基于python的超市历年数据可视化分析

人生苦短 我用python

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一、数据描述

数据集中9994条数据，横跨1237天，
销售额为2,297,200.8603美元，
利润为286,397.0217美元，
他们的库存中有1862件独特的物品，
它们被分为3类，
所有这些物品都在美国4个地区的49个州销售，
来着793位客户的5009个订单。

数据集： Superstore.csv 来源：kaggle

一共21列数据，每一列属性描述如下：

Row ID => 每一行唯一的ID.
Order ID => 每个客户的唯一订单ID.
Order Date => 产品的订单日期.
Ship Date => 产品发货日期.
Ship Mode=> 客户指定的发货模式.
Customer ID => 标识每个客户的唯一ID.
Customer Name => 客户的名称.
Segment => The segment where the Customer belongs.
Country => 客户居住的国家.
City => 客户居住的城市.
State => 客户所在的州.
Postal Code => 每个客户的邮政编码.
Region => “客户”所属地区.
Product ID => 产品的唯一ID.
Category => 所订购产品的类别.
Sub-Category => 所订购产品的子类别.
Product Name => 产品名称
Sales =>产品的销售.
Quantity => 产品数量.
Discount => 提供折扣.
Profit => 已发生的利润/亏损.

1、数据概览

9994行，21列数据

print(df.info())

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 9994 entries, 0 to 9993
Data columns (total 21 columns):#   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  0   Row ID         9994 non-null   int64  1   Order ID       9994 non-null   object 2   Order Date     9994 non-null   object 3   Ship Date      9994 non-null   object 4   Ship Mode      9994 non-null   object 5   Customer ID    9994 non-null   object 6   Customer Name  9994 non-null   object 7   Segment        9994 non-null   object 8   Country        9994 non-null   object 9   City           9994 non-null   object 10  State          9994 non-null   object 11  Postal Code    9994 non-null   int64  12  Region         9994 non-null   object 13  Product ID     9994 non-null   object 14  Category       9994 non-null   object 15  Sub-Category   9994 non-null   object 16  Product Name   9994 non-null   object 17  Sales          9994 non-null   float6418  Quantity       9994 non-null   int64  19  Discount       9994 non-null   float6420  Profit         9994 non-null   float64
dtypes: float64(3), int64(3), object(15)
memory usage: 1.6+ MB
None

二、数据预处理

0、导入包和数据

import pandas as pd
from pyecharts.charts import *
from pyecharts import options as opts
from pyecharts.commons.utils import JsCodedata = pd.read_csv(r'./data/Superstore.csv')

1、列名重命名

重命名后的列名：

data.columns = ['行ID', '订单ID', '订单日期', '发货日期', '发货方式', '客户ID', '客户名称', '客户类型', '国家', '城市', '州', '邮政编码', '所属区域', '产品ID','产品类别', '产品子类别', '产品名称', '销售额', '产品数量', '提供折扣', '利润/亏损']

2、提取数据中时间，方便后续分析绘图

data['年份'] = data['订单日期'].apply(lambda x: x[-4:])
data['日期'] = pd.to_datetime(data['订单日期'], format='%m/%d/%Y')
data['月份'] = data['日期'].dt.month
data['年-月'] = data['年份'].astype('str') + '-' + data['月份'].astype('str')

三、数据可视化

1、美国各个地区销售额的分布（地图）

包含：Order_Date Sales Quantity Profit year month

usa_sale = data[['州', '销售额']].groupby('州').sum().round(2).reset_index()
print(usa_sale.head())def echarts_map(province, data, title='主标题', subtitle='副标题', label='图例'):"""province：传入省份Listdata：传入各省对应的数据Listtitle：主标题subtitle：副标题label：图例"""map_ = Map(init_opts=opts.InitOpts(bg_color='#080b30'theme='dark'width='980px'height='700px'))map_.add(label, [list(i) for i in zip(province, data)],maptype='美国')map_.set_global_opts(title_opts=opts.TitleOpts(title=titlesubtitle=subtitlepos_left='center'title_textstyle_opts=dict(color='#fff') legend_opts=opts.LegendOpts(is_show=True pos_left='right'pos_top='3%'orient='horizontal' ),visualmap_opts=opts.VisualMapOpts(max_=int(max(data)), is_piecewise=False))return map_.render(title + '-' + subtitle + '.html')echarts_map(usa_sale['州'].tolist(), usa_sale['销售额'].tolist(), title='美国各地区销售额分布', subtitle='销售额分布地图', label='销售额')

