我正在尝试找到一种有效的方法来在给定分组和日期范围的情况下在 pandas 中生成滚动计数或总和。最终，我希望能够添加条件，即。评估“类型”字段，但我还没有到那儿。我已经写了一些东西来完成工作，但我觉得可能有更直接的方法来达到预期的结果。

我的 pandas 数据框目前看起来像这样，所需的输出放在最后一列“rolling_sales_180”中。

    name       date  amount  rolling_sales_180
0  David 2015-01-01     100              100.0
1  David 2015-01-05     500              600.0
2  David 2015-05-30      50              650.0
3  David 2015-07-25      50              100.0
4   Ryan 2014-01-04     100              100.0
5   Ryan 2015-01-19     500              500.0
6   Ryan 2016-03-31      50               50.0
7    Joe 2015-07-01     100              100.0
8    Joe 2015-09-09     500              600.0
9    Joe 2015-10-15      50              650.0

我当前的解决方案和环境可以在下面获取。我一直在根据 stackoverflow 中的 R Q&A 为我的解决方案建模。 Efficient way to perform running total in the last 365 day window

import pandas as pd
import numpy as np 

def trans_date_to_dist_matrix(date_col):  #  used to create a distance matrix
    x = date_col.tolist()
    y = date_col.tolist()
    data = []
    for i in x:
        tmp = []
        for j in y:
            tmp.append(abs((i - j).days))
        data.append(tmp)
        del tmp

    return pd.DataFrame(data=data, index=date_col.values, columns=date_col.values)


def lower_tri(x_col, date_col, win):  # x_col = column user wants a rolling sum of ,date_col = dates, win = time window
    dm = trans_date_to_dist_matrix(date_col=date_col)  # dm = distance matrix
    dm = dm.where(dm <= win)  # find all elements of the distance matrix that are less than window(time)
    lt = dm.where(np.tril(np.ones(dm.shape)).astype(np.bool))  # lt = lower tri of distance matrix so we get only future dates
    lt[lt >= 0.0] = 1.0  # cleans up our lower tri so that we can sum events that happen on the day we are evaluating
    lt = lt.fillna(0)  # replaces NaN with 0's for multiplication
     return pd.DataFrame(x_col.values * lt.values).sum(axis=1).tolist()


def flatten(x):
    try:
        n = [v for sl in x for v in sl]
        return [v for sl in n for v in sl]
    except:
        return [v for sl in x for v in sl]


data = [
['David', '1/1/2015', 100], ['David', '1/5/2015', 500], ['David', '5/30/2015', 50], ['David', '7/25/2015', 50],
['Ryan', '1/4/2014', 100], ['Ryan', '1/19/2015', 500], ['Ryan', '3/31/2016', 50],
['Joe', '7/1/2015', 100], ['Joe', '9/9/2015', 500], ['Joe', '10/15/2015', 50]
]

list_of_vals = []

dates_df = pd.DataFrame(data=data, columns=['name', 'date', 'amount'], index=None)
dates_df['date'] = pd.to_datetime(dates_df['date'])
list_of_vals.append(dates_df.groupby('name', as_index=False).apply(
lambda x: lower_tri(x_col=x.amount, date_col=x.date, win=180)))

new_data = flatten(list_of_vals)
dates_df['rolling_sales_180'] = new_data

print dates_df

感谢您的宝贵时间和反馈。

最佳答案

Pandas 支持 time-aware rolling通过 rolling方法，因此您可以使用它而不是从头开始编写自己的解决方案:

def get_rolling_amount(grp, freq):
    return grp.rolling(freq, on='date')['amount'].sum()

df['rolling_sales_180'] = df.groupby('name', as_index=False, group_keys=False) \
                            .apply(get_rolling_amount, '180D')

结果输出:

    name       date  amount  rolling_sales_180
0  David 2015-01-01     100              100.0
1  David 2015-01-05     500              600.0
2  David 2015-05-30      50              650.0
3  David 2015-07-25      50              100.0
4   Ryan 2014-01-04     100              100.0
5   Ryan 2015-01-19     500              500.0
6   Ryan 2016-03-31      50               50.0
7    Joe 2015-07-01     100              100.0
8    Joe 2015-09-09     500              600.0
9    Joe 2015-10-15      50              650.0

关于python - 按组在日期范围内高效的 p​​andas 滚动聚合 - Python 2.7 Windows - Pandas 0.19.2，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/41642320/