Category: Programming

There are four major ways to install the Solidity compiler:

1. Install Solidity Compiler via npm
2. Install Solidity Compiler via Docker on Ubuntu
3. Install Solidity Compiler via Source Code Compilation
4. Install Solidity Compiler via Static Binary and Linux Packages

In this tutorial, we’ll have a quick look at each of them and give you a link to a more detailed resource so you can set up your Solidity compiler as quickly and efficiently as possible.

Video: For your convenience, I embedded the video tutorial provided by our Solidity expert Matija so you don’t even need to leave this page.

Without further ado, let’s get started!

Method 1: Install Solidity Compiler via npm

As you watch the video or go through this tutorial, feel free to download the following slides as well — for your convenience:

Full Tutorial: How to Install the Solidity Compiler via npm?

Method 2: Install Solidity Compiler via Docker on Ubuntu

Before we go into details about the Docker installation of solc, let’s first get introduced to what Docker is.

Docker is an open platform for developing, shipping, and running applications… Docker provides the ability to package and run an application in a loosely isolated environment called a container… Containers are lightweight and contain everything needed to run the application, so you do not need to rely on what is currently installed on the host.

Source: https://docs.docker.com/get-started/overview/

There are some parts of the description I’ve deliberately left out (separated by the symbol …) because they’re not essential to our understanding of the technology.

Full Tutorial: How to Install the Solidity Compiler via Docker on Ubuntu?

Method 3: Install Solidity Compiler via Source Code Compilation

This is a very complex way to install the Solidity compiler and I wouldn’t recommend it for most people. Due to the complexity, I’ll only give a quick overview of the associated article (tutorial).

Feel free to dive into it after scanning through these three contributions:

1. First, we listed and explained the software prerequisites needed for compiling a Solidity compiler. In some cases, we reached a complete explanation, and in others, we just gave a brief introductory explanation and announced an entire topic, such as in the case of the Satisfiability Modulo Theorem, SMT.
2. Second, we installed the prerequisites by following the first part of a step-by-step tutorial. All the examples have been checked and validated at the time of writing the article, so I expect that we’ll be able to follow them without issues. We also concluded that a compilation process can in some cases take a substantial amount of time; it took almost 40 minutes to compile the z3 SMT solver on my machine.
3. Third, we compiled a Solidity compiler following a step-by-step tutorial. I explained for each command example to broaden our learning process even outside of the strict scope of Solidity, to Linux (as far as we needed to go). Finally, when the compilation ended, we confirmed that our home-compiled Solidity compiler works at least as charming as the ones we’ve simply downloaded or installed in a precompiled state.

Full Tutorial: How to Install the Solidity Compiler via Source Code Compilation?

Method 4: Install Solidity Compiler via Static Binary and Linux Packages

You’ll just download the compiler’s static binary, or in short, binary, and simply run it, without any additional prerequisites or preparations required.

First, downloading the file solc-static-linux and giving it an executable privilege:

$ cd ~ && wget https://github.com/ethereum/solidity/releases/download/v0.8.16/solc-static-linux
$ chmod +x ~/solc-static-linux

Second, running solc:

$ ~/solc-static-linux 1_Storage.sol -o output – abi – bin
Compiler run successful. Artifact(s) can be found in directory "output".

When checking our solidity_src directory, we’ll discover a new directory output, created by the Solidity compiler, containing both .abi and .bin files.

Full Tutorial: How to Install the Solidity Compiler via Static Binary and Linux Packages?

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Learn Solidity Course

Solidity is the programming language of the future.

It gives you the rare and sought-after superpower to program against the “Internet Computer”, i.e., against decentralized Blockchains such as Ethereum, Binance Smart Chain, Ethereum Classic, Tron, and Avalanche – to mention just a few Blockchain infrastructures that support Solidity.

In particular, Solidity allows you to create smart contracts, i.e., pieces of code that automatically execute on specific conditions in a completely decentralized environment. For example, smart contracts empower you to create your own decentralized autonomous organizations (DAOs) that run on Blockchains without being subject to centralized control.

