2024-10-04 21:59:56 +04:00
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1 Датасет: NASA - Nearest Earth Objects\n",
"#### https://www.kaggle.com/datasets/sameepvani/nasa-nearest-earth-objects \n",
"\n",
"There is an infinite number of objects in the outer space. Some of them are closer than we think. Even though we might think that a distance of 70,000 Km can not potentially harm us, but at an astronomical scale, this is a very small distance and can disrupt many natural phenomena. These objects/asteroids can thus prove to be harmful. Hence, it is wise to know what is surrounding us and what can harm us amongst those. Thus, this dataset compiles the list of NASA certified asteroids that are classified as the nearest earth object.\n",
"\n",
"В
"\n",
"- Из этого описания очевидно что объектами иследования являются околоземные объекты.\n",
"- Атрибуты объектов: id, name, est_diameter_min, est_diameter_max, relative_velocity, miss_distance, orbiting_body, sentry_object, absolute_magnitude, hazardous\n",
"- Очевидная цель этого датасета - это научиться определять опасность объекта автоматически.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"количество колонок: 10\n",
"колонки: id, name, est_diameter_min, est_diameter_max, relative_velocity, miss_distance, orbiting_body, sentry_object, absolute_magnitude, hazardous\n"
]
}
],
"source": [
"import pandas as pd\n",
"df = pd.read_csv(\"..//static//csv//neo_v2.csv\", sep=\",\")\n",
"print('количество колонок: ' + str(df.columns.size)) \n",
"print('колонки: ' + ', '.join(df.columns))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Получение сведений о
"\n",
"Типы пропущенных данных:\n",
"- None - представление пустых данных в Python\n",
"- NaN - представление пустых данных в Pandas\n",
"- '' - пустая строка"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"id 0\n",
"name 0\n",
"est_diameter_min 0\n",
"est_diameter_max 0\n",
"relative_velocity 0\n",
"miss_distance 0\n",
"orbiting_body 0\n",
"sentry_object 0\n",
"absolute_magnitude 0\n",
"hazardous 0\n",
"dtype: int64\n",
"\n",
"id False\n",
"name False\n",
"est_diameter_min False\n",
"est_diameter_max False\n",
"relative_velocity False\n",
"miss_distance False\n",
"orbiting_body False\n",
"sentry_object False\n",
"absolute_magnitude False\n",
"hazardous False\n",
"dtype: bool\n",
"\n"
]
}
],
"source": [
"# Количество пустых значений признаков\n",
"print(df.isnull().sum())\n",
"\n",
"print()\n",
"\n",
"# Есть ли пустые значения признаков\n",
"print(df.isnull().any())\n",
"\n",
"print()\n",
"\n",
"# Процент пустых значений признаков\n",
