'Convert row to column header for Pandas DataFrame,

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

Solution 1:^[1]

This works (pandas v'0.19.2'):

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

Solution 2:^[2]

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)

Solution 3:^[3]

To rename the header without reassign df:

df.rename(columns=df.iloc[0], inplace = True)

To drop the row without reassign df:

df.drop(df.index[0], inplace = True)

Solution 4:^[4]

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

Solution 5:^[5]

Just for fun :

df.T.set_index(0).T

Transposes the dataframe, sets index with the first row and transposes again, so, no need to remove the fist row :)

Sources

This article follows the attribution requirements of Stack Overflow and is licensed under CC BY-SA 3.0.

Source: Stack Overflow

Solution	Source
Solution 1	Rob
Solution 2	shahar_m
Solution 3	Govinda
Solution 4
Solution 5	Arnaud Desplanche

'Convert row to column header for Pandas DataFrame,

Solution 1:[1]

Solution 2:[2]

Solution 3:[3]

Solution 4:[4]

Solution 5:[5]