Python 如何向行中添加值？

如何向行添加值：

我在数据框中创建了一列，并将值赋给0

现在更新这些列值的书面逻辑，但不反映 输入：

>>> parafix_df = main_df[["line_width", "para_num", "bbox" ]]
>>> parafix_df
   line_width para_num                             bbox
0     238.546      NaN  (50.0, 579.3, 288.546, 598.022)
1         318        1    (64.0, 564.9, 382.0, 583.622)
2         332        2    (50.0, 550.5, 382.0, 569.222)
3         332        2    (50.0, 536.1, 382.0, 554.822)
4     328.977        2  (50.0, 521.7, 378.977, 540.422)
5         318        3    (64.0, 507.3, 382.0, 526.022)
6         332        3    (50.0, 492.9, 382.0, 511.622)
7         332        3    (50.0, 478.5, 382.0, 497.222)
8         332        3    (50.0, 464.1, 382.0, 482.822)
9         332        3    (50.0, 449.7, 382.0, 468.422)
10      59.04        3   (50.0, 435.3, 109.04, 454.022)
11    304.007        4  (64.0, 420.9, 368.007, 439.622)
12        318        5    (64.0, 406.5, 382.0, 425.222)
13        332        5    (50.0, 392.1, 382.0, 410.822)
14        332        5    (50.0, 377.7, 382.0, 396.422)
15        332        5    (50.0, 363.3, 382.0, 382.022)
16     43.252        5   (50.0, 348.9, 93.252, 367.622)
17        318        6    (64.0, 334.5, 382.0, 353.222)
18        332        6    (50.0, 320.1, 382.0, 338.822)
19        332        6    (50.0, 305.7, 382.0, 324.422)
20        332        6    (50.0, 291.3, 382.0, 310.022)
21        332        6    (50.0, 276.9, 382.0, 295.622)
22     317.02        6   (50.0, 262.5, 367.02, 281.222)
23        318        7    (64.0, 248.1, 382.0, 266.822)
24        332        7    (50.0, 233.7, 382.0, 252.422)
25     47.014        7   (50.0, 219.3, 97.014, 238.022)
26        318        8    (64.0, 204.9, 382.0, 223.622)
27    316.723        8  (50.0, 190.5, 366.723, 209.222)
28        318        9    (64.0, 176.1, 382.0, 194.822)
29    326.766        9  (50.0, 161.7, 376.766, 180.422)
30        318       10    (64.0, 147.3, 382.0, 166.022)
31        332       10    (50.0, 132.9, 382.0, 151.622)
32        332       10    (50.0, 118.5, 382.0, 137.222)
33    305.393       11  (64.0, 104.1, 369.393, 122.822)
34        318       12     (64.0, 89.7, 382.0, 108.422)
35        318       13      (64.0, 75.3, 382.0, 94.022)
36    319.165       13    (50.0, 60.9, 369.165, 79.622)
37    308.165       14    (64.0, 46.5, 372.165, 65.222)
38        318       15      (64.0, 32.1, 382.0, 50.822)
39    329.153       15    (50.0, 17.7, 379.153, 36.422)
40        318       16       (64.0, 3.3, 382.0, 22.022)
41    324.335       16    (50.0, -11.1, 374.335, 7.622)

para1: 2
para1: 3
para1: 4
para1: 5
para1: 6
para1: 7
para1: 8
para1: 9
para1: 10
para1: 11
para1: 12
para1: 13
para1: 14
para1: 15
para1: 16

parafix_df = main_df[["line_text", "line_width", "para_num", "bbox" ]]
parafix_df["new_para_num"] = 0

max_width = parafix_df['line_width'].max()
bbox_max_width = parafix_df.loc[selected['line_width'] == max_width].iloc[0]["bbox"]

para1 = 1
for indx, current in enumerate(parafix_df.iterrows(), start=0):
    if indx!=0:
        bbox_current = current[1]["bbox"]
        if bbox_current[0]>bbox_max_width[0]:
            para1 += 1
    parafix_df.iloc[indx, 4] = para1

代码：

parafix_df = main_df[["line_text", "line_width", "para_num", "bbox" ]]
parafix_df["new_para_num"] = 0

max_width = parafix_df['line_width'].max()
bbox_max_width = parafix_df.loc[selected['line_width'] == max_width].iloc[0]["bbox"]

previous = None
para1 = 1
for current, next in izip(parafix_df.iterrows(), parafix_df.iloc[1:].iterrows()):
    if previous==None:
        current[1]["new_para_num"] = para1
    else:
        bbox_current = current[1]["bbox"]
        bbox_next = next[1]["bbox"]
        bbox_previous = previous[1]["bbox"]
        if bbox_current[0]>bbox_max_width[0]:
            para1 += 1
            print "para1:", para1
        current[1]["new_para_num"] = para1

    previous = current

                              bbox  new_para_num  
0   (50.0, 579.3, 288.546, 598.022)             0  
1     (64.0, 564.9, 382.0, 583.622)             0  
2     (50.0, 550.5, 382.0, 569.222)             0  
3     (50.0, 536.1, 382.0, 554.822)             0  
4   (50.0, 521.7, 378.977, 540.422)             0  
5     (64.0, 507.3, 382.0, 526.022)             0  
6     (50.0, 492.9, 382.0, 511.622)             0  
7     (50.0, 478.5, 382.0, 497.222)             0  
8     (50.0, 464.1, 382.0, 482.822)             0  
9     (50.0, 449.7, 382.0, 468.422)             0  
10   (50.0, 435.3, 109.04, 454.022)             0  
11  (64.0, 420.9, 368.007, 439.622)             0  
12    (64.0, 406.5, 382.0, 425.222)             0  
13    (50.0, 392.1, 382.0, 410.822)             0  
14    (50.0, 377.7, 382.0, 396.422)             0  
15    (50.0, 363.3, 382.0, 382.022)             0  
16   (50.0, 348.9, 93.252, 367.622)             0  
17    (64.0, 334.5, 382.0, 353.222)             0  
18    (50.0, 320.1, 382.0, 338.822)             0  
19    (50.0, 305.7, 382.0, 324.422)             0  
20    (50.0, 291.3, 382.0, 310.022)             0  
21    (50.0, 276.9, 382.0, 295.622)             0  
22   (50.0, 262.5, 367.02, 281.222)             0  
23    (64.0, 248.1, 382.0, 266.822)             0  
24    (50.0, 233.7, 382.0, 252.422)             0  
25   (50.0, 219.3, 97.014, 238.022)             0  
26    (64.0, 204.9, 382.0, 223.622)             0  
27  (50.0, 190.5, 366.723, 209.222)             0  
28    (64.0, 176.1, 382.0, 194.822)             0  
29  (50.0, 161.7, 376.766, 180.422)             0  
30    (64.0, 147.3, 382.0, 166.022)             0  
31    (50.0, 132.9, 382.0, 151.622)             0  
32    (50.0, 118.5, 382.0, 137.222)             0  
33  (64.0, 104.1, 369.393, 122.822)             0  
34     (64.0, 89.7, 382.0, 108.422)             0  
35      (64.0, 75.3, 382.0, 94.022)             0  
36    (50.0, 60.9, 369.165, 79.622)             0  
37    (64.0, 46.5, 372.165, 65.222)             0  
38      (64.0, 32.1, 382.0, 50.822)             0  
39    (50.0, 17.7, 379.153, 36.422)             0  
40       (64.0, 3.3, 382.0, 22.022)             0  
41    (50.0, -11.1, 374.335, 7.622)             0  

上述代码的输出：

parafix_df = main_df[["line_text", "line_width", "para_num", "bbox" ]]
parafix_df["new_para_num"] = 0

max_width = parafix_df['line_width'].max()
bbox_max_width = parafix_df.loc[selected['line_width'] == max_width].iloc[0]["bbox"]

previous = None
para1 = 1
for current, next in izip(parafix_df.iterrows(), parafix_df.iloc[1:].iterrows()):
    if previous==None:
        current[1]["new_para_num"] = para1
    else:
        bbox_current = current[1]["bbox"]
        bbox_next = next[1]["bbox"]
        bbox_previous = previous[1]["bbox"]
        if bbox_current[0]>bbox_max_width[0]:
            para1 += 1
            print "para1:", para1
        current[1]["new_para_num"] = para1

    previous = current

                              bbox  new_para_num  
0   (50.0, 579.3, 288.546, 598.022)             0  
1     (64.0, 564.9, 382.0, 583.622)             0  
2     (50.0, 550.5, 382.0, 569.222)             0  
3     (50.0, 536.1, 382.0, 554.822)             0  
4   (50.0, 521.7, 378.977, 540.422)             0  
5     (64.0, 507.3, 382.0, 526.022)             0  
6     (50.0, 492.9, 382.0, 511.622)             0  
7     (50.0, 478.5, 382.0, 497.222)             0  
8     (50.0, 464.1, 382.0, 482.822)             0  
9     (50.0, 449.7, 382.0, 468.422)             0  
10   (50.0, 435.3, 109.04, 454.022)             0  
11  (64.0, 420.9, 368.007, 439.622)             0  
12    (64.0, 406.5, 382.0, 425.222)             0  
13    (50.0, 392.1, 382.0, 410.822)             0  
14    (50.0, 377.7, 382.0, 396.422)             0  
15    (50.0, 363.3, 382.0, 382.022)             0  
16   (50.0, 348.9, 93.252, 367.622)             0  
17    (64.0, 334.5, 382.0, 353.222)             0  
18    (50.0, 320.1, 382.0, 338.822)             0  
19    (50.0, 305.7, 382.0, 324.422)             0  
20    (50.0, 291.3, 382.0, 310.022)             0  
21    (50.0, 276.9, 382.0, 295.622)             0  
22   (50.0, 262.5, 367.02, 281.222)             0  
23    (64.0, 248.1, 382.0, 266.822)             0  
24    (50.0, 233.7, 382.0, 252.422)             0  
25   (50.0, 219.3, 97.014, 238.022)             0  
26    (64.0, 204.9, 382.0, 223.622)             0  
27  (50.0, 190.5, 366.723, 209.222)             0  
28    (64.0, 176.1, 382.0, 194.822)             0  
29  (50.0, 161.7, 376.766, 180.422)             0  
30    (64.0, 147.3, 382.0, 166.022)             0  
31    (50.0, 132.9, 382.0, 151.622)             0  
32    (50.0, 118.5, 382.0, 137.222)             0  
33  (64.0, 104.1, 369.393, 122.822)             0  
34     (64.0, 89.7, 382.0, 108.422)             0  
35      (64.0, 75.3, 382.0, 94.022)             0  
36    (50.0, 60.9, 369.165, 79.622)             0  
37    (64.0, 46.5, 372.165, 65.222)             0  
38      (64.0, 32.1, 382.0, 50.822)             0  
39    (50.0, 17.7, 379.153, 36.422)             0  
40       (64.0, 3.3, 382.0, 22.022)             0  
41    (50.0, -11.1, 374.335, 7.622)             0  

但我需要新的参数值：

>>> parafix_df = main_df[["line_width", "para_num", "bbox" ]]
>>> parafix_df
   line_width para_num                             bbox
0     238.546      NaN  (50.0, 579.3, 288.546, 598.022)
1         318        1    (64.0, 564.9, 382.0, 583.622)
2         332        2    (50.0, 550.5, 382.0, 569.222)
3         332        2    (50.0, 536.1, 382.0, 554.822)
4     328.977        2  (50.0, 521.7, 378.977, 540.422)
5         318        3    (64.0, 507.3, 382.0, 526.022)
6         332        3    (50.0, 492.9, 382.0, 511.622)
7         332        3    (50.0, 478.5, 382.0, 497.222)
8         332        3    (50.0, 464.1, 382.0, 482.822)
9         332        3    (50.0, 449.7, 382.0, 468.422)
10      59.04        3   (50.0, 435.3, 109.04, 454.022)
11    304.007        4  (64.0, 420.9, 368.007, 439.622)
12        318        5    (64.0, 406.5, 382.0, 425.222)
13        332        5    (50.0, 392.1, 382.0, 410.822)
14        332        5    (50.0, 377.7, 382.0, 396.422)
15        332        5    (50.0, 363.3, 382.0, 382.022)
16     43.252        5   (50.0, 348.9, 93.252, 367.622)
17        318        6    (64.0, 334.5, 382.0, 353.222)
18        332        6    (50.0, 320.1, 382.0, 338.822)
19        332        6    (50.0, 305.7, 382.0, 324.422)
20        332        6    (50.0, 291.3, 382.0, 310.022)
21        332        6    (50.0, 276.9, 382.0, 295.622)
22     317.02        6   (50.0, 262.5, 367.02, 281.222)
23        318        7    (64.0, 248.1, 382.0, 266.822)
24        332        7    (50.0, 233.7, 382.0, 252.422)
25     47.014        7   (50.0, 219.3, 97.014, 238.022)
26        318        8    (64.0, 204.9, 382.0, 223.622)
27    316.723        8  (50.0, 190.5, 366.723, 209.222)
28        318        9    (64.0, 176.1, 382.0, 194.822)
29    326.766        9  (50.0, 161.7, 376.766, 180.422)
30        318       10    (64.0, 147.3, 382.0, 166.022)
31        332       10    (50.0, 132.9, 382.0, 151.622)
32        332       10    (50.0, 118.5, 382.0, 137.222)
33    305.393       11  (64.0, 104.1, 369.393, 122.822)
34        318       12     (64.0, 89.7, 382.0, 108.422)
35        318       13      (64.0, 75.3, 382.0, 94.022)
36    319.165       13    (50.0, 60.9, 369.165, 79.622)
37    308.165       14    (64.0, 46.5, 372.165, 65.222)
38        318       15      (64.0, 32.1, 382.0, 50.822)
39    329.153       15    (50.0, 17.7, 379.153, 36.422)
40        318       16       (64.0, 3.3, 382.0, 22.022)
41    324.335       16    (50.0, -11.1, 374.335, 7.622)

para1: 2
para1: 3
para1: 4
para1: 5
para1: 6
para1: 7
para1: 8
para1: 9
para1: 10
para1: 11
para1: 12
para1: 13
para1: 14
para1: 15
para1: 16

parafix_df = main_df[["line_text", "line_width", "para_num", "bbox" ]]
parafix_df["new_para_num"] = 0

max_width = parafix_df['line_width'].max()
bbox_max_width = parafix_df.loc[selected['line_width'] == max_width].iloc[0]["bbox"]

para1 = 1
for indx, current in enumerate(parafix_df.iterrows(), start=0):
    if indx!=0:
        bbox_current = current[1]["bbox"]
        if bbox_current[0]>bbox_max_width[0]:
            para1 += 1
    parafix_df.iloc[indx, 4] = para1

你能帮我吗

以下是我的最终工作代码：

>>> parafix_df = main_df[["line_width", "para_num", "bbox" ]]
>>> parafix_df
   line_width para_num                             bbox
0     238.546      NaN  (50.0, 579.3, 288.546, 598.022)
1         318        1    (64.0, 564.9, 382.0, 583.622)
2         332        2    (50.0, 550.5, 382.0, 569.222)
3         332        2    (50.0, 536.1, 382.0, 554.822)
4     328.977        2  (50.0, 521.7, 378.977, 540.422)
5         318        3    (64.0, 507.3, 382.0, 526.022)
6         332        3    (50.0, 492.9, 382.0, 511.622)
7         332        3    (50.0, 478.5, 382.0, 497.222)
8         332        3    (50.0, 464.1, 382.0, 482.822)
9         332        3    (50.0, 449.7, 382.0, 468.422)
10      59.04        3   (50.0, 435.3, 109.04, 454.022)
11    304.007        4  (64.0, 420.9, 368.007, 439.622)
12        318        5    (64.0, 406.5, 382.0, 425.222)
13        332        5    (50.0, 392.1, 382.0, 410.822)
14        332        5    (50.0, 377.7, 382.0, 396.422)
15        332        5    (50.0, 363.3, 382.0, 382.022)
16     43.252        5   (50.0, 348.9, 93.252, 367.622)
17        318        6    (64.0, 334.5, 382.0, 353.222)
18        332        6    (50.0, 320.1, 382.0, 338.822)
19        332        6    (50.0, 305.7, 382.0, 324.422)
20        332        6    (50.0, 291.3, 382.0, 310.022)
21        332        6    (50.0, 276.9, 382.0, 295.622)
22     317.02        6   (50.0, 262.5, 367.02, 281.222)
23        318        7    (64.0, 248.1, 382.0, 266.822)
24        332        7    (50.0, 233.7, 382.0, 252.422)
25     47.014        7   (50.0, 219.3, 97.014, 238.022)
26        318        8    (64.0, 204.9, 382.0, 223.622)
27    316.723        8  (50.0, 190.5, 366.723, 209.222)
28        318        9    (64.0, 176.1, 382.0, 194.822)
29    326.766        9  (50.0, 161.7, 376.766, 180.422)
30        318       10    (64.0, 147.3, 382.0, 166.022)
31        332       10    (50.0, 132.9, 382.0, 151.622)
32        332       10    (50.0, 118.5, 382.0, 137.222)
33    305.393       11  (64.0, 104.1, 369.393, 122.822)
34        318       12     (64.0, 89.7, 382.0, 108.422)
35        318       13      (64.0, 75.3, 382.0, 94.022)
36    319.165       13    (50.0, 60.9, 369.165, 79.622)
37    308.165       14    (64.0, 46.5, 372.165, 65.222)
38        318       15      (64.0, 32.1, 382.0, 50.822)
39    329.153       15    (50.0, 17.7, 379.153, 36.422)
40        318       16       (64.0, 3.3, 382.0, 22.022)
41    324.335       16    (50.0, -11.1, 374.335, 7.622)

para1: 2
para1: 3
para1: 4
para1: 5
para1: 6
para1: 7
para1: 8
para1: 9
para1: 10
para1: 11
para1: 12
para1: 13
para1: 14
para1: 15
para1: 16

parafix_df = main_df[["line_text", "line_width", "para_num", "bbox" ]]
parafix_df["new_para_num"] = 0

max_width = parafix_df['line_width'].max()
bbox_max_width = parafix_df.loc[selected['line_width'] == max_width].iloc[0]["bbox"]

para1 = 1
for indx, current in enumerate(parafix_df.iterrows(), start=0):
    if indx!=0:
        bbox_current = current[1]["bbox"]
        if bbox_current[0]>bbox_max_width[0]:
            para1 += 1
    parafix_df.iloc[indx, 4] = para1

---

我们可以优化更多吗？

更新：

>>> parafix_df = main_df[["line_width", "para_num", "bbox" ]]
>>> parafix_df
   line_width para_num                             bbox
0     238.546      NaN  (50.0, 579.3, 288.546, 598.022)
1         318        1    (64.0, 564.9, 382.0, 583.622)
2         332        2    (50.0, 550.5, 382.0, 569.222)
3         332        2    (50.0, 536.1, 382.0, 554.822)
4     328.977        2  (50.0, 521.7, 378.977, 540.422)
5         318        3    (64.0, 507.3, 382.0, 526.022)
6         332        3    (50.0, 492.9, 382.0, 511.622)
7         332        3    (50.0, 478.5, 382.0, 497.222)
8         332        3    (50.0, 464.1, 382.0, 482.822)
9         332        3    (50.0, 449.7, 382.0, 468.422)
10      59.04        3   (50.0, 435.3, 109.04, 454.022)
11    304.007        4  (64.0, 420.9, 368.007, 439.622)
12        318        5    (64.0, 406.5, 382.0, 425.222)
13        332        5    (50.0, 392.1, 382.0, 410.822)
14        332        5    (50.0, 377.7, 382.0, 396.422)
15        332        5    (50.0, 363.3, 382.0, 382.022)
16     43.252        5   (50.0, 348.9, 93.252, 367.622)
17        318        6    (64.0, 334.5, 382.0, 353.222)
18        332        6    (50.0, 320.1, 382.0, 338.822)
19        332        6    (50.0, 305.7, 382.0, 324.422)
20        332        6    (50.0, 291.3, 382.0, 310.022)
21        332        6    (50.0, 276.9, 382.0, 295.622)
22     317.02        6   (50.0, 262.5, 367.02, 281.222)
23        318        7    (64.0, 248.1, 382.0, 266.822)
24        332        7    (50.0, 233.7, 382.0, 252.422)
25     47.014        7   (50.0, 219.3, 97.014, 238.022)
26        318        8    (64.0, 204.9, 382.0, 223.622)
27    316.723        8  (50.0, 190.5, 366.723, 209.222)
28        318        9    (64.0, 176.1, 382.0, 194.822)
29    326.766        9  (50.0, 161.7, 376.766, 180.422)
30        318       10    (64.0, 147.3, 382.0, 166.022)
31        332       10    (50.0, 132.9, 382.0, 151.622)
32        332       10    (50.0, 118.5, 382.0, 137.222)
33    305.393       11  (64.0, 104.1, 369.393, 122.822)
34        318       12     (64.0, 89.7, 382.0, 108.422)
35        318       13      (64.0, 75.3, 382.0, 94.022)
36    319.165       13    (50.0, 60.9, 369.165, 79.622)
37    308.165       14    (64.0, 46.5, 372.165, 65.222)
38        318       15      (64.0, 32.1, 382.0, 50.822)
39    329.153       15    (50.0, 17.7, 379.153, 36.422)
40        318       16       (64.0, 3.3, 382.0, 22.022)
41    324.335       16    (50.0, -11.1, 374.335, 7.622)

