如何更改python数据帧中的标题行

Question

我在使用python中的pandas更改现有DataFrame中的标题行时遇到了问题.导入pandas和csv文件后,我将标题行设置为None,以便能够在转置后删除重复的日期.但是这给我留下了一个我不想要的行标题(实际上是一个索引列).

df = pd.read_csv(spreadfile, header=None)

df2 = df.T.drop_duplicates([0], take_last=True)
del df2[1]

indcol = df2.ix[:,0]
df3 = df2.reindex(indcol)

然而,上述缺乏想象力的代码在两个方面都失败了.索引列现在是必需的列,但所有条目现在都是NaN.我对python的理解还不足以识别python正在做什么.以下所需的输出是我需要的,任何帮助将不胜感激!

重建索引前的df2:

     0             2             3             4             5
0        NaN  XS0089553282  XS0089773484  XS0092157600  XS0092541969
1  01-May-14         131.7         165.1         151.8          88.9
3  02-May-14           131         164.9         151.7          88.5
5  05-May-14         131.1           165         151.8          88.6
7  06-May-14         129.9         163.4         151.2          87.1

重建索引后的df2:

             0    2    3    4    5
0                                 
NaN        NaN  NaN  NaN  NaN  NaN
01-May-14  NaN  NaN  NaN  NaN  NaN
02-May-14  NaN  NaN  NaN  NaN  NaN
05-May-14  NaN  NaN  NaN  NaN  NaN
06-May-14  NaN  NaN  NaN  NaN  NaN

df2期望:

       XS0089553282  XS0089773484  XS0092157600  XS0092541969
01-May-14         131.7         165.1         151.8          88.9
02-May-14           131         164.9         151.7          88.5
05-May-14         131.1           165         151.8          88.6
06-May-14         129.9         163.4         151.2          87.1

Answer 1

直接分配列：

indcol = df2.ix[:,0]
df2.columns = indcol

问题reindex在于它将使用 df 的现有索引和列值，因此您传入的新列值不存在，因此为什么您得到所有NaNs

您尝试执行的操作的更简单方法：

In [147]:
# take the cols and index values of interest
cols = df.loc[0, '2':]
idx = df['0'].iloc[1:]
print(cols)
print(idx)

2    XS0089553282
3    XS0089773484
4    XS0092157600
5    XS0092541969
Name: 0, dtype: object

1    01-May-14
3    02-May-14
5    05-May-14
7    06-May-14
Name: 0, dtype: object

In [157]:
# drop the first row and the first column
df2 = df.drop('0', axis=1).drop(0)
# overwrite the index values
df2.index = idx.values
df2

Out[157]:
               2      3      4     5
01-May-14  131.7  165.1  151.8  88.9
02-May-14    131  164.9  151.7  88.5
05-May-14  131.1    165  151.8  88.6
06-May-14  129.9  163.4  151.2  87.1

In [158]:
# now overwrite the column values    
df2.columns = cols.values
df2

Out[158]:
          XS0089553282 XS0089773484 XS0092157600 XS0092541969
01-May-14        131.7        165.1        151.8         88.9
02-May-14          131        164.9        151.7         88.5
05-May-14        131.1          165        151.8         88.6
06-May-14        129.9        163.4        151.2         87.1