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columns() to Convert Row to Column Header. You can use df. columns=df. iloc[0] to set the column labels by extracting the first row.
The transpose() function is used to transpose index and columns. Reflect the DataFrame over its main diagonal by writing rows as columns and vice-versa. If True, the underlying data is copied. Otherwise (default), no copy is made if possible.
You can replace the header with the first row of the dataframe by using df. columns = df. iloc[0]. You can use the below code snippet to replace the header with the first row of the pandas dataframe.
To select the first row we are going to use iloc - df. iloc[0] . For other rows we can change the index - 0. For example to use the last row as header: -1 - df.
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)
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)
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