NFTs, DeFi, DAOs, and Blockchain-based games are all based on smart contracts.

This course is a simple, low-friction introduction to creating your first smart contract using the Remix IDE on the Ethereum testnet – without fluff, significant upfront costs to purchase ETH, or unnecessary complexity.

Problem Formulation

Programming Challenge: Given a list of lists (nested list). Find and return the shortest inner list from the outer list of lists.

Here are some examples:

• [[1], [2, 3], [4, 5, 6]] [1]
• [[1, [2, 3], 4], [5, 6], [7]] [7]
• [[[1], [2], [3]], [4, 5, [6]], [7, 8, 9, 10]] [[1], [2], [3]]

Also, you’ll learn how to solve a variant of this challenge.

Bonus challenge: Find only the length of the shortest list in the list of lists.

Here are some examples:

• [[1], [2, 3], [4, 5, 6]] 1
• [[1, [2, 3], 4], [5, 6], [7]] 1
• [[[1], [2], [3]], [4, 5, [6]], [7, 8, 9, 10]] 3

So without further ado, let’s get started!

Method 1: min(lst, key=len)

Use Python’s built-in min() function with a key argument to find the shortest list in a list of lists. Call min(lst, key=len) to return the shortest list in lst using the built-in len() function to associate the weight of each list, so that the shortest inner list will be the minimum.

Here’s an example:

def get_shortest_list(lst): return min(lst, key=len) print(get_shortest_list([[1], [2, 3], [4, 5, 6]]))
# [1] print(get_shortest_list([[1, [2, 3], 4], [5, 6], [7]]))
# [7] print(get_shortest_list([[[1], [2], [3]], [4, 5, [6]], [7, 8, 9, 10]]))
# [[1], [2], [3]]

A beautiful one-liner solution, isn’t it? Let’s have a look at a slight variant to check the length of the shortest list instead.

Method 2: len(min(lst, key=len))

To get the length of the shortest list in a nested list, use the len(min(lst, key=len)) function. First, you determine the shortest inner list using the min() function with the key argument set to the len() function. Second, you pass this shortest list into the len() function itself to determine the minimum.

Here’s an analogous example:

def get_length_of_shortest_list(lst): return len(min(lst, key=len)) print(get_length_of_shortest_list([[1], [2, 3], [4, 5, 6]]))
# 1 print(get_length_of_shortest_list([[1, [2, 3], 4], [5, 6], [7]]))
# 1 print(get_length_of_shortest_list([[[1], [2], [3]], [4, 5, [6]], [7, 8, 9, 10]]))
# 3

Method 3: min(len(x) for x in lst)

A Pythonic way to check the length of the shortest list is to combine a generator expression or list comprehension with the min() function without key. For instance, min(len(x) for x in lst) first turns all inner list into length integer numbers and passes this iterable into the min() function to get the result.

Here’s this approach on the same examples as before:

def get_length_of_shortest_list(lst): return min(len(x) for x in lst) print(get_length_of_shortest_list([[1], [2, 3], [4, 5, 6]]))
# 1 print(get_length_of_shortest_list([[1, [2, 3], 4], [5, 6], [7]]))
# 1 print(get_length_of_shortest_list([[[1], [2], [3]], [4, 5, [6]], [7, 8, 9, 10]]))
# 3

A good training effect can be obtained by studying the following tutorial on the topic—feel free to do so!

Training: Understanding List Comprehension in Python

Method 4: Naive For Loop

A not so Pythonic but still fine approach is to iterate over all lists in a for loop, check their length using the len() function, and compare it against the currently shortest list stored in a separate variable. After the termination of the loop, the variable contains the shortest list.

