基于具有特定条件pandas或numpy的上述行中一列的值创建新行

Question

我有一个如下所示的数据框

B_ID   no_show  Session  slot_num  walkin   ns_w   c_ns_w     c_walkin
    1     0.4       S1        1       0.2    0.2    0.2       0.2
    2     0.3       S1        2       0.5   -0.2    0.2       0.7 
    3     0.8       S1        3       0.5    0.3    0.5       1.2  
    4     0.3       S1        4       0.8   -0.5    0.0       2.0
    5     0.6       S1        5       0.4    0.2    0.2       2.4 
    6     0.8       S1        6       0.2    0.6    0.8       2.6 
    7     0.9       S1        7       0.1    0.8    1.4       2.7
    8     0.4       S1        8       0.5   -0.1    1.3       3.2
    9     0.6       S1        9       0.1    0.5    1.8       3.3
    12    0.9       S2        1       0.9    0.0    0.0       0.9
    13    0.5       S2        2       0.4    0.1    0.1       1.3  
    14    0.3       S2        3       0.1    0.2    0.3       1.4    
    15    0.7       S2        4       0.4    0.3    0.6       1.8  
    20    0.7       S2        5       0.1    0.6    1.2       1.9
    16    0.6       S2        6       0.3    0.3    1.5       2.2
    17    0.8       S2        7       0.5    0.3    1.8       2.7
    19    0.3       S2        8       0.8   -0.5    1.3       3.5

在哪里,

df[ns_w] = df['no_show'] - df['walkin']

c_ns_w = cumulaitve of ns_w

df['c_ns_w'] = df.groupby(['Session'])['ns_w'].cumsum()

c_walkin = cumulative of walkin

df['c_walkin'] = df.groupby(['Session'])['walkin'].cumsum()

根据上面的内容，我想计算两列u_ns_w和u_c_walkin

当u_c_walkin > 0.9创建一个带有no_show = 0的新行时，walkin=0和所有其他值将与上述行相同。其中B_ID = walkin1, 2, etc,并从上述u_c_walkin中减去1

同时u_c_ns_w > 0.8添加一个新行，其中包含B_ID = overbook1, 2 etc，以及与上述行相同的所有其他值，并从上面的u_c_ns_w中减去0.5

预期产出：

B_ID   no_show  Session  slot_num  walkin   ns_w   c_ns_w  c_walkin  u_c_walkin  u_c_ns_w
    1     0.4       S1        1       0.2    0.2    0.2    0.2       0.2          0.2
    2     0.3       S1        2       0.5   -0.2    0.2    0.7       0.7          0.2
    3     0.8       S1        3       0.5    0.3    0.5    1.2       1.2          0.5
walkin1   0.0       S1        3       0.0    0.3    0.5    1.2       0.2          0.5
    4     0.3       S1        4       0.8   -0.5    0.0    2.0       1.0          0.0
walkin2   0.0       S1        4       0.0   -0.5    0.0    2.0       0.0          0.0
    5     0.6       S1        5       0.4    0.2    0.2    2.4       0.4          0.2
    6     0.8       S1        6       0.2    0.6    0.8    2.6       0.6          0.8
    7     0.9       S1        7       0.1    0.8    1.4    2.7       0.7          1.4
overbook1 0.5       S1        7       0.0    0.5    1.4    2.7       0.7          0.9
    8     0.4       S1        8       0.5   -0.1    1.3    3.2       1.2          0.8
walkin3   0.0       S1        8       0.0   -0.1    1.3    3.2       0.2          0.8
    9     0.6       S1        9       0.1    0.5    1.8    3.3       0.1          1.3
overbook2 0.5       S1        9       0.0    0.5    1.8    3.3       0.1          0.8
    12    0.9       S2        1       0.9    0.0    0.0    0.9       0.9          0.0     
    13    0.5       S2        2       0.4    0.1    0.1    1.3       1.3          0.1
walkin1   0.0       S2        2       0.0    0.1    0.1    1.3       0.3          0.1
    14    0.3       S2        3       0.1    0.2    0.3    1.4       0.4          0.3
    15    0.7       S2        4       0.4    0.3    0.6    1.8       0.8          0.6
    20    0.7       S2        5       0.1    0.6    1.2    1.9       0.9          1.2
overbook1 0.5       S2        5       0.0    0.5    1.2    1.9       0.9          0.7
    16    0.6       S2        6       0.3    0.3    1.5    2.2       1.2          1.0
walkin2   0.0       S2        6       0.3    0.3    1.5    2.2       0.2          1.0
overbook2 0.5       S2        6       0.0    0.5    1.5    2.2       0.2          0.5
    17    0.8       S2        7       0.5    0.3    1.8    2.7       0.7          0.8
    19    0.3       S2        8       0.8   -0.5    1.3    3.5       1.5          0.3
walkin3   0.0       S2        8       0.8   -0.5    1.3    3.5       0.5          0.3

