分析某医院半年内的药品销售数据，使用pyecharts对数据进行可视化

ゝ一纸荒年。 2024-05-03 14:31 60阅读 0赞

![在这里插入图片描述][2b08ee8a248f41b09c705bfedadb7907.gif_pic_center]

> Author： 无聊大侠hello word  
> 有一点思考，有一点想法，有一点理性！  
> CSDN@无聊大侠hello word

#### 文章目录 ####

*  前言
 *  PyeCharts官方文档（中文）
 *  1. 导入模块
 *  2. 读取数据
 *  3. 查看索引，数据类型，内存信息。
 *  4. 统计空格数据
 *  5. 输出包含空值的行，
 *  6. 删除购药时间为空的行
 *  7. 对社保卡用0进行填充
 *  8. 社保卡号，商品编码为设置为str类型，销售数量为int类型
 *  9. 查看销售数量，应收金额，实收金额分布情况
 *  10. 过滤负数值
 *   *  a.检索负数
     *  b．负数转正数
     *  查询描述情况:
 *  11. 绘图
 *   *  列拆分（购药时间列拆分为两列）
     *  a. 一周各天药品销量图
     *  b. 药品销量前十
     *  c. 药品销售额前十
     *  d. 一周每天订单量
     *  e. 自然月每天订单量
 *  提示

## 前言 ##

> ?前言?  
> ?博客：【无聊大侠hello word】?  
> ✍本文由在下【无聊大侠hello word】原创，首发于CSDN✍

## PyeCharts官方文档（中文） ##

官方文档（中文）：https://pyecharts.org/#/zh-cn/intro

## 1. 导入模块 ##

import jieba
    import stylecloud
    import pandas as pd
    from PIL import Image
    from collections import Counter
    from pyecharts.charts import Geo
    from pyecharts.charts import Bar
    from pyecharts.charts import Line
    from pyecharts.charts import Pie
    from pyecharts.charts import Calendar
    from pyecharts.charts import WordCloud
    from pyecharts import options as opts
    from pyecharts.commons.utils import JsCode
    from pyecharts.globals import ThemeType,SymbolType,ChartType

## 2. 读取数据 ##

df = pd.read_excel('./医院药品销售数据.xlsx')
    df.head()

![在这里插入图片描述][25f78b8ca2f54fcf8e7df58266cc857b.png]

## 3. 查看索引，数据类型，内存信息。 ##

df.info()

![在这里插入图片描述][b2737d54080845319a0abd82978ea048.png]

## 4. 统计空格数据 ##

df.isnull().sum()

![在这里插入图片描述][5fcd92db148c46d88467849a17fa0eae.png]

## 5. 输出包含空值的行， ##

df[df.isnull().T.any()]

![在这里插入图片描述][d4110e7a2e734d678a3c954467b05cad.png]

## 6. 删除购药时间为空的行 ##

"""
    因为购药时间在后面的分析中会用到，所以我们将购药时间为空的行删除，
    社保卡号用"000"填充，社保卡号、商品编码为一串数字，
    应为str类型，销售数量应为int类型：
    """
    # 拷贝一份副本
    df1 = df.copy()
    df1 = df1.dropna(subset=['购药时间'])
    df1[df1.isnull().T.any()]

![在这里插入图片描述][385cd01ddf8f44d1bd67b2ae6efe9834.png]

## 7. 对社保卡用0进行填充 ##

df1['社保卡号'].fillna('0000',inplace=True)

df1.isnull().sum()

![在这里插入图片描述][e764b3bdc4234b12962a9e74c6ffab12.png]

## 8. 社保卡号，商品编码为设置为str类型，销售数量为int类型 ##

df1['社保卡号'] = df1['社保卡号'].astype(str)
    df1['商品编码'] = df1['商品编码'].astype(str)
    df1['销售数量'] = df1['销售数量'].astype(int)
    #df1.dtypes
    df1.info()
    df1.head()

![在这里插入图片描述][2766621bfdfd48689dbbda360d7a72a0.png]

![在这里插入图片描述][0f532ad632454a0ab2277b1af640b4c3.png]

## 9. 查看销售数量，应收金额，实收金额分布情况 ##

#df1.describe()
    df1[['销售数量', '应收金额', '实收金额']].describe()

![在这里插入图片描述][4046baeffd63454ab20371cf19e7cae3.png]

## 10. 过滤负数值 ##

### a.检索负数 ###

df1[(df1['销售数量']<0) | (df1['应收金额']<0) | (df1['实收金额']<0)]

![在这里插入图片描述][28c141a7f1854e26b4b4f66d7c300286.png]

### b．负数转正数 ###

df2 = df1.copy()
    # abs()返回数字的绝对值。
    df2['销售数量'] = df2['销售数量'].abs()
    df2['应收金额'] = df2['应收金额'].abs()
    df2['实收金额'] = df2['实收金额'].abs()

df2[(df2['销售数量']<0) | (df2['应收金额']<0) | (df2['实收金额']<0)].sum()

![在这里插入图片描述][0f07d2b8e22d458cb2d583604fdb67e9.png]

### 查询描述情况: ###

df2.describe()

![在这里插入图片描述][fc9a2aef85a342289749549d4efa2aa1.png]

## 11. 绘图 ##

### 列拆分（购药时间列拆分为两列） ###

df3 = df2.copy()
    df3[['购药日期', '星期']] = df3['购药时间'].str.split(' ', 2, expand=True)
    df3 = df3[['购药日期', '星期', '社保卡号', '商品编码', '商品名称', '销售数量', '应收金额', '实收金额']]
    df3.head()

![在这里插入图片描述][bd08814a9b2b4f47b2189cca6a5fb519.png]

### a. 一周各天药品销量图 ###

color_js = """new echarts.graphic.LinearGradient(0, 1, 0, 0,
        [{offset: 0, color: '#FFFFFF'}, {offset: 1, color: '#ed1941'}], false)"""

g1 = df3.groupby('星期').sum()
    g1

![在这里插入图片描述][c7c8f9015f3f4fd5988b9a18bca3945a.png]

x_data = g1.index.tolist()
    y_data = g1['销售数量'].values.tolist()
    x_data, y_data

![在这里插入图片描述][0e1891b850ee4ba3a037d0ad48942391.png]  
用柱形图展示

# 用柱形图展示
    bar1 = (
        Bar()
        .add_xaxis(x_data)
        .add_yaxis('',y_data,itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_js)))
        .set_global_opts(title_opts=opts.TitleOpts(title='一周各天药品销量', pos_top='2%', pos_left='center'),
            legend_opts = opts.LegendOpts(is_show=False),
            xaxis_opts = opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-15)),
            yaxis_opts = opts.AxisOpts(name='销量', name_location='middle', name_gap=50, name_textstyle_opts=opts.TextStyleOpts(font_size=16)))
    )
    bar1.render_notebook()

![在这里插入图片描述][60231b58c716484a872bffb57aeb30d3.png]

*  每天销量整理相差不大，周五、周六偏于购药高峰。

### b. 药品销量前十 ###

color_js = """new echarts.graphic.LinearGradient(0, 1, 0, 0,
        [{offset: 0, color: '#FFFFFF'}, {offset: 1, color: '#08519c'}], false)"""

g2 = df3.groupby('商品名称').sum().sort_values(by='销售数量',ascending=False)[:10]
    g2

![在这里插入图片描述][ed9339222e384689af2e6bddfd694206.png]

x_data = g2.index.tolist()
    y_data = g2['销售数量'].values.tolist()
    x_data, y_data

![在这里插入图片描述][e38f95b7b73e4c68ba75df4d620cfb3e.png]

# 用柱形图展示
    bar2 = (
        Bar()
        .add_xaxis(x_data)
        .add_yaxis('', y_data, itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_js)))
        .set_global_opts(
            title_opts=opts.TitleOpts(title='药品销量前十',pos_top='2%', pos_left='center'),
            legend_opts=opts.LegendOpts(is_show=False),
            xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-15)),
            yaxis_opts=opts.AxisOpts(name='销量', name_location='middle', name_gap=50, name_textstyle_opts=opts.TextStyleOpts(font_size=16))
        )
    )
    # 渲染
    bar2.render_notebook()

![在这里插入图片描述][6f3db2963fdd439cb083d0219bac662f.png]

*  可以看出：苯磺酸氨氯地平片(安内真)、开博通、酒石酸美托洛尔片(倍他乐克)等治疗高血压、心绞痛药物购买量比较多。

### c. 药品销售额前十 ###

查看下目前数据情况：

df3.head()

