Pandas groupby和聚合输出应包括所有原始列(包括未聚合的列)

问题描述

我具有以下数据框，并希望:

I have the following data frame and want to:

• 按month
分组记录
• 每个唯一UPC_ID的QTY_SOLD和NET_AMT(每月)
• 将其余的列也包含在结果数据框中

• Group records by month
• Sum QTY_SOLDand NET_AMT of each unique UPC_ID(per month)
• Include the rest of the columns as well in the resulting dataframe

我以为我可以做到这一点的方法是第一:创建一个month列以汇总D_DATES，然后将QTY_SOLD加到UPC_ID.

The way I thought I can do this is 1st: create a month column to aggregate the D_DATES, then sum QTY_SOLD by UPC_ID.

脚本:

# Convert date to date time object
df['D_DATE'] = pd.to_datetime(df['D_DATE'])

# Create aggregated months column
df['month'] = df['D_DATE'].apply(dt.date.strftime, args=('%Y.%m',))

# Group by month and sum up quantity sold by UPC_ID
df = df.groupby(['month', 'UPC_ID'])['QTY_SOLD'].sum()

---

当前数据帧:

---

Current data frame:

UPC_ID | UPC_DSC | D_DATE | QTY_SOLD | NET_AMT
----------------------------------------------
111      desc1    2/26/2017   2         10 (2 x $5)
222      desc2    2/26/2017   3         15
333      desc3    2/26/2017   1         4
111      desc1    3/1/2017    1         5
111      desc1    3/3/2017    4         20

所需的输出:

MONTH | UPC_ID | QTY_SOLD | NET_AMT | UPC_DSC
----------------------------------------------
2017-2      111     2         10       etc...
2017-2      222     3         15
2017-2      333     1         4
2017-3      111     5         25

实际输出:

MONTH | UPC_ID
----------------------------------------------
2017-2      111     2
            222     3
            333     1
2017-3      111     5
...

问题:

• 如何为每一行包括月份?
• 如何包含数据框的其余列?
• 除了QTY_SOLD之外，还如何求和NET_AMT?

• How do I include the month for each row?
• How do I include the rest of the columns of the dataframe?
• How do also sum NET_AMT in addition to QTY_SOLD?

推荐答案

agg具有功能dict

创建一个函数dict并将其传递给agg.您还需要as_index=False来防止组列成为输出中的索引.

agg with a dict of functions

Create a dict of functions and pass it to agg. You'll also need as_index=False to prevent the group columns from becoming the index in your output.

f = {'NET_AMT': 'sum', 'QTY_SOLD': 'sum', 'UPC_DSC': 'first'}
df.groupby(['month', 'UPC_ID'], as_index=False).agg(f)

     month  UPC_ID UPC_DSC  NET_AMT  QTY_SOLD
0  2017.02     111   desc1       10         2
1  2017.02     222   desc2       15         3
2  2017.02     333   desc3        4         1
3  2017.03     111   desc1       25         5

---

毯子sum

只需调用sum，而无需任何列名.这将处理数字列.对于UPC_DSC，您需要单独处理.

---

Blanket sum

Just call sum without any column names. This handles the numeric columns. For UPC_DSC, you'll need to handle it separately.

g = df.groupby(['month', 'UPC_ID'])
i = g.sum()
j = g[['UPC_DSC']].first()

pd.concat([i, j], 1).reset_index()

     month  UPC_ID  QTY_SOLD  NET_AMT UPC_DSC
0  2017.02     111         2       10   desc1
1  2017.02     222         3       15   desc2
2  2017.02     333         1        4   desc3
3  2017.03     111         5       25   desc1

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