For a two-dimensional NumPy array, use a.T to swap rows and columns. NumPy also provides a.transpose(), np.transpose(), and targeted axis operations; for a plain nested list, use zip(*matrix). The right choice depends on the data type and, for multidimensional arrays, which axes you want to rearrange.
Transpose a 2D NumPy array
Here is a non-square array so the row-and-column change is easy to see:
import numpy as np
a = np.array([[1, 2, 3],
[4, 5, 6]])Its shape is (2, 3): two rows and three columns. A transpose exchanges those dimensions and produces:
[[1, 4],
[2, 5],
[3, 6]]1. Use the .T property
a_t = a.TFor a two-dimensional NumPy array, .T is the concise way to exchange rows and columns. NumPy documents it as equivalent to the ndarray transpose method: ndarray.T.
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2. Call .transpose()
a_t = a.transpose()With no axes specified, this method reverses the order of all axes. It can be useful when a method call fits the style of a transformation pipeline. NumPy returns a view when possible; see ndarray.transpose.
3. Call np.transpose()
a_t = np.transpose(a)The function form gives the same 2D result and lets you specify the output axis order for higher-dimensional arrays. For a three-dimensional input, np.transpose(a, (1, 0, 2)) swaps the first two axes and leaves the third in place. The axes must form a permutation of the input axes; negative axis indices are also accepted. See numpy.transpose.
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Change selected axes in a multidimensional array
For more than two dimensions, decide whether you intend to exchange a pair of axes, move axes to new positions, or reverse the full axis order. A default transpose reverses every axis; it is not always the same as swapping just the last two dimensions.
4. Use swapaxes or moveaxis
b = np.swapaxes(a, 0, 1)
c = np.moveaxis(a, 0, 1)On a 2D input, both examples produce the familiar row-and-column exchange. For higher-dimensional inputs, swapaxes exchanges the two named axes. moveaxis moves the selected source axis to the destination position while keeping the other axes in their relative order. Pick the function that describes the change you mean; neither is a general synonym for reversing all axes. See numpy.moveaxis.
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Transpose a plain nested list without NumPy
5. Use zip(*matrix)
matrix = [[1, 2, 3],
[4, 5, 6]]
transposed = list(zip(*matrix))
# [(1, 4), (2, 5), (3, 6)]Unpacking the rows as arguments to zip groups their first elements, then their second elements, and so on. The result contains tuples. To get a list of lists instead, convert each tuple:
transposed = [list(row) for row in zip(*matrix)]
# [[1, 4], [2, 5], [3, 6]]The Python documentation describes zip() as turning rows into columns and columns into rows: Python built-in functions. By default, zip stops at the shortest row, so a ragged nested list can lose trailing values. In Python 3.10 and later, zip(*matrix, strict=True) raises ValueError when the rows have different lengths. The Python tutorial also shows the transpose idiom: Nested list comprehensions.
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Handle 1D and n-dimensional NumPy arrays correctly
A 1D array stays one-dimensional
Transposing a one-dimensional array does not turn it into a row or column vector: np.transpose(a) returns an unchanged view. To make a column vector, add an axis explicitly:
column = np.atleast_2d(a).T
# or
column = a[:, np.newaxis]NumPy documents this behavior in numpy.transpose.
Default transpose reverses all axes
For an array with shape (2, 3, 4), a default transpose produces shape (4, 3, 2). Supply an axis permutation when you need a different arrangement, such as swapping only the first two axes. Do not infer the intended result from the word “transpose” alone; specify the axes that should move.
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A transpose may share storage with its input
NumPy returns a view whenever possible, so modifying a transposed array may affect the original array. If you need independent storage, make an explicit copy, for example a.T.copy(). The applicable behavior is described in numpy.transpose and ndarray.transpose.
Transpose a pandas DataFrame
For a DataFrame, use df.T or df.transpose() to exchange its index and columns. If the DataFrame contains mixed data types, the transposed frame has a homogeneous object dtype. In pandas 3.0, the method’s copy argument is ignored and deprecated; the method uses lazy Copy-on-Write behavior, and a copy is always required for mixed-dtype DataFrames or extension types. Consult the current DataFrame.transpose documentation for details.
Quick Recap
Choose the method that matches your data
| Data or goal | Recommended form | What to keep in mind |
|---|---|---|
| 2D NumPy array | a.T |
Concise row-and-column exchange. |
| NumPy array with explicit axis order | np.transpose(a, axes=...) |
Controls the position of every output axis. |
| Exchange two selected NumPy axes | np.swapaxes(a, axis1, axis2) |
Only the named pair is exchanged. |
| Move selected NumPy axes | np.moveaxis(a, source, destination) |
Other axes keep their relative order. |
| pandas DataFrame | df.T or df.transpose() |
Mixed dtypes produce an object-dtype transposed frame. |
| Rectangular nested list | list(zip(*matrix)) |
Elements are tuples; unequal rows truncate by default. Use strict=True in Python 3.10 or later to detect unequal lengths. |
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