![在这里插入图片描述][423ffea84572414891bf3e4e38794ba2.png]

color_js = """new echarts.graphic.LinearGradient(0, 1, 0, 0,
        [{offset: 0, color: '#FFFFFF'}, {offset: 1, color: '#55ccbf'}], false)"""

# 总销售额=总销售量×单价
    # 总销售量
    sales_z = df3['销售数量'].sum()
    # 单价=价格总额/总商品数量。
    price = df3['实收金额'] / sales_z
    df3['销售额'] = sales_z*price

处理后的数据情况：

df3.head()

![在这里插入图片描述][172fc9113805427786d903b7343cdb71.png]

g3 = df3.groupby('商品名称').sum().sort_values(by='销售额', ascending=False)[:10]
    g3

![在这里插入图片描述][b948b4f4e45f4b26adc381493837d2d3.png]

x_data = g3.index.tolist()
    # 四舍五入round(留小数位)
    y_data = g3['销售额'].values.round(2).tolist()
    x_data, y_data

![在这里插入图片描述][246d6b9ae4af4ef99027fd8150abc243.png]

# 用柱形图展示（c.	药品销售额前十）
    bar3 = (
        Bar()
        .add_xaxis(x_data)
        .add_yaxis('', y_data, itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_js)))
        .set_global_opts(
            title_opts=opts.TitleOpts(title='药品销售额前十', pos_top='2%', pos_left='center'),
            legend_opts=opts.LegendOpts(is_show=False),
            xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-15)),
                yaxis_opts=opts.AxisOpts(name='销量额', name_location='middle', name_gap=50, name_textstyle_opts=opts.TextStyleOpts(font_size=16)),
        )
    )
    # 渲染
    bar3.render_notebook()

![在这里插入图片描述][72a573b02839440ba7e0ab5c4e9a0ef0.png]

### d. 一周每天订单量 ###

g4 = df3.groupby('星期').count() 
    week_index = g4.index.tolist()
    order_count = g4['购药日期'].values.tolist()
    g4

![在这里插入图片描述][b1bc2d818f684178a47dfa815befc9e1.png]

background_color_js = (
        "new echarts.graphic.LinearGradient(0, 0, 0, 1, "
        "[{offset: 0, color: '#e22247'}, {offset: 1, color: '#2bb9a9'}], false)"
    )
    area_color_js = (
        "new echarts.graphic.LinearGradient(0, 0, 0, 1, "
        "[{offset: 0, color: '#2bb9a9'}, {offset: 1, color: '#3fbbff0d'}], false)"
    )
    # 实例化一个折线图
    line = Line(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js)))
    line.add_xaxis(xaxis_data=week_index)
    line.add_yaxis(series_name='', y_axis=order_count, symbol='circle',symbol_size=6, linestyle_opts=opts.LineStyleOpts(color="#fff"),
                  label_opts=opts.LabelOpts(is_show=True, position="top", color="white"),
                    itemstyle_opts=opts.ItemStyleOpts(color="red", border_color="#fff", border_width=3),
                  areastyle_opts=opts.AreaStyleOpts(color=JsCode(area_color_js), opacity=1),)
    line.set_global_opts(
        title_opts=opts.TitleOpts(title=' 一周每天订单量', pos_left='center', pos_top='2%', title_textstyle_opts=opts.TextStyleOpts(color='white')),
        xaxis_opts=opts.AxisOpts(
            type_="category",
            boundary_gap=False,
            axislabel_opts=opts.LabelOpts(margin=3, color="#ffffff63"),
            axisline_opts=opts.AxisLineOpts(is_show=False),
            axistick_opts=opts.AxisTickOpts(
                    is_show=True,
                    length=25,
                    linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
                ),
          splitline_opts=opts.SplitLineOpts(
                is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")
            ),
        ),
        yaxis_opts=opts.AxisOpts(
            type_="value",
            position="left",
            axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63"),
            axisline_opts=opts.AxisLineOpts(
                linestyle_opts=opts.LineStyleOpts(width=2, color="#fff",)
            ),
            splitline_opts=opts.SplitLineOpts(
                is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")
            ),
        ),
    )
    line.render_notebook()

![在这里插入图片描述][6419716b9ff4458d94a4267b04e5bab7.png]

