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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"static\\\\swagger.png\" style=\"height: 400px;\">"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Импортируем библиотеку `pandas` и загружаем датасет (разделителем указываем точку с
]
},
{
"cell_type": "code",
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"execution_count": 28,
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"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"df = pd.read_csv(\"csv\\\\dataset25.csv\", sep=\";\")\n",
"\n",
"df[\"Valuation ($B) \"] = pd.to_numeric(df[\"Valuation ($B) \"].str.slice(0, -4).str.replace(\",\", \".\"))\n",
"df[\"Total Funding\"] = pd.to_numeric(df[\"Total Funding\"].str.slice(1, -2).str.replace(\",\", \"\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Посмотрим краткое содержание датасета. Видим, что датасет состоит из 100 строк и 10 столбцов"
]
},
{
"cell_type": "code",
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"execution_count": 29,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 100 entries, 0 to 99\n",
"Data columns (total 10 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Company 100 non-null object \n",
" 1 Valuation ($B) 100 non-null float64\n",
" 2 Country 100 non-null object \n",
" 3 State 79 non-null object \n",
" 4 City 99 non-null object \n",
" 5 Industries 99 non-null object \n",
" 6 Founded Year 100 non-null int64 \n",
" 7 Name of Founders 100 non-null object \n",
" 8 Total Funding 100 non-null float64\n",
" 9 Number of Employees 100 non-null object \n",
"dtypes: float64(2), int64(1), object(7)\n",
"memory usage: 7.9+ KB\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Company</th>\n",
" <th>Valuation ($B)</th>\n",
" <th>Country</th>\n",
" <th>State</th>\n",
" <th>City</th>\n",
" <th>Industries</th>\n",
" <th>Founded Year</th>\n",
" <th>Name of Founders</th>\n",
" <th>Total Funding</th>\n",
" <th>Number of Employees</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Bytedance</td>\n",
" <td>140.0</td>\n",
" <td>China</td>\n",
" <td>Beijing</td>\n",
" <td>Beijing</td>\n",
" <td>Content, Data Mining, Internet</td>\n",
" <td>2012</td>\n",
" <td>Yiming Zhang</td>\n",
" <td>7440.0</td>\n",
" <td>10.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>SpaceX</td>\n",
" <td>100.3</td>\n",
" <td>United States</td>\n",
" <td>California</td>\n",
" <td>Hawthorne</td>\n",
" <td>Aerospace, Manufacturing, Space Travel, Transp...</td>\n",
" <td>2002</td>\n",
" <td>Elon Musk</td>\n",
" <td>383.0</td>\n",
" <td>5,000-10,000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Stripe</td>\n",
" <td>95.0</td>\n",
" <td>United States</td>\n",
" <td>California</td>\n",
" <td>San Francisco</td>\n",
" <td>Finance, FinTech, Mobile Payments, SaaS</td>\n",
" <td>2010</td>\n",
" <td>John Collison, Patrick Collison</td>\n",
" <td>300.0</td>\n",
" <td>1,000-5,000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Klarna</td>\n",
" <td>45.6</td>\n",
