import pandas as pd from pyecharts import options as opts from pyecharts.charts import Line # 读取Excel文件 data = pd.read_excel('6004021055.xlsx') # 提取数据 week = data['week'] need = data['need'] # 创建折线图对象 line = Line() line.set_global_opts( xaxis_opts=opts.AxisOpts(name='week', split_number=18, interval=10), yaxis_opts=opts.AxisOpts(name='need', split_number=10, interval=10, axislabel_opts={"interval": 10}), title_opts=opts.TitleOpts(title='6004021055') ) # 设置图例 legend_data = ['需求'] line.add_xaxis(week) line.add_yaxis(legend_data[0], need, linestyle_opts=opts.LineStyleOpts(width=2), label_opts=opts.LabelOpts(is_show=True, position='top')) # 显示中文标签 line.set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='right')) # 生成HTML文件 line.render('line_chart_6004021055.html')我想要实现生成的图像中的点上不显示数据，只有鼠标放上去才显示，把代码修改好给我

import pandas as pd from pyecharts import options as opts from pyecharts.charts import Line # 读取Excel文件 data = pd.read_excel('6004021055.xlsx') # 提取数据 week = data['week'] need = data['need'] # 创建折线图对象 line = Line() line.set_global_opts( xaxis_opts=opts.AxisOpts(name='week', split_number=18, interval=10), yaxis_opts=opts.AxisOpts(name='need', split_number=10, interval=10, axislabel_opts={"interval": 10}), title_opts=opts.TitleOpts(title='6004021055') ) # 设置图例 legend_data = ['需求'] line.add_xaxis(week) line.add_yaxis(legend_data[0], need, linestyle_opts=opts.LineStyleOpts(width=2)) # 显示中文标签 line.set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='right')) # 生成HTML文件 line.render('line_chart_6004021055.html')我希望我的代码实现的图像上的点不显示数据，只有鼠标放上去才显示

line.add_yaxis(legend_data[0], need, linestyle_opts=opts.LineStyleOpts(width=2), label_opts=opts.LabelOpts(is_show=False)) 这样，图像上的点就不会显示数据，只有当鼠标放上去时才会显示数据。

import pandas as pd from pyecharts import options as opts from pyecharts.charts import Scatter # 读取Excel文件 data = pd.read_excel('6004021055.xlsx') # 提取数据 week = data['week'] need = data['need'] # 创建散点图对象 scatter = Scatter() scatter.set_global_opts( xaxis_opts=opts.AxisOpts(name='week', split_number=18, interval=10), yaxis_opts=opts.AxisOpts(name='need', split_number=10, interval=10, axislabel_opts={"interval": 10}), title_opts=opts.TitleOpts(title='6004021055') ) # 设置图例 legend_data = ['需求'] scatter.add_xaxis(week) scatter.add_yaxis(legend_data[0], need, symbol_size=10) # 显示中文标签 scatter.set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='right')) # 生成HTML文件 scatter.render('scatter_chart_6004021055.html')我想把这个方法改一下，我想绘制成折线图，其他要求不变

from pyecharts import options as opts from pyecharts.charts import Line # 读取Excel文件 data = pd.read_excel('6004021055.xlsx') # 提取数据 week = data['week'] need = data['need'] # 创建折线图对象 ...

from pyecharts import options as opts from pyecharts.charts import Line import pandas as pd # 读取Excel文件 data = pd.read_excel('6004021055.xlsx') # 提取数据 week = data['week'] need = data['need'] # 创建折线图对象 line = Line() line.set_global_opts( xaxis_opts=opts.AxisOpts(name='week', split_number=18, interval=10), yaxis_opts=opts.AxisOpts(name='need', split_number=10, interval=10, axislabel_opts={"interval": 10}), title_opts=opts.TitleOpts(title='6004021055'), tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross") ) # 设置图例 legend_data = ['需求'] line.add_xaxis(week) line.add_yaxis(legend_data[0], need, is_smooth=True, label_opts=opts.LabelOpts(is_show=False)) # 显示中文标签 line.set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='top')) # 生成HTML文件 line.render('line_chart_6004021055.html')我这个代码中实现的效果里数据点上有没有对应数据显示出来

from pyecharts import options as opts from pyecharts.charts import Line import pandas as pd # 读取Excel文件 data = pd.read_excel('6004021055.xlsx') # 提取数据 week = data['week'] need = data['need']...

运行import pandas as pd from pyecharts import options as opts from pyecharts.charts import Line # 读取 Excel 文件并指定列 df = pd.read_excel('所有评论数据(1).xlsx', usecols=[1], engine='openpyxl') # 对数据进行抽样或分组，例如每隔100条数据取一个样本 sampled_df = df.iloc[::100, :] # 创建 Line 图表 line_chart = Line(init_opts=opts.InitOpts(width="1500px", height="700px", )) # 添加数据 x_data = range(len(sampled_df)) # 保留两位小数 y_data = [round(value, 2) for value in sampled_df.iloc[:, 0].tolist()] #line_chart.add_xaxis(x_data) line_chart.add_xaxis(xaxis_data=x_data) # 修改此处，将 x_data 作为 xaxis_data 参数传入 line_chart.add_yaxis("情感倾向", y_data, is_smooth=True, symbol="circle", symbol_size=6) # 设置图表标题和坐标轴标签 line_chart.set_global_opts( title_opts=opts.TitleOpts(title="情感倾向动态图"), xaxis_opts=opts.AxisOpts(name="时间"), yaxis_opts=opts.AxisOpts(name="情感倾向"), ) # 生成 HTML 文件并在浏览器中打开 line_chart.render("3_情感倾向图.html")

在您运行这段代码之后，它会读取名为"所有评论数据(1).xlsx"的Excel文件，并从中抽样每隔100条数据取一个样本。然后，它会创建一个Line图表，并添加抽样的数据。最后，它会设置图表的标题和坐标轴标签，并生成一个名...

import pandas as pd from pyecharts import options as opts from pyecharts.charts import Boxplot, Line, Grid # 读取数据 df = pd.read_excel('200马力拖拉机明细.xlsx') # 创建DataFrame df = pd.DataFrame({ 'FactoryName': df['FactoryName'], 'JiJXH': df['JiJXH'], 'sale': df['sale'] }) # 将FactoryName和JiJXH合并为一列 df['FactoryName-JiJXH'] = df['FactoryName'] + '-' + df['JiJXH'].astype(str) # 对FactoryName-JiJXH进行分组 grouped = df.groupby('FactoryName-JiJXH') # 绘制箱线图 box = Boxplot() box_data = [] for name, group in grouped: box_data.append([round(i, 2) for i in group['sale'].tolist()]) box.add_xaxis([name]) box.add_yaxis('', box.prepare_data(box_data), tooltip_opts=opts.TooltipOpts(trigger='axis', axis_pointer_type='cross')) box.set_global_opts( title_opts=opts.TitleOpts(title='Sale Boxplot', subtitle=''), xaxis_opts=opts.AxisOpts( axislabel_opts=opts.LabelOpts(interval=0, formatter='{value|换行}'.replace('换行', '\n')) ) ) box.set_series_opts(label_opts=opts.LabelOpts(is_show=False)) # 绘制折线图 line = Line() for name, group in grouped: line.add_xaxis([name]) line.add_yaxis('Median', [round(group['sale'].median(), 2)], label_opts=opts.LabelOpts(is_show=False)) line.set_global_opts( title_opts=opts.TitleOpts(title='Sale Median Line', subtitle=''), xaxis_opts=opts.AxisOpts( axislabel_opts=opts.LabelOpts(interval=0, formatter='{value|换行}'.replace('换行', '\n')) ) ) # 合并图表 grid = Grid( init_opts=opts.InitOpts( width='1400px', height='800px', page_title='Boxplot and Median Line', theme='white' ) ) grid.add(box, grid_opts=opts.GridOpts(pos_left='10%', pos_right='10%')) grid.add(line, grid_opts=opts.GridOpts(pos_left='10%', pos_right='10%')) grid.render('boxplot_and_line.html') 提示list index out of range

这个错误通常是由于没有找到所需的列名或行数不够导致的。...或者您可以在读取 excel 文件之后添加下面这行代码来查看 DataFrame 的行数： print(df.shape[0]) 希望这可以帮助您找出问题所在。

