204 lines
163 KiB
Plaintext
204 lines
163 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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"Выгружаем данные при помощи Pandas из csv файла в количестве 10000 строк."
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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": 4,
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"metadata": {},
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"outputs": [],
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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(\"data/age.csv\", nrows=10000)\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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"Смотрим количество и название столбцов таблицы"
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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": 40,
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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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"<class 'pandas.core.frame.DataFrame'>\n",
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"Index: 12359 entries, 0 to 9999\n",
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"Data columns (total 10 columns):\n",
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" # Column Non-Null Count Dtype \n",
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"--- ------ -------------- ----- \n",
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" 0 Id 12359 non-null object \n",
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" 1 Name 12359 non-null object \n",
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" 2 Short description 12354 non-null object \n",
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" 3 Gender 12273 non-null object \n",
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" 4 Country 12080 non-null object \n",
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" 5 Occupation 12188 non-null object \n",
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" 6 Birth year 12359 non-null int64 \n",
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" 7 Death year 12358 non-null float64\n",
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" 8 Manner of death 2486 non-null object \n",
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" 9 Age of death 12358 non-null float64\n",
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"dtypes: float64(2), int64(1), object(7)\n",
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"memory usage: 1.0+ MB\n",
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" count mean std min 25% 50% \\\n",
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"Birth year 12359.0 1773.275993 298.845499 -2284.0 1787.0 1870.0 \n",
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"Death year 12358.0 1840.944813 303.842150 -2200.0 1853.0 1938.0 \n",
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"Age of death 12358.0 67.686519 17.137864 4.0 57.0 70.0 \n",
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"\n",
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" 75% max \n",
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"Birth year 1905.0 1996.0 \n",
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"Death year 1979.0 2021.0 \n",
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"Age of death 80.0 107.0 \n"
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]
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}
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],
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"source": [
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"print(df.describe().transpose())"
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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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"Узнаем количество людей относящихся к той или иной стране"
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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 640x480 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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"df['Country'] = df['Country'].str.split('; ')\n",
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"df1 = df.explode('Country')\n",
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"countrys = df1.groupby(\"Country\").size().reset_index(name=\"Count\") # type: ignore\n",
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"country_counts_sorted = countrys.sort_values(by='Count', ascending=False)\n",
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"top_countries = country_counts_sorted.head(50)\n",
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"\n",
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"top_countries.plot.bar(x='Country', y='Count', color=['green'])\n",
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"plt.title('Top Countries by count of people')\n",
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"plt.xlabel('Country')\n",
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"plt.ylabel('Number of People')\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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"Длительность жизни по полу"
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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": 11,
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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": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"dfgender = df[df['Gender'].isin(['Male', 'Female'])]\n",
|
||
|
|
"dfgender.boxplot(column='Age of death', by='Gender')\n",
|
||
|
|
"plt.title('Lifespan by Gender')\n",
|
||
|
|
"plt.suptitle('') # Убираем автоматический заголовок\n",
|
||
|
|
"plt.xlabel('Gender')\n",
|
||
|
|
"plt.ylabel('Lifespan')\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Количество людей родившихся в тот или иной год"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<Axes: ylabel='Frequency'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 10,
|
||
|
|
"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": [
|
||
|
|
"from matplotlib.pyplot import xlim\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"df.plot.hist(column=[\"Birth year\"], xlim=(1900, 2000), bins=4000)"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": ".venv",
|
||
|
|
"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
|
||
|
|
}
|