我有以下数据帧：
pd.Dataframe({'Col1': {0: '04/01/2021',
1: '04/01/2021',
2: '04/01/2021',
3: '04/01/2021',
4: '04/01/2021',
5: '05/01/2021',
6: '05/01/2021',
7: '05/01/2021',
8: '06/01/2021',
9: '06/01/2021',
10: '06/01/2021',
11: '06/01/2021',
12: '06/01/2021',
13: '06/01/2021',
14: '06/01/2021',
15: '06/01/2021',
16: '07/01/2021',
17: '07/01/2021',
18: '07/01/2021',
19: '07/01/2021',
20: '07/01/2021',
21: '07/01/2021',
22: '07/01/2021',
23: '08/01/2021',
24: '08/01/2021',
25: '08/01/2021',
26: '08/01/2021',
27: '08/01/2021',
28: '08/01/2021',
29: '08/01/2021',
30: '08/01/2021',
31: '08/01/2021',
32: '08/01/2021',
33: '08/01/2021',
34: '08/01/2021',
35: '08/01/2021',
36: '08/01/2021',
37: '08/01/2021',
38: '11/01/2021',
39: '11/01/2021',
40: '11/01/2021',
41: '11/01/2021',
42: '11/01/2021',
43: '11/01/2021',
44: '11/01/2021',
45: '11/01/2021',
46: '11/01/2021',
47: '11/01/2021',
48: '11/01/2021',
49: '11/01/2021',
50: '11/01/2021',
51: '11/01/2021',
52: '11/01/2021',
53: '11/01/2021',
54: '12/01/2021',
55: '12/01/2021',
56: '12/01/2021',
57: '12/01/2021',
58: '12/01/2021',
59: '12/01/2021',
60: '12/01/2021',
61: '12/01/2021',
62: '12/01/2021',
63: '12/01/2021',
64: '12/01/2021',
65: '12/01/2021',
66: '12/01/2021',
67: '12/01/2021',
68: '12/01/2021',
69: '12/01/2021',
70: '12/01/2021',
71: '13/01/2021',
72: '13/01/2021',
73: '13/01/2021',
74: '13/01/2021',
75: '13/01/2021',
76: '13/01/2021',
77: '13/01/2021',
78: '13/01/2021',
79: '13/01/2021',
80: '13/01/2021',
81: '13/01/2021',
82: '14/01/2021',
83: '14/01/2021',
84: '14/01/2021',
85: '14/01/2021',
86: '14/01/2021',
87: '14/01/2021',
88: '14/01/2021',
89: '14/01/2021',
90: '14/01/2021',
91: '14/01/2021',
92: '14/01/2021',
93: '14/01/2021',
94: '14/01/2021',
95: '14/01/2021',
96: '14/01/2021',
97: '14/01/2021',
98: '14/01/2021',
99: '14/01/2021',
100: '15/01/2021',
101: '15/01/2021',
102: '15/01/2021',
103: '15/01/2021',
104: '15/01/2021',
105: '15/01/2021',
106: nan,
107: nan},
'Col2': {0: 17.0,
1: 93.0,
2: 76.0,
3: 93.0,
4: 72.0,
5: 52.0,
6: 97.0,
7: 78.0,
8: 38.0,
9: 42.0,
10: 40.0,
11: 25.0,
12: 18.0,
13: 23.0,
14: 69.0,
15: 12.0,
16: 5.0,
17: 82.0,
18: 92.0,
19: 98.0,
20: 85.0,
21: 39.0,
22: 37.0,
23: 84.0,
24: 79.0,
25: 40.0,
26: 46.0,
27: 92.0,
28: 53.0,
29: 92.0,
30: 60.0,
31: 36.0,
32: 19.0,
33: 100.0,
34: 36.0,
35: 39.0,
