create new rows based on values of one of the columns in the above row with specific condition - pandas or numpy

Question

I have a data frame as shown below

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         


        

Answer 1

Here you can modify the function this way to do both check at the same time. Please check that it is exactly the conditions you want to apply for the walkin and overbook dataframes.

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)

and you get:

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