所以我有一个数据框,我想要的标题目前没有排队:
In [1]: df = pd.read_excel('example.xlsx')
print (df.head(10))
Out [1]: Portfolio Asset Country Quantity
Unique Identifier Number of fund B24 B65 B35 B44
456 2 General Type A UNITED KINGDOM 1
123 3 General Type B US 2
789 2 General Type C UNITED KINGDOM 4
4852 4 General Type C UNITED KINGDOM 4
654 1 General Type A FRANCE 3
987 5 General Type B UNITED KINGDOM 2
321 1 General Type B GERMANY 1
951 3 General Type A UNITED KINGDOM 2
357 4 General Type C UNITED KINGDOM 3
我们可以看到;在前2列标题上面有2个空白单元格,在接下来的4列标题下方是“B”数字,我不在乎。
所以2个问题;如何在没有列标题的情况下向上移动前两列(由于上面的空白单元格)?
如何删除其余列中的第2行并将下面的数据移到上面取代“B”数字?
我发现了一些类似的问题python: shift column in pandas dataframe up by one,但没有解决上述特定错综复杂的问题我不认为。
此外,我对Python和Pandas都很陌生,所以如果这是非常基本的我道歉!
答案 0 :(得分:1)
您可以使用的IIUC:
#create df from multiindex in columns
df1 = pd.DataFrame([x for x in df.columns.values])
print df1
0 1
0 Unique Identifier
1 Number of fund
2 Portfolio B24
3 Asset B65
4 Country B35
5 Quantity B44
#if len of string < 4, give value from column 0 to column 1
df1.loc[df1.iloc[:,1].str.len() < 4, 1] = df1.iloc[:,0]
print df1
0 1
0 Unique Identifier
1 Number of fund
2 Portfolio Portfolio
3 Asset Asset
4 Country Country
5 Quantity Quantity
#set columns by first columns of df1
df.columns = df1.iloc[:,1]
print df
0 Unique Identifier Number of fund Portfolio Asset Country \
0 456 2 General Type A UNITED KINGDOM
1 123 3 General Type B US
2 789 2 General Type C UNITED KINGDOM
3 4852 4 General Type C UNITED KINGDOM
4 654 1 General Type A FRANCE
5 987 5 General Type B UNITED KINGDOM
6 321 1 General Type B GERMANY
7 951 3 General Type A UNITED KINGDOM
8 357 4 General Type C UNITED KINGDOM
0 Quantity
0 1
1 2
2 4
3 4
4 3
5 2
6 1
7 2
8 3
通过评论编辑:
print df.columns
Index([u'Portfolio', u'Asset', u'Country', u'Quantity'], dtype='object')
#set first row by columns names
df.iloc[0,:] = df.columns
#reset_index
df = df.reset_index()
#set columns from first row
df.columns = df.iloc[0,:]
df.columns.name= None
#remove first row
print df.iloc[1:,:]
Unique Identifier Number of fund Portfolio Asset Country Quantity
1 456 2 General Type A UNITED KINGDOM 1
2 123 3 General Type B US 2
3 789 2 General Type C UNITED KINGDOM 4
4 4852 4 General Type C UNITED KINGDOM 4
5 654 1 General Type A FRANCE 3
6 987 5 General Type B UNITED KINGDOM 2
7 321 1 General Type B GERMANY 1
8 951 3 General Type A UNITED KINGDOM 2
9 357 4 General Type C UNITED KINGDOM 3