2、各产品类别销售额对比（柱状图）

pro_category = data[['产品类别', '销售额', '利润/亏损']].groupby('产品类别').sum().round(2).reset_index()
pro_category.head()def echarts_bar(x, y, y2, title='主标题', subtitle='副标题', label='图例', label2='图例2'):"""x: 函数传入x轴标签数据y：函数传入y轴数据title：主标题subtitle：副标题label：图例"""bar = Bar(init_opts=opts.InitOpts(bg_color='#080b30'theme='dark'width='900px'height='600px'  ))bar.add_xaxis(x)bar.add_yaxis(label, y,label_opts=opts.LabelOpts(is_show=True), category_gap="70%"  , yaxis_index=0)bar.add_yaxis(label2, y2,label_opts=opts.LabelOpts(is_show=True) , category_gap="70%"  , yaxis_index=1)bar.set_series_opts( label_opts=opts.LabelOpts(is_show=True,position='top'font_size=15,color='white',font_weight='bolder'font_style='oblique'itemstyle_opts={"normal": {"color": JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{offset: 0,color: 'rgba(0, 244, 255, 1)'},{offset: 1,color: 'rgba(0, 77, 167, 1)'}], false)""")'shadowBlur': 15"barBorderRadius": [100, 100, 100, 100]"shadowColor": "#0EEEF9"'shadowOffsetY': 2,'shadowOffsetX': 2}})bar.set_global_opts(title_opts=opts.TitleOpts(title=titlesubtitle=subtitlepos_left='center'title_textstyle_opts=dict(color='#fff') ),legend_opts=opts.LegendOpts(is_show=Truepos_left='right'pos_top='3%'orient='horizontal' ),tooltip_opts=opts.TooltipOpts(is_show=Truetrigger='axis'is_show_content=True,trigger_on='mousemove|click'axis_pointer_type='cross'),yaxis_opts=opts.AxisOpts(is_show=True,splitline_opts=opts.SplitLineOpts(is_show=False), axistick_opts=opts.AxisTickOpts(is_show=False),  axislabel_opts=opts.LabelOpts(  font_size=13, font_weight='bolder' ),)xaxis_opts=opts.AxisOpts(boundary_gap=Trueaxistick_opts=opts.AxisTickOpts(is_show=True)splitline_opts=opts.SplitLineOpts(is_show=False)axisline_opts=opts.AxisLineOpts(is_show=True)axislabel_opts=opts.LabelOpts( font_size=13font_weight='bolder' ),),)bar.extend_axis(yaxis=opts.AxisOpts())return bar.render(title + '-' + subtitle + '.html')echarts_bar(pro_category['产品类别'].tolist(), pro_category['销售额'].tolist(),pro_category['利润/亏损'].tolist(), title='不同产品类别销售额对比', subtitle='销售额对比柱状图',label='销售额', label2='利润')

3、不同客户类别销售额对比（饼图）

customer_sale = data[['客户类型', '销售额', '利润/亏损']].groupby('客户类型').sum().round(2).reset_index()def echarts_pie(x, y, title='主标题', subtitle='副标题', label='图例'):pie = Pie(init_opts=opts.InitOpts(bg_color='#080b30'theme='dark'width='900px'height='600px'))pie.add('', [list(z) for z in zip(x, y)])pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}",font_size='15',font_style='oblique',font_weight='bolder'))pie.set_global_opts(title_opts=opts.TitleOpts(title=titlesubtitle=subtitlepos_left='center'title_textstyle_opts=dict(color='white')subtitle_textstyle_opts=dict(color='white')),legend_opts=opts.LegendOpts(is_show=True,pos_left='right'pos_top='3%', orient='vertical', textstyle_opts=opts.TextStyleOpts(color='white', font_size='13', font_weight='bolder', ),))return pie.render(title + '-' + subtitle + '.html')echarts_pie(customer_sale['客户类型'], customer_sale['销售额'], title='不同客户类别销售额对比', subtitle=' ', label='销售额')
echarts_pie(customer_sale['客户类型'], customer_sale['利润/亏损'], title='不同客户类别利润对比', subtitle=' ', label='利润/亏损')

4、每月各产品销售额top10榜单

month_lis = data.sort_values(by='日期')['年-月'].unique().tolist()
month_sale = []
for i in month_lis:month_data = data[data['年-月'] == i][['产品名称', '销售额']].groupby(['产品名称']). \\sum().round(2).reset_index().sort_values(by='销售额', ascending=False)[:10]month_data = month_data.sort_values(by='销售额', ascending=True)month_sale.append(month_data)def echart_line(x, y, title='主标题', subtitle='副标题', label='图例'):tl = Timeline(init_opts=opts.InitOpts(bg_color='#080b30'theme='dark'width='1200px'height='700px' ))tl.add_schema(is_auto_play=Trueplay_interval=1500is_loop_play=True)for i, data1 in zip(x, y):day = ibar = Bar(init_opts=opts.InitOpts(bg_color='#080b30'theme='dark'width='1200px'height='700px'))bar.add_xaxis(data1.iloc[:, 0].tolist())bar.add_yaxis(label,data1.iloc[:, 1].round(2).tolist(),category_gap="40%")bar.reversal_axis()bar.set_series_opts( label_opts=opts.LabelOpts(is_show=True,position="right",font_style='oblique',font_weight='bolder',font_size='13',),itemstyle_opts={"normal": {"color": JsCode("""new echarts.graphic.LinearGradient(1, 0, 0, 0, [{offset: 0,color: 'rgba(0, 244, 255, 1)'},{offset: 1,color: 'rgba(0, 77, 167, 1)'}], false)""")'shadowBlur': 8"barBorderRadius": [100, 100, 100, 100]"shadowColor": "#0EEEF9"'shadowOffsetY': 6,'shadowOffsetX': 6, }})bar.set_global_opts(title_opts=opts.TitleOpts(title=title, subtitle=subtitle, pos_left='center', title_textstyle_opts=dict(color='white'), subtitle_textstyle_opts=dict(color='#white')),legend_opts=opts.LegendOpts(is_show=True, pos_left='right', pos_top='3%',  orient='vertical', textstyle_opts=opts.TextStyleOpts(color='white', font_size='13', font_weight='bolder', font_style='oblique',),),tooltip_opts=opts.TooltipOpts(is_show=True, trigger='axis',  is_show_content=True,trigger_on='mousemove|click',  axis_pointer_type='cross',  ),yaxis_opts=opts.AxisOpts(is_show=True,splitline_opts=opts.SplitLineOpts(is_show=False), axistick_opts=opts.AxisTickOpts(is_show=False), axislabel_opts=opts.LabelOpts( font_size=13, font_weight='bolder' ),), xaxis_opts=opts.AxisOpts(boundary_gap=True,  axistick_opts=opts.AxisTickOpts(is_show=True), splitline_opts=opts.SplitLineOpts(is_show=False), axisline_opts=opts.AxisLineOpts(is_show=True), axislabel_opts=opts.LabelOpts(  font_size=13,  font_weight='bolder',                  ),),)tl.add(bar, day)return tl.render(title + '-' + subtitle + '.html')echart_line(month_lis, month_sale, title='每月各产品销售额top10榜单', subtitle=' ', label='销售额')

