Python 3.x 熊猫数据清理

因此，我正在将表格从PDF读入pandas数据框，但我对pandas还是相当陌生，阅读文档非常令人畏惧。我相信有一个相当简单的方法来做我需要做的事情，但我只是不知道如何做

          0                    1           2        3                4                5       6       7                      8              9        10               11          12   13
0        NaN                 col0        col1     col2             col3             col4    col5    col6                   col7           col8     col9            col10       col11  NaN
1        NaN             Location        Date      NaN              NaN              NaN     NaN     NaN                    NaN            NaN      NaN              NaN         NaN  NaN
2        NaN             measure1         1**     40**             30**             20**      20  0.02**                    3**           10**      5**            100**        15**  NaN
3        NaN             measure2         100      400              300              200     200       2                    300            100       50            1,000         150  NaN
4        NaN            location1   1/15/1994     5900            28000             7600   25000     150                    ---            ---      ---              ---         ---  ---
5        NaN                  NaN   3/16/1994     4900            12000             4400   11000      60                    ---            ---      ---              ---         ---  ---
6        NaN                  NaN    1/4/1995        1                1                1       1       8                    ---            ---      ---              ---         ---  ---
7        NaN                  NaN   4/12/2004     8400            34000             4600   17000   <1000                    ---            ---      ---              ---         ---  ---
8        NaN                  NaN   7/28/2008     3200            15400             4430   17100  172  I                    ---            ---      ---              ---         ---  ---
9        NaN                  NaN   5/19/2011     2000            11000             2500    9200  0.2  1                    ---            ---      ---              ---         ---  ---
10       NaN                  NaN    8/6/2013     2700            20000             5300   20000    2  6                    ---            ---      ---              ---         ---  ---
11       NaN                  NaN  11/13/2013     2600            14000             5400   20000  0.1  3                    ---            ---      ---              ---         ---  ---
12       NaN                  NaN    2/5/2014     3200            19000             6400   25000   18  0                    ---            ---      ---              ---         ---  ---
13       NaN                  NaN    5/7/2014     2000            15000             4100   16000   22  0                    ---            ---      ---              ---         ---  ---
14       NaN                  NaN  12/18/2014     2500            32000             5200   20000    8  8                    ---            ---      ---              ---         ---  ---
15       NaN                  NaN    6/4/2015     1700            15000             5200   21000   44  0                    ---            ---      ---              ---         ---  ---
16       NaN                  NaN   1/20/2017     1400           15,000            6,300  21,000    1  2                    ---            ---      ---              ---         ---  ---
17       NaN            location2   1/15/1994      210              290               39     180      69                    ---            ---      ---              ---         ---  ---
18       NaN                  NaN   3/24/1994     1500            12000             4100   18000  400  0                    ---            ---      ---              ---         ---  ---
19       NaN                  NaN    1/4/1995        1                1                1       1       8                    ---            ---      ---              ---         ---  ---
20       NaN                  NaN    2/1/2000    <1000             8900             5200   58000  <10000                    ---            ---      ---              ---         ---  ---
21       NaN                  NaN   4/12/2004     <5.0               42               78     540     150                    ---            ---      ---              ---         ---  ---
22       NaN                  NaN   7/28/2008     23.3             27.9               28     409    9.34                    ---            ---      ---              ---         ---  ---
23       NaN                  NaN   5/19/2011      1.8               12               22     170  0.2  1                    ---            ---      ---              ---         ---  ---
24       NaN                  NaN    8/6/2013      4.3               23               71     590  0.1  3                    ---            ---      ---              ---         ---  ---
25       NaN                  NaN   1/19/2017   0.21 I           0.26 I              7.7      42  0.2  4                    ---            ---      ---              ---         ---  ---
26       NaN            location3   3/21/1994       <1               <1               <1      <1      <8                    ---            ---      ---              ---         ---  ---
27  2/1/2000                   <1          <1       <1               <2              <10     ---     ---                    ---            ---      ---              ---         NaN  NaN

01123456789101123
0列NaN列0列1列2列3列4列5列6列7列8列9列10列11列NaN
1楠位置日期楠楠楠楠
2 NaN测量1**40**30**20**20 0.02**3**10**5**100**15**NaN
3 NaN测量2 100 400 300 200 2 300 100 50 1000 150 NaN
4南区1/15/1994 5900 28000 7600 25000 150---
5南南3/16/1994 4900 12000 4400 11000 60-------
6楠楠1/4/1995 18--------
7楠楠4/12/2004 8400 34000 4600 17000关于第一点，您可以尝试以下方法：

df = df.T
df.iloc[:,-1] = df.iloc[:,-1].shift(1)
df = df.T
df = df.drop(df.columns[0], axis=1)

最后一点：

df['1'] = df['1'].ffill()

非常感谢，菲尔的工作很神奇。至于其他两点，我通过使用df.T转置数据帧并在列上使用shift（）使它们工作。我不知道切片表示法在数据帧上有效。只需使用axis=1进行移位即可。不需要换位。@Nick不客气！请考虑投票并接受答案：
df['1'] = df['1'].ffill()