Problem Formulation: Given a Python set. How to convert it to a tuple? And how to convert the tuple back to a set?

There are three main ways:

• Method 1: To convert a Python set to a tuple, use the tuple(my_set) function.
• Method 2: To convert a Python tuple to a set, use the set(my_tuple) function.
• Method 3: To convert a Python tuple of mutable elements to a set, use the expression set(tuple(x) for x in my_tuple) to avoid a TypeError.

I’ll also give you a bonus method 4 that shows you what to do to retain the ordering information when converting a tuple to a set—so keep reading!

Method 1: Convert Set to Tuple with tuple()

To convert a set to a tuple, pass the set into the tuple() function. This is a built-in Python function, so you don’t need to import or install any library to use it. The return value is a new tuple from the values in the set.

Here’s an example where you convert the set {1, 2, 3} to a tuple (1, 2, 3):

my_set = {1, 2, 3}

my_tuple = tuple(my_set)

print(my_tuple)

# (1, 2, 3)

By the way, here’s an explainer video on this function:

Method 2: Convert Tuple to Set with set()

To convert a tuple to a set, pass the tuple into the set() function. This is a built-in Python function, so you don’t need to import or install any library. The return value is a new set from the values in the tuple.

Here’s an example where you convert the tuple (1, 2, 3) to a set {1, 2, 3}:

my_tuple = (1, 2, 3)

my_set = set(my_tuple)

print(my_set)

# {1, 2, 3}

Problem: However, the conversion process from a tuple to set doesn’t always work because if you try to convert a tuple of mutable values, Python will raise the TypeError: unhashable type!

Read on to learn more about this problem—and how to resolve it easily:

Method 3: Convert Tuple to Set with Set Comprehension

A Python set is an unordered collection of unique immutable elements. Each element must define explicitly or implicitly the __hash__() dunder method, i.e., must be hashable.

If you attempt to convert a tuple of mutable elements (e.g., lists) to a set, Python will raise an error such as the TypeError: unhashable type: 'list':

my_tuple = ([1, 2], [3, 4], [5, 6])

my_set = set(my_tuple)

# TypeError: unhashable type: 'list'

In this case, you can use set comprehension to convert each inner tuple element to an immutable type. For example, the expression set(tuple(x) for x in my_tuple) converts each inner list to a tuple. The result is a set of immutable tuples.

Here’s the solution to this problem in a minimal code example:

my_tuple = ([1, 2], [3, 4], [5, 6])

my_set = set(tuple(x) for x in my_tuple)

print(my_set)

# {(1, 2), (3, 4), (5, 6)}

The final “bonus” section introduces another elegant way to retain the ordering information in a set:

Bonus Method 4: Enumerate Elements

Sometimes, you want to associate each set or tuple element with a specific numerical “index” value, i.e., a unique integer identifier. The enumerate() method to the rescue!

• Use tuple(enumerate(my_set)) to convert a set to an enumerated tuple.
• Use set(enumerate(my_tuple)) to convert a tuple to an enumerated set.

The result is the respective container data structure with (identifier, value) tuples:

my_set = {'Alice', 'Bob', 'Carl'} my_tuple = tuple(enumerate(my_set))

print(my_tuple)

# ((0, 'Carl'), (1, 'Bob'), (2, 'Alice')) my_set = set(enumerate(('Alice', 'Bob', 'Carl')))

print(my_set)

# {(2, 'Carl'), (0, 'Alice'), (1, 'Bob')}

Especially in the case where you convert a tuple to a set, this makes a lot of sense because you can retain the information on the ordering of elements that would be otherwise lost after converting to a set.

Thanks for reading the whole article, my friend! You can join us here (we have cheat sheets too):

If you are looking for a client-side solution to detect a mobile device, your search stops here :-). There are properties to detect this on the client side by using JavaScript.

Two methods of detecting if a current device is a mobile device are listed below.

1. By using JavaScript window.matchMedia() method.
2. By using navigator.userAgent property.

Both are using JavaScript basic methods to create the mobile device detector code.

