Question

我有一个像这样的数据框

DF

    order_date    amount
0   2015-10-02      1
1   2015-12-21      15
2   2015-12-24      3
3   2015-12-26      4
4   2015-12-27      5
5   2015-12-28      10

我想根据df [“order_date”]到df [“order_date”] + 6天的范围总结df [“amount”]

    order_date    amount   sum
0   2015-10-02      1       1 
1   2015-12-21      15      27  //comes from 15 + 3 + 4 + 5
2   2015-12-24      3       22  //comes from 3 + 4 + 5 + 10
3   2015-12-26      4       19
4   2015-12-27      5       15
5   2015-12-28      10      10

order_date的数据类型是datetime 试图使用iloc但是效果不好...... 如果有人有任何关于谁在这方面工作的想法/例子，请让我知道。

Answer 1

如果pandas rolling允许左对齐窗口（默认为右对齐），那么答案将是一个简单的单行：df.set_index('order_date').amount.rolling('7d',min_periods=1,align='left').sum()，但前瞻性尚未实现（即{ {1}}不接受rolling参数）。所以，我想出的伎俩就是暂时“扭转”日期。解决方案：

align

输出：

df.index = pd.to_datetime(pd.datetime.now() - df.order_date)
df['sum'] = df.sort_index().amount.rolling('7d',min_periods=1).sum()
df.reset_index(drop=True)

Answer 2

扩展我的评论：

from datetime import timedelta

df['sum'] = 0
for i in range(len(df)):
    dt1 = df['order_date'][i]
    dt2 = dt1 + timedelta(days=6)
    df['sum'][i] = sum(df['amount'][(df['order_date'] >= dt1) & (df['order_date'] <= dt2)])

可能有更好的方法来做到这一点，但它有效...

Answer 3

我有办法解决这个问题。它有效..（我相信应该有更好的方法来做到这一点。）

    import pandas as pd

    df['order_date']=pd.to_datetime(pd.Series(df.order_date))
    Temp=pd.DataFrame(pd.date_range(start='2015-10-02', end='2017-01-01'),columns=['STDate'])
    Temp=Temp.merge(df,left_on='STDate',right_on='order_date',how='left')
    Temp['amount']=Temp['amount'].fillna(0)
    Temp.sort(['STDate'],ascending=False,inplace=True)
    Temp['rolls']=pd.rolling_sum(Temp['amount'],window =7,min_periods=0)
    Temp.loc[Temp.STDate.isin(df.order_date),:].sort(['STDate'],ascending=True)


    STDate  Unnamed: 0 order_date  amount  rolls


0  2015-10-02         0.0 2015-10-02     1.0    1.0
80 2015-12-21         1.0 2015-12-21    15.0   27.0
83 2015-12-24         2.0 2015-12-24     3.0   22.0
85 2015-12-26         3.0 2015-12-26     4.0   19.0
86 2015-12-27         4.0 2015-12-27     5.0   15.0
87 2015-12-28         5.0 2015-12-28    10.0   10.0

Answer 4

将order_date设置为DatetimeIndex，以便您可以使用df.ix[time1:time2]获取时间范围行，然后过滤amount列并对其求和。

您可以尝试：

from datetime import timedelta
df = pd.read_fwf('test2.csv')
df.order_date = pd.to_datetime(df.order_date)
df =df.set_index(pd.DatetimeIndex(df['order_date']))
sum_list = list()
for i in range(len(df)):
    sum_list.append(df.ix[df.ix[i]['order_date']:(df.ix[i]['order_date'] + timedelta(days=6))]['amount'].sum())
df['sum'] = sum_list
df

输出：

            order_date  amount  sum
2015-10-02  2015-10-02  1       1
2015-12-21  2015-12-21  15      27
2015-12-24  2015-12-24  3       22
2015-12-26  2015-12-26  4       19
2015-12-27  2015-12-27  5       15
2015-12-28  2015-12-28  10      10

Answer 5

import datetime

df['order_date'] = pd.to_datetime(df['order_date'], format='%Y-%m-%d')
df.set_index(['order_date'], inplace=True)

# Sum rows within the range of six days in the future
d = {t: df[(df.index >= t) & (df.index <= t + datetime.timedelta(days=6))]['amount'].sum()
        for t in df.index}

# Assign the summed values back to the dataframe
df['amount_sum'] = [d[t] for t in df.index]

df现在是：

            amount  amount_sum
order_date                    
2015-10-02     1.0         1.0
2015-12-21    15.0        27.0
2015-12-24     3.0        22.0
2015-12-26     4.0        19.0
2015-12-27     5.0        15.0
2015-12-28    10.0        10.0

如何在特定时间范围内对python pandas数据帧进行求和

5 个答案: