2024-09-19 16:11:49 +04:00
|
|
|
|
{
|
|
|
|
|
|
"cells": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"## Начало лабораторной\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"Выгрузка данных из csv файла в датафрейм"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": 1,
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
|
"text": [
|
|
|
|
|
|
"Index(['work_year', 'experience_level', 'employment_type', 'job_title',\n",
|
|
|
|
|
|
" 'salary', 'salary_currency', 'salary_in_usd', 'employee_residence',\n",
|
|
|
|
|
|
" 'remote_ratio', 'company_location', 'company_size'],\n",
|
|
|
|
|
|
" dtype='object')\n"
|
|
|
|
|
|
]
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"import pandas as pd\n",
|
|
|
|
|
|
"df = pd.read_csv(\"..//static//csv//ds_salaries.csv\")\n",
|
|
|
|
|
|
"print(df.columns)"
|
|
|
|
|
|
]
|
2024-10-12 09:49:04 +04:00
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": 6,
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
|
"text": [
|
|
|
|
|
|
"Index(['work_year', 'experience_level', 'employment_type', 'job_title',\n",
|
|
|
|
|
|
" 'salary', 'salary_currency', 'salary_in_usd', 'employee_residence',\n",
|
|
|
|
|
|
" 'remote_ratio', 'company_location', 'company_size'],\n",
|
|
|
|
|
|
" dtype='object')\n"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
|
|
|
|
|
"image/png": "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
|
|
|
|
|
|
"text/plain": [
|
|
|
|
|
|
"<Figure size 1000x600 with 1 Axes>"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"import matplotlib.pyplot as plt\n",
|
|
|
|
|
|
"import seaborn as sns\n",
|
|
|
|
|
|
"import pandas as pd\n",
|
|
|
|
|
|
"df = pd.read_csv(\"..//static//csv//ds_salaries.csv\")\n",
|
|
|
|
|
|
"print(df.columns)\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"# Группировка данных по уровню опыта и вычисление средней зарплаты\n",
|
|
|
|
|
|
"avg_salary_by_exp = df.groupby('experience_level')['salary_in_usd'].mean().reset_index()\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"# Создание столбчатой диаграммы\n",
|
|
|
|
|
|
"plt.figure(figsize=(10, 6))\n",
|
|
|
|
|
|
"sns.barplot(x='experience_level', y='salary_in_usd', data=avg_salary_by_exp)\n",
|
|
|
|
|
|
"plt.title('Средняя зарплата по уровню опыта')\n",
|
|
|
|
|
|
"plt.xlabel('Уровень опыта')\n",
|
|
|
|
|
|
"plt.ylabel('Средняя зарплата (USD)')\n",
|
|
|
|
|
|
"plt.show()"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"По данной диаграмме можно сделать вывод, что у работников Executive-level зарплата выше всех, а у Entry-level зарплата ниже всех"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": 7,
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
|
|
|
|
|
"image/png": "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
|
|
|
|
|
|
"text/plain": [
|
|
|
|
|
|
"<Figure size 1000x600 with 1 Axes>"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"import matplotlib.pyplot as plt\n",
|
|
|
|
|
|
"import seaborn as sns\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"plt.figure(figsize=(10, 6))\n",
|
|
|
|
|
|
"sns.histplot(df['salary_in_usd'], bins=30, kde=True)\n",
|
|
|
|
|
|
"plt.title('Распределение зарплат')\n",
|
|
|
|
|
|
"plt.xlabel('Зарплата (USD)')\n",
|
|
|
|
|
|
"plt.ylabel('Частота')\n",
|
|
|
|
|
|
"plt.show()"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"Средняя зарплата составляет 150000 USD"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": 9,
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
|
|
|
|
|
"image/png": "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
|
|
|
|
|
|
"text/plain": [
|
|
|
|
|
|
"<Figure size 800x800 with 1 Axes>"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"import matplotlib.pyplot as plt\n",
|
|
|
|
|
|
"import seaborn as sns\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"# Подсчет количества работников по уровню опыта\n",
|
|
|
|
|
|
"experience_counts = df['experience_level'].value_counts()\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"# Создание круговой диаграммы\n",
|
|
|
|
|
|
"plt.figure(figsize=(8, 8))\n",
|
|
|
|
|
|
"plt.pie(experience_counts, labels=experience_counts.index, autopct='%1.1f%%', startangle=140)\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"# Настройка заголовка\n",
|
|
|
|
|
|
"plt.title('Распределение работников по уровню опыта')\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"# Отображение диаграммы\n",
|
|
|
|
|
|
"plt.show()"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"По данной диаграмме можно сделать вывод, что большую часть сотрудников составляют Senior-level"
|
|
|
|
|
|
]
|
2024-09-19 16:11:49 +04:00
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"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
|
|
|
|
|
|
}
|
