transpose(self, *args, copy: 'bool' = False) -> 'DataFrame'
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
.
Transposing a DataFrame with mixed dtypes will result in a homogeneous DataFrame with the :None:None:`object` dtype. In such a case, a copy of the data is always made.
Accepted for compatibility with NumPy.
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.
The transposed DataFrame.
Transpose index and columns.
numpy.transpose
Permute the dimensions of a given array.
Square DataFrame with homogeneous dtype
This example is valid syntax, but we were not able to check execution>>> d1 = {'col1': [1, 2], 'col2': [3, 4]}This example is valid syntax, but we were not able to check execution
... 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:
This example is valid syntax, but we were not able to check execution>>> df1.dtypes col1 int64 col2 int64 dtype: objectThis example is valid syntax, but we were not able to check execution
>>> df1_transposed.dtypes 0 int64 1 int64 dtype: object
Non-square DataFrame with mixed dtypes
This example is valid syntax, but we were not able to check execution>>> d2 = {'name': ['Alice', 'Bob'],This example is valid syntax, but we were not able to check execution
... '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 :None:None:`object` dtype:
>>> df2.dtypes name object score float64 employed bool kids int64 dtype: objectThis example is valid syntax, but we were not able to check execution
>>> df2_transposed.dtypes 0 object 1 object dtype: objectSee :
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