1960 lines
460 KiB
Plaintext
1960 lines
460 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1c43aa8f-8dc8-4965-a718-978a5fb526bd",
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"metadata": {},
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"outputs": [],
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"source": [
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"Вывод Hello world! на экран"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "d9886918-b2bd-4271-820e-1872b022fe81",
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"metadata": {
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"jupyter": {
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"source_hidden": true
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Hello world!\n"
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]
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}
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],
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"source": [
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"print(\"Hello world!\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5ad2a7be-c859-496b-b2d8-398340bd6085",
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"metadata": {},
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"outputs": [],
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"source": [
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"Вывод произведения 5 и 3"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "1c6f0b5c-9a46-42b6-8b90-8fcbc8f0e2f4",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"15\n"
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]
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}
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],
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"source": [
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"a=5\n",
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"b=3\n",
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"print(a*b)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "899b4f35-99e2-48d2-9047-bfdd71a36d7b",
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"metadata": {},
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"outputs": [],
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"source": [
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"Удаление точек из строки"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "f5dd712f-98d2-4d04-abd2-b4ec0183f27f",
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"metadata": {
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"jupyter": {
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"source_hidden": true
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Введите строку\n"
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]
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},
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{
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"name": "stdin",
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"output_type": "stream",
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"text": [
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" 192.168.1.1\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Количество удаленных точек в строке = 3\n",
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"Строка без точек: 19216811\n"
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]
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}
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],
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"source": [
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"print(\"Введите строку\")\n",
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"str = input()\n",
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"result_str = \"\"\n",
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"n = 0\n",
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"for i in range(0, len(str)):\n",
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" if str[i] == \".\":\n",
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" n = n+1\n",
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" else:\n",
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" result_str = result_str + str[i]\n",
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"\n",
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"print(\"Количество удаленных точек в строке =\", n)\n",
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"print(\"Строка без точек:\", result_str)\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "79e04f2e-7116-4ca8-9284-c4fbeed6bd20",
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"metadata": {},
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"source": [
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"________________________________________________________________________\n",
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"Работа с pandas\n",
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"________________________________________________________________________"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "3f8d4ccc-7b2e-4a1b-b2fa-4e276cb25a97",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Первый взгляд на данные:\n",
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" Имя Возраст Баллы\n",
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"0 Анна 21 89\n",
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"1 Борис 22 76\n",
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"2 Виктор 23 95\n",
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"3 Галина 24 82\n",
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"<class 'pandas.core.frame.DataFrame'>\n",
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"RangeIndex: 4 entries, 0 to 3\n",
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"Data columns (total 3 columns):\n",
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" # Column Non-Null Count Dtype \n",
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"--- ------ -------------- ----- \n",
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" 0 Имя 4 non-null object\n",
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" 1 Возраст 4 non-null int64 \n",
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" 2 Баллы 4 non-null int64 \n",
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"dtypes: int64(2), object(1)\n",
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"memory usage: 228.0+ bytes\n",
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"None\n",
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" Возраст Баллы\n",
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"count 4.000000 4.000000\n",
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"mean 22.500000 85.500000\n",
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"std 1.290994 8.266398\n",
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"min 21.000000 76.000000\n",
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"25% 21.750000 80.500000\n",
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"50% 22.500000 85.500000\n",
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"75% 23.250000 90.500000\n",
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"max 24.000000 95.000000\n",
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"Имя 0\n",
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"Возраст 0\n",
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"Баллы 0\n",
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"dtype: int64\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"\n",
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"# Создадим DataFrame\n",
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"data = {\n",
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" \"Имя\": [\"Анна\", \"Борис\", \"Виктор\", \"Галина\"],\n",
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" \"Возраст\": [21, 22, 23, 24],\n",
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" \"Баллы\": [89, 76, 95, 82]\n",
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"}\n",
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"df = pd.DataFrame(data)\n",
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"\n",
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"print(\"Первый взгляд на данные:\")\n",
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"print(df.head())\n",
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"print(df.info())\n",
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"print(df.describe())\n",
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"print(df.isnull().sum())"
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]
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},
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{
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"cell_type": "markdown",
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"id": "905b7b2c-784e-4471-a3c2-2c38a4712226",
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"metadata": {},
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"source": [
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"1) Поместить df в последнюю строчку блока."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "7806cff8-5714-4d78-9d25-eb717c7ad35f",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Первый взгляд на данные:\n",
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" Имя Возраст Баллы\n",
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"0 Анна 21 89\n",
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"1 Борис 22 76\n",
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"2 Виктор 23 95\n",
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"3 Галина 24 82\n",
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"<class 'pandas.core.frame.DataFrame'>\n",
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"RangeIndex: 4 entries, 0 to 3\n",
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"Data columns (total 3 columns):\n",
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" # Column Non-Null Count Dtype \n",
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"--- ------ -------------- ----- \n",
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" 0 Имя 4 non-null object\n",
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" 1 Возраст 4 non-null int64 \n",
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" 2 Баллы 4 non-null int64 \n",
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"dtypes: int64(2), object(1)\n",
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"memory usage: 228.0+ bytes\n",
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"None\n",
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" Возраст Баллы\n",
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"count 4.000000 4.000000\n",
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"mean 22.500000 85.500000\n",
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"std 1.290994 8.266398\n",
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"min 21.000000 76.000000\n",
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"25% 21.750000 80.500000\n",
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"50% 22.500000 85.500000\n",
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"75% 23.250000 90.500000\n",
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"max 24.000000 95.000000\n",
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"Имя 0\n",
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"Возраст 0\n",
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"Баллы 0\n",
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"dtype: int64\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"\n",
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"# Создадим DataFrame\n",
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"data = {\n",
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" \"Имя\": [\"Анна\", \"Борис\", \"Виктор\", \"Галина\"],\n",
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" \"Возраст\": [21, 22, 23, 24],\n",
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" \"Баллы\": [89, 76, 95, 82]\n",
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"}\n",
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"\n",
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"print(\"Первый взгляд на данные:\")\n",
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"print(df.head())\n",
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"print(df.info())\n",
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"print(df.describe())\n",
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"print(df.isnull().sum())\n",
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"df = pd.DataFrame(data)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "24b75088-3248-40c4-bbe2-4ce163db6ead",
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"metadata": {},
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"source": [
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"2) Добавить новый столбец с вычисляемыми значениями (df[\"Новый столбец\"] = df[\"Баллы\"] * 1.1)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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||
"id": "7c3b4991-702b-4c30-922a-0425160e8fcb",
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||
"metadata": {},
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||
"outputs": [
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||
{
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||
"name": "stdout",
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||
"output_type": "stream",
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"text": [
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"Первый взгляд на данные:\n",
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" Имя Возраст Баллы Новый столбец\n",
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"0 Анна 21 89 97.9\n",
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"1 Борис 22 76 83.6\n",
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"2 Виктор 23 95 104.5\n",
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"3 Галина 24 82 90.2\n",
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"<class 'pandas.core.frame.DataFrame'>\n",
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"RangeIndex: 4 entries, 0 to 3\n",
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"Data columns (total 4 columns):\n",
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" # Column Non-Null Count Dtype \n",
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"--- ------ -------------- ----- \n",
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" 0 Имя 4 non-null object \n",
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" 1 Возраст 4 non-null int64 \n",
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" 2 Баллы 4 non-null int64 \n",
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" 3 Новый столбец 4 non-null float64\n",
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"dtypes: float64(1), int64(2), object(1)\n",
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"memory usage: 260.0+ bytes\n",
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"None\n",
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" Возраст Баллы Новый столбец\n",
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"count 4.000000 4.000000 4.000000\n",
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"mean 22.500000 85.500000 94.050000\n",
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"std 1.290994 8.266398 9.093038\n",
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"min 21.000000 76.000000 83.600000\n",
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"25% 21.750000 80.500000 88.550000\n",
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"50% 22.500000 85.500000 94.050000\n",
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"75% 23.250000 90.500000 99.550000\n",
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"max 24.000000 95.000000 104.500000\n",
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"Имя 0\n",
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"Возраст 0\n",
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"Баллы 0\n",
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"Новый столбец 0\n",
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"dtype: int64\n"
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]
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}
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],
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"source": [
|
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"import pandas as pd\n",
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"\n",
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"# Создадим DataFrame\n",
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"data = {\n",
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" \"Имя\": [\"Анна\", \"Борис\", \"Виктор\", \"Галина\"],\n",
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" \"Возраст\": [21, 22, 23, 24],\n",
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" \"Баллы\": [89, 76, 95, 82]\n",
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"}\n",
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"df = pd.DataFrame(data)\n",
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"df[\"Новый столбец\"] = df[\"Баллы\"] * 1.1\n",
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"\n",
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"print(\"Первый взгляд на данные:\")\n",
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"print(df.head())\n",
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"print(df.info())\n",
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"print(df.describe())\n",
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"print(df.isnull().sum())"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d901198c-9d40-4da2-b7ff-e56866607b55",
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"metadata": {},
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"source": [
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"3) Применить .groupby() и .agg(), чтобы сгруппировать данные."
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]
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},
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{
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||
"cell_type": "code",
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||
"execution_count": 6,
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||
"id": "a40456c1-11f2-42e1-95e7-572761b19f37",
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||
"metadata": {},
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||
"outputs": [
|
||
{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Первый взгляд на данные:\n",
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" Имя Возраст Баллы Новый столбец\n",
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"0 Анна 21 89 97.9\n",
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"1 Борис 22 76 83.6\n",
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"2 Виктор 23 95 104.5\n",
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"3 Галина 24 82 90.2\n",
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"4 Илья 21 89 97.9\n",
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"<class 'pandas.core.frame.DataFrame'>\n",
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"RangeIndex: 8 entries, 0 to 7\n",
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"Data columns (total 4 columns):\n",
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" # Column Non-Null Count Dtype \n",
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"--- ------ -------------- ----- \n",
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" 0 Имя 8 non-null object \n",
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" 1 Возраст 8 non-null int64 \n",
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" 2 Баллы 8 non-null int64 \n",
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" 3 Новый столбец 8 non-null float64\n",
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"dtypes: float64(1), int64(2), object(1)\n",
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||
"memory usage: 388.0+ bytes\n",
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"None\n",
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" Возраст Баллы Новый столбец\n",
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"count 8.000000 8.000000 8.000000\n",
|
||
"mean 22.500000 85.500000 94.050000\n",
|
||
"std 1.195229 7.653197 8.418517\n",
|
||
"min 21.000000 76.000000 83.600000\n",
|
||
"25% 21.750000 80.500000 88.550000\n",
|
||
"50% 22.500000 85.500000 94.050000\n",
|
||
"75% 23.250000 90.500000 99.550000\n",
|
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"max 24.000000 95.000000 104.500000\n",
|
||
"Имя 0\n",
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||
"Возраст 0\n",
|
||
"Баллы 0\n",
|
||
"Новый столбец 0\n",
|
||
"dtype: int64\n",
|
||
" Баллы\n",
|
||
"Возраст \n",
|
||
"21 178\n",
|
||
"22 152\n",
|
||
"23 190\n",
|
||
"24 164\n"
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||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import pandas as pd\n",
|
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"\n",
|
||
"# Создадим DataFrame\n",
|
||
"data = {\n",
|
||
" \"Имя\": [\"Анна\", \"Борис\", \"Виктор\", \"Галина\", \"Илья\", \"Игорь\", \"Игнат\", \"Ильдар\"],\n",
|
||
" \"Возраст\": [21, 22, 23, 24, 21, 22, 23, 24],\n",
|
||
" \"Баллы\": [89, 76, 95, 82, 89, 76, 95, 82]\n",
|
||
"}\n",
|
||
"df = pd.DataFrame(data)\n",
|
||
"df[\"Новый столбец\"] = df[\"Баллы\"] * 1.1\n",
|
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"\n",
|
||
"print(\"Первый взгляд на данные:\")\n",
|
||
"print(df.head())\n",
|
||
"print(df.info())\n",
|
||
"print(df.describe())\n",
|
||
"print(df.isnull().sum())\n",
|
||
"print(df.groupby('Возраст').agg({'Баллы':'sum'}))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "87592057-2ab2-4d85-8c39-d309397f8364",
|
||
"metadata": {},
|
||
"source": [
|
||
"4) Фильтровать записи по условиям (df[df[\"Возраст\"] > 21])."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "f3c133ad-d5cb-484e-8177-b8550baadc5d",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Первый взгляд на данные:\n",
|
||
" Имя Возраст Баллы Новый столбец\n",
|
||
"0 Анна 21 89 97.9\n",
|
||
"1 Борис 22 76 83.6\n",
|
||
"2 Виктор 23 95 104.5\n",
|
||
"3 Галина 24 82 90.2\n",
|
||
"4 Илья 21 89 97.9\n",
|
||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||
"RangeIndex: 8 entries, 0 to 7\n",
|
||
"Data columns (total 4 columns):\n",
|
||
" # Column Non-Null Count Dtype \n",
|
||
"--- ------ -------------- ----- \n",
|
||
" 0 Имя 8 non-null object \n",
|
||
" 1 Возраст 8 non-null int64 \n",
|
||
" 2 Баллы 8 non-null int64 \n",
|
||
" 3 Новый столбец 8 non-null float64\n",
|
||
"dtypes: float64(1), int64(2), object(1)\n",
|
||
"memory usage: 388.0+ bytes\n",
|
||
"None\n",
|
||
" Возраст Баллы Новый столбец\n",
|
||
"count 8.000000 8.000000 8.000000\n",
|
||
"mean 22.500000 85.500000 94.050000\n",
|
||
"std 1.195229 7.653197 8.418517\n",
|
||
"min 21.000000 76.000000 83.600000\n",
|
||
"25% 21.750000 80.500000 88.550000\n",
|
||
"50% 22.500000 85.500000 94.050000\n",
|
||
"75% 23.250000 90.500000 99.550000\n",
|
||
"max 24.000000 95.000000 104.500000\n",
|
||
"Имя 0\n",
|
||
"Возраст 0\n",
|
||
"Баллы 0\n",
|
||
"Новый столбец 0\n",
|
||
"dtype: int64\n",
|
||
" Баллы\n",
|
||
"Возраст \n",
|
||
"21 178\n",
|
||
"22 152\n",
|
||
"23 190\n",
|
||
"24 164\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"\n",
|
||
"# Создадим DataFrame\n",
|
||
"data = {\n",
|
||
" \"Имя\": [\"Анна\", \"Борис\", \"Виктор\", \"Галина\", \"Илья\", \"Игорь\", \"Игнат\", \"Ильдар\"],\n",
|
||
" \"Возраст\": [21, 22, 23, 24, 21, 22, 23, 24],\n",
|
||
" \"Баллы\": [89, 76, 95, 82, 89, 76, 95, 82]\n",
|
||
"}\n",
|
||
"df = pd.DataFrame(data)\n",
|
||
"df[\"Новый столбец\"] = df[\"Баллы\"] * 1.1\n",
|
||
"df[df[\"Возраст\"] > 23]\n",
|
||
"\n",
|
||
"print(\"Первый взгляд на данные:\")\n",
|
||
"print(df.head())\n",
|
||
"print(df.info())\n",
|
||
"print(df.describe())\n",
|
||
"print(df.isnull().sum())\n",
|
||
"print(df.groupby('Возраст').agg({'Баллы':'sum'}))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "b05fb130-5231-4996-9e88-b4852a174921",
|
||
"metadata": {},
|
||
"source": [
|
||
"______________________________________________________________\n",
|
||
"numpy: массивы и вычисления\n",
|
||
"______________________________________________________________"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "c176a6c4-7d3a-4e75-8fbf-2c8f96981462",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Сумма элементов массива: 15\n",
|
||
"Среднее значение: 3.0\n",
|
||
"Медиана: 3.0\n",
|
||
"Стандартное отклонение: 1.4142135623730951\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"arr = np.array([1, 2, 3, 4, 5])\n",
|
||
"print(\"Сумма элементов массива:\", np.sum(arr))\n",
|
||
"print(\"Среднее значение:\", np.mean(arr))\n",
|
||
"print(\"Медиана:\", np.median(arr))\n",
|
||
"print(\"Стандартное отклонение:\", np.std(arr))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "fbd69334-5b9a-4c47-9bef-82ad1af6535a",
|
||
"metadata": {},
|
||
"source": [
|
||
"1) Создать двумерный массив (np.array([[1, 2], [3, 4]]))."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "7c368e08-8977-493f-8f63-0c57109f5ab9",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Сумма элементов массива: 10\n",
|
||
"Среднее значение: 2.5\n",
|
||
"Медиана: 2.5\n",
|
||
"Стандартное отклонение: 1.118033988749895\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"arr = np.array([[1, 2], [3, 4]])\n",
|
||
"print(\"Сумма элементов массива:\", np.sum(arr))\n",
|
||
"print(\"Среднее значение:\", np.mean(arr))\n",
|
||
"print(\"Медиана:\", np.median(arr))\n",
|
||
"print(\"Стандартное отклонение:\", np.std(arr))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "7521e5d4-8ff7-4a53-98e1-967655afa77a",
|
||
"metadata": {},
|
||
"source": [
|
||
"np.linspace()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"id": "06e234c7-2aac-4431-8c55-c638ae5c7fea",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Массив: [ 0. 11.11111111 22.22222222 33.33333333 44.44444444\n",
|
||
" 55.55555556 66.66666667 77.77777778 88.88888889 100. ]\n",
|
||
"Сумма элементов массива: 500.0\n",
|
||
"Среднее значение: 50.0\n",
|
||
"Медиана: 50.0\n",
|
||
"Стандартное отклонение: 31.91423692521127\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"arr = np.linspace(0,100,10)\n",
|
||
"print(\"Массив: \")\n",
|
||
"print(arr)\n",
|
||
"print(\"Сумма элементов массива:\", np.sum(arr))\n",
|
||
"print(\"Среднее значение:\", np.mean(arr))\n",
|
||
"print(\"Медиана:\", np.median(arr))\n",
|
||
"print(\"Стандартное отклонение:\", np.std(arr))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f08199ce-dfcb-4c9d-8243-fb6012b51888",
|
||
"metadata": {},
|
||
"source": [
|
||
"np.random.randn()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"id": "27e500e2-1823-470f-893e-5be42f6b959a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Массив: \n",
|
||
"[[ 1.48745644 -0.85267656 -1.40561138]\n",
|
||
" [ 0.26239148 -0.72135624 0.32342215]\n",
|
||
" [ 0.59682331 -0.13625498 -0.41690415]\n",
|
||
" [-1.65509578 1.29242409 -1.19270658]]\n",
|
||
"Сумма элементов массива: -2.418088186972355\n",
|
||
"Среднее значение: -0.20150734891436292\n",
|
||
"Медиана: -0.276579562006014\n",
|
||
"Стандартное отклонение: 0.9790596842752675\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"arr = np.random.randn(4,3)\n",
|
||
"print(\"Массив: \")\n",
|
||
"print(arr)\n",
|
||
"print(\"Сумма элементов массива:\", np.sum(arr))\n",
|
||
"print(\"Среднее значение:\", np.mean(arr))\n",
|
||
"print(\"Медиана:\", np.median(arr))\n",
|
||
"print(\"Стандартное отклонение:\", np.std(arr))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "39435227-867f-45e5-8e66-9bf968f2c756",
|
||
"metadata": {},
|
||
"source": [
|
||
"np.dot()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"id": "9c1e87f0-f43e-4edb-baa6-fca71fe40c2a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Первый массив: \n",
|
||
"[[ 1.73230072 -0.31469419]\n",
|
||
" [-1.30366728 -1.50926594]\n",
|
||
" [-0.13028058 -0.94534568]]\n",
|
||
"Второй массив: \n",
|
||
"[[-0.75037165 2.08896712 0.79244038]\n",
|
||
" [ 0.65389115 -0.81650709 1.37065976]]\n",
|
||
"Массив: \n",
|
||
"[[-1.5056451 3.87566929 0.94140637]\n",
|
||
" [-0.00866068 -1.49099173 -3.10176868]\n",
|
||
" [-0.52039432 0.49972961 -1.39898688]]\n",
|
||
"Сумма элементов массива: -2.709642116719168\n",
|
||
"Среднее значение: -0.3010713463021298\n",
|
||
"Медиана: -0.5203943235006268\n",
|
||
"Стандартное отклонение: 1.8761673246530655\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"st = np.random.randn(3,2)\n",
|
||
"print(\"Первый массив: \")\n",
|
||
"print(st)\n",
|
||
"nd = np.random.randn(2,3)\n",
|
||
"print(\"Второй массив: \")\n",
|
||
"print(nd)\n",
|
||
"arr = np.dot(st,nd)\n",
|
||
"print(\"Массив: \")\n",
|
||
"print(arr)\n",
|
||
"print(\"Сумма элементов массива:\", np.sum(arr))\n",
|
||
"print(\"Среднее значение:\", np.mean(arr))\n",
|
||
"print(\"Медиана:\", np.median(arr))\n",
|
||
"print(\"Стандартное отклонение:\", np.std(arr))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "a8a95925-880b-4ae2-9811-66eccbc1323f",
|
||
"metadata": {},
|
||
"source": [
|
||
"____________________________________________________\n",
|
||
"matplotlib: построение графиков\n",
|
||
"____________________________________________________"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"id": "fefe1866-ae14-43e2-823a-ae041e182e2b",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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L8sBjI9pgQr94eHpe+hJ12e9MtvGQ6tQy8pJfVILur+oT5O58bjgCfOoXFhw8eFAFHrfddhtatGihHuvXr5/6KK85CQK190O5L/nAL7/8svpcRock1WXt2rW4/vrr1WMnTpxQaTOV30PlvqTGSMJ4SkqKek3LbFB4eHiVtkiStwRvNb3/Sh9LccpBgwapPq6sctDltsGSRKDSwZXJ59Kh0nEyHCe36p4j31sTWVkntwvJC8QRJyZHHdcVlZaV461FB/DW4gOQ9ICWEYEY37c5buzRDOGBPtV+T7PGQRjVuSlyCorx664UvLfsEA6k5mLSZ5tw76BEPDqiDbwtdafnuVM/64197Rzs59r9uisZD3+5FeeKS1UwdG2XGNw3OBHtoqt/0x3RyR8jOsWq+/uSc/DUnO3YeDQTL88/gAV70/DqTV2QENnoktogsxsymiPTTdpNL5fy87t3766mvbRFUTJYICNDYWFh6vfRfidNly5dqnwuW4+cOXPG9piM/MjMzIU//9Zbb1Ur39988021uKtt27YXtUW+T76/prbL49Ke6v4e6vv3YepgSaJcba5TI5GpFv3K3GjPnj3V8J22zFHmNuVzmXMl50rPLcTDX21VV25CgqRnftcBft71WxkR5OeNm3s2U1eEL/68G7PXHcOsZYew5nA63h7XHc0bs0IuEVn3DJu17DBe+3WvuiiTPKRXbuiMuPD6nyPaRgfh8z/2xlOfzscvJ32wIeksRv1jBf52Y2d1cddQ/t4W7H5hJPQgP7u+LBaLej9dvXq1SkF5++238dRTT2HdunXVPv/CoESCl8o5WhERESo15kIyTSYr1eXnyWhWdSRnKTExEY5kqNVwMqcrJQDkppUGkPvHjh2zTY9NmDDB9nyZ4zx8+LBajig5SP/617/wv//9D4888ojtOTKd9sEHH6j51D179uD+++9XJQokgYycZ+fJLPzu7ZUqUJI/2Jm/74qXb+hc70CpMn8fizrxvTu+B4L9vLDteCbGvL0CO05kOaTtRGQchSWl+MvX2/HqfGugNKFfC3wysfclBUoamXYbGF2On//UH4PaRKKopAyPfr0Nc7acaHD7JIiQqTA9bpe6OMbDw0PlFEne0pYtW9QAhJbUfalkpErqJl3o0UcfVSNDX3/9tQrIFi9efNFzdu7cqb7fkQw1srRx40ZbMpjQ8obuvPNOVdPh9OnTtsBJyPznzz//rIIjqfsg85offvihGjKsPMQnQ4HPPvusmvOUhLP58+dflPRNjpOUlocJH69HRl4REiID8e74nuqq7XJd0zkGnZuFYMrsLdh6PBMTP1mPr+/rd8nD5ERkDrmFJfjjJxuwPilDTbtNv7aDyjW6XE1D/fHZpN54eu5OfL7uGB793zZ4eXri2q7WKTszWrdunZqFkek32aBWPpf3UinTs3379ks+nrwvy6BFZfL+LblNq1atUkWl//KXv6j3ezm+TPeJpKQknDx58qKV7249sjR48GA1fHrhTSt+JR+lYveF3yMRryz1lxoOkih2IZlyO3r0qHqO/IdLFW9yDlmae+cn1kCpc9MQfP/glXYJlDTNwgLwn7v6oFPTYKTnFeEPH61HSnaB3Y5PRMZQVlau8pMkUAry9cLHE3vbJVCqPMry4vWdMK53HMrKoVIKftlxGmYVHBysVpeNHj0abdq0UcUkpaah1ENsCFlZt2vXLuzbt099LoHXXXfdheeee07twiHkvgxkyKxR5ZIDErBpSeaOYqiRJTIXWfVx16cbcDQ9H3Hh/urkJXlH9ibH/HRSH9wya42qoTLho/X43739EBLAxFcid/Har/vw254U+Hh54t939UH35taRCXuSaTlJASguLce3m0/gT19swb88PTCiY80Lhoyqffv2ahamOhdW775wEENcuF2ZrHCTgQspFP3ee+8hMjJSzfYILbdJ8p5khqlySQBZLTd79mw4mqFGlsg8pJjkn2ZvwbYTWQgL8MZnk/ogMujiFYb2Ist///3HPmgS5It9KTm467MNqrglEZnfd5tPqMUe4vWbuzgkUKocML12cxeM7RarCudKGgDzJetHEsRlhKi+xTkl7ebJJ59UeVOOxmCJnE6mTp/9YRcW7U2Fr5cnPryzl1PyiCSBU64oJel749GzePTrrdy+gMjkNh09iye+3aHuP3h1Iq7v1tThP1Pyod64pSuGtY9CkVwYfrFZ5UtR7WSLFAl+6lu+QPKYpBK4MzBYIqebu/WkWtYvCy/+Ma47eraoWmDMkaR+ikz3SZHLeTuS8e3mk0772UTkXCczz+He/2xUAcvIjlF4dPjFNXocxcviib/f0hWxIX5ISs/Hs9/vdNrPJvtjsEROlZxVgOnf71L3HxnWBqM6OX8uv1d8OB4e1kbdf+6HXeqESkTmK3D74OebkZZbhPYxwZj5+24NqrB9OSQv8h+3dYf82O82n7yskgKkLwZL5DQy5TXtu+3ILihBl2YheGCwY4uI1ebeQQno0TxUDY3/9budavUKEZnHJ6uOqJIhQX5eaqo/0Fef9Uy948Px0FDrxdnTc3biaHpetc9jSoDj2KNvGSyR03y98QSW7DujVqPI8LQMU+tFfrZcaQb4WNR+T8tOO/eKk4gc53hGPv6+YL+6/9To9qoOkp6mDGmFPi3DkVdUij9/sUUVr7ywsnV9N3SlS6f17eVs/cPSAeQUMtX1wk/W6qyPDm+D1lH2q6XUUPERgXhqTHs8NWcnfjrmiXtSctGhmeNWyRCRs0awd6j93q5ICMetveP0bpJK+H7z1m645h8r1Argfyzaj8dGtrN+zWJRic2pqam2fc4utZK22ZWVlakyAbIh7qXsnSevBQmUpG+lj6WvG4rBEjmcvGD/+s12NeXVs0UY7h6YAFdxe5/mWLArGcv2p+Ev3+7A3AcHqJEvIjKmbzadwMqDaWql7d9u7OIygUdsqL/aN+7+zzfj/eWHcVOPZrZVwNrG7VrARBe/h8hGuf7+/g36/5RASevjhmKwRA4n5f/l5OXn7alqnMhVlquQP7xXxnbE8JlLsft0Dj5dfQT3DNIvl4qIGu5MTiFe+nmPuv/I8DZq9NiVyBZMg9tGYum+M6qdsjJXOw/FxMSobUOKi4v1bqbLKS4uVtXCBw0adMlTafL8yxlR0jBYIofKyi/G679ay9c/PrKdS+7LJoUqr29Rhi8OWfD24oO4uWccwgN99G4WEV0iWd2ada5YbW9094CWcEXP/K4DVh5YjsV7U7FkXyqubtvE9jV5U7fHG7vZWCwWlJSUwM/P77Lyji4H5xvIod5ZelCdvNpENcKd/e23D5O99YksR7voIOQUlOCtRQf0bg4RXSIJPH7ecVqNXL96UxddF5DUJjGyESZdaT0Xvvjj7irJ3uS6XPPVRKZZkfLpqiR1f9o17V1q+u1C0rRpo6zLe/+79igOncnVu0lEdAmb5L76y151/49XxqNjbAhc2Z+GtkZEIx8cTsvDZ6ut50hybQyWyGH+vmCfqpzbP7Gxmqd3ddLOoe2aqP2cZsyznniJyPX9uP0U9ibnqJpKD17dCq4u2M9bpSUIGcmWXCtybQyWyCFk48i5W0+p+0+Obu8yK1LqMm20dQRMdidffShN7+YQUR1kGkurqXTfVYkIDTBGvuHNPZup4rw5hSV4/VdenLk6BkvkkGWer8yzrki5oXtTdGrq2kPilbVq0kiVExAv/7xHDe8Tkev6asMxHMvIR0QjX1sukBHI1ivTr+2o7n+96QR2ncrSu0lUCwZLZHeyLHbN4XRVr+jREdY8ICN5eFhrBPl6YdepbHy3hRvtErmq/KISvLX4oLr/0NBWCPAx1gJvqTv3uy4xkN043l5k/T3INTFYIrsqKS3DjF+so0qT+sejWVgAjKZxI188OKTV+bwrrlYhckmfrEpS+T5x4f64tbd1RNho/jy0NSRLYf6uZOxNzta7OVQDBktkVz9tP439KbkIDfDGAwZItKzJxP7xiAzyxemsAszl6BKRy8nML8KsZYfU/UeHtzVs5f02UUG4ppO1uvQ/K0bJyPUY89VFLpur9O5S68lr8sAEhPjrUzzMHvy8LZg80FrUTk7IpcxdInIp7y47pOqiSX2067rGwsimXN1afZQ6UQdTWbbEFTFYIrsWhduXkoNGvl6444oWMLrb+7ZQAZ/UQpm/M1nv5hBRhbN5Rfj36qPq/mMj26pkaSPrEBuM4R2iVO7SO0s4uuSKGCyR3WijSuP7Njf0qJJGgj6t6vi/lh5UI2dEpD8pHHuuuBQdYoIxpN357UKM7M9DrKNL3289iaS0PL2bQxdgsER2sTEpAxuSzsLH4ok/uuieTA0hSer+3ha1Mm7Z/jN6N4fI7RUUl+KzNdaq1/delWCYGm516dwsBFe3jYTM+MvFGbkWBktkF1qi5U09myIq2A9mERbog9v7WlfZ/Kti5IyI9CMLLtJyixAb4ofRnWNgJrINivhu80m1XRS5DgZLdNn2Jefgtz2pavmrJHabzd0DW8Lb4oH1RzLUCBoR6UOKxH6w4rC6LyPY3i66WW5D9WgehgGtItSWS5LATq7DXK800sV7FX/Usvw1IbIRzCYmxB839Wim7nN0iUg/i/em4tCZPFU09tbecTCjP1XUePt20wmVyE6ugcESXZYTZ/Px/bZTtn2ZzOreqxIhC27kZL37FAvHEenh/YpRpduvaI4gP+MvIqlOn5bh6BgbjMKSMny54bjezaEKDJbosny44oiqQXRlq8bo0iwUZtUyItCWH/Hp6iN6N4fI7Ww9nqmmwr08PTCpv3kWkVxIEta1Vbiy6k92RSD9MViiBsspKMb/Nh43/aiSRtuk8/utp1T1YCJyHi1X6bpusYgOMc8ikupIkc2wAG+czDyn8kFJfwyW6LJWpeQXlaJVk0YqKdHsJPmyfYx1ePybTSf0bg6R2ziWno9fdpxW9+8ZZL5FJNXtIDCuj3UV7merrWUSSF8MlqhBpEDjf9YetRWhNEutk9rI7zihn7UyufzusjKHiBzvP2uTVP2hga0j0C46GO5AdkGQPMk1h9PVimPSl+GCpXfeeQfx8fHw8/ND3759sX79+hqfO3jwYPUGd+FtzJgxtudMnDjxoq+PGjXKSb+NcUkBStkwVwo23lixUswdXN8tVq3EOZqejxUH0/RuDpFbFKHURnJlg2t30TTUHyM6WDfY1Ypwkn4MFSx99dVXmDp1KqZPn47Nmzeja9euGDlyJFJTq5/T/e6773D69GnbbefOnbBYLLjllluqPE+Co8rP++KLL5z0GxmXJB5qc+tm2NqkvgJ8vHBTT2tw+B+ewIgc7tddyTibX6yKUA5ua46tTepLS/Ses/kksvKL9W6OWzNUsDRz5kxMnjwZkyZNQocOHTBr1iwEBATg448/rvb54eHhiI6Ott0WLlyonn9hsOTr61vleWFhYU76jYwpLbcQv+y05g+YYcPcS/WHiqm4RXtTWWWXyME+X3dMfby1d3NYDL5h7qW6IiEc7aKD1D54X29iGQE9ecEgioqKsGnTJkybNs32mKenJ4YNG4Y1a9bU6xgfffQRxo0bh8DAwCqPL126FE2aNFFB0pAhQ/DSSy+hcePGNR6nsLBQ3TTZ2da6O8XFxepmL9qx7HlMe/hy3VEUl5ajS9NgtIsKcLn2Obqfm4f6on9COFYfzsB/1yThLyOsWxSQcV/TZmOWfpYClFIuQGKkG7pFu9zv44x+vqNvHJ7+frdK9L6jTzO3Cxgd3c/1PaZHuUG2Uj916hSaNm2K1atXo1+/frbHH3/8cSxbtgzr1q2r9fslt0lynOR5ffr0sT3+5ZdfqtGmli1b4tChQ3jyySfRqFEjFYDJlF11nnvuOTz//PMXPT579mx1LDOTJMsXt1iQUeiB2xNL0beJIV4+drct3QMf77cg0KscL/QshZehxmiJjGFOkieWnvZEp7AyTG7nnvWGikqB6ZssyC/1wD3tStExzD3PuY6Sn5+P22+/HVlZWQgODjb+yNLlklGlzp07VwmUhIw0aeTrXbp0QWJiohptGjp0aLXHktEtyZ2qPLIUFxeHESNG1NrZDYl4Zepw+PDh8PZ2jbygpfvPIGPtFoT4e2Ha+KFqiavRNaSfR5SW4eeZK5CSXYjyZt0wulusw9tpBq74mjYjM/RzYXEppr++XH4bPPS7nhjcJhLu2s+7vfbhk9VHccQjBo+N7gZ3U+zAftZmhupimGApIiJCjfSkpKRUeVw+lzyj2uTl5akRpBdeeKHOn5OQkKB+1sGDB2sMliTHSW4Xkv9ER/zBOOq4DfHlhpPq48094xAUYK7CcJfSz/K08X1bYObC/fh8wwnc3Nv9crfM8po2MyP38887U5F5rlitChvSPsalp58c3c/j+rRQwdKSfWeQXViGxo0ufv9xB94O6Of6Hs8wkwc+Pj7o2bMnFi1aZHusrKxMfV55Wq46X3/9tcoxuuOOO+r8OSdOnEB6ejpiYqxbW9B5ksy8eF+qrbaSuxvXJ05tvbDlWCYOpLAOCpE9zbYldse5dKDkDG2jg9C1WQhKysoxZ4v1gpWcyzDBkpCprw8++ACfffYZ9uzZg/vvv1+NGsnqODFhwoQqCeCVp+DGjh17UdJ2bm4uHnvsMaxduxZJSUkq8Lr++uvRqlUrVZKAqvp60wlIhpvsA5cQ2QjurkmQLGW2Tg18s5kVvYnsRS4+1idlqCBJgiUCbu5l7QepOWWQVGNTMVSwdOutt+KNN97As88+i27dumHr1q2YP38+oqKi1NePHTum6iRVtm/fPqxcuRJ33XXXRceTab3t27fjuuuuQ5s2bdRzZPRqxYoV1U6zuTOpVv1dRUDw+4o/WpLpyGa2Oijc8JLIPmavt44qDW3XBFHB5prubyipaefr5Ym9yTnYcTJL7+a4HcPkLGmmTJmibtWRpOwLtW3btsYo3N/fH7/++qvd22hGG5IycOLsOTTy9bJVlSXg6nZNEBrgjdScQqw8mOZ2RfOI7K2wpBTfbbZONd3G6X4bKf47smM0fth2Cl9vPIEuzUL1bpJbMdTIEunn24pRpdGdo+HvY/wVcPbi62XB9V2tK+G4uS7R5Vu8JxVZ54oRE+KHQa1dbwWcnrRR/e+3nlTbwJDzMFiiOp0rKsW8Hcnq/k1utA9cfcnKQLFgd4o6yRNRw31XkcB8fbembp/YfaH+iY3V6sDsghJ1viHnYbBEdVqwOxm5hSWIC/dH7/hwvZvjcjo1DUabqEYoKinDz9ur5swRUf2dzSvC0ooVtzf2aKp3c1yOp6eHbW/Krzdy+xNnYrBEdfq2In/ghu7N1B8rVeXh4WFL9P6G+zcRNdhPO06rrZQ6xsoFSJDezXFJt1ScayRH8mTmOb2b4zYYLFGtUrILsPLAGXX/Jl7p1Whst6Zq/6rNxzJx+Eyu3s0hMqQ5FbmRN3TnuaYmceEB6JfQWJVx+Y55kk7DYIlqJQXQZD+4Xi3C0KJx1Q2I6bwmwX64qmI7Bi0ZnojqLyktT11syEWHLJOnmt3Sq2IqjjWXnIbBEtVI/gi/rbhy0ebJqWZaH8my51KJMImo3rTK1ANaR6qLD6rZNZ1iEOBjwbGMfGw9nql3c9wCgyWq0c6T2TiQmgsfL0+M7sztX+oyrH0Ugv28cDqrAGsOpevdHCJDXZjN3WoNlm7kFFydpHzL8A7WYsxSd4kcj8ES1UibThrRIUoVRKPa+XlbcG3F9MF3WzgVR1Rfm4+dxdH0fDVaMqKjNQig2mlTlT9tP82RbCdgsETVkq07tCsWTsHVn9SGEQt3pbBoHNElTsGN6hiNAB/DbSyhi4GtI9VF7JmcQqw7zJFsR2OwRNVafSgdGXlFaBzog4GtIvRujmFIInx0sB9yCkuwfL91FSER1Uzqk8noiLiBK27rzZoeYd16ilNxjsdgiar103brH9+oTtHwsvBlUl9Sh2pMF2t+148sUElUpyX7UpGZX4yoYF/0T+SF2aXQpv1/2Zmsgk5yHL4L0kXkj+7XXdZS+tobP9Xf7yr6bNGeFLVVDBHVTBsVkRwcbm9yafq2bIwmQb5qmyWOZDsWgyW6yKqDaeqPLzLIV/0x0qXpFheKZmH+yC8qxeK91q0biOhicjEhG+dWHiWh+pPgUrug5VScYzFYoov8WDEFN7pTNK/0Grj9ye+6WE/8P/IERlTrFNy54lJ1cdG5aYjezTH0qriFu1OQX1Sid3NMi8ESVSEruGQll/gdr/Qa7NquMbY3g5yCYr2bQ+SStI2nZXRELjKoYSPZzcMDVND5W8UoHdkfgyWqYsWBNLWSS1Z09WwepndzDKtDTDASIgJRWFKG3/ZYg08iOk9GQbRp6jEsettgEmRqF2c/bOVItqMwWKJqV8FJxW5Z2UWXMRWnFY3bxlVxRBdasvcMp+Ds5Lqu1pILy/anIiufI9mOwGCJqkzB/bZbm4Ljld7lurYi8XL5gTM8gRFdYN4OTsHZS9voILSNCkJxaTnm7+LFmSMwWCKbJXtTkVdUiqah/ugeF6p3cwyvddT5E9ivu5L1bg6Ry+AUnP1pU3FSc4nsj8ES2fzEKz2HncC0FYZEdH4KLi6cU3D2MqpTjK30SzYXldgdgyU6f6VXsZJCK6pIl08rISDbx6TnFurdHCKXmoKT3EhemNlHqyaN1E1GsrVzOdkPgyVSFu2x1juRJai80rOf+IhAtTJOdgWXPiZyd3JhtmhvxQ4BnIKzK9mIWMznVJzdMViiKn9cvNKzP9lfTzBvicg6BVdQXMYpOAeea5buT2WBSjtjsERqFdzSfalV/tjIfkZWXO1JDavcQp7AyL1xCs5xOsYGq1IMEoxyrzj7YrBEKiFQVsFJIcouvNKzuzZRjdAyIhBFpWW2oJTIHXEKzrEk+Lym4oKXq+Lsi8ESYUHF9iYjOkaxEKWDTmDSt+LXir4mckfL96epUQ8WonQcbXZAkrwLS0r1bo5pMFhyc5J4rG3HoU0XkeMSL6WWFU9g5K4W7LaOdozoEM0pOAfpHheGJkG+atsqWYVL9sFgyc1tTMpAel4RQvy90adluN7NMa2uzUIRFeyrcpZWH+QJjNxPSWmZrRClNtJK9iezA9qF7/wdnIqzFwZLbk6bFhravgm8LXw5OPIEJlfTgst6yR1tSDqLzPxihAV4o1cLbtLtSFre0sI9KSpIpcvHd0c3Vl5+fhsO7Y2cHJ9LINOeMv1J5I5TcEPbR8GLF2YOJbMEEpRm5BVhfVKG3s0xBcO9Yt955x3Ex8fDz88Pffv2xfr162t87qeffqrmxSvf5PsuDBieffZZxMTEwN/fH8OGDcOBAwfgDnadysbJzHPw8/bEVW0i9W6OW5zAZLpTpj1l+pPIXch51raQpAOn4BxNgtHhFf38K0ey3S9Y+uqrrzB16lRMnz4dmzdvRteuXTFy5Eikpta8HDs4OBinT5+23Y4ePVrl66+99hreeustzJo1C+vWrUNgYKA6ZkFBAcxuQcWo0qDWkfD3sejdHNOTac5h7a0nsPksUEluZPfp8xdmA1vzwsyZI9lyrinjSLZ7BUszZ87E5MmTMWnSJHTo0EEFOAEBAfj4449r/B4ZTYqOjrbdoqKiqlztvPnmm3j66adx/fXXo0uXLvj3v/+NU6dOYe7cuTC7Bbu5Cs7ZRlYktspVtrz+iNyBNqrECzPnubJVBAJ9LEjJLsSOk1l6N8fwvGAQRUVF2LRpE6ZNm2Z7zNPTU02brVmzpsbvy83NRYsWLVBWVoYePXrglVdeQceOHdXXjhw5guTkZHUMTUhIiJrek2OOGzeu2mMWFhaqmyY7O1t9LC4uVjd70Y5lz2NqjmbkY29yDiyeHhjUKtwhP8MoHNnPF+rXMhT+3p7qKnvbsQxVcdedOLOv3Zmr9bM2ij20XYTLtMmM/XzhSMjA1hGYvysFC3adRofoQBhVsQP7ub7HNEywlJaWhtLS0iojQ0I+37t3b7Xf07ZtWzXqJCNGWVlZeOONN9C/f3/s2rULzZo1U4GSdowLj6l9rTozZszA888/f9HjCxYsUCNd9rZw4UK7H3PxKalxYkFiUClWL7X/8Y3IEf1cnTZBntiW4Yl//bAaY5q750oVZ/W1u3OFfk4vAPYke8ED5Sg9tg3zTm+D2bhCP1cnotB6np+7/hDaFO6H0S10QD/n5+ebK1hqiH79+qmbRgKl9u3b47333sOLL77Y4OPK6JbkTlUeWYqLi8OIESNUjpQ9I155cQwfPhze3t6wp88+kMT4TIwb0AGjr2gOd+bIfq725zU9jW3f7MDRkmCMHt0f7sTZfe2uXKmfP11zFNiyD73jw3HL9b1hJq7Uz9Xpl1+E2X9bipP5Huja/2o0DfWHERU7sJ+1mSHTBEsRERGwWCxISam6XYR8LrlI9SGd3L17dxw8eFB9rn2fHENWw1U+Zrdu3Wo8jq+vr7pVd3xH/MHY+7hncgqx5Ximun9Nl1iX/CPXg6P+/y40rEM0LJ47sS8lFym5xWgWZv/RSFfnrL52d67Qz4v2Wjd0HdkpRve2mLmfq9MkRGpahavyAcsPZmBCv3gYmbcD+rm+xzNMgrePjw969uyJRYsW2R6TPCT5vPLoUW1kGm/Hjh22wKhly5YqYKp8TIkyZVVcfY9pREv2pUJyi2VvppgQY15pGFlogA96VhTlW7SHG+uSeZ2VOj9HrGUyWDJAH8M6NFEfF1Ys6KGGMUywJGTq64MPPsBnn32GPXv24P7770deXp5aHScmTJhQJQH8hRdeUHlEhw8fVqUG7rjjDlU64O6777atlHv44Yfx0ksv4YcfflCBlBwjNjYWY8eOhVktqtgLbkg76x8ROd+w9ta+1/blIzKjRXtTIavW28cEIy7c/UZQXYEUARVrD6cjp8D1EtGNwjDTcOLWW2/FmTNnVBFJScCWqbL58+fbErSPHTumVshpzp49q0oNyHPDwsLUyNTq1atV2QHN448/rgKue+65B5mZmRgwYIA65oXFK81CNnFdcSBN3ddq/pA+J7BX5u3FusMZar+4Rr6G+lMkqpeFFVW7tQKJ5HyJkY2QEBGIw2l5WL4/DWO6nE85ofoz3Bl6ypQp6ladpUuXVvn8//7v/9StNjK6JCNQcnMHaw9nIL+oVO1K7W7L1l2JnLziGwcgKT0fKw+cwahOPIGReS/MOAWnr2EdovD+8sNqVoHBkhtMw9HlW7zn/Ma5srkr6UOCdG14/DfmLZEJSa4SL8xcw9CKlIvF+1K5sW4DMVhyI1IxWntjHtKOV3p6k4BVLNmbyo11yXS0xQuSGykXB6QfWVASGuCNzPxibDp6Vu/mGBKDJTeyPyVXVY729fLEgFYRejfH7UndmSA/L7Wx7taKUg5EZrkwW7SXC0lcaWPdIW2b2JLu6dIxWHIj2smrf2Jj7s/kIhvrDtZOYFwVRyZy6Ewujmecg4+Xp9qjjPRnm/ZnCYEGYbDkhsPi2h8NuU4JAdZbIjNZXDF6cUVCYwRypadLGNQmAt4WD7UqToJZujQMltxERl4RNh+zzlVzWNx1XNUmUm1mvC8lB8cz6rdHEZFhLsx4rnEZQX7eKngVHF26dAyW3IQkEUvV7g4xwYg16P5AZq/mrV2NExlZVn4xNlYkEfPCzLVotfV4rrl0DJbchPbHoa3AItfBat5kJssOnFGrO1s3acSq3S7m6oocSQlms1nN+5IwWHIDRSVlWLbfupkl85VcezsCqeZNZPRRbDGEF2Yup3njACRGBqpgdmVFwVCqHwZLbmBDknVLjYhGPujSNETv5lA12xG0jAhEcSlPYGRs8iYsG3WLoazl5tKjS1pQS/XDYMmNki3lj4RVu13T4LaR6uPSijcaIiPacuysKnwY4u+NHs1D9W4OVePqijyypfvPoIzFcOuNwZIb0N6AmWzpurR6S0v3nVEF/YiMnBspqzylECK5nl7xYQj0seBMTiF2n87WuzmGwVezyR1Nz1N1Nbw8PTCgNYvDuaq+LcPh5+2J5OwC7E3O0bs5RA3ChSSuz9fLYisUyqm4+mOwZHIyUiFkebrU2SDX5OdtQf/EiCr/Z0RGIlspSaAvM/0yskSuPxWn5ZdR3RgsuckUnPbHQa7r6oq8JZ7AyIi0UYoezWXTVh+9m0P1yJHccjxTFSymujFYMrGC4lKsOZxe5Y+DXD9vSXYFZw0UMhqtPAkvzFxfTIg/2kUHqULFyyv+36h2DJZMTOr2FBSXITrYD22jgvRuDtVBCvixBgoZtZbb6oPW1yyn4IxBW/DDkez6YbBkYlrui4wqeXiwZICxVsXxBEbGsTEpA3lFpYho5Ku2VCLXp40AyoigXKBR7RgsucGwuPYGTMYpGMcSAmQkUrNHG1ViLTdj6B4XimA/L1UXa+vxTL2b4/IYLJlUUloejlSUDLiylXWnaXJ9vVuGIcDHglTWQCEDWVYxin0VcyMNQ+pgDaqYMuVIdt0YLJmU9uKXAmQsGWCsGigsIUBGcirzHPalWEsGDGItN2NufcJgqU4Mlkw+LK79MZBxcOsTMuJ0f7e4UJYMMBhtJHDnyWykZhfo3RyXxmDJrCUDDmklAxgsGTVYkhICWfksIUAGmYJrw3ON0UhCfpdm1s3Vl3MFbq0YLJmQ1FYqLClDTIgf2kQ10rs5dImahQWgdZNGkAUqKw5yKo5cV3FpGVZVlAxgLTdj0ko9aCOEVD0GSya+0mPJABNsR7CXJzByXTL6mVNYgsaBPujc1DpCQcaiJXmvOMASArVhsGRCWq4Lp+CMa3DFCWz5AZYQINelLUKQN1yWDDBuCYGgihICO05m6d0cl8VgyYQlA5LS8+FtkZIBXJliVD3jw+DvbcGZnELsOZ2jd3OIqqVN3bBqt7FLCAyoeK/QZiXoYgyWTEZGIkTPFmFo5Ould3PoMkoI9Eu01sdiLgG5opTsAuw5nQ2Z6demcsiYtP8/7f2DLsZgyWS0TRF58jI+rWYNN7okV6SNQnRpForwQJYMMDLt/WLLMa7ArQmDJZNtZqmVDBjUmsGS0V1VkXO28WgG8gpL9G4OURWcgjOPpqH+aFWxAnfVIZYQqA6DJZOtTLFuZunDzSxNIL5xAOLC/VFcWo61h61BMJErKCktU6unBIMlk5UQYN6SOYKld955B/Hx8fDz80Pfvn2xfv36Gp/7wQcfYODAgQgLC1O3YcOGXfT8iRMnquX1lW+jRo2CEWnzzQNbc2WKGchrURsh5FQcuZLtJ7OQXVCiNmLtWlHUkMwxFScjhlyBa/Bg6auvvsLUqVMxffp0bN68GV27dsXIkSORmlr9thBLly7FbbfdhiVLlmDNmjWIi4vDiBEjcPLkySrPk+Do9OnTttsXX3wBY+crcRWcGU9gRK52rhnQOkKtpiLj69syHL5enkjOLsCB1Fy9m+NyDPUqnzlzJiZPnoxJkyahQ4cOmDVrFgICAvDxxx9X+/zPP/8cDzzwALp164Z27drhww8/RFlZGRYtWlTleb6+voiOjrbdZBTKaGSJ+a5T2baRJTKH/omN4eXpocpBHEvP17s5RFUvzHiuMQ0/bwuuSKhYgcupuIsYZm15UVERNm3ahGnTptke8/T0VFNrMmpUH/n5+SguLkZ4ePhFI1BNmjRRQdKQIUPw0ksvoXFj64umOoWFheqmyc62BilybLnZi3as+hxz2d5k9bFDTBBCfD3t2g6zu5R+djY/i2xQGoKNRzOxeG8yxveJg5G5cl+biSP7OftcMbYez1T3+7UMdev/S7O9nq9MDFej2FLYeGK/OLfo5+J6HtMwwVJaWhpKS0sRFRVV5XH5fO/evfU6xl//+lfExsaqAKvyFNyNN96Ili1b4tChQ3jyySdxzTXXqADMYrFUe5wZM2bg+eefv+jxBQsWqJEue1u4cGGdz/nygAwSeiLWIwvz5s2zexvcQX36WQ9RZZJ/ZsG3K3chLG0HzMBV+9psHNHPW9M9UFZuQZR/ObauXoKtdv8JxmOa1/M5+ccL6w6nYe6P8+BT/VugqfpZBlFMFSxdrr/97W/48ssv1SiSJIdrxo0bZ7vfuXNndOnSBYmJiep5Q4cOrfZYMroluVOVR5a0fKjg4GC7Rrzy4hg+fDi8vb1rfF5ZWTle2L5Mxt8wcVQfNfdM9u9nvcSdzMLPs9bhcL43ho+8Gt4GzhFx9b42C0f28+rvdwM4gZFdW2D06HZwZ2Z7PUti96dHVuBUVgHC2vZ2mZWOxQ7sZ21myDTBUkREhBrpSUlJqfK4fC55RrV54403VLD022+/qWCoNgkJCepnHTx4sMZgSXKc5HYh+U90xB9MXcfdeTIL6XlFCPCxoE9CJLy9jPtmqidH/f9drm7NG6uifxl5RdhxKhd9K/IKjMxV+9ps7N3P8ma68qC1jMXV7aL4f2jC17PUd/ti/TGsPHQWwzrGwuz97F3P4xnmXdXHxwc9e/askpytJWv369evxu977bXX8OKLL2L+/Pno1atXnT/nxIkTSE9PR0xMDIxixQFrEbF+CY3hw0DJdKQMxMCKat5cFUd6OpKWh5OZ5+Bj8UTfBI5gm9FVFauptTpaZGWod1aZ+pLaSZ999hn27NmD+++/H3l5eWp1nJgwYUKVBPBXX30VzzzzjFotJ7WZkpOT1S0317osUj4+9thjWLt2LZKSklTgdf3116NVq1aqJIFRcIsT87PVW+IJjFzgXNMrPgwBPoaZmKBL0C8xAlKm79AZa2BMBgyWbr31VjWl9uyzz6pyAFu3blUjRlrS97Fjx1SdJM27776rVtHdfPPNaqRIu8kxhEzrbd++Hddddx3atGmDu+66S41erVixotppNlck22DIdhiCwZJ5Day42tt5MhtpuedXYhI50/KKUWyea8wrxN8b3eJC1f2VvDizMdylwZQpU9StOpKUXZmMFtXG398fv/76K4xMtsGQ7TBkWwzZHoPMqUmQH9rHBKtd3lceSMPY7k31bhK5mcKSUu496SakVt/mY5kqOL61d3O9m+MSDDWyRLUXh5PtMci8BlXkLWk5akTO3nvyXLHsPemLdtFBejeHHEjbBWLVwTSUyu66xGDJ6LQ3Tg6Lm59WmV0SL7l3E+l2rmkdwb0nTa5rs1AE+XohM79YrbYmBkuGduJsPg6n5cHi6YF+icZfTk61k6Ra2bspNaeQezeRbqPYWv4cmZfs96e9p3BVnBWDJQOT3BUhyXjBfuao8UG1793Up6LgqPbGReQM3HvS/QysmK3gtL8VgyUD017EWg0eMj8tsZYnMHImyV0RHWKCVc4SuU+O5OZjZ5FbWAJ3x2DJoCTpbtUhBkvuRpsCWXckXa1OInLqhRmn4NxGi8aBaB4eoFZbrztsXQXpzhgsGZQk3UnynSThSTIeuYe2UUGIDPJFQXEZNiWd1bs55AZkMYGWt8KSAe5FuxBfwZFsBktGtbJiWFyS8CQZj9yDlIcY2KriBFbxGiByJFlMIIsKZHFBzxZhejeHnEjLT1vOJG8GS8ZfmcIrPXczwHa1xxMYOZ42qiCLC2SRAbkPuRiXKhGHz+Sp1dfujMGSAckWJ5J0VzkJj9zHgFbntz5J59Yn5GCcgnNfVbc+SYM7Y7BkQJLcq21xIkl45F6aBPvZKiivqth+gsgRZBHBusMZVUY0yU2L4R5ksEQGs3y/tgqOV3pw98RL1lsiB9p8NJNbnLg5bn1ixWDJwMndWqIvufPWJ2nc+oQcPgUnwTn3nnRPlbc+2eHGW58wWDKY01nncDA1VyXd9U9ksOSuJNnWx8sTydkFOHSGW5+QYy/MtDw5cu+tT1a58VQcgyWDrkzp0iwUIQHc4sSttz6J17Y+cd8TGDnO2bwi20gC85Xc20CuwGWwZOSdv8m9aScw7eqfyJ5khwCZ4ZVCqFHBfno3h3Q0oGLaf9PRs8gvcs+tTxgsGUiZbHGiDYszudvtaVf7aw6lo6ikTO/mkMloS8U5qkTxjQPQNNTfuvXJEevqSHfDYMlAdp/ORkZeEQJ9LOjenFucuLv20cFoHOijVitpdbeI7LfFCYMlqrRzQOsIt663xGDJQLSTlyTbeXOLE7fn6emBKysSb9058ZLs70haHk5mnoOPxRN9W1pz48i9DWCwREax8qA1uY4rU+jirU/c8wRGjqHlwclecAE+Xno3h1zAlYlSPgLYl5KD1OwCuBsGSwZRUFyKDRW7zDNfiTTa0Pj2E5nIyi/WuzlkEtoKS07BkSYs0AedYkPcdlEJgyWD2JCUoZJ4o4P9kBjJLU7IKibEX70epLDumsPudwIj+ysuLcPaw+lVgnEid5+KY7BkwJUprKRLlWnTspyKI3uQUcrcwhKEBnijY8VIAlHlc42MLLnbzgEMlgxCeyPklR5dSJuWdcehcXLcuUZyVCyyVQBRBclh8/XyRGpOIfanuNfOAQyWDCA9t1CVDRDc4oQudEVCuHpTO5qej+MZ+Xo3hwyO9ZWo1p0DWoa7ZTVvBksGsPqwtQiY7PodGeSrd3PIxQT5eaN7nLXuFkeX6HLkFBRjy/FMdZ+rbqk6A9105wAGSwaw+pA1WOIUHNXEnRMvyX7WHc5AaVk5WjQOQFx4gN7NIRc0oFWk7bXiTjsHMFhycZJDt+qQdWUKSwZQTbRAWvbzkjc7oobQRgs4qkQ1aRcdhIhG7rdzAIMlF5daAJzOKlCVdLVd5oku1KVZKBr5eiEzvxi7Tll3iie6VFoeCkexqT47B6x0o5FsBksubl+mdTVKr/gw+PtY9G4OuSjZ/uaKhMbqPksIUEOczjqHQ2fyIAvg+iUwWKJ6lCs56D7nmnoHS6dOnXJsS6ha+7OswZIWyRPVORXnRicwsh9tlKBzs1CEBHjr3RxyYQMrUkJ2uNHOAfUOljp27IjZs2dDb++88w7i4+Ph5+eHvn37Yv369bU+/+uvv0a7du3U8zt37ox58+ZV+boU1nr22WcRExMDf39/DBs2DAcOHIArKCktw4Fsa7DEYXGqb5L3xqSzOFdUqndzyKD5SgN5YUZ1iA7xQ6smjdTOAasPucfFWb2DpZdffhn33nsvbrnlFmRkWFdnOdtXX32FqVOnYvr06di8eTO6du2KkSNHIjU1tdrnr169GrfddhvuuusubNmyBWPHjlW3nTt32p7z2muv4a233sKsWbOwbt06BAYGqmMWFOi/UeD2k9koKPVAqD8r6VLdEiICERvih6LSMqxP0udvlIyprKzcNiLJ+kp0qdW83UG9g6UHHngA27dvR3p6Ojp06IAff/wRzjZz5kxMnjwZkyZNUm2QACcgIAAff/xxtc//xz/+gVGjRuGxxx5D+/bt8eKLL6JHjx745z//aRtVevPNN/H000/j+uuvR5cuXfDvf/9bTTnOnTsXelt10LoKrl9F0UGi2sg2OOdLCLhXwTi6PLKTfFpuEfy9LejRPEzv5pABDHCzYMnrUp7csmVLLF68WAUbN954owpAvLyqHkJGfByhqKgImzZtwrRp02yPeXp6qmmzNWvWVPs98riMRFUmo0ZaIHTkyBEkJyerY2hCQkLU9J5877hx46o9bmFhobppsrOt1bWLi4vVzV60F+EVLUPtelyqSutbM/TxFS3D8L+NJ7Bi/xkUj3C938dMfe3KLrWfl+1LUR/7xIfBo7wUxcWcxq0Pd34992weDK+KnQMOp2YhLizAkP1c32NeUrAkjh49iu+++w5hYWFqNObCYMlR0tLSUFpaiqioqCqPy+d79+6t9nskEKru+fK49nXtsZqeU50ZM2bg+eefv+jxBQsWqJEueygtB06ny+o3D5Se3IV583bZ5bhUs4ULF8LorLmWXtibkosv585DsA9ckhn62gjq289zd8skgyfCilIuyuukurnr67l5oAWHczzw3txl6B9Vbsh+zs+v3xZRlxTpfPDBB3j00UfVSMyuXbsQGemeRRJldKvyiJWMLMXFxWHEiBEIDg62288ZNaIY3/y8EDePGQ5vb65OcRS5spA/wuHDzdHPn59Ygz3JOQho2R2ju8bAlZitr13VpfRzYXEp/rpxiWQuYfK1A9AmKshp7TQ6d389H/I7hLeWHEK2fyxGj+5qyH7WZobsFixJ7o+sPJMpuAkTJsDZIiIiYLFYkJJiHS7WyOfR0dHVfo88XtvztY/ymKyGq/ycbt261dgWX19fdbuQ/Cfa+z8yxMcxx6WLmaWfB7WJVMHSmiNncVOv5nBFZulrV1efft5wLAsFxWVq38kOTcNU7htdGnd9PV/VrokKltYcyYCnxcvhubWO6Of6Hq/eCd4yBSYJ3noESsLHxwc9e/bEokWLbI+VlZWpz/v161ft98jjlZ8vJDrVni85WBIwVX6ORJmyKq6mYxK5usrVdWURA1F96itJwi4DJboUXZuFIshNdg6od7AkQUazZs2gJ5n6kqnAzz77DHv27MH999+PvLw8tTpOSCBXOQH8oYcewvz58/H3v/9d5TU999xz2LhxI6ZMmaK+LieGhx9+GC+99BJ++OEH7NixQx0jNjZWlRggMqI+LcPh4+WJ5OwCVZGZqDbcD44aykt2Dkhs7Bar4pyTnW0nt956K86cOaOKSEoCtkyVSTCkJWgfO3ZMrZDT9O/fXxXSlNIATz75JFq3bq1WwnXq1Mn2nMcff1wFXPfccw8yMzMxYMAAdUwpYklkRH7eFvSOD1OlJ6SEgBSPI6rO2bwi7DhpHRFgfSVqiAGtIrBwd4oaoXxgcCuYlaGCJSGjQtrI0IWWLl160WNSRFNuNZHRpRdeeEHdiMxiQKtIa7B0MA0Tr2ypd3PIRa0+lA6ZqW0T1QhRwbxApMvfOcDfpHuYciNdIhPStsdZezgDxaVlejeHXNTKg2dswTVRQyS4yc4BDJaITKhDTDDCAryRW1iCbccz9W4Oufp+cJyCowby8PCwLSox8ybeDJaITMjT0wP9K05gKypWOxFVdjQ9D8czzsHb4qEWBRBd7lTcChOfaxgsEZmUtnu82VepUMNob2zdm4ch0Ndw6avkQq6sONfsOZ2NMznntwIzEwZLRCa/2tt6PBPZBe63dxXVr76SFlQTNVREI1819S9WHzLnxRmDJSKTahYWgPjGASgtK8e6w+ZNvKRLJ68J7U2NJQPIHgaYfCqOwRKRG5zApN4SkUZqK2UXlCDYzwtdmoXq3RwygYGtzb1zAIMlIhPTloSvYN4SVaIFz/0TIxy+nxe5h97xlXcOyIXZMFgiMrF+iY0h74WHz+ThVOY5vZtDLkKbKuEUHNlz54A+8eGmnYpjsERkYiH+3ugaF1oloZfcW15hCTYfO6vus74S2dMAE+ctMVgiMjlttdNy5i0RgPVHpKp7OZqF+aN5eIDezSETGVBxrll7OB1FJebaOYDBEpHJDWwTaauuW1ZmvsRLujTaVb+MKkn1ZSJ76RATjMaBPsgvKsWWitFLs2CwRGRy3eJC0cjXC2fzi7HrVLbezSGdaVtScD84csTOAVeatBgugyUik/O2eOKKhMbq/oqKjVPJPaVmF2BfSg5kQKl/ovU1QeSIvKXlJstbYrBE5Aa0RN4V+811AqOGTcF1aRqCsEAfvZtDJj7X7DiRiax88+wcwGCJyI2u9jYdPYtzRaV6N4d0sqIiyX9ga07BkWPEhPgjMTIQkh5ppq1PGCwRuYGEiEA0DfVHUWkZ1h1J17s5pANJ7tfySFhfiRxpYGvzFcNlsETkBmTVk7as14w1UKhue5NzkJZbhAAfC3o0D9O7OeQO0/4HzJMjyWCJyE0MbHN+7yZyP9obV7+ExmpbCiJH6ZvQGF6eHjiecQ5H0/NgBvyLIXITVyZKXR2o1VAp2QV6N4d0rK9E5EiNfL1so5dmGclmsETkJmT1U6fYEHWfo0vupaC4FOuTMtT9AUzuJicYUBGUm+Vcw2CJyI1oowpmKxhHdW9xIttPxIb4qZVKRM4616w6lIaSUuNvfcJgichNN7osL+fWJ+5YMoBbnJAzdGkWimA/L+QUlGDbiSwYHYMlIjfSs0UY/L0tSMstVKujyD1oeSMsGUDOYvH0qHRxZvxVcQyWiNyIr5cFfRPCTXMCo/ptcSKBsQwoaft2ETnDoIr8uOX7jX+uYbBE5K4F40ySeEm10/6fOzcNQTi3OCEnGtjGeq7ZejwTWeeMvfUJgyUiNzOoYmh83ZEMbn3iBmxVuzmqRE7WNPT81idrDL71CYMlIjfTqkkjxIT4qdVR3PrE/FucnK+vxJIB5HwDK153ywy+iTeDJSI3I6uhtFwCTsWZ276UXJXMr7Y4aRGqd3PIDV3V5nzekpFX4DJYInJDgyqdwMi8VlZMfVyR0Fgl9xM5W9+EcPhYPHEy8xyOpBl36xMGS0Ru6MpWjeHpARxIzcWpzHN6N4ccZOUB6zQr85VILwE+XugVb/ytTwwTLGVkZGD8+PEIDg5GaGgo7rrrLuTm5tb6/D/96U9o27Yt/P390bx5c/z5z39GVlbWRVMSF96+/PJLJ/xGRPoJDfBRReMESwiYU2EpsOHoWXX/qrbMVyJXWIF7BkZlmGBJAqVdu3Zh4cKF+Omnn7B8+XLcc889NT7/1KlT6vbGG29g586d+PTTTzF//nwVZF3ok08+wenTp223sWPHOvi3IXKhqTgDX+1RzQ5me6C4tFytSEqI4BYnpP/WJ2sOpauFJUbkBQPYs2ePCnQ2bNiAXr16qcfefvttjB49WgVDsbGxF31Pp06d8O2339o+T0xMxMsvv4w77rgDJSUl8PI6/6vLSFV0dLSTfhsi13BVmwi8teiA2uiytKxcVdwl89ib6WELirnFCempQ0wwIhr5IC23CJuPnVU5dEZjiGBpzZo1KqDRAiUxbNgweHp6Yt26dbjhhhvqdRyZgpNpvMqBknjwwQdx9913IyEhAffddx8mTZpU68mlsLBQ3TTZ2dnqY3FxsbrZi3Ysex6TLuau/dwhKhBBfl6qWNzmpDR0i3P8ail37Wtnk/7VgqUBiWHsbwfh67n++ic0xg/bT2Pp3hT0jAt2mX6u7zENESwlJyejSZMmVR6TgCc8PFx9rT7S0tLw4osvXjR198ILL2DIkCEICAjAggUL8MADD6hcKMlvqsmMGTPw/PPPX/S4fL8cx95k6pEczx37OSHAE9sKPPHxvDUYFee8Zb3u2NfOlF4ApBZ4wRPlyDm4CfOS9G6RufH1XLfgfAneLfh502G0Lz4AV+nn/Px81w+WnnjiCbz66qt1TsFdLhn5GTNmDDp06IDnnnuuyteeeeYZ2/3u3bsjLy8Pr7/+eq3B0rRp0zB16tQqx4+Li8OIESPUyJU9I155cQwfPhze3t52Oy5V5c79nB15Att+2I1Uz8YYPbqPw3+eO/e1M/1nTRKwZb8aLbzpur56N8e0+Hquv945hfjva8twIt8Dfa8ahsaXsPWOI/tZmxly6WDp0UcfxcSJE2t9jkyNST5Rampqlccl70hWvNWVa5STk4NRo0YhKCgIc+bMqbOj+/btq0agZJrN19e32ufI49V9TY7tiD8YRx2XqnLHfr66fRTww25sPZGFc6VAsJ9zfn937GtnWnMk01YQkP3seHw91y023BvtY4Kx53Q21iVl4vpuTeEK/Vzf4+kaLEVGRqpbXfr164fMzExs2rQJPXv2VI8tXrwYZWVlKripLWIcOXKkCmx++OEH+Pn51fmztm7dirCwsBoDJSIzaRYWgITIQBw+k4fVB9MwqlOM3k2iy1RcWobVh631lQa2Nl4iLZnXoDYRKlhavj+tQcGSngxROqB9+/ZqdGjy5MlYv349Vq1ahSlTpmDcuHG2lXAnT55Eu3bt1Ne1QEmmxWRa7aOPPlKfS36T3EpLrZuH/vjjj/jwww9VaYGDBw/i3XffxSuvvKLqMxG5i0Em2buJrDYfPYu8wlIEepWjY4z90gKI7LX1ybL9Z9S+hUZiiARv8fnnn6sAaejQoWoV3E033YS33nqrypzmvn37bMlamzdvVivlRKtWraoc68iRI4iPj1fDb++88w4eeeQRtWeNPG/mzJkqKCNypxPYp6uTbHs3cZm5sS2vKPzXLrQcniwHQS6kV4twBPpY1H6Fu09no1PTEL2bZL5gSVa+zZ49u8avS/BTeZO+wYMH17lpn4xWyY3InVXeu+lwWh4SIxvp3SS6DHLVrgVLRK7Ex8sT/VtFYOHuFPU6NVKwZIhpOCJy7N5NfVqGq/tL9xl3OwKCumLfedK6uqddCIMlcuGpuH3GOtcwWCIiDK7YO2zpvqqrTslYtL232kcHIbj+K7OJnB4sbTp2VhXENQoGS0RkC5bWHcnAuSLrAggyHlllJLgKjlxVXHgAEiMD1RZLsgLXKBgsEZHKU5INV2WTyzWHjXMCo/NkdZE2sjSwlXXjUiJXdFWbJoab9mewRERqBdz5qTjjnMDovF2nstVGpQE+FvRo7vh9/ogaSjvXSJJ3XQuxXAWDJSJSBrc9f7VnlBMYnaflm13ZKkKtOiJyVX1ahsPP2xPJ2QXYl5IDI+BfFBEp/RIbw9vigWMZ+TiSlqd3c+gSLakIlq6uCHqJXJWftwX9EhobalUcgyUiUhr5eqF3PEsIGFFGXhG2HM+sMsVBZIRVcUsNcq5hsERENra8pYrChmQMktgtM6ftooMQG+qvd3OI6nRVxQjoxqMZyC0sgatjsEREF+UtrTucjoJilhAwCu3qXPv/I3J1LSMC0aJxAIpLjVFCgMESEdm0btIIsSF+KFQlBKw715Nrk3o12hYnnIIjo26s6+oYLBFRlRIC2vC4URIv3d32E5kqZynI1ws9W4Tp3RyiBuUtufoKXAZLRFQFtz4xliUVQe3ANhHwtvCUTsYhK3ClzIVs4n0wNReujH9ZRFSF1OmREgJJ6flIYgkBl6cFtcxXIiNu4n1FRQmBxXtd++KMwRIRXVRCoFcLrYSAa5/A3N2ZnEJsP5Gl7g+umNIgMpIhFSPZixgsEZFRp+IWM2/JpS2vSIztGBuMJsF+ejeH6JINaRelPm46ehZZ+cVwVQyWiOgiQ9tbp3TWHkpHngFqoLgrVu0mo2veOACtmjRSqzqXV2wE7YoYLBHRRRIjG6F5eACKSsuwygA1UNxRSWmZbWTp6nacgiPjGtquicvnLTFYIqJqSwgMMcAJzJ3J9ibZBSUIDfBGtziWDCDjurriXCM5kjLC5IoYLBFRrVNxEiyVuegJzJ1pyfeDWkfC4umhd3OIGkzqgwX5eeFsfjG2Hj8LV8RgiYiq1adlOAJ9LEjNKcSuU9l6N4cusGiPVjKAU3BkbN4WT1uBSlcdyWawRETV8vWyYGBrbVlvit7NoUpOnM3H3uQcyIASk7vJDIbYpv1dM8mbwRIR1WhIpak4cr1RJamHFRboo3dziC6bFFX18AD2nM7GqcxzcDUMloioRtqohRQ+TMku0Ls5VOG3PSlV8sqIjC480Afd40KrlMRwJQyWiKhGkUG+6KqdwDi65BJyCoqx9nC6uj+0vbWgH5GZpuKWuOC5hsESEdWrBoqrb0fgLlYcSENxaTlaRgQiMTJQ7+YQ2b2a98qDaSgoLoUrYbBERPW62lt5wPVOYG49BddOcjxYMoDMo31MEGJC/FBQXIY1FaOnroLBEhHVSvYdiw72w7niUtv0D+lDCvZpUxScgiOz8fDwsBWoXFyxiMFVMFgiorqreXNVnEvYfOysKtwX7OeFXvGs2k3mM6ziXLNwdwrKy12nGC6DJSKqf97SnlSXOoG56xScXH1LIT8is+mfGIEAHwuSswuw42QWXAX/2oioXicwXy9PnMw8h30pOXo3B+5eX4lTcGRWft4WWzVvGV1yFYYJljIyMjB+/HgEBwcjNDQUd911F3Jzc2v9nsGDB6sphMq3++67r8pzjh07hjFjxiAgIABNmjTBY489hpKSEgf/NkTG4u8j1bwj1P0Fu1znBOZOktLycDA1F16eHrY3EyIzGt7BejHAYKkBJFDatWsXFi5ciJ9++gnLly/HPffcU+f3TZ48GadPn7bdXnvtNdvXSktLVaBUVFSE1atX47PPPsOnn36KZ5991sG/DZHxjOgQrT4u2J2sd1Pcegqud3w4Qvy99W4OkUNX4Mrm0LKlz7H0fLgCQwRLe/bswfz58/Hhhx+ib9++GDBgAN5++218+eWXOHXqVK3fKyNG0dHRtpuMTGkWLFiA3bt347///S+6deuGa665Bi+++CLeeecdFUAR0XlSLVr2Itt5MlvtTUb6TMENq7jqJjKr0AAf9IkPd6mLMy8YwJo1a9TUW69evWyPDRs2DJ6enli3bh1uuOGGGr/3888/V8GQBErXXnstnnnmGRVAacft3LkzoqLOn3xGjhyJ+++/X41ide/evdpjFhYWqpsmO9u6I3txcbG62Yt2LHseky7Gfq6fYF9P9GwRhg1JZzF/xync2a/FJR+Dfd0wWeeKsT4pQ92/qnV4nf3HfnYO9rPjDGkXoWot/borGbf3jHZYP9f3mIYIlpKTk1U+UWVeXl4IDw9XX6vJ7bffjhYtWiA2Nhbbt2/HX//6V+zbtw/fffed7biVAyWhfV7bcWfMmIHnn3/+osdlpEoLxOxJph7J8djPdWta7oENsOCrlXsQeXZXg4/Dvr40G894oLTMgij/cuxauxT17Xn2s3Own+3Porai9MLGpAzMmbcQjbwd08/5+fmuHyw98cQTePXVV+ucgmuoyjlNMoIUExODoUOH4tChQ0hMTGzwcadNm4apU6dWGVmKi4vDiBEjqkzz2SPilRfH8OHD4e3NHAVHYT/XX+ez+Zg7cyUO51rQb/AQhAVc2o737OuG+fmLrQBScWOfBIwe1rrO57OfnYP97Fj/O71G5S15NO0EpO50SD9rM0MuHSw9+uijmDhxYq3PSUhIUFNoqalVi+HJijVZISdfqy/JdxIHDx5UwZJ87/r166s8JyXFmkRZ23F9fX3V7ULyn+iIPxhHHZeqYj/XLaFJCNrHBGPP6WwsP3gWN/ds1qDjsK/r71xRKZYfSFP3x3Rpekn9xn52DvazY4zsGK2CpSX7M/C7UMf0c32Pp2uCd2RkJNq1a1frzcfHB/369UNmZiY2bdpk+97FixejrKzMFgDVx9atcnUGNcIk5Lg7duyoEojJVYKMDnXo0MGuvyuRWYyoSDCWXAJyvGX7z6i9spqG+qutZ4jcrYTAyoNpKNJ5W0pDrIZr3749Ro0apcoAyEjQqlWrMGXKFIwbN07lI4mTJ0+q4EobKZKpNlnZJgFWUlISfvjhB0yYMAGDBg1Cly5d1HNk2kyCoj/84Q/Ytm0bfv31Vzz99NN48MEHqx05IiLr1Z5YceCMGvUgx9KC0lGdorlxLrmVjrHB6iLhXHEZ9mfp+9o3RLCkrWqTYEhyjkaPHq3KB7z//vtV5o4leVtL1pIRqd9++00FRPJ9MuV300034ccff7R9j8ViUTWb5KOMMt1xxx0qoHrhhRd0+R2JjLIzeLMwfzXasfzAGb2bY2pFJWVYVFFfSYIlInfi4eFhG13anqFvsGSI1XBCVr7Nnj27xq/Hx8dX2bNKEq6XLVtW53Fltdy8efPs1k4idziByejSRyuPqFEPbaSJ7G/t4XRkF5QgopEvejTnxrnkfoZ3iMKnq5Ow66ysCC2HXplhhhlZIiLXy1uSQoklpWV6N8e05ldMwY3oGKUqGhO5mz4twxHs54XcEg9sOZ6pWzsYLBHRJesVH47wQB9rscQj1mKJZF9yFa3twzeKo3fkprwtnnh4aCv8oVUp2kYF6dYOBktEdMlklGNYe2uhWK6Kc4zNx84iLbdQXVVfkdBY7+YQ6eYPVzRHr8hyBPnplznEYImIGkTLVfp1VwrKys7nC5J9zN9pDUKHtY+CjxdP1UR64l8gETXIgNYRCPL1QnJ2gRoFIfuRxSpasDSSq+CIdMdgiYgaxNfLguEdrYneP20/rXdzTGXXqWyczDwHf28LBrWO1Ls5RG6PwRIRNdjvulir4c/bcZpTcXakjSoNbhsJfx+L3s0hcnsMloiowQa0ilRJl6k5hdiQxFVx9pqCm7fTOlLHGlZEroHBEhE1mCQea2/oP+/gVJw97D6djcNn8uDr5YmhFSsOiUhfDJaI6LKMsU3FJavaQHR5ftxmDTqHtGuCID/uZE/kChgsEdFluTIxAiH+3qomEAtUXv4U3I/bTqn713a1bhJORPpjsEREdpiKs66K+3mH9Y2eGka2c5BVcIE+FlzdllNwRK6CwRIRXbYxXWJtq7i4V1zDaaNKsnkoV8ERuQ4GS0R02fonNkZYgEzFFXEqroEk30urV8UpOCLXwmCJiOyy2eWoikrTP3FVXIOsO5KOMzmFKv9rIAtRErkUBktEZBdjOnMqzh6r4K7pFM294IhcDP8iicgurkgIR3igDzLyirD6ULrezTGU4tIy/FJRiJJTcESuh8ESEdmFl8UToztbp+Lmbjmpd3MMZeXBNGTmFyOikS+uSGisd3OI6AIMlojIbm7s0Ux9/GVnMvIKS/RujmH8uNW6Cm5M52hYPD30bg4RXYDBEhHZTfe4ULSMCMS54lLbZrBUu4LiUizYnaLucwqOyDUxWCIiu/Hw8MAN3Zuq+3M4FVcvi/emIrewBLEhfujRPEzv5hBRNRgsEZFdacHSqkNpOJ11Tu/muLxvNp1QH6/r1hSenIIjckkMlojIruLCA9AnPhzl5ZLoze1PapOaXYBl+8+o+7f0suZ7EZHrYbBERHZ3Yw/r6NJ3m0+ozWGpenO3nlSVu3s0D0ViZCO9m0NENWCwRER2d03nGFVY8UBqLnadyta7OS5JgsivN1qn4G7uGad3c4ioFgyWiMjuZMsO2QxWfLeZid7V2X4iSwWTvl6e+F3XGL2bQ0S1YLBERA5xU8VU3A/bTqoK1VTV15uOq4+yp16wn7fezSGiWjBYIiKHkM1gGwf6IC23CCsOWJOY6XxtpR8qClHe3JOJ3USujsESETmEt8UT13WzFln8llNxVSzcnYLsAmttpf6JEXo3h4jqwGCJiBzmportTxbuSlEb7FLV2kqyPQy3NyFyfQyWiMhhOjUNQeemISgqLcM3FTk67i45q8A2LckpOCJjMEywlJGRgfHjxyM4OBihoaG46667kJubW+Pzk5KS1NYL1d2+/vpr2/Oq+/qXX37ppN+KyPzG922uPs5edwxlZay59N2WE5Bu6B0fhviIQL2bQ0RmCpYkUNq1axcWLlyIn376CcuXL8c999xT4/Pj4uJw+vTpKrfnn38ejRo1wjXXXFPluZ988kmV540dO9YJvxGRe5DNYYN8vZCUno81RzLgziRY/KaittItrK1EZBheMIA9e/Zg/vz52LBhA3r16qUee/vttzF69Gi88cYbiI29eKdui8WC6OjoKo/NmTMHv//971XAVJmMVF34XCKyj0BfL9zQoyn+veYovlh/HKND4LZkv7zDaXlo5OuF0V1YW4nIKAwRLK1Zs0YFNFqgJIYNGwZPT0+sW7cON9xwQ53H2LRpE7Zu3Yp33nnnoq89+OCDuPvuu5GQkID77rsPkyZNUtNxNSksLFQ3TXa2tUJxcXGxutmLdix7HpMuxn52vN/3iFXB0m97zuDKHu7b15+uOqI+3tAtBr6e5Q7rB76mnYP9bPx+ru8xDREsJScno0mTJlUe8/LyQnh4uPpafXz00Udo3749+vfvX+XxF154AUOGDEFAQAAWLFiABx54QOVC/fnPf67xWDNmzFBTeheS75fj2JtMPZLjsZ8dq2WQBUdygLWpHghxw75OLwAW77VIpiTiCo9g3jxr4ORIfE07B/vZuP2cn5/v+sHSE088gVdffbXOKbjLde7cOcyePRvPPPPMRV+r/Fj37t2Rl5eH119/vdZgadq0aZg6dWqVkSXJkRoxYoRKQLdnxCsvjuHDh8PbmxV+HYX97BxFsafw2Lc7sTrFE6/eeTX8fH3gTl5fsB/lSEL/xHBMuun8KLkj8DXtHOxn4/ezNjPk0sHSo48+iokTJ9b6HJkak3yi1NTUKo+XlJSoFXL1yTX65ptvVPQ4YcKEOp/bt29fvPjii2qazdfXt9rnyOPVfU3+Ex3xB+Oo41JV7GfHurZbM7w8bx8yzxVjTVIWRnS6ONfQzBW7v95kLcw5sX9Lp73O+Jp2Dvazcfu5vsfTNViKjIxUt7r069cPmZmZKu+oZ8+e6rHFixejrKxMBTf1mYK77rrr6vWzJK8pLCysxkCJiBrGz9uCG7vH4uPVR/HFhuNuFSz9uO0UzuYXo2moP4a2t24wTETGYYjSAZJrNGrUKEyePBnr16/HqlWrMGXKFIwbN862Eu7kyZNo166d+nplBw8eVGUGJIH7Qj/++CM+/PBD7Ny5Uz3v3XffxSuvvII//elPTvvdiNzJrb2sRRiX7k/DibP1yxUwuvLycpXcLsZf0ZwVu4kMyBDBkvj8889VMDR06FBVMmDAgAF4//33q8xp7tu376JkrY8//hjNmjVT+UTVDb/J6jgZuerWrRvee+89zJw5E9OnT3fK70TkbhIiA9E6uAzl5cB/KgIIs9t6PBM7TmbBx8sTt/ZibSUiIzLEajghK98kSbsm8fHx6gruQjJSJLfqyGiV3IjIeQbHlONAtrWi94NDWiHYz9y5Htqo0rVdYtG4Eaf3iYzIMCNLRGQOHcLK0SoyEDmFJfhi3TGYWVpuIX7eflrdv7N/C72bQ0QNxGCJiJxKUnbuHhCv7n+86ggKS0phVv9de1RtItw1LhRdmoXq3RwiaiAGS0TkdNd2iUFUsC9Ssgvx/dZTMKPcwhJ8sipJ3b97QEu9m0NEl4HBEhE5nSQ731URQLy//LDaYNZsPl97FFnnilVS++jO3AeOyMgYLBGRLm7r0xxBvl44mJqLxXurFp01QxHKD1ZYtzO5/6pElgsgMjgGS0SkiyA/b4y/wpr0/N7yQzCTrzYcV8ndUoRybPemejeHiC4TgyUi0s2kK+PhY/HEhqSz2HQ0A2ZQVFKG95ZZg7/7BifC28LTLJHR8a+YiHQTFeyHGypGXmYtOwwzmLPlBE5lFaBJkC9u6WmtWE5ExsZgiYh0NXlQAjw8gIW7U7D9RCaMrKS0DO8utY4q3TMoQe2HR0TGx2CJiHTVqkkj3NDNOrr0t1/2VluJ3yh+3nEaSen5CAvwxu19m+vdHCKyEwZLRKS7R4a3UblLqw+lY8WBNBhRaVk53llyUN2XsggBPobZTYqI6sBgiYh0FxcegD/0a2EbXTJi3aWvNx7H/pRcBPt54Q/9rBXKicgcGCwRkUt48OpWqu7S7tPZ+HG7sap65xQU440F+9T9h4e1QYi/uTcHJnI3DJaIyCWEB/qopfbi9V/3GWrPuHeWHEJabpGq1q2NkBGReTBYIiKXqrskS+5PnD2H2euOwQiOpefj45XWat1Pj2nPukpEJsS/aiJyGZIULdNY4u3FB9X0lqub8cseFJWWYWDrCFzdtonezSEiB2CwREQu5fe9mqnprIy8IhUwubK1h9Pxy85kyNZvz/yuAzykYBQRmQ6DJSJyKV4WTzWdJT5ccRhbjp2Fq5YKeOHH3er++L4t0CYqSO8mEZGDMFgiIpczpF0UxnaLhVQQePyb7S6Z7P3lhmNq5V6Qn5eqE0VE5sVgiYhc0vRrOyKikS8OpObi7UWuNR13JC0PL/+8R91/ZFgbtZKPiMyLwRIRuaSwQB+8NLajuv/uskPYeTILrqC4tAwPf7UV+UWluCIhHHf2ZwFKIrNjsERELmtUpxiM6Ryj8oP+8vU2FJWU6d0kvL3oALYdz1SVumf+vhsskt1NRKbGYImIXNrz13dU01x7k3Pwr6X6TsdtSMrAPyv2f3v5hs6IDfXXtT1E5BwMlojIpUne0nPXWafj/rn4IFYd1Gej3eyCYjzy1VaVdH5j96a4tmusLu0gIudjsERELu/aLjFqdVxJWTnu++8mHEzNcerPLy8vx/Tvd6nK4nHh/mq0i4jcB4MlInJ5Uuzxbzd1Qc8WYcgpKMGkTzcgLbfQaT//H4sOYM6Wk6r45P/9vhuC/LhRLpE7YbBERIbg523B+3/oiebhATiecQ6T/70RBcWOr78khTHf/O2Auv/s7zqgV3y4w38mEbkWBktEZBiNG/nik0m9EeLvjS3HMvHo19tQJklEDiKb+b5UUU/pLyPaYOKVLR32s4jIdTFYIiJDSYxshFl39IS3xQM/bz+NJ+fscEhJge+3nsRTc3eo+/delYAHr25l959BRMbAYImIDKdfYmP87cYukH1rv9xwHLd/sBapOQV2O/6cLScw9X/bUF4O3HFFczwxqh03ySVyYwyWiMiQburZDB/d2QtBvl7YePQsrnt7lSoWeTky84vwpy+24JGvtqlCmFIi4IXrOjFQInJzhgmWXn75ZfTv3x8BAQEIDQ2t93LfZ599FjExMfD398ewYcNw4IA1UVOTkZGB8ePHIzg4WB33rrvuQm5uroN+CyKy94a7c6dcicTIQCRnF+CW99bgy/XHGpTHtHz/GYx8czl+3HZKVeX+89DWeO3mLvBkhW4it2eYYKmoqAi33HIL7r///np/z2uvvYa33noLs2bNwrp16xAYGIiRI0eioOD8cL0ESrt27cLChQvx008/Yfny5bjnnnsc9FsQkSNymOY+eCWGtY9SuUtPfLcDV/99KT5eeQQ5BcV1XlBtP5GJad9tx4SP1yMluxAJEYH49v7+mDq8DbwshjlFEpEDecEgnn/+efXx008/rdfz5ST45ptv4umnn8b111+vHvv3v/+NqKgozJ07F+PGjcOePXswf/58bNiwAb169VLPefvttzF69Gi88cYbiI1lhV4iI5C6R1JWYNbyQ5i19BCOpufjhZ92Y+bC/bi5ZzNVnynIz6vi5o303CL8uisZC3Yl41TW+YunO/u1wBPXtIe/j0XX34eIXIthgqVLdeTIESQnJ6upN01ISAj69u2LNWvWqGBJPsrUmxYoCXm+p6enGom64YYbqj12YWGhummys7PVx+LiYnWzF+1Y9jwmXYz9bJ6+nnxlC4zv3RRzt57Gv9cew6Ezefh0dZK61STAx4JBrSNwe59m6JfQGEAZiov137D3cvA17RzsZ+P3c32PadpgSQIlISNJlcnn2tfkY5MmTap83cvLC+Hh4bbnVGfGjBm2ka7KFixYoHKq7E2mCMnx2M/m6WvJavxTIrAvwgPrUj2QXQwUlHrgXIl8hKrE3T60HF3Dy9EmpAQ+lpM4u/ck5u2FqfA17RzsZ+P2c35+vusHS0888QReffXVWp8jU2Xt2rWDK5k2bRqmTp1aZWQpLi4OI0aMUIni9ox45cUxfPhweHtzewVHYT+bt6/HwD3xNe0c7Gfj97M2M+TSwdKjjz6KiRMn1vqchISEBh07OjpafUxJSVGr4TTyebdu3WzPSU1NrfJ9JSUlaoWc9v3V8fX1VbcLyX+iI/5gHHVcqor97Dzsa+dgPzsH+9m4/Vzf4+kaLEVGRqqbI7Rs2VIFPIsWLbIFRxJBSi6StqKuX79+yMzMxKZNm9CzZ0/12OLFi1FWVqZym4iIiIgMsy722LFj2Lp1q/pYWlqq7sutck0kma6bM2eOui9F5B5++GG89NJL+OGHH7Bjxw5MmDBBrXAbO3asek779u0xatQoTJ48GevXr8eqVaswZcoUlfzNlXBERERkqARvKS752Wef2T7v3r27+rhkyRIMHjxY3d+3bx+ysrJsz3n88ceRl5en6ibJCNKAAQNUqQA/Pz/bcz7//HMVIA0dOlStgrvppptUbSYiIiIiQwVLUl+prhpLUlupMhldeuGFF9StJrLybfbs2XZrJxEREZmLYabhiIiIiPTAYImIiIioFgyWiIiIiGrBYImIiIioFgyWiIiIiGrBYImIiIioFgyWiIiIiGrBYImIiIioFgyWiIiIiMxQwduVaZXDZaNeeyouLkZ+fr46Lne0dhz2s/Owr52D/ewc7Gfj97P2vn3hDiAXYrBkBzk5OepjXFyc3k0hIiKiBryPh4SE1Ph1j/K6wimqU1lZGU6dOoWgoCC1H509I14JwI4fP47g4GC7HZeqYj87D/vaOdjPzsF+Nn4/SwgkgVJsbCw8PWvOTOLIkh1IBzdr1sxhx5cXB/8QHY/97Dzsa+dgPzsH+9nY/VzbiJKGCd5EREREtWCwRERERFQLBksuzNfXF9OnT1cfyXHYz87DvnYO9rNzsJ/dp5+Z4E1ERERUC44sEREREdWCwRIRERFRLRgsEREREdWCwRIRERFRLRgsubB33nkH8fHx8PPzQ9++fbF+/Xq9m2QqM2bMQO/evVXl9SZNmmDs2LHYt2+f3s0yvb/97W+q0v3DDz+sd1NM5+TJk7jjjjvQuHFj+Pv7o3Pnzti4caPezTKV0tJSPPPMM2jZsqXq48TERLz44ot17i1GdVu+fDmuvfZaVU1bzhFz586t8nXp42effRYxMTGq74cNG4YDBw7AGRgsuaivvvoKU6dOVcslN2/ejK5du2LkyJFITU3Vu2mmsWzZMjz44INYu3YtFi5cqDZrHDFiBPLy8vRummlt2LAB7733Hrp06aJ3U0zn7NmzuPLKK9VGo7/88gt2796Nv//97wgLC9O7aaby6quv4t1338U///lP7NmzR33+2muv4e2339a7aYaXl5en3utkoKA60s9vvfUWZs2ahXXr1iEwMFC9LxYUFDi+cVI6gFxPnz59yh988EHb56WlpeWxsbHlM2bM0LVdZpaamiqXhuXLli3TuymmlJOTU966devyhQsXll911VXlDz30kN5NMpW//vWv5QMGDNC7GaY3ZsyY8j/+8Y9VHrvxxhvLx48fr1ubzAhA+Zw5c2yfl5WVlUdHR5e//vrrtscyMzPLfX19y7/44guHt4cjSy6oqKgImzZtUkOMlfefk8/XrFmja9vMLCsrS30MDw/XuymmJKN4Y8aMqfK6Jvv54Ycf0KtXL9xyyy1qWrl79+744IMP9G6W6fTv3x+LFi3C/v371efbtm3DypUrcc011+jdNFM7cuQIkpOTq5w/ZE83SVFxxvsiN9J1QWlpaWpePCoqqsrj8vnevXt1a5eZlZWVqRwamcbo1KmT3s0xnS+//FJNJ8s0HDnG4cOH1fSQTN8/+eSTqq///Oc/w8fHB3feeafezTONJ554AtnZ2WjXrh0sFos6V7/88ssYP3683k0zteTkZPWxuvdF7WuOxGCJqGLUY+fOneoKkezr+PHjeOihh1RemCxWIMcF/DKy9Morr6jPZWRJXtOS38FgyX7+97//4fPPP8fs2bPRsWNHbN26VV1oSVIy+9m8OA3ngiIiItQVS0pKSpXH5fPo6Gjd2mVWU6ZMwU8//YQlS5agWbNmejfHdGRKWRYm9OjRA15eXuomyfWSqCn35cqcLp+sEOrQoUOVx9q3b49jx47p1iYzeuyxx9To0rhx49Rqwz/84Q945JFH1OpachztvU+v90UGSy5Ihs179uyp5sUrXzXK5/369dO1bWYiOYQSKM2ZMweLFy9WS4HJ/oYOHYodO3aoK3DtJiMgMm0h9+XCgC6fTCFfWPpC8mpatGihW5vMKD8/X+WQViavYTlHk+PI+VmCosrvizIdKqvinPG+yGk4FyV5BzKkK28qffr0wZtvvqmWVU6aNEnvpplq6k2G0r///ntVa0mb95akQanhQfYhfXthHpgs+ZVaQMwPsx8Z3ZDkY5mG+/3vf6/qsr3//vvqRvYjdYAkR6l58+ZqGm7Lli2YOXMm/vjHP+rdNMPLzc3FwYMHqyR1ywWVLLqR/pbpzpdeegmtW7dWwZPUu5LpT6mR53AOX29HDfb222+XN2/evNzHx0eVEli7dq3eTTIVeflXd/vkk0/0bprpsXSAY/z444/lnTp1Usup27VrV/7+++/r3STTyc7OVq9dOTf7+fmVJyQklD/11FPlhYWFejfN8JYsWVLtOfnOO++0lQ945plnyqOiotRrfOjQoeX79u1zSts85B/Hh2RERERExsScJSIiIqJaMFgiIiIiqgWDJSIiIqJaMFgiIiIiqgWDJSIiIqJaMFgiIiIiqgWDJSIiIqJaMFgiIiIiqgWDJSKiSmRjX9k25MYbb6zyeFZWFuLi4vDUU0/p1jYi0gcreBMRXUA2oO3WrRs++OADteGvmDBhArZt24YNGzaoza6JyH0wWCIiqsZbb72F5557Drt27VKb0t5yyy0qUOrataveTSMiJ2OwRERUDTk1DhkyBBaLBTt27MCf/vQnPP3003o3i4h0wGCJiKgGe/fuRfv27dG5c2ds3rwZXl5eejeJiHTABG8iohp8/PHHCAgIwJEjR3DixAm9m0NEOuHIEhFRNVavXo2rrroKCxYswEsvvaQe++233+Dh4aF304jIyTiyRER0gfz8fEycOBH3338/rr76anz00UcqyXvWrFl6N42IdMCRJSKiCzz00EOYN2+eKhUg03Divffew1/+8heV7B0fH693E4nIiRgsERFVsmzZMgwdOhRLly7FgAEDqnxt5MiRKCkp4XQckZthsERERERUC+YsEREREdWCwRIRERFRLRgsEREREdWCwRIRERFRLRgsEREREdWCwRIRERFRLRgsEREREdWCwRIRERFRLRgsEREREdWCwRIRERFRLRgsEREREdWCwRIRERERavb/TomZ4u59KgkAAAAASUVORK5CYII=",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"x = np.linspace(0, 10, 100)\n",
|
||
"y = np.sin(x)\n",
|
||
"\n",
|
||
"plt.plot(x, y, label='sin(x)')\n",
|
||
"plt.xlabel(\"X\")\n",
|
||
"plt.ylabel(\"Y\")\n",
|
||
"plt.title(\"График синуса\")\n",
|
||
"plt.legend()\n",
|
||
"plt.grid()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "b1fedc7b-24f0-44ef-8154-be386d6479d5",
|
||
"metadata": {
|
||
"jupyter": {
|
||
"source_hidden": true
|
||
}
|
||
},
|
||
"source": [
|
||
"1) Изменить цвет"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "0d6d4753-b46f-4f3b-b7e0-14acca36e699",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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3l/3OZBsPqU4tIy8ych5SrBiMEC8BSPbsPj13z549KvB49NFHUaJECfVY/fr11Ud5zUkQqL8fyn3JB3733XfV5zI6JKkuq1evRtu2bdVjhw8fVmkznu+hcl9SYyRh/MSJE+o1LbNBefPmTdIWSfKW4C2191/pYylO2bhxY62PPXgGXY4NliQClQ72JJ9Lh0rHyXCc3FJ6jnxtamRlndySkxdIIE5MgTquKckqiCFDgMGDtSuscuWAPn3geuwxuPLnTznJTv5QJY9NXvQy7Dt8OFx//omw1q0lY1A7ng/956h+Nhj7OjjYz2mQFdWdO2sjOTI906kT8OqrCKlaNeVzjbyx33xzx7ZtiO/TB2HLlwMvvqide774AihfPl1NkNkNWd0t002qOnUGAi5/Sc/3r1mzppr20hdFyWCBjAzlyZNH/Tz6z6SrVq1aks9l65F//vkn4TEZ+ZGZmeQVujt27KhWvn/00UdqcVeFChVuaYt8nXx9atW95XFpT0p/D77+fVgqwTu9JMqVoTlPEpnq0a/MjdauXTvJc2RuUz7Xn0NB9M8/gAQ4MsUpgdJTT6mrPrzwApA/f9pfL1cVjz8OrFsH9O6tHeP994FGjQBv03hE5Cz6uaF9ey1Qio4Gdu8G/vc/mdPx7RhVqiBuwQJs7tUL7ttukwxliQiAb77J/FTYxYvG3NKRRhIaGqreT3/99Ve1OGr06NEqkJFV6ClJHpRI8OKZo5U/f36VGpOcTJPJSnX5fjKalRLJWSpQoAACyVLBkszpSgkAuQn5pcj9gwcPJkyPde3aNeH5Mse5b98+tRxRcpA+/fRTfPfdd3hB3nxvkum0CRMmqPnUHTt2oE+fPqpEgSSQURBt2ADUqiWJX9of7NdfA599BiQbMvWJfP3YsZJ9KNtNA1I3S469fn0gWk5EViKLc+T8/tprWtAk02eSQFyqVPqPFRKCA61b48bGjbJ6SDu2XLBJ0JVRkjMkU2FG3NK5OMblcqmcIslb2rhxoxqA0JO600tGqqRuUnIvvviiGhn6/vvvVUC2aNGiW56zbds29fWBZKlg6Y8//lAdoneKBDpyf8CAAerzY8eOJQROQuY/f/nlFxX9ylChlBD4/PPP1ZCh5xCfJI/JMSTZTIKvuXPn3pL0TQEkVwvyOzl8GJAh1jVrgMcey/xxZQmqBNb16gGymqVVK7XShYgc6sIFbRRp0iRt2u2TT7Sb5D9mhqQC/PqrNhouAZgETNOmwc7WrFmD9957T70vy/vu9OnT1bSalOnJCHlflvIBnuT9W3KbvvnmG5UI/tJLL+Hxxx9PMgJ14MABHDly5JaV7/5mqZylJk2aqOV/qUmpCJZ8jUS83sgyR7mRAaR2ScuWgCRy164NyFWDH5Pk1UlMRqtkxYWMXsXEACtXAkWL+u97EJH5yZRPly7A779r55jvvtMu0vxFRmWkYLKstvr8cy0XSoKwdNYNsoqcOXOq1WWSSyRJ0pLkLQMSUg9Rij2nl6ysk1mgXbt2qek8Cbx69OiBd955R+3CId9D7svgh8wa6d9DSg5IvpSeZB4olgqWyGakXtJ99wF792pD4FJDyZ+Bkk6OKVd9krskeQlygly2DMiTx//fi4jM6Y03gFmzZIWOdgElI87+JgnG48Zp9d9k9EoSxiUdQE8Kt5FKlSqpWRhfBi5k27Hkkm9XJivcZNBCCkWPGzdO5SDJCjeh5zZJ3pOMZHmWBJDVclOmTEGgWWoajmxErr7kRCLJ2PnyAfJHF8ipz4IFtROkLAHetg24/37japkQUXBJ0rUkdAvZnSEQgZJnwCSr4mRkSc5zUoKA+ZI+efPNN9UIka/FOWX674033lB5U4HGYImCT6ZSZdpz9mwtgVuu9tK53DZDSpYEZF9ASfpesULLK+D2BUT2JrsxPPlk4ujS//1f4L+n5EPJ6MoDD8jwh3ZhKPlS5JVskSLBT2olAJIrW7asqgQeDAyWKPgmT9aGqmWOX4ZPGzQI3veWZcESpMkyVhkel1V3RGRPsuCnXTstYJFabFJzLVgkX0kCpqgobREL82ItjcESBdeRI8Azz2j3pZ6Sj5si+5UM2b7zjnZf2vL338FvAxEFvsCtTIHJIpLq1bULo2AXfJS8SLkglO8r3z8zJQXIUAyWKHhkyqtXL+DsWaBOHSmMZVxbpLK3FB69cAGhMkRvsQ0siSgNo0ZpZUhkj0iZ6pfCkUaQhSX6hrF9+mgLWlLgbaU3ZY4/+pbBEgXPl19qxd9kNYqsFMlsbZPMkO8tV3rZsyNk6VKUlqk5IrIHqSL91lva/REjANlP0khvvgk0bqxVyX70UW1aMFlla183dKX00/s2M1v/sHQABS934PnntfuSN1C5stEtkuxA4IMPVCG5yt98g3iZkqtRw+hWEZE/RrDlDbJJE6BHD6NbpCV8yxScTAfKCmBJQbi5qaxs4yGJzSdluvDmPmdSGZsSyeo4KRMgG+L6mvytjyhJoCR9K30sfZ1RDJYoOCcvOWHJahBJ5u7fH6bRqxfif/oJob/+ihDZAkGG7bNmNbpVRJRRMmq9YIG20nbChHRv4REwkugt7Xn4YeC//9VW495cBaxv3K4HTHRr0CMb5UZERGQokJRASe/jjGKwRIEnK9/k5BURoU3FZSK69zuXC3Fjx+JGlSrIKlujSJ7DSy8Z3SoiyogTJxIvxmT0RkaPzUSqecu2S1IkV9p5c/pfAoAiRYqgYMGCiJWClpSE9IlUC2/cuHG6p9Lk+ZkZUdIxWKLAkj18pLaJGDYsOPWU0qtIEWzv1g01R48Ghg4FunWTLbCNbhURpZdMpcs5RzbONtMItqcPPwTmz9d2LJCgSYKnm+RN3R9v7HYTGhqKGzduIDw8PFN5R5nBBG8KrPfe005ed9xh6jojB2XfwWrVtA13Bw82ujlElF4SeHz/vTZyLRW0jVxA4o1sFv7cc9p9yeP0SPYm82KwRIFz4IA2rSWGDzfX9FtyoaGIkzaKzz4Ddu0yukVE5Csp/fHaa4kBiNkXarz9trYF019/ATKiTabHYIkCR5buylVT06ZJhprNyi3tlI19ZT8nqcNERNYwdSqwZYtWU0mf9jczaaekJQgZyZZcKzI1BksUGLJxpGxrImTlh1lWpKRF2iojYFLEbvFio1tDRGmRCzIZqRFykZM3LyxBciOlOO/589YI8ByOwRIFplTAyy9r97t00ZItraJiRUDfmPHFF1nZm8jsPv8c2LcPKFQoMRfICqRekJ6mIKuEZTUumRaDJQpMoqWMykilblldZjWyb1zOnMDGjcA33xjdGiJKzaVLiZvjDhigKvJbimy51LGjdoEZzE1+Kd0YLJF/eeb7PPssUKIELKdAAW17As+8KyIyHxmZOX4cKFUKkD0erUimECVNYfp0YOtWo1tDqWCwRP41bRqwfbuWN2DleXgJ9KTi6+HD3CmcyIxOnwbef1+7L6MyVq28L2VVpFiluLkFCpkPgyXyHxlK/s9/EvN9cueGZclWCfIzCDkhx8UZ3SIi8iR/l1IXTeqjyea0VqZv+vvdd8DOnUa3hlLAYIn8Z84cYNs2IEcOoG9fWJ4keufJo9VCkSFyIjKHf/8FPvkkcTQmHZurmpJssNu2rXbBydElU7L4K4xMRR9Veuopa48q6STok+0ThNREkRMZERlPCsdevqwVn2zTBraglz+YMgXYs8fo1lAyDJbIP1asAJYv1/IGpIKuXUjuUmSktjLut9+Mbg0RXb2aWPVaSpRYpYZbWmrXBlq31sqV6AUryTQYLJF/6ImWjz8OFC0K28iXL7HuEk9gRMaTBRcnTwJRUUCHDrAVfXTp66+17aLINBgsUeZJntLPP2tXeC+9BNuR3ctlp+tly7QRNCIyhoy6fPCBdl9GsA3agT5g7roLaN5cK8GipzWQKTBYoszTN6CV5a/ly8N2ihXTRswER5eIjPPLL9pqMSkaa9W6Sr6OLk2apCWykykwWKLM+ftvLSFRvPoqbEsKbcqKGzlZb95sdGuInGnEiMRFJBIw2dHddwM1a2q5WbKVC5kCgyXKHBkSlxpEzZppm0LaVblyifkR+n5ORBQ8a9dqU+FhYdrCC7uSdAZ9Fe6nn2pTcmQ4BkuUcbJb9sSJ9h9V0uknaBlJk+rBRBQ8eq7S//0fcPvtsLVOnbTFJQcPavmgZDgGS5S5VSmykWWlSlpSot3JppdSPE6Gx7/6yujWEDnHvn3ADz9o9+24iCS5iAigZ0/tvl4mgQzFYIkyRgo0yhCxnj9gl1on3sjP+PTT2n352WVlDhEFnv73FhMDVK0KR+jTR8uTXLxYW3FMhrJcsDRmzBiULFkS4eHhqFevHtbKPHYqmjRpApfLdcutjUfF127dut3y/y1btgzST2NhUoBSNsyVgo1du8IxZApAEkv37gXmzze6NUT25zmSq+fyOEHx4kC7dtp9fWsXMoylgqVp06ahf//+GDhwIDZs2IDq1aujRYsWOCkFylIwffp0HDt2LOG2bds2hIaGokOyQmYSHHk+79tvvw3ST2Tx7QaEbGBph61NfJU9u0TY2v0xY4xuDZH9yb6MsoReilC2agVH0YPDb74BzpwxujWOZqlgaeTIkejZsye6d++OypUrY+zYsYiMjMREPck4mbx586Jw4cIJt/nz56vnJw+WsmXLluR5eWTzVEqdBKd6/oAMFTuNvknw7NmssksUaOPGaR+lrlJoKBzlnnu0aUfZB+/LL41ujaOFwSKuX7+O9evX4/XXX094LCQkBM2bN8eqVat8OsYXX3yBTp06IbuMDnhYsmQJChYsqIKkpk2bYujQocgnKxFSce3aNXXTnZdVYQBiY2PVzV/0Y/nzmP4QMmECQmNjEV+nDuKqVZMGwsrS3c+lSyO0aVOELFqEuE8/RTx3Cbf8a9pubNPPO3ciy7JlcIeE4MZjj5nuXBOMfnb17YuwPn3gHjMGN+RCzWkBIwLbz74e0zLB0qlTpxAXF4dChQoleVw+3ykVXdMguU0yDScBU/IpuPbt26NUqVLYu3cv3njjDbRq1UoFYDJll5Jhw4Zh0KBBtzw+b948NXLlbzIiZhpxcWg+ahQk3NxUvz4OzZkDu0hPPxepWxd3LlqEG+PGYd6ddyLebtsuBJipXtM2ZvV+vmPiRJQFcLxOHazdsgWQm8P6OTRvXsTcdhuy7tuH9e++ixN2rmdnQD9fllE7H7jcblnWZH5Hjx7F7bffjpUrV6K+LOG+6ZVXXsHSpUuxZs0ar1/fu3dvFQBtSeOPbd++fShTpgwWLFiAZlJo0ceRpaioKBXQ5fRjVVmJeOXFER0djSwmeTN2/forwtq2hTtPHtyQKShZ4mpxGernGzcQVq4cXEeO4MaXX8LduXOgm2kLZnxN25Et+vnqVYSVLAnX6dO48dNPcJswXylY/Rzy8ssI/fhjxLdti7jvv4fTxAawn+X9O3/+/Dh37pzX92/LjCzJDyMjPSdOnEjyuHwueUbeXLp0CVOnTsXgwYPT/D6lS5dW32vPnj2pBkuS4yS35OSXGIg/mEAdN0MmTFAfXN26IYvNthtIVz/L83r3BgYMQJjkVOhJ32S917SNWbqfv/tOK/5avDjCZAWziaefAt7Pkq/18ccI+eUXhJw9CxQoACfKEoB+9vV4lknwzpo1K2rXro2FCxcmPBYfH68+9xxpSsn333+vRoK6dOmS5vc5fPgw/v33XxQpUsQv7bYVGUmSvdH02kpOJ0XjZOuF1auBP/80ujVE9uLkxO7kqlQB6tbVtj6RYsAUdJYJloSUDZgwYQImTZqEHTt2oE+fPmrUSFbHia5duyZJANdJnlK7du1uSdq+ePEiXn75ZaxevRoHDhxQgVfbtm1RtmxZVZKAkpHVGDJrKyNu5csb3RrjyYimPjUgO4QTkX/Ixcfvv2tBUo8eRrfGHG6+zyWchymoLBUsdezYESNGjMCAAQNQo0YNbNq0CXPnzk1I+j548KCqk+Rp165dWL58OXqk8Acn03qSw/TAAw+gfPny6jkyevX777+nOM3maFI99+uvtftPPGF0a8xDn36TOijc8JLIP8aP1z7efz9QtKjRrTEHqWkXHg5s3QqsX290axzHMjlLun79+qlbSqQEQHIVKlRAajnsERER+O233/zeRttW7JZpuBw5EqvKEiC5FHnzAhKkL1ggyyuNbhGRtcniGf3CrFcvo1tjHlL898EHASmaLKNLDl4VZwRLjSyRgfRpJinoGYDyCJYlI5CyBYrg5rpEmSfFXqVadbFi2l5wlEgf1Z8yRdsGhoKGwRKlTepQ6MtVH3/c6NaYdypu5kxAVqoQUcbJlLaQchxOT+xOrmlTbc84Oc/I+YaChsESpU3+KC9cAEqVAho1Mro15lOrFnDHHdr0gSx3JqKMkT3g9EK3UrGbkgoJSbw4S2WbLwoMBkuUNj1/QE5e8sdKSblciScwTsURZZxcbMj2EzVrahcgdCv9XCM5kgcPGt0ax+A7H3l39KjUmNfud+1qdGvMS6YMJJCUfQr/+svo1hBZewrOh5p4jiUj/Pfeq5UP0C9kKeAYLJF3UgBNygY0bAiUKWN0a8xLipjqK+FYc4ko/fbs0S425KJDlslT6lhzKegYLFHq5I9Qf+NnYnfa9D6Sq724OKNbQ2QtemXq6Gjt4oNS99BDQPbsspmp7BJvdGscgcESpW7DBq2SriyPl5IB5N0DD2i1UA4fBhYvNro1RNa6MNODJSZ2p03Kt7Rtq92XuksUcAyWKHX6fLgUoZQggLyT6rqdOiXNvSCitMn029692mgJi976Rp+qnDaNI9lBwGCJUiZbd0jhM8EpON/pBSql3AKLxhH5Rh9Vat9eC5gobVKwM08e4PhxYOlSo1tjewyWKGWLFgGnTgEFCmg5BOQbSYS//Xbg/HmAW+kQpe36dW10RHAKzndZswIPP6zd51RcwDFYopTpJy9JJAyz3BaCxpGVPI88ot2fOtXo1hCZnxShPH1a2zBXKlRT+qfifvxRCzopYBgs0a3kj27GDO2+/sZPvuvYUfv488/aVjFElDp9VETe+Lm9Sfo0bqytHJS99DiSHVAMluhWUhlW/vgKF9b+GCl97rwTKFkSuHQJ+OUXo1tDZF5yMSEb5wp9cQT5ToJL/YKWU3EBxWCJUp+Ck/lwXullbPsTfXSJU3FE3qfgJGCSi4vatY1ujbWn4n76SbtAo4BgsERJyQoufTdr/Q2f0k+/SpY3A0n2JqJb6RtPy+iIXGRQxkayS5fWgk6Z+qeAYLBESc2bp725y4quBg2Mbo11Va8OlC+vBZ+zZhndGiLz8ZymZtHbjJMgU78441RcwDBYopSn4OTkJSu7KPMnML1PiSgRp+D8PxX3669avin5Hd8NKdGVK4mjIJyCyzy9D2WVCk9gREl9/732kVNwmVelinaLjQWmTze6NbbEYImSXuldvAgULw7Uq2d0a6yvcuXEE5heioGIOAUXCPpIttRcIr9jsESJmGzpf5yKI0p9Cq5UKU7B+YsUENZLv5w7Z3RrbIfBEiVe6en1TjgF5z96Xy5cCPzzj9GtITLXFJyMKvHCzD8qVgQqVdJGsvVzOfkNgyXSyB+XXOnJElRe6flP2bJAjRraruBc1kuU9MKMU3D+JRsRC+Yt+R2DJUo6z80rvcCdwJi3RKRNwcliEk7BBW4qTlbFsUClXzFYIq0WkPxxeb6xk/88+GBiDasLF4xuDZGxOAUXODKKLaUYJBjlXnF+xWCJtIRAWQUnhSjr1DG6NfZzxx1AuXLaBsV6UErkRJyCCywJPvXRJa6K8ysGS5S4vUm7dixEGagTmD66xKk4cjIZXZVRDxaiDBx9dkCC0mvXjG6NbfCd0ekk8VgvRKm/oVPgTmBSW4YnMHIqzwszTsEFxl13AUWKaNtWLVpkdGtsg8GS061YoS1pz5MHaNzY6NbYV926QNGiWs6SlBEgcpobNxKn4CRYosCQ2QH9wpdTcX7DYMnp9Gmh++8HsmQxujX2PoHpbxBc1ktOtHw5cPo0kC8f0LCh0a2xNz1v6aeftCCVMo3BkpO53YnBEq/0gjcVJ9OeMv1J5MQpOLkwCwszujX2JrMEEpSeOgX8/rvRrbEFywVLY8aMQcmSJREeHo569eph7dq1qT73q6++gsvlSnKTr/PkdrsxYMAAFClSBBEREWjevDl2794NR9i0Cfj7byAiAmjRwujWOOMEJtOdMu0p059ETrow88xXosCSYLRtW+0+R7KdFyxNmzYN/fv3x8CBA7FhwwZUr14dLVq0wMmTJ1P9mpw5c+LYsWMJt78lOPAwfPhwjBo1CmPHjsWaNWuQPXt2dcyrUnvI7vRRJQmUIiONbo39yTTnAw9o93kCIyfZvDnxwiw62ujWOK+ad3y80a2xPEsFSyNHjkTPnj3RvXt3VK5cWQU4kZGRmDhxYqpfI6NJhQsXTrgVKlQoyajSRx99hLfeegtt27ZFtWrV8PXXX+Po0aOYqV8F2Zn+M3IVXPB4lhCQq20iJ9DPNbwwC57mzYHbbgOOHgXWrze6NZZnmYnj69evY/369Xj99dcTHgsJCVHTZqtWrUr16y5evIgSJUogPj4etWrVwnvvvYc7pEgggP379+P48ePqGLpcuXKp6T05Zid9x/hkrl27pm6687JEE7J/Yay6+Yt+LH8eM8HevciydSvcoaG4ISewQHwPiwhoPyd3770Ii4yE6+BBxK5bB9SsCScJal87mNn6OWzmTEihgBv33Qe3Sdpkx35OIiQEoTExCJk+HXEzZyJeqntbVGwA+9nXY1omWDp16hTi4uKSjAwJ+Xznzp0pfk2FChXUqJOMGJ07dw4jRoxAgwYNsH37dhQrVkwFSvoxkh9T/7+UDBs2DIMGDbrl8Xnz5qmRLn+bP3++349ZZuZMVJF+veMOrFy92u/Ht6JA9HNK6lavjqKrVmHfiBHY2bkznChYfe10ZujniBMnELN5M9whIZiXNStiZW84mzFDP6ekWFQUpPTnxSlTsOTOO2F18wPQz5dlA3k7BUsZUb9+fXXTSaBUqVIljBs3DkOGDMnwcWV0S3KnPEeWoqKiEBMTo3Kk/BnxyosjOjoaWfy8rD/0/ffVx7xPPIHWrVvDyQLZzylxnT0LrFqF8jt2oLTD+j7Yfe1UZurnkNGj1Ud3o0aITmW03qrM1M8pqlcP7tGjkevAAbSWGZUSJWBFsQHsZ31myDbBUv78+REaGooTJ04keVw+l1wkX0gn16xZE3v27FGf618nx5DVcJ7HrOFlyDJbtmzqltLxA/EH4/fjSh/eHE0KfeghhJrxj9wAgfr93UKWToeGwrVtG7JIPoFFT2CW6GuHM0U///yz+hDy4IMIMbotdu7nlMh7nNS0+v13ZJGNdZ9+GlaWJQD97OvxLJPgnTVrVtSuXRsLPaofSx6SfO45euSNTONt3bo1ITAqVaqUCpg8jylRpqyK8/WYliTD4JJcLHszFStmdGucJ2/exKJ8N99IiGzp33+BZcu0+/pSdgoufQWuvq0VZYhlgiUhU18TJkzApEmTsGPHDvTp0weXLl1Sq+NE165dkySADx48WOUR7du3T5Ua6NKliyod8OSTTyaslHv++ecxdOhQzJo1SwVScoyiRYuinZ1rgehv0PfdZ3RLnEtGlwSDJbIz2d5Elq1Xry5Xp0a3xtnnmsWLtf3iKEMsMw0nOnbsiH/++UcVkZQEbJkqmzt3bkKC9sGDB9UKOd2ZM2dUqQF5bp48edTI1MqVK1XZAd0rr7yiAq5evXrh7NmzaNSokTpm8uKVtiGr+GTnb88/Igo+6fuXXwaWLNH2i8uRw+gWEfmfbLchOKpknAoVgPLlgb/+AmQqrkMHo1tkSZYKlkS/fv3ULSVL5I3Hw4cffqhu3sjokoxAyc0RpI8uXdJ2pXbYsnVTkZNX2bKA5M/JCg+9gByRHS/MGCwZPxU3YoQ2ks1gyf7TcOQH+q7fMgXnMQpHQeZycSqO7E1ylXhhZg76ueaXX7ixbgbx3dJJJKmb+UrmPIFxY12y64VZmzbaxQEZp0EDbWHJ6dPAypVGt8aSGCw5yfbt2v5Mko/lUbWcDNKokZSM1zbW9bIhNJHl8MLMfBvrStAqOJKdIQyWnHil17Qp92cyA6nv0aqVdp8nMLIT2VVh/34pSgc0a2Z0a8hzJJslBDKEwZKT6G/IXAVnHsxbIjuSqWXRpIm2mSsZT/YAlQs0WRW3a5fRrbEcBktOceqU2mJD0YdjyXgtW6pq3ti2DThwwOjWEPl/IQmZg2zFde+92n2OLqUbgyWn0Kt2yzYuUVFGt4ZSquatv8EQWdmZM8Dy5dp9XpiZd1EJpQuDJafglZ55cSqO7EQKH8rqTin+y6rd5qJv3C3B7LlzRrfGUhgsOcH168Dcudp95iuZezsCqeZNZGX6qAUvzMyndGmgYkUtmJViuOQzBktO8Pvv2ptwwYJAnTpGt4ZS2o6gXDkgNhZYsMDo1hBlnLwJy5S/YLBk7tEl/fdEPmGw5LTicKzabU48gZEdrF6tFT7MkweoX9/o1pC3c82vv2qbHJNP+M7pBPobMJMtzUuvtyQnMEnEJ7LyFJys8pRCiGTOYrhSzuH4cWDTJqNbYxkMluxu716troacuKKjjW4Npeaee4CICODIEWDrVqNbQ5QxXEhiflIoVN/BgSPZPmOwZHcyUiFkebrU2SBzki1opLK65++MyEoOHtQCfZnql5ElMi9O+6cbgyW709949T8OMi+ewMjK9Net5CpJ/TAy/7S/5JhJwWJKE4MlO7tyRVuO7vnHQeal/45WrGANFLIevTwJL8zMr1gxoFo1LT9S6mJRmhgs2dnSpVrAdPvtQJUqRreG0iIF/FgDhaxay23hQu0+p+CsQV/ww5FsnzBYcsIUnIxYuFxGt4bSuyqOyCqkIvTFi0ChQtqWSmR++gigjAjKBRp5xWDJzpivZO0aKCwhQFY718ioEmu5WcNddwG5c2t1sdauNbo1psdXtV3t2QPs3q2VDGjWzOjWkK/uvhvInh04dgzYvNno1hClL1+JU3DWIe8NLVpo9zkVlyYGS3a/0pMCZCwZYK0aKHpwyxMYWcGhQ8C2bdqIUkyM0a2h9OAKXJ8xWLIrTsFZF/OWyIqjSvXqsWSA1egjgRs2aKPZlCoGS3bEkgHWpv/OVq4EzpwxujVE3nEKzro8N1efN8/o1pgagyU7WrIEuHpVq6Vxxx1Gt4bSq0QJoHJlbZNLlhAgM4uNBRYs0O7zwsya9CBXD3opRQyW7IglA6yPuQRkBTL6ef48UKAAULu20a2hjNCTvGVkiSUEUsVgyY6Yr2R9+lW6VNdlCQEy+7lG3nBZMsC6JQRy5dJKCKxfb3RrTIuvbjuWDJBbliwsGWBlsvFxZCRw/DiwZYvRrSFKGfOV7FFCoHlz7T6n4lLFYMlu9H1+5M02Rw6jW0OZKSFw773afZ7AyIyOHtVqgclUvz6VQ9ak//64T1yqGCzZjf5i58nL+ngCIzPTg/i6dYH8+Y1uDfnjXLN6NVfgpoLBkt02s9RLBjBYsj59akPfd4vITDgFZx/FiwOVKmkrcPUNkSkJBkt2W5kib6pSO6N6daNbQ5lVtixQqpS2PFvKQRCZxY0biWUtGCzZA0sI2CtYGjNmDEqWLInw8HDUq1cPa71sADhhwgTcfffdyJMnj7o1b978lud369YNLpcrya2lVf/49eka2XKAK1OszzMXhFNxZCZ//AGcPattxCrTcGR9+rlGgiWuwL2Fpd5Rp02bhv79+2PgwIHYsGEDqlevjhYtWuDkyZMpPn/JkiV49NFHsXjxYqxatQpRUVGIiYnBkSNHkjxPgqNjx44l3L799ltYEvOV7H0CIzLbuUZWUclqKrK+xo2B8HBA3h///NPo1piOpYKlkSNHomfPnujevTsqV66MsWPHIjIyEhMnTkzx+ZMnT0bfvn1Ro0YNVKxYEZ9//jni4+OxMNmcbLZs2VC4cOGEm4xCWc6JE8DGjdp9bmZpH02bam9GUg5i3z6jW0Ok4YWZ/UREAE2aaPd5cXYLy1wSXL9+HevXr8frr7+e8FhISIiaWpNRI19cvnwZsbGxyJtss0cZgSpYsKAKkpo2bYqhQ4ciX758qR7n2rVr6qY7LxVsVeX/WHXzF/1YvhzT9euv6pfprlEDNyTY82M77C49/Rx0EREIvesuhCxfjrg5cxDfuzeszNR9bSMB7eezZxG2Zg1kb4BYKW/h4N+l3V7PIc2bI3TuXMTPnYu4Z5+FE/o51sdjWiZYOnXqFOLi4lCoUKEkj8vnO3fu9OkYr776KooWLaoCLM8puPbt26NUqVLYu3cv3njjDbRq1UoFYKGhoSkeZ9iwYRg0aNAtj8+bN0+NdPnbfB/2B6v11VeIArC7TBns4BYZAetnI5QvUQKVli/HyW++wdoo+S1bn1n72m4C0c9FVq7EnfHxuFCsGBZt2wbIzeHs8nq+LTwcUsrYvXQpfpsxA3FS783m/Xz58mV7BUuZ9Z///AdTp05Vo0iSHK7r1KlTwv2qVauiWrVqKFOmjHpes1QqYMvoluROeY4s6flQOXPm9GvEKy+O6OhoZJGK3KmJj0dYr17qbumnnkKpe+7xWxucwOd+NohLLhAmT0bhHTvQOjpaq85uUWbva7sIZD+HzJ6tPka2a4fWDt9SyXavZ7cb7uHDEXrwIFpGRMBtksVOsQHsZ31myDbBUv78+dVIzwnJzfEgn0uekTcjRoxQwdKCBQtUMORN6dKl1ffas2dPqsGS5DjJLTn5JQbiDybN40qukiS5Z8+OMEnSs8MfrQEC9fvLtDvvVEX/XKdOIYusQpLfscWZtq9txu/9LKukbl7dh7ZqhVD+Du33epYAafx4hMnv+f77Yfd+zuLj8SyT4J01a1bUrl07SXK2nqxdv379VL9u+PDhGDJkCObOnYs6deqk+X0OHz6Mf//9F0WKFIFlyG7RQvIHsmY1ujXkb1IGQk/aZ+IlGWn3buDvv7XzDEew7UlP2rfJ1KK/WCZYEjL1JbWTJk2ahB07dqBPnz64dOmSWh0nunbtmiQB/P3338fbb7+tVstJbabjx4+r28Wb1ZDl48svv4zVq1fjwIEDKvBq27YtypYtq0oSWAZXptgf6y2RGeivv0aN1Eg22XQFrlygSS7wwYNGt8Y0LBUsdezYUU2pDRgwQJUD2LRpkxox0pO+Dx48qOok6T777DO1iu7hhx9WI0X6TY4hZFpvy5YteOCBB1C+fHn06NFDjV79/vvvKU6zmZIEfrIdhmCwZF/6yNKGDdqUK5EReGFmf1JotF497T5Hl6yXs6Tr16+fuqVEkrI9yWiRNxEREfjN6lfq8jPL0kfZFkO2xyB7krw82cJGdnmXE1jnzka3iJxGyqVw70nnXJxJSR5J8ejRw+jWmIKlRpYojSs92R6D7D+6xKs9MmrvSVlmLSP5Vasa3RoKxrlmwQIgLs7o1pgCgyWr05O7eaXnnBOY/M65dxMZda7h3pP2JytwpQzO6dPa1D8xWLI0WZXy11+SfKWthCN7k6RaqREmeXncu4mM3Kib7E22WJJEb88g2eEYLFmZPh0jyXi5chndGgo0CZT0Gks8gVEwce9J5+G0fxIMluwyLE7Om4ojChbJXRE1agAFCxrdGgrmuUZy1S5cgNMxWLIqSbrTC3TKFhjkrBPY0qXa6iSiYNBHF3hh5hxlysiWFtpq66VL4XQMlqxKku4k+U6S8CQZj5yhShWtjMCVK8CKFUa3hpxAFhNwFNuZOJKdgMGS1a/0JAlPkvHIGaQ8hD6SyFwCCgZZTCCLCiRnrmFDo1tDwcRgKQGDJavilZ5z6cEST2AUDHpQLosLJGAi55BV1lImYtcubfW1gzFYsiLZ4kSS7gSDJedp3jxxKvaff4xuDdkdL8yci1ufJGCwZEWSbKdvcSJJeOQsRYoA1app9/Ukf6JAkEUEenIvF5I4E0sIKAyWrIhXesSpOAoG2R+MW5w4G7c+URgsWZEe4fNKz7m49QkFgx6My7mGe086k+fWJ+vXw6kYLFnN4cPAjh1a0p1ejp6c5+67gWzZgCNHgJ07jW4N2RUvzMhz65MFN4uTOhCDJauevOrWBfLkMbo1ZJSICC1gEpyKo0D499/EkQR9UQE5UzTLlTBYshrmK5GOiZcUSLJ4QKZ4pRBq0aJGt4bMECytWAFcugQnYrBkJfHxicOgHBYn/TWweDFw/brRrSG74RQc6cqWBUqU0FZhL1sGJ2KwZCWbNgGnTgG33QbcdZfRrSGjSfmAAgW01UqyaonIX2REicES6VzcOYDBkpXoL1Kpqpoli9GtIaNJkr+eS+LgxEsKgN27tYrNWbNqlbuJohkskVXwSo+Sc/gJjAJEfz3JXnDZsxvdGjKDZs20EaZt27S9Ah2GwZJVyC7zy5dr9xkskU5/LaxbB5w5Y3RryI71lYhEvnxArVqOHclmsGQVEijJ1gO33w5UqGB0a8gsihUDKlbUkv8l0ZsosySJV38tMVgiT9HOHclmsGTFKThW0iVPet6SA09gFAAySnnhApA3L1CzptGtITNp7pEj6bCdAxgsWQXzlSg1Dr7aowDQX0eSoxIaanRryEwaNgTCw7Wcpe3b4SQMlqzg5EmtbIB+AiPy1KSJ9qa2dy+wf7/RrSGr44UZpSY8PHF1pMMuzhgsWYBr0aLEujqy+zeRJ9nkUq+75cDES/Kj8+eB1au1+wyWKCXRzhzJZrBkASF6sMSTF6XGoScw8rOlS4G4OKBMGaBkSaNbQ2Y+1yxd6qidAxgsmZ3bDZfs0SQYLFFq9NeGvFbkzY4oIzgFR2mpWhUoWNBxOwcwWDK5244ehevQIa2Srr7LPFFydesCOXIAp08DGzca3RqyKgZLlJ6dA+Y7ZySbwZLJFdATuxs1AiIjjW4OmZVsfyPb4DjsBEZ+dPgwsHOn9maov5aIUhLtvGl/n4Olo0ePBrYllKICmzdrd/RIniitExiTvCkj9NdNnTpAnjxGt4ascK754w/H7Bzgc7B0xx13YMqUKTDamDFjULJkSYSHh6NevXpYu3at1+d///33qFixonp+1apVMWfOnCT/73a7MWDAABQpUgQRERFo3rw5dssmkmZw4wbyyz48gsPilBb9NSLV3iWfgCg9OAVHvrr9dqBSJW3nAH0Bks35HCy9++676N27Nzp06IDTkhdhgGnTpqF///4YOHAgNmzYgOrVq6NFixY4KXWIUrBy5Uo8+uij6NGjBzZu3Ih27dqp2zY9AAEwfPhwjBo1CmPHjsWaNWuQPXt2dcyrV6/CaK5165Dl8mW4WUmXfFG+PBAVpa1Q+f13o1tDViJvevrIEoMl8kW0s0ayfQ6W+vbtiy1btuDff/9F5cqV8fPPPyPYRo4ciZ49e6J79+6qDRLgREZGYuLEiSk+/+OPP0bLli3x8ssvo1KlShgyZAhq1aqFTz75JGFU6aOPPsJbb72Ftm3bolq1avj666/VlOPMmTNhNNfNF6Fb8gdYSZfSItvgODCXgPxALiDlolPyIuvXN7o1ZAXNnZXkHZaeJ5cqVQqLFi1SwUb79u1VABIWlvQQMuITCNevX8f69evx+uuvJzwWEhKips1WpbJ8UR6XkShPMmqkB0L79+/H8ePH1TF0uXLlUtN78rWdOnVK8bjXrl1TN915KeSm9p+MVTd/CblZMiD23nsR4sfjUlL678yfvzujuJo0QdjEiXDPm4cbJvx57NTXZpbefg6ZOxdyORbfuDHiJOjm78cnjn49N2yo3v9de/ci9q+/JECwZD/7esx0BUvi77//xvTp05EnTx41GpM8WAqUU6dOIS4uDoWSVbCWz3fKCo4USCCU0vPlcf3/9cdSe05Khg0bhkGDBt3y+Lx589RIlz+44uJwz5EjyAVgWZYsuJws14r8b74NrpCyxsejlbx+tm7FwilTcC13bpiRHfraCnzt57umToWcBbcXLYp9PNekm1Nfz43KlUO+HTuw/aOP8HeLFpbs58s+5nemK9KZMGECXnzxRTUSs337dhQoUABOJKNbniNWMrIUFRWFmJgY5JStJ/wktmVL/DZtGu7u2BFZZGk4BYRcWcgfYXR0tC362f3hh3Bt3ozmLhfcrVvDTOzW12aVrn6+ehVhjz6q7lbs1w8Vq1QJTiNtwOmv55D164EhQ1Dt5EncEcBzTSD7WZ8Z8luwJLk/svJMpuC6du2KYMufPz9CQ0Nx4sSJJI/L54ULF07xa+Rxb8/XP8pjshrO8zk1atRItS3ZsmVTt+Tkl+jvX+TVvHkDcly6lW36OSYG2LwZYYsXAwb8rTqqr03Op36W1ZNXrsgJEVnkvCfTcJQujn09t2ypgqWQxYtVWkygc2sD0c++Hs/nBG+ZApMEbyMCJZE1a1bUrl0bC/WtP9QCjnj1ef1UEhLlcc/nC4lO9edLDpYETJ7PkShTVsWldkwiSyVeut1Gt4bMTp/akNcNAyVKjzvv1DbydsDOAT4HSxJkFCtWDEaSqS+ZCpw0aRJ27NiBPn364NKlS2p1nJBAzjMB/LnnnsPcuXPxwQcfqLymd955B3/88Qf69eun/t/lcuH555/H0KFDMWvWLGzdulUdo2jRoqrEAJElybY4MvJ55Aiwa5fRrSGzY30lyqiwsMRq7zYvIWCp7U46duyIESNGqCKSMk22adMmFQzpCdoHDx7EsWPHEp7foEEDVUhz/PjxqibTDz/8oFbCVfGYk3/llVfwzDPPoFevXqhbty4uXryojilFLIksKSJC2x5HODTxlHz077+A5J0I7hJAGdHcGSUEgrOUzY9kVEgfGUpuyZIltzwmRTTllhoZXRo8eLC6EdmGjBLI9LKcwJ55xujWkFlJ9WWZqr3jDqBoUaNbQ3bYOSDSnnuYWmpkiYjSeQKTCwgn1oAh33AKjjKrvDN2DmCwRGRHsqopXz7gwgUgjf0TycG4xQlllsuVOBVn47wlBktEdiTLeJs1c0QuAWXQ3r2yjYGsnQYaNza6NWRl0fbfZonBEpFdOeAERpmgvy6kTMpttxndGrKyZjcvzDZvlkKFsCMGS0R2D5bWrAHOnTO6NWQ2zFcifylYUJv6F8lqG9oFgyUiuypRAihbVirKAkuXGt0aMhN5TchKOMFgifyhub1LCDBYIrIzTsVRSqS20tmzgGy0XKeO0a0hO4iJsfXOAQyWiOyMwRKlRH89NG0a8P28yCEaNUrcOWDnTtgNgyUiO5OtCGRlnGx7cuiQ0a0hs2C+EgVi54C777btxRmDJSI7k2kW2ezSpicwyoCLF4GVK7X7DJbIn6Jvvp7mzYPdMFgisjsbn8AoA5Yt06q6lywJlC5tdGvIrjsHXL8OO2GwROSUxEuprhsfb3RryExTcFJ9mchfqlcHChQALl0CVq+GnTBYIrK7evWAHDm0HeY3bjS6NWQ0bnFCgRISYtsSAgyWiOxOtrOQRG8bnsAonY4dA7Zt00aUZCUckb9F23Pan8ESkRPY9ARG6aQHy1JbSTZaJgrUueaPP4AzZ2AXDJaInHQCW7ECuHzZ6NaQUfRgWc9jI/K3YsWAihW1/Ei9SrwNMFgicoLy5YHixbUVKrIaipxH3rxYX4mCXc3bJhgsETmB5KhwKs7Ztm4FTp4EsmcH6tc3ujVkZ9H2O9cwWCJyChte7VE66G9ckuyfNavRrSE7u+ceICwM2L8f2LsXdsBgicgpmjXTRphkNdTRo0a3hoKN+UoULDlyJI5e2uTijMESkVPI6qdatZLW2iFnuHIF+P137T7zlSgYou21iTeDJSIn4VScM0mgdO0aEBUFVKhgdGvISeeahQuBGzdgdQyWiJx6ted2G90aMmIKjlucUDDUqaNt5H3uHLBuHayOwRKRkzRoAERGAidOaKujyFnBEqfgKFhCQxO3PrHBqjgGS0ROki2btlLFJicw8nGLEwmMZURJkvyJgqVFC+3jb7/B6hgsETk1l4DBkjPo+Wm1awP58xvdGnLiuWbNGuDsWVgZgyUip57ApJI3tz6xP1btJqMUL26brU8YLBE5TaVK2v5NsjqKW584Z4sT1lciI8TE2GIqjsESkdNI7oqeS8CpOHuTXCVJ5ucWJ2SGvCW3dVfgMlgiciIbJV5S6kL04qNNmmjJ/UTBds892vY6f/8N7N4Nq2KwROREsioqJAT480/g0CGjW0MB4mK+Ehkte3agUSPLj2RbJlg6ffo0OnfujJw5cyJ37tzo0aMHLl686PX5zzzzDCpUqICIiAgUL14czz77LM5JgSwPLpfrltvUqVOD8BMRGShvXqBuXcufwCh1oVevwrV8ufZJy5ZGN4ecLMb6K3AtEyxJoLR9+3bMnz8fs2fPxrJly9CrV69Un3/06FF1GzFiBLZt24avvvoKc+fOVUFWcl9++SWOHTuWcGvXrl2AfxoiE2Dekq3l27YNruvXgRIlgPLljW4OOVnMzWBp8WJAXpMWFAYL2LFjhwp01q1bhzpSQh3A6NGj0bp1axUMFS1a9JavqVKlCn788ceEz8uUKYN3330XXbp0wY0bNxAWlvijy0hV4cKFg/TTEJkoWBo8WFstFRenVdwl2yi0cWPi75lbnJCRqlcHChYETp4EVq1KLIxrIZYIllatWqUCGj1QEs2bN0dISAjWrFmDBx980KfjyBScTON5Bkri6aefxpNPPonSpUvjqaeeQvfu3dV0XGquXbumbrrz58+rj7GxsermL/qx/HlMupVj+7lmTYTlygXXmTO4sXo13HfeGfBv6di+DjLp34I3g6UbzZvDzf4OCL6efRfarBlCvv0WcXPmIF62XTJJP/t6TEsES8ePH0dBiUo9SMCTN29e9X++OHXqFIYMGXLL1N3gwYPRtGlTREZGYt68eejbt6/KhZL8ptQMGzYMgwYNuuVx+Xo5jr/J1CMFnhP7uW7lyii6ahV2jxmDv06dCtr3dWJfB1PkiROIPnoU8SEh+C02FjfmzDG6SbbG13PaihUsiNoALvz4I5amM1gKZD9f9rEwr8vtNq7wwWuvvYb3338/zSm46dOnY9KkSdi1a1eS/5MASoKWPn36eD2GjPxER0er4GrWrFnIkiVLqs8dMGCAymE65GWFUEojS1FRUSogk5Erf0a88uKQtntrM2WOk/vZ9fnnCOvbV13pxS1ZEvDv5+S+Dib3Z58h63PPIa5BA8QH4ffqVHw9p8Px48hSvDjcLhduHD4MFChgin6W9+/8+fMnzDyZcmTpxRdfRLdu3bw+R6bGJJ/opMx1epC8I1nxllau0YULF9CyZUvkyJEDM2bMSLOj69Wrp0agJBjKlkpdEnk8pf+TYwfiDyZQx6WkHNnPrVurDyFr1iBErrBy5QrKt3VkXwdR/MKF2p0WLdjPQcDXsw+iolTukmvzZmRZuhR49FGYoZ99PZ6hwVKBAgXULS3169fH2bNnsX79etSWzSAh28wsQnx8vApuvEWMLVq0UIGNjCiFh4en+b02bdqEPHnypBooEdmKrJSqUAGQUVt5g23f3ugWUWbFxsIlq45khIlbnJCZtGgBbN6sFcPNQLBkJEuUDqhUqZIaHerZsyfWrl2LFStWoF+/fujUqVPCSrgjR46gYsWK6v/1QCkmJgaXLl3CF198oT6X/Ca5xcnKHwA///wzPv/8c1VaYM+ePfjss8/w3nvvqfpMRI7Bat72smoVXBcu4FrOnHDXrGl0a4gS6fW+5s7V9i20EEskeIvJkyerAKmZZNSHhOChhx7CqFGjksxpSk6Tnqy1YcMGtVJOlC1bNsmx9u/fj5IlS6rhtzFjxuCFF16ApG7J80aOHKmCMiJHBUvyt6Tv3cRl5tZ2M+g9WaMGCkuVdiKzaNgQuO02bb9CGWGyUDBvmWBJkrOnTJmS6v9L8OOZq96kSZMkn6dERqvkRuRonns3/fWXNi1H1iVX7QD+qVkTrB5HppI1q7bV0k8/Ab/+aqlgiZcdRE4nezc1bqzdlxMYWZcshNmwQbtbo4bRrSHyPhVnIQyWiAho1Ur7yGDJ2m5uXeOuXh3X8uQxujVEqQdLK1cCZ8/CKhgsEVFisCRLen0s0kbmzVeKj442uiVEKStZEqhYUdtiSS9xYQEMlohIO3lJGQEptnpz2TlZjKwu0keWWDKArDC69Kt1RrIZLBGRtgKOU3HWJnvBSc5S9uxwZ3A7CaKgaNUqMW/JuE1E0oXBEhFpPIMli5zAyIMe5DZvrq06IjKrxo2BiAgpkAhs2wYrYLBERJp775Xa/8C+fcDu3Ua3htJL3yz35hY2RKYVHq6dbyy0Ko7BEhFpcuQA7r5bu8+pOGs5dQpYvTrpCCGRmbW0Vt4SgyUiSsS8JWuSxG6ZOq1aVduwlMgqwdLy5bLjPcyOwRIRpVxC4MoVo1tDvtKDW07BkVWUKweUKaM2fsaiRTA7BktElKhyZW1k4upVYMkSo1tDvpB6NXreB6fgyEpaWqeaN4MlIkrEEgLW88cfWs5SzpwASwaQVfOW3OZegctgiYiSYrBkzVVwUohSVjMSWYWsiMuWTdvEe8cOmBmDJSJKSnYFlzfdPXu0G5kbSwaQlTfxbtJEu//LLzAzBktEdGsJgUaNtPscXTK3Eye0aTjPKQ0iK2nTRvs4ezbMjMESEaU+FWfyqz3Hu7lxLmrWBIoUMbo1RBkPllasAM6cgVkxWCKiW913n/ZRNtW9eNHo1lBqOAVHVle6NFCpkraq8+ZG0GbEYImIblWxonYSu34dWLjQ6NZQSm7cSBxZYrBEdrg4m23eqTgGS0SUcgkBC5zAHE22Nzl7FsibF6hXz+jWEGV+Kk5yJGWEyYQYLBFRyvRgSfKW4uONbg0lpyfft2gBhIYa3RqijJP6YLlyAf/+C6xZAzNisEREKWvcGLjtNuDYMWDjRqNbQ8n9/LP2kVW7yeqyZElczWnSRSUMlogoZVIsTgodCk7FmYsU8du6FQgJYb4S2Wsq7hcGS0RkNcxbMveoUsOGQL58RreGKPNkhFRyJTdvBg4dgtkwWCKi1OmjFlL48OhRo1tDulmztI/33290S4j8I39+4K67kpbEMBEGS0SUukKFgDvvNO0JzJHOnweWLNHuM1giO2lj3qk4BktE5B2n4sxFCvfFxgLlygEVKhjdGiL/n2sWLACuXIGZMFgiIt9OYPPnA1evGt0a0vOVZFRJcjyI7KJaNaBYMS1Q0kdPTYLBEhF5V6MGcPvtwOXLpjuBOY4U7NOnKDgFR3bjcpl2Y10GS0TkHat5m8eqVVrhvty5tZVwRHZz/82LgJ9+AtxumAWDJSJKm2ewZKITmGOn4GSVohTyI7KbZs2A7NmBI0eA9ethFgyWiChtTZsC4eFaMcRt24xujXN55isR2VF4eGI1bxldMgnLBEunT59G586dkTNnTuTOnRs9evTAxYsXvX5NkyZN4HK5ktyeeuqpJM85ePAg2rRpg8jISBQsWBAvv/wybshu3kSUKDISiI7W7s+caXRrnGnPHmDHDiAsLPHNhMiO2rbVPjJYSj8JlLZv34758+dj9uzZWLZsGXr16pXm1/Xs2RPHjh1LuA0fPjzh/+Li4lSgdP36daxcuRKTJk3CV199hQEDBgT4pyGyoHbttI8MlowdVbr7bi1niciu2rTRNoeWLX327YMZWCJY2rFjB+bOnYvPP/8c9erVQ6NGjTB69GhMnToVR9OoKiwjRoULF064yciUbt68efjzzz/xv//9DzVq1ECrVq0wZMgQjBkzRgVQRORBpn5kL7ING7TpODImWHrgAaNbQhRYefNqG3mbaHQpDBawatUqNfVWp06dhMeaN2+OkJAQrFmzBg8++GCqXzt58mQVDEmgdP/99+Ptt99WAZR+3KpVq6KQVCm+qUWLFujTp48axapZs2aKx7x27Zq66c5LRV1InbhYdfMX/Vj+PCbdiv3so9y5EdqwIUJ+/x1x06cjvl+/dB+CfZ1BZ84gbNkySFWlWJmCS6P/2M/BwX4OnJD77kPo4sWInzEDsb17B6yffT2mJYKl48ePq3wiT2FhYcibN6/6v9T83//9H0qUKIGiRYtiy5YtePXVV7Fr1y5Mnz494biegZLQP/d23GHDhmHQoEG3PC4jVXog5k8y9UiBx35OW+ly5VD1999x+ssvsbJ06Qwfh32dPsWWLkXtuDhcKFYMi3btAuTmA/ZzcLCf/S/ittsQI5VLVqzA0h9/BHLmDEg/X5b6cWYPll577TW8//77aU7BZZRnTpOMIBUpUgTNmjXD3r17UaZMmQwf9/XXX0f//v2TjCxFRUUhJiYmyTSfPyJeeXFER0cjC5cJBwz7OR0qVQImTkT+P/9E63r10r3jPfs6Y0K/+kp9jOzSBa31zY29YD8HB/s5sNxjxsC1ZQuaXrmCuTlzBqSf9ZkhUwdLL774Irp16+b1OaVLl1ZTaCdPnkzyuKxYkxVy8n++knwnsWfPHhUsydeuXbs2yXNOnDihPno7brZs2dQtOfklBuIPJlDHpaTYzz4oXx6oXh2uzZuR5bffgMcfz9Bh2NfpIFe+0tcSNHXogNB09Bv7OTjYzwEiKTZbtiCLVK1/4omA9LOvxzM0wbtAgQKoWLGi11vWrFlRv359nD17Fus9ClQtWrQI8fHxCQGQLzZt2qQ+ygiTkONu3bo1SSAmVwkyOlS5cmW//qxEtlsVN2OG0S1xBgmUZK+sEiWAVPIoiexcQsC1YAFCPPKEjWCJ1XCVKlVCy5YtVRkAGQlasWIF+vXrh06dOql8JHHkyBEVXOkjRTLVJivbJMA6cOAAZs2aha5du6Jx48aoJpv1AWraTIKixx57DJs3b8Zvv/2Gt956C08//XSKI0dEdPNqT8ybp416UGDdzLFE+/bcOJecty9l8eJwXb6MAps3G9oUSwRL+qo2CYYk50jm7KV8wPjx45PMHUvytp6sJSNSCxYsUAGRfJ1M+T300EP4WV9+K0PaoaGqZpN8lFGmLl26qIBq8ODBhvyMRJYgFxslS2qjHRIwUeBICRN9Pz4JloicxOVKGF0qsmaNoU2xxGo4ISvfpkyZkur/lyxZEm6PPask4Xrp0qVpHldWy82ZM8dv7SRyxAlMRpc+/FCbitOn5cj/liwBzp6VZbqSN2B0a4iCT4Kl0aNR+I8/pJK0YXsiWmZkiYhMRA+QZKSW2wMFfgpO+lsqGhM5TePGcOfOjWznzsG1erVhzWCwRETp17AhkD+/KpaIZcuMbo09yVW0vrUMp+DIqbJkQfw77+CPF16Au2pVw5rBYImI0k9GOfRtN7gqLjBWrZJaJto+cE2aGN0aIsPE9+2LI/fcowpTGoXBEhFlblWcBEvx8Ua3xr5TcLInX9asRreGyNEYLBFRxkRHa1d6R45ooyDkP7JYxbNkABEZisESEWWM1CLTE72/+87o1tjLxo3A338DstdkjOyQRURGYrBERBn3yCPax++/51ScP+mjSq1aaQETERmKwRIRZW4qLlcu4NgxYPlyo1tjnym4H35ImhdGRIZisEREGSeJx/obOqfi/EO2ddi1CwgP15K7ichwDJaIyD9TcTIaIrWBKHOmTtU+tmlj6FJpIkrEYImIMqdZMyBPHq0m0O+/G90a60/B6cFSp05Gt4aIbmKwRESZw6k4/5HNQmUV3G23Aa1bG90aIrqJwRIR+W8q7scfuVdcZuijSrJ5KFfBEZkGgyUiyrymTYF8+YCTJ7lXXEZJvte0adp9TsERmQqDJSLKvCxZEitNcyouYyTIPH5cy/9iIUoiU2GwRET+wak4/0zBPfQQ94IjMhkGS0TkH02aAPnzA6dOAYsWGd0aa4mNTSxEySk4ItNhsERE/hEWBnTooN3/3/+Mbo21LFgAnD4NFCqkBZ1EZCoMlojIf7p2TZyKu3jR6NZYx7ffah8l2AwNNbo1RJQMgyUi8p969YBy5YDLlxM3gyXvrlwBZs7U7nMKjsiUGCwRkf+4XMBjj2n3v/nG6NZYwy+/ABcuAFFRQP36RreGiFLAYImI/KtLF+3jwoXA4cNGt8b8vvpK+/h//weE8JRMZEb8yyQi/ypVCrj7bm2fs8mTjW6NuR07Bsydq93v3t3o1hBRKhgsEVHgEr2//loLmihlEkxK5W6ZfqtQwejWEFEqGCwRkf89/DCQLRvw55/Axo1Gt8acJIj88kvtfrduRreGiLxgsERE/pc7t7YZrGCid8r++EMLJsPDgY4djW4NEXnBYImIAjsVN2WKVqGaktJHlWRPvVy5jG4NEXnBYImIAkM2gy1QADh5Epg3z+jWmMvVq4mFKDkFR2R6DJaIKDCyZNGWw+uJ3pTop5+As2e12kpNmxrdGiJKA4MlIgr8VJxUqJYNdilpbSXpH25vQmR6DJaIKHBq1QJq1wauX08MEJzuyJHEaUlOwRFZgmWCpdOnT6Nz587ImTMncufOjR49euCil406Dxw4AJfLleLt+++/T3heSv8/derUIP1URA7w1FPax3HjgPh4o1tjPFkdKP3QqBFQtqzRrSEiOwVLEiht374d8+fPx+zZs7Fs2TL06tUr1edHRUXh2LFjSW6DBg3CbbfdhlatWiV57pdffpnkee3atQvCT0TkELI5bM6cwJ49cC1eDEeTIElfBceK3USWEQYL2LFjB+bOnYt169ahTp066rHRo0ejdevWGDFiBIoWLXrL14SGhqJw4cJJHpsxYwYeeeQRFTB5kpGq5M8lIj+RvzfZXHfMGISMHw88/jgcS/bL++svIEcOoEMHo1tDRHYKllatWqUCGj1QEs2bN0dISAjWrFmDBx98MM1jrF+/Hps2bcKYMWNu+b+nn34aTz75JEqXLo2nnnoK3bt3V9Nxqbl27Zq66c6fP68+xsbGqpu/6Mfy5zHpVuznIHjiCWQZMwauWbMQfv/9ju3r0FGj1HB+3GOPIV6KUQaoH/iaDg72s/X72ddjWiJYOn78OAoWLJjksbCwMOTNm1f9ny+++OILVKpUCQ0aNEjy+ODBg9G0aVNERkZi3rx56Nu3r8qFevbZZ1M91rBhw9SUXnLy9XIcf5OpRwo89nNgNapUCfl27EDxBQswP29eOE3EiROInjNH3V9SuTIu3rwfSHxNBwf72br9fPnyZfMHS6+99href//9NKfgMuvKlSuYMmUK3n777Vv+z/OxmjVr4tKlS/jvf//rNVh6/fXX0b9//yQjS5IjFRMToxLQ/RnxyosjOjoaWaRmDQUE+zk4XKdPqxGmEvPmocTYscgiIysOEvLGG3DFxyO+WTM09pJv6Q98TQcH+9n6/azPDJk6WHrxxRfRLY2lszI1JvlEJ6UKsIcbN26oFXK+5Br98MMPKnrsqtd88aJevXoYMmSImmbLJhuBpkAeT+n/5JcYiD+YQB2XkmI/B1inTnC/9BIiT53CjUWLEKbvHecEV64kJHaHPPMMQoL0OuNrOjjYz9btZ1+PZ2iwVKBAAXVLS/369XH27FmVd1RbarYAWLRoEeLj41Vw48sU3AMPPODT95K8pjx58qQaKBFRBkVEIL5rV4R+9JGW6O2kYGnaNODff4HixYH77jO6NURkx9IBkmvUsmVL9OzZE2vXrsWKFSvQr18/dOrUKWEl3JEjR1CxYkX1/5727NmjygxIAndyP//8Mz7//HNs27ZNPe+zzz7De++9h2eeeSZoPxuRk8T36KE+un79Ffj7bziC2w188ol2v08fVuwmsiBLBEti8uTJKhhq1qyZKhnQqFEjjJerU485zV27dt2SrDVx4kQUK1ZM5ROlNPwmq+Nk5KpGjRoYN24cRo4ciYEDBwblZyJynAoV8E/VqnBJAJHCylRbkgu49etl/h64GSwSkbVYYjWckJVvkqSdmpIlS8ItJ+BkZKRIbimR0Sq5EVHw7L3/fhTYulWr6P3mm0CuXLA1fVRJinP6kApAROZjmZElIrKHE3XqwF2pkixDATxGh21JFqZ89512v18/o1tDRBnEYImIgiskBHEvvqjd/+gjqfIK2/rsM20T4TvvBDyK6hKRtTBYIqKgc8uUlCzOOHoU8DK9bmkXLgAff6zd96jLRkTWw2CJiIIva1bghRe0+//9r7bBrN2MHQucOaOS2vHww0a3hogygcESERlDqlhLxXup0v/LL7BdEcoPPtDuv/YaywUQWRyDJSIyhgRKUndIDB8OW/niC+DECaBECaBzZ6NbQ0SZxGCJiIwjezDKlNzy5cDKlbAFSejWg79XX5WCbka3iIgyicESERlHkrwfe8xeo0vffAMcOgQUKQJ07250a4jIDxgsEZGxXnoJcLmAn34C/vgDlnbjBvCf/yT+XOHhRreIiPyAwRIRGatiRaBLl8RpqxQq8VvG99/LhpRAvnxA795Gt4aI/ITBEhEZb/BgLXdp0SJg/nxYUlwc8O672n0pi5A9u9EtIiI/YbBERMYrWRJ4+unE0SUr1l368ktg+3Ygd+7En4WIbIHBEhGZwxtvaOUENm0Cpk6Fpcg+d7IpsHjnHS1gIiLbYLBEROaQP782qiQk8LDSnnHvvadtmivVuvv2Nbo1RORnDJaIyDyee05bcn/gADBuHCxh3z7gww+1+1K1m3WViGyHwRIRmYckRcs0lhgyRJveMrtXXtEKUcbEAK1bG90aIgoABktEZC5PPKFNZ506pQVMZrZ0KfDjj0BICDBypFYviohsh8ESEZlLWFjiJrQSgKxZA9OWCnj+ee3+U08Bd9xhdIuIKEAYLBGR+bRpo21AKyUEZKTJjMnen3+urdzLlQsYNMjo1hBRADFYIiJz+vhjoFAh4M8/zTcdt3s38OKL2n0JlGQlHxHZFoMlIjIn2TLk00+1+7Lf2oYNMIXYWG17lkuXgCZNgH79jG4REQUYgyUiMq/27YEOHbT8oO7dtVVnRpNRrrVrtcKTX38NhIYa3SIiCjAGS0Rkbp98ok1zbdkCDBtmbFuWL0/c/23sWCAqytj2EFFQMFgiInMrWBAYPVq7P3QosHChMe04dw547DEt6Vw+duxoTDuIKOgYLBGR+UlgIqvjbtwAHnoI2LEjuN/f7dZyk6SyeKlS2mgXETkGgyUiMj8p9ihL9Rs00EZ4pLSA7MUWLIMHA//7n1Z88ptvtA1/icgxGCwRkTWEhwMzZwKlSwP79wNt2wJXrgT++0phTH0Llo8+Aho2DPz3JCJTYbBERNZRoAAwZw6QJw+wejXQrZuWQxQo48cn1lOSfKlnngnc9yIi02KwRETWIvvGTZ8OZMkCfPcd0Lt3YEoKfPutto2JvlnuG2/4/3sQkSUwWCIi65FikJLDpOcyNW0KHD/uv+NLfpKseJPE7j59tKKY3CSXyLEYLBGRNXXtCvz8s5ZsvWIFUKcOsG5d5o55+jTw6KNaoCSFMOWjrHxjoETkaJYJlt599100aNAAkZGRyC2Vc33gdrsxYMAAFClSBBEREWjevDl2y55OHk6fPo3OnTsjZ86c6rg9evTAxYsXA/RTEJFfyao4qaZdsSJw5Ahw993aSFNG8pjmzQOqVgWmTtWqcg8YAEycqK2AIyJHs8xZ4Pr16+jQoQP6yJC4j4YPH45Ro0Zh7NixWLNmDbJnz44WLVrg6tWrCc+RQGn79u2YP38+Zs+ejWXLlqFXr14B+imIKCA5TGvWAA88AFy7BvTsCZQvr23Ee/6896+VabY//gDkb75FC+DoUe1rV67UNsgNCwvWT0FEJmaZM8EgOXEB+Oqrr3weVfroo4/w1ltvoa0sMYZs4/Q1ChUqhJkzZ6JTp07YsWMH5s6di3Xr1qGODOFDCgWPRuvWrTFixAgULVo0gD8REfmNTMXNmCFXSMD77wN79wLPPw+8/ba2p5zUZ8qVS3uefPznH+35cjt0KPE4UnhSvj4y0sifhohMxjLBUnrt378fx48fV1Nvuly5cqFevXpYtWqVCpbko0y96YGSkOeHhISokagHH3wwxWNfu3ZN3XTnb169xsbGqpu/6Mfy5zHpVuxnG/W1LPN/6imETJ6MkE8+gWvnTmDUKO2WCnf27HC3aIH43r3hvvdevaGwMr6mg4P9bP1+9vWYtg2WJFASMpLkST7X/08+FpR9pzyEhYUhb968Cc9JybBhwxJGujzNmzdP5VT5m0wRUuCxn23U18WKqU13C2zahOKLFiHbmTPIcvkysly6hLArV+AOCcHJWrVw9K678E/16ojPlk0rcCk1nGyEr+ngYD9bt58vX75s/mDptddew/sy5O2FTJVVlORNE3n99dfRv3//JCNLUVFRiImJUYni/ox45cURHR2NLFJThgKC/WzjvpYE8DffTPG/ity82RFf08HBfrZ+P+szQ6YOll588UV0kwq8XpSWrQ0yoHDhwurjiRMn1Go4nXxeo0aNhOecTLa/1I0bN9QKOf3rU5ItWzZ1S05+iYH4gwnUcSkp9nPwsK+Dg/0cHOxn6/azr8czNFgqUKCAugVCqVKlVMCzcOHChOBIIkjJRdJX1NWvXx9nz57F+vXrUbt2bfXYokWLEB8fr3KbiIiIiCxTOuDgwYPYtGmT+hgXF6fuy82zJpJM182Q1S1qk3IXnn/+eQwdOhSzZs3C1q1b0bVrV7XCrV27duo5lSpVQsuWLdGzZ0+sXbsWK1asQL9+/VTyN1fCERERkaUSvKW45KRJkxI+r1mzpvq4ePFiNJGtDwDs2rUL586dS3jOK6+8gkuXLqm6STKC1KhRI1UqIFx2L79p8uTJKkBq1qyZWgX30EMPqdpMRERERJYKlqS+Ulo1lqS2kicZXRo8eLC6pUZWvk2ZMsVv7SQiIiJ7scw0HBEREZERGCwRERERecFgiYiIiMgLBktEREREXjBYIiIiIvKCwRIRERGRFwyWiIiIiLxgsERERETkBYMlIiIiIjtU8DYzvXK4bNTrT7Gxsbh8+bI6Lne0Dhz2c/Cwr4OD/Rwc7Gfr97P+vp18B5DkGCz5wYULF9THqKgoo5tCREREGXgfz5UrV6r/73KnFU5RmuLj43H06FHkyJFD7Ufnz4hXArBDhw4hZ86cfjsuJcV+Dh72dXCwn4OD/Wz9fpYQSAKlokWLIiQk9cwkjiz5gXRwsWLFAnZ8eXHwDzHw2M/Bw74ODvZzcLCfrd3P3kaUdEzwJiIiIvKCwRIRERGRFwyWTCxbtmwYOHCg+kiBw34OHvZ1cLCfg4P97Jx+ZoI3ERERkRccWSIiIiLygsESERERkRcMloiIiIi8YLBERERE5AWDJRMbM2YMSpYsifDwcNSrVw9r1641ukm2MmzYMNStW1dVXi9YsCDatWuHXbt2Gd0s2/vPf/6jKt0///zzRjfFdo4cOYIuXbogX758iIiIQNWqVfHHH38Y3SxbiYuLw9tvv41SpUqpPi5TpgyGDBmS5t5ilLZly5bh/vvvV9W05Rwxc+bMJP8vfTxgwAAUKVJE9X3z5s2xe/duBAODJZOaNm0a+vfvr5ZLbtiwAdWrV0eLFi1w8uRJo5tmG0uXLsXTTz+N1atXY/78+WqzxpiYGFy6dMnoptnWunXrMG7cOFSrVs3optjOmTNn0LBhQ7XR6K+//oo///wTH3zwAfLkyWN002zl/fffx2effYZPPvkEO3bsUJ8PHz4co0ePNrpplnfp0iX1XicDBSmRfh41ahTGjh2LNWvWIHv27Op98erVq4FvnJQOIPO588473U8//XTC53Fxce6iRYu6hw0bZmi77OzkyZNyaeheunSp0U2xpQsXLrjLlSvnnj9/vvuee+5xP/fcc0Y3yVZeffVVd6NGjYxuhu21adPG/cQTTyR5rH379u7OnTsb1iY7AuCeMWNGwufx8fHuwoULu//73/8mPHb27Fl3tmzZ3N9++23A28ORJRO6fv061q9fr4YYPfefk89XrVplaNvs7Ny5c+pj3rx5jW6KLckoXps2bZK8rsl/Zs2ahTp16qBDhw5qWrlmzZqYMGGC0c2ynQYNGmDhwoX466+/1OebN2/G8uXL0apVK6ObZmv79+/H8ePHk5w/ZE83SVEJxvsiN9I1oVOnTql58UKFCiV5XD7fuXOnYe2ys/j4eJVDI9MYVapUMbo5tjN16lQ1nSzTcBQY+/btU9NDMn3/xhtvqL5+9tlnkTVrVjz++ONGN882XnvtNZw/fx4VK1ZEaGioOle/++676Ny5s9FNs7Xjx4+rjym9L+r/F0gMlohujnps27ZNXSGSfx06dAjPPfecyguTxQoUuIBfRpbee+899bmMLMlrWvI7GCz5z3fffYfJkydjypQpuOOOO7Bp0yZ1oSVJyexn++I0nAnlz59fXbGcOHEiyePyeeHChQ1rl13169cPs2fPxuLFi1GsWDGjm2M7MqUsCxNq1aqFsLAwdZPkeknUlPtyZU6ZJyuEKleunOSxSpUq4eDBg4a1yY5efvllNbrUqVMntdrwsccewwsvvKBW11Lg6O99Rr0vMlgyIRk2r127tpoX97xqlM/r169vaNvsRHIIJVCaMWMGFi1apJYCk/81a9YMW7duVVfg+k1GQGTaQu7LhQFlnkwhJy99IXk1JUqUMKxNdnT58mWVQ+pJXsNyjqbAkfOzBEWe74syHSqr4oLxvshpOJOSvAMZ0pU3lTvvvBMfffSRWlbZvXt3o5tmq6k3GUr/6aefVK0lfd5bkgalhgf5h/Rt8jwwWfIrtYCYH+Y/MrohyccyDffII4+oumzjx49XN/IfqQMkOUrFixdX03AbN27EyJEj8cQTTxjdNMu7ePEi9uzZkySpWy6oZNGN9LdMdw4dOhTlypVTwZPUu5LpT6mRF3ABX29HGTZ69Gh38eLF3VmzZlWlBFavXm10k2xFXv4p3b788kujm2Z7LB0QGD///LO7SpUqajl1xYoV3ePHjze6SbZz/vx59dqVc3N4eLi7dOnS7jfffNN97do1o5tmeYsXL07xnPz4448nlA94++233YUKFVKv8WbNmrl37doVlLa55J/Ah2RERERE1sScJSIiIiIvGCwRERERecFgiYiIiMgLBktEREREXjBYIiIiIvKCwRIRERGRFwyWiIiIiLxgsERERETkBYMlIiIPsrGvbBvSvn37JI+fO3cOUVFRePPNNw1rGxEZgxW8iYiSkQ1oa9SogQkTJqgNf0XXrl2xefNmrFu3Tm12TUTOwWCJiCgFo0aNwjvvvIPt27erTWk7dOigAqXq1asb3TQiCjIGS0REKZBTY9OmTREaGoqtW7fimWeewVtvvWV0s4jIAAyWiIhSsXPnTlSqVAlVq1bFhg0bEBYWZnSTiMgATPAmIkrFxIkTERkZif379+Pw4cNGN4eIDMKRJSKiFKxcuRL33HMP5s2bh6FDh6rHFixYAJfLZXTTiCjIOLJERJTM5cuX0a1bN/Tp0wf33nsvvvjiC5XkPXbsWKObRkQG4MgSEVEyzz33HObMmaNKBcg0nBg3bhxeeukllexdsmRJo5tIREHEYImIyMPSpUvRrFkzLFmyBI0aNUryfy1atMCNGzc4HUfkMAyWiIiIiLxgzhIRERGRFwyWiIiIiLxgsERERETkBYMlIiIiIi8YLBERERF5wWCJiIiIyAsGS0REREReMFgiIiIi8oLBEhEREZEXDJaIiIiIvGCwREREROQFgyUiIiIipO7/AYH4F+Dfs+FcAAAAAElFTkSuQmCC",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"x = np.linspace(0, 10, 100)\n",
