基于条件的数据帧分组

Question

我发现最相似的问题是here，但没有正确的答案。你知道吗

基本上，我遇到了一个问题，我试图在数据帧上使用groupby来为公交线路生成唯一的id。问题是，我所拥有的数据有时（尽管很少）具有与groupby列相同的值，因此它们被认为是相同的总线，即使它们不是

我能想到的唯一其他方法是根据另一个名为“站点类型”的栏对公交车进行分组，其中有一个起始、中间和结束的指示器。我想使用groupby来创建基于此列的组，其中每个组从“type of stop”=Start开始，从“type of stop”=End结束。你知道吗

考虑以下数据：

df = pd.DataFrame({'Vehicle_ID': ['A']*18,
    'Position': ['START', 'MID', 'MID', 'END', 'MID', 'START']*3)})

   Cond   Position
0     A   START
1     A   MID  
2     A   MID   
3     A   END    
4     A   MID    
5     A   START   
6     A   START   
7     A   MID    
8     A   MID    
9     A   END    
10    A   MID   
11    A   START    
12    A   START    
13    A   MID    
14    A   MID    
15    A   END     
16    A   MID    
17    A   START

我想出的将这些总线准确地分组在一起的唯一方法是用总线序列id生成一个附加列，但是考虑到我处理大量数据，这不是一个非常有效的解决方案。我希望能够管理我想用一个groupby做什么，如果可能的话，以便生成以下输出

   Cond   Position   Group
0     A   START      1
1     A   MID        1
2     A   MID        1
3     A   END        1
4     A   MID        
5     A   START      2
6     A   START      2
7     A   MID        2
8     A   MID        2
9     A   END        2 
10    A   MID        
11    A   START      3
12    A   START      3 
13    A   MID        3
14    A   MID        3
15    A   END        3 
16    A   MID        
17    A   START      4

Answer 1

一个想法是通过np.select分解，然后通过numba使用自定义循环：

from numba import njit

df = pd.DataFrame({'Vehicle_ID': ['A']*18,
                   'Position': ['START', 'MID', 'MID', 'END', 'MID', 'START']*3})

@njit
def grouper(pos):
    res = np.empty(pos.shape)
    num = 1
    started = 0
    for i in range(len(res)):
        current_pos = pos[i]
        if (started == 0) and (current_pos == 0):
            started = 1
            res[i] = num
        elif (started == 1) and (current_pos == 1):
            started = 0
            res[i] = num
            num += 1
        elif (started == 1) and (current_pos in [-1, 0]):
            res[i] = num
        else:
            res[i] = 0
    return res

arr = np.select([df['Position'].eq('START'), df['Position'].eq('END')], [0, 1], -1)

df['Group'] = grouper(arr).astype(int)

结果：

print(df)

   Position Vehicle_ID  Group
0     START          A      1
1       MID          A      1
2       MID          A      1
3       END          A      1
4       MID          A      0
5     START          A      2
6     START          A      2
7       MID          A      2
8       MID          A      2
9       END          A      2
10      MID          A      0
11    START          A      3
12    START          A      3
13      MID          A      3
14      MID          A      3
15      END          A      3
16      MID          A      0
17    START          A      4

在我看来，您应该而不是包含“blank”值，因为这将迫使您的序列成为object数据类型，对任何后续处理都没有效率。如上所述，您可以使用0。你知道吗

绩效基准

numba比一只纯熊猫快约10倍方法：-你知道吗

import pandas as pd, numpy as np
from numba import njit

df = pd.DataFrame({'Vehicle_ID': ['A']*18,
                   'Position': ['START', 'MID', 'MID', 'END', 'MID', 'START']*3})


df = pd.concat([df]*10, ignore_index=True)

assert joz(df.copy()).equals(jpp(df.copy()))

%timeit joz(df.copy())  # 18.6 ms per loop
%timeit jpp(df.copy())  # 1.95 ms per loop

基准功能：

def joz(df):
    # identification of sequences
    df['Position_Prev'] = df['Position'].shift(1)
    df['Sequence'] = 0
    df.loc[(df['Position'] == 'START') & (df['Position_Prev'] != 'START'), 'Sequence'] = 1
    df.loc[df['Position'] == 'END', 'Sequence'] = -1
    df['Sequence_Sum'] = df['Sequence'].cumsum()
    df.loc[df['Sequence'] == -1, 'Sequence_Sum'] = 1

    # take only items between START and END and generate Group number
    df2 = df[df['Sequence_Sum'] == 1].copy()
    df2.loc[df['Sequence'] == -1, 'Sequence'] = 0
    df2['Group'] = df2['Sequence'].cumsum()

    # merge results to one dataframe
    df = df.merge(df2[['Group']], left_index=True, right_index=True, how='left')
    df['Group'] = df['Group'].fillna(0)
    df['Group'] = df['Group'].astype(int)
    df.drop(['Position_Prev', 'Sequence', 'Sequence_Sum'], axis=1, inplace=True)    
    return df

@njit
def grouper(pos):
    res = np.empty(pos.shape)
    num = 1
    started = 0
    for i in range(len(res)):
        current_pos = pos[i]
        if (started == 0) and (current_pos == 0):
            started = 1
            res[i] = num
        elif (started == 1) and (current_pos == 1):
            started = 0
            res[i] = num
            num += 1
        elif (started == 1) and (current_pos in [-1, 0]):
            res[i] = num
        else:
            res[i] = 0
    return res

def jpp(df):
    arr = np.select([df['Position'].eq('START'), df['Position'].eq('END')], [0, 1], -1)
    df['Group'] = grouper(arr).astype(int)
    return df

Answer 2

我有办法。你必须避免循环，并尝试使用滑动，切片和合并。你知道吗

这是我的第一个原型（应该重构）

# identification of sequences
df['Position_Prev'] = df['Position'].shift(1)
df['Sequence'] = 0
df.loc[(df['Position'] == 'START') & (df['Position_Prev'] != 'START'), 'Sequence'] = 1
df.loc[df['Position'] == 'END', 'Sequence'] = -1
df['Sequence_Sum'] = df['Sequence'].cumsum()
df.loc[df['Sequence'] == -1, 'Sequence_Sum'] = 1

# take only items between START and END and generate Group number
df2 = df[df['Sequence_Sum'] == 1].copy()
df2.loc[df['Sequence'] == -1, 'Sequence'] = 0
df2['Group'] = df2['Sequence'].cumsum()

# merge results to one dataframe
df = df.merge(df2[['Group']], left_index=True, right_index=True, how='left')
df['Group'] = df['Group'].fillna(0)
df['Group'] = df['Group'].astype(int)
df.drop(columns=['Position_Prev', 'Sequence', 'Sequence_Sum'], inplace=True)
df

结果：

Vehicle_ID Position  Group
0           A    START      1
1           A      MID      1
2           A      MID      1
3           A      END      1
4           A      MID      0
5           A    START      2
6           A    START      2
7           A      MID      2
8           A      MID      2
9           A      END      2
10          A      MID      0
11          A    START      3
12          A    START      3
13          A      MID      3
14          A      MID      3
15          A      END      3
16          A      MID      0
17          A    START      4