1) By using JavaScript window.matchMedia() method

This method is the best one compared to the one using JavaScript navigator.userAgent. Because the userAgent is a setting that can be configured by the end users. They can change it!

matchmedia.html

function isMobileDevice() { return window .matchMedia("only screen and (max-width: 760px)").matches;

}

if (isMobileDevice()) { document.write("<b>Detected device is a mobile.</b>");

} else { document .write("<b>Detected device is not a mobile.</b>");

}

The matchMedia() JavaScript custom method is used to do this mobile device detection.

It accepts a media query string and returns an object. This object will contain the current device’s media property and its relevancy.

Then, this object is used to find the match between the current device’s media property with the media query passed.

This program sends the media query containing a max-width of a mobile device that is expected to have.

If the current device’s media screen properties are matched with this argument, then this JavaScript function returns a boolean true.

View Demo

This screenshot is taken from my computer that prints the result of the above quick example.

Media match with CSS

It can also be done by CSS instead of JavaScript. Follow the below steps to implement this using CSS.

• Keep two possible messages in the HTML markup with  style=’display:none’.
• Write CSS media query section with @media screen and (max-width: 600px).
• Show and hide the appropriate UI notification element according to the screen width.

<!DOCTYPE html>

<html>

<head>

<title>How to Detect Mobile Device using JavaScript</title>

<meta name="viewport" content="width=device-width, initial-scale=1">

<style>

.mobile { display: none;

} .not-mobile { display: block;

} @media only screen and (max-width: 600px) { .mobile { display: block; } .not-mobile { display: none; }

}

</style>

</head>

<body> <b class="mobile">Detected device is a mobile.</b> <b class="not-mobile">Detected device is not a mobile.</b>

</body>

</html>

2) By using a navigator.userAgent property

The alternate method is for checking the current userAgent to detect if it is a mobile device.

The isMobileDevice() function in the below example does the test with a regex pattern. The regex contains the most possible values of a mobile device’s userAgent.

We have already used regex pattern matching for a JavaScript email validation utility.

The script compares the current device’s userAgent property with the pattern. If a match is found, then this function returns true to print the appropriate result.

navigator.html

<!DOCTYPE html>

<html>

<head>

<title>How to Detect Mobile Device using JavaScript</title>

</head>

<body> <h1>How to Detect Mobile Device using JavaScript</h1> <p>Note: Browser users can change value of "userAgent" via UA spoofing. So be aware of that and do not use this feature to provide a critical function of your website.</p> <script> function isMobileDevice() { return /Android|webOS|iPhone|iPad|iPod|BlackBerry|IEMobile|Opera Mini/i .test(navigator.userAgent); /* for a more detailed test /(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|ipad|iris|kindle|Android|Silk|lge |maemo|midp|mmp|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows (ce|phone)|xda|xiino/i .test(navigator.userAgent) || /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i .test(navigator.userAgent.substr(0, 4)) */ } if (isMobileDevice()) { document.write("<b>Detected device is a mobile.</b>"); } else { document .write("<b>Detected device is not a mobile.</b>"); } </script>

</body>

</html>

Note: Browser users can change the value of “userAgent” via UA spoofing. So be aware of that and do not use this feature to provide a critical function of your website.

View Demo Download

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In our latest blog post, we tackle a topic that's been at the forefront of the community for some time - the rev split.
We also provide a related update around monetization for a subset of Partners.
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Python Longest String in NumPy Array

To find the longest string in a given NumPy array, say arr, you can use the max(arr, key=len) function that determines the maximum by comparing the length of the array elements using the len() function as a key for comparison.

import numpy as np arr = np.array(['Alice', 'Bob', 'Carl'])

print(max(arr, key=len))

# Alice

You can find more about the powerful max() function in our detailed blog tutorial:

Recommended Tutorial: Python Maximum Function

Python Length of Longest String in NumPy Array

To find the length of the longest string in a NumPy array arr, use the max(arr, key=len) function to obtain the string with the maximum length and then pass this max string into the len() function to obtain the number of characters of the max string.