### e. 自然月每天订单量 ###

查看下目前数据情况：

df.head()

![在这里插入图片描述][f0723fa492054510b0e4e2350d2a86ff.png]

## 提示 ##

渲染可能不支持：

bar.render_notebook()

可以采用或其他方式

bar.render()

**先赞后看，养成习惯！！！^ \_ ^ ❤️ ❤️ ❤️  
码字不易，大家的支持就是我的坚持下去的动力。点赞后不要忘了关注我哦！**

[2b08ee8a248f41b09c705bfedadb7907.gif_pic_center]: https://image.dandelioncloud.cn/pgy_files/images/2024/05/03/0dec7d00cc97490988a84d11685a15cc.gif
[25f78b8ca2f54fcf8e7df58266cc857b.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/05/03/191e53baad98421d9ad66f0db3cb39e3.png
[b2737d54080845319a0abd82978ea048.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/05/03/dc3147e991d64d189eded2204cdad9cb.png
[5fcd92db148c46d88467849a17fa0eae.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/05/03/4b5ac240cdaa40f3878e5434165f632d.png
[d4110e7a2e734d678a3c954467b05cad.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/05/03/4842170518dc4203a9d0e5f7b650511f.png
[385cd01ddf8f44d1bd67b2ae6efe9834.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/05/03/a016a365893c49658003455b8925e58f.png
[e764b3bdc4234b12962a9e74c6ffab12.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/05/03/cdf32da43ee24f54a34f83cb9b58ab92.png
[2766621bfdfd48689dbbda360d7a72a0.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/05/03/3d04b06147454fa3af2a44303263b64a.png
[0f532ad632454a0ab2277b1af640b4c3.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/05/03/7965fbb72eb340f3abc1bf7321f2d586.png
[4046baeffd63454ab20371cf19e7cae3.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/05/03/78686855b6b94023b9292d51571af76b.png
[28c141a7f1854e26b4b4f66d7c300286.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/05/03/dc4aabd3fe1b445283502a87ba19c164.png
[0f07d2b8e22d458cb2d583604fdb67e9.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/05/03/83715f31098748c48a6895612a26b700.png
[fc9a2aef85a342289749549d4efa2aa1.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/05/03/edcbc9b076eb4f97813313b5e16c5266.png
[bd08814a9b2b4f47b2189cca6a5fb519.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/05/03/ff42564da819426298b4cd59986ad244.png
[c7c8f9015f3f4fd5988b9a18bca3945a.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/05/03/406b4d72f796413cb7abf9ec1741a409.png
[0e1891b850ee4ba3a037d0ad48942391.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/05/03/18e2607359774f64b83f62954e615bc2.png
[60231b58c716484a872bffb57aeb30d3.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/05/03/25e89f9fe905493dae60bedff2430c72.png
[ed9339222e384689af2e6bddfd694206.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/05/03/b0ad7f54a32e4d75b5108fed210bf1b9.png
[e38f95b7b73e4c68ba75df4d620cfb3e.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/05/03/325f3b156ab64d929a66190693c7337c.png
[6f3db2963fdd439cb083d0219bac662f.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/05/03/aa2657b849574b67949baf5c70637ff8.png
[423ffea84572414891bf3e4e38794ba2.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/05/03/a8e41f2389d14464a8ffb905fc986080.png
[172fc9113805427786d903b7343cdb71.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/05/03/bd412dc5ed65412599985bb8e1d21b0c.png
[b948b4f4e45f4b26adc381493837d2d3.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/05/03/749bf7c612274068aac764043ea1a053.png
[246d6b9ae4af4ef99027fd8150abc243.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/05/03/636fe35ba5b24b969b1adcbcd16a039b.png
[72a573b02839440ba7e0ab5c4e9a0ef0.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/05/03/ee42bc7ff4ba4d1a8043f5991367c5f6.png
[b1bc2d818f684178a47dfa815befc9e1.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/05/03/cadf8f6fc7de44ed88c6d11d35cce0fb.png
[6419716b9ff4458d94a4267b04e5bab7.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/05/03/e2875ce6e6dd414c8f788ae20815b487.png
[f0723fa492054510b0e4e2350d2a86ff.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/05/03/7fb9e6ef24b34d9b86a5f054e9803c7d.png