pandas.DataFrame.transpose#

DataFrame.transpose(*args, copy=False)[source] #

Transpose index and columns.

Reflect the DataFrame over its main diagonal by writing rows as columns and vice-versa. The property T is an accessor to the method transpose().

Parameters:

*argstuple, optional

Accepted for compatibility with NumPy.

copybool, default False

Whether to copy the data after transposing, even for DataFrames with a single dtype.

Note that a copy is always required for mixed dtype DataFrames, or for DataFrames with any extension types.

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

Returns:

DataFrame

The transposed DataFrame.

See also

numpy.transpose

Permute the dimensions of a given array.

Notes

Transposing a DataFrame with mixed dtypes will result in a homogeneous DataFrame with the object dtype. In such a case, a copy of the data is always made.

Examples

Square DataFrame with homogeneous dtype

>>> d1 = {'col1': [1, 2], 'col2': [3, 4]}
>>> df1 = pd.DataFrame(data=d1)
>>> df1
 col1 col2
0 1 3
1 2 4

>>> df1_transposed = df1.T # or df1.transpose()
>>> df1_transposed
 0 1
col1 1 2
col2 3 4

When the dtype is homogeneous in the original DataFrame, we get a transposed DataFrame with the same dtype:

>>> df1.dtypes
col1 int64
col2 int64
dtype: object
>>> df1_transposed.dtypes
0 int64
1 int64
dtype: object

Non-square DataFrame with mixed dtypes

>>> d2 = {'name': ['Alice', 'Bob'],
...  'score': [9.5, 8],
...  'employed': [False, True],
...  'kids': [0, 0]}
>>> df2 = pd.DataFrame(data=d2)
>>> df2
 name score employed kids
0 Alice 9.5 False 0
1 Bob 8.0 True 0

>>> df2_transposed = df2.T # or df2.transpose()
>>> df2_transposed
 0 1
name Alice Bob
score 9.5 8.0
employed False True
kids 0 0

When the DataFrame has mixed dtypes, we get a transposed DataFrame with the object dtype:

>>> df2.dtypes
name object
score float64
employed bool
kids int64
dtype: object
>>> df2_transposed.dtypes
0 object
1 object
dtype: object