我有一个数据框架,如下图所示
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
其中:
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()
从上面我想计算两个列,分别叫做 u_ns_w
和 u_c_walkin
.
而当... u_c_walkin > 0.9
新建一行 no_show = 0
, walkin=0
而其他所有的值都会和上面的行一样,其中 B_ID = walkin1, 2, etc,
并减去 1
从上 u_c_walkin
.
同时,每当 u_c_ns_w > 0.8
添加一行 B_ID = overbook1, 2 etc
,与 no_show = 0.5, walkin=0, ns_w = 0.5
和其他所有的值与上面一行相同,然后减去。0.5
从上 u_c_ns_w
.
预期的输出。
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
我试着用下面的代码来创建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)
这里你可以这样修改函数,同时进行两个检查。请检查一下,你想对walkin和overbook数据帧应用的条件是否准确。
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)
就可以得到。
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