0ypxTUMd/916NAB69at84QtxEX8LaO6v6MUJ1PR81RV+JgxDQS4nqfKn/jr8AVd5az6NBWuFpV/oQmUvtzLTZ4qPxRHruIRURUY4PIZkqVn8+H9YDhQvVOnTnj77bexZ88etG7dGlWrVpWtf/zxx00zjujD//JU+TeKoqqcNMquUtFFozMqgqfK7BdFQXHF6P4rKhEqvKfKHw+v0APTHAWa9C7z5f1gWFS98sorAIAJEyY4rLNYLCipIPNJlSf8LU+Vv6MUQF3RNUdF8NS4Q0WYPNjsI8ivIJ4qsxJGEmN4wlMVYDEppsr9KlzGsKjSM9s98S70VBlDafRfRfdUmTWPXnnFEyOVvI3Zt2hF8VQVW8uPp8rljzc/PD5PfKiZNeiq3ASqE/+EIVXGUO7+84EhXqQCaAq3qBCeKpNfFJ7wNPiCEqtQjj6KKtDoPw/cP6XvsvLtqnJJVK1btw6PPfYYbrzxRtx44414/PHHnSbOJJ6DgerGUBJV5SXQ1VVKhIrxAnUFAUBRBfDUmS38K4qosgrlJ/y7In28FXjo/inv3X+GRdUPP/yAhIQEVKlSBa+99hpee+01hIeHo0OHDk6zlhPP4Hd5qvwcpZiqXa4m5SsnVARPjTtU9pgyJSrKObFaBf+fG6sC4onUOWbNYFCuRv+NHTsW48ePx+uvvy4ue+211zBhwgSMHj0azzzzjKkGEudQUhFnKCU8rUy4Pveff3DqUp7pX9/+nk9OL1bBP7vHlKhQyT89EJNXYlJ8XLnyVB09ehSPPfaYw/LHH38cx44dM8UoYowARsYRJ1SA3i/XEcq/V+be8WtM//quCMH7QOkL9ItyMp9eRQpULyw23ygz5vsMDACujw43wRrXMPw6rlu3LpKSkhyWr1q1CnXr1jXFKGIMplQgzqjMo/8EADkF5T97uNnHUNm7hH3BzpOXfW2CaXjMU+WmggywWFAtLNgki4xjuPvvjTfewGuvvYbU1FTcfffdAICNGzdixowZmDRpkukGEucwpIo4o4I4JSo1xy7kmlpfRUiIWt6oSIHq/hxT5UsMi6qXX34ZcXFx+Oyzz/DTTz8BAJo0aYL58+fjiSeeMN1A4hwGqhNn8AVa/jl63lxRVQHeX5UGfxzf6InRo2bFVPkSw6IKAJ588kk8+eSTZttCXISaijijsk9TUxHIyM73tQmEiHjCU3U68yo2HNI3x6W/YjimqmHDhrh48aLD8szMTDRs2NAUo4gx6KkizmD4TPmnouSVIsbxx4EWnrof08v5x4NhUXX8+HHF+f0KCgpw+vRpU4wixqCkIs6ozIHqFQUzRkYRYhYU+cro7v5bsmSJ+Pcff/yBqKgo8XdJSQmSkpLQoEEDU40j+mBGdeIMvo/LP5U91xjxLypKnjOz0S2qunTpAqD0Bd67d2/ZuuDgYDRo0ACfffaZqcYRfXBCZeKMvMKKMXluZYZxccSfKCiiqFJCt6iyXus+iI+Px7Zt21CzZk2PGUWMQU8VccaineyaL+/QU0X8Cd6Nyhge/ces6f4HPVWEVHzoqCLE/9EdqJ6cnIxly5bJls2aNQvx8fGIiYnBgAEDUFBQYLqBxDl0VBFS8WH3HyH+j25RNWrUKKSlpYm/9+zZg379+iEhIQFvv/02li5dinHjxnnESKINu/8IqfhQUhHi/+gWVampqejQoYP4e968eWjbti2++eYbDB06FJMnTxYzrBPvwjxVhBBCiO/RLaouX76M2NhY8fe6devQqVMn8fcdd9yBU6dOmWsd0QUlFSGEEOJ7dIuq2NhYMUi9sLAQO3bswF133SWuv3LlCoKDfTczdGWGnipCCCHE9+gWVY888gjefvttbNiwAcOHD0eVKlVw7733iut3796NRo0aecRIog01FSGEVHzY1Ps/ulMqjB49Gk899RTuv/9+REREYObMmQgJCRHXf//99+jYsaNHjCTaUFQRQgghvke3qKpZsybWr1+PrKwsREREIDAwULZ+wYIFiIiIMN1A4hx2/xFCCCG+x3DyT+mcf1Jq1KjhtjHENSipCCGk4sO0Gv6P7pgq4r8EMKW6V+BZJoQQogVFVQWAL3tCCCHE91BUVQCYUZ0QQgjxPRRVFQD2/hFCCCG+h6KqAkBPFSGEEOJ7KKoqAPRUEUIIIb6HoqoCYGGoOiGEEOJzKKoqAOz9I4QQQnwPRVUFoDJkVK/4R0gIIaS8Q1FVAagEmooQQgjxeyiqKgCVwVPlD3CKCEIIIVpQVFUAOPqPEEII8T0UVRUAOqoIIYQQ30NRVQFg8k9CCCHE91BUVQAoqQghhBDfQ1FVAaCnihBCCPE9FFUVAEGo+OPSKv4REkIIKe9QVFUAKDgIIYQQ31OuRNVHH30Ei8WCIUOGiMvy8/MxcOBAXHfddYiIiEDXrl2RkZEh2+7kyZPo3LkzqlSpgpiYGAwbNgzFxcWyMmvXrkWrVq0QGhqKG2+8ETNmzHDY/1dffYUGDRogLCwMbdu2xdatWz1xmIapDJ4qQgghxN8pN6Jq27Zt+Prrr3HrrbfKlr/++utYunQpFixYgHXr1uHMmTN46qmnxPUlJSXo3LkzCgsLsWnTJsycORMzZszAyJEjxTLHjh1D586d8eCDDyI1NRVDhgzBCy+8gD/++EMsM3/+fAwdOhTvvfceduzYgZYtWyIxMRHnzp3z/ME7gZqKEEII8T3lQlTl5OSgV69e+Oabb1C9enVxeVZWFr777jtMmDAB//d//4fWrVtj+vTp2LRpEzZv3gwA+PPPP7Fv3z788MMPuO2229CpUyeMHj0aX331FQoLCwEAU6dORXx8PD777DM0adIEgwYNQrdu3fD555+L+5owYQL69++Pvn37omnTppg6dSqqVKmC77//3rsnQwErRRUhhBDic8qFqBo4cCA6d+6MhIQE2fKUlBQUFRXJljdu3Bj16tVDcnIyACA5ORktWrRAbGysWCYxMRHZ2dlIS0sTy9jXnZiYKNZRWFiIlJQUWZmAgAAkJCSIZXyJwKgqQgghxOcE+doAZ8ybNw87duzAtm3bHNalp6cjJCQE0dHRsuWxsbFIT08Xy0gFlW29bZ1WmezsbFy9ehWXL19GSUmJYpkDBw6o2l5QUICCggLxd3Z2tpOjdQ12/xFCCCG+x689VadOncLgwYMxZ84chIWF+docw4wbNw5RUVHiv7p163pkP3VrVPFIvYQQQgjRj1+LqpSUFJw7dw6tWrVCUFAQgoKCsG7dOkyePBlBQUGIjY1FYWEhMjMzZdtlZGQgLi4OABAXF+cwGtD221mZyMhIhIeHo2bNmggMDFQsY6tDieHDhyMrK0v8d+rUKZfOgzMiQoNw6/VRHqmbEEIIIfrwa1HVoUMH7NmzB6mpqeK/Nm3aoFevXuLfwcHBSEpKErc5ePAgTp48iXbt2gEA2rVrhz179shG6a1cuRKRkZFo2rSpWEZah62MrY6QkBC0bt1aVsZqtSIpKUkso0RoaCgiIyNl/zxFaLBfX0pCCCGkwuPXMVXVqlVD8+bNZcuqVq2K6667Tlzer18/DB06FDVq1EBkZCReffVVtGvXDnfddRcAoGPHjmjatCmeffZZjB8/Hunp6Xj33XcxcOBAhIaGAgBeeuklfPnll3jzzTfx/PPPY/Xq1fjpp5+wfPlycb9Dhw5F79690aZNG9x5552YOHEicnNz0bdvXy+dDUIIIYT4M34tqvTw+eefIyAgAF27dkVBQQESExPxv//9T1wfGBiIZcuW4eWXX0a7du1QtWpV9O7dG6NGjRLLxMfHY/ny5Xj99dcxadIk3HDDDfj222+RmJgolvnXv/6F8+fPY+TIkUhPT8dtt92GFStWOASvE0IIIaRyYhGYjttrZGdnIyoqCllZWaZ3BXafugnbjl82tU5CCCGkPBEcaEHvdg3w7qNNTa1X7/ubgTiEEEIIISZAUUUIIYQQYgIUVYQQQgghJkBRRQghhBBiAhRVhBBCCCEmQFFFCCGEEGICFFWEEEIIISZAUUUIIYQQYgIUVYQQQgghJkBRRQghhBBiAhRVhBBCCCEmQFFFCCGEEGICFFWEEEIIISZAUUUIIYQQYgIUVYQQQgghJkBRRQghhBBiAhRVhBBCCCEmQFFFCCGEEGICFFWEEEIIISZAUUUIIYQQYgIUVYQQQgghJkBRRQghhBBiAhRVhBBCCCEmQFFFCCGEEGICFFWEEEIIISZAUUUIIYQQYgIUVYQQQgghJkBRRQghhBBiAhRVhBBCCCEmQFFFCCGEEGICFFWEEEIIISZAUUUIIYQQYgIUVYQQQgghJkBRRQghhBBiAhRVhBBCCCEmQFFFCCGEEGICFFWEEEIIISZAUUUIIYQQYgIUVYQQQgghJkBRRQghhBBiAhRVhBBCCCEmQFFFCCGEEGICFFWEEEIIISZAUUUIIYQQYgIUVYQQQgghJkBRRQghhBBiAhRVhBBCCCEmQFFFCCGEEGICFFWEEEIIISZAUUUIIYQQYgJ+LarGjRuHO+64A9WqVUNMTAy6dOmCgwcPysrk5+dj4MCBuO666xAREYGuXbsiIyNDVubkyZPo3LkzqlSpgpiYGAwbNgzFxcWyMmvXrkWrVq0QGhqKG2+8ETNmzHCw56uvvkKDBg0QFhaGtm3bYuvWraYfMyGEEELKJ34tqtatW4eBAwdi8+bNWLlyJYqKitCxY0fk5uaKZV5//XUsXboUCxYswLp163DmzBk89dRT4vqSkhJ07twZhYWF2LRpE2bOnIkZM2Zg5MiRYpljx46hc+fOePDBB5GamoohQ4bghRdewB9//CGWmT9/PoYOHYr33nsPO3bsQMuWLZGYmIhz585552QQQgghxK+xCIIg+NoIvZw/fx4xMTFYt24d7rvvPmRlZaFWrVqYO3cuunXrBgA4cOAAmjRpguTkZNx11134/fff8eijj+LMmTOIjY0FAEydOhVvvfUWzp8/j5CQELz11ltYvnw59u7dK+6rR48eyMzMxIoVKwAAbdu2xR133IEvv/wSAGC1WlG3bl28+uqrePvtt3XZn52djaioKGRlZSEyMtLMU4PuUzdh2/HLptZJCCGElCeCAy3o3a4B3n20qan16n1/+7Wnyp6srCwAQI0aNQAAKSkpKCoqQkJCglimcePGqFevHpKTkwEAycnJaNGihSioACAxMRHZ2dlIS0sTy0jrsJWx1VFYWIiUlBRZmYCAACQkJIhllCgoKEB2drbsHyGEEEIqJuVGVFmtVgwZMgTt27dH8+bNAQDp6ekICQlBdHS0rGxsbCzS09PFMlJBZVtvW6dVJjs7G1evXsWFCxdQUlKiWMZWhxLjxo1DVFSU+K9u3brGD5wQQggh5YJyI6oGDhyIvXv3Yt68eb42RTfDhw9HVlaW+O/UqVO+NokQQgghHiLI1wboYdCgQVi2bBnWr1+PG264QVweFxeHwsJCZGZmyrxVGRkZiIuLE8vYj9KzjQ6UlrEfMZiRkYHIyEiEh4cjMDAQgYGBimVsdSgRGhqK0NBQ4wdMCCGEkHKHX3uqBEHAoEGDsGjRIqxevRrx8fGy9a1bt0ZwcDCSkpLEZQcPHsTJkyfRrl07AEC7du2wZ88e2Si9lStXIjIyEk2bNhXLSOuwlbHVERISgtatW8vKWK1WJCUliWUIIYQQUrnxa0/VwIEDMXfuXPz666+oVq2aGL8UFRWF8PBwREVFoV+/fhg6dChq1KiByMhIvPrqq2jXrh3uuusuAEDHjh3RtGlTPPvssxg/fjzS09Px7rvvYuDAgaIX6aWXXsKXX36JN998E88//zxWr16Nn376CcuXLxdtGTp0KHr37o02bdrgzjvvxMSJE5Gbm4u+fft6/8QQQgghxO/wa1E1ZcoUAMADDzwgWz59+nT06dMHAPD5558jICAAXbt2RUFBARITE/G///1PLBsYGIhly5bh5ZdfRrt27VC1alX07t0bo0aNEsvEx8dj+fLleP311zFp0iTccMMN+Pbbb5GYmCiW+de//oXz589j5MiRSE9Px2233YYVK1Y4BK8TQgghpHJSrvJUlXeYp4oQQgjxHMxTRQghhBBSAaCoIoQQQggxAYoqQgghhBAToKgihBBCCDEBiipCCCGEEBOgqCKEEEIIMQGKKkIIIYQQE6CoIoQQQggxAYoqQgghhBAToKgihBBCCDEBiipCCCGEEBOgqCKEEEIIMQGKKkIIIYQQE6CoIoQQQggxAYoqQgghhBAToKgihBBCCDEBiipCCCGEEBOgqCKEEEIIMQGKKkIIIYQQE6CoIoQQQggxAYoqQgghhBAToKgihBBCCDEBiipCCCGEEBOgqCKEEEIIMQGKKkIIIYQQE6CoIoQQQggxAYoqQgghhBAToKgihBBCCDEBiipCCCGEEBOgqCKEEEIIMQGKKkIIIYQQE6CoIoQQQggxAYoqQgghhBAToKgihBBCCDEBiipCCCGEEBOgqCKEEEIIMQGKKkIIIYQQE6CoIoQQQggxAYoqQgghhBAToKgihBBCCDEBiipCCCGEEBOgqCKEEEIIMQGKKkIIIYQQE6CoIoQQQggxAYoqQgghhBAToKgihBBCCDEBiipCCCGEEBOgqCKEEEIIMQGKKkIIIYQQE6CoMshXX32FBg0aICwsDG3btsXWrVt9bRIhhBBC/ACKKgPMnz8fQ4cOxXvvvYcdO3agZcuWSExMxLlz53xtGiGEEEJ8DEWVASZMmID+/fujb9++aNq0KaZOnYoqVarg+++/97VphBBCCPExFFU6KSwsREpKChISEsRlAQEBSEhIQHJysg8tI4QQQog/EORrA8oLFy5cQElJCWJjY2XLY2NjceDAAcVtCgoKUFBQIP7OysoCAGRnZ5tuX+HVXFgL8kyvlxBCCCkvFADIz8sx/T1rq08QBM1yFFUeZNy4cfjggw8cltetW9cH1hBCCCEVn7ETgbEeqvvKlSuIiopSXU9RpZOaNWsiMDAQGRkZsuUZGRmIi4tT3Gb48OEYOnSo+NtqteLSpUu47rrrYLFYTLMtOzsbdevWxalTpxAZGWlavcR1eE38C14P/4PXxP/gNVFHEARcuXIFderU0SxHUaWTkJAQtG7dGklJSejSpQuAUpGUlJSEQYMGKW4TGhqK0NBQ2bLo6GiP2RgZGckHwc/gNfEveD38D14T/4PXRBktD5UNiioDDB06FL1790abNm1w5513YuLEicjNzUXfvn19bRohhBBCfAxFlQH+9a9/4fz58xg5ciTS09Nx2223YcWKFQ7B64QQQgipfFBUGWTQoEGq3X2+IjQ0FO+9955DVyPxHbwm/gWvh//Ba+J/8Jq4j0VwNj6QEEIIIYQ4hck/CSGEEEJMgKKKEEIIIcQEKKoIIYQQQkyAoooQQgghxAQoqso5X331FRo0aICwsDC0bdsWW7du9bVJFZZx48bhjjvuQLVq1RATE4MuXbrg4MGDsjL5+fkYOHAgrrvuOkRERKBr164OWfhPnjyJzp07o0qVKoiJicGwYcNQXFzszUOpkHz00UewWCwYMmSIuIzXw/ucPn0a//73v3HdddchPDwcLVq0wPbt28X1giBg5MiRqF27NsLDw5GQkIBDhw7J6rh06RJ69eqFyMhIREdHo1+/fsjJyfH2oVQISkpKMGLECMTHxyM8PByNGjXC6NGjZXPY8ZqYiEDKLfPmzRNCQkKE77//XkhLSxP69+8vREdHCxkZGb42rUKSmJgoTJ8+Xdi7d6+QmpoqPPLII0K9evWEnJwcscxLL70k1K1bV0hKShK2b98u3HXXXcLdd98tri8uLhaaN28uJCQkCDt37hR+++03oWbNmsLw4cN9cUgVhq1btwoNGjQQbr31VmHw4MHicl4P73Lp0iWhfv36Qp8+fYQtW7YIR48eFf744w/h8OHDYpmPPvpIiIqKEhYvXizs2rVLePzxx4X4+Hjh6tWrYpmHH35YaNmypbB582Zhw4YNwo033ij07NnTF4dU7hk7dqxw3XXXCcuWLROOHTsmLFiwQIiIiBAmTZokluE1MQ+KqnLMnXfeKQwcOFD8XVJSItSpU0cYN26cD62qPJw7d04AIKxbt04QBEHIzMwUgoODhQULFohl9u/fLwAQkpOTBUEQhN9++00ICAgQ0tPTxTJTpkwRIiMjhYKCAu8eQAXhypUrwk033SSsXLlSuP/++0VRxevhfd566y3hnnvuUV1vtVqFuLg44ZNPPhGXZWZmCqGhocKPP/4oCIIg7Nu3TwAgbNu2TSzz+++/CxaLRTh9+rTnjK+gdO7cWXj++edly5566imhV69egiDwmpgNu//KKYWFhUhJSUFCQoK4LCAgAAkJCUhOTvahZZWHrKwsAECNGjUAACkpKSgqKpJdk8aNG6NevXriNUlOTkaLFi1kWfgTExORnZ2NtLQ0L1pfcRg4cCA6d+4sO+8Ar4cvWLJkCdq0aYPu3bsjJiYGt99+O7755htx/bFjx5Ceni67JlFRUWjbtq3smkRHR6NNmzZimYSEBAQEBGDLli3eO5gKwt13342kpCT8/fffAIBdu3bhr7/+QqdOnQDwmpgNM6qXUy5cuICSkhKHKXJiY2Nx4MABH1lVebBarRgyZAjat2+P5s2bAwDS09MREhLiMGl2bGws0tPTxTJK18y2jhhj3rx52LFjB7Zt2+awjtfD+xw9ehRTpkzB0KFD8c4772Dbtm147bXXEBISgt69e4vnVOmcS69JTEyMbH1QUBBq1KjBa+ICb7/9NrKzs9G4cWMEBgaipKQEY8eORa9evQCA18RkKKoIcYGBAwdi7969+Ouvv3xtSqXl1KlTGDx4MFauXImwsDBfm0NQ+rHRpk0bfPjhhwCA22+/HXv37sXUqVPRu3dvH1tXOfnpp58wZ84czJ07F82aNUNqaiqGDBmCOnXq8Jp4AHb/lVNq1qyJwMBAh5FMGRkZiIuL85FVlYNBgwZh2bJlWLNmDW644QZxeVxcHAoLC5GZmSkrL70mcXFxitfMto7oJyUlBefOnUOrVq0QFBSEoKAgrFu3DpMnT0ZQUBBiY2N5PbxM7dq10bRpU9myJk2a4OTJkwDKzqlWuxUXF4dz587J1hcXF+PSpUu8Ji4wbNgwvP322+jRowdatGiBZ599Fq+//jrGjRsHgNfEbCiqyikhISFo3bo1kpKSxGVWqxVJSUlo166dDy2ruAiCgEGDBmHRokVYvXo14uPjZetbt26N4OBg2TU5ePAgTp48KV6Tdu3aYc+ePbIGauXKlYiMjHR4GRFtOnTogD179iA1NVX816ZNG/Tq1Uv8m9fDu7Rv394hzcjff/+N+vXrAwDi4+MRFxcnuybZ2dnYsmWL7JpkZmYiJSVFLLN69WpYrVa0bdvWC0dRscjLy0NAgPxVHxgYCKvVCoDXxHR8HSlPXGfevHlCaGioMGPGDGHfvn3CgAEDhOjoaNlIJmIeL7/8shAVFSWsXbtWOHv2rPgvLy9PLPPSSy8J9erVE1avXi1s375daNeundCuXTtxvW0If8eOHYXU1FRhxYoVQq1atTiE3ySko/8EgdfD22zdulUICgoSxo4dKxw6dEiYM2eOUKVKFeGHH34Qy3z00UdCdHS08Ouvvwq7d+8WnnjiCcXh+7fffruwZcsW4a+//hJuuukmDt93kd69ewvXX3+9mFJh4cKFQs2aNYU333xTLMNrYh4UVeWcL774QqhXr54QEhIi3HnnncLmzZt9bVKFBYDiv+nTp4tlrl69KrzyyitC9erVhSpVqghPPvmkcPbsWVk9x48fFzp16iSEh4cLNWvWFN544w2hqKjIy0dTMbEXVbwe3mfp0qVC8+bNhdDQUKFx48bCtGnTZOutVqswYsQIITY2VggNDRU6dOggHDx4UFbm4sWLQs+ePYWIiAghMjJS6Nu3r3DlyhVvHkaFITs7Wxg8eLBQr149ISwsTGjYsKHw3//+V5YyhNfEPCyCIEmrSgghhBBCXIIxVYQQQgghJkBRRQghhBBiAhRVhBBCCCEmQFFFCCGEEGICFFWEEEIIISZAUUUIIYQQYgIUVYQQQgghJkBRRQghhBBiAhRVhJAKzfnz5/Hyyy+jXr16CA0NRVxcHBITE7Fx40axjMViweLFi31m4/vvv4/bbrtN9ttiscBisSAoKAg1a9bEfffdh4kTJ6KgoMBndhJCtAnytQGEEOJJunbtisLCQsycORMNGzZERkYGkpKScPHiRdP3VVRUhODgYFPqatasGVatWgWr1YqLFy9i7dq1GDNmDGbPno21a9eiWrVqpuyHEGIe9FQRQiosmZmZ2LBhAz7++GM8+OCDqF+/Pu68804MHz4cjz/+OACgQYMGAIAnn3wSFotF/A0AU6ZMQaNGjRASEoJbbrkFs2fPltVvsVgwZcoUPP7446hatSrGjh0LAPj111/RqlUrhIWFoWHDhvjggw9QXFxsyPagoCDExcWhTp06aNGiBV599VWsW7cOe/fuxccff+z6SSGEeAyKKkJIhSUiIgIRERFYvHixarfZtm3bAADTp0/H2bNnxd+LFi3C4MGD8cYbb2Dv3r148cUX0bdvX6xZs0a2/fvvv48nn3wSe/bswfPPP48NGzbgueeew+DBg7Fv3z58/fXXmDFjhii43KFx48bo1KkTFi5c6HZdhBDzoagihFRYgoKCMGPGDMycORPR0dFo37493nnnHezevVssU6tWLQBAdHQ04uLixN+ffvop+vTpg1deeQU333wzhg4diqeeegqffvqpbB/PPPMM+vbti4YNG6JevXr44IMP8Pbbb6N3795o2LAhHnroIYwePRpff/21KcfUuHFjHD9+3JS6CCHmQlFFCKnQdO3aFWfOnMGSJUvw8MMPY+3atWjVqhVmzJihud3+/fvRvn172bL27dtj//79smVt2rSR/d61axdGjRoleskiIiLQv39/nD17Fnl5eW4fjyAIsFgsbtdDCDEfBqoTQio8YWFheOihh/DQQw9hxIgReOGFF/Dee++hT58+btddtWpV2e+cnBx88MEHeOqppxTtcJf9+/cjPj7e7XoIIeZDTxUhpNLRtGlT5Obmir+Dg4NRUlIiK9OkSRNZ2gUA2LhxI5o2bapZd6tWrXDw4EHceOONDv8CAtxrcg8cOIAVK1aga9eubtVDCPEM9FQRQiosFy9eRPfu3fH888/j1ltvRbVq1bB9+3aMHz8eTzzxhFiuQYMGSEpKQvv27REaGorq1atj2LBhePrpp3H77bcjISEBS5cuxcKFC7Fq1SrNfY4cORKPPvoo6tWrh27duiEgIAC7du3C3r17MWbMGN22FxcXIz093SGlwm233YZhw4a5fE4IIZ6DoooQUmGJiIhA27Zt8fnnn+PIkSMoKipC3bp10b9/f7zzzjtiuc8++wxDhw7FN998g+uvvx7Hjx9Hly5dMGnSJHz66acYPHgw4uPjMX36dDzwwAOa+0xMTMSyZcswatQofPzxxwgODkbjxo3xwgsvGLI9LS0NtWvXRmBgIKKiotC0aVMMHz4cL7/8MkJDQ105HYQQD2MRBEHwtRGEEEIIIeUdxlQRQgghhJgARRUhhBBCiAlQVBFCCCGEmABFFSGEEEKICVBUEUIIIYSYAEUVIYQQQogJUFQRQgghhJgARRUhhBBCiAlQVBFCCCGEmABFFSGEEEKICVBUEUIIIYSYAEUVIYQQQogJ/D+gLSWjPNMBLwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot(x=\"Store ID \", y=\"Store_Sales\", kind=\"area\")\n",
+    "\n",
+    "plt.xlabel(\"Store ID\") \n",
+    "plt.ylabel(\"Store Sales\")\n",
+    "plt.title(\"Store Sales by Store ID\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "6. Диаграмма рассеяния (scatter). Зависимость продаж от количества посетителей"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot(kind=\"scatter\", x=\"Daily_Customer_Count\", y=\"Store_Sales\")\n",
+    "\n",
+    "plt.xlabel(\"Daily Customer Count\") \n",
+    "plt.ylabel(\"Store Sales (US $)\")\n",
+    "plt.title(\"Store Sales vs. Daily Customer Count\") \n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "7. Круговая диаграмма (pie). Количество товаров"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Определение порога для объединения редких значений\n",
+    "threshold = 0.02  # Порог 2%\n",
+    "\n",
+    "# Подсчёт количества уникальных значений и расчёт частот\n",
+    "value_counts = df[\"Daily_Customer_Count\"].value_counts()\n",
+    "total_count = value_counts.sum()\n",
+    "\n",
+    "# Условие для агрегации значений ниже порога\n",
+    "other_values = value_counts[value_counts / total_count < threshold].sum()\n",
+    "main_values = value_counts[value_counts / total_count >= threshold]\n",
+    "\n",
+    "# Добавление категории \"Other\"\n",
+    "main_values[\"Other\"] = other_values\n",
+    "\n",
+    "# Построение диаграммы\n",
+    "main_values.plot(kind=\"pie\", \n",
+    "                 autopct='%1.1f%%',  # Проценты\n",
+    "                 startangle=90,      # Начальный угол\n",
+    "                 counterclock=False, # По часовой стрелке\n",
+    "                 cmap=\"Set3\",        # Цветовая схема\n",
+    "                 wedgeprops={'edgecolor': 'black'}) # Границы сегментов\n",
+    "\n",
+    "plt.title(\"Distribution of Daily Customer Count in Stores (Aggregated)\")\n",
+    "plt.subplots_adjust(left=0.3, right=0.7, top=0.9, bottom=0.1)\n",
+    "plt.show()\n"
    ]
   }
  ],