"for i in df.columns:\n",
" null_rate = df[i].isnull().sum() / len(df) * 100\n",
" if null_rate > 0:\n",
" print(f\"{i} процент пустых значений: %{null_rate:.2f}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Итог: пропущеных значений нет"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"инфографика на сайте и в datawrangelere показывает, что в столбцах orbiting_body и sentry_object у
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"количество колонок: 8\n",
"колонки: id, name, est_diameter_min, est_diameter_max, relative_velocity, miss_distance, absolute_magnitude, hazardous\n"
]
}
],
"source": [
"df = df.drop(columns=['orbiting_body'])\n",
"df = df.drop(columns=['sentry_object'])\n",
"print('количество колонок: ' + str(df.columns.size)) \n",
"print('колонки: ' + ', '.join(df.columns))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"я быстро посмотрев данные зашумленности не выявил"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"поля id и name в предсказании не помогут, но я их пока выкидывать не буду"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"посмотрим выбросы:"
]
},
{
"cell_type": "code",
2024-10-04 22:01:22 +04:00
"execution_count": 4,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Колонка est_diameter_min:\n",
" Есть выбросы: Да\n",
" Количество выбросов: 8306\n",
" Минимальное значение: 0.0006089126\n",
" Максимальное значение: 37.8926498379\n",
" 1-й квартиль (Q1): 0.0192555078\n",
" 3-й квартиль (Q3): 0.1434019235\n",
"\n",
"Колонка est_diameter_max:\n",
" Есть выбросы: Да\n",
" Количество выбросов: 8306\n",
" Минимальное значение: 0.00136157\n",
" Максимальное значение: 84.7305408852\n",
" 1-й квартиль (Q1): 0.0430566244\n",
" 3-й квартиль (Q3): 0.320656449\n",
"\n",
"Колонка relative_velocity:\n",
" Есть выбросы: Да\n",
" Количество выбросов: 1574\n",
" Минимальное значение: 203.34643253\n",
" Максимальное значение: 236990.1280878666\n",
" 1-й квартиль (Q1): 28619.02064490995\n",
" 3-й квартиль (Q3): 62923.60463276395\n",
"\n",
"Колонка miss_distance:\n",
" Есть выбросы: Нет\n",
" Количество выбросов: 0\n",
" Минимальное значение: 6745.532515957\n",
" Максимальное значение: 74798651.4521972\n",
" 1-й квартиль (Q1): 17210820.23576468\n",
" 3-й квартиль (Q3): 56548996.45139917\n",
"\n",
"Колонка absolute_magnitude:\n",
" Есть выбросы: Да\n",
" Количество выбросов: 101\n",
" Минимальное значение: 9.23\n",
" Максимальное значение: 33.2\n",
" 1-й квартиль (Q1): 21.34\n",
" 3-й квартиль (Q3): 25.7\n",
"\n"
]
}
],
"source": [
"numeric_columns = ['est_diameter_min', 'est_diameter_max', 'relative_velocity', 'miss_distance', 'absolute_magnitude']\n",
