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将Pandas DataFrame的行转换为列标题，

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问题：将Pandas DataFrame的行转换为列标题，
回答 0
回答 1
回答 2
回答 3

问题：将Pandas DataFrame的行转换为列标题，

我必须使用的数据有点混乱。它的数据中包含标头名称。如何从现有的pandas数据框中选择一行并使其（重命名为）列标题？

我想做类似的事情：

header = df[df['old_header_name1'] == 'new_header_name1']

df.columns = header
点击查看英文原文

The data I have to work with is a bit messy.. It has header names inside of its data. How can I choose a row from an existing pandas dataframe and make it (rename it to) a column header?

I want to do something like:

header = df[df['old_header_name1'] == 'new_header_name1']

df.columns = header

回答 0

In [21]: df = pd.DataFrame([(1,2,3), ('foo','bar','baz'), (4,5,6)])

In [22]: df
Out[22]: 
     0    1    2
0    1    2    3
1  foo  bar  baz
2    4    5    6

将列标签设置为等于第二行（索引位置1）中的值：

In [23]: df.columns = df.iloc[1]

如果索引具有唯一标签，则可以使用以下命令删除第二行：

In [24]: df.drop(df.index[1])
Out[24]: 
1 foo bar baz
0   1   2   3
2   4   5   6

如果索引不是唯一的，则可以使用：

In [133]: df.iloc[pd.RangeIndex(len(df)).drop(1)]
Out[133]: 
1 foo bar baz
0   1   2   3
2   4   5   6

使用df.drop(df.index[1])删除所有与第二行具有相同标签的行。因为非唯一索引可能会导致像这样的绊脚石（或潜在的错误），所以通常最好注意索引的唯一性（即使Pandas不需要它）。

点击查看英文原文 
In [21]: df = pd.DataFrame([(1,2,3), ('foo','bar','baz'), (4,5,6)])

In [22]: df
Out[22]: 
     0    1    2
0    1    2    3
1  foo  bar  baz
2    4    5    6

Set the column labels to equal the values in the 2nd row (index location 1):

In [23]: df.columns = df.iloc[1]

If the index has unique labels, you can drop the 2nd row using:

In [24]: df.drop(df.index[1])
Out[24]: 
1 foo bar baz
0   1   2   3
2   4   5   6

If the index is not unique, you could use:

In [133]: df.iloc[pd.RangeIndex(len(df)).drop(1)]
Out[133]: 
1 foo bar baz
0   1   2   3
2   4   5   6

Using df.drop(df.index[1]) removes all rows with the same label as the second row. Because non-unique indexes can lead to stumbling blocks (or potential bugs) like this, it’s often better to take care that the index is unique (even though Pandas does not require it).

回答 1

这有效（熊猫v’0.19.2’）：

df.rename(columns=df.iloc[0])
点击查看英文原文

This works (pandas v’0.19.2′):

df.rename(columns=df.iloc[0])

回答 2

重新创建数据框会更容易。这也将从头开始解释列的类型。

headers = df.iloc[0]
new_df  = pd.DataFrame(df.values[1:], columns=headers)
点击查看英文原文

It would be easier to recreate the data frame. This would also interpret the columns types from scratch.

headers = df.iloc[0]
new_df  = pd.DataFrame(df.values[1:], columns=headers)

回答 3

您可以通过代表的参数在read_csv或read_html构造函数中指定行索引。这样的优点是可以自动删除所有先前被认为是垃圾的行。headerRow number(s) to use as the column names, and the start of the data

import pandas as pd
from io import StringIO

In[1]
    csv = '''junk1, junk2, junk3, junk4, junk5
    junk1, junk2, junk3, junk4, junk5
    pears, apples, lemons, plums, other
    40, 50, 61, 72, 85
    '''

    df = pd.read_csv(StringIO(csv), header=2)
    print(df)

Out[1]
       pears   apples   lemons   plums   other
    0     40       50       61      72      85
点击查看英文原文

You can specify the row index in the read_csv or read_html constructors via the header parameter which represents Row number(s) to use as the column names, and the start of the data. This has the advantage of automatically dropping all the preceding rows which supposedly are junk.

import pandas as pd
from io import StringIO

In[1]
    csv = '''junk1, junk2, junk3, junk4, junk5
    junk1, junk2, junk3, junk4, junk5
    pears, apples, lemons, plums, other
    40, 50, 61, 72, 85
    '''

    df = pd.read_csv(StringIO(csv), header=2)
    print(df)

Out[1]
       pears   apples   lemons   plums   other
    0     40       50       61      72      85