Question

我需要对分组的数据帧执行累加和，但是当前一个值为负而当前值为正时，我需要重置它。

在R中，我可以使用ave（）函数将条件应用于groupby，但是在python中无法做到这一点，因此在考虑解决方案时遇到了一些麻烦。谁能帮我吗？

以下是示例：

import pandas as pd

df = pd.DataFrame({'PRODUCT': ['A'] * 40, 'GROUP': ['1'] * 40, 'FORECAST': [100, -40, -40, -40]*10, })

df['CS'] = df.groupby(['GROUP', 'PRODUCT']).FORECAST.cumsum()

# Reset cumsum if
# condition: (df.FORECAST > 0) & (df.groupby(['GROUP', 'PRODUCT']).FORECAST.shift(-1).fillna(0) <= 0)

Answer 1

对于要求和的值从负变为正的任何示例，此解决方案都将重置其总和（无论数据集是否像示例中的那样是周期性的）

import numpy as np
import pandas as pd

df = pd.DataFrame({'PRODUCT': ['A'] * 40, 'GROUP': ['1'] * 40, 'FORECAST': [100, -40, -40, -40]*10, })

cumsum = np.cumsum(df['FORECAST'])

# Array of indices where sum should be reset
reset_ind = np.where(df['FORECAST'].diff() > 0)[0]

# Sums that need to be subtracted at resets
subs = cumsum[reset_ind-1].values

# Repeat subtraction values for every entry BETWEEN resets and values after final reset
rep_subs = np.repeat(subs, np.hstack([np.diff(reset_ind), df['FORECAST'].size - reset_ind[-1]]))

# Stack together values before first reset and resetted sums
df['CS'] = np.hstack([cumsum[:reset_ind[0]], cumsum[reset_ind[0]:] - rep_subs])

或者，基于on this solution to a similar question（以及我对groupby有用性的认识）

import pandas as pd
import numpy as np

df = pd.DataFrame({'PRODUCT': ['A'] * 40, 'GROUP': ['1'] * 40, 'FORECAST': [100, -40, -40, -40]*10, })

# Create indices to group sums together
df['cumsum'] = (df['FORECAST'].diff() > 0).cumsum()

# Perform group-wise cumsum
df['CS'] = df.groupby(['cumsum'])['FORECAST'].cumsum()

# Remove intermediary cumsum column
df = df.drop(['cumsum'], axis=1)

前一个值为负时，熊猫重置累计值

1 个答案: