pandas.DataFrame.transpose — pandas 2.2.3 documentation (original) (raw)

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 methodtranspose().

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-Writewill be enabled by default, which means that all methods with acopy 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.

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