"for column in numeric_columns:\n",
" if pd.api.types.is_numeric_dtype(df[column]): # Проверяем, является ли колонка числовой\n",
" q1 = df[column].quantile(0.25) # Находим 1-й квартиль (Q1)\n",
" q3 = df[column].quantile(0.75) # Находим 3-й квартиль (Q3)\n",
" iqr = q3 - q1 # Вычисляем межквартильный размах (IQR)\n",
"\n",
" # Определяем границы для выбросов\n",
" lower_bound = q1 - 1.5 * iqr # Нижняя граница\n",
" upper_bound = q3 + 1.5 * iqr # Верхняя граница\n",
"\n",
" # Подсчитываем количество выбросов\n",
" outliers = df[(df[column] < lower_bound) | (df[column] > upper_bound)]\n",
" outlier_count = outliers.shape[0]\n",
"\n",
" print(f\"Колонка {column}:\")\n",
" print(f\" Есть выбросы: {'Да' if outlier_count > 0 else 'Нет'}\")\n",
" print(f\" Количество выбросов: {outlier_count}\")\n",
" print(f\" Минимальное значение: {df[column].min()}\")\n",
" print(f\" Максимальное значение: {df[column].max()}\")\n",
" print(f\" 1-й квартиль (Q1): {q1}\")\n",
" print(f\" 3-й квартиль (Q3): {q3}\\n\")\n"
]
2024-10-04 22:01:22 +04:00
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"построим графики в надежде найти какие то зависимости опасности от других колонок"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 400x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAaAAAAK9CAYAAABilriBAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABV9klEQVR4nO3deZyN9f//8eeZfTMzllnQmLFlp0+kECOGEUqptGfpW31CWT4SLZZUkpLI0mr5pE+b9hChlIRIJUvIFmbGOvt+rt8fZs7PmYWZ4/BmPO6329zMeV/v6zqvc5lzPc91Xe9zXTbLsiwBAHCeeZguAABwaSKAAABGEEAAACMIIACAEQQQAMAIAggAYAQBBAAwggACABhBAAEAjCCAcNGLiYlRv379HI+/++472Ww2fffdd8ZqwsVj3LhxstlsOnLkiOlS3GLu3Lmy2Wzas2eP6VLOyK0BNHv2bMXHxysiIkLe3t6KjIxUbGys5s+fL7vd7s6nwkXqvffe09SpU02XYVxGRobGjRtHSOKS5uXOhc2bN0/Vq1fX008/reDgYJ04cUI///yz+vXrp8WLF+t///ufO58OF6H33ntPmzdv1tChQ8/Zc3To0EGZmZny8fE5Z89xtjIyMjR+/HhJUseOHc0WAxji1gBatWqVvL29ndoeffRRVa1aVa+99pomTpyomJgYdz4lUIyHh4f8/PxMl2FEenq6AgMDTZeBc8hutysnJ6dC/I279RBc0fApVBg6Hh7//+k+//xz9ejRQzVq1JCvr6/q1q2rCRMmKD8/32nejh07ymazOX6qVaumHj16aPPmzU79bDabxo0b59Q2efJk2Wy2Yp8ws7KyNG7cOF1++eXy8/NT9erV1bt3b+3atUuStGfPHtlsNs2dO9dpvkGDBslmszmdbyg83urj46PDhw879V+zZo2j7l9++cVp2kcffaSWLVvK399f1apV0z333KMDBw4UW3fbtm1Tnz59FBYWJn9/fzVo0EBPPvmkpP9/7Pp0P4WHeDp27KimTZsWW35ZrV27Vt26dVNISIgCAgIUGxur1atXO/VJTU3V0KFDFRMTI19fX4WHh6tLly7auHGjo4avv/5ae/fuddRXng8klmXp2Wef1WWXXaaAgABdd911+vPPP4v1K+kc0A8//KDbbrtNtWrVkq+vr6KiojRs2DBlZmY6zduvXz8FBQVp37596tmzp4KCglSzZk3NmDFDkvTHH3+oU6dOCgwMVHR0tN57771iz3/ixAkNHTpUUVFR8vX1Vb169TRp0iTHYeg9e/YoLCxMkjR+/HjHujj173fbtm269dZbVaVKFfn5+alVq1b64osvnJ6n8G/v+++/18CBAxUeHq7LLrusTOuy8G/8pZde0owZM1SnTh0FBASoa9eu2r9/vyzL0oQJE3TZZZfJ399fvXr10rFjx5yWUZb38NatW+Xv76/77rvPad4ff/xRnp6eevzxx89Y64oVK9S+fXsFBgYqNDRUvXr10tatWx3Ty/M+OJ0TJ06oX79+Cg0NVUhIiPr376+MjAynPnPmzFGnTp0UHh4uX19fNW7cWLNmzXLqc7p6Tt12vPTSS2rbtq2qVq0qf39/tWzZUh9//HGxumw2mwYPHqwFCxaoSZMm8vX11ZIlSyRJf/75pzp16iR/f39ddtllevbZZ0s93TFz5kzH/DVq1NCgQYN04sQJpz5Fz6cW6tixY7Ht6PTp09WkSRMFBASocuXKatWqVYnvh9Nx6x5QoRMnTigvL0+pqanasGGDXnrpJd1xxx2qVauWo8/cuXMVFBSk4cOHKygoSCtWrNCYMWOUkpKiyZMnOy2vYcOGevLJJ2VZlnbt2qUpU6aoe/fu2rdv32lrmDhxYrH2/Px89ezZU8uXL9cdd9yhIUOGKDU1VcuWLdPmzZtVt27dEpe3c+dOvfnmm6U+n6enp959910NGzbM0TZnzhz5+fkpKyvLqe/cuXPVv39/XXXVVZo4caISExP16quvavXq1fr1118VGhoqSfr999/Vvn17eXt768EHH1RMTIx27dqlL7/8Us8995x69+6tevXqOZY7bNgwNWrUSA8++KCjrVGjRqXWXFYrVqzQ9ddfr5YtW2rs2LHy8PBwvBF/+OEHtW7dWpL073//Wx9//LEGDx6sxo0b6+jRo/rxxx+1detWXXnllXryySeVnJysf/75R6+88ookKSgoqMx1jBkzRs8++6y6d++u7t27a+PGjeratatycnLOOO9HH32kjIwMPfzww6patarWrVun6dOn659//tFHH33k1Dc/P1/XX3+9OnTooBdffFELFizQ4MGDFRgYqCeffFJ33323evfurdmzZ+u+++5TmzZtVLt2bUknD63FxsbqwIEDeuihh1SrVi399NNPGj16tA4dOqSpU6cqLCxMs2bN0sMPP6ybb75ZvXv3liQ1b95c0smNSrt27VSzZk2NGjVKgYGB+vDDD3XTTTdp4cKFuvnmm53qHThwoMLCwjRmzBilp6eXeX1K0oIFC5STk6NHHnlEx44d04svvqg+ffqoU6dO+u677/T4449r586dmj59ukaMGKF33nnHMW9Z3sONGjXShAkT9Nhjj+nWW2/VjTfeqPT0dPXr108NGzbUM888c9r6vv32W11//fWqU6eOxo0bp8zMTE2fPl3t2rXTxo0bFRMT47b3QZ8+fVS7dm1NnDhRGzdu1FtvvaXw8HBNmjTJ0WfWrFlq0qSJbrzxRnl5eenLL7/UwIEDZbfbNWjQIEkqVo8kbdiwQVOnTlV4eLij7dVXX9WNN96ou+++Wzk5OXr//fd122236auvvlKPHj2c5l+xYoU+/PBDDR48WNWqVVNMTIwSEhJ03XXXKS8vz/F38sYbb8jf37/Yaxs3bpzGjx+vuLg4Pfzww9q+fbtmzZql9evXa/Xq1aXuPJTmzTff1KOPPqpbb71VQ4YMUVZWln7//XetXbtWd911V9kXZJ0DDRo0sCQ5fu677z4rNzfXqU9GRkax+R566CErICDAysrKcrTFxsZasbGxTv2eeOIJS5KVlJTkaJNkjR071vF45MiRVnh4uNWyZUun+d955x1LkjVlypRiz2+32y3Lsqzdu3dbkqw5c+Y4pvXp08dq2rSpFRUVZfXt29fRPmfOHEuSdeedd1rNmjVztKenp1vBwcHWXXfdZUmy1q9fb1mWZeXk5Fjh4eFW06ZNrczMTEf/r776ypJkjRkzxtHWoUMHq1KlStbevXtLrLOo6Ohop9pOFRsbazVp0qTEaadjt9ut+vXrW/Hx8U7Pm5GRYdWuXdvq0qWLoy0kJMQaNGjQaZfXo0cPKzo6utx1JCUlWT4+PlaPHj2c6ij8Wzj1da9cudKSZK1cudKp3qImTpxo2Ww2p/Xbt29fS5L1/PPPO9qOHz9u+fv7WzabzXr//fcd7du2bSv2dzdhwgQrMDDQ+uuvv5yea9SoUZanp6e1b98+y7Is6/Dhw8XmLdS5c2erWbNmTu8Du91utW3b1qpfv76jrfBv79prr7Xy8vJKWGulK/wbDwsLs06cOOFoHz16tCXJatGihdN79s4777R8fHycairrezg/P9+69tprrYiICOvIkSPWoEGDLC8vL8d74nSuuOIKKzw83Dp69Kij7bfffrM8PDys++67r8R5Tvc+KMnYsWMtSdaAAQOc2m+++WaratWqTm0lveb4+HirTp06pS7/8OHDVq1ataxmzZpZaWlppS4rJyfHatq0qdWpUyendkmWh4eH9eeffzq1Dx061JJkrV271tGWlJRkhYSEWJKs3bt3O9p8fHysrl27Wvn5+Y6+r732miXJeueddxxtpa27otvhXr16ubQ9KeqcDMOeM2eOli1bpgULFuj+++/XggULnD6NSHJK6dTUVB05ckTt27dXRkaGtm3b5tQ3NzdXR44c0eHDh7VmzRp9+umnat68uapVq1bi8x84cEDTp0/X008/XewT9sKFC1WtWjU98sgjxeaz2WwlLm/Dhg366KOPNHHiRKfDiKe69957tW3bNsehtoULFyokJESdO3d26vfLL78oKSlJAwc