36: 48.0,
37: 59.0,
38: 78.0,
39: 30.0,
40: 22.0,
41: 41.0,
42: 71.0,
43: 73.0,
44: 34.0,
45: 91.0,
46: 47.0,
47: 15.0,
48: 75.0,
49: 2.0,
50: 82.0,
51: 68.0,
52: 46.0,
53: 85.0,
54: 100.0,
55: 93.0,
56: 13.0,
57: 12.0,
58: 14.0,
59: 35.0,
60: 27.0,
61: 75.0,
62: 49.0,
63: 100.0,
64: 98.0,
65: 19.0,
66: 50.0,
67: 88.0,
68: 85.0,
69: 47.0,
70: 91.0,
71: 24.0,
72: 45.0,
73: 86.0,
74: 80.0,
75: 70.0,
76: 73.0,
77: 47.0,
78: 65.0,
79: 19.0,
80: 30.0,
81: 43.0,
82: 73.0,
83: 10.0,
84: 57.0,
85: 3.0,
86: 56.0,
87: 76.0,
88: 51.0,
89: 65.0,
90: 94.0,
91: 23.0,
92: 78.0,
93: 24.0,
94: 94.0,
95: 74.0,
96: 95.0,
97: 11.0,
98: 1.0,
99: 90.0,
100: 78.0,
101: 20.0,
102: 11.0,
103: 69.0,
104: 75.0,
105: 33.0,
106: nan,
107: nan},
'Col3': {0: '06/01/2021',
1: '06/01/2021',
2: '06/01/2021',
3: '06/01/2021',
4: '06/01/2021',
5: '06/01/2021',
6: '06/01/2021',
7: '07/01/2021',
8: '07/01/2021',
9: '07/01/2021',
10: '07/01/2021',
11: '07/01/2021',
12: '07/01/2021',
13: '07/01/2021',
14: '08/01/2021',
15: '11/01/2021',
16: '11/01/2021',
17: '11/01/2021',
18: '11/01/2021',
19: '11/01/2021',
20: '11/01/2021',
21: '11/01/2021',
22: '11/01/2021',
23: '11/01/2021',
24: '12/01/2021',
25: '12/01/2021',
26: '12/01/2021',
27: '12/01/2021',
28: '12/01/2021',
29: '12/01/2021',
30: '12/01/2021',
31: '12/01/2021',
32: '12/01/2021',
33: '12/01/2021',
34: '12/01/2021',
35: '12/01/2021',
36: '12/01/2021',
37: '12/01/2021',
38: '12/01/2021',
39: '12/01/2021',
40: '12/01/2021',
41: '13/01/2021',
42: '13/01/2021',
43: '13/01/2021',
44: '13/01/2021',
45: '06/01/2021',
46: '07/01/2021',
47: '07/01/2021',
48: '07/01/2021',
49: '07/01/2021',
50: '07/01/2021',
51: '07/01/2021',
52: '07/01/2021',
53: '08/01/2021',
54: '08/01/2021',
55: '08/01/2021',
56: '08/01/2021',
57: '08/01/2021',
58: '08/01/2021',
59: '08/01/2021',
60: '08/01/2021',
61: '08/01/2021',
62: '08/01/2021',
63: '08/01/2021',
64: '08/01/2021',
65: '08/01/2021',
66: '08/01/2021',
67: '08/01/2021',
68: '06/01/2021',
69: '06/01/2021',
70: '07/01/2021',
71: '07/01/2021',
72: '07/01/2021',
73: '07/01/2021',
74: '07/01/2021',
75: '07/01/2021',
76: '07/01/2021',
77: '08/01/2021',
78: '08/01/2021',
79: '08/01/2021',
80: '08/01/2021',
81: '08/01/2021',
82: '08/01/2021',
83: '08/01/2021',
84: '08/01/2021',
85: '08/01/2021',
86: '08/01/2021',
87: '08/01/2021',