para1: 2
para1: 3
para1: 4
para1: 5
para1: 6
para1: 7
para1: 8
para1: 9
para1: 10
para1: 11
para1: 12
para1: 13
para1: 14
para1: 15
para1: 16

parafix_df = main_df[["line_text", "line_width", "para_num", "bbox" ]]
parafix_df["new_para_num"] = 0

max_width = parafix_df['line_width'].max()
bbox_max_width = parafix_df.loc[selected['line_width'] == max_width].iloc[0]["bbox"]

para1 = 1
for indx, current in enumerate(parafix_df.iterrows(), start=0):
    if indx!=0:
        bbox_current = current[1]["bbox"]
        if bbox_current[0]>bbox_max_width[0]:
            para1 += 1
    parafix_df.iloc[indx, 4] = para1

IIUC，你可以这样做：

df.new_para_num = 1

In [210]: df.loc[df.line_width == df.line_width.max(), 'new_para_num'].cumsum() + 1
Out[210]:
2      2
3      3
6      4
7      5
8      6
9      7
13     8
14     9
15    10
18    11
19    12
20    13
21    14
24    15
31    16
32    17
Name: new_para_num, dtype: int64

如果要有条件地更新原始DF中的

new_para_num

列：

In [223]: df.new_para_num = 1

In [224]: selected = df.loc[df.line_width == df.line_width.max()].copy()

In [226]: selected.new_para_num = selected.new_para_num.cumsum() + 1

In [227]: selected
Out[227]:
    line_width  para_num                           bbox  new_para_num
2        332.0       2.0  [50.0, 550.5, 382.0, 569.222]             2
3        332.0       2.0  [50.0, 536.1, 382.0, 554.822]             3
6        332.0       3.0  [50.0, 492.9, 382.0, 511.622]             4
7        332.0       3.0  [50.0, 478.5, 382.0, 497.222]             5
8        332.0       3.0  [50.0, 464.1, 382.0, 482.822]             6
9        332.0       3.0  [50.0, 449.7, 382.0, 468.422]             7
13       332.0       5.0  [50.0, 392.1, 382.0, 410.822]             8
14       332.0       5.0  [50.0, 377.7, 382.0, 396.422]             9
15       332.0       5.0  [50.0, 363.3, 382.0, 382.022]            10
18       332.0       6.0  [50.0, 320.1, 382.0, 338.822]            11
19       332.0       6.0  [50.0, 305.7, 382.0, 324.422]            12
20       332.0       6.0  [50.0, 291.3, 382.0, 310.022]            13
21       332.0       6.0  [50.0, 276.9, 382.0, 295.622]            14
24       332.0       7.0  [50.0, 233.7, 382.0, 252.422]            15
31       332.0      10.0  [50.0, 132.9, 382.0, 151.622]            16
32       332.0      10.0  [50.0, 118.5, 382.0, 137.222]            17

In [228]: df.loc[df.line_width == df.line_width.max(), 'new_para_num'] = selected