"text/plain": [
"<Figure size 400x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 400x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAALMCAYAAADkXsVPAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABIGElEQVR4nO3deXxMZ///8feEZJLIhiQIEdvPVlRLKUG0VGqraqslWkH39i6Vuwu3W5toS5dvVS2lrbtoVbWo9m5VbaW62JfaKY2laguVIERkzu8PJ3Mbk0SMkYO8no9HHpzrXOfMZ04m13vOMnNshmEYAgAUez5WFwAAuDoQCAAASQQCAMBEIAAAJBEIAAATgQAAkEQgAABMBAIAQBKBAAAwEQi4akyaNEk2m027du2yuhQnm82m5ORk5/TVWOP1aPHixbLZbJoxY4bVpXjFrl27ZLPZNGnSJKtLKZAlgTB+/HjFx8erXLly8vX1Vfny5RUXF6ePP/5YDofDipKAKyYzM1PJyclavHix1aUABbIkECZPnqxSpUppyJAh+s9//qN//etfqlixonr37q2ePXtaURKuAg899JBOnTqlmJgYq0vJlyc1ZmZmKiUlhUDAVa+kFQ+6ZMkS+fr6urT169dPZcuW1ZgxYzR8+HBVqVLFitJgoRIlSqhEiRJWl1Gga6FGWCMzM1OBgYFWl3FZLNlDuDAMcuWGgI/P/8r6+uuv1bFjR0VFRclut6t69ep65ZVXlJOT47Js69atZbPZnD/h4eHq2LGjNm7c6NLvwmPCkvTWW2/JZrOpdevWLu2nT59WcnKyatasKX9/f1WoUEH33HOPdu7cKSn/44JPP/20bDabevfu7WzLPfbs5+enw4cPu/RfunSps+5Vq1a5zJs+fboaNWqkgIAAhYeH68EHH9S+ffvctt3WrVt1//33KyIiQgEBAapVq5YGDx4sSUpOTnbZNnn95L57bd26terVq+e2/sLIXXb9+vWKi4tTYGCgatSo4TwO/OOPP6pp06bO+hYsWOCyfF7H51etWqX4+HiFh4crICBAVatWVd++fV2WmzZtmho1aqTg4GCFhISofv36evfddy+p9qysLA0YMEAREREKDg7WXXfdpT///NOt36XWuGvXLkVEREiSUlJSnNs79zW4fv169e7dW9WqVZO/v7/Kly+vvn376siRIy6Pm/s73LFjh3r37q2wsDCFhoaqT58+yszMdKtzypQpatKkiQIDA1W6dGm1atVK8+bNc+kzZ84ctWzZUqVKlVJwcLA6duyoTZs2FWp7/fHHH+rWrZvKlCmjwMBA3XrrrZo9e7Zzfu45gIJ+Lvw7zIvD4dBrr72mSpUqyd/fX23atNGOHTtc+vz000/q1q2bKleuLLvdrujoaA0YMECnTp0qVD3nv/m8lPGmXr16Wr16tVq1aqXAwED961//kiQdO3ZMvXv3VmhoqMLCwpSYmKhjx47l+fx++OEH5+8gLCxMXbp00ZYtW1z69O7dO883yLmvifPNnz9fLVq0UFhYmIKCglSrVi1nXYVhyR5CrmPHjuns2bM6fvy4Vq9erf/7v/9T9+7dVblyZWefSZMmKSgoSElJSQoKCtIPP/ygl156SRkZGXrrrbdc1le7dm0NHjxYhmFo586dGjFihDp06KA9e/YUWMPw4cPd2nNyctSpUyctXLhQ3bt3V//+/XX8+HHNnz9fGzduVPXq1fNc344dO/Thhx/m+3glSpTQlClTNGDAAGfbxIkT5e/vr9OnT7v0nTRpkvr06aNbbrlFw4cP18GDB/Xuu+/ql19+0dq1axUWFibp3KDSsmVL+fr66rHHHlOVKlW0c+dOffPNN3rttdd0zz33qEaNGs71DhgwQHXq1NFjjz3mbKtTp06+NV+Kv//+W506dVL37t3VrVs3jRs3Tt27d9enn36qZ599Vk888YQSEhL01ltv6b777tPevXsVHByc57oOHTqkdu3aKSIiQgMHDlRYWJh27dqlL7/80tln/vz56tGjh9q0aaM33nhDkrRlyxb98ssv6t+/f6HrfuSRRzRlyhQlJCSoefPm+uGHH9SxY8eLLnexGiMiIjRu3Dg9+eST6tq1q+655x5JUoMGDZz1//HHH+rTp4/Kly+vTZs26YMPPtCmTZu0bNkytz/4+++/X1WrVtXw4cO1Zs0aTZgwQZGRkc7nLp0LnuTkZDVv3lxDhw6Vn5+fli9frh9++EHt2rWTJH3yySdKTExUfHy83njjDWVmZmrcuHFq0aKF1q5dW+Ae+sGDB9W8eXNlZmY69+wnT56su+66SzNmzFDXrl1Vp04dffLJJ85lPvjgA23ZskXvvPOOsy13GxTk9ddfl4+Pj5577jmlp6frzTffVM+ePbV8+XJnn+nTpyszM1NPPvmkypYtqxUrVmj06NH6888/NX36dElyq0c697eflJSkyMhIZ9uljDdHjhxR+/bt1b17dz344IMqV66cDMNQly5d9PPPP+uJJ55QnTp1NGvWLCUmJro9twULFqh9+/aqVq2akpOTderUKY0ePVqxsbFas2bNJR8l2bRpkzp16qQGDRpo6NChstvt2rFjh3755ZfCr8SwUK1atQxJzp9evXoZ2dnZLn0yMzPdlnv88ceNwMBA4/Tp0862uLg4Iy4uzqXfv/71L0OScejQIWebJOPll192Tr/wwgtGZGSk0ahRI5flP/roI0OSMWLECLfHdzgchmEYRmpqqiHJmDhxonPe/fffb9SrV8+Ijo42EhMTne0TJ040JBk9evQw6tev72w/efKkERISYiQkJBiSjJUrVxqGYRhnzpwxIiMjjXr16hmnTp1y9v/2228NScZLL73kbGvVqpURHBxs7N69O886LxQTE+NS2/ni4uKMG264Ic95FxMXF2dIMqZOneps27p1qyHJ8PHxMZYtW+Zsnzt3rtu2y91GqamphmEYxqxZs1y2SV769+9vhISEGGfPnvWoZsMwjHXr1hmSjKeeesqlPfd3cv7rxZMaDx8+7LaeXHm9vj/77DNDkrFkyRJn28svv2xIMvr27evSt2vXrkbZsmWd07///rvh4+NjdO3a1cjJyXHpm/t6OH78uBEWFmY8+uijLvMPHDhghIaGurVf6NlnnzUkGT/99JOz7fjx40bVqlWNKlWquD2uYRhGYmKiERMTU+B6z7do0SJDklGnTh0jKyvL2f7uu+8akowNGzY42/LahsOHDzdsNpvb30Quh8NhdOrUyQgKCjI2bdpU4LryG28kGePHj3fp+9VXXxmSjDfffNPZdvbsWaNly5Zur/eGDRsakZGRxpEjR5xtv/32m+Hj42P06tXL2Zbftst9TeR65513DEnG4cOH83zOhWHpZacTJ07U/Pnz9emnn+rhhx/Wp59+6vKuVZICAgKc/z9+/LjS0tLUsmVLZWZmauvWrS59s7OzlZaWpsOHD2vp0qWaNWuWGjRooPDw8Dwff9++fRo9erSGDBmioKAgl3kzZ85UeHi4nnnmGbflLnzXlmv16tWaPn26hg8f7nLY63wPPfSQtm7d6jw0NHPmTIWGhqpNmzYu/VatWqVDhw7pqaeekr+/v7O9Y8eOql27tnP3/PDhw1qyZIn69u3rsmdVUJ0Xk5OTo7S0NKWlpenMmTOXtGxQUJC6d+/unK5Vq5bCwsJUp04dNW3a1Nme+/8//vgj33Xl7gF9++23ys7OzrfPyZMnNX/+/Euq83zfffedpHPnsc737LPPXnTZwtRYkPNf36dPn1ZaWppuvfVWSdKaNWvc+j/xxBMu0y1bttSRI0eUkZEhSfrqq6/kcDj00ksvub0Gc18P8+fP17Fjx9SjRw/n7zktLU0lSpRQ06ZNtWjRogJr/u6779SkSRO1aNHC2RYUFKTHHntMu3bt0ubNmy9hCxSsT58+8vPzc063bNlSkuvr5vxtePLkSaWlpal58+YyDENr167Nc72vvPKKvv32W02aNEl169bNc10XG2/sdrv69Onj0vbdd9+pZMmSevLJJ51tJUqUcBtH9u/fr3Xr1ql3794qU6aMs71Bgwa64447nK/JS5H7Wvz
"text/plain": [
"<Figure size 400x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 400x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"# Список числовых колонок, для которых мы будем строить графики\n",
"numeric_columns = ['est_diameter_min', 'est_diameter_max', 'relative_velocity', 'miss_distance', 'absolute_magnitude']\n",
"\n",
"# Создание диаграмм зависимости\n",
"for column in numeric_columns:\n",
" plt.figure(figsize=(4, 8)) # Установка размера графика\n",
" plt.scatter(df['hazardous'], df[column], alpha=0.5) # Создаем диаграмму рассеяния\n",
" plt.title(f'Зависимость {column} от hazardous')\n",
" plt.xlabel('hazardous (0 = нет, 1 = да)')\n",
" plt.ylabel(column)\n",
" plt.xticks([0, 1]) # Установка меток по оси X\n",
" plt.grid() # Добавление сетки для удобства восприятия\n",
" plt.show() # Отображение графика"
]
2024-10-04 21:59:56 +04:00
}
],
"metadata": {
"kernelspec": {
"display_name": "laba",
"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
}
