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@ -3,154 +3,143 @@ from pyecharts.charts import Bar,Page,Line,Timeline
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from pyecharts.commons.utils import JsCode
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from pyecharts.globals import ThemeType
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from pyecharts.charts import Pie
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import sql
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from pyecharts.faker import Faker
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from main import book_list_data
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# from main import book_list_data
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from collections import Counter
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book_list_data_sortBystart = sorted(book_list_data, key=lambda x: (x.star, x.star_people), reverse=True)
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book_list_data_sortBystart=book_list_data_sortBystart[:10]
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book_list_data_sortBystart=book_list_data_sortBystart[::-1]
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# 从book_list_data中提取书籍名称和评分数据
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book_names = [book.name for book in book_list_data_sortBystart]
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book_stars = [book.star for book in book_list_data_sortBystart]
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x_data = list(range(1, len(book_names) + 1))
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# 创建柱状图
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bar = (
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Bar(init_opts=opts.InitOpts(theme="shine",width="850px",height='400px'))
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.add_xaxis(book_names)
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.add_yaxis("评分", book_stars,color="red")
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.reversal_axis() # 实现旋转
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.set_global_opts(
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title_opts=opts.TitleOpts(title="评分前10榜", pos_bottom="bottom", pos_left="center"),
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xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=0)),
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yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-45))
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class Book:
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def __init__(self, name, url, star, star_people, author, translater, publisher, pub_year, price, comment):
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self.name = name
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self.url = url
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self.star = star
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self.star_people = star_people
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self.author = author
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self.translater = translater
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self.publisher = publisher
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self.pub_year = pub_year
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self.price = price
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self.comment = comment
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db = sql.BookDatabase(host='localhost', user='root', password='123456', database='xiaosuo', table_name='books')
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db.initialize_table()
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def show():
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data = db.get_book_list()
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book_list_data = []
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for book in data:
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book_list_data.append(
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Book(book["name"], book["url"], book["star"], book["star_people"], book["author"], book["translater"],
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book["publisher"], book["pub_year"], book["price"], book["comment"]))
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book_list_data_sortBystart = sorted(book_list_data, key=lambda x: (x.star, x.star_people), reverse=True)
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book_list_data_sortBystart = book_list_data_sortBystart[:10]
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book_list_data_sortBystart = book_list_data_sortBystart[::-1]
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# 从book_list_data中提取书籍名称和评分数据
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book_names = [book.name for book in book_list_data_sortBystart]
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book_stars = [book.star for book in book_list_data_sortBystart]
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x_data = list(range(1, len(book_names) + 1))
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# 创建柱状图
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bar = (
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Bar(init_opts=opts.InitOpts(theme="shine", width="850px", height='400px'))
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.add_xaxis(book_names)
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.add_yaxis("评分", book_stars, color="red")
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.reversal_axis() # 实现旋转
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.set_global_opts(
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title_opts=opts.TitleOpts(title="评分前10榜", pos_bottom="bottom", pos_left="center"),
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xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=0)),
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yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-45))
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)
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# .render("bar_datazoom_slider.html")
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)
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# .render("bar_datazoom_slider.html")
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)
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publisher_list = [book.publisher for book in book_list_data]
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# 使用Counter来计算每个出版商的数量
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publisher_counter = Counter(publisher_list)
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publisher_names = list(publisher_counter.keys())
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publisher_counts = list(publisher_counter.values())
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colors = [
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"#5470c6", "#91cc75", "#fac858", "#ee6666", "#73c0de",
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"#3ba272", "#fc8452", "#9a60b4", "#ea7ccc", "#bb60b4",
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"#8B008B", "#FF1493", "#1E90FF", "#20B2AA", "#2E8B57",
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"#B22222", "#FF4500", "#4682B4", "#DAA520", "#32CD32"
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]
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# 使用Pyecharts创建饼图
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pie = (
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Pie()
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.add("", [list(z) for z in zip(publisher_names, publisher_counts)])
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.set_colors(colors) # 设置颜色
