Pandas+Pyecharts | Visualization of Double Eleven beauty sales data analysis

This issue uses python to analyze Double Eleven beauty sales data, see Look:

• The number of beauty orders and total sales in the days before and after Double Eleven

• Each beauty brandSales situation

• Proportion of primary/secondary classification of beauty brands

• Price box distribution of each beauty brand

• Average price of each beauty brand

• Beauty brand word cloud

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I hope it will be helpful to everyone. If you have any questions or areas that need improvement, please contact the editor.

Involved libraries:
Pandas — Data processing
Pyecharts — Data visualization

1. Import module

import pandas as pd
from pyecharts.charts import Line
from pyecharts.charts import Bar
from pyecharts.charts import Scatter
from pyecharts.charts import Boxplot
from pyecharts.charts import Pie
from pyecharts.charts import WordCloud
from pyecharts import options as opts
from pyecharts.commons.utils import JsCode
import warnings
warnings.filterwarnings('ignore')

##2. Pandas data processing

##2.1 Read data

df_school = pd.read_excel('data.xlsx')

2.2 Data information

df.info()

2.3 筛选有销量的数据 

df1 = df.copy()
df1 = df1[df1['销量']>0]

数据过滤后还有24479条。

3. Pyecharts数据可视化

3.1 双十一前后几天美妆订单数量

def get_line1():
    line1 = (
        Line()
        .add_xaxis(x_data)
        .add_yaxis("", y_data,
                   is_smooth=True)
        .set_global_opts(
            legend_opts=opts.LegendOpts(is_show=False),
            visualmap_opts=opts.VisualMapOpts(
                is_show=False,
                min_ = 1500,
                max_ = max(y_data),
                range_color=range_color
            ),
            title_opts=opts.TitleOpts(
                title='1-双十一前后几天美妆订单数量',
                subtitle='-- 制图@公众号：Python当打之年 --',
                pos_top='1%',
                pos_left="1%",
                title_textstyle_opts=opts.TextStyleOpts(color='#fff200',font_size=20)
            )
        )
    )

在11号前几天订单量持续在比较高的状态，在11月11号后趋于平稳，应该是双十一商家提前预热，消费者的预购订单量比较大。

3.2 双十一前后几天美妆销量

化妆品的购买高峰在11号前几天，在11月9号达到高峰，消费者的预购销量比较大，和订单量趋势基本保持一致。

3.3 各美妆品牌订单数量 

def get_bar1():
    bar1 = (
        Bar()
        .add_xaxis(x_data)
        .add_yaxis("", y_data,label_opts=opts.LabelOpts(position='right'))
        .set_global_opts(
            legend_opts=opts.LegendOpts(is_show=False),
            visualmap_opts=opts.VisualMapOpts(
                is_show=False,
                min_ = min(y_data),
                max_ = max(y_data),
                dimension=0,
                range_color=range_color
            ),
            title_opts=opts.TitleOpts(
                title='3-各美妆品牌订单数量',
                subtitle='-- 制图@公众号：Python当打之年 --',
                pos_top='1%',
                pos_left="1%",
                title_textstyle_opts=opts.TextStyleOpts(color='#fff200',font_size=20)
            ),
        )
        .reversal_axis()
    )

悦诗风吟的商品数量最多，其次为佰草集、欧莱雅。

3.4 各美妆品牌总销量

相宜本草的销售额、销量都是最高的，美宝莲、悦诗风吟、妮维雅、欧莱雅分列第二至五位。

3.5 一级分类占比

def get_pie1():
    pie1 = (
        Pie()
        .add(
            "", 
            [list(z) for z in zip(x_data, y_data)],
            radius=["40%", "70%"],
            center=["50%", "50%"],
            label_opts=opts.LabelOpts(formatter="{b}: {d}%",font_size=14,font_weight=500), 
        )
        .set_global_opts(
            title_opts=opts.TitleOpts(
                title='5-一级分类占比',
                subtitle='-- 制图@公众号：Python当打之年 --',
                pos_top='1%',
                pos_left="1%",
                title_textstyle_opts=opts.TextStyleOpts(color='#fff200',font_size=20)
            ),
            legend_opts=opts.LegendOpts(is_show=False) 
        )
    )

销量第一的还要是护肤品，其次是套装系列和化妆品。

3.6 二级分类占比

按二级分类来看，订单量前五的分别是：套装类、清洁类、面霜类、化妆水和乳液类。

3.7 二级分类销量

3.8 Price Box Chart of Each Beauty Brand

##3.9 Average Price of Each Beauty Brand

In terms of average price, brands such as Guerlain, Sulwhasoo, Estee Lauder, Lancôme, and Shiseido are slightly more expensive.

3.10 Beauty brand classification word cloud

##

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