我尝试在下面的代码中创建walkin行，但无法创建overbook行

def create_u_columns (ser):
    l_index = []
    arr_ns = ser.to_numpy()
    # array for latter insert
    arr_idx = np.zeros(len(ser), dtype=int)
    walkin_id = 1
    for i in range(len(arr_ns)-1):
        if arr_ns[i]>0.8:
            # remove 1 to u_no_show
            arr_ns[i+1:] -= 1
            # increment later idx to add
            arr_idx[i] = walkin_id
            walkin_id +=1
    #return a dataframe with both columns
    return pd.DataFrame({'u_cumulative': arr_ns, 'mask_idx':arr_idx}, index=ser.index)

df[['u_c_walkin', 'mask_idx']]= df.groupby(['Session'])['c_walkin'].apply(create_u_columns)


# select the rows
df_toAdd = df.loc[df['mask_idx'].astype(bool), :].copy()
# replace the values as wanted
df_toAdd['no_show'] = 0
df_toAdd['walkin'] = 0
df_toAdd['EpisodeNumber'] = 'walkin'+df_toAdd['mask_idx'].astype(str)
df_toAdd['u_c_walkin'] -= 1
# add 0.5 to index for later sort
df_toAdd.index += 0.5 

new_df = pd.concat([df,df_toAdd]).sort_index()\
           .reset_index(drop=True).drop('mask_idx', axis=1)

Answer 1

在这里，您可以通过这种方式修改函数，以同时执行这两项检查。请检查它是否正是您想要为walkin和overbook数据帧应用的条件

def create_columns(dfg):
    arr_walkin = dfg['c_walkin'].to_numpy()
    arr_ns = dfg['c_ns_w'].to_numpy()
    # array for latter insert
    arr_idx_walkin = np.zeros(len(arr_walkin), dtype=int)
    arr_idx_ns = np.zeros(len(arr_ns), dtype=int)
    walkin_id = 1
    oberbook_id = 1
    for i in range(len(arr_ns)):
        # condition on c_walkin
        if arr_walkin[i]>0.9:
            # remove 1 to u_no_show
            arr_walkin[i+1:] -= 1
            # increment later idx to add
            arr_idx_walkin[i] = walkin_id
            walkin_id +=1
        # condition on c_ns_w
        if arr_ns[i]>0.8:
            # remove 1 to u_no_show
            arr_ns[i+1:] -= 0.5
            # increment later idx to add
            arr_idx_ns[i] = oberbook_id
            oberbook_id +=1
    #return a dataframe with both columns
    return pd.DataFrame({'u_c_walkin': arr_walkin, 
                         'u_c_ns_w': arr_ns,
                         'mask_idx_walkin':arr_idx_walkin, 
                         'mask_idx_ns': arr_idx_ns }, index=dfg.index)

df[['u_c_walkin', 'u_c_ns_w', 'mask_idx_walkin', 'mask_idx_ns']]=\
   df.groupby(['Session'])[['c_walkin', 'c_ns_w']].apply(create_columns)