88: '08/01/2021',
89: nan,
90: nan,
91: nan,
92: nan,
93: nan,
94: nan,
95: nan,
96: nan,
97: nan,
98: nan,
99: nan,
100: nan,
101: nan,
102: nan,
103: nan,
104: nan,
105: nan,
106: nan,
107: nan},
'Col4': {0: 40.0,
1: 45.0,
2: 63.0,
3: 10.0,
4: 52.0,
5: 71.0,
6: 24.0,
7: 44.0,
8: 10.0,
9: 64.0,
10: 43.0,
11: 5.0,
12: 34.0,
13: 63.0,
14: 86.0,
15: 81.0,
16: 81.0,
17: 4.0,
18: 25.0,
19: 68.0,
20: 48.0,
21: 68.0,
22: 33.0,
23: 32.0,
24: 70.0,
25: 10.0,
26: 11.0,
27: 70.0,
28: 44.0,
29: 75.0,
30: 80.0,
31: 52.0,
32: 67.0,
33: 27.0,
34: 38.0,
35: 49.0,
36: 26.0,
37: 23.0,
38: 95.0,
39: 9.0,
40: 29.0,
41: 47.0,
42: 19.0,
43: 21.0,
44: 88.0,
45: 81.0,
46: 95.0,
47: 62.0,
48: 32.0,
49: 75.0,
50: 82.0,
51: 39.0,
52: 43.0,
53: 55.0,
54: 70.0,
55: 50.0,
56: 82.0,
57: 5.0,
58: 2.0,
59: 89.0,
60: 100.0,
61: 43.0,
62: 61.0,
63: 43.0,
64: 68.0,
65: 41.0,
66: 76.0,
67: 49.0,
68: 12.0,
69: 36.0,
70: 13.0,
71: 54.0,
72: 64.0,
73: 66.0,
74: 19.0,
75: 45.0,
76: 66.0,
77: 51.0,
78: 47.0,
79: 92.0,
80: 90.0,
81: 79.0,
82: 69.0,
83: 67.0,
84: 72.0,
85: 13.0,
86: 50.0,
87: 12.0,
88: 69.0,
89: nan,
90: nan,
91: nan,
92: nan,
93: nan,
94: nan,
95: nan,
96: nan,
97: nan,
98: nan,
99: nan,
100: nan,
101: nan,
102: nan,
103: nan,
104: nan,
105: nan,
106: nan,
107: nan},
'Col5': {0: '12/01/2021',
1: '12/01/2021',
2: '12/01/2021',
3: '12/01/2021',
4: '12/01/2021',
5: '12/01/2021',
6: '12/01/2021',
7: '12/01/2021',
8: '12/01/2021',
9: '13/01/2021',
10: '13/01/2021',
11: '13/01/2021',
12: '13/01/2021',
13: '13/01/2021',
14: '13/01/2021',
15: '13/01/2021',
16: '13/01/2021',
17: '13/01/2021',
18: '13/01/2021',
19: '13/01/2021',
20: '14/01/2021',
21: '14/01/2021',
22: '14/01/2021',
23: '14/01/2021',
24: '14/01/2021',
25: '14/01/2021',
26: '14/01/2021',
27: '14/01/2021',
28: '14/01/2021',
29: '14/01/2021',
30: '14/01/2021',
31: '14/01/2021',
32: '14/01/2021',
33: '14/01/2021',
34: '14/01/2021',
35: '14/01/2021',
36: '14/01/2021',
37: '14/01/2021',
38: '15/01/2021',
39: '15/01/2021',
40: '15/01/2021',
41: '15/01/2021',
42: '15/01/2021',
43: '15/01/2021',
44: nan,
45: nan,
46: nan,
47: nan,
48: nan,
49: nan,
50: nan,
51: nan,
52: nan,
53: nan,
54: nan,
55: nan,
56: nan,
57: nan,
58: nan,
59: nan,
60: nan,
61: nan,
62: nan,
63: nan,
64: nan,
65: nan,
66: nan,
67: nan,
68: nan,
69: nan,
70: nan,
71: nan,
72: nan,
