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数据的透视表

• 数据的透视表：

  • 使用函数 pivot_table( )

    # 引用pandas
    import pandas as pd
    # pivot_table 使用
    pd.pivot_table(data,values,index,aggfunc,fill_value,columns)参数1:data = DataFrame的源数据参数2:values = '要进行聚合操作的列'参数3:index = '进行分组的数据列' 最终显示到透视表的行参数4:aggfunc = '要进行的聚合操作' 默认值为求平均值 'mean'参数5:fill_value = 空值替换的数据 如果结果有空值，则指定要填充的字段参数6:columns = '列索引'按列索引进行聚合操作
    

• 例如：

  import pandas as pd
  data = {"班级":["一班","一班","二班","二班","三班","三班"],"学生":["小红","小红","小强","小李","小王","小赵"],"科目":["语文","数学","语文","数学","语文","数学"],"成绩":[89,78,88,92,93,85]
  }df = pd.DataFrame(data)# 按班级 汇总平均成绩  =>  按班级分组,得到每个班级的平均成绩
  df1 = pd.pivot_table(data = df,values = '成绩',index = '班级',aggfunc = 'mean')
  print(df1)
  

  结果

        成绩
  班级      
  一班  83.5
  三班  89.0
  二班  90.0
  

  使用columns案例

  import pandas as pd
  data = {"班级":["一班","一班","二班","二班","三班","三班"],"学生":["小红","小红","小强","小李","小王","小赵"],"科目":["语文","数学","语文","数学","语文","数学"],"成绩":[89,78,88,92,93,85]
  }df = pd.DataFrame(data)# 求每个班的科目平均成绩
  df2 = pd.pivot_table(data = df,values = '成绩',index = '班级',columns = '科目',)
  print(df2)
  

  结果：

  科目    数学    语文
  班级
  一班  78.0  89.0
  三班  85.0  93.0
  二班  92.0  88.0
  

• 案例2：

  import pandas as pd
  data = {"产品":["A","B","A","B","A","B"],"区域":["东区","东区","西区","西区","东区","西区"],"销售额":[200,160,300,234,450,321],"数量":[19,18,15,12,13,10]
  }df = pd.DataFrame(data)
  print("源数据:\n",df)
  # 创建透视表,按区域分组,计算每个区域的销售总额和数量
  df1 = pd.pivot_table(df,values = ['销售额','数量'],index = '区域',aggfunc = {'销售额':'sum','数量':'sum'})
  print(df1)
  

  结果：

  源数据:产品  区域  销售额  数量
  0  A  东区  200  19
  1  B  东区  160  18
  2  A  西区  300  15
  3  B  西区  234  12
  4  A  东区  450  13
  5  B  西区  321  10数量  销售额
  区域
  东区  50  810
  西区  37  855