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Classe « DataFrame »

Méthode pandas.DataFrame.transpose

Signature de la méthode transpose

def transpose(self, *args, copy: 'bool' = False) -> 'DataFrame' 

Description

help(DataFrame.transpose)

Transpose index and columns.

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

Parameters
----------
*args : tuple, optional
    Accepted for compatibility with NumPy.
copy : bool, 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
        <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
        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

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