# 5 Best Ways to Create Numpy Arrays of Tuples in Python : Emily Rosemary Collins

**5 Best Ways to Create Numpy Arrays of Tuples in Python**

**by: Emily Rosemary Collins**

*blow post content copied from Be on the Right Side of Change*

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**Problem Formulation:**

When working with large datasets in Python, it’s common to need to create Numpy arrays that hold tuples as their elements. This problem revolves around transforming a collection of tuples, representing multidimensional data points, into a structured Numpy array where each tuple becomes an array element. Suppose the input is a list of tuples like `[(1, 2), (3, 4)]`

, the desired output would be a Numpy array, potentially for further vectorized operations.

## Method 1: Using numpy.array

This method involves the direct use of the `numpy.array()`

function to transform a list of tuples into a Numpy array. The function delineates the structure of the resultant array, allowing for multidimensional array creation that can be tailored through data types and order parameters.

Here’s an example:

import numpy as np tuples_list = [(1, 2), (3, 4), (5, 6)] np_array_of_tuples = np.array(tuples_list) print(np_array_of_tuples)

Output:

[[1 2] [3 4] [5 6]]

This code snippet first defines a list of tuples. It then uses the `numpy.array()`

function to convert this list into a Numpy array, preserving the tuple structure as the elements of the array.

## Method 2: Using numpy.asarray

The `numpy.asarray()`

function is an alternative that converts an input sequence into an array. It’s similar to `numpy.array()`

, but does not copy the object if it is already an ndarray with matching dtype and order. It is efficient when you’re dealing with sequences already in a numpy-like structure.

Here’s an example:

import numpy as np tuples_list = [(7, 8), (9, 10), (11, 12)] np_array_of_tuples = np.asarray(tuples_list) print(np_array_of_tuples)

Output:

[[ 7 8] [ 9 10] [11 12]]

This example starts with a list of tuples and uses `numpy.asarray()`

to create a numpy array. If the provided list of tuples were already an array, `asarray()`

would not needlessly make a copy of the data.

## Method 3: Using numpy.fromiter

The `numpy.fromiter()`

function creates a new one-dimensional array from an iterable object, such as a generator expression or iterator. To create an array of tuples, the iterable should yield tuples. This method can be more memory-efficient than creating a list first.

Here’s an example:

import numpy as np tuples_iterator = iter([(13, 14), (15, 16), (17, 18)]) np_array_of_tuples = np.fromiter(tuples_iterator, dtype='i,i') print(np_array_of_tuples)

Output:

[(13, 14) (15, 16) (17, 18)]

Here, we create an iterator from a list of tuples and pass it to `numpy.fromiter()`

, specifying the data type as a pair of integers (`i,i`

). The output is a one-dimensional array of tuple elements, making it ideal for sequence conversions.

## Method 4: Using numpy.zeros with a structured dtype

NumPy’s `numpy.zeros()`

can create an array filled with zeros. By specifying a structured data type (dtype), you can form an array of zeros where each zero element is replaced with a tuple structure.

Here’s an example:

import numpy as np tuple_shape = (3,) # the desired shape of the array dtype = [('x', 'int32'), ('y', 'int32')] # specifying tuple structure as a list of pairs np_array_of_tuples = np.zeros(tuple_shape, dtype=dtype) print(np_array_of_tuples)

Output:

[(0, 0) (0, 0) (0, 0)]

In this code snippet, we use `numpy.zeros()`

with a specified structured dtype to create a structured array. While the initial array is filled with zeros, it can be subsequently populated with the actual tuple data.

## Bonus One-Liner Method 5: List Comprehension with numpy.array

A one-liner method involves using a list comprehension inside the `numpy.array()`

function call. This method is great for cases where the tuples need to be generated or transformed before creating the array.

Here’s an example:

import numpy as np np_array_of_tuples = np.array([(x, x+1) for x in range(3)]) print(np_array_of_tuples)

Output:

[[0 1] [1 2] [2 3]]

This one-liner uses list comprehension to create tuples on the fly and immediately converts them into a Numpy array using `numpy.array()`

.

## Summary/Discussion

**Method 1: numpy.array**. Straightforward. Ideal for lists. Can be memory intensive.**Method 2: numpy.asarray**. Efficient for existing sequences. Less intuitive for tuple creation.**Method 3: numpy.fromiter**. Memory-efficient for large sequences. Limited to one-dimensional arrays.**Method 4: numpy.zeros with structured dtype**. Allows preset array structure. Initial values will be zeros.**Method 5: List Comprehension with numpy.array**. Compact and convenient for on-the-fly transformations. Less readable with complex transformations.

February 20, 2024 at 06:50PM

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