# select the rows for walkin
df_walkin = df.loc[df['mask_idx_walkin'].astype(bool), :].copy()
# replace the values as wanted
df_walkin['no_show'] = 0
df_walkin['walkin'] = 0
df_walkin['B_ID'] = 'walkin'+df_walkin['mask_idx_walkin'].astype(str)
df_walkin['u_c_walkin'] -= 1
# add 0.5 to index for later sort
df_walkin.index += 0.2 

# select the rows for ns_w
df_ns = df.loc[df['mask_idx_ns'].astype(bool), :].copy()
# replace the values as wanted
df_ns['no_show'] = 0.5
df_ns['walkin'] = 0
df_ns['ns_w'] = 0.5
df_ns['B_ID'] = 'overbook'+df_ns['mask_idx_ns'].astype(str)
df_ns['u_c_ns_w'] -= 0.5
# add 0.5 to index for later sort
df_ns.index += 0.4

new_df = pd.concat([df,df_walkin, df_ns]).sort_index()\
           .reset_index(drop=True).drop(['mask_idx_walkin','mask_idx_ns'], axis=1)

你会得到：

print (new_df)
         B_ID  no_show Session  slot_num  walkin  ns_w  c_ns_w  c_walkin  \
0           1      0.4      S1         1     0.2   0.2     0.2       0.2   
1           2      0.3      S1         2     0.5  -0.2     0.2       0.7   
2           3      0.8      S1         3     0.5   0.3     0.5       1.2   
3     walkin1      0.0      S1         3     0.0   0.3     0.5       1.2   
4           4      0.3      S1         4     0.8  -0.5     0.0       2.0   
5     walkin2      0.0      S1         4     0.0  -0.5     0.0       2.0   
6           5      0.6      S1         5     0.4   0.2     0.2       2.4   
7           6      0.8      S1         6     0.2   0.6     0.8       2.6   
8           7      0.9      S1         7     0.1   0.8     1.4       2.7   
9   overbook1      0.5      S1         7     0.0   0.5     1.4       2.7   
10          8      0.4      S1         8     0.5  -0.1     1.3       3.2   
11    walkin3      0.0      S1         8     0.0  -0.1     1.3       3.2   
12          9      0.6      S1         9     0.1   0.5     1.8       3.3   
13  overbook2      0.5      S1         9     0.0   0.5     1.8       3.3   
14         12      0.9      S2         1     0.9   0.0     0.0       0.9   
15         13      0.5      S2         2     0.4   0.1     0.1       1.3   
16    walkin1      0.0      S2         2     0.0   0.1     0.1       1.3   
17         14      0.3      S2         3     0.1   0.2     0.3       1.4   
18         15      0.7      S2         4     0.4   0.3     0.6       1.8   
19         20      0.7      S2         5     0.1   0.6     1.2       1.9   
20  overbook1      0.5      S2         5     0.0   0.5     1.2       1.9   
21         16      0.6      S2         6     0.3   0.3     1.5       2.2   
22    walkin2      0.0      S2         6     0.0   0.3     1.5       2.2   
23  overbook2      0.5      S2         6     0.0   0.5     1.5       2.2   
24         17      0.8      S2         7     0.5   0.3     1.8       2.7   
25         19      0.3      S2         8     0.8  -0.5     1.3       3.5   
26    walkin3      0.0      S2         8     0.0  -0.5     1.3       3.5   

    u_c_walkin  u_c_ns_w  
0          0.2       0.2  
1          0.7       0.2  
2          1.2       0.5  
3          0.2       0.5  
4          1.0       0.0  
5          0.0       0.0  
6          0.4       0.2  
7          0.6       0.8  
8          0.7       1.4  
9          0.7       0.9  
10         1.2       0.8  
11         0.2       0.8  
12         0.3       1.3  
13         0.3       0.8  
14         0.9       0.0  
15         1.3       0.1  
16         0.3       0.1  
17         0.4       0.3  
18         0.8       0.6  
19         0.9       1.2  
20         0.9       0.7  
21         1.2       1.0  
22         0.2       1.0  
23         1.2       0.5  
24         0.7       0.8  
25         1.5       0.3  
26         0.5       0.3