厉害了，用Python在Excel表格当中绘制可视化大屏！！

# 读取Excel文档并且指定工作表的名称
file_name = 'Bike_Sales_Playground.xlsx'
df = pd.read_excel(file_name,sheet_name='bike_buyers')

# 新建一张工作表
with pd.ExcelWriter(file_name,#文档的名称
                  engine='openpyxl',#调用模块的名称
                  mode='a',#添加的模式
                 if_sheet_exists="replace" #如果已经存在，就替换掉
                 ) as writer:
df.to_excel(writer, sheet_name='Working_Sheet',index = False)# 设置Index为False

# 从新的工作表当中来读取数据
df = pd.read_excel(file_name,sheet_name='Working_Sheet')

# 去掉重复值
df.drop_duplicates(keep='first', inplace=True, ignore_index=False)
    
# 针对婚姻状况这一列，“已婚”替换成“M”，“单身”替换成“S”
df['Marital Status'] = df['Marital Status'].replace('M','Married').replace('S','Single')
    
# 针对性别这一列，“男性”替换成“F”，而“女性”替换成“M”
df['Gender'] = df['Gender'].replace('F','Female').replace('M','Male')

# 查看表格的前5行
df.head()

# 年龄数值的处理
df['Age brackets'] = df['Age'].apply(lambda x: 'Less than 30' if x<=30 else('Greater than 55' if x>55 else '31 to 55'))

# 通勤距离的数值的替换
df['Commute Distance'] = df['Commute Distance'].replace('10+ Miles','More than 10 Miles')

      ID Marital Status  Gender  ...  Age  Purchased Bike     Age brackets
0  12496        Married  Female  ...   42              No         31 to 55
1  24107        Married    Male  ...   43              No         31 to 55
2  14177        Married    Male  ...   60              No  Greater than 55
3  24381         Single    Male  ...   41             Yes         31 to 55
4  25597         Single    Male  ...   36             Yes         31 to 55

# 透视表1
# 制作数据透视表
avg_gender_income_df = np.round(pd.pivot_table(bike_df,
               values = 'Income',
               index = ['Gender'],
               columns = ['Purchased Bike'],
               aggfunc = np.mean
              ),2)

# 将数据透视表放入Excel表格中，并且指定工作表
with pd.ExcelWriter(file_name,#工作表的名称
                    engine='openpyxl',#引擎的名称
                    mode='a',#Append模式
                   if_sheet_exists="replace" #如果已经存在，就替换掉
                   ) as writer:  
    
    avg_gender_income_df.to_excel(writer, sheet_name='Average_Gender_Income')

# 加载文档，并且指定工作表
wb = load_workbook(file_name)
sheet = wb['Average_Gender_Income']

# 创建柱状图
chart1 = BarChart()
chart1.type = "col"
chart1.style = 10
chart1.title = "基于性别与消费数据之下的平均收入"
chart1.y_axis.title = '性别'
chart1.x_axis.title = '收入'

# 将绘制出来的柱状图放在单元格中去
data1 = Reference(sheet, min_col=2, min_row=1, max_row=3, max_col=3)#Including Headers
cats1 = Reference(sheet, min_col=1, min_row=2, max_row=3)#Not including headers
chart1.add_data(data1, titles_from_data=True)
chart1.dataLabels = DataLabelList() 
chart1.dataLabels.showVal = True
chart1.set_categories(cats1)
chart1.shape = 4
sheet.add_chart(chart1, "A10")
wb.save(file_name)

# 创建一个空的DataFrame表格
title_df = pd.DataFrame()

# 将结果放入至Excel文件当中去
with pd.ExcelWriter(file_name,#工作表的名称
                    engine='openpyxl',#引擎的名称
                    mode='a',#Append模式
                   if_sheet_exists="replace" #如果已经存在，就替换掉
                   ) as writer:  
    
    title_df.to_excel(writer, sheet_name='Dashboard')

# 加载文档，指定工作表是哪个
wb = load_workbook(file_name)
sheet = wb['Dashboard']
for x in range(1,22):
    sheet.merge_cells('A1:R4')
    
cell = sheet.cell(row=1, column=1)  
cell.value = 'Bike Sales Dashboard'  
cell.alignment = Alignment(horizontal='center', vertical='center')   
cell.font  = Font(b=True, color="F8F8F8",size = 46)
cell.fill = PatternFill("solid", fgColor="2591DB")

# 将绘制出来的图表放置到Excel文档中
sheet.add_chart(chart1,'A5')
sheet.add_chart(chart2,'J5')
chart3.width = 31
sheet.add_chart(chart3,'A20')
wb.save(file_name)

import Bikes_Sales_Report_Automation as auto

# 填入文件的名称
auto.automate_excel_dashboard('Bike_Sales_Playground.xlsx')