Here’s a simple example:

def get_shortest_list(lst): shortest = lst[0] if lst else None for x in lst: if len(x) < len(shortest): shortest = x return shortest print(get_shortest_list([[1], [2, 3], [4, 5, 6]]))
# [1] print(get_shortest_list([[1, [2, 3], 4], [5, 6], [7]]))
# [7] print(get_shortest_list([[[1], [2], [3]], [4, 5, [6]], [7, 8, 9, 10]]))
# [[1], [2], [3]] print(get_shortest_list([]))
# None

So many lines of code! At least does the approach also work when passing in an empty list due to the ternary operator used in the first line.

lst[0] if lst else None

If you need a refresher on the ternary operator, you should check out our blog tutorial.

Training Tutorial: The Ternary Operator — A Powerful Python Device

Note: If you need the length of the shortest list, you could simply replace the last line of the function with return len(shortest) , and you’re done!

Summary

You have learned about four ways to find the shortest list and its length from a Python list of lists (nested list):

• Method 1: min(lst, key=len)
• Method 2: len(min(lst, key=len))
• Method 3: min(len(x) for x in lst)
• Method 4: Naive For Loop

I hope you found the tutorial helpful, if you did, feel free to consider joining our community of likeminded coders—we do have lots of free training material!

Also, check out our tutorial on How to Find the Minimum of a List of Lists in Python?—it’s a slight variation!

Recommended Tutorial: Python Find Longest List in Dict of Lists

Problem Formulation and Solution Overview

This article will show you how to delete a line from a file in Python.

To make it more interesting, we have the following running scenario:

Rivers Clothing has a flat text file, rivers_emps.txt containing employee data. What happens if an employee leaves? They would like you to write code to resolve this issue.

Contents of rivers_emps.txt

100:Jane Smith 101:Daniel Williams 102:Steve Markham 103:Howie Manson 104:Wendy Wilson 105:Anne McEvans 106:Bev Doyle 107:Hal Holden 108:Mich Matthews 109:Paul Paulson

---

Question: How would we write code to remove this line?

We can accomplish this task by one of the following options:

• Method 1: Use List Comprehension
• Method 2: Use List Comprehension and Slicing
• Method 3: Use List Comprehension, Slicing and np.savetxt(),
• Method 4: Use pop()
• Method 5: Use remove()
• Method 6: Remove row(s) from a DataFrame

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Method 1: Use List Comprehension

This example uses List Comprehension to remove a specific line from a flat text file.

orig_lines = [line.strip() for line in open('rivers_emps.txt')]
new_lines = [l for l in orig_lines if not l.startswith('102')] with open('rivers_01.txt', 'w') as fp: print(*new_lines, sep='\n', file=fp)

The above code uses List Comprehension to read in the contents of a flat text file to a List, orig_lines. If output to the terminal, the following displays.

['100:Jane Smith', '101:Daniel Williams', '102:Steve Markham', '103:Howie Manson', '104:Wendy Wilson', '105:Anne McEvans', '106:Bev Doyle', '107:Hal Holden', '108:Mich Matthews', '109:Paul Paulson']

Then, List Comprehension is used again to append each element to a new List only if the element does not start with 102. If output to the terminal, the following displays.

['100:Jane Smith', '101:Daniel Williams', '103:Howie Manson', '104:Wendy Wilson', '105:Anne McEvans', '106:Bev Doyle', '107:Hal Holden', '108:Mich Matthews', '109:Paul Paulson']

As you can see, the element starting with 102 has been removed.

Next, a new file, rivers_01.txt, is opened in write (w) mode and the List created above is written to the file with a newline (\n) character appended to each line. The contents of the file are shown below.

100:Jane Smith 101:Daniel Williams 103:Howie Manson 104:Wendy Wilson 105:Anne McEvans 106:Bev Doyle 107:Hal Holden 108:Mich Matthews 109:Paul Paulson

---

Method 2: Use List Comprehension and Slicing

This example uses List Comprehension and Slicing to remove a specific line from a flat text file.

orig_lines = [line.strip() for line in open('rivers_emps.txt')]
new_lines = orig_lines[0:2] + orig_lines[3:] with open('rivers_02.txt', 'w') as fp: fp.write('\n'.join(new_lines))

The above code uses List Comprehension to read in the contents of a flat text file to a List, orig_lines. If output to the terminal, the following displays.

