2021年家庭设计装修大 🦟 全：如何在土巴兔上找到最 🐦 适合你的装修方案
1、2021年家庭设计装修大全：如何 🦊 在 🌷 土巴兔上找到最适合你的装修方案
2021 年家庭设计装修大全：如 ☘ 何在土巴兔上找到最适合你的装修方案
土巴兔是一个领先的家庭装修平台，提，供各种装修服务包括设计装修、和材料采购。使，用土巴兔。你。可以轻松地找到适合你需求和预算的装修方案本文将指导你如何利用土 🌼 巴兔的平台找到最适合你的家庭设计装修解决方案
步骤 🐎 1：创建项目 🦉
访问土 🌲 巴兔官网或下载移动 🦈 应用程序。
创建一个 🦋 帐 🐺 户并登录。
点击“装 🌾 修”选项卡，然“后选 🌷 择创建项目”。
输入你的装修项目详细信息，包括房屋类型、面、积 🐬 预算和期望的完工日期。
步骤 🐅 2：选择设计师 🐱
根据你的项 ☘ 目要求，土巴 🍁 兔会推荐合格的设计 💮 师。
查看设计 🐧 师的个人资料，了解他们的专业领域、风格和过往项目。
阅读客户评价以了解他们的 🐒 可靠性和沟通能力。
联系多位设计师，讨论你的想法并 🦄 获 💮 取 🦈 报价。
步骤 3：设计方案 🐅
一旦你选择了设计师 🦁 ，他们将与你合作制定设计方案。
提供你 🌴 的灵感图片、风格偏好和功能性要求 💮 。
设 🦄 计师将创建多个设计方案并向你展 🐡 示。
协商细 🦈 节 🌵 并选择 🐕 最适合你的方案。
步 🌵 骤 4：材料选择
土巴兔与领先的材料供 🌷 应商合作，提供各种材料选择。
浏览材料目录 🐡 ，比较价 🐘 格和质 🐘 量。
获得 🌴 材料样品以亲自查看和感受。
与设 💐 计师协商最终材 🌷 料 🐒 清单。
步 🐘 骤 5：装修 🌺 计划 🐦
根据设计方 🐶 案制定装修计划。
确定装修时 🦟 间表、承包商 🐴 和工期 🐅 。
获取 🐼 材料 🍀 清单并安排交付。
监督装 🌹 修 🐋 过程以确保按计划和 🐋 预算进行。
步 🐅 骤 🐬 6：质量控制 🦊
土巴兔 🌸 提供质量控制服务以确 🦈 保施工符合标准。
定期 🌵 检查装修进度 🌷 并 🐒 解决任何问题。
验收最终成果并确保 🐡 达到 🐞 你的满意度。
小贴士利 🦆 用土巴兔的在线设计工具来创建 💐 自己的 🐕 设计布局。
参与土巴兔的社区 🐡 论坛以获取灵感和建议。
与多位设计师交谈 🐼 以获得不同的观点并找到最合适的人选。
在签署合同之前仔细阅读所有文件并了 🐒 解条款和条件 🐈 。
定期 🌼 与设计师和承 🌳 包商沟通 🐴 以确保项目按计划进行。
结论通过使用土巴兔，你可以轻松地找到最适合你的 🐎 家庭设计装修方案。遵，循本指南中的步骤你可以获得专业的设计、高。质，量的。材料和流畅的装修体验土巴兔将帮助你打造一个美 🍀 丽的家满足你的生活方式和预算
2、
"cells": [
{"cell_type": "markdown",
"metadata": {},
"source": [
" Explore a publicly available shelter dataset \n",
"\n",
"In this notebook, we will use Python to explore a publicly available dataset on shelter admissions in New York City. The dataset contains information on over 100,000 shelter admissions from 2011 to 2018. We will use this dataset to explore the following questions:\n",
"\n",
"1. What are the different types of shelter admissions in New York City?\n",
"2. How has the number of shelter admissions changed over time?\n",
"3. What are the characteristics of people who are admitted to shelters?\n",
"4. What are the outcomes for people who are admitted to shelters?\n",
"\n",
"We will use the following libraries to explore the dataset:\n",
"\n",
" Pandas: for data manipulation and analysis\n",
" Matplotlib: for data visualization\n",
" Seaborn: for statistical data visualization"
]},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
]},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" 1. Load the data"
]},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('nyc_shelter_admissions.csv')"
]},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" 2. Explore the data"
]},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's start by exploring the data to get a better understanding of the different types of shelter admissions in New York City."
]},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"df.head()"
]},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `df.head()` function shows the first five rows of the dataframe. We can see that the dataset contains the following information:\n",
"\n",
" `DateAdmission`: The date on which the person was admitted to the shelter.\n",
" `ClientAge`: The age of the person when they were admitted to the shelter.\n",
" `Gender`: The gender of the person who was admitted to the shelter.\n",
" `ClientFamilyType`: The type of family that the person was admitted with.\n",
" `Borough`: The borough in which the person was admitted to the shelter.\n",
" `FacilityType`: The type of shelter to which the person was admitted."
]},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can use the `df.info()` function to get more information about the dataset."
]},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"df.info()"
]},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `df.info()` function shows that the dataset contains 101,254 rows and 12 columns. There are no missing values in the dataset."
]},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" 3. What are the different types of shelter admissions in New York City?"
]},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"df['FacilityType'].value_counts()"
]},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `df['FacilityType'].value_counts()` function shows that there are four different types of shelter admissions in New York City:\n",
"\n",
" Adult family shelters\n",
" Domestic violence shelters\n",
" Homeless family shelters\n",
" Single adult shelters"
]},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" 4. How has the number of shelter admissions changed over time?"
]},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"df['DateAdmission'] = pd.to_datetime(df['DateAdmission'])\n",
"df['Year'] = df['DateAdmission'].dt.year"
]},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
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