# 5 Best Ways to Convert a Python String to a Float Array

Rate this post

π‘ Problem Formulation: Converting a string to a float array in Python is a common task in data processing. For instance, you may have input like `"3.14, 1.59, 2.65"` and need to transform it into a float array `[3.14, 1.59, 2.65]`. This article illustrates different approaches to achieve this, each with its own nuances and best use cases.

## Method 1: Using the `split()` method and a list comprehension

One straightforward method to convert a string to a float array is by using the `split()` function to separate the string into a list of substrings, followed by a list comprehension to convert each substring to a float.

Here’s an example:

```numbers_string = "3.14, 1.59, 2.65"
float_array = [float(number) for number in numbers_string.split(", ")]
print(float_array)```

Output:

`[3.14, 1.59, 2.65]`

This code snippet first splits the input string by the separator “, ” to create a list of number strings. Then, it maps each string to a float using a list comprehension, resulting in the desired float array.

## Method 2: Using the `map()` function

This method employs `map()`, in conjunction with `split()`, to apply the `float()` function to each element in the list of strings, converting them into a list of floats.

Here’s an example:

```numbers_string = "3.14, 1.59, 2.65"
float_array = list(map(float, numbers_string.split(", ")))
print(float_array)```

Output:

`[3.14, 1.59, 2.65]`

This snippet splits the string into a list of number strings and then maps each number string to a float, effectively converting the whole list into a float array.

## Method 3: Using `numpy.fromstring()`

For those using the NumPy library, `numpy.fromstring()` is a powerful function that converts a string to a float array in one step, making it a concise and efficient method.

Here’s an example:

```import numpy as np

numbers_string = "3.14, 1.59, 2.65"
float_array = np.fromstring(numbers_string, sep=', ')
print(float_array)```

Output:

`[3.14 1.59 2.65]`

By passing the input string and the separator to `np.fromstring()`, NumPy returns an array of floats without the need for intermediate steps.

## Method 4: Using regular expressions with `re.findall()`

Regular expressions provide a way to match patterns in strings. Here, `re.findall()` is used to find all floats in a given string, which are then converted into a float array.

Here’s an example:

```import re

numbers_string = "Pi: 3.14, E: 1.59, Phi: 2.65"
float_array = [float(number) for number in re.findall(r"[-+]?\d*\.\d+|\d+", numbers_string)]
print(float_array)```

Output:

`[3.14, 1.59, 2.65]`

The regular expression pattern matches floats and integers within the string, allowing the creation of a float array even when the numeric values are embedded within text.

## Bonus One-Liner Method 5: Using the `eval()` function

The `eval()` function can evaluate a string as a Python expression. If the string is formatted correctly, it can be directly converted to a list of floats with one line of code. Caution is advised with `eval()` due to potential security risks if the source of the string is untrusted.

Here’s an example:

```numbers_string = "[3.14, 1.59, 2.65]"
float_array = eval(numbers_string)
print(float_array)```

Output:

`[3.14, 1.59, 2.65]`

Ensure the input string is safe to execute because `eval()` can run arbitrary code, which could be a security concern.

## Summary/Discussion

• Method 1: Using split and a list comprehension. It is intuitive and pythonic. However, it assumes a consistent separator and clean data.
• Method 2: Using the map function. It is a functional programming approach, and efficient but less readable than a list comprehension for some developers.
• Method 3: Using numpy.fromstring. Highly efficient for large datasets and works well within NumPyβs ecosystem, but requires installing NumPy.
• Method 4: Using regular expressions. Extremely flexible in handling irregular data formats but may be overkill for simple use cases and could be less efficient.
• Method 5: Using eval. Offers a one-liner solution for well-formatted strings, but poses a security risk and is discouraged for untrusted input.