|
||
"y = np.sin(x)\n",
|
||
"plt.plot(x, y, 'r', label='sin(x)')\n",
|
||
"plt.xlabel(\"X\")\n",
|
||
"plt.ylabel(\"Y\")\n",
|
||
"plt.color = 'r'\n",
|
||
"plt.title(\"График синуса\")\n",
|
||
"plt.legend()\n",
|
||
"plt.grid()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6a8e6151-d6e3-449a-9a06-bcdeba90b489",
|
||
"metadata": {},
|
||
"source": [
|
||
"2) Добавить несколько графиков (plt.plot(x, np.cos(x), label='cos(x)'))."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "eba73f2c-f4ca-4e2f-92ba-7e6f5f0ba83d",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"x = np.linspace(0, 10, 100)\n",
|
||
"y = np.sin(x)\n",
|
||
"plt.plot(x, y, 'r', label='sin(x)')\n",
|
||
"plt.plot(x, np.cos(x), 'g', label='cos(x)')\n",
|
||
"plt.xlabel(\"X\")\n",
|
||
"plt.ylabel(\"Y\")\n",
|
||
"plt.color = 'r'\n",
|
||
"plt.title(\"График синуса и косинуса\")\n",
|
||
"plt.legend()\n",
|
||
"plt.grid()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "a6388696-7ffa-48c2-bbca-a2a7d2d89143",
|
||
"metadata": {},
|
||
"source": [
|
||
"3) bar"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "dd37f5ae-9669-4859-b483-4131dd4ba760",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAjIAAAGwCAYAAACzXI8XAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvc2/+5QAAAAlwSFlzAAAPYQAAD2EBqD+naQAAGRRJREFUeJzt3QuQlXX9+PEPlxV2DTYULxCrkFmhmEx5ycsUJis1apozTKaNpKWWV9K8lZKkhtrkkJcRpUibQm2mTGvMYbMBx/GCSBpZ3hrHLEcRL4uK4Qr7n+fxB/9WrgqH53wOr9fMM2f2uOeZr893zzlvnuc55+nV3d3dHQAACfWuegAAAO+XkAEA0hIyAEBaQgYASEvIAABpCRkAIC0hAwCk1Tca3PLly+O5556LAQMGRK9evaoeDgCwHoqvuXvttddi6NCh0bt37803ZIqIaWtrq3oYAMD78Oyzz8awYcM235Ap9sSs2BADBw6sejh1o6urK2bNmhUHHXRQNDU1VT0cNiJz25jMa+Myt6u3ePHickfEivfxzTZkVhxOKiJGyPR84rS0tJTbxBOnsZjbxmReG5e5Xbt1nRbiZF8AIC0hAwCkJWQAgLSEDACQlpABANISMgBAWkIGAEhLyAAAaQkZACAtIQMApCVkAIC0Kg2Zu+++Ow499NDyEt3FtRR+97vfrXIJ70mTJsWQIUOiubk5xo4dG08++WRl4wUA6kulIfPGG2/E7rvvHtdcc81q//vll18eV155ZUybNi0eeOCB2HLLLWPcuHHx3//+d5OPFQCoP5Ve/foLX/hCuaxOsTdm6tSpcf7558dhhx1W3veLX/witttuu3LPzZFHHrmJRwsA1JtKQ2Ztnn766Xj++efLw0krtLa2xt577x333XffGkNm6dKl5bLC4sWLV14mvVh4x4ptYZs0HnPbmMxr4zK3q7e+26NuQ6aImEKxB+Z/FT+v+G+rM2XKlJg8efIq98+aNStaWlpqMNLcOjo6qh4CNWJuG5N5bVzmtqclS5ZE6pB5v84777w444wzeuyRaWtri4MOOigGDhxY6djqrXSLJ017e3s0NTVVPRw2InPbmMzrurRGVl1dzdHRMSPa24+LpqY3I5/Omqx1xRGVtCGz/fbbl7cvvPBC+amlFYqfR48evcbH9evXr1zerXjie/KvynZpXOa2MZnXNckYAD0VEZMzZJpqs9b1/Duv2++RGTFiRBkzd911V486Kz69tM8++1Q6NgCgPlS6R+b111+Pp556qscJvg8//HBstdVWscMOO8TEiRPj4osvjp133rkMmwsuuKD8zpnDDz+8ymEDAHWi0pCZN29eHHDAASt/XnFuy4QJE+KGG26Is88+u/yumRNOOCFeffXV2H///ePOO++M/v37VzhqAKBeVBoyY8aMKb8vZk2Kb/v9wQ9+UC4AAGnOkQEAWBchAwCkJWQAgLSEDACQlpABANISMgBAWkIGAEhLyAAAaQkZACAtIQMApCVkAIC0hAwAkJaQAQDSEjIAQFpCBgBIS8gAAGkJGQAgLSEDAKQlZACAtIQMAJCWkAEA0hIyAEBaQgYASEvIAABpCRkAIC0hAwCkJWQAgLSEDACQlpABANISMgBAWkIGAEhLyAAAaQkZACAtIQMApCVkAIC0hAwAkJaQAQDSEjIAQFpCBgBIS8gAAGkJGQAgLSEDAKQlZACAtIQMAJCWkAEA0hIyAEBaQgYASEvIAABpCRkAIC0hAwCkJWQAgLSEDACQlpABANISMgBAWkIGAEhLyAAAaQkZACAtIQMApCVkAIC0hAwAkFZdh8yyZcviggsuiBEjRkRzc3PstNNOcdFFF0V3d3fVQwMA6kDfqGOXXXZZXHvttXHjjTfGrrvuGvPmzYtjjz02Wltb47TTTqt6eABAxeo6ZO6999447LDD4uCDDy5/Hj58eNx0000xd+7cNT5m6dKl5bLC4sWLy9uurq5y4R0rtoVt0njMbWMyr+vSHFl1dTX3uM2nqzZrXc+/9boOmX333Teuv/76eOKJJ+KjH/1oPPLII3HPPffEFVdcscbHTJkyJSZPnrzK/bNmzYqWlpYajzifjo6OqodAjZjbxmRe1+SmyK6jY0bkdEdN1rpkyZL1+r1e3XV8wsny5cvju9/9blx++eXRp0+f8pyZSy65JM4777z3tEemra0tFi1aFAMHDtxEI69/RekWL4jt7e3R1NRU9XDYiMxtYzKv69IaWRV7YoqIaW8/Lpqa3ox8Omuy1uL9e/DgwdHZ2bnW9++63iPz61//On71q1/FzJkzy3NkHn744Zg4cWIMHTo0JkyYsNrH9OvXr1zerXjie/KvynZpXOa2MZnXNckYAD0VEZMzZJpqs9b1/Duv65A566yz4txzz40jjzyy/Hm33XaLZ555pjx8tKaQAQA2H3X98evi+Fjv3j2HWBxiKg45AQDU9R6ZQw89tDwnZocddigPLf3lL38pT/Q97rjjqh4aAFAH6jpkrrrqqvIL8U466aRYuHBheW7MiSeeGJMmTap6aABAHajrkBkwYEBMnTq1XAAAUp0jAwCwNkIGAEhLyAAAaQkZACAtIQMApCVkAIC0hAwAkJaQAQDSEjIAQFpCBgBIS8gAAGkJGQAgLSEDAKQlZACAtIQMAJCWkAEA0hIyAEBaQgYASEvIAABpCRkAIC0hAwCkJWQAgLSEDACQlpABANISMgBAWkIGAEhLyAAAaQkZYDPTK+nS+n/jb62DsbzfBTY+IQMApCVkAIC0hAwAkJaQAQDSEjIAQFpCBgBIS8gAAGkJGQAgLSEDAKQlZACAtIQMAJCWkAEA0hIyAEBaQgYASEvIAABpCRkAIC0hAwCkJWQAgLSEDACQlpABANISMgBAWkIGAEhLyAAAaQkZACAtIQMApCVkAIC0hAwAkJaQAQDSEjIAQFpCBgBIS8gAAGnVfcj85z//ia9+9aux9dZbR3Nzc+y2224xb968qocFANSBvlHHXnnlldhvv/3igAMOiD/+8Y+xzTbbxJNPPhmDBg2qemgAQB2o65C57LLLoq2tLX7+85+vvG/EiBGVjgkAqB91HTK33357jBs3LsaPHx9z5syJD33oQ3HSSSfF8ccfv8bHLF26tFxWWLx4cXnb1dVVLrxjxbawTRqPuV2X5sioq6u5x21OtfybzLtd8s9tV23Wup6vYb26u7u7o07179+/vD3jjDPKmHnwwQfj9NNPj2nTpsWECRNW+5gLL7wwJk+evMr9M2fOjJaWlpqPGQDYcEuWLImjjjoqOjs7Y+DAgTlDZosttog99tgj7r333pX3nXbaaWXQ3Hfffeu9R6Y4PLVo0aK1bojNTVG6HR0d0d7eHk1NTVUPh43I3K5La2RU/Gu9o2NGtLcfF01Nb0ZOnTVcd855bYy57azJWov378GDB68zZOr60NKQIUNil1126XHfyJEj4ze/+c0aH9OvX79yebfiBd2L+qpsl8Zlbtck4xvF/1e80eV8syvU8u8x6zZphLltqs1a1/P1q64/fl18Yunxxx/vcd8TTzwRO+64Y2VjAgDqR12HzLe//e24//7744c//GE89dRT5Xku119/fZx88slVDw0AqAN1HTJ77rln3HrrrXHTTTfFqFGj4qKLLoqpU6fG0UcfXfXQAIA6UNfnyBQOOeSQcgEASLVHBgBgbYQMAJCWkAEA0hIyAEBaQgYASEvIAABpCRkAIC0hAwCkJWQAgLSEDACQlpABANISMgBAWkIGAEhLyAAAaQkZAKDxQ+a5556r7UgAAGoVMrvuumvMnDnzva4fAKD6kLnkkkvixBNPjPHjx8fLL79cuxEBAGzskDnppJPir3/9a7z00kuxyy67xO9///v1fSgAQE30fS+/PGLEiPjzn/8cV199dRxxxBExcuTI6Nu35yrmz5+/sccIALDhIVN45pln4re//W0MGjQoDjvssFVCBgBgU3lPFTJ9+vQ488wzY+zYsfHoo4/GNttsU7uRAQBsrJD5/Oc/H3Pnzi0PKx1zzDHr+zAAgOpDZtmyZeXJvsOGDavdaAAAahEyHR0d72W9AAA15xIFAEBaQgYASEvIAABpCRkAIC0hAwCkJWQAgLSEDACQlpABANISMgBAWkIGAEhLyAAAaQkZACAtIQMApCVkAIC0hAwAkJaQAQDSEjIAQFpCBgBIS8gAAGkJGQAgLSEDAKQlZACAtIQMAJCWkAEA0hIyAEBaQgYASEvIAABpCRkAIC0hAwCkJWQAgLSEDACQlpABANISMgBAWkIGAEgrVchceuml0atXr5g4cWLVQwEA6kCakHnwwQfjuuuui0984hNVDwUAqBMpQub111+Po48+OqZPnx6DBg2qejgAQJ3oGwmcfPLJcfDBB8fYsWPj4osvXuvvLl26tFxWWLx4cXnb1dVVLrxjxbawTRqPuV2X5sioq6u5x21OtfybzLtd8s9tV23Wup6vYXUfMjfffHPMnz+/PLS0PqZMmRKTJ09e5f5Zs2ZFS0tLDUaYW0dHR9VDoEbM7ZrcFJl1dMyIvO6o4bpzz2vuub2jJmtdsmTJev1er+7u7u6oU88++2zsscce5QvyinNjxowZE6NHj46pU6eu9x6Ztra2WLRoUQwcOHCTjb3eFaVbbNf29vZoamqqejh1qDWyKv5VV7wgtrcfF01Nb0Y+nTVef865zT+vtZ7bnPPaGHPbWZO1Fu/fgwcPjs7OzrW+f9f1HpmHHnooFi5cGJ/85CdX3rds2bK4++674+qrry6DpU+fPj0e069fv3J5t+LN2hv2qmyXNcn4YtJT8YKY80Wx1n+PGbdJI8xrrec26zZphLltqs1a1/O9qa5D5sADD4wFCxb0uO/YY4+Nj3/843HOOeesEjEAwOalrkNmwIABMWrUqB73bbnllrH11luvcj8AsPlJ8fFrAIB0e2RWZ/bs2VUPAQCoE/bIAABpCRkAIC0hAwCkJWQAgLSEDACQlpABANISMgBAWkIGAEhLyAAAaQkZACAtIQMApCVkAIC0hAwAkJaQAQDSEjIAQFpCBgBIS8gAAGkJGQAgLSEDAKQlZACAtIQMAJCWkAEA0hIyAEBaQgYASEvIAABpCRkAIC0hAwCkJWQAgLSEDACQlpABANISMgBAWkIGAEhLyAAAaQkZACAtIQMApCVkAIC0hAwAkJaQAQDSEjIAQFpCBgBIS8gAAGkJGQAgLSEDAKQlZACAtIQMAJCWkAEA0hIyAEBaQgYASEvIAABpCRkAIC0hAwCkJWQAgLSEDACQlpABANISMgBAWkIGAEhLyAAAaQkZACAtIQMApCVkAIC06jpkpkyZEnvuuWcMGDAgtt122zj88MPj8ccfr3pYAECdqOuQmTNnTpx88slx//33R0dHR3R1dcVBBx0Ub7zxRtVDAwDqQN+oY3feeWePn2+44YZyz8xDDz0Un/nMZ1b7mKVLl5bLCosXLy5viwgqFt6xYlvYJmvSHFl1dTX3uM2n1n+TObdL/nmt9dzm3S7557arNmtdz/enXt3d3d2RxFNPPRU777xzLFiwIEaNGrXa37nwwgtj8uTJq9w/c+bMaGlp2QSjBAA21JIlS+Koo46Kzs7OGDhwYP6QWb58eXzxi1+MV199Ne655541/t7q9si0tbXFokWL1rohNjdF6RaH69rb26Opqanq4dSh1siq+FddR8eMaG8/Lpqa3ox8Omu8/pxzm39eaz23Oee1Mea2syZrLd6/Bw8evM6QqetDS/+rOFfmb3/721ojptCvX79yebfizdob9qpslzXJ+GLSU/GCmPNFsdZ/jxm3SSPMa63nNus2aYS5barNWtfzvSlFyJxyyinxhz/8Ie6+++4YNmxY1cMBAOpEXYdMcdTr1FNPjVtvvTVmz54dI0aMqHpIAEAd6Vvvh5OKk3Rvu+228rtknn/++fL+1tbWaG7OenY3ALBZfI/MtddeW57kM2bMmBgyZMjK5ZZbbql6aABAHajrPTJJPlAFAFSkrvfIAACsjZABANISMgBAWkIGAEhLyAAAaQkZACAtIQMApCVkAIC0hAwAkJaQAQDSEjIAQFpCBgBIS8gAAGkJGQAgLSEDAKQlZACAtIQMAJCWkAEA0hIyAEBaQgYASEvIAABpCRkAIC0hAwCkJWQAgLSEDACQlpABANISMgBAWkIGAEhLyGyQXomX1v/7f2itg7G8nwUAhAwAkJiQAQDSEjIAQFpCBgBIS8gAAGkJGQAgLSEDAKQlZACAtIQMAJCWkAEA0hIyAEBaQgYASEvIAABpCRkAIC0hAwCkJWQAgLSEDACQlpABANISMgBAWkIGAEhLyAAAaQkZACAtIQMApCVkAIC0hAwAkJaQAQDSEjIAQFpCBgBIS8gAAGkJGQAgrRQhc80118Tw4cOjf//+sffee8fcuXOrHhIAUAfqPmRuueWWOOOMM+L73/9+zJ8/P3bfffcYN25cLFy4sOqhAQAVq/uQueKKK+L444+PY489NnbZZZeYNm1atLS0xIwZM6oeGgBQsb5Rx95666146KGH4rzzzlt5X+/evWPs2LFx3333rfYxS5cuLZcVOjs7y9uXX345urq6NvII+0dWXV39Y8mSJfHSS/2jqak78nmpxus3t9Uxt405r7We25zz2hhz+1JN1vraa6+Vt93d3XlDZtGiRbFs2bLYbrvtetxf/PzYY4+t9jFTpkyJyZMnr3L/iBEjajbOnP4bEUdFXoOrHkAdM7eNKfu8FsxtY87t4JquvQia1tbWnCHzfhR7b4pzalZYvnx5uTdm6623jl69elU6tnqyePHiaGtri2effTYGDhxY9XDYiMxtYzKvjcvcrl6xJ6aImKFDh8ba1HXIDB48OPr06RMvvPBCj/uLn7fffvvVPqZfv37l8r8++MEP1nScmRVPGk+cxmRuG5N5bVzmdlVr2xOT4mTfLbbYIj71qU/FXXfd1WMPS/HzPvvsU+nYAIDq1fUemUJxmGjChAmxxx57xF577RVTp06NN954o/wUEwCweav7kPnyl78cL774YkyaNCmef/75GD16dNx5552rnADMe1Mcfiu+m+fdh+HIz9w2JvPauMzthunVva7PNQEA1Km6PkcGAGBthAwAkJaQAQDSEjLQAMaMGRMTJ06sehjABvJcfu+EDACQlpABANISMpuZ4jt49t9///KyDcX1pw455JD45z//WfWw2AjefvvtOOWUU8qv9C4u73HBBRes86qx5FB8o/nll18eH/nIR8rvGtlhhx3ikksuqXpYbKDiy12POeaY+MAHPhBDhgyJH//4x1UPKSUhsxk+cYpvS543b155qYfevXvHl770pfKFktxuvPHG6Nu3b8ydOzd+8pOfxBVXXBE//elPqx4WG+liuJdeemkZp3//+99j5syZvhS0AZx11lkxZ86cuO2222LWrFkxe/bsmD9/ftXDSscX4m3mFi1aFNtss00sWLAgRo0aVfVw2IATBBcuXBiPPvroyqu8n3vuuXH77beXb3zkVVz9t3iOXn311fGNb3yj6uGwkbz++uvlXvFf/vKXMX78+PK+l19+OYYNGxYnnHBCeTke1o89MpuZJ598Mr7yla/Ehz/84fIqq8OHDy/v/9e//lX10NhAn/70p1dGTKG4sGox38uWLat0XGyYf/zjH7F06dI48MADqx4KG1FxSP+tt96Kvffee+V9W221VXzsYx+rdFwZ1f21lti4Dj300Nhxxx1j+vTpMXTo0PKQUrEnpnhCAfWnubm56iFAXbNHZjPy0ksvxeOPPx7nn39++a+7kSNHxiuvvFL1sNhIHnjggR4/33///bHzzjtHnz59KhsTG66YwyJminPaaBw77bRTNDU19XjeFq/HTzzxRKXjysgemc3IoEGDymOy119/fXmGfHE4qTiPgsZQzGdxIveJJ55YnjB41VVX+RREA+jfv3+cc845cfbZZ8cWW2wR++23X7z44ovl+VBf//rXqx4e71PxSaVi/ooTfovX5W233Ta+973vlR/A4L0RMpuR4gly8803x2mnnVYeTiqOxV555ZXliaLkV3yM880334y99tqr3Atz+umnlycNkl/xaaXiE2mTJk2K5557rvyHyDe/+c2qh8UG+tGPflSe9Fsc8h8wYECceeaZ0dnZWfWw0vGpJQAgLfuwAIC0hAwAkJaQAQDSEjIAQFpCBgBIS8gAAGkJGQAgLSEDAKQlZACAtIQMkMqyZcti3333jSOOOKLH/cVXu7e1tZXXqwE2Hy5RAKRTXCF49OjRMX369Dj66KNXXmvqkUceiQcffLC8uCKweRAyQErFBU8vvPDC8irQc+fOjfHjx5cRs/vuu1c9NGATEjJASsVL1+c+97nySt8LFiyIU089Nc4///yqhwVsYkIGSOuxxx6LkSNHxm677Rbz58+Pvn37Vj0kYBNzsi+Q1owZM6KlpSWefvrp+Pe//131cIAK2CMDpHTvvffGZz/72Zg1a1ZcfPHF5X1/+tOfolevXlUPDdiE7JEB0lmyZEl87Wtfi29961txwAEHxM9+9rPyhN9p06ZVPTRgE7NHBkjn9NNPjzvuuKP8uHVxaKlw3XXXxXe+853yxN/hw4dXPURgExEyQCpz5syJAw88MGbPnh37779/j/82bty4ePvttx1igs2IkAEA0nKODACQlpABANISMgBAWkIGAEhLyAAAaQkZACAtIQMApCVkAIC0hAwAkJaQAQDSEjIAQGT1/wBh/J9EgnVaCwAAAABJRU5ErkJggg==",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"x = ['a', 'b', 'c', 'd']\n",
|
||
"y = np.linspace(1,10,4)\n",
|
||
"plt.bar(x,y, color = 'yellow')\n",
|
||
"\n",
|
||
"plt.xlabel(\"X\")\n",
|
||
"plt.ylabel(\"Y\")\n",
|
||
"\n",
|
||
"plt.grid()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "b1580082-d5ce-4a56-b262-97e2db397e55",
|
||
"metadata": {},
|
||
"source": [
|
||
"4) scatter"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"id": "3df64232-a2d2-4158-9d44-ba4f44bb5a71",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"x = np.random.rand(100)\n",
|
||
"y = np.random.rand(100)\n",
|
||
"colors = np.random.rand(100)\n",
|
||
"\n",
|
||
"plt.scatter(x, y, c=colors, cmap='viridis')\n",
|
||
"plt.colorbar()\n",
|
||
"plt.title('График рассеяния')\n",
|
||
"plt.xlabel('x')\n",
|
||
"plt.ylabel('y')\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "ab82d6a8-a982-425b-8777-d382f7b7648f",
|
||
"metadata": {},
|
||
"source": [
|
||
"5) hist"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"id": "3f507e9c-0e30-4b65-96cc-c46bfd4fe56b",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"data = np.random.randn(100)\n",
|
||
"\n",
|
||
"plt.hist(data, bins=10, color='green')\n",
|
||
"plt.title('Гистограмма')\n",
|
||
"plt.xlabel('Значения')\n",
|
||
"plt.ylabel('Частота')\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "be503adf-e323-4dea-90b8-36e0b7459789",
|
||
"metadata": {},
|
||
"source": [
|
||
"____________________________________________\n",
|
||
"seaborn\n",
|
||
"____________________________________________"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "9ae9a9b1-9f96-4665-afab-c6e7d9b7e0bb",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import seaborn as sns\n",
|
||
"import pandas as pd\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"data = {\n",
|
||
" \"Имя\": [\"Анна\", \"Борис\", \"Виктор\", \"Галина\"],\n",
|
||
" \"Возраст\": [21, 22, 23, 24],\n",
|
||
" \"Баллы\": [89, 76, 95, 82]\n",
|
||
"}\n",
|
||
"df = pd.DataFrame(data)\n",
|
||
"df[\"Категория\"] = [\"A\", \"B\", \"A\", \"B\"] # догадайтесь откуда df и её содержимое взялось. zagadka?\n",
|
||
"sns.boxplot(x=\"Категория\", y=\"Баллы\", data=df)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d34c49de-384c-4fa0-b752-cc6ea267bd23",
|
||
"metadata": {},
|
||
"source": [
|
||
"histplot"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "43c9c98c-da98-4253-87b9-7f4a571919c6",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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AAIQQT4NMUlKSVqxYkXZ51apVWrRokUqXLq3Y2FhdffXVztLrKVOmKCUlRZs2bXLuZ7cXKFDAw5YDAABFepD56aefFBcXl3a5X79+zteePXvqv//9ryZPnuxcbtSoUYbvs96Z1q1b53JrAQBAqPE0yFgYsQm8WTnWbQAAAL5YtQQAAJAZggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtggwAAPAtT4PMnDlz1KlTJ1WsWFFRUVGaOHFihttTU1P18MMPKzY2VoUKFVLbtm2VkJDgWXsBAEBo8TTI7NmzRw0bNtTIkSMzvf2ZZ57R8OHDNXr0aP3www8qUqSI2rdvr/379+d6WwEAQOjJ5+WDX3rppc4pM9YbM2zYMD344IO64oornOvefvttlS9f3um5uf7663O5tQAAINR4GmSOZdWqVdq0aZMznBRQokQJNW/eXPPmzcsyyCQnJzungF27duVKewEgFKxdu1aJiYlBPWZ8fHxQjwdERJCxEGOsByY9uxy4LTNDhw7VkCFDcrx9ABCKIaZu3Xrat29vjhw/KWl3jhwXCMsgk12DBg1Sv379MvTIVK5c2dM2AUBusJ4YCzFduryrsmXrBe24CQlTNWvWQ8xPREgK2SBToUIF5+vmzZudVUsBdrlRo0ZZfl90dLRzAoBIZSEmNvbsoB0vMZGhJYSukK0jU716dSfMzJgxI0Pviq1eOu+88zxtGwAACA2e9sgkJSVpxYoVGSb4Llq0SKVLl1aVKlXUt29fPf7446pdu7YTbB566CGn5kznzp29bDYAAAgRngaZn376SXFxcWmXA3NbevbsqTFjxui+++5zas3cdttt2rFjh84//3xNmzZNBQsW9LDVAAAgVHgaZFq3bu3Ui8mKVft99NFHnRMAAIBv5sgAAAAcD0EGAAD4FkEGAAD4FkEGAAD4FkEGAAD4FkEGAAD4FkEGAAD4FkEGAAD4FkEGAAD4FkEGAAD4FkEGAAD4FkEGAAD4FkEGAAD4FkEGAAD4FkEGAAD4FkEGAAD4FkEGAAD4FkEGAAD4FkEGAAD4FkEGAAD4FkEGAAD4FkEGAAD4FkEGAAD4FkEGAAD4FkEGAAD4FkEGAJCl1FTpwIECkiopKamwdu6UkpPd64FQkM/rBgAAQoOFky1bpDVrpD//lDZvlrZvlw4evEbSNfrqKzknU6CAVKaMVK6cVLWqVL26VLKk1z8BIhFBBgAinIWXRYukZcvc4JK5ZOXLl0eHDuXXoUPWSyNt3OieFi927xEbK9WvL511llS0aC7+AIhoBBkAiEAWRuLjpR9+kNatO3x9vnxuD0vlylLFilLp0nb7B5o06QZdfvk01a/fXn//LWeIKTFR2rBBWr1aWr/+cLCZOdMNMy1aSDExXv6UiAQEGQCIsOGjX3+V5syR/vrLvS4qSqpbV2rQQKpZ0x02Sm/DhkNp5+2++fO7AcVO9n1m715p6VK3Z8dCzc8/u+cbNZLi4qRixXLzp0QkIcgAQISwuS9ffun2mpiCBaVmzaQmTU49aBQu7B7HTtbD8+230vLlbqBZssQNM82bS3lYYoIgI8gAQJiz3hKbpBuYy2I9Li1busEiOjr4j2fDUtdf7waa6dPdr/b4Fmg6d5bKlg3+YyJyEWQAIIzZcM/nn7thxpx9tts7khuTcS3Q9Orl9spYkLH5NK++Kl16qdS4sTtMBZwqggwAhCGr9fLFF4d7YWyZdMeObrjITRZWLDzVri1NmiT98Yf02WfuBOFOnXK3LQhPBBkACDPbthXUJ5/YVzdInH++dOGFUt683rXJ5uB06yZ99527qskmHNuqpzZt8nvXKIQFggwAhJVrNXFiHWeJdIkS0pVXSlWqKCRYqLK5OZUqSR995E46njChjqQGXjcNPsb8cQAIAxZcXnyxkqQP9fffeVWjhnTbbaETYtKrVk269VZ30u/evbbWe44WLqSCHrKHIAMAPmf1YNq1k959t7xzuVGjTc4wji2JDlWlSkk33SRVqJAkqaTuvLOWpk71ulXwI4IMAPiYTZq14ZpZsyy4pEi6Ss2abfBFvRarY3PZZQmSJunAgTzq0kWaMsXrVsFvfPCrDgDIjFXOPe88t/DcaadJb721XNKn8pN8+Wwb7at10UXbnf2bbE4PYQYngyADAD70f/8ntWolbdrkbi0wb55Uq9Z++dPfeuKJVbrmGttpW7r6aunrr71uE/yCIAMAPvP++25Rud27pdatpblz3R4ZP7P9m+znuuIKtwaO1ZiZP9/rVsEPCDIA4CNvvCF17+6uUrruOmnaNHeZdTiwnbfHjbPaMlJSks2fkVau9LpVCHUhHWRSUlL00EMPqXr16ipUqJBq1qypxx57TKm2fSsARJj//U+65RZ3B+s77nB7MHJirySvJwBPnOhWA7aCeVaNeMcOr1uFUBbSBfGefvppjRo1SmPHjtWZZ56pn376Sb169VKJEiV09913e908AMg1L74o9evnnr/nHun558N3ryKrAmzbGNjO3PHx7pwZ227Bhp8AX/XIfPfdd7riiivUoUMHVatWTVdffbXatWunH3/80eumAUCueeqpwyFm0KDwDjEBFSu6q5eKFJFmzHB7oOiMh+96ZFq0aKFXX31Vv//+u04//XQtXrxY33zzjV544YUsvyc5Odk5BezatSuXWgvkjrVr1yrR+txzQExMjKqEYinYCH4N3367nF56yZ3J27v3Bl111SZnN+nMxFv3RRhp1MidM2MTgF9/XapTR7r3Xq9bhVAT0kHm/vvvd4JI3bp1lTdvXmfOzBNPPKFuVrIyC0OHDtWQIUNytZ1Abr4B1q1bT/v27c2R4xcqVFjLlsUTZkLmNbxV0qv/nB+sV14ZqldeOf53JSXtVriwOTL22bVvX+m++9www67Z8E2Q+eijj/Tee+/p/fffd+bILFq0SH379lXFihXVs2fPTL9n0KBB6hfog/2nR6Zybu9bD+QQ+xRvb4BduryrsmXrBfXYW7fGa8KE7s5jEGS8fw1XrCilmTOrpW050KzZ1U7huGNJSJiqWbMe0v79fq0nkzmbEmlF/0aNknr0kBYulLOXFBDyQWbAgAFOr8z111/vXG7QoIHWrFnj9LpkFWSio6OdExDO7A0wNvZsr5uBHHoNExIOF4Rr0sSWIVdQVFSF4x4zMTG8hpYCbD7QsGFyhtS+/96d/Pvtt9aD6HXLEApCerLv3r17leeIDUNsiOnQoUOetQkActKaNdYbLdmfOavYa7VUwn1i74koUED6+GObx+UGmrvu8rpFCBUhHWQ6derkzIn5/PPPtXr1ak2YMMGZ6NvFdhYDgDCzYYNbG8aK3Z1+ujvJlRBzmFUv/uAD9zmxwoBvveV1ixAKQjrIvPzyy86S6zvuuEP16tXTvffeq969eztF8QAgnGzdKr37rpyNE6tVk7PvUN68Xrcq9LRtKz36qHvelmTbxpmIbCE9R6ZYsWIaNmyYcwKAcLV9u/TOO9K+fW79FJsWaOX6kbnBg91NMqdOdefL2FCTFdFDZMr2f5U9e/Zo9uzZzlLCA/YRIh2q7gLAibGNHy3E2NeyZSWrLsF6hWOzqZP2nDVuLP3xhztfZswYr1sFXwWZn3/+WZdddpkzGdcCTenSpZ0lhYULF1a5cuUIMgBwAqwHxoaTrEemZEl3aXHhwl63yh9Kl5bee0+68EJp7Fh3N3DbRBORJ1tzZO655x5nIu727dudzRy///57Z1n0Oeeco+eeey74rQSAMHPwYB7njXjLFqloUenGGxkeOVnnny898IB7vndvd8UXIk+2gowVpuvfv7+zNNqWQ9uWAFZ07plnntFgG7wEABxDtL76qobWr3droVhPTKlSXrfJnx56SGreXNq5030eU1K8bhF8EWTy58+fVt/FhpJsnoyxXanXrVsX3BYCQBixpdXSOK1fX9ypjWJzYsqV87pV/mU7YlvPlvVqzZ1r29R43SL4Isg0btxY8+fPd85feOGFevjhh52tBGz7gPr16we7jQAQFqzI3aOPVpXUWXnzHlLXrlKlSl63yv9q1pRGjnTP//e/bvVfRI5sBZknn3xSsbGxznkrWFeqVCndfvvt2rp1q7NbNQAgo9RU6T//kT7/vIz1y6ht21VOvRgEhw0rWTC0oSXbwWZvzuyrinBZtdTENv/4hw0tTZs2LZhtAoCw8/DD0ogRVpU2VampN6pq1Xu9blJYsWq/1isze7b0++/uJOAXX/S6VQjZHpk2bdpox44dwW8NAIQhW8z5+OPu+YEDbR7hB143KSzZhOnXX3fPv/SSG2oQ/rIVZL7++uujiuABAI722mvSgAHueZuIes01iV43KaxZPZlbbnGH8nr1kpKSvG4RQnavpSh2MgOAY/rwQ7e+iRk4ULr/fq9bFBmef16qWlVatepwiET4yvYWBbYDdQFbO5iJmTNnnkqbAMD3vvhC6t7d7RmwMMOy4NxTvLi7M3abNtLo0dKVV0oXX+x1qxByQea8885TUVu4DwDIwOqZXHWVWzPGNoC0Sah0YueuuDipTx93gvVNN0m//eYGHISffNkdVhowYICzYgkAcNjChVLHju4+Sh06SG+/LeXN63WrItNTT7k9Y7axpA3r/e9/XrcIITNHJtX6SgEAGSxbJrVvL+3aJbVqJX38sVt5Ft4oUsSdbG1GjZLmzPG6RQiZIPPII48wrAQA6axeLbVtKyUmSuecI332mbuPErwfYrr1Vve8rWaynjKEl2wHmcKFCzuVfL/55hvnZOcBIBJt3OhOJrVNIOvVk6xGKPMxQsczz0gVK0oJCbZFhNetQUgEmb179+qmm25SxYoV1apVK+dk52+++WbnNgCIFPYZznpiVqyQs+XA9OlSTIzXrUJ6JUu6Q0vm2WfdeUyI8CBzzz33aPbs2Zo8ebJT4ddOkyZNcq7r379/8FsJACFo+3apXTtp6VJ388cZM9gEMlRdfrl07bXuXkw33ywdPOh1i+DpqqXx48frk08+UevWrdOuu+yyy1SoUCFde+21GhWIvgCQw9auXatEm5gSZDExMapSpUqWt+/e7VaRXbTI9pxzQ0yNGkFvRsSIj48P+jGTk5MVHR2ddvmWW/Lpyy/P0KJF+XTPPet1002bc+z3AyEeZGz4qHz58kddb8uxGVoCkJshpm7detq3L/h/dwoVKqxly+IzfbOyP3OdOkk//ODu72PDSXXqBL0JESEpaaMV9VB3qx4YdFa858hVtvY472jkyDIaObKNpN+D/vsBHwQZK4ZnE37ffvttFSxY0Llu3759GjJkiHMbAOQG64mxENOly7sqW7Ze0I67dWu8Jkzo7hz/yDeq5GS3UqxtSGgTer/6SjrrrKA9dMTZv982IE5VXNwI1a4dvPePhISpmjXroaOOa9VDpk3bqXXrSqhChQXq1On3ky5WeKzfD/gkyAwbNkyXXHKJTjvtNDVs2NC5bvHixU6o+fLLL4PdRgA4JgsxsbFn5/jj2LyK666T7M9c4cLS1KlSkyY5/rARoVSpWkF9DRMT47M8rgVRK463aVNR/fnn2WrWLGgPC78EmQYNGighIUHvvfeellkFKEldu3ZVt27dnHkyABBubLuBHj2kSZMkm3YxebLUsqXXrUJ2VzHZSjOr+mtzm+rWZbl8xAWZOXPmqEWLFro1UGUIAMKY9cR063a4Uu/48dJFF3ndKpyKpk2lX3+V/vzTDTTW04YIWn4dFxenbdu2Bb81ABCCIaZr14whxvZQgr/ZvBjbEytPHndriX8GF+BD7LUEAFk4eDDKqT1i4aVAAWnCBHe1EsKDLb4NrE+xXhmbyI0IGVoy8+bNUylbd5gJq/QLAP5WQAMHVndWJ9mcGAsxVjcG4eXCC6XffpN27JBmzZIuucTrFiHXgkyXLl0yvT4qKkopVjoRAHwqJcXW447X7NklZRUmJk50d7VG+LHhQhsqfO896ccf3aX0ti8TwnxoyWzatEmHDh066kSIAeD31UlffWUlejsqOvqQs4s1ISa81aol1a/v1pix1/vQIa9bhBwPMtbrAgDhxuZIvP++nGJp0l4NG7bCWaaL8Gdh1XrfNm1yKzbDP5jsCwD/bDvwzjvSqlU23GA9y5epWbMkr5uFXFK0qHTxxe55myuzc6fXLUKOBhkbQrJ9lQAgHNgGkGPGSOvX2x46NmciQdJsr5uFXNa4sWQ7DtiSe6vazGf2MA4yQ4cO1ZtvvnnU9Xbd008/HYx2AUCusJJY9uds61apWDGpVy/bAJfNbyO9tszvv9uO3F63CDkWZF555RXVtZrORzjzzDM1evTo7BwSAHLdxo3SW2+5S2+tmsRNN9m+TV63Cl6y1//88w/Xltm/3+sWIUeCjK1Yio2NPer6smXLaqP9ZQCAELdihRtikpLcwmgWYmwPHuCCC6TSpd3fjZkzvW4NciTIVK5cWd9+++1R19t1FVmADyDELVzork6yuRDVq0v/+pc72RMw+fK5Q0xm/nx3PyaEWUE82yyyb9++OnjwoNq0aeNcN2PGDN13333q379/sNsIAEFhkze//to2vnUvN2zobjmQN6/XLUOosYBrvx+LF0tTptj7Hr8nYRVkBgwYoL/++kt33HGHDhw44FxXsGBBDRw4UIMGDQp2GwHglFnvixU7sx2Pje2k0rq1O8ETyEy7du6k382bpe+/l1q29LpFCGpBPFudtHXrVn3//fdavHixsxv2ww8/nJ3DAUCuLK+2EGPBxXph4uIIMTi2woXdMGOsJ2/7dq9bhKBuUWCKFi2qpk2bqn79+oq2XdUAIMRYbZhXX5U2bHBrxPToIZ19ttetgl/Y8JINM9nWFZ9/Tm2ZsNo08qefftJHH32ktWvXpg0vBXz66afBaBsAnJJffpEmT7ZNIN1ltV27ususgRNlvXa2qeSoUdIff7g7ZZcp43WrcMo9MuPGjVOLFi0UHx+vCRMmOJN+f/vtN82cOVMlStgeJQDgHfv0bJVZJ0xwQ0ydOtLNNxNikD0WXGxJtpk2zfbkYtav74PMk08+qRdffFGfffaZChQooJdeeknLli3TtddeqypW3zmI1q9fr+7du6tMmTIqVKiQGjRo4PQGAUBmrLid1YexZbPG3oCuu05i9BunworkxcRIe/bYppKVvG4OTjXI/PHHH+pgfW2SE2T27NnjTAC+55579KoNRgfJ9u3b1bJlS+XPn19ffPGFli5dqueff16l+FgFIBO2wuSVV9z5MLaTsQ0lWYUIJvXiVNnSa5skbpYti7Fo43WTcCpzZCxI7LZlAJIqVaqkJUuWOD0lO3bs0F7bQjZIbGWUFd97yz5e/aO6zboCgHRSUqL01VfSvHnu5UqVpKuvplIvgssGHGyiuBVUlF7RgQPJXjcJ2Q0yrVq10vTp053wcs011+g///mPMz/GrrvooouC1rjJkyerffv2zmPMnj3bCU1Wu8YK8mUlOTnZOQXs2rUraO0BToZNhE9MTAzqMW1emh/lxHNx+PmorwkT6jqbP5pmzdwlsxQvQ05o29Z+7w5q374zNHbsBp17rtctQraCzIgRI7T/n520HnjgAWfo57vvvtNVV12lBx98MGiNW7lypUaNGqV+/fpp8ODBmj9/vu6++25nOKtnz55Z7sw9ZMiQoLUByO4bd9269bRvX87sopyU5PaIRvZzYeNF99gaSm3bFu3U/Lj8cndiL5BTbAn/eef9qZkzq+vNNyvonnuk00/3ulWR7aSCTKB3I1++fE4NmcBl6yWxU7AdOnRITZo0cSYXm8aNGzvDWLbDdlZBxioLW/BJ32YbngJyk/U+2Bt3ly7vqmzZekE7bkLCVM2a9VDaB4lIfS527SqgOXOqasOGYs7lChW2qFu3cuyXhFxRs+Z2zZy5TAcOXKp//9u26GEelm+CTMmSJZ1JvceTYusdg8B22D7jjDMyXFevXj2NHz8+y++xwnwU50OosDfu2NjgVV9LTPTn0FKwngv702Kl4q3Kqi2xzpv3b6Wk3KHzzrtKRYu2D1pbgWNx3wbvUHT0H5o1K4/GjnU3HoUPgsysWbMyXE5NTdVll12m119/3Zm/Emy2Ymn58uUZrvv9999VtWrVoD8WgNCv0Gt7Jdm+N8bm/deu/bm++uo1RUVd5XXzEHFW69//3qCXXjpNtleyLeS1oosI8SBz4YUXHnVd3rx5de6556pGjRoKNlvObYX3bGjJatT8+OOPzvLuYC7xBhDabCGk9cBY+SgrD29zFGwyr5WOX7IkyevmIYJ17bpFX399mrNDtoWZt9/2ukWR6ZT2Wsppto+TVQ7+4IMPnP2cHnvsMQ0bNkzdunXzumkAclhgGOnll93idhZiGjSQ7rxTatSIOQnwXv787j5e9rv4zjvS//2f1y2KTNnea8msW7fOqRtjVXdzSseOHZ0TgMhggcUK202fLv31l3td+fJS+/bucBIQSmy5f58+buC2ib+2w7r1GiJEg8zw4cMzrESwnpI2bdqwvxKAoASYlSvdYaQ//3SvK1LErcxrPTB5Qrr/GJHs8cdts2R3U0k7/8QTXrcospxUkLH9lYytXIqJiVGnTp2CWjcGQGQGmNWrbTGB9fK61+XLJzVv7u6TxCJEhLrixa2+mtSli/TMM+7WGPXre92qyHFSQWbVqlU51xIAEeXQIckWJdq2AoEAY9V4mzRxN+ijJgz8pHNn9zRxotS7tzR3Lr2IvpgjAwAn68AB6eefbQdh2xj2cIA55xw3wBRza9wBvmPzZKw43nffuZOAbc4Mch5BBkCu+OuvQlq0SPrlFylQmNh2qLYeGJswSYCB3512mjs/5u67pYED3S0zKlb0ulXhjyADIMckJVlXu61qnKfx4w9vT1C6tJzN9qwWTIECnjYRCCrbrefdd6Uff5T+8x/p44+9blH4I8gACPrQ0ZdfSu+/bzvYW0E7q8RdVVFRqapbN0pnn2171VAHBuHJhkltWMmGSj/5RJoyxcqIeN2q8MZUJABBCy82ybFCBbdLfdw4typvlSo2jnSfunX7VddeK9WqRYhBeLOexsDexVbA0XomkXMIMgCyZedO6YMPpOuvd/eYueQS95OoTeCNjZX69nW71z/9dKmkZ1W48N9eNxnINY88IlWrJq1dKz38sNetCW8MLQE4YVaozoaLbImpFa47ePDwbYGemOuus33Z3C52s3ChZ80FPGPFHEeNki69VHrpJbe2TNOmXrcqPBFkAByzWN2SJdKkSe7JNm5Mr149t3bGFVe4f6SpmwEcZr2UtjXge+9Jt9zi/v+x/ZkQXAQZAEcVqrMidRMmHC67HmBzW1q0cIOLnU4/3cuWAqHPCuJPm+aWHXjuOWnQIK9bFH4IMgCcnab//NMKuYzQpZfWV2Li4dtsi4CLL3aDS6dO7gaOAE6MzR8bNkzq0UMaMkS68kqpTh2vWxVeCDJABA8b2URE+6S4dKkVqastqbYTYqw4nS0ZtT+61j3OdgFA9tnwktWWsZV9t93m7ivGMGzwEGSACLNtm7R4sRtgduw4fH3Bgge1f/8YDR8ep9tuq8VmjUCQ2JDs6NHuRpJz5kivv+4GGgQHQQaIkHkvCQnS/PkZ57xYVd0zzpDOOsvO/6rXX79NLVsuIMQAQWZLsW37AitLMGCA2+PJ9gXBQZABwti+fdKCBe5qCav7EmBF6Sy81K17eBXFxo2eNROICH36uBWvrb6SnbfJ9Dh1BBkgDFlFXVt5ZH8wrequKVRIatzY3aSxVCmvWwhEHqutZMNKtk1HYFWgzUPDqSHIAGFkzx7pu+/cIaRAsbpy5dwNGm18nhoWgLcaNJDuv196/HF3+4K4OD5YnCqCDBAG/v7b7YGZO/dwgLFKu1Zh15Z6srdR9sTHx/vimPCXBx5wd8Vevly67z7ptdcO37Z27Volpq9/EEQxMTGqUqWKwg1BBvD5Euply6Svvjq8AskmEFqAqV2bAJNdSUk2YShK3bt3z8HH2J1jx0ZoK1jQDS+tWrlDTTfc4PbMWIipW7ee9u3bmyOPW6hQYS1bFh92YYYgA/jU1q3SF19Iq1a5l632S9u2btc1AebU7N9vqTBVcXEjVLv2eUE9dkLCVM2a9ZD277ddwRGpLrhAuv12dz8mW4pt5RCsJ8ZCTJcu76ps2XpBfbytW+M1YUJ35zEIMgA8lke//17N2fvIKvLaBELbNuD8893l1AieUqVqKTb27KAeMzGRoSW4hg51N2FdsUJ69FHpmmvc6y3EBPv3LpwRZAAf2b3bthH4RkuW1ElbRn3ZZUwWBPyoRAnpf/9zt/949lnrTS3kdZN8iSAD+GQuzA8/SDNmXOb8t82X76Auuyy/GjViGAnws8svd3tibPLvY49VtUXaXjfJd9jtAQhxNpXiww/dfVoOHbLPHl+pbdvvnJowhBjA/4YPd3tVly0rLKmv183xHYIMEMI2b5ZefdVdpmlzYRo2/FFSexUuzERRIFxYqYTnnw9celS7djHZ7WQQZIAQZRs72tLM7dvdsfSbbpJq1kzwulkAcsC//iU1bbpLUmHNnVvFGU7GiSHIACG4wePUqdLEiW6hO5vQa8sz2WAOCF82TPzAA2tthzStX1/c+SCDE0OQAUKIVeX96CN3iwFjhe2sWFZhGzoHENYqV7aN0R52ztucuKQkr1vkDwQZIIT2SRo79vB8GFvJ0Lo1E3qByPKiYmL2OpP8reAljo8gA4SAv/6S3nhDWr/e3aX6xhulM87wulUAcl+KWrVa43yAWbrU9ubyuj2hjyADeGzDBjfE2KReW4J5881SmFUQB3ASYmL2qWVL97zNl9u3z+sWhTaCDOChP/+U3n7b/UNlk3ktxJQp43WrAHjN5sfZ3wKbJzN9utetCW0EGcAja9dK77wjJSe7PTA2nFSkiNetAhAK8uVzq/6an3+WVq70ukWhiyADeGD1aundd6UDB6Tq1aVu3aToaK9bBSCU2Aecpk3d85995v69wNEIMkAu++MP6b333KXWNWtKXbuyazWAzF10kVsQc8cOaeZMr1sTmggyQC5as0YaN84tdFe7tnT99VL+/F63CkCosp7ajh3d87ZxrM2rQ0YEGSCXbNwoffDB4Wq9117rjoMDwLHY34uGDd3zkye7f0NwGEEGyAWJie6cGJvYW7UqIQbAyWnXzl0MsHWrNHeu160JLQQZIIfZ2LYtsd67V4qNdefEMJwE4GTYNiWXXeae/+YbafNmr1sUOggyQA7auzefs8R6924rciV1787qJADZU6+eVLeuu7GsDTHZVxBkgBxUWF9+WVPbtkklS0o9erD5I4Dss20LrFfGPgxZRfDvv/e6RaHBV0HmqaeeUlRUlPr27et1U4BjSkmxfz/Q1q1FnL2TrCemeHGvWwXA74oVk9q3d8/PmiXng1Kk802QmT9/vl555RWdddZZXjcFOKbUVOnZZytLulx58x5y5sSw7QCAYGnUyC2kaauXJk92/+ZEMl8EmaSkJHXr1k2vvfaaStmuekAIe/556eOPy0o6pLi41apsmQYAgjjE1KmTu2hgzRpp4UJFNF8sAL3zzjvVoUMHtW3bVo8//vgx75ucnOycAnbt2pULLQRcH30kDRgQuNRfNWr08LZBAMKSfaZv00b68kt3U0krsHkiw9fx8fFBb0tMTIyq2H4KHgn5IDNu3DgtXLjQGVo6EUOHDtWQIUNyvF3AkexXtGdP9/z112/RuHHDJBFkAOSMZs2kJUuk9eulzz93K4Vbb01mkpI2Wl+OutuEvSArVKiwli2L9yzMhHSQWbdunf7zn/9o+vTpKliw4Al9z6BBg9SvX78MPTKV6dtHLlTt7dxZ2r9f6tBB6tfvT2crAgDIKXnyuDtkv/KK9Pvv0m+/SfXrZ37f/ft32Aw+xcWNUO3a5wWtDVu3xmvChO5KTEwkyGRmwYIF2rJli84+++y061JSUjRnzhyNGDHCGULKmzdvhu+Jjo52TkBusfDSpYu7HNLqPLz/vrRihdetAhAJypWTWrWSvv5a+uILqUaNY5d5KFWqlmJjD7+nhoOQDjIXXXSRfv311wzX9erVS3Xr1tXAgQOPCjFAbrPVArfd5m7mZmPWtoKAZdYActP550tLl0pbtkjTpklXXqmIEtJBplixYqp/RD9ZkSJFVKZMmaOuB7xaoWSVey1Tf/yxu7kbAOSmvHndIaY33pDss7+9PZ5+uiKGL5ZfA6Fo6lTpvvvc88OGWQ+i1y0CEKkqVZLOPdc9P2WKu0FtpAjpHpnMfG0DgYDHli1zN3+0oaVbb7USAV63CECki4tz/zZt3+4uye7YURGBHhngJNkfCevGtRJFF1wgjRiR9ZJHAMgt+fO7hfLMggXS6tWKCAQZ4CT3ULrhBikhQapaVRo/XipQwOtWAYDLti4ILPT97DPp4EGFPYIMcBKs1qKtCrCNICdOlMraTgQAEEIuvtjdXNI2lIyE2RgEGeAE2aebxx5zz7/2mrtxGwCEmoIF3cKcZt48t8ZVOCPIACfACtz1+Ge3gbvukrp187pFAJC1OnXcZdi2IMHqWx06FL4T+QgywHHs2eMWmNq5U2rRQnruOa9bBADHd8kl7jD45s22hcGZClcEGeAEKvdakany5d2id0zuBeAHRYpIl17qnl+2zIrI1lM4IsgAx2BLq23vJKuc+dFHUsWKXrcIAE5c/fpS7do2tGRb+rzufDgLNwQZIAvffmu7WLvnn33W3ZgNAPwkKsqd+Jsvn63DbqE//vBmh+qcRJABMrFpk3TNNdLff0vXXSf17et1iwAge0qUsJ6Zn53zv/1WWzt2KKwQZIAjWAGpa6+VNm6UzjxTev11KvcC8Lfq1RMkzVZKSj6nlEQ4DTERZIAj2EaQc+e6BaU+/VQqWtTrFgHAqYlyPozdqjx5UrRypbR4scIGQQZIZ9w4dydr8/bb0umne90iAAiWBJ1xxgrn3JdfSklJCgsEGeAfS5ZIN9/snh80SOrc2esWAUBw1aq1RrGx0v790tSpCgsEGUBusTsrerd3r9S27eGtCAAgnOTJk6rLL7evUny8tHSpfI8gg4h36JDUs6e7o3WVKtIHH7h1YwAgHFWoILVs6Z63Xpl9++RrBBlEvKeekiZNciv2jh8vxcR43SIAyFmtWrl/62wLlq++kq8RZBDR7D/wgw+650eOlJo08bpFAJDz8uWTM8RkFi1yN8b1K4IMItaaNdINN7j1FG65xT0BQKSoXFlq1sw9b7VlkpPlS/m8bgDgBZuxf9VV0l9/ub0wL7/sdYtCR7zNAAzh4wGhIhz+r1x0kTs/cPt2t4e6Uyf5DkEGEalPH2nBAqlMGemTT6SCBb1ukfeSkjZa2Sx17949h46/O0eOC+S2cPq/UqCAO8Q0dqy0cKFbzbxGDfkKQQYRx7YceOMNd/mhFcCrWtXrFoWG/fttA5ZUxcWNUO3a5wXtuAkJUzVr1kPab91gQBgIt/8r1apJTZtK8+dLkydLt98uRUfLNwgyiCj2H/XOO93zjz/u1oxBRqVK1VJs7NlBO15iIkNLCE/h9H+lbVt3iMk2lJw+XerYUb7BZF9EjMREd17MgQNu1d777/e6RQAQGgr8M8RkbNjd9mPyC4IMIsLff0vXXy+tW+funzRmDDtaA0B61asfLkFhQ0x+WcVEkEFE6N9fmjFDKlLE3dG6RAmvWwQAoadtW/fvo23b8n//J18gyCDs2cTe4cPd8++8487KBwAczSb5XnGFe/6nn6RVqxTyCDIIa99+687AN0OGSF26eN0iAAj9IaZzzjk8xGTzCkMZQQZhy+bD2I7WBw9KV199eCsCAMCxXXyxO8Rkq5hCfYiJIIOwtHevuzJpyxapYUN3cq/VjQEAnNgQU2AVk5WtWL1aIYs/7Qg7tnfSTTe5VSptd1fb2dom+QIATpxV+D37nzI59nc0VIeYCDIIO089JX34obu76/jxVO4FgOxq104qXvxwobxQRJBBWLGJaQ884J63jSBbtfK6RQAQHkNMP/0krVihkEOQQdiwoaSuXd2hpX//2z0BAE5NzZruXkyBD4v79imkEGQQNiuUbG8Qm+Rrs+0DdWMAAKfO/q6WKSPt3i1NnaqQQpCB7+3aJXXoIG3c6Ba7+/hjKX9+r1sFAOEjf363Dpdt7bJkiXsKFQQZ+H4Ppeuuk379VSpfXvr8c7YfAICcUKmSdMEF7nn7W2sfIkMBQQa+ZXNh7rpLmjZNKlRI+uwzVigBQE6yBRSxsdL+/e58Gfs77DWCDHzrySel0aPdrs733z88GQ0AkDPy5nWHmKy8xR9/SPHxMfIaQQa+9Nprh7ccGDbMreILAMh5ZctKF13knv/++0qSaslLBBn4zsSJh5dWDx4s3X231y0CgMjSvLm7ueTff+eV9LYzX9ErBBn4ypw50vXXS4cOSTffLD3+uNctAoDIExUlXXGFVKCAJZjzNHZsBc/aQpCBb/zyi1thMjnZ/Q8UmB8DAMh9tkK0Zcs/JcWrRYud8kpIB5mhQ4eqadOmKlasmMqVK6fOnTtr+fLlXjcLHli2zC3ItHOndP750gcfuJPNAADeqVVrm6RGqlfPu3K/IR1kZs+erTvvvFPff/+9pk+froMHD6pdu3bas2eP101DLrK9Pdq0kbZskRo1cpf82XJrAIC33F5xb7fFDunPtNOsQEg6Y8aMcXpmFixYoFbsBhgR1qxxZ8cHqvba7qulSnndKgBAqAjpHpkj7bRxBUmlS5f2uinIBevXuz0xa9dKdepIM2ZIMd6XLAAAhJCQ7pFJ79ChQ+rbt69atmyp+vXrZ3m/5ORk5xSwK1RqKEeAtWvXKjExMSjH2ro1v3r3rq01awqqUqVkPfPMEq1fH+WEm2Cy35Vo26c+yOLj44N+TACAj4OMzZVZsmSJvvnmm+NOEB4yZEiutQuHQ0zduvW0b9/eIBytiqQZkgra4JLWr2+lK65YZ5sSKPhsgDfnamwnJe3OsWMDAHwSZPr06aMpU6Zozpw5Ou20045530GDBqlfv34ZemQqV66cC62MbNYTYyGmS5d3VbZsvWwfZ+fOaE2ZUlt79hRQsWLJ6thxtzZtulWzZj2kuLgRql37vKC1OSFhao4cN/2x99uGJACAyAwyqampuuuuuzRhwgR9/fXXqm5lBI/DhglyYqgAJ8ZCTGzs2dn63q1b3R1VbVFamTLSjTdGq3jx+kpOXuzcXqpUrWwfOzOJifE5ctz0xwYARHCQseGk999/X5MmTXJqyWzatMm5vkSJEirE+tuwYi/tO+9Ie/dK5cpJPXpIRYt63SoAQKgL6VVLo0aNclYqtW7dWrGxsWmnDz/80OumIYhWrpTeessNMRUrSj17EmIAAGEytITw33Zg0iR376SqVd19lAraHF8AAPweZBC+LKN++61bG8bYinrbP4ltBwAAJ4O3DeQ6632xos3z57uXzzvP3UeJDSABACeLIINctW+f9Mkn7rwYc8klUvPmXrcKAOBXBBnkGtv0cdw4aft2KX9+qUsXqV72S84AAECQQe6wiv0TJkgHD0olS7qTesuX97pVAAC/I8ggx+fDzJ4tzZnjXraahldfLRUu7HXLAADhgCCDHGP7dX76qbRmjXvZ5sK0ayflCenqRQAAPyHIIEf8/rs0caI7ubdAAaljR6lBA69bBQAINwQZBFVKSpS+/FL6/nv3cmysO5RUurTXLQMAhCOCDIKoiT79tK6zKikwlNS2LUXuAAA5h7cYnLLkZGnEiIqS5mn79nwqUkTq1EmqU8frlgEAwh1BBqfkxx+lXr2kpUsrOJdr1dqmLl1KsyoJAJArWD+CbPnrL6l3b+nccy3ESGXKHJTUWW3arCbEAAByDUEGJyUlRXr1Ven0092vtvnjjTdKH320VNIkr5sHAIgwBBmcsLlz3Q0erSdm2zZ3ObUVuhs71qr1pnjdPABABCLI4LiWLHEn77Zq5e5YXby4NGyYtHChdMEFXrcOABDJmOyLLFlF3kcekd5+2x1CyptXuuUW6b//lSq4c3sBAPAUQQZHSUiQnnrKDTB//+1eZ0XtHn+cJdUAgNBCkEGGIaQnn5Q+/NDd7NG0aeNeZ8XtAAAINQSZCGeBZdo0afhwOVsLBHToID3wgDu5FwCAUEWQOQVr165VYmJijhw7OTlZ0dHROXLsmJgYlSxZRWPGSC+/LK1Y4V4fFSVddZU0eLDUuHGOPDQAAEFFkDmFEFO3bj3t27c3hx4hSlJqDhy3pfLm7a0CBbpp3z530VqJEtLNN0t33inVqJEDDwkAQA4hyGST9cRYiOnS5V2VLVsvqMdOSJiqWbMeUlzcCNWufepjO0lJ+bViRWktX15GO3cWdIra7dsn1asn3XWX1KOHVLRoUJoOAECuIsicIgsxsbFnB/WYiYnxztdSpWpl+9h79rhbB9gE3rVrD1+fL1+K/v57jN5443z16lXHGU4CAMCvCDJhZOdOafly97RqlVv7JaBKFalhQ9sT6VeNGXOLGjVaQIgBAPgeQcbHLKhs2nQ4vNj59GJjpfr13ZNV4zUbN/6zrhoAgDBAkPEZm9tivS0rV7qF63btOnyb9bBUruxu6Fi3rvW+eNlSAAByHkEmxNnE3HXr3OBipw0bMg4Z5c8v1azpVtytXVsqUsTL1gIAkLsIMiHGtgRITCwrabC++eYcffaZdPBgxvvExLjLpC3AVK/uhhkAACIRQcZjBw64PS62QaOtLvrzT+uFaSepnbZsce9TuPDh4GJfA/NdAACIdAQZD1YWWVixkwWYI4eKTHT0PiUnT1HDhvV17rn1VL68O/8FAABkRJDJ4WGijRvdwBIIL7t3H30/q6xbterh0/r1n2rChO6qWXOaKlQIbrE9AADCCUEmSKxXJX1vi50sxAR2kQ6wnpUKFaRKldwVRlbfpWTJjPexXhoAAHB8BJls2r/fxnpaavHicpozxw0uSUlH389WEZ122uFTxYpSgQJetBgAgPBDkMmmwYOrS/pGP/xw+Lo8edzelvTBxXpbmN8CAEDOIMhkU/36ezR79l5Vq1ZItWqVcoaJrJIuS6EBAMg9eXLxscJKjx6bJVVSu3ar1LKlO9eFEAMAQO4iyGQToQUAAO8RZAAAgG8RZAAAgG8RZAAAgG8RZAAAgG8RZAAAgG/5IsiMHDlS1apVU8GCBdW8eXP9+OOPXjcJAACEgJAPMh9++KH69eunRx55RAsXLlTDhg3Vvn17bdmyxeumAQAAj4V8kHnhhRd06623qlevXjrjjDM0evRoFS5cWG+++abXTQMAAB4L6SBz4MABLViwQG3btk27Lk+ePM7lefPmedo2AADgvZDeaykxMVEpKSkqX758huvt8rJlyzL9nuTkZOcUsHPnTufrrl27gtq2pH+2ut6wYYEOHMhk2+tTsHVr/D9ff9WaNYWCeuzExOXOVwuIgZ8hGJYvX+675yMnn2fanPPHzclj+7HNOXls2pw7x/ZjmxP/eU+x95Ngv88GjpeamnrsO6aGsPXr11vrU7/77rsM1w8YMCC1WbNmmX7PI4884nwPJ06cOHHixEm+P61bt+6YWSGke2RiYmKUN29ebd5sGzQeZpcrVKiQ6fcMGjTImRwccOjQIW3btk1lypRRVFRUjrcZJ560K1eurHXr1ql48eJeNwdH4PUJbbw+oY3XJzisJ2b37t2qWLHiMe8X0kGmQIECOuecczRjxgx17tw5LZjY5T59+mT6PdHR0c4pvZIlS+ZKe3Hy7D85/9FDF69PaOP1CW28PqeuRIkSx71PSAcZY70rPXv2VJMmTdSsWTMNGzZMe/bscVYxAQCAyBbyQea6667T1q1b9fDDD2vTpk1q1KiRpk2bdtQEYAAAEHlCPsgYG0bKaigJ/mTDf1bk8MhhQIQGXp/QxusT2nh9cleUzfj1uhEAAABhVxAPAADgWAgyAADAtwgyAADAtwgyAADAtwgy8NTq1at18803q3r16ipUqJBq1qzpzPa3DUMRGp544gm1aNHC2XWe4pLeGzlypKpVq6aCBQuqefPm+vHHH71uEv4xZ84cderUyalEa5XkJ06c6HWTIgJBBp6yzT+tWvMrr7yi3377TS+++KJGjx6twYMHe900/MNC5TXXXKPbb7/d66ZEvA8//NApEmphf+HChWrYsKHat2+vLVu2eN00SE6xVntNLGwi97D8GiHn2Wef1ahRo7Ry5Uqvm4J0xowZo759+2rHjh1eNyViWQ9M06ZNNWLECOeyfQiwPX3uuusu3X///V43D+lYj8yECRPSttdBzqFHBiFn586dKl26tNfNAEKuZ2zBggVq27Zt2nV58uRxLs+bN8/TtgFeIsggpKxYsUIvv/yyevfu7XVTgJCSmJiolJSUo7Znscu2fQsQqQgyyBHWzW1dq8c62fyY9NavX69LLrnEmY9x6623etb2SJCd1wcAQpEv9lqC//Tv31//+te/jnmfGjVqpJ3fsGGD4uLinNUxr776ai60MLKd7OsD78XExChv3rzavHlzhuvtcoUKFTxrF+A1ggxyRNmyZZ3TibCeGAsx55xzjt566y1n3B+h8/ogNBQoUMD5PzJjxoy0CaQ22dcus6kuIhlBBp6yENO6dWtVrVpVzz33nLZu3Zp2G58yQ8PatWu1bds256vN0Vi0aJFzfa1atVS0aFGvmxdRbOl1z5491aRJEzVr1kzDhg1zlvz26tXL66ZBUlJSkjPPL2DVqlXO/xdbvFClShVP2xbOWH4Nz5f0ZvVHmF/N0GBDUGPHjj3q+lmzZjkhFLnLll5biQKb4NuoUSMNHz7cWZYN73399ddO7/KRLHza3zrkDIIMAADwLSYjAAAA3yLIAAAA3yLIAAAA3yLIAAAA3yLIAAAA3yLIAAAA3yLIAAAA3yLIAAAA3yLIAMi2W265RbVr11bhwoVVqlQpnXfeeXr33Xe9bhaACMJeSwCyrUyZMnr99dedfZf27t2refPm6d///rez54x9BYCcRo8MgGx7+umndeGFF6pSpUpOz8yNN96odu3aac6cOc7t1apVczY2PHLvpsDuzWbatGk6//zzVbJkSScYdezYUX/88Ufa7bZHjd2Wnu3x1Ldv37TLycnJuvfee512FClSxNl7yPa9OdYxVq9eraioqLRNMO3+dnnHjh1p9+nRo4dz3cSJE9Ous7BmPU+2YabdZifb8wiANwgyAILCtm1bsGCBvvvuO11yySUn/H22e7Pt6vzTTz9pxowZypMnj7p06aJDhw6d8DH69OnjBIxx48bpl19+0TXXXOO0ISEhIZs/jZyfZfLkyUddf/XVV6ty5cr6+eeftXHjRvXv3z/bjwHg1BFkAJwS662w3okCBQqoadOm6t27t9Mzc6KuuuoqXXnllc7wlPVsvPnmm/r111+1dOlS5/ZChQpp//79WX7/2rVr9dZbb+njjz/WBRdcoJo1azq9M9bLY9dnl4WrAQMGZLhuy5Yt2rBhg9MbZD1QFSpUcH52AN4hyAA4JRdffLEzPDN//nyNGjVKL730kkaPHp12+8CBA503+8Dpvffey/D91mvStWtX1ahRQ8WLF3eGowIBxZx55pnO0NH48eMzfXwLPSkpKTr99NMzPM7s2bMzDFHt3Lkzw+123GOFs5UrVx7V21K6dGmVKFFCH330kQ4ePJjNZwxAMDHZF8ApsTkp1ptirEdl69ateu6559Im+1qvhs2LSR9sLHgEdOrUSVWrVtVrr72mihUrOkNK9evX14EDB5zb7bx9jw0XFSxY0Bl62rdvX9q8FJtYnDdvXmcoyL6ml763pFixYlq4cGHa5fXr1ztzbY5kAeW+++7TE0884fQGpZcvXz698847uv322zVixAinPdbOM84445SfRwDZQ5ABEPS5Munnt8TExKQFnUCgCEyo/euvv7R8+XInxNiwkPnmm2+OOuZTTz2lwYMHO0M7plu3bmm3NW7c2AlGdlvgGJmxAJS+HRZKMmO9ShaAbKJvZix4WZixwPPss89q+PDhaZObAeQ+ggyAbNm1a5dTR+a2225TnTp1nF6SuXPnOm/uDz744Akdw2rP2EqlV199VbGxsc5w0v3335/pfW3YyU4mfU+JDSlZsLF5Oc8//7wTbKxXyCYOn3XWWerQocNJ/VzPPPOMPvvsM2c1UmZeeOGFtKE0G2ay4SYA3iHIAMgWG1axEGLzSGwpsw3rNGjQQG+88YYzDHQirJfEVhrdfffdzhCSBSLr4chsyOdYbFLv448/7rTFhoysF+jcc891lnKfrLi4OOeUGQtqQ4YMcXqNLMQA8F5UqvUDAwAA+BCrlgAAgG8RZAAAgG8RZAAAgG8RZAAAgG8RZAAAgG8RZAAAgG8RZAAAgG8RZAAAgG8RZAAAgG8RZAAAgG8RZAAAgG8RZAAAgPzq/wENcajZTzawdgAAAABJRU5ErkJggg==",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import seaborn as sns\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"data = np.random.randn(100)\n",
|
||
"\n",
|
||
"sns.histplot(data, bins=20, kde=True, color='blue')\n",
|
||
"plt.title('Гистограмма с плотностью')\n",
|
||
"plt.xlabel('Значения')\n",
|
||
"plt.ylabel('Частота')\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6d2dc93e-d285-4089-970e-01e893d62595",
|
||
"metadata": {},
|
||
"source": [
|
||
"scatterplot"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "c9a84e8b-8208-4a65-a84e-76b012512dd0",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import seaborn as sns\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"x = np.random.rand(100)\n",
|
||
"y = np.random.rand(100)\n",
|
||
"sizes = np.random.rand(100) * 100\n",
|
||
"\n",
|
||
"sns.scatterplot(x=x, y=y, size=sizes, sizes=(20, 200), hue=sizes, palette='viridis', alpha=0.6)\n",
|
||
"plt.title('График рассеяния')\n",
|
||
"plt.xlabel('x')\n",
|
||
"plt.ylabel('y')\n",
|
||
"plt.legend(title='Размеры', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "35b2fe6b-c334-4310-9b0f-181341a71eb2",
|
||
"metadata": {},
|
||
"source": [
|
||
"Добавить sns.pairplot(df), sns.heatmap(df.corr(), annot=True)."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "db2b5bec-84dc-4b9d-9f04-6e8534033973",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"ename": "ValueError",
|
||
"evalue": "could not convert string to float: 'Анна'",
|
||
"output_type": "error",
|
||
"traceback": [
|
||
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
|
||
"\u001b[31mValueError\u001b[39m Traceback (most recent call last)",
|
||
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[12]\u001b[39m\u001b[32m, line 13\u001b[39m\n\u001b[32m 11\u001b[39m sns.boxplot(x=\u001b[33m\"\u001b[39m\u001b[33mКатегория\u001b[39m\u001b[33m\"\u001b[39m, y=\u001b[33m\"\u001b[39m\u001b[33mБаллы\u001b[39m\u001b[33m\"\u001b[39m, data=df)\n\u001b[32m 12\u001b[39m sns.pairplot(df)\n\u001b[32m---> \u001b[39m\u001b[32m13\u001b[39m sns.heatmap(\u001b[43mdf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcorr\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m, annot=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m 14\u001b[39m plt.show()\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32mC:\\WINDOWS\\system32\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:11049\u001b[39m, in \u001b[36mDataFrame.corr\u001b[39m\u001b[34m(self, method, min_periods, numeric_only)\u001b[39m\n\u001b[32m 11047\u001b[39m cols = data.columns\n\u001b[32m 11048\u001b[39m idx = cols.copy()\n\u001b[32m> \u001b[39m\u001b[32m11049\u001b[39m mat = \u001b[43mdata\u001b[49m\u001b[43m.\u001b[49m\u001b[43mto_numpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_value\u001b[49m\u001b[43m=\u001b[49m\u001b[43mnp\u001b[49m\u001b[43m.\u001b[49m\u001b[43mnan\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m 11051\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m method == \u001b[33m\"\u001b[39m\u001b[33mpearson\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m 11052\u001b[39m correl = libalgos.nancorr(mat, minp=min_periods)\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32mC:\\WINDOWS\\system32\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:1993\u001b[39m, in \u001b[36mDataFrame.to_numpy\u001b[39m\u001b[34m(self, dtype, copy, na_value)\u001b[39m\n\u001b[32m 1991\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m dtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 1992\u001b[39m dtype = np.dtype(dtype)\n\u001b[32m-> \u001b[39m\u001b[32m1993\u001b[39m result = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_mgr\u001b[49m\u001b[43m.\u001b[49m\u001b[43mas_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_value\u001b[49m\u001b[43m=\u001b[49m\u001b[43mna_value\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1994\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m result.dtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m dtype:\n\u001b[32m 1995\u001b[39m result = np.asarray(result, dtype=dtype)\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32mC:\\WINDOWS\\system32\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:1694\u001b[39m, in \u001b[36mBlockManager.as_array\u001b[39m\u001b[34m(self, dtype, copy, na_value)\u001b[39m\n\u001b[32m 1692\u001b[39m arr.flags.writeable = \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[32m 1693\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1694\u001b[39m arr = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_interleave\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_value\u001b[49m\u001b[43m=\u001b[49m\u001b[43mna_value\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1695\u001b[39m \u001b[38;5;66;03m# The underlying data was copied within _interleave, so no need\u001b[39;00m\n\u001b[32m 1696\u001b[39m \u001b[38;5;66;03m# to further copy if copy=True or setting na_value\u001b[39;00m\n\u001b[32m 1698\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m na_value \u001b[38;5;129;01mis\u001b[39;00m lib.no_default:\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32mC:\\WINDOWS\\system32\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:1753\u001b[39m, in \u001b[36mBlockManager._interleave\u001b[39m\u001b[34m(self, dtype, na_value)\u001b[39m\n\u001b[32m 1751\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 1752\u001b[39m arr = blk.get_values(dtype)\n\u001b[32m-> \u001b[39m\u001b[32m1753\u001b[39m \u001b[43mresult\u001b[49m\u001b[43m[\u001b[49m\u001b[43mrl\u001b[49m\u001b[43m.\u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m]\u001b[49m = arr\n\u001b[32m 1754\u001b[39m itemmask[rl.indexer] = \u001b[32m1\u001b[39m\n\u001b[32m 1756\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m itemmask.all():\n",
|
||
"\u001b[31mValueError\u001b[39m: could not convert string to float: 'Анна'"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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UTavnn332WUlLSyt0vwMHDjQ1Anmn7t27l+RUAQDwnl7ob775ply5csV8/9JLL0nlypVl+/bt0qdPHxk/fnyx97NlyxYZNmyYCfFr167JuHHjpFu3bvLdd99JUFCQnD592kyvvvqq/PrXv5Yff/xRnn76abPsww8/LHTfGtgJCQmOecaoAwDE1wM8LCzM8X2FChVkzJgxJfrhiYmJ+eYXLVpkSuK7d+82pfxmzZrJypUrHesbNmwoM2bMkEcffdQEfqVKNz98DeyIiIgSHRcAAF47Dvz69etmCJkOJVNaQo6Liys0VItirxrP+wHB2TYhISFF/pzNmzebDwPaXq/3a58+fbrcdtttTrfNzs42k116enqJzwHAL7iuAA9sA9cx33feeacMGDDAhLhO+r3eTtXeoe1W5ebmmgekdOzY0ZS8nUlJSZFp06bJ4MGDi6w+X7JkiXnAit7yVavqe/ToYT503KwtXsev2yd92hqA0uG6AsqXn81ms93qi9q3b2+e/b148WJTwlUXL140ncd+/vln0x5+q4YOHSqffvqpbNu2TerUqXPDev30/oc//MGUzteuXWva3Yvrhx9+MNXv//rXv5x2snNWUtA/NvbSPnzbN998I23atJE/vJQgYfWaiC+4cOKwbJjxuGnOat26dYn2wXUFlK8S1Xfv3bvX3ErVHt5Kv9f26ZLcG3348OGybt062bp1q9Pw1iFrWqrW3upa2r+V8FYNGjSQ8PBwOXLkiNMA1/ZyOrkBZYvrCvDAKnStPj979uwNy8+dOyeNGjUq9n608K/hraGst2GNjo6+YRv91K490/WGMVry1seW3qpTp06Zx59GRkbe8msBAPCaEri2bel47MmTJ8vdd99tlu3cuVOmTp1q2pzzdlYprKpMh5AtW7ZM1qxZY0rXycnJZrm2l+m4b3t4681hli5daubt+9Yq/IoVK5rvY2JizDH17t1bMjIyZMqUKWZIm/ZC17vGjR492nywiI2NlbKkD3LRdnlfolWivlaqsnfUBADLB/j9999vvuq9z/UmKcrelP7AAw845nXdzTqOqfnz55uvnTt3zrdcx29re7q2Pe7atcssK1iyP3bsmNSvX998rzd/sfdg11Dfv3+/aZ/XO8NFRUWZDwHa+a0sg0fDOybmV5KV5WN3ntP3+9a7TXiFnOz8T90DAMsF+GeffVYmP7yo/nMa7MXpY5d3Gy25r1+/Xsqblrw1vNsNmiQhkb98kPB2Z77dIQfWLpSWj7woNaNjxFfYz1vvPQAAlg7w3/3ud2V/JBal4e0rPZPTzxw3X6vVqucz55z3vAHA8p3YlLZJazW1ln51nLW2ieud1AAAgIeWwPXWqfrY0KpVq5p26hUrVpgbsGjbtfZOf/HFF8v+SAEAQOlK4Fr6Xr58uXz++efmwSZz586Vjz76yHRKe+edd0qySwAAUN4lcL3bWtOmTc1NV3Rctt6lSv32t7+VkydPlmSXAACgvEvgelcz+/hnfXyoPjREZWZmmvHcAADAA0vgjz32mBljrcaOHetYrp3ZSnIrVQAA4KI7sd3sgSQ6AQCA8lXyh3f/py1c74KmmjRpYm5vCgAAPLQNXNu6Bw0aZG5T2qlTJzPp90888YS5bzkAAPDAAH/++edNe7c+HUzbwnXSB5LoshdeeKHsjxIAAJS+Cn3lypXy4Ycf5nsIyX333WfuQ64POLE/pAQAAHhQCVyryWvXrn3Dch1ORhU6AAAeGuDt27eXSZMmyZUrVxzLsrKyzHO4dR0AAPDAKvS//vWvEhsba+7Edtddd5ll+/btM8/b/uc//1nWxwgAAMoiwJs1ayZJSUny3nvvyaFDh8yy+Ph46d+/v2kHBwAAHliFfv78efMksqeeekr+9Kc/SVBQkBkP/vXXX5f9EQIAgNIF+Lfffiv169c3ndViYmJk79698pvf/EZef/1183jRe++9V1avXn0ruwQAAOUd4KNHj5bmzZvL1q1bzRCy+++/X3r27ClpaWly8eJFGTJkiMyaNaskxwEAAMqrDfyrr76STZs2SYsWLUznNS11P/PMM1Khwi+fA0aMGCF33333rewSAACUdwn8woULEhERYb6vVq2aafuuUaOGY71+f+nSpZIcBwAAKM9ObH5+foXO3+pTzfTxo/oMcW1X79Wrl+PhKPYPDFqq1welaO/2evXqybPPPmuq7Atjs9lk4sSJEhkZaV7XtWtX02segOdKu3xVjp7LkD0nLsrRnzPMPIAyHEY2cOBAM95b6Y1cnn76aVMSV9nZ2be0L713+rBhw0yIX7t2TcaNGyfdunWT7777zuzz9OnTZnr11Vfl17/+tfz444/m5+kyvZXrzcyZM0fmzZsnixcvlujoaJkwYYIZt677DQgIuNVTBlDOTqdmyYsr98vnSSmOZZ0ah8usPi0kqjpDU4FSB/iAAQPyzT/66KM3bPPYY48Ve3+JiYn55hctWmRK4rt37zZPONPx5nrfdbuGDRvKjBkzzM/VwK9UqZLT0vfcuXNl/PjxEhcXZ5YtWbLE3PpVe8j369ev2McHoPxpSbtgeKutSSkyZuV+eSO+lYRW9Xfb8QFeEeAJCQnleyH/p2o8LCys0G1CQkKchrc6duyYJCcnm2pzu9DQUGnXrp3s2LHDaYBrzUHe2oP09PRSngmA4l5XKRlXbwjvvCGu6wlwoIxu5FIecnNzZeTIkdKxY0dT8nYmJSVFpk2bJoMHD77pfjS8VcGHrei8fZ2ztngNeftUt27dUp0LgOJfV+lXcgrdz6Ui1gO+ymMCXNvCDxw4ICtWrHC6Xj+965hzbQufPHlymf7ssWPHmpK9fTp58mSZ7h/wRcW9rkICKhe6n+Ai1gO+qkT3Qi9rw4cPl3Xr1pkbxOgDUgrSoWndu3c3vdVXrVollSvf/IK2D3M7e/as6YVup/MtW7Z0+hrtlGfvmAegbBT3ugqv5m86rGl1eUG6XNcD8LASuHY40/DWUNYbxGiPcWclb+2Z7u/vL2vXri2yF7nuQ0N848aN+faxa9cuHnUKeCBt39be5hrWeen87D4taP8GPLEErtXmy5YtkzVr1pjStb2NWtvLdPy2PbwvX74sS5cuNfP2jjA1a9aUihUrmu/1vuza3ta7d28zLl3b0qdPny6NGzd2DCOLiooy48wBeB4dKqa9zbXDmrZ5a7W5lrwJb8BDA3z+/Pnmq95XvWBvdx1v/s0335iSs2rUqNENvc31wSpKb/6S9+Yues/2zMxM09ktNTVV7rnnHjNkjTHggOfSsCawAYsEuFahF0aDvahtnO1HS+FTp041EwAA3shjeqEDAIDiI8ABALAgAhwAAAsiwAEAsCACHAAACyLAAQCwIAIcAAALIsABALAgAhwAAAsiwAEAsCACHAAACyLAAQCwIAIcAAALIsABALAgAhwAAAsiwAEAsCACHAAACyLAAQCwIAIcAAALIsABALAgAhwAAAtya4DPnDlT2rZtK8HBwVKrVi3p1auXHD58ON82CxculM6dO0tISIj4+flJampqkfudPHmy2TbvFBMTU45nAgCADwX4li1bZNiwYbJz507ZsGGD5OTkSLdu3SQzM9OxzeXLl6V79+4ybty4W9p306ZN5cyZM45p27Zt5XAGAAC4RyVxo8TExHzzixYtMiXx3bt3S6dOncyykSNHmq+bN2++pX1XqlRJIiIiyvBoAQDwHG4N8ILS0tLM17CwsFLvKykpSaKioiQgIEDat29vquvr1avndNvs7Gwz2aWnp5f65wO+jusK8JFObLm5uaa03bFjR2nWrFmp9tWuXTtTmtcS/vz58+XYsWPy29/+Vi5duuR0ew330NBQx1S3bt1S/XwAXFeAzwS4toUfOHBAVqxYUep99ejRQx566CFp0aKFxMbGyj/+8Q/T+e2DDz5wuv3YsWNN6d8+nTx5stTHAPg6rivAB6rQhw8fLuvWrZOtW7dKnTp1ynz/1atXlzvvvFOOHDnidH2VKlXMBKDscF0BXlwCt9lsJrxXrVolmzZtkujo6HL5ORkZGXL06FGJjIwsl/0DAOBTAa7V5kuXLpVly5aZseDJyclmysrKcmyj83v37nWUnr/99lszf+HCBcc2Xbp0kTfffNMx/+c//9kMUTt+/Lhs375devfuLRUrVpT4+HgXnyEAAF4Y4NrBTNvG9EYtWjq2T++//75jmwULFkirVq3kqaeeMvM6vEzn165d69hGS9cpKSmO+VOnTpmwbtKkifTt21duu+02M9a8Zs2aLj5DAAC8sA1cq9CLc1c1nQqjJe28yqIjHAAAnsxjeqEDAIDiI8ABALAgAhwAAAsiwAEAsCACHAAACyLAAQCwIAIcAAALIsABALAgAhwAAAsiwAEAsCACHAAACyLAAQCwIAIcAAALIsABALAgAhwAAAsiwAEAsCACHAAACyLAAQCwIAIcAAALIsABALAgtwb4zJkzpW3bthIcHCy1atWSXr16yeHDh/Nts3DhQuncubOEhISIn5+fpKamFmvff/vb36R+/foSEBAg7dq1ky+//LKczgIAAB8L8C1btsiwYcNk586dsmHDBsnJyZFu3bpJZmamY5vLly9L9+7dZdy4ccXe7/vvvy/PP/+8TJo0Sb755hu56667JDY2Vs6dO1dOZwIAKIm0y1fl6LkM2XPiohz9OcPMo3gqiRslJibmm1+0aJEpie/evVs6depklo0cOdJ83bx5c7H3+9prr8lTTz0ljz/+uJlfsGCBfPLJJ/LOO+/ImDFjyvQcAAAlczo1S15cuV8+T0pxLOvUOFxm9WkhUdUD3XpsVuBRbeBpaWnma1hYWIn3cfXqVfMBoGvXro5lFSpUMPM7duwok+MEAJSOlrQLhrfampQiY1bupyTu6SXwvHJzc01pu2PHjtKsWbMS7yclJUWuX78utWvXzrdc5w8dOuT0NdnZ2WayS09PL/HPB/ALrisUJiXj6g3hnTfEdX1oVX+XH5eVeEwJXNvCDxw4ICtWrHBLZ7rQ0FDHVLduXZcfA+BtuK5QmPQrOYWuv1TEenhIgA8fPlzWrVsnn332mdSpU6dU+woPD5eKFSvK2bNn8y3X+YiICKevGTt2rKm+t08nT54s1TEA4LpC4UICKhe6PriI9XBzgNtsNhPeq1atkk2bNkl0dHSp9+nv7y9t2rSRjRs35que1/n27ds7fU2VKlXMMLW8E4DS4bpCYcKr+ZsOa87ocl0PDw5wrTZfunSpLFu2zIwFT05ONlNWVpZjG53fu3evHDlyxMx/++23Zv7ChQuObbp06SJvvvmmY16HkL399tuyePFiOXjwoAwdOtQMTbP3SgcAuJe2b2tv84IhrvOz+7Sg/dvTO7HNnz/ffNUbteSVkJAgAwcOdAwBmzJlimOdfXhZ3m2OHj1qOq/ZPfzww/Lzzz/LxIkTzQeAli1bmiFrBTu2AQDcR4eKvRHfynRY0zZvrTbXkjfhbYEA1yr0okyePNlMhTl+/PgNy7RqXicAgOfSsCawLdyJDQAA3BoCHAAAC/KYG7l4EnvVfmE3nsjIyDBf0079ILnXrosvyDj3k/mannzc9DD2Fb543peSf3T8nhd1AxbtgKoPGiqL6wpA8a8rP1txGqJ9zKlTp7jpBFBMOsa7OEPEuK6Asr2uCHAndNz46dOnC/0EpKUI/WOkN6fwlfGtvnjOvnret3LOxS2BF+e68mS++HvgaXzpPQguxnVCFboT+vCT4t4RzhdvUOGL5+yr512W53wr15Un88XfA0/De/ALOrEBAGBBBDgAABZEgJeQ9kaeNGmSz/RK9tVz9tXz9sVzLgr/J+7He5AfndgAALAgSuAAAFgQAQ4AgAUR4AAAWBABDgCABRHgTmi/Pr3jD/37gLLDdQWULQLciUuXLkloaKj5CqBscF0BZYsABwDAgghwAAAsiAAHAMCCCHAAACzIkgGunWBGjhwpd9xxhwQGBkqHDh3kq6++cqwfOHCgeY5q3ql79+5uPWYAAMqSJZ8H/uSTT8qBAwfk3XfflaioKFm6dKl07dpVvvvuO7n99tvNNhrYCQkJjtdw83uURNrlq5KScVXSr+RISGBlCQ/yl9Cq/u4+LACwXoBnZWXJypUrZc2aNdKpUyezbPLkyfLxxx/L/PnzZfr06Y7AjoiIcPPRwspOp2bJiyv3y+dJKY5lnRqHy6w+LSSqeqBbjw0ALFeFfu3aNbl+/boEBATkW65V6du2bXPMb968WWrVqiVNmjSRoUOHyvnz591wtLByybtgeKutSSkyZuV+sx4A3MlyJfDg4GBp3769TJs2TX71q19J7dq1Zfny5bJjxw5p1KiRo/r8wQcflOjoaDl69KiMGzdOevToYbapWLHiDfvMzs42k53eLQq+TavNC4Z33hDX9VSlF66k19WJEyckJcX5/703Cw8Pl3r16okv8cX3OrwM32fLBbjStu9BgwaZ9m4N5NatW0t8fLzs3r3brO/Xr59j2+bNm0uLFi2kYcOGplTepUuXG/Y3c+ZMmTJlikvPAZ5N27wLc6mI9SjZdaV/0GNifiVZWZfF1wQGVpVDhw76TIj76nsdWIbvsyUDXMN4y5YtkpmZaT7VR0ZGysMPPywNGjRwur0u1089R44ccRrgY8eOleeff94xr/usW7duuZ4DPFtIQOVC1wcXsR4lu660NKZ/0NsNmiQhkfXFV6SfOS673plizt9XAtwX3+v0Mn6fLRngdkFBQWa6ePGirF+/XubMmeN0u1OnTpk2cA16Z7TDG73UkVd4NX/TYU2rywvS5boehSvNdaV/0MPqNSnzY4Ln4b32oU5sSsM6MTFRjh07Jhs2bJB7771XYmJi5PHHH5eMjAwZNWqU7Ny5U44fPy4bN26UuLg40z4eGxvr7kOHRWj7tvY217DOS+dn92lB+zcAt7NkCTwtLc1Uz2nJOiwsTPr06SMzZsyQypUrm17q+/fvl8WLF0tqaqoZJ96tWzfT6Y1SNm6FDhV7I76V6bCmbd5aba4lb8IbgCewZID37dvXTM7ocDItoQNlQcOawAbgiSxZhQ4AgK8jwAEAsCACHAAACyLAAQCwIAIcAAALIsABALAgAhwAAAsiwAEAsCACHAAACyLAAQCwIAIcAAALIsABALAgAhwAAAsiwAEAsCACHAAACyLAAQCwIAIcAAALIsABALAgAhwAAAsiwAEAsCACHAAACyLAAQCwIAIcAAALIsABALAgAhwAAAsiwAEAsCACHAAACyLAAQCwIAIcAAALIsABALAgAhwAAAsiwAEAsCACHAAACyLAAQCwIAIcAAALIsABALAgAhwAAAsiwAEAsCACHAAACyLAAQCwIEsG+KVLl2TkyJFyxx13SGBgoHTo0EG++uorx3qbzSYTJ06UyMhIs75r166SlJTk1mMGAEB8PcCffPJJ2bBhg7z77rvy7bffSrdu3UxI//TTT2b9nDlzZN68ebJgwQLZtWuXBAUFSWxsrFy5csXdhw4AgG8GeFZWlqxcudKEdKdOnaRRo0YyefJk83X+/Pmm9D137lwZP368xMXFSYsWLWTJkiVy+vRpWb16tbsPHwAA3wzwa9euyfXr1yUgICDfcq0q37Ztmxw7dkySk5NNidwuNDRU2rVrJzt27HC6z+zsbElPT883ASgdriugfFkuwIODg6V9+/Yybdo0U6rWMF+6dKkJ5zNnzpjwVrVr1873Op23ryto5syZJuTtU926dV1yLoA347oCypflAlxp27dWld9+++1SpUoV094dHx8vFSqU7HTGjh0raWlpjunkyZNlfsyAr+G6AspXJbGghg0bypYtWyQzM9NUy2lv84cfflgaNGggERERZpuzZ8+a5XY637JlS6f70w8BOgEoO1xXQPmyZAncTnuXa0hfvHhR1q9fbzqtRUdHmxDfuHGjYzsNee2NrlXvAAB4A0uWwDWstQq9SZMmcuTIERk1apTExMTI448/Ln5+fmaM+PTp06Vx48Ym0CdMmCBRUVHSq1cvdx86AAC+G+Danqbta6dOnZKwsDDp06ePzJgxQypXrmzWjx492lSvDx48WFJTU+Wee+6RxMTEG3quAwBgVZYM8L59+5rpZrQUPnXqVDOVh7TLVyUl46qkX8mRkMDKEh7kL6FV/cvlZwEA4DUB7k6nU7PkxZX75fOkFMeyTo3DZVafFhJVPdCtxwYA8B2W7sTmalryLhjeamtSioxZud+sBwDAFQjwW6DV5gXDO2+I63oAAFyBAL8F2uZdmEtFrAcAoKwQ4LcgJOCXXu43E1zEegAAygoBfgvCq/mbDmvO6HJdDwCAKxDgt0CHimlv84IhrvOz+7RgKBkAwGUYRnaLdKjYG/GtTIc1bfPWanMteRPeAABXIsBLQMOawAYAuBNV6AAAWBABDgCABRHgAABYEAEOAIAFEeAAAFgQAQ4AgAUR4AAAWBABDgCABRHgAABYEAEOAIAFufRWqhUqVBA/P7+brr9+/borDwcAAMtyaYCvWrXKlT8OAACv5dIAj4uLyzf/0Ucfyd69e6VZs2bSt29fVx4KAACW5rY28NmzZ0t8fLwkJibKE088IVOmTHHXoQAAYDluC/DFixfL3//+d/nyyy9lzZo1kpCQ4K5DAQDActwW4KdPn5a7777bfK9ff/rpJ3cdCgAAluO2ANce55Uq/dIEX7FiRcnNzXXXoQAAYDku7cTWqlUrxzCyrKwseeCBB8Tf319sNpsrDwMAAMtzaYD36tXrpj3S864DAAAeFOCTJk1y5Y8DAMBruTTA09PTC10fEhLismMBAMDKXBrg1atXd3orVW0D1+XcShUAAA8McPXhhx9KWFiYq38sAABexeUB3rFjR6lVq5arfywAAF7F5QH+3Xffyfnz5yUoKEgiIiLMMDIAAODhN3Lp0qWLNG3aVKKjo02IN2/eXF5//XVXHwYAAJbm0hL4sWPHTIe1nJwc0yNdb6eq90KfMGGCXLt2TUaNGuXKwwEAwLJcGuB33HFHvvk2bdqYu7HdeeedMnXqVAIcAABPbQN3pl+/fqZaHQAAWOxGLo0aNXLloQAAYGkVXH0jlxo1atww2ZcXh97sRdvMtRNcYGCgNGzYUKZNm5bvgSgDBw40N4bJO3Xv3r0czwwAANey3I1cZs+eLfPnz5fFixebavevv/5aHn/8cQkNDZVnn33WsZ0GdkJCgmO+SpUqpT52AAA8heVu5LJ9+3bzJLOePXua+fr168vy5ctNb/a8NLB1nDkAAN6ogjtu5HLw4EE5ceKEXL169ZZf36FDB9m4caN8//33Zn7fvn2ybds26dGjR77tNm/ebD4oNGnSRIYOHWpuHgMAgLeo5I4budgfXlKhQgWJiYmRQYMGyXPPPVes148ZM8Z0htPXVaxY0bSJz5gxQ/r375+v+vzBBx807eRHjx6VcePGmYDfsWOHeU1B2dnZZiruU9MAFI3rCihflruRywcffCDvvfeeLFu2zLSB7927V0aOHClRUVEyYMAAx7A0O73TW4sWLUxnNy2V6weIgmbOnClTpkwp47MFfBvXFeBFVeh6Ixdts27cuLHjJi7ag1w7pS1cuLBY+9CQ11K4hrSG8x//+EdTetc/FjfToEEDCQ8PlyNHjjhdP3bsWElLS3NMJ0+eLPE5AvgF1xVQvix3I5fLly+bqve8tFo8Nzf3pq85deqUaQOPjIx0ul47vNFLHShbXFeADwR45cqVpXXr1sXaVkvt2uZdr149E/p79uyR1157zbSjq4yMDFNt16dPH9MLXdvAR48ebW4UExsbW85nAgCAFwa4djjTJ49pO7azXugXLlwoch9vvPGGaTN/5pln5Ny5c6bte8iQITJx4kRHaXz//v1mnHhqaqpZ361bN1NVT2kAAOAtXBrgWjL++9//Li+88IKMHz9eXnrpJTl+/LisXr3aEcBFCQ4Olrlz55rJGb072/r168v4yAEA8OFObNp7/O233zYBXqlSJYmPjzeBruG9c+dOVx4KAACW5tIAT05ONj3HVbVq1UzPVHX//ffLJ5984spDAQDA0lwa4HXq1JEzZ86Y73Vc9j//+U/z/VdffUX7NAAAnhrgvXv3NrdBVSNGjDCd0XRM+GOPPeboRQ4AADysE9usWbMc3z/88MPmxi76cBINcR0eBgAAPPRhJnZabf7xxx+boWD6KFAAAOBhJXC96YrecOW2224z8xs2bDCPA9V2b72r2quvvmoeCfrQQw+54nAAALA8l5TA9VamehMXu1deecUMIdMbrVy8eNHciGXOnDmuOBQAALyCW6rQ9Xng2olN75qmJXD9PikpyR2HAgCAJbklwLXkHRYW5pivUaOGXLp0yR2HAgCAJbkkwP38/MxUcBkAAPDgTmw2m00GDhzouFnLlStX5Omnn5agoCAzn52d7YrDAADAa7gkwAcMGJBv/tFHH71hG72ZCwAA8KAAT0hIcMWPAQDAZ7jtRi4AAKDkCHAAACyIAAcAwIIIcAAALIgABwDAgghwAAAsyKXPAwfg+dIuX5WUjKuSfiVHQgIrS3iQv4RW9Xf3YQEogAAH4HA6NUteXLlfPk9KcSzr1DhcZvVpIVHVA916bADyowodgKPkXTC81dakFBmzcr9ZD8BzEOAADK02LxjeeUNc1wPwHAQ4AEPbvAtzqYj1AFyLAAdghARULnR9cBHrAbgWAQ7ACK/mbzqsOaPLdT0Az0GAAzB0qJj2Ni8Y4jo/u08LhpIBHoZhZAAcdKjYG/GtTIc1bfPWanMteRPegOchwAHko2FNYAOejyp0AAAsiAAHAMCCCHAAACyIAAcAwIIIcAAALIgABwDAgghwAAAsiAAHAMCCCHAAACzIcgF+/fp1mTBhgkRHR0tgYKA0bNhQpk2bJjabzbGNfj9x4kSJjIw023Tt2lWSkpLcetwAAPh0gM+ePVvmz58vb775phw8eNDMz5kzR9544w3HNjo/b948WbBggezatUuCgoIkNjZWrly54tZjBwDAZ++Fvn37domLi5OePXua+fr168vy5cvlyy+/dJS+586dK+PHjzfbqSVLlkjt2rVl9erV0q9fP7cePwAAPlkC79Chg2zcuFG+//57M79v3z7Ztm2b9OjRw8wfO3ZMkpOTTbW5XWhoqLRr10527NjhtuMGAMCnS+BjxoyR9PR0iYmJkYoVK5o28RkzZkj//v3Neg1vpSXuvHTevq6g7OxsM9np/gGUDtcVUL4sVwL/4IMP5L333pNly5bJN998I4sXL5ZXX33VfC2pmTNnmlK6fapbt26ZHjPgi7iugPJluQAfNWqUKYVrW3bz5s3lj3/8ozz33HPmj4WKiIgwX8+ePZvvdTpvX1fQ2LFjJS0tzTGdPHnSBWcCeDeuK6B8Wa4K/fLly1KhQv7PHVqVnpuba77X4WUa1NpO3rJlS0fVnfZGHzp0qNN9VqlSxUwAyg7XFVC+LBfgDzzwgGnzrlevnjRt2lT27Nkjr732mgwaNMis9/Pzk5EjR8r06dOlcePGJtB13HhUVJT06tXL3YcPAIBvBriO99ZAfuaZZ+TcuXMmmIcMGWJu3GI3evRoyczMlMGDB0tqaqrcc889kpiYKAEBAW49dgAAfDbAg4ODzThvnW5GS+FTp041EwAA3shyndgAAAABDgCAJRHgAABYEAEOAIAFEeAAAFgQAQ4AgAUR4AAAWBABDgCABRHgAABYEAEOAIAFEeAAAFgQAQ4AgAUR4AAAWBABDgCABRHgAABYEAEOAIAFEeAAAFgQAQ4AgAUR4AAAWBABDgCABRHgAABYEAEOAIAFEeAAAFgQAQ4AgAUR4AAAWBABDgCABRHgAABYEAEOAIAFEeAAAFgQAQ4AgAVVcvcBwBrSLl+VlIyrkn4lR0ICK0t4kL+EVvV392EBgM8iwFGk06lZ8uLK/fJ5UopjWafG4TKrTwuJqh7o1mMDAF9FFTqKLHkXDG+1NSlFxqzcb9YDAFyPAEehtNq8YHjnDXFdDwBwPQIchdI278JcKmI9AKB8EOAoVEhA5ULXBxexHgBQPghwFCq8mr/psOaMLtf1AADXI8BRKB0qpr3NC4a4zs/u04KhZADgJgwjQ5F0qNgb8a1MhzVt89Zqcy15E94A4D4EOIpFw5rABgDPQRU6AAAWRIADAGBBBDgAABZEG7gTNpvNfE1PT3f3oQAeLzg4WPz8/MrkusrIyDBf0079ILnXrouvuJT8o/n6zTffOP4PvN3333/vc+/1pf+8z/oeF5Uvxbmu/Gz2qwoOp06dkrp167r7MABLSEtLk5CQkCK347oCyva6IsCdyM3NldOnTxf6CUg/Pekfo5MnTxbrj5c38MVz9tXzvpVzLm4JvDjXlSfzxd8DT+NL70FwMa4TqtCdqFChgtSpU6dY2+ovkbf/IhXki+fsq+ddlud8K9eVJ/PF3wNPw3vwCzqxAQBgQQQ4AAAWRICXUJUqVWTSpEnmq6/wxXP21fP2xXMuCv8n7sd7kB+d2AAAsCBK4AAAWBABDgCABRHgAABYEAEOAIAFEeCFmDlzprRt29bcEadWrVrSq1cvOXz4cL5tFi5cKJ07dzY3FdC75qSmpoq3n/eFCxdkxIgR0qRJEwkMDJR69erJs88+a279583v9ZAhQ6Rhw4bmnGvWrClxcXFy6NAh8eZzttO+rj169DC/46tXrxZvdf36dZkwYYJER0eb91nf72nTpjnu4670+4kTJ0pkZKTZpmvXrpKUlOTW4/Y2ly5dkpEjR8odd9xh/o87dOggX331lWM978EvCPBCbNmyRYYNGyY7d+6UDRs2SE5OjnTr1k0yMzMd21y+fFm6d+8u48aNE185b70dpk6vvvqqHDhwQBYtWiSJiYnyxBNPiDe/123atJGEhAQ5ePCgrF+/3vwR0W30j763nrPd3LlzLXn701s1e/ZsmT9/vrz55pvmfdb5OXPmyBtvvOHYRufnzZsnCxYskF27dklQUJDExsbKlStX3Hrs3uTJJ580v5PvvvuufPvtt+b3UkP6p59+Mut5D/5Dh5GheM6dO6cfw21btmy5Yd1nn31m1l28eNHmS+dt98EHH9j8/f1tOTk5Nl8553379pltjhw5YvPmc96zZ4/t9ttvt505c8asX7Vqlc1b9ezZ0zZo0KB8yx588EFb//79zfe5ubm2iIgI2yuvvOJYn5qaaqtSpYpt+fLlLj9eb3T58mVbxYoVbevWrcu3vHXr1raXXnqJ9yAPSuC3wF5FHBYWJr6kOOdtf3JOpUqVfOKctZSqpXGtavWWJ2w5O2etYXrkkUfkb3/7m0RERIi306rajRs3Oh51uW/fPtm2bZtpPlDHjh2T5ORkUxq0Cw0NlXbt2smOHTvcdtze5Nq1a6ZWKyAgIN9yrSrX94L34P8jwItJn6SkbTIdO3aUZs2aia8oznmnpKSYdsLBgweLt5/zW2+9JdWqVTPTp59+aqr5/P39xVvP+bnnnjOhpu39vmDMmDHSr18/iYmJkcqVK0urVq3M/0v//v3Neg0OVbt27Xyv03n7OpSO9slo3769+ZuiTXUa5kuXLjXhfObMGd6DPLyjuOQC2lao7b36CdCXFHXe+ni/nj17yq9//WuZPHmyePs56x/yP/zhD+YPifYB6Nu3r3zxxRc3lBa84ZzXrl0rmzZtkj179oiv+OCDD+S9996TZcuWSdOmTWXv3r0mwKOiomTAgAHuPjyfoW3fgwYNkttvv10qVqworVu3lvj4eNm9e7e7D82z5K1Ph3PDhg2z1alTx/bDDz/cdBtvbAMv6rzT09Nt7du3t3Xp0sWWlZVl85X32i47O9tWtWpV27Jly2zeeM5/+tOfbH5+fqY90j7p73iFChVsv/vd72zeSP8f3nzzzXzLpk2bZmvSpIn5/ujRo+b/QPsF5NWpUyfbs88+69Jj9QUZGRm206dPm+/79u1ru++++3gP8qAKvRDay3j48OGyatUqUxLR9k5fUJzz1pK39gzV6mMtqVm9BFqS91pfo1N2drZ44zlrdfL+/ftNKdQ+qddff920/3sjbfPX55bnpSVAbWJQ+n+kfQG0nTzvtaA9obXaF2VLe5frULGLFy+akR/alMN7kEfeNEd+Q4cOtYWGhto2b95seuDaJ+0laafz+knw7bffNp8Kt27daubPnz9v89bzTktLs7Vr187WvHlz0wM77zbXrl2zeeM566f+l19+2fb111/bfvzxR9sXX3xhe+CBB2xhYWG2s2fP2rz197sgb++FPmDAANPjXntAHzt2zPbRRx/ZwsPDbaNHj3ZsM2vWLFv16tVta9asse3fv98WFxdni46O9ppaKE+QmJho+/TTT02t0D//+U/bXXfdZf7mXL161aznPfgFAV4I/WPlbEpISHBsM2nSpCK38bbztjcXOJv0j543nvNPP/1k69Gjh61WrVq2ypUrm6rWRx55xHbo0CGbN/9++1qAa7OQNh3Uq1fPFhAQYGvQoIEZuqTNJXY6jGnChAm22rVrm6FL2oR0+PBhtx63t3n//ffN/70OTdUhY9rMo0PF7HgPfsHjRAEAsCDawAEAsCACHAAACyLAAQCwIAIcAAALIsABALAgAhwAAAsiwAEAsCACHAAACyLAUWIDBw4UPz8/x3TbbbdJ9+7dzf2zAZTv9Waf6tSp4+5Dg5sQ4CgVDWx9tKZO+nCBSpUqyf333+/uwwK8/nqzT770uFfkR4CjVKpUqWKeDKRTy5YtzROsTp48KT///LNZ/+2338rvf/97CQwMNCX0wYMHS0ZGhuP1ur0+a1mfaqbP/n3xxRcdT37avHmzKWF88skn0qJFC/PEs7vvvts8t9ru/Pnz5jnB+tqqVatK8+bNZfny5fmOUfc3Z84cadSokTneevXqyYwZM8w6ZyUa+6Q/H/DU680+1axZ07Fer58777zTXAsNGjSQCRMmSE5OTr59HD9+3Onve2pqarG3mTx5srnenVm9erXZFuWPAEeZ0WBeunSpCUoN68zMTImNjZUaNWrIV199Jf/3f/8n//rXv8wjLO30kaTr1q2TI0eOyN///ndZuHCh2Udeo0aNkr/85S9mH/rH6oEHHnD8Ubpy5Yq0adPGhLwGu35A+OMf/yhffvml4/Vjx46VWbNmmT9m3333nSxbtkxq165t1uUtyaiVK1c65jt06OCi/zmgbAQHB8uiRYvM7/lf//pXefvtt83jX/OyP/5Cr0X9Pdff+YKKsw08wH8eagKU6NGLFStWtAUFBZlJf50iIyNtu3fvNusXLlxoq1Gjhi0jI8Pxmk8++cRWoUIFW3Jy8g3700cH6uvfeeedfE89W7FihWMbfUxrYGCgeVrRzfTs2dP2wgsvOJ4upU8r0se9FkV/lv5MwArXmz72tGvXrrb169ff9DWvvPKKrU2bNvmW6VO79Hf9wIED+a6zixcvFnsbfQqjPuLTGX1aHdHiGpTAUSr33nuv7N2710xa6tUSd48ePeTHH3+UgwcPyl133SVBQUGO7Tt27GiqtA8fPuxY9vLLLzuq/Pr06SOPPfZYvp/Rvn17x/dhYWHSpEkTs291/fp1mTZtmqk613XVqlWT9evXy4kTJ8x63S47O1u6dOnigv8NwHXX20cffSSRkZHSs2dP2bVrl1n//vvvm2tMq9b1Whg/frzjWrBLT083X/NelwUVZxttHtOfERoaKr/61a9MLRdcq5KLfx68jF7gWmVup9XgekFr1V1xPf300/Lggw/K7t27ZeTIkeZ7/UNVHK+88oqpKpw7d64JcT0e3cfVq1fNem17B7z1envnnXdMkGu7s34w7t+/v0yZMsV8kNbrcMWKFab5Ka/Tp09LhQoVTMjfTHG20Q/Sa9euNR+id+7cKU899ZQ5Nu3ICtegBI4ypZ1X9MLPysoyn8r37dtn2sLtvvjiC7NeL347LTnHxMSYPz733HPPDe1t+sfB7uLFi/L999+bfdv3FxcXJ48++qgp7WspXtfbNW7c2IS49pAHvI1eSzppiG7fvl3uuOMOeemll+S//uu/zO++1oQVpH1J9HrTTqE3U5xttOOpBrZeywMGDDDXn9YMwHUIcJSKVk8nJyebSaurR4wYYTqzaUczDWT9A6AXt3Yw++yzz8x67WRm70T21ltvyb///W/T61U7r23YsEFatWqV72dMnTrVBLDuQ8fChoeHS69evcw6/SOlr9E/XvrzhwwZImfPnnW8Vn++9swdPXq0LFmyRI4ePWo+EPzv//6vi/+ngPK73u677z5zLWh1uZa69fd83rx5smrVKsdrtVbq3Xfflddee00ef/xxp/svzjZ22m1EO5HqB/RNmzaZjnPNmjUr83NGIVzU1g4v7VSjv0L2KTg42Na2bVvbhx9+6Nhm//79tnvvvdcWEBBgCwsLsz311FO2S5cuOdbfd999tvDwcNPRrFGjRrYZM2bYcnNz83Wc+fjjj21Nmza1+fv7237zm9/Y9u3bl69TW1xcnK1atWq2WrVq2caPH2977LHHzDK769ev26ZPn2674447bJUrV7bVq1fP9vLLL99wPnRig9Wut9atW9uWL1/u2GbUqFG22267zVwPDz/8sO3111+3hYaGmnVff/21rUGDBraZM2eaa8Iubwe14mxj78RmPw7tlFq3bl3bhAkTzDo6sbmOn/5TWMAD7qLjsLUtXKvNq1ev7u7DAQCPQhU6AAAWRIADAGBBVKEDAGBBlMABALAgAhwAAAsiwAEAsCACHAAACyLAAQCwIAIcAAALIsABALAgAhwAAAsiwAEAEOv5f/xTImF0BWcVAAAAAElFTkSuQmCC",
|
||
"text/plain": [
|
||
"<Figure size 500x500 with 6 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import seaborn as sns\n",
|
||
"import pandas as pd\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"data = {\n",
|
||
" \"Имя\": [\"Анна\", \"Борис\", \"Виктор\", \"Галина\"],\n",
|
||
" \"Возраст\": [21, 22, 23, 24],\n",
|
||
" \"Баллы\": [89, 76, 95, 82]\n",
|
||
"}\n",
|
||
"df = pd.DataFrame(data)\n",
|
||
"df[\"Категория\"] = [\"A\", \"B\", \"A\", \"B\"]\n",
|
||
"sns.boxplot(x=\"Категория\", y=\"Баллы\", data=df)\n",
|
||
"sns.pairplot(df)\n",
|
||
"sns.heatmap(df.corr(), annot=True)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "286e6c40-b631-47df-acda-a98a583707a4",
|
||
"metadata": {},
|
||
"source": [
|
||
"_________________________________________________\n",
|
||
"Прогресс-бар с tqdm\n",
|
||
"_________________________________________________"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "99628b67-6486-47e5-9739-5c1bc2e9472a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" 48%|█████████████████████████████████████▉ | 480/1000 [00:05<00:05, 94.64it/s]"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from tqdm import tqdm\n",
|
||
"import time\n",
|
||
"\n",
|
||
"for i in tqdm(range(1000)):\n",
|
||
" time.sleep(0.01) # Симуляция долгого процесса"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "acecb529-3549-444e-92e2-6b5460c1891b",
|
||
"metadata": {},
|
||
"source": [
|
||
"Использовать tqdm для обработки данных (tqdm(df.iterrows()))."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "c8634a4e-f3d0-4ca8-b932-b90b1b80464e",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000000/1000000 [00:20<00:00, 49500.87it/s]\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" A B C\n",
|
||
"0 1 1000001 1000002\n",
|
||
"1 2 1000002 1000004\n",
|
||
"2 3 1000003 1000006\n",
|
||
"3 4 1000004 1000008\n",
|
||
"4 5 1000005 1000010\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from tqdm import tqdm\n",
|
||
"import pandas as pd\n",
|
||
"\n",
|
||
"data = {\n",
|
||
" 'A': range(1, 1000001),\n",
|
||
" 'B': range(1000001, 2000001)\n",
|
||
"}\n",
|
||
"df = pd.DataFrame(data)\n",
|
||
"\n",
|
||
"results = []\n",
|
||
"for index, row in tqdm(df.iterrows(), total=df.shape[0]):\n",
|
||
" result = row['A'] + row['B']\n",
|
||
" results.append(result)\n",
|
||
"df['C'] = results\n",
|
||
"print(df.head())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "20fa00fd-4c6c-49e3-a271-aac75ab729a7",
|
||
"metadata": {},
|
||
"source": [
|
||
"Добавить кастомные стилизации (tqdm(range(100), desc='Загрузка'))."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "a18e7fc8-4dc2-4b78-9ace-2a3582021d13",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Загрузка: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000000/1000000 [00:20<00:00, 49161.98it/s]\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" A B C\n",
|
||
"0 1 1000001 1000002\n",
|
||
"1 2 1000002 1000004\n",
|
||
"2 3 1000003 1000006\n",
|
||
"3 4 1000004 1000008\n",
|
||
"4 5 1000005 1000010\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from tqdm import tqdm\n",
|
||
"import time\n",
|
||
"import pandas as pd\n",
|
||
"\n",
|
||
"data = {\n",
|
||
" 'A': range(1, 1000001),\n",
|
||
" 'B': range(1000001, 2000001)\n",
|
||
"}\n",
|
||
"df = pd.DataFrame(data)\n",
|
||
"\n",
|
||
"results = []\n",
|
||
"for index, row in tqdm(df.iterrows(), desc='Загрузка', total=df.shape[0]):\n",
|
||
" result = row['A'] + row['B']\n",
|
||
" results.append(result)\n",
|
||
"df['C'] = results\n",
|
||
"print(df.head())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "0f93d97e-5bd6-45bc-a18b-715a757693c2",
|
||
"metadata": {
|
||
"jupyter": {
|
||
"source_hidden": true
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "03a29ece-315c-4554-a65d-09f5ffb51c0c",
|
||
"metadata": {},
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "11bc033e-004d-4f52-be14-7d3c631bc9ba",
|
||
"metadata": {},
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "939f8b25-1e04-49cc-b661-5fdea19875dc",
|
||
"metadata": {
|
||
"jupyter": {
|
||
"source_hidden": true
|
||
}
|
||
},
|
||
"source": [
|
||
"САМОСТОЯТЕЛЬНОЕ ЗАДАНИЕ"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 51,
|
||
"id": "23fca7ae-0c4a-4054-910b-24e131ac18d6",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Краткая информация о датафрейме:\n",
|
||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||
"RangeIndex: 234 entries, 0 to 233\n",
|
||
"Data columns (total 22 columns):\n",
|
||
" # Column Non-Null Count Dtype \n",
|
||
"--- ------ -------------- ----- \n",
|
||
" 0 Date 234 non-null object \n",
|
||
" 1 HomeTeam 234 non-null object \n",
|
||
" 2 AwayTeam 234 non-null object \n",
|
||
" 3 FTHG 234 non-null int64 \n",
|
||
" 4 FTAG 234 non-null int64 \n",
|
||
" 5 FTR 234 non-null object \n",
|
||
" 6 HTHG 234 non-null int64 \n",
|
||
" 7 HTAG 234 non-null int64 \n",
|
||
" 8 HTR 234 non-null object \n",
|
||
" 9 Referee 0 non-null float64\n",
|
||
" 10 HS 234 non-null int64 \n",
|
||
" 11 AS 234 non-null int64 \n",
|
||
" 12 HST 234 non-null int64 \n",
|
||
" 13 AST 234 non-null int64 \n",
|
||
" 14 HF 234 non-null int64 \n",
|
||
" 15 AF 234 non-null int64 \n",
|
||
" 16 HC 234 non-null int64 \n",
|
||
" 17 AC 234 non-null int64 \n",
|
||
" 18 HY 234 non-null int64 \n",
|
||
" 19 AY 234 non-null int64 \n",
|
||
" 20 HR 234 non-null int64 \n",
|
||
" 21 AR 234 non-null int64 \n",
|
||
"dtypes: float64(1), int64(16), object(5)\n",
|
||
"memory usage: 40.3+ KB\n",
|
||
"Статистика (include=\"object\"):\n",
|
||
" Date HomeTeam AwayTeam FTR HTR\n",
|
||
"count 234 234 234 234 234\n",
|
||
"unique 77 18 18 3 3\n",
|
||
"top 24/08/24 Stuttgart Freiburg H H\n",
|
||
"freq 6 14 14 97 81\n",
|
||
"Статистика:\n",
|
||
" FTHG FTAG HTHG HTAG Referee HS \\\n",
|
||
"count 234.000000 234.000000 234.000000 234.000000 0.0 234.000000 \n",
|
||
"mean 1.675214 1.457265 0.764957 0.700855 NaN 14.282051 \n",
|
||
"std 1.446197 1.225748 0.917340 0.846756 NaN 5.580232 \n",
|
||
"min 0.000000 0.000000 0.000000 0.000000 NaN 0.000000 \n",
|
||
"25% 1.000000 0.250000 0.000000 0.000000 NaN 10.250000 \n",
|
||
"50% 1.000000 1.000000 1.000000 0.000000 NaN 14.000000 \n",
|
||
"75% 2.750000 2.000000 1.000000 1.000000 NaN 17.000000 \n",
|
||
"max 7.000000 6.000000 4.000000 4.000000 NaN 34.000000 \n",
|
||
"\n",
|
||
" AS HST AST HF AF HC \\\n",
|
||
"count 234.000000 234.000000 234.000000 234.000000 234.000000 234.00000 \n",
|
||
"mean 11.495726 5.102564 4.158120 10.594017 11.115385 5.57265 \n",
|
||
"std 4.741605 2.652668 2.278044 3.408165 3.639250 2.91162 \n",
|
||
"min 2.000000 0.000000 0.000000 2.000000 3.000000 0.00000 \n",
|
||
"25% 8.000000 3.000000 3.000000 8.000000 9.000000 4.00000 \n",
|
||
"50% 11.000000 5.000000 4.000000 10.000000 10.500000 5.00000 \n",
|
||
"75% 14.000000 7.000000 5.000000 12.750000 14.000000 7.00000 \n",
|
||
"max 25.000000 14.000000 13.000000 25.000000 23.000000 18.00000 \n",
|
||
"\n",
|
||
" AC HY AY HR AR \n",
|
||
"count 234.000000 234.000000 234.000000 234.000000 234.000000 \n",
|
||
"mean 4.149573 1.876068 2.153846 0.081197 0.098291 \n",
|
||
"std 2.630104 1.370138 1.426995 0.317293 0.325849 \n",
|
||
"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
|
||
"25% 2.000000 1.000000 1.000000 0.000000 0.000000 \n",
|
||
"50% 4.000000 2.000000 2.000000 0.000000 0.000000 \n",
|
||
"75% 6.000000 3.000000 3.000000 0.000000 0.000000 \n",
|
||
"max 14.000000 8.000000 7.000000 3.000000 2.000000 \n",
|
||
"Датафрейм: \n",
|
||
" Date HomeTeam AwayTeam FTHG FTAG FTR HTHG HTAG HTR \\\n",
|
||
"0 23/08/24 M'gladbach Leverkusen 2 3 A 0 2 A \n",
|
||
"1 24/08/24 Augsburg Werder Bremen 2 2 D 2 1 H \n",
|
||
"2 24/08/24 Freiburg Stuttgart 3 1 H 1 1 D \n",
|
||
"3 24/08/24 Hoffenheim Holstein Kiel 3 2 H 2 0 H \n",
|
||
"4 24/08/24 Mainz Union Berlin 1 1 D 0 0 D \n",
|
||
"5 24/08/24 RB Leipzig Bochum 1 0 H 0 0 D \n",
|
||
"6 24/08/24 Dortmund Ein Frankfurt 2 0 H 0 0 D \n",
|
||
"7 25/08/24 Wolfsburg Bayern Munich 2 3 A 0 1 A \n",
|
||
"8 25/08/24 St Pauli Heidenheim 0 2 A 0 0 D \n",
|
||
"9 30/08/24 Union Berlin St Pauli 1 0 H 1 0 H \n",
|
||
"10 31/08/24 Bochum M'gladbach 0 2 A 0 0 D \n",
|
||
"11 31/08/24 Ein Frankfurt Hoffenheim 3 1 H 2 0 H \n",
|
||
"12 31/08/24 Holstein Kiel Wolfsburg 0 2 A 0 2 A \n",
|
||
"13 31/08/24 Stuttgart Mainz 3 3 D 2 1 H \n",
|
||
"14 31/08/24 Werder Bremen Dortmund 0 0 D 0 0 D \n",
|
||
"15 31/08/24 Leverkusen RB Leipzig 2 3 A 2 1 H \n",
|
||
"16 01/09/24 Heidenheim Augsburg 4 0 H 2 0 H \n",
|
||
"17 01/09/24 Bayern Munich Freiburg 2 0 H 1 0 H \n",
|
||
"18 13/09/24 Dortmund Heidenheim 4 2 H 3 1 H \n",
|
||
"19 14/09/24 Freiburg Bochum 2 1 H 0 1 A \n",
|
||
"20 14/09/24 Hoffenheim Leverkusen 1 4 A 1 2 A \n",
|
||
"21 14/09/24 M'gladbach Stuttgart 1 3 A 1 1 D \n",
|
||
"22 14/09/24 RB Leipzig Union Berlin 0 0 D 0 0 D \n",
|
||
"23 14/09/24 Wolfsburg Ein Frankfurt 1 2 A 0 1 A \n",
|
||
"24 14/09/24 Holstein Kiel Bayern Munich 1 6 A 0 4 A \n",
|
||
"25 15/09/24 Augsburg St Pauli 3 1 H 0 0 D \n",
|
||
"26 15/09/24 Mainz Werder Bremen 1 2 A 1 1 D \n",
|
||
"27 20/09/24 Augsburg Mainz 2 3 A 1 2 A \n",
|
||
"28 21/09/24 Bochum Holstein Kiel 2 2 D 2 1 H \n",
|
||
"29 21/09/24 Heidenheim Freiburg 0 3 A 0 0 D \n",
|
||
"30 21/09/24 Union Berlin Hoffenheim 2 1 H 2 0 H \n",
|
||
"31 21/09/24 Werder Bremen Bayern Munich 0 5 A 0 2 A \n",
|
||
"32 21/09/24 Ein Frankfurt M'gladbach 2 0 H 1 0 H \n",
|
||
"33 22/09/24 Leverkusen Wolfsburg 4 3 H 2 3 A \n",
|
||
"34 22/09/24 Stuttgart Dortmund 5 1 H 2 0 H \n",
|
||
"35 22/09/24 St Pauli RB Leipzig 0 0 D 0 0 D \n",
|
||
"36 27/09/24 Dortmund Bochum 4 2 H 1 2 A \n",
|
||
"37 28/09/24 Freiburg St Pauli 0 3 A 0 2 A \n",
|
||
"38 28/09/24 Mainz Heidenheim 0 2 A 0 1 A \n",
|
||
"39 28/09/24 M'gladbach Union Berlin 1 0 H 0 0 D \n",
|
||
"40 28/09/24 RB Leipzig Augsburg 4 0 H 2 0 H \n",
|
||
"41 28/09/24 Wolfsburg Stuttgart 2 2 D 1 1 D \n",
|
||
"42 28/09/24 Bayern Munich Leverkusen 1 1 D 1 1 D \n",
|
||
"43 29/09/24 Holstein Kiel Ein Frankfurt 2 4 A 1 1 D \n",
|
||
"44 29/09/24 Hoffenheim Werder Bremen 3 4 A 3 3 D \n",
|
||
"45 04/10/24 Augsburg M'gladbach 2 1 H 1 0 H \n",
|
||
"46 05/10/24 Bochum Wolfsburg 1 3 A 0 2 A \n",
|
||
"47 05/10/24 Leverkusen Holstein Kiel 2 2 D 2 1 H \n",
|
||
"48 05/10/24 Union Berlin Dortmund 2 1 H 2 0 H \n",
|
||
"49 05/10/24 Werder Bremen Freiburg 0 1 A 0 0 D \n",
|
||
"50 05/10/24 St Pauli Mainz 0 3 A 0 2 A \n",
|
||
"51 06/10/24 Heidenheim RB Leipzig 0 1 A 0 0 D \n",
|
||
"52 06/10/24 Ein Frankfurt Bayern Munich 3 3 D 2 2 D \n",
|
||
"53 06/10/24 Stuttgart Hoffenheim 1 1 D 0 1 A \n",
|
||
"54 18/10/24 Dortmund St Pauli 2 1 H 1 0 H \n",
|
||
"55 19/10/24 Freiburg Augsburg 3 1 H 3 0 H \n",
|
||
"56 19/10/24 Hoffenheim Bochum 3 1 H 1 0 H \n",
|
||
"57 19/10/24 Leverkusen Ein Frankfurt 2 1 H 1 1 D \n",
|
||
"58 19/10/24 Mainz RB Leipzig 0 2 A 0 2 A \n",
|
||
"59 19/10/24 M'gladbach Heidenheim 3 2 H 1 1 D \n",
|
||
"60 19/10/24 Bayern Munich Stuttgart 4 0 H 0 0 D \n",
|
||
"61 20/10/24 Holstein Kiel Union Berlin 0 2 A 0 1 A \n",
|
||
"62 20/10/24 Wolfsburg Werder Bremen 2 4 A 1 1 D \n",
|
||
"63 25/10/24 Mainz M'gladbach 1 1 D 0 0 D \n",
|
||
"64 26/10/24 Augsburg Dortmund 2 1 H 1 1 D \n",
|
||
"65 26/10/24 RB Leipzig Freiburg 3 1 H 0 1 A \n",
|
||
"66 26/10/24 St Pauli Wolfsburg 0 0 D 0 0 D \n",
|
||
"67 26/10/24 Stuttgart Holstein Kiel 2 1 H 1 0 H \n",
|
||
"68 26/10/24 Werder Bremen Leverkusen 2 2 D 0 1 A \n",
|
||
"69 27/10/24 Bochum Bayern Munich 0 5 A 0 2 A \n",
|
||
"70 27/10/24 Union Berlin Ein Frankfurt 1 1 D 0 1 A \n",
|
||
"71 27/10/24 Heidenheim Hoffenheim 0 0 D 0 0 D \n",
|
||
"72 01/11/24 Leverkusen Stuttgart 0 0 D 0 0 D \n",
|
||
"73 02/11/24 Bayern Munich Union Berlin 3 0 H 2 0 H \n",
|
||
"74 02/11/24 Ein Frankfurt Bochum 7 2 H 4 1 H \n",
|
||
"75 02/11/24 Hoffenheim St Pauli 0 2 A 0 1 A \n",
|
||
"76 02/11/24 Holstein Kiel Heidenheim 1 0 H 1 0 H \n",
|
||
"77 02/11/24 Wolfsburg Augsburg 1 1 D 0 1 A \n",
|
||
"78 02/11/24 Dortmund RB Leipzig 2 1 H 1 1 D \n",
|
||
"79 03/11/24 Freiburg Mainz 0 0 D 0 0 D \n",
|
||
"80 03/11/24 M'gladbach Werder Bremen 4 1 H 3 0 H \n",
|
||
"81 08/11/24 Union Berlin Freiburg 0 0 D 0 0 D \n",
|
||
"82 09/11/24 Bochum Leverkusen 1 1 D 0 1 A \n",
|
||
"83 09/11/24 Mainz Dortmund 3 1 H 2 1 H \n",
|
||
"84 09/11/24 St Pauli Bayern Munich 0 1 A 0 1 A \n",
|
||
"85 09/11/24 Werder Bremen Holstein Kiel 2 1 H 1 0 H \n",
|
||
"86 09/11/24 RB Leipzig M'gladbach 0 0 D 0 0 D \n",
|
||
"87 10/11/24 Augsburg Hoffenheim 0 0 D 0 0 D \n",
|
||
"88 10/11/24 Stuttgart Ein Frankfurt 2 3 A 0 1 A \n",
|
||
"89 10/11/24 Heidenheim Wolfsburg 1 3 A 0 2 A \n",
|
||
"90 22/11/24 Bayern Munich Augsburg 3 0 H 0 0 D \n",
|
||
"91 23/11/24 Dortmund Freiburg 4 0 H 2 0 H \n",
|
||
"92 23/11/24 Hoffenheim RB Leipzig 4 3 H 1 2 A \n",
|
||
"93 23/11/24 Leverkusen Heidenheim 5 2 H 2 2 D \n",
|
||
"94 23/11/24 Stuttgart Bochum 2 0 H 0 0 D \n",
|
||
"95 23/11/24 Wolfsburg Union Berlin 1 0 H 0 0 D \n",
|
||
"96 23/11/24 Ein Frankfurt Werder Bremen 1 0 H 1 0 H \n",
|
||
"97 24/11/24 Holstein Kiel Mainz 0 3 A 0 2 A \n",
|
||
"98 24/11/24 M'gladbach St Pauli 2 0 H 2 0 H \n",
|
||
"99 29/11/24 St Pauli Holstein Kiel 3 1 H 1 0 H \n",
|
||
"100 30/11/24 Augsburg Bochum 1 0 H 1 0 H \n",
|
||
"101 30/11/24 Freiburg M'gladbach 3 1 H 1 0 H \n",
|
||
"102 30/11/24 RB Leipzig Wolfsburg 1 5 A 0 3 A \n",
|
||
"103 30/11/24 Union Berlin Leverkusen 1 2 A 1 1 D \n",
|
||
"104 30/11/24 Werder Bremen Stuttgart 2 2 D 1 1 D \n",
|
||
"105 30/11/24 Dortmund Bayern Munich 1 1 D 1 0 H \n",
|
||
"106 01/12/24 Mainz Hoffenheim 2 0 H 2 0 H \n",
|
||
"107 01/12/24 Heidenheim Ein Frankfurt 0 4 A 0 1 A \n",
|
||
"108 06/12/24 Stuttgart Union Berlin 3 2 H 0 1 A \n",
|
||
"109 07/12/24 Bayern Munich Heidenheim 4 2 H 1 0 H \n",
|
||
"110 07/12/24 Bochum Werder Bremen 0 1 A 0 0 D \n",
|
||
"111 07/12/24 Ein Frankfurt Augsburg 2 2 D 0 0 D \n",
|
||
"112 07/12/24 Holstein Kiel RB Leipzig 0 2 A 0 1 A \n",
|
||
"113 07/12/24 Leverkusen St Pauli 2 1 H 2 0 H \n",
|
||
"114 07/12/24 M'gladbach Dortmund 1 1 D 0 0 D \n",
|
||
"115 08/12/24 Wolfsburg Mainz 4 3 H 1 2 A \n",
|
||
"116 08/12/24 Hoffenheim Freiburg 1 1 D 0 0 D \n",
|
||
"117 13/12/24 Freiburg Wolfsburg 3 2 H 1 0 H \n",
|
||
"118 14/12/24 Augsburg Leverkusen 0 2 A 0 2 A \n",
|
||
"119 14/12/24 Mainz Bayern Munich 2 1 H 1 0 H \n",
|
||
"120 14/12/24 M'gladbach Holstein Kiel 4 1 H 3 1 H \n",
|
||
"121 14/12/24 Union Berlin Bochum 1 1 D 1 1 D \n",
