extend pandas dataframe based on columns values range-CodePudding

I have following dataframe(short sample):

cond_ID  tow1_ID  tow2_ID
0       10        0        4
1       17        6       12
3       13       14       15
4       13       16       16
5       13       17       18

I want to extend it based on range between tow1_ID and tow2_ID. For instance, I want to add records with values 1,2,3 and 4 below value 0. Here is desired output:

cond_ID  tow1_ID
0       10        0
0       10        1
0       10        2
0       10        3
0       10        4
1       17        6
1       17        7
1       17        8
1       17        9
1       17        10
1       17        11
1       17        12
1       13        14
1       13        15
1       13        16
1       13        17
1       13        18

How can I do this with vectorized approach ( without using apply ) ? Any help is highly appreciated.

CodePudding user response：

Try this:

df.assign(tow1_ID=[np.arange(s,f 1) for s, f in zip(df['tow1_ID'], df['tow2_ID'])])\
  .explode('tow1_ID')\
  .drop(['tow2_ID'], axis=1)

Output:

   cond_ID tow1_ID
0       10       0
0       10       1
0       10       2
0       10       3
0       10       4
1       17       6
1       17       7
1       17       8
1       17       9
1       17      10
1       17      11
1       17      12
3       13      14
3       13      15
4       13      16
5       13      17
5       13      18

CodePudding user response：

def foo(r):
    return pd.DataFrame({"cond_ID": r.cond_ID,
                         "tow_ID": range(r.tow1_ID, r.tow2_ID   1),
                         "index": r.name}).set_index("index")
    
print(pd.concat([foo(r) for _, r in df.iterrows()]))
#        cond_ID  tow_ID
# index                 
# 0           10       0
# 0           10       1
# 0           10       2
# 0           10       3
# 0           10       4
# 1           17       6
# 1           17       7
# 1           17       8
# 1           17       9
# 1           17      10
# 1           17      11
# 1           17      12
# 3           13      14
# 3           13      15
# 4           13      16
# 5           13      17
# 5           13      18