len(max(arr, key=len))

Here’s a more detailed code example of a simple 1D NumPy Array:

import numpy as np arr = np.array(['Alice', 'Bob', 'Carl']) # Print Longest String:

print(max(arr, key=len))

# Alice # Print Length of Longest String

print(len(max(arr, key=len)))

# 5

Get Longest String from NumPy Axis (2D, Column or Row)

To get the longest string from a certain NumPy array axis (e.g., row or column), first use simple NumPy slicing and indexing to get that axis (e.g., arr[0, :] to get the first row) and pass it into the max() function with the key argument set to the length function like so: max(arr[0, :], key=len).

Here’s an example to get the longest string of the first row of a 2D array:

import numpy as np arr = np.array([['Alice', 'Bob', 'Carl'], ['Ann', 'Zoe', 'Leonard']]) print(max(arr[0, :], key=len))

# Alice

Here’s an example to get the longest string of the third column of a 2D array:

print(max(arr[:, 2], key=len))

# Leonard

You get the idea.

If you want to get the longest string from the whole NumPy array, not only from a column or row or axis, first flatten it and then pass the flattened array into the max() function using the key=len argument.

Recommended Tutorial: How to Flatten a NumPy Array?

Summary: You can combine images represented in the form of Numpy arrays using the concatenate function of the Numpy library as np.concatenate((numpydata_1, numpydata_2), axis=1). This combines the images horizontally. Use syntax: np.concatenate((numpydata_1, numpydata_2), axis=0) to combine the images vertically.

Problem Formulation

Consider you have two images represented as Numpy arrays of pixels. How will you combine the two images represented in the form of Numpy pixel arrays?

Combining two images that are in the form of Numpy arrays will create a new Numpy array having pixels that will represent a new combined image formed by either concatenating the two images horizontally or vertically. Let’s understand this with the help of an example:

Given: Let’s say we have two different images as given below (Both images have similar dimensions) –

When you convert them to Numpy arrays this is how you can represent the two images:

Challenge: Combine the two images – (i) horizontally (ii) vertically

Expected Output:

So, are you up for the challenge? Well! If it looks daunting – don’t worry. This tutorial will guide you through the techniques to solve the programming challenge. So, without further delay let us dive into the solution.

Prerequisite: To understand how the solutions to follow work it is essential to understand – “How to concatenate two Numpy arrays in Python.”

NumPy’s concatenate() method joins a sequence of arrays along an existing axis. The first couple of comma-separated array arguments are joined. If you use the axis argument, you can specify along which axis the arrays should be joined. For example, np.concatenate(a, b, axis=0) joins arrays along the first axis and np.concatenate(a, b, axis=None) joins the flattened arrays.

To learn more about concatenating arrays in Python, here’s a wonderful tutorial that will guide you through numerous methods of doing so: How to Concatenate Two NumPy Arrays?

Combine Images “Horizontally” with Numpy

Approach: The concatenate() method of the Numpy library allows you combine matrices of different images along different axes. To combine the two image arrays horizontally, you must specify the axis=1.

Code: Please go through the comments mentioned in the script in order to understand how each line of code works.

from PIL import Image

import numpy as np

# Reading the given images img_1 = Image.open('img_1.JPG')

img_2 = Image.open('img_2.JPG')

# Converting the two images into Numpy Arrays

numpydata_1 = np.asarray(img_1)

numpydata_2 = np.asarray(img_2)

# Combining the two images horizontally

horizontal = np.concatenate((numpydata_1, numpydata_2), axis=1)

# Display the horizontally combined image as a Numpy Array

print(horizontal)

# converting the combined image in the Numpy Array form to an image format

data = Image.fromarray(horizontal)

# Saving the combined image

data.save('combined_pic.png')

Output:

[[[184 186 201] [184 186 201] [184 187 202] ... [242 245 252] [240 243 250] [241 244 251]] [[184 186 201] [184 186 201] [184 187 202] ... [242 245 252] [241 244 251] [241 244 251]] [[183 186 203] [183 186 203] [184 187 204] ... [243 246 253] [243 246 253] [241 244 251]] ... [[ 43 64 55] [ 45 66 57] [ 48 69 60] ... [ 72 108 106] [ 73 109 107] [ 77 113 111]] [[ 49 70 61] [ 50 71 62] [ 53 73 64] ... [ 69 104 100] [ 69 104 100] [ 71 106 102]] [[ 53 74 65] [ 52 73 64] [ 56 73 65] ... [ 65 97 94] [ 60 92 89] [ 64 96 93]]]

Here’s how the horizontally combined image looks like when saved to a file:

Wonderful! Isn’t it?

Combine Images “Vertically” with Numpy

In the previous solution, we combined the images horizontally. In this soution you will learn how to combine two images represented in the form of Numpy arrays vertically.

Approach: The idea is quite similar to the previous solution with the only difference in the axis parameter of the concatenate() method. To combine the two image arrays vertically, you must specify the axis=0.

Code:

from PIL import Image

import numpy as np

# Reading the given images

img_1 = Image.open('img_1.JPG')

img_2 = Image.open('img_2.JPG')

# Converting the two images into Numpy Arrays

numpydata_1 = np.asarray(img_1)

numpydata_2 = np.asarray(img_2)

# Combining the two images horizontally

vertical = np.concatenate((numpydata_1, numpydata_2), axis=0)

# Display the vertically combined image as a Numpy Array

print(vertical)

# converting the combined image in the Numpy Array form to an image format

data = Image.fromarray(vertical)

# Saving the combined image

data.save('combined_pic.png')

Output:

[[[184 186 201] [184 186 201] [184 187 202] ... [174 189 218] [174 189 218] [173 188 217]] [[184 186 201] [184 186 201] [184 187 202] ... [174 189 218] [173 188 217] [173 188 217]] [[183 186 203] [183 186 203] [184 187 204] ... [173 188 217] [173 188 217] [172 187 216]] ... [[115 152 144] [111 151 142] [ 96 142 131] ... [ 72 108 106] [ 73 109 107] [ 77 113 111]] [[ 75 118 108] [ 79 125 114] [ 82 132 120] ... [ 69 104 100] [ 69 104 100] [ 71 106 102]] [[ 42 90 78] [ 47 97 85] [ 64 116 103] ... [ 65 97 94] [ 60 92 89] [ 64 96 93]]]

Here’s how the horizontally combined image looks like when saved to a file:

Hurrah! We have successfully combined the two images vertically.

Exercises

Before we wrap this tutorial, here’s a set of challenges to further enhance your knowledge.

Challenge 1: Consider that you have been given an image. How will you convert this image to a Numpy array?

Given Image

Solution:

from PIL import Image

from numpy import asarray

img = Image.open('img.png')

img_to_array = asarray(img)

print(img_to_array)

Challenge 2: Consider that you have two images of different dimensions. How will you combine the two images horizontally using OpenCV?

Given Images:

Solution:

import cv2

import numpy as np

img_1 = cv2.imread('Imgage_1.png')

img_2 = cv2.imread('Image_2.png')

h1, w1 = img_1.shape[:2]

h2, w2 = img_2.shape[:2]

img_3 = np.zeros((max(h1, h2), w1 + w2, 3), dtype=np.uint8)

img_3[:, :] = (255, 255, 255)

img_3[:h1, :w1, :3] = img_1

img_3[:h2, w1:w1 + w2, :3] = img_2

cv2.imwrite('Img_3.png', img_3)

Output:

Want to learn about OpenCV? Here’s an amazing tutorial to get you started with OpenCV – Python OpenCV Image Processing.

Conclusion

Phew! That was some coding challenge! I hope you can now successfully combine images given as Numpy arrays in both dimensions – horizontally as well as vertically. With that we come to the end of this tutorial. Please subscribe and stay tuned for more interesting tutorials and solutions in the future.

Happy coding!