In [229]: df
Out[229]:
    line_width  para_num                             bbox  new_para_num
0      238.546       NaN  [50.0, 579.3, 288.546, 598.022]             1
1      318.000       1.0    [64.0, 564.9, 382.0, 583.622]             1
2      332.000       2.0    [50.0, 550.5, 382.0, 569.222]             2
3      332.000       2.0    [50.0, 536.1, 382.0, 554.822]             3
4      328.977       2.0  [50.0, 521.7, 378.977, 540.422]             1
5      318.000       3.0    [64.0, 507.3, 382.0, 526.022]             1
6      332.000       3.0    [50.0, 492.9, 382.0, 511.622]             4
7      332.000       3.0    [50.0, 478.5, 382.0, 497.222]             5
8      332.000       3.0    [50.0, 464.1, 382.0, 482.822]             6
9      332.000       3.0    [50.0, 449.7, 382.0, 468.422]             7
10      59.040       3.0   [50.0, 435.3, 109.04, 454.022]             1
11     304.007       4.0  [64.0, 420.9, 368.007, 439.622]             1
12     318.000       5.0    [64.0, 406.5, 382.0, 425.222]             1
13     332.000       5.0    [50.0, 392.1, 382.0, 410.822]             8
14     332.000       5.0    [50.0, 377.7, 382.0, 396.422]             9
15     332.000       5.0    [50.0, 363.3, 382.0, 382.022]            10
16      43.252       5.0   [50.0, 348.9, 93.252, 367.622]             1
17     318.000       6.0    [64.0, 334.5, 382.0, 353.222]             1
18     332.000       6.0    [50.0, 320.1, 382.0, 338.822]            11
19     332.000       6.0    [50.0, 305.7, 382.0, 324.422]            12
20     332.000       6.0    [50.0, 291.3, 382.0, 310.022]            13
21     332.000       6.0    [50.0, 276.9, 382.0, 295.622]            14
22     317.020       6.0   [50.0, 262.5, 367.02, 281.222]             1
23     318.000       7.0    [64.0, 248.1, 382.0, 266.822]             1
24     332.000       7.0    [50.0, 233.7, 382.0, 252.422]            15
25      47.014       7.0   [50.0, 219.3, 97.014, 238.022]             1
26     318.000       8.0    [64.0, 204.9, 382.0, 223.622]             1
27     316.723       8.0  [50.0, 190.5, 366.723, 209.222]             1
28     318.000       9.0    [64.0, 176.1, 382.0, 194.822]             1
29     326.766       9.0  [50.0, 161.7, 376.766, 180.422]             1
30     318.000      10.0    [64.0, 147.3, 382.0, 166.022]             1
31     332.000      10.0    [50.0, 132.9, 382.0, 151.622]            16
32     332.000      10.0    [50.0, 118.5, 382.0, 137.222]            17
33     305.393      11.0  [64.0, 104.1, 369.393, 122.822]             1
34     318.000      12.0     [64.0, 89.7, 382.0, 108.422]             1
35     318.000      13.0      [64.0, 75.3, 382.0, 94.022]             1
36     319.165      13.0    [50.0, 60.9, 369.165, 79.622]             1
37     308.165      14.0    [64.0, 46.5, 372.165, 65.222]             1
38     318.000      15.0      [64.0, 32.1, 382.0, 50.822]             1
39     329.153      15.0    [50.0, 17.7, 379.153, 36.422]             1
40     318.000      16.0       [64.0, 3.3, 382.0, 22.022]             1
41     324.335      16.0    [50.0, -11.1, 374.335, 7.622]             1