|
||
"122 14/12/24 St Pauli Werder Bremen 0 2 A 0 1 A \n",
|
||
"123 15/12/24 Heidenheim Stuttgart 1 3 A 1 2 A \n",
|
||
"124 15/12/24 Dortmund Hoffenheim 1 1 D 0 0 D \n",
|
||
"125 15/12/24 RB Leipzig Ein Frankfurt 2 1 H 1 1 D \n",
|
||
"126 20/12/24 Bayern Munich RB Leipzig 5 1 H 3 1 H \n",
|
||
"127 21/12/24 Ein Frankfurt Mainz 1 3 A 0 2 A \n",
|
||
"128 21/12/24 Hoffenheim M'gladbach 1 2 A 0 1 A \n",
|
||
"129 21/12/24 Holstein Kiel Augsburg 5 1 H 4 1 H \n",
|
||
"130 21/12/24 Stuttgart St Pauli 0 1 A 0 1 A \n",
|
||
"131 21/12/24 Werder Bremen Union Berlin 4 1 H 3 1 H \n",
|
||
"132 21/12/24 Leverkusen Freiburg 5 1 H 1 0 H \n",
|
||
"133 22/12/24 Bochum Heidenheim 2 0 H 2 0 H \n",
|
||
"134 22/12/24 Wolfsburg Dortmund 1 3 A 0 3 A \n",
|
||
"135 10/01/25 Dortmund Leverkusen 2 3 A 1 3 A \n",
|
||
"136 11/01/25 Freiburg Holstein Kiel 3 2 H 2 0 H \n",
|
||
"137 11/01/25 Heidenheim Union Berlin 2 0 H 1 0 H \n",
|
||
"138 11/01/25 Hoffenheim Wolfsburg 0 1 A 0 1 A \n",
|
||
"139 11/01/25 Mainz Bochum 2 0 H 1 0 H \n",
|
||
"140 11/01/25 St Pauli Ein Frankfurt 0 1 A 0 1 A \n",
|
||
"141 11/01/25 M'gladbach Bayern Munich 0 1 A 0 0 D \n",
|
||
"142 12/01/25 RB Leipzig Werder Bremen 4 2 H 2 1 H \n",
|
||
"143 12/01/25 Augsburg Stuttgart 0 1 A 0 0 D \n",
|
||
"144 14/01/25 Holstein Kiel Dortmund 4 2 H 3 0 H \n",
|
||
"145 14/01/25 Ein Frankfurt Freiburg 4 1 H 1 1 D \n",
|
||
"146 14/01/25 Leverkusen Mainz 1 0 H 0 0 D \n",
|
||
"147 14/01/25 Wolfsburg M'gladbach 5 1 H 1 0 H \n",
|
||
"148 15/01/25 Bochum St Pauli 1 0 H 0 0 D \n",
|
||
"149 15/01/25 Bayern Munich Hoffenheim 5 0 H 3 0 H \n",
|
||
"150 15/01/25 Stuttgart RB Leipzig 2 1 H 0 1 A \n",
|
||
"151 15/01/25 Union Berlin Augsburg 0 2 A 0 2 A \n",
|
||
"152 15/01/25 Werder Bremen Heidenheim 3 3 D 1 1 D \n",
|
||
"153 17/01/25 Ein Frankfurt Dortmund 2 0 H 1 0 H \n",
|
||
"154 18/01/25 Bayern Munich Wolfsburg 3 2 H 2 1 H \n",
|
||
"155 18/01/25 Bochum RB Leipzig 3 3 D 0 3 A \n",
|
||
"156 18/01/25 Heidenheim St Pauli 0 2 A 0 1 A \n",
|
||
"157 18/01/25 Holstein Kiel Hoffenheim 1 3 A 0 2 A \n",
|
||
"158 18/01/25 Stuttgart Freiburg 4 0 H 3 0 H \n",
|
||
"159 18/01/25 Leverkusen M'gladbach 3 1 H 1 0 H \n",
|
||
"160 19/01/25 Union Berlin Mainz 2 1 H 2 1 H \n",
|
||
"161 19/01/25 Werder Bremen Augsburg 0 2 A 0 2 A \n",
|
||
"162 24/01/25 Wolfsburg Holstein Kiel 2 2 D 0 1 A \n",
|
||
"163 25/01/25 Augsburg Heidenheim 2 1 H 1 0 H \n",
|
||
"164 25/01/25 Dortmund Werder Bremen 2 2 D 1 0 H \n",
|
||
"165 25/01/25 Freiburg Bayern Munich 1 2 A 0 1 A \n",
|
||
"166 25/01/25 Mainz Stuttgart 2 0 H 1 0 H \n",
|
||
"167 25/01/25 RB Leipzig Leverkusen 2 2 D 1 2 A \n",
|
||
"168 25/01/25 M'gladbach Bochum 3 0 H 1 0 H \n",
|
||
"169 26/01/25 Hoffenheim Ein Frankfurt 2 2 D 0 1 A \n",
|
||
"170 26/01/25 St Pauli Union Berlin 3 0 H 1 0 H \n",
|
||
"171 31/01/25 Werder Bremen Mainz 1 0 H 1 0 H \n",
|
||
"172 01/02/25 Bayern Munich Holstein Kiel 4 3 H 2 0 H \n",
|
||
"173 01/02/25 Bochum Freiburg 0 1 A 0 1 A \n",
|
||
"174 01/02/25 Heidenheim Dortmund 1 2 A 0 1 A \n",
|
||
"175 01/02/25 St Pauli Augsburg 1 1 D 1 0 H \n",
|
||
"176 01/02/25 Stuttgart M'gladbach 1 2 A 0 1 A \n",
|
||
"177 01/02/25 Union Berlin RB Leipzig 0 0 D 0 0 D \n",
|
||
"178 02/02/25 Ein Frankfurt Wolfsburg 1 1 D 0 0 D \n",
|
||
"179 02/02/25 Leverkusen Hoffenheim 3 1 H 2 0 H \n",
|
||
"180 07/02/25 Bayern Munich Werder Bremen 3 0 H 0 0 D \n",
|
||
"181 08/02/25 Dortmund Stuttgart 1 2 A 0 0 D \n",
|
||
"182 08/02/25 Freiburg Heidenheim 1 0 H 1 0 H \n",
|
||
"183 08/02/25 Hoffenheim Union Berlin 0 4 A 0 1 A \n",
|
||
"184 08/02/25 Mainz Augsburg 0 0 D 0 0 D \n",
|
||
"185 08/02/25 Wolfsburg Leverkusen 0 0 D 0 0 D \n",
|
||
"186 08/02/25 M'gladbach Ein Frankfurt 1 1 D 1 1 D \n",
|
||
"187 09/02/25 Holstein Kiel Bochum 2 2 D 1 2 A \n",
|
||
"188 09/02/25 RB Leipzig St Pauli 2 0 H 2 0 H \n",
|
||
"189 14/02/25 Augsburg RB Leipzig 0 0 D 0 0 D \n",
|
||
"190 15/02/25 Bochum Dortmund 2 0 H 2 0 H \n",
|
||
"191 15/02/25 St Pauli Freiburg 0 1 A 0 0 D \n",
|
||
"192 15/02/25 Stuttgart Wolfsburg 1 2 A 0 0 D \n",
|
||
"193 15/02/25 Union Berlin M'gladbach 1 2 A 0 2 A \n",
|
||
"194 15/02/25 Leverkusen Bayern Munich 0 0 D 0 0 D \n",
|
||
"195 16/02/25 Werder Bremen Hoffenheim 1 3 A 1 2 A \n",
|
||
"196 16/02/25 Ein Frankfurt Holstein Kiel 3 1 H 2 0 H \n",
|
||
"197 16/02/25 Heidenheim Mainz 0 2 A 0 1 A \n",
|
||
"198 21/02/25 Freiburg Werder Bremen 5 0 H 2 0 H \n",
|
||
"199 22/02/25 Holstein Kiel Leverkusen 0 2 A 0 2 A \n",
|
||
"200 22/02/25 Mainz St Pauli 2 0 H 0 0 D \n",
|
||
"201 22/02/25 M'gladbach Augsburg 0 3 A 0 0 D \n",
|
||
"202 22/02/25 Wolfsburg Bochum 1 1 D 0 0 D \n",
|
||
"203 22/02/25 Dortmund Union Berlin 6 0 H 2 0 H \n",
|
||
"204 23/02/25 RB Leipzig Heidenheim 2 2 D 1 2 A \n",
|
||
"205 23/02/25 Bayern Munich Ein Frankfurt 4 0 H 1 0 H \n",
|
||
"206 23/02/25 Hoffenheim Stuttgart 1 1 D 0 1 A \n",
|
||
"207 28/02/25 Stuttgart Bayern Munich 1 3 A 1 1 D \n",
|
||
"208 01/03/25 Bochum Hoffenheim 0 1 A 0 0 D \n",
|
||
"209 01/03/25 Heidenheim M'gladbach 0 3 A 0 2 A \n",
|
||
"210 01/03/25 RB Leipzig Mainz 1 2 A 1 0 H \n",
|
||
"211 01/03/25 St Pauli Dortmund 0 2 A 0 0 D \n",
|
||
"212 01/03/25 Werder Bremen Wolfsburg 1 2 A 0 1 A \n",
|
||
"213 01/03/25 Ein Frankfurt Leverkusen 1 4 A 1 3 A \n",
|
||
"214 02/03/25 Union Berlin Holstein Kiel 0 1 A 0 1 A \n",
|
||
"215 02/03/25 Augsburg Freiburg 0 0 D 0 0 D \n",
|
||
"216 07/03/25 M'gladbach Mainz 1 3 A 0 1 A \n",
|
||
"217 08/03/25 Bayern Munich Bochum 2 3 A 2 1 H \n",
|
||
"218 08/03/25 Dortmund Augsburg 0 1 A 0 1 A \n",
|
||
"219 08/03/25 Holstein Kiel Stuttgart 2 2 D 1 1 D \n",
|
||
"220 08/03/25 Leverkusen Werder Bremen 0 2 A 0 1 A \n",
|
||
"221 08/03/25 Wolfsburg St Pauli 1 1 D 0 1 A \n",
|
||
"222 08/03/25 Freiburg RB Leipzig 0 0 D 0 0 D \n",
|
||
"223 09/03/25 Ein Frankfurt Union Berlin 1 2 A 1 0 H \n",
|
||
"224 09/03/25 Hoffenheim Heidenheim 1 1 D 1 0 H \n",
|
||
"225 14/03/25 St Pauli Hoffenheim 1 0 H 0 0 D \n",
|
||
"226 15/03/25 Augsburg Wolfsburg 1 0 H 0 0 D \n",
|
||
"227 15/03/25 Mainz Freiburg 2 2 D 1 0 H \n",
|
||
"228 15/03/25 Union Berlin Bayern Munich 1 1 D 0 0 D \n",
|
||
"229 15/03/25 Werder Bremen M'gladbach 2 4 A 2 2 D \n",
|
||
"230 15/03/25 RB Leipzig Dortmund 2 0 H 1 0 H \n",
|
||
"231 16/03/25 Bochum Ein Frankfurt 1 3 A 0 2 A \n",
|
||
"232 16/03/25 Heidenheim Holstein Kiel 3 1 H 1 0 H \n",
|
||
"233 16/03/25 Stuttgart Leverkusen 3 4 A 1 0 H \n",
|
||
"\n",
|
||
" Referee HS AS HST AST HF AF HC AC HY AY HR AR \n",
|
||
"0 NaN 14 25 7 9 11 7 2 4 1 0 0 0 \n",
|
||
"1 NaN 10 12 3 6 11 10 4 2 3 1 0 0 \n",
|
||
"2 NaN 15 9 4 2 7 10 6 3 2 1 0 0 \n",
|
||
"3 NaN 20 15 8 6 8 14 8 6 2 3 0 1 \n",
|
||
"4 NaN 15 15 3 4 9 15 8 9 1 3 0 0 \n",
|
||
"5 NaN 12 10 3 4 9 17 9 3 1 3 1 0 \n",
|
||
"6 NaN 17 7 5 2 6 3 7 7 0 1 0 0 \n",
|
||
"7 NaN 11 14 2 8 13 11 1 7 2 3 0 0 \n",
|
||
"8 NaN 11 6 3 3 10 15 4 2 2 3 0 0 \n",
|
||
"9 NaN 10 8 5 2 10 11 5 4 3 1 0 0 \n",
|
||
"10 NaN 16 15 4 6 9 10 3 4 1 0 0 0 \n",
|
||
"11 NaN 17 10 7 3 10 10 1 4 0 3 0 0 \n",
|
||
"12 NaN 12 12 5 4 17 14 1 7 4 7 0 0 \n",
|
||
"13 NaN 28 9 13 7 6 12 11 2 4 2 0 0 \n",
|
||
"14 NaN 5 9 1 4 11 16 5 6 2 7 0 1 \n",
|
||
"15 NaN 27 10 10 4 7 8 18 2 3 1 0 0 \n",
|
||
"16 NaN 17 15 9 1 15 11 5 6 0 3 0 0 \n",
|
||
"17 NaN 11 8 4 4 13 12 2 4 1 0 0 0 \n",
|
||
"18 NaN 13 11 7 4 13 18 8 1 2 2 0 0 \n",
|
||
"19 NaN 27 13 9 2 5 16 5 6 0 3 0 0 \n",
|
||
"20 NaN 14 20 5 8 9 10 4 12 2 2 0 0 \n",
|
||
"21 NaN 15 12 7 7 14 12 6 2 2 2 0 0 \n",
|
||
"22 NaN 16 11 3 3 9 19 6 5 2 3 0 0 \n",
|
||
"23 NaN 17 15 4 7 7 18 5 4 1 2 0 0 \n",
|
||
"24 NaN 5 23 1 13 12 11 2 10 0 1 0 0 \n",
|
||
"25 NaN 18 12 10 3 12 10 7 7 4 3 0 0 \n",
|
||
"26 NaN 11 11 5 5 12 7 10 4 3 1 0 1 \n",
|
||
"27 NaN 30 5 9 3 7 12 14 1 3 5 1 1 \n",
|
||
"28 NaN 14 13 3 4 20 9 2 5 4 1 0 0 \n",
|
||
"29 NaN 7 14 2 5 6 7 3 9 1 1 0 0 \n",
|
||
"30 NaN 12 6 2 3 17 18 5 5 3 3 0 0 \n",
|
||
"31 NaN 0 25 0 7 4 13 1 4 0 3 0 0 \n",
|
||
"32 NaN 14 22 4 7 10 12 8 5 1 1 0 0 \n",
|
||
"33 NaN 20 8 7 4 6 14 6 2 4 5 0 1 \n",
|
||
"34 NaN 16 6 9 3 10 6 6 4 1 3 0 0 \n",
|
||
"35 NaN 15 10 4 5 6 9 3 1 2 1 0 0 \n",
|
||
"36 NaN 23 10 8 6 12 9 11 0 2 0 0 0 \n",
|
||
"37 NaN 6 10 2 4 7 18 4 2 3 3 0 0 \n",
|
||
"38 NaN 7 14 2 7 10 10 7 6 4 4 1 1 \n",
|
||
"39 NaN 8 9 3 3 10 12 2 4 3 1 0 0 \n",
|
||
"40 NaN 14 7 7 1 9 13 8 5 1 2 0 0 \n",
|
||
"41 NaN 5 17 2 5 20 10 1 3 5 2 0 1 \n",
|
||
"42 NaN 18 3 3 3 11 15 6 1 1 3 0 0 \n",
|
||
"43 NaN 10 18 5 10 12 13 4 9 1 3 0 0 \n",
|
||
"44 NaN 10 18 5 10 12 19 2 6 3 3 1 0 \n",
|
||
"45 NaN 13 11 5 5 13 15 6 5 2 4 0 0 \n",
|
||
"46 NaN 17 15 5 5 11 12 7 6 1 2 0 0 \n",
|
||
"47 NaN 24 8 7 4 5 9 12 3 2 2 0 0 \n",
|
||
"48 NaN 13 9 5 5 15 17 3 3 4 4 0 0 \n",
|
||
"49 NaN 11 13 3 3 7 9 7 5 1 2 0 0 \n",
|
||
"50 NaN 17 8 4 4 12 16 2 2 0 3 0 0 \n",
|
||
"51 NaN 15 12 4 2 12 13 3 2 2 2 0 0 \n",
|
||
"52 NaN 5 22 4 13 10 6 0 11 1 1 0 0 \n",
|
||
"53 NaN 18 7 7 5 10 7 10 5 1 4 0 0 \n",
|
||
"54 NaN 20 10 7 3 8 7 7 1 2 1 0 0 \n",
|
||
"55 NaN 15 9 6 3 11 7 5 4 1 1 0 0 \n",
|
||
"56 NaN 12 13 7 4 10 12 4 2 1 1 0 0 \n",
|
||
"57 NaN 27 9 9 2 9 5 8 3 2 1 0 0 \n",
|
||
"58 NaN 16 11 2 5 12 15 5 3 1 3 0 0 \n",
|
||
"59 NaN 20 19 10 6 15 9 6 3 3 3 0 0 \n",
|
||
"60 NaN 22 4 10 1 6 10 7 3 0 2 0 0 \n",
|
||
"61 NaN 12 14 5 4 8 11 7 3 2 1 0 0 \n",
|
||
"62 NaN 14 15 6 7 15 9 4 4 1 1 1 0 \n",
|
||
"63 NaN 23 9 6 5 16 16 2 4 3 2 0 0 \n",
|
||
"64 NaN 12 11 4 4 13 10 3 5 3 3 0 1 \n",
|
||
"65 NaN 12 15 9 5 9 7 3 3 3 2 0 0 \n",
|
||
"66 NaN 14 10 5 4 9 12 7 6 0 3 0 0 \n",
|
||
"67 NaN 16 9 6 3 6 17 9 5 3 6 1 1 \n",
|
||
"68 NaN 13 15 8 6 9 11 3 5 1 1 0 0 \n",
|
||
"69 NaN 6 17 2 9 15 6 2 5 1 1 0 0 \n",
|
||
"70 NaN 13 17 4 5 13 7 4 5 5 3 0 1 \n",
|
||
"71 NaN 17 12 3 4 12 16 7 3 1 2 0 0 \n",
|
||
"72 NaN 19 4 5 1 13 14 10 2 3 4 0 0 \n",
|
||
"73 NaN 16 8 7 1 2 9 7 4 0 2 0 0 \n",
|
||
"74 NaN 18 11 9 8 6 15 5 2 1 2 0 0 \n",
|
||
"75 NaN 12 7 6 3 10 10 7 2 0 2 0 0 \n",
|
||
"76 NaN 11 17 6 4 16 14 5 6 4 5 0 0 \n",
|
||
"77 NaN 18 3 6 1 7 14 8 1 1 3 0 0 \n",
|
||
"78 NaN 19 7 7 3 11 12 4 0 3 4 0 0 \n",
|
||
"79 NaN 10 9 2 2 9 12 8 4 1 2 0 0 \n",
|
||
"80 NaN 16 15 8 5 12 10 7 7 1 4 0 1 \n",
|
||
"81 NaN 12 14 6 4 11 8 4 1 2 0 0 0 \n",
|
||
"82 NaN 15 11 3 3 10 9 6 4 0 1 0 0 \n",
|
||
"83 NaN 13 2 9 1 17 5 8 0 3 1 0 1 \n",
|
||
"84 NaN 3 13 0 6 10 7 5 10 2 2 0 0 \n",
|
||
"85 NaN 16 12 5 5 10 19 6 3 3 2 0 0 \n",
|
||
"86 NaN 11 14 4 4 14 7 8 4 2 2 0 0 \n",
|
||
"87 NaN 16 11 4 2 16 7 10 6 2 3 0 0 \n",
|
||
"88 NaN 22 8 11 5 10 15 8 3 2 3 0 0 \n",
|
||
"89 NaN 19 13 4 6 9 23 5 5 1 6 0 0 \n",
|
||
"90 NaN 33 2 14 1 9 9 8 0 0 2 0 1 \n",
|
||
"91 NaN 19 7 7 3 11 4 7 0 1 2 0 2 \n",
|
||
"92 NaN 20 14 7 6 14 12 4 3 2 1 0 0 \n",
|
||
"93 NaN 20 6 10 3 8 11 6 0 0 0 0 0 \n",
|
||
"94 NaN 15 9 5 2 7 16 7 4 2 3 0 0 \n",
|
||
"95 NaN 10 11 5 4 7 15 8 5 1 2 0 0 \n",
|
||
"96 NaN 15 8 5 1 15 9 5 6 2 2 0 0 \n",
|
||
"97 NaN 7 18 2 7 11 7 2 6 1 0 0 0 \n",
|
||
"98 NaN 15 15 4 3 7 8 5 4 1 0 0 0 \n",
|
||
"99 NaN 11 10 4 3 10 11 4 4 2 5 0 0 \n",
|
||
"100 NaN 16 7 4 1 8 22 6 1 3 3 0 0 \n",
|
||
"101 NaN 17 15 7 4 5 14 4 6 1 1 0 0 \n",
|
||
"102 NaN 9 13 4 8 11 6 7 3 2 1 0 0 \n",
|
||
"103 NaN 8 8 2 3 18 11 1 3 4 2 0 0 \n",
|
||
"104 NaN 20 10 6 4 8 13 11 2 1 1 0 0 \n",
|
||
"105 NaN 7 14 2 5 11 7 2 3 2 2 0 0 \n",
|
||
"106 NaN 18 10 8 2 13 12 8 3 1 3 0 0 \n",
|
||
"107 NaN 8 14 1 8 7 9 6 5 0 2 0 0 \n",
|
||
"108 NaN 7 14 5 3 14 19 1 6 1 6 0 0 \n",
|
||
"109 NaN 21 2 10 2 4 5 8 0 1 0 0 0 \n",
|
||
"110 NaN 12 9 2 1 12 10 9 5 1 1 0 0 \n",
|
||
"111 NaN 16 8 4 3 17 9 4 0 2 2 0 0 \n",
|
||
"112 NaN 17 9 5 5 11 13 6 1 4 3 0 0 \n",
|
||
"113 NaN 6 12 2 4 8 10 7 4 1 3 0 0 \n",
|
||
"114 NaN 9 16 3 3 16 11 7 4 3 2 1 0 \n",
|
||
"115 NaN 10 12 5 5 10 15 3 5 3 4 0 0 \n",
|
||
"116 NaN 15 11 5 4 10 5 6 1 1 1 0 0 \n",
|
||
"117 NaN 18 20 9 5 11 3 5 6 4 1 0 0 \n",
|
||
"118 NaN 8 14 4 3 12 5 4 9 3 0 0 0 \n",
|
||
"119 NaN 8 13 4 1 21 12 3 2 3 2 0 0 \n",
|
||
"120 NaN 15 11 6 3 10 14 5 2 1 1 0 0 \n",
|
||
"121 NaN 22 13 4 3 11 9 7 6 2 2 0 1 \n",
|
||
"122 NaN 15 8 4 4 10 9 4 5 2 3 0 0 \n",
|
||
"123 NaN 9 18 2 7 25 13 9 6 4 3 0 0 \n",
|
||
"124 NaN 6 12 2 1 9 10 3 9 5 2 0 0 \n",
|
||
"125 NaN 19 16 9 3 15 10 6 7 1 3 0 0 \n",
|
||
"126 NaN 22 4 9 1 9 10 8 1 5 2 0 0 \n",
|
||
"127 NaN 34 9 9 3 10 10 17 2 1 1 0 1 \n",
|
||
"128 NaN 18 5 8 3 14 6 8 4 0 0 0 0 \n",
|
||
"129 NaN 8 17 6 4 11 13 3 8 0 4 0 0 \n",
|
||
"130 NaN 25 11 5 5 10 11 9 1 2 2 0 0 \n",
|
||
"131 NaN 11 16 6 4 9 12 4 9 2 2 0 0 \n",
|
||
"132 NaN 20 8 12 2 8 9 4 2 1 2 0 0 \n",
|
||
"133 NaN 14 10 4 2 18 15 5 2 2 7 0 0 \n",
|
||
"134 NaN 14 12 5 4 8 8 10 4 3 2 0 1 \n",
|
||
"135 NaN 12 7 5 5 8 6 6 1 1 2 0 0 \n",
|
||
"136 NaN 14 14 5 7 9 12 7 1 2 3 0 0 \n",
|
||
"137 NaN 16 10 9 6 8 10 5 4 0 1 0 1 \n",
|
||
"138 NaN 15 12 2 3 15 14 4 8 1 2 0 0 \n",
|
||
"139 NaN 10 11 5 2 6 16 5 6 0 2 0 0 \n",
|
||
"140 NaN 18 14 4 6 8 7 3 3 0 3 0 0 \n",
|
||
"141 NaN 5 23 0 10 10 7 4 10 3 1 0 0 \n",
|
||
"142 NaN 18 7 7 2 7 12 4 1 1 1 0 0 \n",
|
||
"143 NaN 8 13 2 5 11 14 3 9 5 1 0 0 \n",
|
||
"144 NaN 11 16 7 4 13 11 1 9 2 2 1 0 \n",
|
||
"145 NaN 23 7 8 5 8 11 8 1 1 1 0 0 \n",
|
||
"146 NaN 20 7 9 4 4 14 8 4 2 3 0 0 \n",
|
||
"147 NaN 16 9 5 5 10 7 4 7 1 0 0 0 \n",
|
||
"148 NaN 13 12 5 1 7 15 6 7 1 3 0 1 \n",
|
||
"149 NaN 25 5 10 2 8 10 13 1 1 1 0 0 \n",
|
||
"150 NaN 14 6 6 3 10 19 5 0 2 7 0 2 \n",
|
||
"151 NaN 13 11 2 5 15 9 7 4 1 2 0 0 \n",
|
||
"152 NaN 12 14 5 5 10 15 4 2 5 3 0 0 \n",
|
||
"153 NaN 13 12 5 3 8 6 5 7 0 2 0 0 \n",
|
||
"154 NaN 25 7 10 3 10 9 6 2 2 3 0 0 \n",
|
||
"155 NaN 14 11 3 3 14 7 6 3 4 0 0 0 \n",
|
||
"156 NaN 8 7 1 4 11 17 4 4 1 2 0 0 \n",
|
||
"157 NaN 12 11 3 6 13 14 4 3 2 1 0 0 \n",
|
||
"158 NaN 12 4 7 0 8 12 4 1 5 3 0 0 \n",
|
||
"159 NaN 10 7 5 4 12 10 7 6 0 2 0 0 \n",
|
||
"160 NaN 12 9 4 1 12 10 2 2 2 2 0 0 \n",
|
||
"161 NaN 20 9 2 4 8 14 6 2 1 2 0 0 \n",
|
||
"162 NaN 22 5 5 2 9 10 10 3 2 4 0 0 \n",
|
||
"163 NaN 11 11 7 6 8 9 6 4 1 0 0 0 \n",
|
||
"164 NaN 8 13 4 4 11 15 5 5 1 4 1 0 \n",
|
||
"165 NaN 7 7 2 3 15 9 2 4 4 2 0 0 \n",
|
||
"166 NaN 11 10 4 5 12 8 9 2 4 3 0 0 \n",
|
||
"167 NaN 8 13 4 7 12 6 7 3 0 0 0 0 \n",
|
||
"168 NaN 16 10 6 4 6 15 8 7 1 3 0 0 \n",
|
||
"169 NaN 24 12 8 7 16 8 9 3 3 0 0 0 \n",
|
||
"170 NaN 16 14 5 2 6 15 5 7 0 3 0 0 \n",
|
||
"171 NaN 12 13 5 2 14 13 3 8 8 4 3 0 \n",
|
||
"172 NaN 29 10 12 6 7 10 12 1 1 1 0 0 \n",
|
||
"173 NaN 22 13 4 5 15 10 9 7 3 2 0 0 \n",
|
||
"174 NaN 11 16 5 5 11 9 4 5 0 0 0 0 \n",
|
||
"175 NaN 5 2 1 1 16 18 3 1 1 3 0 0 \n",
|
||
"176 NaN 10 12 3 6 11 11 2 0 2 2 0 0 \n",
|
||
"177 NaN 21 6 6 2 5 12 4 5 3 2 0 0 \n",
|
||
"178 NaN 16 11 4 2 8 8 9 1 1 3 0 0 \n",
|
||
"179 NaN 9 16 5 7 14 7 3 3 4 3 1 0 \n",
|
||
"180 NaN 21 2 12 0 9 8 5 2 1 3 0 0 \n",
|
||
"181 NaN 16 4 5 2 13 14 9 4 5 2 1 0 \n",
|
||
"182 NaN 9 11 3 5 11 8 6 3 3 1 0 0 \n",
|
||
"183 NaN 16 22 4 7 9 16 8 4 2 1 0 0 \n",
|
||
"184 NaN 9 9 5 2 12 9 1 3 3 3 0 0 \n",
|
||
"185 NaN 13 15 8 1 7 8 3 9 2 3 0 0 \n",
|
||
"186 NaN 8 15 3 7 10 9 6 1 3 3 0 0 \n",
|
||
"187 NaN 10 21 3 6 11 17 3 5 2 2 0 0 \n",
|
||
"188 NaN 11 7 6 4 12 8 5 4 2 2 1 0 \n",
|
||
"189 NaN 11 14 2 4 12 12 8 9 2 1 0 0 \n",
|
||
"190 NaN 18 17 9 2 12 4 2 8 1 0 0 0 \n",
|
||
"191 NaN 5 6 1 4 10 10 2 6 1 0 0 0 \n",
|
||
"192 NaN 10 7 4 3 10 15 7 5 3 4 0 0 \n",
|
||
"193 NaN 18 9 3 5 9 8 4 5 0 1 0 0 \n",
|
||
"194 NaN 15 2 3 0 16 13 6 0 2 3 0 0 \n",
|
||
"195 NaN 14 16 1 7 14 12 6 4 2 3 0 0 \n",
|
||
"196 NaN 20 10 9 4 16 10 4 6 2 0 0 0 \n",
|
||
"197 NaN 12 6 3 5 9 16 8 4 1 3 0 0 \n",
|
||
"198 NaN 13 9 8 3 14 8 3 7 3 0 0 0 \n",
|
||
"199 NaN 4 18 3 8 11 10 5 11 4 1 0 0 \n",
|
||
"200 NaN 10 12 5 0 9 10 6 5 3 3 0 0 \n",
|
||
"201 NaN 8 17 5 4 10 9 2 4 3 2 1 0 \n",
|
||
"202 NaN 13 11 6 5 8 15 3 5 2 3 0 0 \n",
|
||
"203 NaN 24 6 8 1 10 11 5 3 2 2 0 0 \n",
|
||
"204 NaN 17 4 6 2 15 15 7 4 3 3 0 0 \n",
|
||
"205 NaN 19 7 12 2 8 7 4 1 1 1 0 0 \n",
|
||
"206 NaN 9 14 2 7 8 10 4 8 0 0 0 0 \n",
|
||
"207 NaN 12 17 3 8 12 13 4 6 5 3 0 0 \n",
|
||
"208 NaN 12 10 3 2 16 17 8 5 0 1 0 0 \n",
|
||
"209 NaN 20 13 5 5 9 15 6 2 2 3 0 0 \n",
|
||
"210 NaN 13 15 4 3 8 15 7 1 1 1 0 0 \n",
|
||
"211 NaN 7 14 1 3 11 13 7 1 1 2 0 0 \n",
|
||
"212 NaN 15 9 7 3 13 9 8 2 2 0 0 0 \n",
|
||
"213 NaN 7 20 3 9 9 7 2 6 1 1 0 0 \n",
|
||
"214 NaN 14 10 3 3 16 12 11 3 4 0 0 0 \n",
|
||
"215 NaN 13 7 5 3 13 9 5 2 1 1 0 0 \n",
|
||
"216 NaN 12 16 4 8 6 6 4 3 0 1 0 0 \n",
|
||
"217 NaN 14 8 5 5 7 14 9 3 0 6 1 0 \n",
|
||
"218 NaN 14 6 1 1 6 12 12 3 1 4 0 0 \n",
|
||
"219 NaN 17 5 5 2 8 9 3 1 1 2 1 0 \n",
|
||
"220 NaN 18 11 4 7 12 10 10 3 4 4 0 0 \n",
|
||
"221 NaN 16 14 4 4 11 13 6 6 1 0 0 0 \n",
|
||
"222 NaN 10 18 2 3 6 11 2 6 0 3 0 0 \n",
|
||
"223 NaN 14 11 3 3 10 8 1 5 3 0 0 0 \n",
|
||
"224 NaN 16 23 3 5 9 11 6 4 2 3 0 0 \n",
|
||
"225 NaN 10 4 5 0 6 8 11 2 0 1 0 0 \n",
|
||
"226 NaN 9 13 2 2 7 9 1 4 1 2 0 0 \n",
|
||
"227 NaN 9 24 4 8 7 7 1 4 1 2 1 0 \n",
|
||
"228 NaN 7 18 3 4 13 8 2 14 1 2 0 0 \n",
|
||
"229 NaN 23 13 2 7 10 7 4 1 1 2 0 1 \n",
|
||
"230 NaN 15 25 5 9 14 10 3 12 3 3 0 0 \n",
|
||
"231 NaN 17 18 7 7 7 9 8 3 4 1 0 0 \n",
|
||
"232 NaN 14 16 5 4 15 13 3 5 1 3 0 0 \n",
|
||
"233 NaN 11 16 6 4 13 14 1 10 3 2 0 0 \n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"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"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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tmDFjorGxsfWYO3fuGq8XAKiioZ+0Jf9mm21W/HnHHXeMadOmxWWXXRbXXnvtMm1Tr0vLHi4AQOdXeo/KJzU3N7eZhwIAVK9Se1TSUE7aM2XQoEHxwQcfxC233BKTJ0+O+++/v8yyAIBMlBpU3nzzzTjmmGNi3rx5xeTYYcOGFSHlS1/6UpllAQCZKDWo3HDDDWW+PACQuezmqAAAtBBUAIBsCSoAQLYEFQAgW6Vv+Mb/euONN4rddoH/9eqrr7Z5BP5PWim78cYbRzWoqVQqleig0ocSpn+s9Abfq1ev6Mgh5dtHHxMff2SjOwA+21pr18bE/7mpw4aV9rx/61HJQPqHSiHl35t+MZq715VdDgAZ67KoMWLWw8V7R0cNKu0hqGQkhZTmHhuWXQYAZMNkWgAgW4IKAJAtQQUAyJagAgBkS1ABALIlqAAA2RJUAIBsCSoAQLYEFQAgW4IKAJAtQQUAyJagAgBkS1ABALIlqAAA2RJUAIBsCSoAQLYEFQAgW4IKAJAtQQUAyJagAgBkS1ABALIlqAAA2RJUAIBsCSoAQLYEFQAgW4IKAJAtQQUAyJagAgBkS1ABALJValBpaGiInXfeOXr27Bl9+/aNQw89NF544YUySwIAMlJqUHn44Yfj1FNPjUcffTQeeOCB+Pjjj2P//fePhQsXllkWAJCJbmW++H333dfmfMKECUXPyvTp02PEiBGl1QUA5KHUoPJJjY2NxWOfPn2W+/XFixcXR4umpqY1VhsAUMWTaZubm+P000+PPfbYI7bddtsVzmmpq6trPerr69d4nQBAFQaVNFfl2WefjVtvvXWFbcaMGVP0urQcc+fOXaM1AgBVOPTzve99L+65556YMmVKDBw4cIXtamtriwMAqA6lBpVKpRLf//7344477ojJkyfHkCFDyiwHAMhMt7KHe2655Za46667ir1U5s+fX1xP80/WWWedMksDAKp9jsrVV19dzDXZe++9o3///q3HbbfdVmZZAEAmSh/6AQDIftUPAMAnCSoAQLYEFQAgW4IKAJAtQQUAyJagAgBkS1ABALIlqAAA2RJUAIBsCSoAQLYEFQAgW4IKAJAtQQUAyJagAgBkS1ABALIlqAAA2RJUAIBsCSoAQLYEFQAgW4IKAJAtQQUAyJagAgBkS1ABALIlqAAA2RJUAIBsCSoAQLYEFQAgW4IKAJAtQQUAyJagAgBkS1ABALIlqAAA2RJUAIBsCSoAQLYEFQAgW4IKAJAtQQUAyFapQWXKlClx8MEHx4ABA6KmpibuvPPOMssBADJTalBZuHBhDB8+PK688soyywAAMtWtzBc/8MADiwMAILug0l6LFy8ujhZNTU3RmXRtfC26/Pv9sssAIGM1Hy2IatKhgkpDQ0OMGzcuOpu6urro0qVrdP/Xk2WXAkAH0KVL1+K9oxrUVCqVSmQgTaa944474tBDD21Xj0p9fX00NjZGr169oiN7/vnnY+7cuWWXAdmYN29e3HjjjXH88cdH//79yy4HslJfXx9bbbVVdFTp/TsFrZV5/+5QPSq1tbXF0Rml/+A68n90sKq9+OKLRVDZddddY4sttii7HKAk9lEBALJVao/KggUL4qWXXmo9nz17djz11FPRp0+fGDRoUJmlAQDVHlSeeOKJ2GeffVrPR48eXTyOGjUqJkyYUGJlAEBUe1DZe++9I5O5vABAhsxRAQCyJagAANkSVACAbAkqAEC2BBUAIFuCCgCQLUEFAMiWoAIAZEtQAQCyJagAANkSVACAbAkqAEC2BBUAIFuCCgCQLUEFAMiWoAIAZEtQAQCyJagAANkSVACAbAkqAEC2BBUAIFuCCgCQLUEFAMiWoAIAZEtQAQCyJagAANkSVACAbAkqAEC2BBUAIFuCCgCQLUEFAMiWoAIAZEtQAQCyJagAANkSVACAbAkqAEC2BBUAIFtZBJUrr7wyNtlkk+jevXvssssu8fjjj5ddEgCQgdKDym233RajR4+OsWPHxpNPPhnDhw+PAw44IN58882ySwMAqj2oXHLJJXHiiSfGcccdF9tss01cc801se6668aNN95YdmkAQMm6lfniH330UUyfPj3GjBnTeq1Lly4xcuTImDp16jLtFy9eXBwtmpqa1litVIdFixbFnDlzyi6DiHj11VfbPFK+QYMGFUP0UDVB5e23344lS5bExhtv3OZ6On/++eeXad/Q0BDjxo1bgxVSbVJIOemkk8oug6VceOGFZZfA/3fdddfFFltsUXYZVJlSg0p7pZ6XNJ9l6R6V+vr6Umui8/3GmH4YA8v//wOqKqhsuOGG0bVr13jjjTfaXE/n/fr1W6Z9bW1tccDqkrq1/cYIkI9SJ9OuvfbaseOOO8aDDz7Yeq25ubk432233cosDQDIQOlDP2koZ9SoUbHTTjvFF77whbj00ktj4cKFxSogAKC6lR5UjjjiiHjrrbfi/PPPj/nz58d2220X99133zITbAGA6lNTqVQq0UGlybR1dXXR2NgYvXr1KrscAGAVv3+XvuEbAMCKCCoAQLYEFQAgW4IKAJAtQQUAyJagAgBkS1ABALIlqAAA2RJUAIBslb6F/n+jZVPdtMMdANAxtLxvr8zm+B06qHzwwQfFY319fdmlAACf4308baXfaT/rp7m5OV5//fXo2bNn1NTUlF0OsIp/40q/hMydO9dneUEnk6JHCikDBgyILl26dN6gAnRePnQUSEymBQCyJagAANkSVIAs1dbWxtixY4tHoHqZowIAZEuPCgCQLUEFAMiWoAIAZEtQAQCyJahAlWtoaIidd9652OG5b9++ceihh8YLL7zQps2iRYvi1FNPjQ022CDWW2+9+PrXvx5vvPFG69f/8Y9/xJFHHlnsJLvOOuvE1ltvHZdddtkKX/Ovf/1rdOvWLbbbbrvPrC/N9z///POjf//+xb1HjhwZM2fObNPmxRdfjEMOOSQ23HDDYnO4PffcMx566KFPve/kyZOL56T79ujRo6jl5ptvXmH7W2+9tdgBO/39AGuOoAJV7uGHHy5CyKOPPhoPPPBAfPzxx7H//vvHwoULW9ucccYZ8fvf/z5+85vfFO3TR1d87Wtfa/369OnTi5AzceLEmDFjRpxzzjkxZsyYuOKKK5Z5vffffz+OOeaY2G+//Vaqvp///Odx+eWXxzXXXBOPPfZYESoOOOCAIjy1+PKXvxz/+c9/4s9//nNRy/Dhw4tr8+fPX+F9//a3v8WwYcPit7/9bTz99NNx3HHHFXXdc889y7R95ZVX4swzz4y99tprpWoGVh3Lk4E23nrrrSJ0pEAyYsSIYgv7jTbaKG655ZY47LDDijbPP/980WsyderU2HXXXZd7nxR+/vnPfxbhYWnf/OY3Y/PNN4+uXbvGnXfeGU899dQKa0k/ntJngfzwhz8sgkKS6tl4441jwoQJxb3efvvtor4pU6a0Bon0GSKpZyUFr9QDs7IOOuig4t433nhj67UlS5YUfw/HH398PPLII0XQSnUDa4YeFaCNFASSPn36FI+phyL1siz9hr/VVlvFoEGDiqDyafdpuUeL8ePHx6xZs4qN3FbG7Nmzi16RpV87ff7PLrvs0vraaThqyy23jJtuuqnoBUo9K9dee20Rtnbcccd2f++frPmnP/1pca8TTjihXfcCVo1uq+g+QCeQPpH89NNPjz322CO23Xbb4loKCmuvvXasv/76bdqmnocVDa2kYZXbbrst/vCHP7ReS/NKzj777KJXIs1PWRkt90+vtaLXTvNGJk2aVMwdSfNs0iexpmBx3333Re/evVf6e//1r38d06ZNK0JOi7/85S9xww03fGqvD7B66VEB2gzXPPvss8XE0c8rPT9NUk29JmmuS8vwybe+9a0YN25cbLHFFst9XprImibqthwp0KyMNDyU6k7hJD3n8ccfL0LLwQcfHPPmzSvaDB06tPW+Bx544DL3SBNv0xyV66+/vmjbMnx09NFHF9fSJF2gJGmOCsCpp55aGThwYGXWrFltrj/44INpHlvlvffea3N90KBBlUsuuaTNtRkzZlT69u1b+fGPf9zmenpuukfXrl1bj5qamtZr6TWampoqM2fObD0+/PDDyssvv1y0+fvf/97mfiNGjKj84Ac/KP48adKkSpcuXSqNjY1t2my22WaVhoaG4s+vvPJK631fe+21Nu0mT55c6dGjR+Xaa69tcz295vJqTkf680svvdTOv2Hg8zD0A1Uu9Uh8//vfjzvuuKNYsjtkyJA2X0/zPNZaa6148MEHi2XJSVq+PGfOnNhtt91a26XVPvvuu2+MGjUqLrzwwjb3SBNbn3nmmTbXrrrqqmKi7e233168ZlrNk4Zulpau9+vXr3jtlqXMTU1Nxeqfk08+uTj/8MMPi8c05LO0dJ6GspLBgwcv93tP329aHXTxxRfHSSed1OZraR7OJ2s+99xzi56WtPQ6LcUGVj9BBapcGjZJK3ruuuuuIii0zP1Ik1bTviXpMU0kHT16dDHRNIWOFGxSSGlZ8ZOGe1JIScuGU7uWe6SVPWlFTgoNLXNeWqShmu7duy9zfWlp/kmaM3PBBRcUK4VScDnvvPOKlUAt+5mkOtJclBSQ0n4rqeY0XJMm4qZVPCuShntSSDnttNOKANZSc5qPk77P5dXWMk/n02oGVrHP1Q8DdBrpx8DyjvHjx7e2+fe//1055ZRTKr17966su+66la9+9auVefPmtX597Nixy73H4MGDV/i66TnDhw//zPqam5sr5513XmXjjTeu1NbWVvbbb7/KCy+80KbNtGnTKvvvv3+lT58+lZ49e1Z23XXXyr333vup9x01atRya/7iF7/4qc855JBDPrNmYNWxjwoAkC2rfgCAbAkqAEC2BBUAIFuCCgCQLUEFAMiWoAIAZEtQAQCyJagAANkSVACAbAkqAEC2BBUAIFuCCgAQufp/oUBQDiqJ+/IAAAAASUVORK5CYII=",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"import numpy as np\n",
|
||
"import seaborn as sns\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"from tqdm import tqdm\n",
|
||
"import time\n",
|
||
"\n",
|
||
"df = pd.read_csv(\n",
|
||
" 'C:/Users/dima0/OneDrive/Desktop/season-2425.csv'\n",
|
||
")\n",
|
||
"pd.set_option('display.max_rows', None) # Отображать все строки\n",
|
||
"pd.set_option('display.max_columns', None) # Отображать все столбцы\n",
|
||
"print('Краткая информация о датафрейме:')\n",
|
||
"df.info() #Краткая информация о датафрейме\n",
|
||
"print('Статистика (include=\"object\"):')\n",
|
||
"statsObj = df.describe(include='object') #Статистическое резюме для числовых столбцов в датафрейме\n",
|
||
"print(statsObj)\n",
|
||
"print('Статистика:')\n",
|
||
"stats = df.describe() #Статистическое резюме для числовых столбцов в датафрейме\n",
|
||
"print(stats)\n",
|
||
"print('Датафрейм: ')\n",
|
||
"print(df)\n",
|
||
"sns.histplot(df['FTHG'], bins = 7)\n",
|
||
"plt.ylabel('Matches')\n",
|
||
"plt.show()\n",
|
||
"df['TotalGoals'] = df['FTHG'] + df['FTAG']\n",
|
||
"sns.scatterplot(x='FTHG', y='FTAG', data=df)\n",
|
||
"plt.show()\n",
|
||
"specificDate = pd.Timestamp('24/08/24')\n",
|
||
"sns.boxplot(x=specificDate, y='FTHG', data=df)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "fe8df6d8-319c-44a8-812c-59bd71f0ba01",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"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": "3.13.2"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|