python对月饼数据进行可视化，看看哪家最划算

前言

嗨喽~大家好呀，这里是魔王呐 !

中秋节，又称拜月节、月光诞、月夕等，节期在每年的农历八月十五日（九月十）。

中秋节自古以来就有祭月、赏月、吃月饼、玩花灯、赏桂花、饮桂花酒等民俗，流传经久不息。

马上有临近中秋，这不得好好准备~于是准备对月饼数据进行可视乎

数据数据集、源码、解答加Q君羊：926207505点击蓝字加入【python学习裙】代码

# 导包import pandas as pdimport numpy as npimport re# author：Dragon少年# 导入爬取得到的数据df = pd.read_csv("月饼.csv", encoding='utf-8-sig', header=None)df.columns = ["商品名", "价格", "购买人数", "店铺", "地址"]# 去除重复的数据df.drop_duplicates(inplace=True)print(df.shape)# 删除购买人数0的记录df['购买人数'] = df['购买人数'].replace(np.nan,'0人付款')df['num'] = [re.findall(r'(\d+\.{0,1}\d*)', i)[0] for i in df['购买人数']]  # 提取数值df['num'] = df['num'].astype('float')  # 转化数值型# 提取单位（万）df['unit'] = [''.join(re.findall(r'(万)', i)) for i in df['购买人数']]  # 提取单位（万）df['unit'] = df['unit'].apply(lambda x:10000 if x=='万' else 1)# 计算销量df['销量'] = df['num'] * df['unit']# 删除没有发货地址的店铺数据 获取省份df = df[df['地址'].notna()]df['省份'] = df['地址'].str.split(' ').apply(lambda x:x[0])# 删除多余的列df.drop(['购买人数', '地址', 'num', 'unit'], axis=1, inplace=True)# 重置索引df = df.reset_index(drop=True)df.to_csv('月饼清洗数据.csv')# 导入包from pyecharts.charts import Barfrom pyecharts import options as opts # 计算月饼总销量Top10的店铺shop_top10 = df.groupby('店铺')['销量'].sum().sort_values(ascending=False).head(10)# 绘制柱形图bar1 = Bar(init_opts=opts.InitOpts(width='600px', height='450px')) bar1.add_xaxis(shop_top10.index.tolist())bar1.add_yaxis('销量', shop_top10.values.tolist()) bar1.set_global_opts(title_opts=opts.TitleOpts(title='销量Top10店铺-Dragon少年'),                     xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-30))) bar1.render("销量Top10店铺-Dragon少年.html")bar1.render_notebook()# 导入包from pyecharts.charts import Barfrom pyecharts import options as opts # 计算销量top10月饼shop_top10 = df.groupby('商品名')['销量'].sum().sort_values(ascending=False).head(10)# 绘制柱形图bar0 = Bar(init_opts=opts.InitOpts(width='750px', height='450px')) bar0.add_xaxis(shop_top10.index.tolist())bar0.add_yaxis('销量', shop_top10.values.tolist()) bar0.set_global_opts(title_opts=opts.TitleOpts(title='销量Top10月饼-Dragon少年'),                     xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-30))) bar0.render("销量Top10月饼-Dragon少年.html")bar0.render_notebook()from pyecharts.charts import Piedef price_range(x): #按照淘宝推荐划分价格区间    if x <= 50:        return '50元以下'    elif x <= 150:        return '50-150元'    elif x <= 500:        return '150-500元'    else:        return '500元以上'df['price_range'] = df['价格'].apply(lambda x: price_range(x)) price_cut_num = df.groupby('price_range')['销量'].sum() data_pair = [list(z) for z in zip(price_cut_num.index, price_cut_num.values)]print(data_pair)# 饼图pie1 = Pie(init_opts=opts.InitOpts(width='750px', height='350px'))# 内置富文本pie1.add(         series_name="销量",        radius=["35%", "55%"],        data_pair=data_pair,        label_opts=opts.LabelOpts(formatter='{b}—占比{d}%'),)pie1.set_global_opts(legend_opts=opts.LegendOpts(pos_left="left", pos_top='30%', orient="vertical"),                      title_opts=opts.TitleOpts(title='不同价格月饼销量占比-Dragon少年'))pie1.render("不同价格月饼销量占比-Dragon少年.html")pie1.render_notebook()from pyecharts.charts import Map # 计算销量province_num = df.groupby('省份')['销量'].sum().sort_values(ascending=False) # 绘制地图map1 = Map(init_opts=opts.InitOpts(width='950px', height='600px'))map1.add("", [list(z) for z in zip(province_num.index.tolist(), province_num.values.tolist())],         maptype='china'        ) map1.set_global_opts(title_opts=opts.TitleOpts(title='各省月饼销量分布-Dragon少年'),                     visualmap_opts=opts.VisualMapOpts(max_=1500000)                    )map1.render("各省月饼销量分布-Dragon少年.html")map1.render_notebook()