['100:Jane Smith', '101:Daniel Williams', '102:Steve Markham', '103:Howie Manson', '104:Wendy Wilson', '105:Anne McEvans', '106:Bev Doyle', '107:Hal Holden', '108:Mich Matthews', '109:Paul Paulson']

Then Slicing is used to extract all elements, except element two (2). The results save to new_lines. If output to the terminal, the following displays.

100:Jane Smith 101:Daniel Williams 103:Howie Manson 104:Wendy Wilson 105:Anne McEvans 106:Bev Doyle 107:Hal Holden 108:Mich Matthews 109:Paul Paulson

As you can see, element two (2) has been removed.

Next, a new file, rivers_02.txt, is opened in write (w) mode and the List created above is written to the file with a newline (\n) character appended to each line. The contents of the file are shown below.

100:Jane Smith 101:Daniel Williams 103:Howie Manson 104:Wendy Wilson 105:Anne McEvans 106:Bev Doyle 107:Hal Holden 108:Mich Matthews 109:Paul Paulson

---

Method 3: Use Slicing and np.savetxt()

This example uses List Comprehension, Slicing and NumPy’s np.savetxt() function to remove a specific line from a flat text file.

Before moving forward, please ensure that the NumPy library is installed to ensure this code runs error-free.

import numpy as np orig_lines = [line.strip() for line in open('rivers_emps.txt')]
new_lines = orig_lines[0:2] + orig_lines[3:] np.savetxt('rivers_03.txt', new_lines, delimiter='\n', fmt='%s')

The first line imports the NumPy library.

The following line uses List Comprehension to read the contents of a flat text file to the List, orig_lines. If output to the terminal, the following displays.

['100:Jane Smith', '101:Daniel Williams', '102:Steve Markham', '103:Howie Manson', '104:Wendy Wilson', '105:Anne McEvans', '106:Bev Doyle', '107:Hal Holden', '108:Mich Matthews', '109:Paul Paulson']

Then Slicing is applied to extract all elements, except element two (2). The results save to new_lines. If output to the terminal, the following displays.

100:Jane Smith 101:Daniel Williams 103:Howie Manson 104:Wendy Wilson 105:Anne McEvans 106:Bev Doyle 107:Hal Holden 108:Mich Matthews 109:Paul Paulson

As you can see, element two (2) has been removed.

The last code line calls np.savetxt() and passes it three (3) arguments:

• The filename (‘rivers_03.txt‘).
• An iterable, in this case, a List (new_lines).
• A delimiter (appended to each line) – a newline character (\n).
• The format. Strings are defined as %s.

The contents of rivers_03.txt displays below.

100:Jane Smith 101:Daniel Williams 103:Howie Manson 104:Wendy Wilson 105:Anne McEvans 106:Bev Doyle 107:Hal Holden 108:Mich Matthews 109:Paul Paulson

---

Method 4: Use pop()

This example uses the pop() function to remove a specific line from a flat text file.

import numpy as np orig_lines = [line.strip() for line in open('rivers_emps.txt')]
orig_lines.pop(2)
np.savetxt('rivers_04.txt', orig_lines, delimiter='\n', fmt='%s')

The first line imports the NumPy library.

The following line uses List Comprehension to read in the contents of a flat text file to the List, orig_lines. If output to the terminal, the following displays.

['100:Jane Smith', '101:Daniel Williams', '102:Steve Markham', '103:Howie Manson', '104:Wendy Wilson', '105:Anne McEvans', '106:Bev Doyle', '107:Hal Holden', '108:Mich Matthews', '109:Paul Paulson']

Then, the pop() method is called and passed one (1) argument, the element’s index to remove.

In this case, it is the second element.

If this List was output to the terminal, the following would display.

