214 lines
117 KiB
Plaintext
214 lines
117 KiB
Plaintext
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{
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"cells": [
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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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"## **НАЧАЛО ЛАБЫ**\n",
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"\n",
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"Выгрузка данных из csv"
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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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"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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"Index(['id', 'name', 'est_diameter_min', 'est_diameter_max',\n",
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" 'relative_velocity', 'miss_distance', 'orbiting_body', 'sentry_object',\n",
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" 'absolute_magnitude', 'hazardous'],\n",
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" dtype='object')\n"
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]
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}
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],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import pandas as pd\n",
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"df = pd.read_csv(\".//static//csv//neo.csv\", nrows=15000)\n",
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"print(df.columns)\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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"## **СТОЛБЦЫ ДАТАСЕТА:**\n",
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"\n",
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"**Id**\n",
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"\n",
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"**name**\n",
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"\n",
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"**est_diameter_min** – минимальный радиус косм. объекта (астероид, комета) рядом с Землёй (км)\n",
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"\n",
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"**est_diameter_max** – максимальный радиус косм. объекта\n",
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"\n",
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"**relative_velocity** – скорость относительно Земли (км/с)\n",
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"\n",
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"**miss_distance** – расстояние, на кот. проходит рядом с Землёй (км)\n",
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"\n",
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"**orbiting_body** – тело, вокруг которого вращается (везде Земля)\n",
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"\n",
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"**sentry_object** – ведётся ли за ним авто мониторинг, как за телом, кот. может столкнуться с Землёй (везде false)\n",
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"\n",
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"**absolute_magnitude** – звёздная величина (яркость)\n",
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"\n",
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"**hazardous** – опасный для Земли / 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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"## Диаграмма ниже показывает разницу между максимальным вероятным диаметром космического объекта и минамальным (на срезе первых 100 объектов). С помощью неё можно увидеть, что у большинства объектов разница между этими двумя значениями составляет меньше 1 км, что говорит о достаточно хорошей точности измерения размера объекта"
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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": 22,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<Axes: title={'center': 'Разница между макс. диаметром (est_diameter_max) и мин диаметром (est_diameter_min) объекта (первые 100 записей)'}, xlabel='Порядковый номер объекта в датасете', ylabel='Разница'>"
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]
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},
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"execution_count": 22,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 1200x600 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"data1 = df[:100].copy()\n",
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"data1['diameter_dif'] = data1['est_diameter_max'] - data1['est_diameter_min']\n",
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"data1['index'] = data1.index\n",
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"data1.plot(kind='scatter', x='index', xlabel='Порядковый номер объекта в датасете', y='diameter_dif', ylabel='Разница', figsize=(12, 6), title='Разница между макс. диаметром (est_diameter_max) и мин диаметром (est_diameter_min) объекта (первые 100 записей)')"
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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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"## На диаграмме ниже показана средняя скорость относительно Земли для космических объектов, которые потенциально опасны (hazardous=true) и неопасны (hazardous=false). С помощью неё можно сделать вывод о том, что потенциально опасны те космические объекты, у которых относительная скорость в среднем больше 60 000 км/с"
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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": 69,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<matplotlib.legend.Legend at 0x2530c87c410>"
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]
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},
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"execution_count": 69,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1000x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"# Группировка данных по значению 'hazardous' и вычисление средней относительной скорости\n",
|
|||
|
|
"data2 = df.groupby('hazardous')['relative_velocity'].mean()\n",
|
|||
|
|
"handles = [plt.Rectangle((0,0),1,1, color='blue'), plt.Rectangle((0,0),1,1, color='red')]\n",
|
|||
|
|
"labels = ['Потенциально не опасный', 'Потенциально опасный']\n",
|
|||
|
|
"data2.plot(kind='bar', figsize=(10, 6), color=['blue', 'red'], xlabel='Значение столбца \"hazardous\"')\n",
|
|||
|
|
"plt.legend(handles, labels)\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"#ПОЧЕМУ НЕ ОТОБРАЖАЕТСЯ ОБЕ ПОЛОСКИ В КОДЕ НИЖЕ без handles?\n",
|
|||
|
|
"# plot = df.groupby('hazardous')['relative_velocity'].mean().plot.bar(color=[\"pink\", \"green\"])\n",
|
|||
|
|
"# plot.legend([\"Потенциально не опасный\", \"Потенциально опасный\"])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"## Диаграмма ниже показывает процентное соотношение космических объектов, за которыми ведётся автоматическое наблюдение, т.к. они представляют серьёзную угрозу, и космических объектов, за которыми такое наблюдение не ведётся. На диаграмме видно, что за всеми космическими объектами наблюдения не ведётся. Это означает, что в данном датасете нет настолько опасных космических объектов, которые требовали бы постоянных наблюдений"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 70,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.legend.Legend at 0x2530c8b6b70>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 70,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 800x800 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"data3=df['sentry_object'].value_counts()\n",
|
|||
|
|
"labels=['Не происходит автомониторинг, как за опасным объектом', 'Происходит автомониторинг, как за опасным объектом']\n",
|
|||
|
|
"colors=['blue', 'red']\n",
|
|||
|
|
"data3.plot(kind='pie', figsize=(8, 8), labels=None, colors=colors, autopct='%1.1f%%')\n",
|
|||
|
|
"handles = [plt.Line2D([0], [0], color=colors[0], lw=4), plt.Line2D([0], [0], color=colors[1], lw=4)]\n",
|
|||
|
|
"plt.legend(handles, labels)"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "aimenv",
|
|||
|
|
"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": "3.12.5"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 2
|
|||
|
|
}
|