了解列之间的相关性 Pandas DataFrame

Question

我有一个数据集，其中包含两种产品在发布前 10 天的每日销售额。下面的数据框显示了每种产品每天销售的一个和几十个项目。它认为，在一件产品售出之前，没有数十件产品被售出。这两种产品（Period_ID）预计销售数量为几十。

d = {'Period_ID':['A12']*10, 'Prod_A_Doz':[1.2]*10, 'Prod_B_Doz':[2.4]*10, 'A_Singles':[0,0,0,1,1,2,2,3,3,4], 'B_Singles':[0,0,1,1,2,2,3,3,4,4],
     'A_Dozens':[0,0,0,0,0,0,0,1,1,1], 'B_Dozens':[0,0,0,0,0,0,1,1,2,2]}
df = pd.DataFrame(data=d)

问题

我想构建一个描述性分析，其中我的一个问题是计算在第 1 次、第 2 次、...、第 10 次售出一打产品之前，每种产品平均售出多少单件？

鉴于df.Period_ID.nunique() = 1568

修改与上述累计销售额相反的每日销售额数据集，并使用Pankaj Joshi解决方案进行少量改动，

print(f'Average number of single items before {index + 1} dozen = {df1.A_Singles[:val+1].mean():0.2f}')


d = {'Period_ID':['A12']*10, 'Prob_A_Doz':[1.2]*10, 'Prod_B_Doz':[2.4]*10, 'A_Singles':[0,0,0,1,0,1,0,1,0,1], 'B_Singles':[0,0,1,0,1,0,1,0,1,0],
 'A_Dozens':[0,0,0,0,0,0,0,1,0,0], 'B_Dozens':[0,0,0,0,0,0,1,0,1,0]}
df1 = pd.DataFrame(data=d)

# For product A
Average number of single items before 1 dozen = 0.38

# For product B
6
Average number of single items before 1 dozen = 0.43
8
Average number of single items before 2 dozen = 0.44, But I want this to be counted from the last Dozens of sales. so rather 0.44, it should be 0.5 

目标是，一旦我获得每个 Period_ID 的信息，然后我将取所有 df.Period_ID.nunique() (= 1568) 的平均值，并尝试优化每个产品的预期“数十”销售数量在 col Prod_A_Doz 和 Prod_B_Doz 下给出

我将不胜感激。

Answer 1

下面是我的做法：

d = {'Period_ID':['A12']*10, 'Prob_A_Doz':[1.2]*10, 'Prod_B_Doz':[2.4]*10, 'A_Singles':[0,0,0,1,1,2,2,3,3,4], 'B_Singles':[0,0,1,1,2,2,3,3,4,4],
 'A_Dozens':[0,0,0,0,0,0,0,1,1,1], 'B_Dozens':[0,0,0,0,0,0,1,1,2,2]}
df1 = pd.DataFrame(data=d)


for per_id in set(df1.Period_ID):
    print(per_id)

    df_temp = df1[df1.Period_ID == per_id]
    for index, val in enumerate(df_temp.index[df_temp.A_Dozens>0]):
        print(val)
        print(f'Average number of single items before {index} dozen = {df_temp.A_Singles[:val].mean():0.2f}')
        print(f'Average number of single items before {index} dozen = {df_temp.B_Dozens[:val].mean():0.2f}')