100:Jane Smith 101:Daniel Williams 103:Howie Manson 104:Wendy Wilson 105:Anne McEvans 106:Bev Doyle 107:Hal Holden 108:Mich Matthews 109:Paul Paulson

As shown in Method 3, the results save to a flat text file. In this case, rivers_04.txt. The contents are the same as in the previous examples.

Note: The pop() function removes the appropriate index and returns the contents to capture if necessary.

---

Method 5: Use remove()

This example uses the remove() function to remove a specific line from a flat text file.

import numpy as np orig_lines = [line.strip() for line in open('rivers_emps.txt')]
orig_lines.remove('102:Steve Markham')
np.savetxt('rivers_05.txt', orig_lines, delimiter='\n', fmt='%s')

This code works exactly like the code in Method 4. However, instead of passing a location of the element to remove, this function requires the contents of the entire line you to remove.

Then, the remove() function is called and passed one (1) argument, the index to remove. In this case, it is the second element. If this List was output to the terminal, the following would display.

100:Jane Smith 101:Daniel Williams 103:Howie Manson 104:Wendy Wilson 105:Anne McEvans 106:Bev Doyle 107:Hal Holden 108:Mich Matthews 109:Paul Paulson

As shown in the previous examples, the results save to a flat text file. In this case, rivers_05.txt.

---

Bonus: Remove row(s) from a DataFrame

CSV files are also known as flat-text files. This code shows you how to easily remove single or multiple rows from a CSV file

import pandas as pd
import numpy as np staff = { 'First' : ['Alice', 'Micah', 'James', 'Mark'], 'Last' : ['Smith', 'Jones', 'Watts', 'Hunter'], 'Rate' : [30, 40, 50, 37], 'Age' : [23, 29, 19, 45]} indexes=['FName', 'LName', 'Rate', 'Age']
df = pd.DataFrame(staff, index=indexes) df1 = df.drop(index=['Age'])
df.to_csv('staff.csv', index=False)

Finxter Challenge
Find 2 Additional Ways to Remove Lines

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Summary

This article has provided five (5) ways to delete a line from a file to select the best fit for your coding requirements.

Good Luck & Happy Coding!

---

Programmer Humor – Blockchain

Problem Formulation

Programming Challenge: Given a list of lists (nested list). Find and return the longest inner list from the outer list of lists.

Here are some examples:

• [[1], [2, 3], [4, 5, 6]] [4, 5, 6]
• [[1, [2, 3], 4], [5, 6], [7]] [1, [2, 3], 4]
• [[[1], [2], [3]], [4, 5, [6]], [7, 8, 9, 10]] [7, 8, 9, 10]

Also, you’ll learn how to solve a variant of this challenge.

Bonus challenge: Find only the length of the longest list in the list of lists.

Here are some examples:

• [[1], [2, 3], [4, 5, 6]] 3
• [[1, [2, 3], 4], [5, 6], [7]] 3
• [[[1], [2], [3]], [4, 5, [6]], [7, 8, 9, 10]] 4

So without further ado, let’s get started!

Method 1: max(lst, key=len)

Use Python’s built-in max() function with a key argument to find the longest list in a list of lists. Call max(lst, key=len) to return the longest list in lst using the built-in len() function to associate the weight of each list, so that the longest inner list will be the maximum.

Here’s an example:

def get_longest_list(lst): return max(lst, key=len) print(get_longest_list([[1], [2, 3], [4, 5, 6]]))
# [4, 5, 6] print(get_longest_list([[1, [2, 3], 4], [5, 6], [7]]))
# [1, [2, 3], 4] print(get_longest_list([[[1], [2], [3]], [4, 5, [6]], [7, 8, 9, 10]]))
# [7, 8, 9, 10]

A beautiful one-liner solution, isn’t it? Let’s have a look at a slight variant to check the length of the longest list instead.

Method 2: len(max(lst, key=len))

To get the length of the longest list in a nested list, use the len(max(lst, key=len)) function. First, you determine the longest inner list using the max() function with the key argument set to the len() function. Second, you pass this longest list into the len() function itself to determine the maximum.

Here’s an analogous example:

def get_length_of_longest_list(lst): return len(max(lst, key=len)) print(get_length_of_longest_list([[1], [2, 3], [4, 5, 6]]))
# 3 print(get_length_of_longest_list([[1, [2, 3], 4], [5, 6], [7]]))
# 3 print(get_length_of_longest_list([[[1], [2], [3]], [4, 5, [6]], [7, 8, 9, 10]]))
# 4

Method 3: max(len(x) for x in lst)

A Pythonic way to check the length of the longest list is to combine a generator expression or list comprehension with the max() function without key. For instance, max(len(x) for x in lst) first turns all inner list into length integer numbers and passes this iterable into the max() function to get the result.

Here’s this approach on the same examples as before:

def get_length_of_longest_list(lst): return max(len(x) for x in lst) print(get_length_of_longest_list([[1], [2, 3], [4, 5, 6]]))
# 3 print(get_length_of_longest_list([[1, [2, 3], 4], [5, 6], [7]]))
# 3 print(get_length_of_longest_list([[[1], [2], [3]], [4, 5, [6]], [7, 8, 9, 10]]))
# 4

A good training effect can be obtained by studying the following tutorial on the topic—feel free to do so!

Training: Understanding List Comprehension in Python

Method 4: Naive For Loop

A not so Pythonic but still fine approach is to iterate over all lists in a for loop, check their length using the len() function, and compare it against the currently longest list stored in a separate variable. After the termination of the loop, the variable contains the longest list.

Here’s a simple example:

def get_longest_list(lst): longest = lst[0] if lst else None for x in lst: if len(x) > len(longest): longest = x return longest print(get_longest_list([[1], [2, 3], [4, 5, 6]]))
# [4, 5, 6] print(get_longest_list([[1, [2, 3], 4], [5, 6], [7]]))
# [1, [2, 3], 4] print(get_longest_list([[[1], [2], [3]], [4, 5, [6]], [7, 8, 9, 10]]))
# [7, 8, 9, 10] print(get_longest_list([]))
# None

So many lines of code! At least does the approach also work when passing in an empty list due to the ternary operator used in the first line.

lst[0] if lst else None

If you need a refresher on the ternary operator, you should check out our blog tutorial.

Training Tutorial: The Ternary Operator — A Powerful Python Device

Note: If you need the length of the longest list, you could simply replace the last line of the function with return len(longest) , and you’re done!

Summary

You have learned about four ways to find the longest list and its length from a Python list of lists (nested list):

• Method 1: max(lst, key=len)
• Method 2: len(max(lst, key=len))
• Method 3: max(len(x) for x in lst)
• Method 4: Naive For Loop

I hope you found the tutorial helpful, if you did, feel free to consider joining our community of likeminded coders—we do have lots of free training material!

Also, check out our tutorial on finding the general maximum of a list of lists—it’s a slight variation!

In the previous articles, we looked at some of the representative examples of smart contracts representing possible real-world scenarios.

Our main focus was on capturing the essence of each case, without particular attention given to the general structure, i.e. layout of the respective source files.

However, in this mini-series starting with this article, we will focus particularly on the source file layout.

The articles will continue with our tradition of going hand in hand with the official Solidity documentation, with the particular topic of our current interest available here.

Info: As we’ve reached such a nice, round number of articles on Solidity, I have a small foreword for my faithful audience.

For those of us who missed or skipped previous articles, the intent behind the content is to supplement and clarify the original documentation and even present it in a style that I find to be more appropriate to us as the audience.

Given that we come from various backgrounds, some less and some more technical, it is my permanent goal to soften the material and make it as close as possible to each reader. Sometimes, completely unannounced and unprovoked, I’ll even try and sprinkle some humor onto the content.