# -- coding: utf-8 -- import pandas as pd from pyecharts import options as opts from pyecharts.charts import Line,Bar,Funnel,Pie from pyecharts.components import Table from pyecharts.options import ComponentTitleOpts from sqlalchemy import create_engine from pyecharts.charts import Page from pyecharts.charts import WordCloud from collections import Counter import jieba import re # 创建数据库连接 engine = create_engine('mysql+pymysql://root:123456@localhost/doubandata') #1. #2. def line_chart2(): # 从数据库中读取数据 query = "SELECT show_time FROM t_music" df = pd.read_sql(query, engine) # 处理日期数据，提取年份 df['year'] = pd.to_datetime(df['show_time'], errors='coerce').dt.year # 统计每年的歌曲发行数量 year_counts = df['year'].dropna().astype(int).value_counts().sort_index() # 准备数据用于生成折线图 years = year_counts.index.astype(str).tolist() counts = year_counts.values.tolist() # 创建折线图 line = Line(init_opts=opts.InitOpts(width="1600px", height="800px")) line.add_xaxis(years) line.add_yaxis("歌曲发行数量", counts, label_opts=opts.LabelOpts(is_show=True, position="top")) # 设置全局配置项 line.set_global_opts( title_opts=opts.TitleOpts(title="每年歌曲发行数量统计"), xaxis_opts=opts.AxisOpts(type_="category", name="年份"), yaxis_opts=opts.AxisOpts(type_="value", name="发行数量"), tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross") ) # 渲染图表到HTML文件 line.render("2.song_release_counts_by_year.html") return line #3. def line_chart3(): # 从数据库中读取数据 query = "SELECT show_time, score FROM t_music" df = pd.read_sql(query, engine) # 将 score 列转换为数值类型，无法转换的会变为 NaN df['score'] = pd.to_numeric(df['score'], errors='coerce') # 处理日期数据，提取年份 df['year'] = pd.to_datetime(df['show_time'], errors='coerce').dt.year # 删除无效数据（如日期或评分为空） df = df.dropna(subset=['year', 'score']) # 按年份分组并计算每年的平均评分，保留一位小数 yearly_avg_scores = df.groupby('year')['score'].mean().round(1).sort_index() # 准备数据用于生成折线图 years = yearly_avg_scores.index.astype(int).astype(str).tolist() # 确保年份为整数且不带小数点 avg_scores = yearly_avg_scores.values.tolist() # 创建折线图 line = Line(init_opts=opts.InitOpts(width="1600px", height="800px")) line.add_xaxis(years) line.add_yaxis("平均评分", avg_scores, label_opts=opts.LabelOpts(is_show=True, position="top")) # 设置全局配置项 line.set_global_opts( title_opts=opts.TitleOpts(title="每年歌曲发行的平均评分"), xaxis_opts=opts.AxisOpts(type_="category", name="年份"), yaxis_opts=opts.AxisOpts(type_="value", name="平均评分", min_=7, max_=10), tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross") ) # 渲染图表到 HTML 文件 line.render("3.song_release_scores_by_year.html") return line page = Page( page_title="基于Python的豆瓣音乐数据分析与可视化", layout=Page.DraggablePageLayout, # 拖拽方式 ) page.add_js_funcs( """ document.body.style.backgroundColor = '#f0f8ff'; // 设置背景颜色为淡蓝色 """ ) # 添加图表到页面 page.add( # 绘制折线图 line_chart2(), line_chart3(), # 绘制表格 ) # 渲染大屏到临时HTML文件 page.render('大屏_临时1.html') 怎么生成好看个性化的可视化大屏网页

from pyecharts.commons.utils import JsCode def generate_table(): table_data = [ ["Year", "Count"], [2019, 100], [2020, 200], ... # 添加实际查询结果填充内容 ] headers = table_data[0] rows =...

import pandas as pd import pyecharts.options as opts from pyecharts.charts import Bar, Line from pyecharts.render import make_snapshot from snapshot_selenium import snapshot as driver x_data = ["1月", "2月", "3月", "4月", "5月", "6月", "7月", "8月", "9月", "10月", "11月", "12月"] # 导入数据 df = pd.read_csv('E:/pythonProject1/第8章实验数据/beijing_AQI_2018.csv') attr = df['Date'].tolist() v1 = df['AQI'].tolist() v2=df['PM'].tolist() # 对AQI进行求平均值 data={'Date':pd.to_datetime(attr),'AQI':v1} df1 = pd.DataFrame(data) total=df1['AQI'].groupby([df1['Date'].dt.strftime('%m')]).mean() d1=total.tolist() y1=[] for i in d1: y1.append(int(i)) # print(d1) # print(y1) # 对PM2.5求平均值 data1={'Date':pd.to_datetime(attr),'PM':v2} df2 = pd.DataFrame(data1) total1=df2['PM'].groupby([df2['Date'].dt.strftime('%m')]).mean() d2=total1.tolist() y2=[] for i in d2: y2.append(int(i)) # print(d2) bar = ( Bar() .add_xaxis(xaxis_data=x_data) .add_yaxis( series_name="PM2.5", y_axis=y2, label_opts=opts.LabelOpts(is_show=False), color="#5793f3" ) .extend_axis( yaxis=opts.AxisOpts( name="平均浓度", type_="value", min_=0, max_=150, interval=30, axislabel_opts=opts.LabelOpts(formatter="{value}"), ) ) .set_global_opts( tooltip_opts=opts.TooltipOpts( is_show=True, trigger="axis", axis_pointer_type="cross" ), xaxis_opts=opts.AxisOpts( type_="category", axispointer_opts=opts.AxisPointerOpts(is_show=True, type_="shadow"), ), ) ) line = ( Line() .add_xaxis(xaxis_data=x_data) .add_yaxis( series_name="AQI", yaxis_index=1, y_axis=y1, label_opts=opts.LabelOpts(is_show=False), color='rgb(192,0, 0,0.2)' ) ) bar.overlap(line).render("five.html") bar.options.update(backgroundColor="#F7F7F7")

