pandas 将第一行与数据框中的列标题合并
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Merge the first row with the column headers in a dataframe
提问by Anna Jeanine
I am trying to clean up a Excel file for some further research. Problem that I have, I want to merge the first and second row. The code which I have now:
我正在尝试清理 Excel 文件以进行进一步研究。我遇到的问题，我想合并第一行和第二行。我现在拥有的代码：
xl = pd.ExcelFile("nanonose.xls")
df = xl.parse("Sheet1")
df = df.drop('Unnamed: 2', axis=1)
## Tried this line but no luck
##print(df.head().combine_first(df.iloc[[0]]))
The output of this is:
这个的输出是：
Nanonose Unnamed: 1 A B C D E \
0 Sample type Concentration NaN NaN NaN NaN NaN
1 Water 9200 95.5 21.0 6.0 11.942308 64.134615
2 Water 9200 94.5 17.0 5.0 5.484615 63.205769
3 Water 9200 92.0 16.0 3.0 11.057692 62.586538
4 Water 4600 53.0 7.5 2.5 3.538462 35.163462
F G H
0 NaN NaN NaN
1 21.498560 5.567840 1.174135
2 19.658560 4.968000 1.883444
3 19.813120 5.192480 0.564835
4 6.876207 1.641724 0.144654
So, my goal is to merge the first and second row to get: Sample type | Concentration | A | B | C | D | E | F | G | H
所以，我的目标是合并第一行和第二行以获得：样本类型 | 浓度| 一个 | 乙 | C | D | E | F | G | H
Could someone help me merge these two rows?
有人可以帮我合并这两行吗？
采纳答案by jezrael
I think you need numpy.concatenate, similar principe like c???s????answer:
我认为你需要numpy.concatenate，像c???s??类似的原理？回答：
df.columns = np.concatenate([df.iloc[0, :2], df.columns[2:]])
df = df.iloc[1:].reset_index(drop=True)
print (df)
Sample type Concentration A B C D E F \
0 Water 9200 95.5 21.0 6.0 11.942308 64.134615 21.498560
1 Water 9200 94.5 17.0 5.0 5.484615 63.205769 19.658560
2 Water 9200 92.0 16.0 3.0 11.057692 62.586538 19.813120
3 Water 4600 53.0 7.5 2.5 3.538462 35.163462 6.876207
G H
0 5.567840 1.174135
1 4.968000 1.883444
2 5.192480 0.564835
3 1.641724 0.144654
回答by cs95
Just reassign df.columns.
只需重新分配df.columns。
df.columns = np.append(df.iloc[0, :2], df.columns[2:])
Or,
或者，
df.columns = df.iloc[0, :2].tolist() + (df.columns[2:]).tolist()
Next, skip the first row.
接下来，跳过第一行。
df = df.iloc[1:].reset_index(drop=True)
df
Sample type Concentration A B C D E F \
0 Water 9200 95.5 21.0 6.0 11.942308 64.134615 21.498560
1 Water 9200 94.5 17.0 5.0 5.484615 63.205769 19.658560
2 Water 9200 92.0 16.0 3.0 11.057692 62.586538 19.813120
3 Water 4600 53.0 7.5 2.5 3.538462 35.163462 6.876207
G H
0 5.567840 1.174135
1 4.968000 1.883444
2 5.192480 0.564835
3 1.641724 0.144654
reset_indexis optional if you want a 0-index for your final output.
reset_index如果您希望最终输出为 0 索引，则是可选的。
回答by vsnahar
Fetch the all columns present in Second row header then First row header. combine them to make a "all columns name header" list. now create a df with excel by taking header as header[0,1]. now replace its headers with all column name headers you created previously.
获取第二行标题中存在的所有列，然后是第一行标题。将它们组合成一个“所有列名称标题”列表。现在通过将标题作为标题 [0,1] 创建一个带有 excel 的 df。现在用您之前创建的所有列名称标题替换其标题。
import pandas as pd
#reading Second header row columns
df1 = pd.read_excel('nanonose.xls', header=[1] , index = False)
cols1 = df1.columns.tolist()
SecondRowColumns = []
for c in cols1:
if ("Unnamed" or "NaN" not in c):
SecondRowColumns.append(c)
#reading First header row columns
df2 = pd.read_excel('nanonose.xls', header=[0] , index = False)
cols2 = df2.columns.tolist()
FirstRowColumns = []
for c in cols2:
if ("Unnamed" or "Nanonose" not in c):
FirstRowColumns.append(c)
AllColumn = []
AllColumn = SecondRowColumns+ FirstRowColumns
df = pd.read_excel('nanonose.xls', header=[0,1] , index=False)
df.columns = AllColumn
print(df)