5、销售额、净利润在时间维度的变化（折线图）

sale_data = data.sort_values(by='日期')[['年份', '日期', '销售额', '利润/亏损']]. \\groupby(['年份', '日期']).sum().round(2).reset_index()
year_lis = sale_data['年份'].unique().tolist()
sale_data1 = sale_data[sale_data['年份'] == '2014']
sale_data2 = sale_data[sale_data['年份'] == '2015']
sale_data3 = sale_data[sale_data['年份'] == '2016']
sale_data4 = sale_data[sale_data['年份'] == '2017']
sale_data_lis = [sale_data1, sale_data2, sale_data3, sale_data4]
print(sale_data4.head())def echarts_two_line(x, y, title='主标题', subtitle='副标题', label='图例', label2='图例2'):"""x: 函数传入x轴table数据y：函数传入y轴dataframe集合title：主标题subtitle：副标题label：图例"""tab = Tab()for table, data in zip(x, y):line1 = Line(init_opts=opts.InitOpts(bg_color='#080b30',  # 设置背景颜色theme='dark',  # 设置主题width='1200px',  # 设置图的宽度height='700px'  # 设置图的高度))line1.add_xaxis(data['日期'].tolist())line1.extend_axis(yaxis=opts.AxisOpts())  # 添加一条Y轴line1.add_yaxis(label,data['销售额'].tolist(),yaxis_index=0,is_symbol_show=False,  # 是否显示数据标签点is_smooth=True,  # 设置曲线平滑label_opts=opts.LabelOpts(is_show=True,  # 是否显示数据),# 线条粗细阴影设置linestyle_opts={"normal": {"color": "#E47085",  # 线条颜色"shadowColor": '#E4708560',  # 阴影颜色和不透明度"shadowBlur": 8,  # 阴影虚化大小"shadowOffsetY": 20,  # 阴影y偏移量"shadowOffsetX": 20,  # 阴影x偏移量"width": 7  # 线条粗细},},)line1.set_global_opts(# 标题设置title_opts=opts.TitleOpts(title=title,  # 主标题subtitle=subtitle,  # 副标题pos_left='center',  # 标题展示位置title_textstyle_opts=dict(color='white'),  # 设置标题字体颜色subtitle_textstyle_opts=dict(color='white')),# 图例设置legend_opts=opts.LegendOpts(is_show=True,  # 是否显示图例pos_left='right',  # 图例显示位置pos_top='3%',  # 图例距离顶部的距离orient='horizontal',  # 图例水平布局textstyle_opts=opts.TextStyleOpts(color='white',  # 颜色font_size='13',  # 字体大小font_weight='bolder',  # 加粗),),tooltip_opts=opts.TooltipOpts(is_show=True,  # 是否使用提示框trigger='axis',  # 触发类型is_show_content=True,trigger_on='mousemove|click',  # 触发条件，点击或者悬停均可出发axis_pointer_type='cross',  # 指示器类型，鼠标移动到图表区可以查看效果# formatter = '{a}<br>{b}:{c}人'  # 文本内容),datazoom_opts=opts.DataZoomOpts(range_start=0,  # 开始范围range_end=25,  # 结束范围# orient='vertical',  # 设置为垂直布局type_='slider',  # slider形式is_zoom_lock=False,  # 锁定区域大小# pos_left='1%'  # 设置位置),yaxis_opts=opts.AxisOpts(is_show=True,splitline_opts=opts.SplitLineOpts(is_show=False),  # 分割线axistick_opts=opts.AxisTickOpts(is_show=False),  # 刻度不显示axislabel_opts=opts.LabelOpts(  # 坐标轴标签配置font_size=13,  # 字体大小font_weight='bolder'  # 字重),),  # 关闭Y轴显示xaxis_opts=opts.AxisOpts(boundary_gap=False,  # 两边不显示间隔axistick_opts=opts.AxisTickOpts(is_show=True),  # 刻度不显示splitline_opts=opts.SplitLineOpts(is_show=False),  # 分割线不显示axisline_opts=opts.AxisLineOpts(is_show=True),  # 轴不显示axislabel_opts=opts.LabelOpts(  # 坐标轴标签配置font_size=13,  # 字体大小font_weight='bolder'  # 字重),),)# 新建一个折线图Lineline2 = Line()line2.add_xaxis(data['日期'].tolist())# 将line数据通过yaxis_index指向后添加的Y轴# line2.extend_axis(yaxis=opts.AxisOpts())line2.add_yaxis(label2,data['利润/亏损'].tolist(),yaxis_index=0,is_symbol_show=False,  # 是否显示数据标签点is_smooth=True,  # 设置曲线平滑label_opts=opts.LabelOpts(is_show=True,  # 是否显示数据),# 线条粗细阴影设置linestyle_opts={"normal": {"color": "#44B2BE",  # 线条颜色"shadowColor": '#44B2BE60',  # 阴影颜色和不透明度"shadowBlur": 8,  # 阴影虚化大小"shadowOffsetY": 20,  # 阴影y偏移量"shadowOffsetX": 20,  # 阴影x偏移量"width": 7  # 线条粗细},},)line1.overlap(line2)tab.add(line1, table)return tab.render(title + '-' + subtitle + '.html')echarts_two_line(year_lis, sale_data_lis, title='销售额、利润在时间维度的变化', subtitle=' ',label='销售额', label2='利润/亏损')

6、销售额

sale_sum = int(data['销售额'].sum())
num_count = int(data['产品数量'].sum())
profit_sum = int(data['利润/亏损'].sum())
print(profit_sum)def big_data(title='主标题', subtitle='副标题'):c = Pie(init_opts=opts.InitOpts(chart_id=1,bg_color='#080b30',theme='dark',width='300px',height='300px',))c.set_global_opts(title_opts=opts.TitleOpts(title=title,subtitle=subtitle,title_textstyle_opts=opts.TextStyleOpts(font_size=36,color='#FFFFFF',),pos_left='center',pos_top='middle'))return c.render(str(title) + '-' + subtitle + '.html')big_data(title=sale_sum, subtitle='销售额')

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