PS，但我仍然不确定我是否正确理解了你的目标

旧答案：

>>> parafix_df = main_df[["line_width", "para_num", "bbox" ]]
>>> parafix_df
   line_width para_num                             bbox
0     238.546      NaN  (50.0, 579.3, 288.546, 598.022)
1         318        1    (64.0, 564.9, 382.0, 583.622)
2         332        2    (50.0, 550.5, 382.0, 569.222)
3         332        2    (50.0, 536.1, 382.0, 554.822)
4     328.977        2  (50.0, 521.7, 378.977, 540.422)
5         318        3    (64.0, 507.3, 382.0, 526.022)
6         332        3    (50.0, 492.9, 382.0, 511.622)
7         332        3    (50.0, 478.5, 382.0, 497.222)
8         332        3    (50.0, 464.1, 382.0, 482.822)
9         332        3    (50.0, 449.7, 382.0, 468.422)
10      59.04        3   (50.0, 435.3, 109.04, 454.022)
11    304.007        4  (64.0, 420.9, 368.007, 439.622)
12        318        5    (64.0, 406.5, 382.0, 425.222)
13        332        5    (50.0, 392.1, 382.0, 410.822)
14        332        5    (50.0, 377.7, 382.0, 396.422)
15        332        5    (50.0, 363.3, 382.0, 382.022)
16     43.252        5   (50.0, 348.9, 93.252, 367.622)
17        318        6    (64.0, 334.5, 382.0, 353.222)
18        332        6    (50.0, 320.1, 382.0, 338.822)
19        332        6    (50.0, 305.7, 382.0, 324.422)
20        332        6    (50.0, 291.3, 382.0, 310.022)
21        332        6    (50.0, 276.9, 382.0, 295.622)
22     317.02        6   (50.0, 262.5, 367.02, 281.222)
23        318        7    (64.0, 248.1, 382.0, 266.822)
24        332        7    (50.0, 233.7, 382.0, 252.422)
25     47.014        7   (50.0, 219.3, 97.014, 238.022)
26        318        8    (64.0, 204.9, 382.0, 223.622)
27    316.723        8  (50.0, 190.5, 366.723, 209.222)
28        318        9    (64.0, 176.1, 382.0, 194.822)
29    326.766        9  (50.0, 161.7, 376.766, 180.422)
30        318       10    (64.0, 147.3, 382.0, 166.022)
31        332       10    (50.0, 132.9, 382.0, 151.622)
32        332       10    (50.0, 118.5, 382.0, 137.222)
33    305.393       11  (64.0, 104.1, 369.393, 122.822)
34        318       12     (64.0, 89.7, 382.0, 108.422)
35        318       13      (64.0, 75.3, 382.0, 94.022)
36    319.165       13    (50.0, 60.9, 369.165, 79.622)
37    308.165       14    (64.0, 46.5, 372.165, 65.222)
38        318       15      (64.0, 32.1, 382.0, 50.822)
39    329.153       15    (50.0, 17.7, 379.153, 36.422)
40        318       16       (64.0, 3.3, 382.0, 22.022)
41    324.335       16    (50.0, -11.1, 374.335, 7.622)

para1: 2
para1: 3
para1: 4
para1: 5
para1: 6
para1: 7
para1: 8
para1: 9
para1: 10
para1: 11
para1: 12
para1: 13
para1: 14
para1: 15
para1: 16

parafix_df = main_df[["line_text", "line_width", "para_num", "bbox" ]]
parafix_df["new_para_num"] = 0

max_width = parafix_df['line_width'].max()
bbox_max_width = parafix_df.loc[selected['line_width'] == max_width].iloc[0]["bbox"]

para1 = 1
for indx, current in enumerate(parafix_df.iterrows(), start=0):
    if indx!=0:
        bbox_current = current[1]["bbox"]
        if bbox_current[0]>bbox_max_width[0]:
            para1 += 1
    parafix_df.iloc[indx, 4] = para1

您可以使用函数访问上一行和下一行：

df.shift(-1)  # df will be shifted one row backwards (will show `next` row) 

df.shift(1)  # df will be shifted one row forwards (will show `prev` row)

例如：

In [142]: df
Out[142]:
   a  b  c
0  8  3  0
1  8  3  4
2  9  4  1
3  2  1  8
4  5  6  3

In [147]: df['prev_a'] = df.a.shift(1)

In [148]: df['next_a'] = df.a.shift(-1)

In [149]: df
Out[149]:
   a  b  c  prev_a  next_a
0  8  3  0     NaN     8.0
1  8  3  4     8.0     9.0
2  9  4  1     8.0     2.0
3  2  1  8     9.0     5.0
4  5  6  3     2.0     NaN

---

谢谢你+10，但我无法反映

new\u para\u num

column@VivekSable，您能否发布示例输入和所需输出数据集（CSV/dict/JSON/Python代码格式的5-7行作为文本，因此在编码时可以使用它），并描述您希望如何处理输入数据以获得输出数据集？我想根据逻辑为新列赋值。（逻辑我将更新）@VivekSable，您是否也可以为输入数据集的前5行（这应该足够了）添加所需的输出-这将有助于更好地理解您的逻辑？实际我的逻辑不正确，我只想为

for

循环的

中的每一行分配
新参数中的值。运行上述代码后，我将获得所有行的
0
。在您的代码中，您总是只访问第二行（索引为：
1
）-
当前[1]
，
下一行[1]
。但是我想你不需要通过你的DF循环来实现你的目标——应该可以用“熊猫”的方式来实现——是的，我是熊猫的新手。你能再检查一下我的代码吗？我更新了代码。