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lab_2/lab2.ipynb
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lab_2/lab2.ipynb
@ -4,7 +4,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Загрузка данных в DataFrame \"Список форбс\"\n",
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"<b>Загрузка данных в DataFrame \"Список форбс\"</b>\n",
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"\n",
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"О рейтинге\n",
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"The World's Billionaires (\"Миллиардеры мира\") - ежегодный рейтинг самых богатых миллиардеров мира, составляемый и публикуемый в марте американским деловым журналом Forbes. Общее состояние каждого человека, включенного в список, оценивается в долларах США на основе его документально подтвержденных активов, а также с учетом долгов и других факторов. Этот рейтинг представляет собой список самых богатых людей, зарегистрированных по документам, за исключением тех, чье благосостояние не может быть полностью установлено.\n",
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@ -23,7 +23,7 @@
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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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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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@ -31,17 +31,17 @@
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"output_type": "stream",
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"text": [
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"<class 'pandas.core.frame.DataFrame'>\n",
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"Index: 2600 entries, Automotive to Food & Beverage \n",
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"Index: 2600 entries, 1 to 2578\n",
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"Data columns (total 6 columns):\n",
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" # Column Non-Null Count Dtype \n",
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"--- ------ -------------- ----- \n",
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" 0 Rank 2600 non-null int64 \n",
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" 1 Name 2600 non-null object \n",
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" 2 Networth 2600 non-null float64\n",
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" 3 Age 2600 non-null int64 \n",
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" 4 Country 2600 non-null object \n",
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" 5 Source 2600 non-null object \n",
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"dtypes: float64(1), int64(2), object(3)\n",
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" 0 Name 2600 non-null object \n",
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" 1 Networth 2600 non-null float64\n",
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" 2 Age 2600 non-null int64 \n",
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" 3 Country 2600 non-null object \n",
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" 4 Source 2600 non-null object \n",
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" 5 Industry 2600 non-null object \n",
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"dtypes: float64(1), int64(1), object(4)\n",
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"memory usage: 142.2+ KB\n",
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"(2600, 6)\n"
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]
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@ -67,15 +67,15 @@
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Rank</th>\n",
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" <th>Name</th>\n",
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" <th>Networth</th>\n",
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" <th>Age</th>\n",
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" <th>Country</th>\n",
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" <th>Source</th>\n",
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" <th>Industry</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Industry</th>\n",
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" <th>Rank</th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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@ -86,73 +86,73 @@
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>Automotive</th>\n",
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" <td>1</td>\n",
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" <th>1</th>\n",
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" <td>Elon Musk</td>\n",
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" <td>219.0</td>\n",
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" <td>50</td>\n",
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" <td>United States</td>\n",
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" <td>Tesla, SpaceX</td>\n",
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" <td>Automotive</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Technology</th>\n",
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" <td>2</td>\n",
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" <th>2</th>\n",
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" <td>Jeff Bezos</td>\n",
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" <td>171.0</td>\n",
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" <td>58</td>\n",
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" <td>United States</td>\n",
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" <td>Amazon</td>\n",
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" <td>Technology</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Fashion & Retail</th>\n",
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" <td>3</td>\n",
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" <th>3</th>\n",
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" <td>Bernard Arnault & family</td>\n",
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" <td>158.0</td>\n",
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" <td>73</td>\n",
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" <td>France</td>\n",
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" <td>LVMH</td>\n",
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" <td>Fashion & Retail</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Technology</th>\n",
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" <td>4</td>\n",
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" <th>4</th>\n",
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" <td>Bill Gates</td>\n",
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" <td>129.0</td>\n",
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" <td>66</td>\n",
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" <td>United States</td>\n",
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" <td>Microsoft</td>\n",
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" <td>Technology</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Finance & Investments</th>\n",
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" <td>5</td>\n",
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" <th>5</th>\n",
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" <td>Warren Buffett</td>\n",
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" <td>118.0</td>\n",
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" <td>91</td>\n",
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" <td>United States</td>\n",
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" <td>Berkshire Hathaway</td>\n",
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" <td>Finance & Investments</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Rank Name Networth Age \\\n",
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"Industry \n",
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"Automotive 1 Elon Musk 219.0 50 \n",
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"Technology 2 Jeff Bezos 171.0 58 \n",
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"Fashion & Retail 3 Bernard Arnault & family 158.0 73 \n",
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"Technology 4 Bill Gates 129.0 66 \n",
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"Finance & Investments 5 Warren Buffett 118.0 91 \n",
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" Name Networth Age Country \\\n",
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"Rank \n",
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"1 Elon Musk 219.0 50 United States \n",
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"2 Jeff Bezos 171.0 58 United States \n",
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"3 Bernard Arnault & family 158.0 73 France \n",
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"4 Bill Gates 129.0 66 United States \n",
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"5 Warren Buffett 118.0 91 United States \n",
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"\n",
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" Country Source \n",
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"Industry \n",
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"Automotive United States Tesla, SpaceX \n",
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"Technology United States Amazon \n",
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"Fashion & Retail France LVMH \n",
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"Technology United States Microsoft \n",
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"Finance & Investments United States Berkshire Hathaway "
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" Source Industry \n",
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"Rank \n",
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"1 Tesla, SpaceX Automotive \n",
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"2 Amazon Technology \n",
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"3 LVMH Fashion & Retail \n",
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"4 Microsoft Technology \n",
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"5 Berkshire Hathaway Finance & Investments "
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]
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},
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"execution_count": 3,
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -160,7 +160,7 @@
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"source": [
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"import pandas as pd\n",
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"\n",
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"df = pd.read_csv(\"..//..//static//csv//Forbes Billionaires.csv\", index_col=\"Industry\")\n",
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"df = pd.read_csv(\"..//..//static//csv//Forbes Billionaires.csv\", index_col=\"Rank\")\n",
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"\n",
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"df.info()\n",
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"\n",
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@ -169,6 +169,52 @@
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"df.head()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<b>2. Проблемная область</b>\n",
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"<br><br>\n",
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"Анализ данных из списка миллиардеров Forbes позволяет не только понять текущее состояние богатства в мире, но и выявить более глубокие тенденции и паттерны, которые могут помочь в принятии бизнес-решений, понимании экономических процессов и определении направлений для дальнейших исследований. Эти данные могут быть основой для многочисленных статей, отчетов и аналитических исследований, что делает их ценными для широкого круга специалистов в различных областях."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<b>3. Анализ содержимого</b>\n",
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"<br><br>\n",
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"\n",
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"1. Объектами наблюдения являются миллиардеры.\n",
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"2. В качестве атбирутов вредставлены: имя, величина богатства, возраст, страна, источник, индустрия\n",
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"3. Связей между объектами нет"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<b>4. Бизнес-цели</b>\n",
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"<br><br>\n",
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"1. Сравнив свой бизнес с другими успешными компаниями, основанными миллиардерами, можно извлечь ценные уроки о сильных сторонами и недостатках своей компании.\n",
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"2. Анализируя, в каких секторах работают миллиардеры и какие компании они развивают, можно выявить растущие рынки и индустрии, в которые стоит инвестировать.\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Привести примеры целей технического проекта для каждой выделенной ранее\n",
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"бизнес-цели. Что поступает на вход, что является целевым признаком?\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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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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@ -188,42 +234,29 @@
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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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"execution_count": 7,
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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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"Survived 0\n",
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"Pclass 0\n",
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"Name 0\n",
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"Sex 0\n",
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"Age 177\n",
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"SibSp 0\n",
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"Parch 0\n",
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"Ticket 0\n",
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"Fare 0\n",
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"Cabin 687\n",
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"Embarked 2\n",
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"Name 0\n",
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"Networth 0\n",
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"Age 0\n",
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"Country 0\n",
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"Source 0\n",
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"Industry 0\n",
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"dtype: int64\n",
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"\n",
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"Survived False\n",
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"Pclass False\n",
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"Name False\n",
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"Sex False\n",
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"Age True\n",
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"SibSp False\n",
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"Parch False\n",
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"Ticket False\n",
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"Fare False\n",
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"Cabin True\n",
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"Embarked True\n",
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"Networth False\n",
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"Age False\n",
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"Country False\n",
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"Source False\n",
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"Industry False\n",
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"dtype: bool\n",
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"\n",
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"Age процент пустых значений: %19.87\n",
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"Cabin процент пустых значений: %77.10\n",
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"Embarked процент пустых значений: %0.22\n"
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"\n"
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]
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}
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],
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