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.set_global_opts(title_opts=opts.TitleOpts(title="出版商分布饼图", pos_left="center",pos_bottom="bottom"))
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.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
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)
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prices = [float(book.price) for book in book_list_data ]
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print(prices)
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# 将价格分组到不同的区间
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price_intervals = [0, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500, float('inf')] # 设置价格区间
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price_counts = [0] * (len(price_intervals) - 1) # 初始化每个区间的计数为0
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prices_intervalsStr=["0-50","50-100","100-150","150-200","200-250","250-300","300-350","350-400","400-450","450-500","500以上"]
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# 统计每个价格区间的数量
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for price in prices:
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for i in range(len(price_intervals) - 1):
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if price>=500:
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price_counts[len(price_intervals) - 1] += 1
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break
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if price_intervals[i] <= price and price < price_intervals[i + 1]:
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price_counts[i] += 1
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break
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# print(price_counts)
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# 生成价格区间与书籍数量折线图
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line = (
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Line()
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.add_xaxis([str(interval) for interval in prices_intervalsStr[:-1]]) # X轴标签为价格区间
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.add_yaxis("书籍数量", price_counts, symbol="circle", is_smooth=True, markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max")]))
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.set_global_opts(title_opts=opts.TitleOpts(title="价格区间与书籍数量折线图", pos_left="center",pos_bottom="bottom"),
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xaxis_opts=opts.AxisOpts(name="价格区间"),
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yaxis_opts=opts.AxisOpts(name="书籍数量"),
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datazoom_opts=opts.DataZoomOpts(range_start=0, range_end=100),)
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)
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# 生成时间与书籍数量折线图# 假设您的书籍数据保存在book_list_data中
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# 提取每本书的出版年份
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publish_dates = [int(book.pub_year.replace('-', '.').split('.')[0]) for book in book_list_data]
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print(publish_dates)
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# 使用Counter来计算每年出版的书的数量
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publish_year_counter = Counter(publish_dates)
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print(publish_year_counter)
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# 确保结果包含连续的年份范围，并且将缺失的年份对应的数量设为 0
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full_year_range = range(min(publish_dates), max(publish_dates) + 1)
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print(full_year_range)
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pub_year_counts = [(year, publish_year_counter[year]) for year in full_year_range]
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# 提取年份和对应的书籍数量
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years = [str(year) for year, count in pub_year_counts]
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counts = [count for year, count in pub_year_counts]
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print(pub_year_counts)
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#
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# # 创建动态的时间曲线图
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# line_year_count = (
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# Line()
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# .add_xaxis(xaxis_data=years)
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# .add_yaxis(
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# series_name="出版数量",
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# y_axis=counts,
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# )
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# .set_global_opts(
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# title_opts=opts.TitleOpts(title="每年出版书籍数量变化"),
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# xaxis_opts=opts.AxisOpts(name="年份"),
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# yaxis_opts=opts.AxisOpts(name="书籍数量"),
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# datazoom_opts=opts.DataZoomOpts(type_="inside"),
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# tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
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# )
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# )
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# timeline = Timeline()
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# for i, year in enumerate(years):
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line_year_count = (
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Line()
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.add_xaxis(years)
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.add_yaxis(
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series_name="出版书籍数量",
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y_axis=counts,
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markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max", name="最大值"),
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opts.MarkPointItem(type_="min", name="最小值")]),
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markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="average", name="平均值")]),
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)
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.set_global_opts(title_opts=opts.TitleOpts(title="年份与出版书籍数量变化"),
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xaxis_opts=opts.AxisOpts(name="年份"),
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yaxis_opts=opts.AxisOpts(name="书籍数量"),
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publisher_list = [book.publisher for book in book_list_data]
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# 使用Counter来计算每个出版商的数量
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publisher_counter = Counter(publisher_list)
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publisher_names = list(publisher_counter.keys())
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publisher_counts = list(publisher_counter.values())
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colors = [
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"#5470c6", "#91cc75", "#fac858", "#ee6666", "#73c0de",
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"#3ba272", "#fc8452", "#9a60b4", "#ea7ccc", "#bb60b4",
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"#8B008B", "#FF1493", "#1E90FF", "#20B2AA", "#2E8B57",
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"#B22222", "#FF4500", "#4682B4", "#DAA520", "#32CD32"
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]
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# 使用Pyecharts创建饼图
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pie = (
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Pie()
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.add("", [list(z) for z in zip(publisher_names, publisher_counts)])
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.set_colors(colors) # 设置颜色
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.set_global_opts(title_opts=opts.TitleOpts(title="出版商分布饼图", pos_left="center", pos_bottom="bottom"))
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.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
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)
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prices = [float(book.price) for book in book_list_data]
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print(prices)
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# 将价格分组到不同的区间
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price_intervals = [0, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500, float('inf')] # 设置价格区间
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price_counts = [0] * (len(price_intervals) - 1) # 初始化每个区间的计数为0
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prices_intervalsStr = ["0-50", "50-100", "100-150", "150-200", "200-250", "250-300", "300-350", "350-400",
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"400-450", "450-500", "500以上"]
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# 统计每个价格区间的数量
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for price in prices:
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for i in range(len(price_intervals) - 1):
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if price >= 500:
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price_counts[len(price_intervals) - 1] += 1
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break
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if price_intervals[i] <= price and price < price_intervals[i + 1]:
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price_counts[i] += 1
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break
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# print(price_counts)
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# 生成价格区间与书籍数量折线图
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line = (
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Line()
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.add_xaxis([str(interval) for interval in prices_intervalsStr[:-1]]) # X轴标签为价格区间
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.add_yaxis("书籍数量", price_counts, symbol="circle", is_smooth=True,
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markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max")]))
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.set_global_opts(
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title_opts=opts.TitleOpts(title="价格区间与书籍数量折线图", pos_left="center", pos_bottom="bottom"),
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xaxis_opts=opts.AxisOpts(name="价格区间"),
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yaxis_opts=opts.AxisOpts(name="书籍数量"),
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datazoom_opts=opts.DataZoomOpts(range_start=0, range_end=100), )
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)
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# timeline.add(line_year_count, time_point=str(year))
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# timeline.add_schema(
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# play_interval=1000, # 播放的时间间隔
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# is_auto_play=False, # 是否自动播放
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# pos_left="center", # 时间轴组件的位置
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# pos_bottom="bottom",
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# )
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# 生成html文件（可选）
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# pie.render("publisher_pie_chart.html")
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# 创建一个页面
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page = Page()
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# 将柱状图和饼图添加到页面中
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page.add(line_year_count)
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page.add(bar)
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page.add(pie)
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page.add(line)
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# 生成HTML文件
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page.render("book_analysis.html")
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# 生成时间与书籍数量折线图# 假设您的书籍数据保存在book_list_data中
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# 提取每本书的出版年份
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publish_dates = [int(book.pub_year.replace('-', '.').split('.')[0]) for book in book_list_data]
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print(publish_dates)
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# 使用Counter来计算每年出版的书的数量
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publish_year_counter = Counter(publish_dates)
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print(publish_year_counter)
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# 确保结果包含连续的年份范围，并且将缺失的年份对应的数量设为 0
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full_year_range = range(min(publish_dates), max(publish_dates) + 1)
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print(full_year_range)
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pub_year_counts = [(year, publish_year_counter[year]) for year in full_year_range]
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# 提取年份和对应的书籍数量
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years = [str(year) for year, count in pub_year_counts]
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counts = [count for year, count in pub_year_counts]
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print(pub_year_counts)
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line_year_count = (
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Line()
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.add_xaxis(years)
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.add_yaxis(
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series_name="出版书籍数量",
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y_axis=counts,
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markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max", name="最大值"),
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opts.MarkPointItem(type_="min", name="最小值")]),
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markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="average", name="平均值")]),
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)
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.set_global_opts(title_opts=opts.TitleOpts(title="年份与出版书籍数量变化"),
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xaxis_opts=opts.AxisOpts(name="年份"),
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yaxis_opts=opts.AxisOpts(name="书籍数量"),
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)
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)
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# 创建一个页面
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page = Page()
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# 将柱状图和饼图添加到页面中
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page.add(line_year_count)
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page.add(bar)
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page.add(pie)
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page.add(line)
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# 生成HTML文件
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page.render("book_analysis.html")
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kun
6 months ago