这段代码的功能是读取一个 csv 文件，分别计算 AQI 和 PM2.5 的每月平均值，并将它们分别用柱状图和折线图展示在同一个图表中。其中，柱状图表示 PM2.5，折线图表示 AQI，两者共用 x 轴（月份），而 y 轴分别是 PM...

请修改以下代码： import pandas as pdimport numpy as np # 新增代码import statsmodels.api as smfrom pyecharts.charts import Lineimport pyecharts.options as opts# 将weather.csv文件的内容追加到weather2.csv中with open('weather2.csv', 'ab')as f: f.write(open('weather.csv', 'rb').read())# 读取数据df = pd.read_csv('weather2.csv', encoding='gb18030', dtype={'最高气温': np.float64}) # 修改代码# 建立ARIMA模型model = sm.tsa.ARIMA(df['最高气温'], order=(2, 1, 1))result = model.fit()# 预测下一年的天气情况forecast = result.forecast(steps=365)# 绘制预测结果的折线图line = Line()line.add_xaxis(pd.date_range(start='2023-01-01', periods=365))line.add_yaxis('预测最高气温', forecast, is_smooth=True)line.set_global_opts( title_opts=opts.TitleOpts(title='2022年西安每月最高气温与最低气温平均数'), legend_opts=opts.LegendOpts(orient='vertical', pos_top='15%', pos_left='2%'),)line.render('weathers5.html') 错误有： TypeError: Cannot cast array data from dtype('O') to dtype('float64') according to the rule 'safe' ValueError: could not convert string to float: '最高气温'

from pyecharts.charts import Line import pyecharts.options as opts # 将weather.csv文件的内容追加到weather2.csv中 with open('weather2.csv', 'ab') as f: f.write(open('weather.csv', 'rb').read()) # ...

import pandas as pd from pyecharts import options as opts from pyecharts.charts import Line 读取Excel文件 data = pd.read_excel('6004020918.xlsx') 提取数据 week = data['week'] need = data 定义三步指数平滑函数 def triple_exponential_smoothing(series, alpha, beta, gamma, n_preds): result = [series[0]] season_length = len(series) // n_preds # 初始化水平、趋势和季节性指数 level, trend, season = series[0], series[1] - series[0], sum(series[:season_length]) / season_length for i in range(1, len(series) + n_preds): if i >= len(series): # 预测新值 m = i - len(series) + 1 result.append(level + m * trend + season) else: # 更新水平、趋势和季节性指数 value = series[i] last_level, level = level, alpha * (value - season) + (1 - alpha) * (level + trend) trend = beta * (level - last_level) + (1 - beta) * trend season = gamma * (value - level) + (1 - gamma) * season result.append(level + trend + season) return result 设置三步指数平滑法参数 alpha = 0.2 beta = 0.3 gamma = 0.4 n_preds = 77 # 预测的值数量进行三步指数平滑预测 predictions = triple_exponential_smoothing(need[:100], alpha, beta, gamma, n_preds) 创建折线图对象 line = Line() line.set_global_opts( title_opts=opts.TitleOpts(title='时间序列预测分析'), legend_opts=opts.LegendOpts(pos_top='5%'), tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross") ) 添加预测值数据 line.add_xaxis(week[100:177]) line.add_yaxis('预测值', predictions, is_smooth=True, label_opts=opts.LabelOpts(is_show=False)) 添加实际值数据 line.add_yaxis('实际值', need[100:177], is_smooth=True, label_opts=opts.LabelOpts(is_show=False)) 生成HTML文件 line.render('time_series_forecast.html')我希望在图中显示的实际值从数据的初始开始，一直绘制到177周，同样，预测的也要从第一周开始预测，预测出第一周到第177周的结果并在图中呈现，如果三步指数平滑法的代码不对你可以进行修改，但是要是三步指数平滑法，把修改好的代码给我

from pyecharts import options as opts from pyecharts.charts import Line # 读取Excel文件 data = pd.read_excel('6004020918.xlsx') # 提取数据 week = data['week'] need = data['need'] # 定义三步指数平滑...

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