" <td>Sweden</td>\n",
" <td>NaN</td>\n",
" <td>Stockholm</td>\n",
" <td>E-Commerce, FinTech, Payments, Shopping</td>\n",
" <td>2005</td>\n",
" <td>Niklas Adalberth, Sebastian Siemiatkowski, Vic...</td>\n",
" <td>3471.7</td>\n",
" <td>5,000-10,000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Epic Games</td>\n",
" <td>42.0</td>\n",
" <td>United States</td>\n",
" <td>North Carolina</td>\n",
" <td>Cary</td>\n",
" <td>Developer Platform, Gaming, Software, Video Games</td>\n",
" <td>1991</td>\n",
" <td>Mark Rein, Tim Sweeney</td>\n",
" <td>544.9</td>\n",
" <td>1,000-5,000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Company Valuation ($B) Country State City \\\n",
"0 Bytedance 140.0 China Beijing Beijing \n",
"1 SpaceX 100.3 United States California Hawthorne \n",
"2 Stripe 95.0 United States California San Francisco \n",
"3 Klarna 45.6 Sweden NaN Stockholm \n",
"4 Epic Games 42.0 United States North Carolina Cary \n",
"\n",
" Industries Founded Year \\\n",
"0 Content, Data Mining, Internet 2012 \n",
"1 Aerospace, Manufacturing, Space Travel, Transp... 2002 \n",
"2 Finance, FinTech, Mobile Payments, SaaS 2010 \n",
"3 E-Commerce, FinTech, Payments, Shopping 2005 \n",
"4 Developer Platform, Gaming, Software, Video Games 1991 \n",
"\n",
" Name of Founders Total Funding \\\n",
"0 Yiming Zhang 7440.0 \n",
"1 Elon Musk 383.0 \n",
"2 John Collison, Patrick Collison 300.0 \n",
"3 Niklas Adalberth, Sebastian Siemiatkowski, Vic... 3471.7 \n",
"4 Mark Rein, Tim Sweeney 544.9 \n",
"\n",
" Number of Employees \n",
"0 10.000 \n",
"1 5,000-10,000 \n",
"2 1,000-5,000 \n",
"3 5,000-10,000 \n",
"4 1,000-5,000 "
]
},
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"execution_count": 29,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.info()\n",
"df.head()"
]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"Выведем названия столбцов для удобства"
]
},
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{
"cell_type": "code",
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"execution_count": 30,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Company', 'Valuation ($B) ', 'Country', 'State', 'City', 'Industries',\n",
" 'Founded Year', 'Name of Founders', 'Total Funding',\n",
" 'Number of Employees'],\n",
" dtype='object')"
]
},
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"execution_count": 30,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Круговая диаграмма показывает распределение компаний по странам"
]
},
{
"cell_type": "code",
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"execution_count": 31,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: ylabel='count'>"
]
},
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"execution_count": 31,
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"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df[\"Country\"].value_counts().plot.pie()"
]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"Стоимость 10 самых дорогих компаний"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Text(0, 0, 'Bytedance'),\n",
" Text(1, 0, 'SpaceX'),\n",
" Text(2, 0, 'Stripe'),\n",
" Text(3, 0, 'Klarna'),\n",
" Text(4, 0, 'Epic Games'),\n",
" Text(5, 0, 'Canva'),\n",
" Text(6, 0, 'Checkout.com'),\n",
" Text(7, 0, 'Instacart'),\n",
" Text(8, 0, 'Databricks'),\n",
" Text(9, 0, 'Revolut'),\n",
" Text(10, 0, 'FTX')]"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAHyCAYAAADBQDVRAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABZ3klEQVR4nO3dd1RU18IF8D2A9KoiRamKHSuJNVaeHTXqswSN3cRYY/cZe8H4osYWMTY0iZoYxWhiNIrYsARFxYIdEZWiEkBQinC+P/yc5zggoMA9g/u31qwV7r2MGwIzm3vPPUclhBAgIiIikoie0gGIiIiIXseCQkRERNJhQSEiIiLpsKAQERGRdFhQiIiISDosKERERCQdFhQiIiKSDgsKERERScdA6QBvIzs7Gw8ePICFhQVUKpXScYiIiCgfhBB48uQJHB0doaf35nMkOllQHjx4ACcnJ6VjEBER0VuIjo5GhQoV3niMThYUCwsLAC++QEtLS4XTEBERUX4kJyfDyclJ/T7+JjpZUF5e1rG0tGRBISIi0jH5GZ7BQbJEREQkHRYUIiIikg4LChEREUlHJ8egEBHRuxFC4Pnz58jKylI6CpUg+vr6MDAwKJQpQFhQiIjeMxkZGYiJicHTp0+VjkIlkKmpKRwcHGBoaPhOz8OCQkT0HsnOzkZkZCT09fXh6OgIQ0NDTnhJhUIIgYyMDDx8+BCRkZHw8PDIczK2N2FBISJ6j2RkZCA7OxtOTk4wNTVVOg6VMCYmJihVqhSioqKQkZEBY2Pjt34uDpIlInoPvctftkRvUlg/W/wJJSIiIumwoBAREZF0ClxQjh49Ch8fHzg6OkKlUmHXrl25Hvv5559DpVLh22+/1diekJAAX19fWFpawtraGoMHD0ZKSkpBoxARUSFSqYrvoZQWLVpg7NixxfJv5fUeWZiaNWuGLVu2aG0/fPgwAgIC3vp5/f394ePj8w7J3l6BC0pqaipq166NVatWvfG4wMBAnDp1Co6Ojlr7fH19cfnyZRw4cAC///47jh49imHDhhU0ChERvSd8fHzQrl27HPcdO3YMKpUK4eHhxZzqhVmzZqFOnTpa22NiYtC+ffsi//d3796NuLg49O7du0Cfp1Kp1A8DAwM4Oztj3LhxSE9PVx8zaNAghIWF4dixY4UdO08FLijt27fHvHnz8PHHH+d6zP379zFq1Cj89NNPKFWqlMa+iIgI7Nu3D+vWrUODBg3QtGlTrFixAtu2bcODBw8K/hUQEVGJN3jwYBw4cAD37t3T2rdx40Z4eXmhVq1aCiTLnb29PYyMjIr831m+fDkGDhyoMTj1/Pnz+Ne//oXu3btj1KhR8PT0xKxZs7Q+d+PGjYiJiUFkZCS+++47/PDDD5g3b556v6GhIT755BMsX768yL+O1xX6GJTs7Gz069cPEydORI0aNbT2nzx5EtbW1vDy8lJv8/b2hp6eHk6fPp3jc6anpyM5OVnjQURE749OnTrB1tZW63JFSkoKtm/fjsGDB+Px48fo06cPypcvD1NTU3h6emLr1q1vfN6cLsNYW1tr/DuTJ09G5cqVYWpqCnd3d0yfPh2ZmZkAgICAAMyePRsXLlxQn414+bmvP/fFixfRqlUrmJiYoEyZMhg2bJjG8IYBAwaga9eu+Oabb+Dg4IAyZcpgxIgR6n8rJw8fPsShQ4c0LsMIIdClSxeYmJjAz88PkyZNwoIFC2BiYqL1+dbW1rC3t4eTkxM6deqELl26ICwsTOMYHx8f7N69G8+ePXvj97KwFXpB+frrr2FgYIDRo0fnuD82NhblypXT2GZgYIDSpUsjNjY2x8/x8/ODlZWV+uHk5FSgTCXxOioR0fvEwMAAn376KQICAiCEUG/fvn07srKy0KdPH6SlpaF+/fr4448/cOnSJQwbNgz9+vXD33///U7/toWFBQICAnDlyhUsW7YMa9euxdKlSwEAvXr1wvjx41GjRg3ExMQgJiYGvXr10nqO1NRUtG3bFjY2NggNDcX27dtx8OBBjBw5UuO44OBg3Lp1C8HBwdi0aRMCAgLeOIbk+PHjMDU1RbVq1dTbHj9+jLt376qLlZOTE3x8fDB58uQ3fp3Xr1/HoUOH0KBBA43tXl5eeP78ea4nEYpKoRaUs2fPYtmyZQgICCjUmQmnTp2KpKQk9SM6OrrQnpuIiHTDoEGDcOvWLRw5ckS9bePGjejevTusrKxQvnx5TJgwAXXq1IG7uztGjRqFdu3a4Zdffnmnf/err75C48aN4erqCh8fH0yYMEH9nCYmJjA3N4eBgQHs7e1hb2+f45mKLVu2IC0tDZs3b0bNmjXRqlUrrFy5Ej/88APi4uLUx9nY2GDlypWoWrUqOnXqhI4dOyIoKCjXbFFRUbCzs9O4vFO2bFlUqVIFc+fOxfnz59/4tfXp0wfm5uYwNjZGlSpVUKNGDUydOlXjGFNTU1hZWSEqKio/365CU6gF5dixY4iPj4ezszMMDAxgYGCAqKgojB8/Hq6urgBeXJOLj4/X+Lznz58jISEB9vb2OT6vkZERLC0tNR5ERPR+qVq1Kho3bowNGzYAAG7evIljx45h8ODBAICsrCzMnTsXnp6eKF26NMzNzbF//37cvXv3nf7dn3/+GU2aNIG9vT3Mzc3x1VdfFfg5IyIiULt2bZiZmam3NWnSBNnZ2bh27Zp6W40aNaCvr6/+2MHBQes981XPnj3LcbbW/fv3w87ODgsWLMDnn3+O1q1b49ChQ1rHLV26FOfPn8eFCxfw+++/4/r16+jXr5/WcSYmJsW+dlOhFpR+/fohPDwc58+fVz8cHR0xceJE7N+/HwDQqFEjJCYm4uzZs+rPO3ToELKzs7VOKxEREb1q8ODB2LFjB548eYKNGzeiYsWKaN68OQDgv//9L5YtW4bJkycjODgY58+fR9u2bZGRkZHr86lUKo1LRgA0xnycPHkSvr6+6NChA37//XecO3cO06ZNe+NzvovXbyxRqVTIzs7O9fiyZcvin3/+0dru4uKCTZs24ZdffsHkyZNRp04dtGvXDpcvX9Y4zt7eHpUqVUKVKlXQsWNHzJ49Gz///DNu3rypcVxCQgJsbW3f4SsruAKvxZOSkqIRPDIyEufPn0fp0qXh7OyMMmXKaBxfqlQp2Nvbo0qVKgCAatWqoV27dhg6dCj8/f2RmZmJkSNHonfv3jnekkxERPRSz549MWbMGGzZsgWbN2/G8OHD1UMKQkJC0KVLF/Tt2xfAi5s2rl+/jurVq+f6fLa2toiJiVF/fOPGDY0zBSdOnICLiwumTZum3vb6pQ5DQ0NkZWW9MXe1atUQEBCA1NRU9VmUkJAQ6Onpqd8f30bdunURGxuLf/75BzY2Njke4+bmhgEDBiAgIACnTp3K8QaWl16evXl1QOytW7eQlpaGunXrvnXOt1HgMyhnzpxB3bp11UHHjRuHunXrYsaMGfl+jp9++glVq1ZF69at0aFDBzRt2hTff/99QaMQEdF7xtzcHL169cLUqVMRExODAQMGqPd5eHjgwIEDOHHiBCIiIvDZZ59pjO/IycuxIOfOncOZM2fw+eefa5zF8PDwwN27d7Ft2zbcunULy5cvR2BgoMZzuLq6qv9Yf/TokcY8Ii/5+vrC2NgY/fv3x6VLlxAcHIxRo0ahX79+sLOze+vvR926dVG2bFmEhISotz148ADjxo1DeHg40tPT8fTpU6xZswaJiYlaJSMxMRGxsbF48OABjhw5gjlz5qBy5coag26PHTsGd3d3VKxY8a1zvhWhg5KSkgQAkZSUlK/jgaJ7EBHpkmfPnokrV66IZ8+eKR3lrZ04cUIAEB06dNDY/vjxY9GlSxdhbm4uypUrJ7766ivx6aefii5duqiPad68uRgzZoz64/v374s2bdoIMzMz4eHhIfbu3SusrKzExo0b1cdMnDhRlClTRpibm4tevXqJpUuXCisrK/X+tLQ00b17d2FtbS0AqD8XgAgMDFQfFx4eLlq2bCmMjY1F6dKlxdChQ8WTJ0/U+/v376+RVQghxowZI5o3b/7G78ekSZNE79691R8/efJEjBo
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot = df.loc[0:10][[\"Company\", \"Valuation ($B) \"]].plot.bar(color=[\"blue\"])\n",
"plot.set_xticklabels(df.loc[0:10][\"Company\"])"
]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"Гистограмма распределение количества компаний по году основания. Диаграмма показывает, что больше всего самых дорогих компаний было основано в период между 2012 и 2015 годом."
]
},
{
"cell_type": "code",
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"execution_count": 33,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: xlabel='Founded Year'>"
]
},
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"execution_count": 33,
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"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df[\"Founded Year\"].value_counts().sort_index().plot.bar()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Точечная диаграмма показывает распределение стоимости компании от количества работников. Из диаграммы видно, что больше всего компаний, у
]
},
{
"cell_type": "code",
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"execution_count": 34,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: xlabel='Number of Employees', ylabel='Valuation ($B) '>"
]
},
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"execution_count": 34,
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"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot.scatter(x=\"Number of Employees\", y=\"Valuation ($B) \", figsize=(12, 4))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
