Question

我正在Pandas DataFrame的子集上创建一个计数函数，并打算导出仅由groupby条件和计数结果组成的字典/电子表格数据。

In [1]: df = pd.DataFrame([[Buy, A, 123, NEW, 500, 20190101-09:00:00am], [Buy, A, 124, CXL, 500, 20190101-09:00:01am], [Buy, A, 125, NEW, 500, 20190101-09:00:03am], [Buy, A, 126, REPLACE, 300, 20190101-09:00:10am], [Buy, B, 210, NEW, 1000, 20190101-09:10:00am], [Sell, B, 345, NEW, 200, 20190101-09:00:00am], [Sell, C, 412, NEW, 100, 20190101-09:00:00am], [Sell, C, 413, NEW, 200, 20190101-09:01:00am], [Sell, C, 414, CXL, 50, 20190101-09:02:00am]], columns=['side', 'sender', 'id', 'type', ''quantity', 'receive_time'])

Out[1]: 
   side  sender  id    type     quantity  receive_time 
0  Buy   A       123   NEW      500       20190101-09:00:00am
1  Buy   A       124   CXL      500       20190101-09:00:01am
2  Buy   A       125   NEW      500       20190101-09:00:03am
3  Buy   A       126   REPLACE  300       20190101-09:00:10am
4  Buy   B       210   NEW      1000      20190101-09:10:00am
5  Buy   B       345   NEW      200       20190101-09:00:00am
6  Sell  C       412   NEW      100       20190101-09:00:00am
7  Sell  C       413   NEW      200       20190101-09:01:00am
8  Sell  C       414   CXL      50        20190101-09:02:00am

count函数如下（mydf作为数据帧的子集传入）：

def ordercount(mydf):
   num = 0.0
   if mydf.type == 'NEW':
      num = num + mydf.qty
   elif mydf.type == 'REPLACE':
      num = mydf.qty
   elif mydf.type == 'CXL':
      num = num - mydf.qty
   else: 
      pass
   orderdict = dict.fromkeys([mydf.side, mydf.sender, mydf.id], num)
   return orderdict

从csv中读取数据后，我按一些标准将其分组，还按时间排序：

df = pd.read_csv('xxxxxxxxx.csv, sep='|', header=0, engine='python', names=col_names)
sorted_df = df.groupby(['side', 'sender', 'id']).apply(lambda_df:_df.sort_values(by=['time']))

然后对排序后的数据调用先前定义的函数：

print(sorted_df.agg(ordercount))

但是值错误不断增加，导致无法调用太多行。

对数据进行计数的功能方式可能并不高效，但它是我想到的与订单类型匹配并相应地对数量进行计数的最直接的方法。我希望程序输出一张只显示边，发件人，身份证和计数数量的表。有什么办法可以做到这一点？谢谢。

预期输出：

   side   sender   total_order_num   trade_date 
0  Buy    A        300               20190101
1  Buy    B        1200              20190101
2  Sell   C        250               20190101

Answer 1

我相信您的函数不容易一次应用，因为您根据行执行不同的操作。如果您仅将+和-作为操作，但是在某个时候replace进行操作，然后继续进行其他操作，则可以。因此，循环可能会更简单，或者您可以花一些时间来拥有一个不错的功能来完成任务。

这就是我所拥有的。我真正要做的就是更改您的ordercount，使其直接作用于子集，而您只需简单地分组即可。您可以在分组之前按时间排序，也可以在ordercount函数中进行排序。希望这会有所帮助。

import pandas as pd
df = pd.DataFrame([['Buy', 'A', 123, 'NEW', 500, '20190101-09:00:00am'],
                   ['Buy', 'A', 124, 'CXL', 500, '20190101-09:00:01am'],
                   ['Buy', 'A', 125, 'NEW', 500, '20190101-09:00:03am'],
                   ['Buy', 'A', 126, 'REPLACE', 300, '20190101-09:00:10am'],
                   ['Buy', 'B', 210, 'NEW', 1000, '20190101-09:10:00am'],
                   ['Buy', 'B', 345, 'NEW', 200, '20190101-09:00:00am'],
                   ['Sell', 'C', 412, 'NEW', 100, '20190101-09:00:00am'],
                   ['Sell', 'C', 413, 'NEW', 200, '20190101-09:01:00am'],
                   ['Sell', 'C', 414, 'CXL', 50, '20190101-09:02:00am']],
columns=['side', 'sender', 'id', 'type', 'quantity', 'receive_time'])

