Python n行后的大熊猫求和

我正在使用（当前）一个索引和一列的数据帧。索引是日期，列是数字。我想首先找到值小于某个级别的行：

list = []
for index,row in df.iterrows():
    if row < -0.1:
        list.append(#sum the next 60 values)

编辑：这是数据的外观：

data

Date        Ticker

2015-06-23 -0.010959
2015-06-24 -0.002770
2015-06-25  0.005556
2015-06-26  0.008287
2015-06-29 -0.013699
2015-06-30  0.005556
2015-07-01  0.006077
2015-07-02 -0.010983

---

您可以在

reversed

dataframe上计算

rolling\u sum

，以实现该技巧

import pandas as pd
import numpy as np

# sample data
# =================================
np.random.seed(0)
df = pd.DataFrame(np.random.randn(25, 2)*0.1, columns=['A', 'B'], index=pd.date_range('2010-01-01', periods=25, freq='B'))
df

                 A       B
2010-01-01  0.1764  0.0400
2010-01-04  0.0979  0.2241
2010-01-05  0.1868 -0.0977
2010-01-06  0.0950 -0.0151
2010-01-07 -0.0103  0.0411
2010-01-08  0.0144  0.1454
2010-01-11  0.0761  0.0122
2010-01-12  0.0444  0.0334
2010-01-13  0.1494 -0.0205
2010-01-14  0.0313 -0.0854
2010-01-15 -0.2553  0.0654
2010-01-18  0.0864 -0.0742
2010-01-19  0.2270 -0.1454
2010-01-20  0.0046 -0.0187
2010-01-21  0.1533  0.1469
2010-01-22  0.0155  0.0378
2010-01-25 -0.0888 -0.1981
2010-01-26 -0.0348  0.0156
2010-01-27  0.1230  0.1202
2010-01-28 -0.0387 -0.0302
2010-01-29 -0.1049 -0.1420
2010-02-01 -0.1706  0.1951
2010-02-02 -0.0510 -0.0438
2010-02-03 -0.1253  0.0777
2010-02-04 -0.1614 -0.0213

# processing
# =================================================
from functools import partial

def sum_over_next_n_days(col, n_days, threshold):
    # sum over next several days, reverse first, and use rolling sum
    res = pd.rolling_sum(col[::-1], window=n_days).shift(1)[::-1]    
    return res[col < threshold].reindex(col.index)

# specify your parameter here, next 3 days, -0.1
apply_func = partial(sum_over_next_n_days, n_days=3, threshold=-0.1)

df.apply(apply_func)

                 A       B
2010-01-01     NaN     NaN
2010-01-04     NaN     NaN
2010-01-05     NaN     NaN
2010-01-06     NaN     NaN
2010-01-07     NaN     NaN
2010-01-08     NaN     NaN
2010-01-11     NaN     NaN
2010-01-12     NaN     NaN
2010-01-13     NaN     NaN
2010-01-14     NaN     NaN
2010-01-15  0.3180     NaN
2010-01-18     NaN     NaN
2010-01-19     NaN  0.1660
2010-01-20     NaN     NaN
2010-01-21     NaN     NaN
2010-01-22     NaN     NaN
2010-01-25     NaN  0.1056
2010-01-26     NaN     NaN
2010-01-27     NaN     NaN
2010-01-28     NaN     NaN
2010-01-29 -0.3469  0.2290
2010-02-01 -0.3376     NaN
2010-02-02     NaN     NaN
2010-02-03     NaN     NaN
2010-02-04     NaN     NaN

将熊猫作为pd导入
将numpy作为np导入
#样本数据
# =================================
np.random.seed（0）
df=pd.DataFrame（np.random.randn（25,2）*0.1，列=['A'，'B'，索引=pd.date_范围（'2010-01-01'，句点=25，频率=B'））
df
A B
2010-01-01  0.1764  0.0400
2010-01-04  0.0979  0.2241
2010-01-05  0.1868 -0.0977
2010-01-06  0.0950 -0.0151
2010-01-07 -0.0103  0.0411
2010-01-08  0.0144  0.1454
2010-01-11  0.0761  0.0122
2010-01-12  0.0444  0.0334
2010-01-13  0.1494 -0.0205
2010-01-14  0.0313 -0.0854
2010-01-15 -0.2553  0.0654
2010-01-18  0.0864 -0.0742
2010-01-19  0.2270 -0.1454
2010-01-20  0.0046 -0.0187
2010-01-21  0.1533  0.1469
2010-01-22  0.0155  0.0378
2010-01-25 -0.0888 -0.1981
2010-01-26 -0.0348  0.0156
2010-01-27  0.1230  0.1202
2010-01-28 -0.0387 -0.0302
2010-01-29 -0.1049 -0.1420
2010-02-01 -0.1706  0.1951
2010-02-02 -0.0510 -0.0438
2010-02-03 -0.1253  0.0777
2010-02-04 -0.1614 -0.0213
#加工
# =================================================
从functools导入部分
定义未来天数的总和（col，n天数，阈值）：
#在接下来的几天里求和，先倒过来，然后使用滚动求和
res=pd.滚动总和（列[：-1]，窗口=n天）。班次（1）[：-1]
返回res[col

通过查找满足条件的所有行的整数索引，尝试构建索引列表，如中所示：

import numpy as np
from pandas import DataFrame

df = DataFrame([["2015-06-23",-0.010959],
["2015-06-24", -0.102770],
["2015-06-25",  0.005556],
["2015-06-26",  0.008287],
["2015-06-29", -0.113699],
["2015-06-30",  0.005556],
["2015-07-01",  0.006077],
["2015-07-02", -0.010983]], columns=['Date','Ticker'])

df.set_index(['Date'])

index_list = list(np.where(df.Ticker < -0.1)[0])

---

是否要查找值小于某个级别的第一行，还是要查找满足此条件的所有行？我要查找所有行。因此，如果2015-01-01小于-0.10，我想对接下来的60天求和。那么，如果2015-02-01是

import numpy as np
from pandas import DataFrame

df = DataFrame([["2015-06-23",-0.010959],
["2015-06-24", -0.102770],
["2015-06-25",  0.005556],
["2015-06-26",  0.008287],
["2015-06-29", -0.113699],
["2015-06-30",  0.005556],
["2015-07-01",  0.006077],
["2015-07-02", -0.010983]], columns=['Date','Ticker'])

df.set_index(['Date'])

index_list = list(np.where(df.Ticker < -0.1)[0])

list = []
for i in index_list:
     list.append(df.loc[i:i+30-1]['Ticker'].sum())