209 lines
153 KiB
Plaintext
209 lines
153 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 лабораторной работы\n",
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"Тут я вывожу все заголовки из моих данных"
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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": 13,
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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', 'Short description', 'Gender', 'Country', 'Occupation',\n",
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" 'Birth year', 'Death year', 'Manner of death', 'Age of death'],\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 pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"df = pd.read_csv(\".//static//csv//csvLab1.csv\", sep =',')\n",
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"print(df.columns)"
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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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"# В какой промежуток времени родилось больше всего выдающихся людей"
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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": 41,
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"metadata": {},
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"outputs": [
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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 1000x600 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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"birth_years = df['Birth year'].dropna()\n",
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"\n",
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"\n",
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"bins = range(int(birth_years.min()), int(birth_years.max()) + 20, 20)\n",
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"labels = [f'{i}-{i+19}' for i in bins[:-1]]\n",
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"\n",
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"\n",
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"birth_year_groups = pd.cut(birth_years, bins=bins, labels=labels, right=False)\n",
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"\n",
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"\n",
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"group_counts = birth_year_groups.value_counts().sort_index()\n",
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"\n",
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"\n",
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"plt.figure(figsize=(10, 6))\n",
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"group_counts.plot(kind='bar', edgecolor='black')\n",
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"\n",
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"\n",
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"plt.title('Number of Famous People Born in different datas', fontsize=14)\n",
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"plt.xlabel('Birth Year Interval', fontsize=12)\n",
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"plt.ylabel('Number of People', fontsize=12)\n",
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"plt.xticks(rotation=45)\n",
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"\n",
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"plt.xlim(left= 190)\n",
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"\n",
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"plt.show()"
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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 данной гистограмме отображено столбцами сколько выдающихся людей родилось в тот или иной промежуток времени (интервал 20 лет)"
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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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"# Во сколько лет умирали люди"
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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": 39,
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"metadata": {},
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"outputs": [
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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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|
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"<Figure size 2000x600 with 1 Axes>"
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]
|
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|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"age_of_death = df['Age of death'].dropna()\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.figure(figsize=(20, 6))\n",
|
|||
|
|
"plt.hist(age_of_death, bins=60, edgecolor='black')\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.title('Distribution of Age of Death', fontsize=14)\n",
|
|||
|
|
"plt.xlabel('Age of Death', fontsize=12)\n",
|
|||
|
|
"plt.ylabel('Number of People', fontsize=12)\n",
|
|||
|
|
"plt.xticks(ticks=range(int(age_of_death.min()), int(age_of_death.max()) + 5, 5))\n",
|
|||
|
|
"plt.xlim(0,120)\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"на данной гистограмме отображены возраста, в которых люди чаще всег покидали наш мир и отправлялись в лучшее место"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Круговая диаграмма гендера\n",
|
|||
|
|
"Круговая диаграмма гендера, которая по выборке из 1000 персон покажет сколько процентов мужчин и сколько женщин"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 44,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 800x800 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"gender_counts = df['Gender'].value_counts()\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"df_subset = df.head(1000)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Подсчет количества записей по каждому гендеру\n",
|
|||
|
|
"gender_counts = df_subset['Gender'].value_counts()\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Построение круговой диаграммы\n",
|
|||
|
|
"plt.figure(figsize=(8, 8))\n",
|
|||
|
|
"plt.pie(gender_counts, labels=gender_counts.index, autopct='%1.1f%%', startangle=140, colors=['#66b3ff','#ff9999'])\n",
|
|||
|
|
"plt.title('Распределение по гендеру (первые 1000 записей)')\n",
|
|||
|
|
"plt.axis('equal') # Чтобы круговая диаграмма была кругом\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Отображение диаграммы\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Данная круговая диаграмма показывает процент мужчин и женщин среди выдающихся людей по выборке из 1000 человек (там еще че-то написано , но мы такое осуждаем !!!!!!!!!!!)"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "MIiLabs",
|
|||
|
|
"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
|
|||
|
|
}
|