df['receive_time'] = pd.to_datetime(df['receive_time'])
df['receive_date'] = df['receive_time'].dt.date # you do not need the time stamps


def ordercount(mydf):
    mydf_ = mydf.sort_values('receive_time')[['type', 'quantity']].copy()
    num = 0
    for val in mydf_.values:
        type_, quantity = val
        # val is going to be a list like ['NEW', 500]. All I am doing above is unpack the list into two variables.
        # You can find many resources on unpacking iterables
        if type_ == 'NEW':
            num += quantity
        elif type_ == 'REPLACE':
            num = quantity
        elif type_ == 'CXL':
            num -= quantity
        else:
            pass
    return num

mydf = df.groupby(['side', 'sender', 'receive_date']).apply(ordercount).reset_index()

输出：

|----|--------|----------|---------------------|------|
|    | side   | sender   | receive_date        |    0 |
|----|--------|----------|---------------------|------|
|  0 | Buy    | A        | 2019-01-01 00:00:00 |  300 |
|----|--------|----------|---------------------|------|
|  1 | Buy    | B        | 2019-01-01 00:00:00 | 1200 |
|----|--------|----------|---------------------|------|
|  2 | Sell   | C        | 2019-01-01 00:00:00 |  250 |
|----|--------|----------|---------------------|------|

您可以根据需要轻松地重命名列“ 0”。我仍然不确定trade_date的定义。您的数据只有一个日期吗？如果您有多个约会，该怎么办？你在忙吗？...

编辑：如果您对此数据框进行过尝试，则可以看到日期按预期工作的组。

df = pd.DataFrame([['Buy', 'A', 123, 'NEW', 500, '20190101-09:00:00am'],
                   ['Buy', 'A', 124, 'CXL', 500, '20190101-09:00:01am'],
                   ['Buy', 'A', 125, 'NEW', 500, '20190101-09:00:03am'],
                   ['Buy', 'A', 126, 'REPLACE', 300, '20190101-09:00:10am'],
                   ['Buy', 'B', 210, 'NEW', 1000, '20190101-09:10:00am'],
                   ['Buy', 'B', 345, 'NEW', 200, '20190101-09:00:00am'],
                   ['Sell', 'C', 412, 'NEW', 100, '20190101-09:00:00am'],
                   ['Sell', 'C', 413, 'NEW', 200, '20190101-09:01:00am'],
                   ['Sell', 'C', 414, 'CXL', 50, '20190101-09:02:00am'],
                   ['Buy', 'A', 123, 'NEW', 500, '20190102-09:00:00am'],
                   ['Buy', 'A', 124, 'CXL', 500, '20190102-09:00:01am'],
                   ['Buy', 'A', 125, 'NEW', 500, '20190102-09:00:03am'],
                   ['Buy', 'A', 126, 'REPLACE', 300, '20190102-09:00:10am'],
                   ['Buy', 'B', 210, 'NEW', 1000, '20190102-09:10:00am'],
                   ['Buy', 'B', 345, 'NEW', 200, '20190102-09:00:00am'],
                   ['Sell', 'C', 412, 'NEW', 100, '20190102-09:00:00am'],
                   ['Sell', 'C', 413, 'NEW', 200, '20190102-09:01:00am'],
                   ['Sell', 'C', 414, 'CXL', 50, '20190102-09:02:00am']],
columns=['side', 'sender', 'id', 'type', 'quantity', 'receive_time'])

熊猫-在子集数据帧上调用用户定义的函数

1 个答案: