Convert row to column header for Pandas DataFrame,

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).

This works (pandas v'0.19.2'):

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

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)

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