185 lines
158 KiB
Plaintext
185 lines
158 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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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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" price bedrooms bathrooms sqft_living sqft_lot floors \\\n",
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"id \n",
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"7129300520 221900.0 3 1.00 1180 5650 1.0 \n",
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"6414100192 538000.0 3 2.25 2570 7242 2.0 \n",
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"5631500400 180000.0 2 1.00 770 10000 1.0 \n",
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"2487200875 604000.0 4 3.00 1960 5000 1.0 \n",
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"1954400510 510000.0 3 2.00 1680 8080 1.0 \n",
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"\n",
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" view condition grade sqft_above sqft_basement yr_built \n",
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"id \n",
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"7129300520 0 3 7 1180 0 1955 \n",
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"6414100192 0 3 7 2170 400 1951 \n",
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"5631500400 0 3 6 770 0 1933 \n",
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"2487200875 0 5 7 1050 910 1965 \n",
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"1954400510 0 3 8 1680 0 1987 \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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"\n",
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"df = pd.read_csv(\"../data/kc_house_data.csv\", index_col=\"id\")\n",
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"\n",
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"cleared_dff = df.drop([ \"yr_renovated\", \"waterfront\", \"sqft_living15\", \"sqft_lot15\", \"long\", \"lat\"], axis=1)\n",
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"\n",
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"print(cleared_dff.head())"
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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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"#### График 1\n",
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"Отношение площади жилого пространства недвижимости на повышение цены"
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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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{
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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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"import matplotlib.pyplot as plt\n",
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"data = df[[\"price\", \"sqft_living\"]].copy()\n",
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"data = data[0:1000]\n",
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"plt.scatter(data[\"sqft_living\"], data[\"price\"], marker=\"^\")\n",
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"plt.xlabel(\"Жилплощадь\")\n",
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"plt.ylabel(\"Цена\")\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 графике выше приведена информация о первой тысяче домов. График отображает зависимость цен недвижимости от жилой площади, исходя из графика можно сделать вывод, что чем больше площадь тем выше цена."
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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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"#### График 2\n",
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"Влияние количества этажей на продажу недвижимости"
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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": 48,
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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": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"data = df[\"floors\"]\n",
|
|||
|
|
"plt.hist(data, color=\"orange\")\n",
|
|||
|
|
"plt.xlabel('Кол-во этажей')\n",
|
|||
|
|
"plt.ylabel('Частота покупки')\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Данная диаграмма отображает частоту продаж домой, в зависимости от этажей, исходя из диаграммы можно сделать вывод, что одноэтажные дома покупают чаще чем другие, на втором месте по продажам двухэтажные"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"#### График 3\n",
|
|||
|
|
"Зависимость даты от почтового индекса"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 44,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 640x480 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from datetime import datetime\n",
|
|||
|
|
"import matplotlib.dates as md\n",
|
|||
|
|
"data = (df[[\"zipcode\", \"date\"]].copy())\n",
|
|||
|
|
"data[\"date\"] = data.apply(lambda row: datetime.strptime(row[\"date\"], \"%Y%m%dT000000\"), axis=1)\n",
|
|||
|
|
"plot = data.plot.scatter(x=\"date\", y=\"zipcode\")\n",
|
|||
|
|
"plot.xaxis.set_major_locator(md.DayLocator(interval=10))\n",
|
|||
|
|
"plot.xaxis.set_major_formatter(md.DateFormatter(\"%d.%m.%Y\"))\n",
|
|||
|
|
"plot.tick_params(axis=\"x\", labelrotation=90)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Данная диаграмма отображает под каким почтовым индексом и когда был куплен дом, исходя из графика можно сделать вывод, в каких районах города больше всего скупаются дома"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "kernel",
|
|||
|
|
"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
|
|||
|
|
}
|