用python编写一段代码关于某品牌手机图片爬虫和评论数据分析，并进行数据获取：采集京东某品牌手机评论数据，数据预处理，可视化分析，图形用户界面，数据存取：使用txt/csv/excel等。网址为https://item.jd.com/100011172363.html，

好的，以下是用 Python 实现该任务的代码示例：

import requests
from bs4 import BeautifulSoup
import json
import pandas as pd
import jieba
import matplotlib.pyplot as plt
from wordcloud import WordCloud
from tkinter import *
from tkinter import ttk
from tkinter import filedialog

# 爬取京东某品牌手机的图片
def crawl_images(url):
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'}
    response = requests.get(url, headers=headers)
    html = response.text
    soup = BeautifulSoup(html, 'html.parser')
    img_tags = soup.select('.spec-items img')
    for img_tag in img_tags:
        img_url = 'https:' + img_tag['data-origin']
        img_name = img_url.split('/')[-1]
        with open(img_name, 'wb') as f:
            img = requests.get(img_url, headers=headers).content
            f.write(img)

# 爬取京东某品牌手机的评论数据
def crawl_comments(url):
    comments_url = url.replace('item', 'comment') + '?pageSize=10&callback=fetchJSON_comment98vv18658'
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'}
    response = requests.get(comments_url, headers=headers)
    json_str = response.text[len('fetchJSON_comment98vv18658('):-2]
    comments = json.loads(json_str)['comments']
    df = pd.DataFrame(comments)
    df = df[['creationTime', 'content', 'score', 'referenceName']]
    return df

# 对爬取到的数据进行预处理
def data_preprocessing(df):
    # 去除无用信息
    df = df.dropna(subset=['content'])
    df = df[df['content'].str.strip() != '']
    # 去除重复数据
    df = df.drop_duplicates()
    # 中文分词
    df['content'] = df['content'].apply(lambda x: ' '.join(jieba.cut(x)))
    return df

# 可视化分析
def data_visualization(df):
    # 绘制评分分布图
    plt.hist(df['score'], bins=5, alpha=0.5)
    plt.xlabel('Score')
    plt.ylabel('Count')
    plt.title('Score Distribution')
    plt.show()
    
    # 绘制词云图
    wordcloud = WordCloud(background_color='white', width=800, height=600, font_path='msyh.ttc').generate(' '.join(df['content']))
    plt.imshow(wordcloud, interpolation='bilinear')
    plt.axis('off')
    plt.show()

# 图形用户界面
def create_gui():
    def open_file():
        file_path = filedialog.askopenfilename(defaultextension='.csv', filetypes=[('CSV Files', '*.csv'), ('Excel Files', '*.xlsx')])
        if file_path:
            df = pd.read_csv(file_path)
            text.delete('1.0', END)
            text.insert('1.0', df.head().to_string())

    def save_file():
        file_path = filedialog.asksaveasfilename(defaultextension='.csv', filetypes=[('CSV Files', '*.csv'), ('Excel Files', '*.xlsx')])
        if file_path:
            df.to_csv(file_path, index=False)

    def crawl_and_analyze():
        url = url_entry.get()
        comments_df = crawl_comments(url)
        comments_df = data_preprocessing(comments_df)
        data_visualization(comments_df)
        global df
        df = comments_df

    def show_data():
        text.delete('1.0', END)
        text.insert('1.0', df.head().to_string())

    root = Tk()
    root.title('JD Phone Comments Analysis')

    url_label = Label(root, text='URL:')
    url_label.grid(row=0, column=0)

    url_entry = Entry(root, width=50)
    url_entry.grid(row=0, column=1)

    crawl_button = Button(root, text='Crawl and Analyze', command=crawl_and_analyze)
    crawl_button.grid(row=1, column=0, sticky=W, pady=5)

    show_data_button = Button(root, text='Show Data', command=show_data)
    show_data_button.grid(row=1, column=1, sticky=W, pady=5)

    save_button = Button(root, text='Save Data', command=save_file)
    save_button.grid(row=1, column=2, sticky=W, pady=5)

    open_button = Button(root, text='Open Data', command=open_file)
    open_button.grid(row=1, column=3, sticky=W, pady=5)

    text = Text(root, width=80, height=30)
    text.grid(row=2, column=0, columnspan=4)

    root.mainloop()

if __name__ == '__main__':
    url = 'https://item.jd.com/100011172363.html'
    crawl_images(url)
    create_gui()

以上代码中，我们首先定义了 `crawl_images` 和 `crawl_comments` 函数，分别用于爬取京东某品牌手机的图片和评论数据，并返回 `pandas.DataFrame` 类型的数据。然后，我们定义了 `data_preprocessing` 函数，用于对爬取到的数据进行预处理，包括去除无用信息、去除重复数据、中文分词等操作。接着，我们定义了 `data_visualization` 函数，用于绘制评分分布图和评论词云图。最后，我们定义了 `create_gui` 函数，用于创建图形用户界面，方便用户进行数据的查看和操作。在 `main` 函数中，我们首先调用 `crawl_images` 函数爬取京东手机的图片，然后调用 `create_gui` 函数创建图形用户界面。