73: nan,
74: nan,
75: nan,
76: nan,
77: nan,
78: nan,
79: nan,
80: nan,
81: nan,
82: nan,
83: nan,
84: nan,
85: nan,
86: nan,
87: nan,
88: nan,
89: nan,
90: nan,
91: nan,
92: nan,
93: nan,
94: nan,
95: nan,
96: nan,
97: nan,
98: nan,
99: nan,
100: nan,
101: nan,
102: nan,
103: nan,
104: nan,
105: nan,
106: nan,
107: nan},
'Col6': {0: 466.0,
1: 421.0,
2: 497.0,
3: 487.0,
4: 426.0,
5: 421.0,
6: 408.0,
7: 461.0,
8: 470.0,
9: 419.0,
10: 492.0,
11: 406.0,
12: 424.0,
13: 496.0,
14: 475.0,
15: 431.0,
16: 457.0,
17: 449.0,
18: 466.0,
19: 458.0,
20: 491.0,
21: 484.0,
22: 404.0,
23: 474.0,
24: 459.0,
25: 421.0,
26: 461.0,
27: 484.0,
28: 493.0,
29: 496.0,
30: 439.0,
31: 465.0,
32: 414.0,
33: 449.0,
34: 490.0,
35: 416.0,
36: 432.0,
37: 472.0,
38: 465.0,
39: 428.0,
40: 422.0,
41: 414.0,
42: 471.0,
43: 450.0,
44: nan,
45: nan,
46: nan,
47: nan,
48: nan,
49: nan,
50: nan,
51: nan,
52: nan,
53: nan,
54: nan,
55: nan,
56: nan,
57: nan,
58: nan,
59: nan,
60: nan,
61: nan,
62: nan,
63: nan,
64: nan,
65: nan,
66: nan,
67: nan,
68: nan,
69: nan,
70: nan,
71: nan,
72: nan,
73: nan,
74: nan,
75: nan,
76: nan,
77: nan,
78: nan,
79: nan,
80: nan,
81: nan,
82: nan,
83: nan,
84: nan,
85: nan,
86: nan,
87: nan,
88: nan,
89: nan,
90: nan,
91: nan,
92: nan,
93: nan,
94: nan,
95: nan,
96: nan,
97: nan,
98: nan,
99: nan,
100: nan,
101: nan,
102: nan,
103: nan,
104: nan,
105: nan,
106: nan,
107: nan}})
Col1是主数据列。我需要将第3列和第5列的日期合并到主栏。最终的数据帧应该是将Col3和Col5合并到Col1(主日期字段(，并保留所有这些日期的值。
最好是自动删除日期字段Col3和Col5，并且只保留末尾的值字段。
Col1(日期(图的值在Col2中，Col3(日期(地图的值在Col4中，Col5(日期(映射的值在Col6中。
基于此，是否有一个简单的Pandas代码可以用于执行上述预期操作？
我不知道你说的"合并"；在这种情况下。你的日期不是唯一的，所以我想你想把Col3/4和Col5/6连接到Col1/2(在这个过程中重命名Col3和Col5(：
df = pd.DataFrame({'Col1': {0: '04/01/2021',
...
107: nan}})
df_12 = df[["Col1", "Col2"]].dropna()
df_34 = df[["Col3", "Col4"]].dropna()
df_34.columns = ["Col1", "Col4"]
df_56 = df[["Col5", "Col6"]].dropna()
df_56.columns = ["Col1", "Col6"]
df_new = pd.concat([df_12, df_34, df_56], ignore_index=True)
print(df_new)
输出：
Col1 Col2 Col4 Col6
0 04/01/2021 17.0 NaN NaN
1 04/01/2021 93.0 NaN NaN
2 04/01/2021 76.0 NaN NaN
3 04/01/2021 93.0 NaN NaN
4 04/01/2021 72.0 NaN NaN
.. ... ... ... ...
234 15/01/2021 NaN NaN 428.0
235 15/01/2021 NaN NaN 422.0
236 15/01/2021 NaN NaN 414.0
237 15/01/2021 NaN NaN 471.0
238 15/01/2021 NaN NaN 450.0