Will I succeed in making it funny and engaging? That’s whole another story

SPDX License Identifier

Smart contracts are somewhat a mystery to unfamiliar folks, and a mystery usually implies a certain amount of distrust. Even so, the more sensitive the subject is, the greater the amount of distrust. The best way to turn distrust into trust is to make the content in question open and available.

When we’re talking about smart contracts, the openness of a smart contract means the availability of its source code. However, making the source code available frequently triggers legal problems regarding copyright.

To alleviate these problems, the Solidity compiler instigates the use of SPDX license identifiers.

Info: SPDX stands for the Software Package Data Exchange, which is “An open standard for communicating software bill of material information, including components, licenses, copyrights, and security references. SPDX reduces redundant work by providing a common format for companies and communities to share important data, thereby streamlining and improving compliance.“

Yes, I agree, it’s a lengthy sentence, but the main takeaway ideas are a communication standard, an instrument of compliance, and a data exchange format:

1. SPDX is a standard used for communicating the information about the software contents;
2. SPDX reduces redundant work and improves compliance;
3. SPDX provides a common format for data sharing between companies and communities;

An SPDX license identifier should be included at the beginning of the source file, e.g.

// SPDX-License-Identifier: GPL-3.0-or-later

Although SPDX license identifiers are machine-readable, the compiler does not check if the license part of the comment is in the list of licenses allowed by SPDX.

Instead, the compiler will just include the string in the bytecode metadata.

We will touch on the subject of contract metadata in future articles, but until then, let’s just remember that there is a thing called metadata.

Info: Metadata can be loosely defined as “data/information about data”, meaning it provides more information or description of certain data.

We don’t have to specify a license or if the case is that the source code is closed-source (opposite of open-source), the recommendation is that we use a special value UNLICENSED.

The UNLICENSED value implies that usage is not allowed, i.e. there is not a corresponding item in SPDX license list; it differs from the value UNLICENSE which grants all rights to everyone.

Solidity documentation authors note that Solidity adheres to the npm recommendation.

If we as developers supply the UNLICENSE comment, we are still tied by the obligation related to licensing, i.e. we have to mention a specific license header or the original copyright holder in the source files.

Although the compiler recognizes the comment placed at any location in the source file, the recommendation, and good practice is to put it at the top of the file.

Pragmas

We’ve mentioned the pragma keyword somewhere in the first few articles, but now we’ll use the opportunity to say a few more words about it.

Pragma keyword is the element of the Solidity programming language that enables specific Solidity compiler (remember solc) features or validations, i.e. checks.

As the pragma keyword scope is its source file, we’d have to add the pragma to all our files to enable it in our whole project.

Note: A pragma from an imported file does not apply to the importer file, i.e. the file that imports the imported file.

Version Pragma

We always use a version pragma for limiting the source file(s) compilation to a specific range of compiler versions.

The intention behind this step is the prevention of incompatible changes introduced with future versions of compilers.

According to the Solidity authors, occurrences of incompatible changes are reduced to an absolute minimum, meaning that in all other cases i.e. cases of compatible changes, the changes in Solidity language semantics visibly coincide with the changes in language syntax.

To stay on the safe side, the recommendation is to study the changelog at least for releases that carry breaking changes, marked x.0.0 (major releases) or 0.x.0 (minor releases).

Info: semantic is relating to meaning in language or logic.

As in every our example so far, we’re using the version pragma as:

Note: pragma solidity ^0.x.y; allows changes that do not modify the left-most non-zero digit in the [major, minor, patch] tuple (docs).

The following line instructs the compilation process to use a compiler with the lowest version of 0.5.2 and with the highest version not exceeding 0.6.0 (this condition is incorporated by a ^ symbol):

pragma solidity ^0.5.2;

By recalling the article about semantic versioning, we’ll remember that no breaking changes are introduced until a minor version of 0.6.0 (in this specific case), therefore we can be sure that our code will compile just as we expect it to.

Also, by using the line above, we didn’t lock on the specific version, so the last part of the version label, i.e. the patch number can increase, leaving enough space for the compiler bug fixes.

Besides this most common way of expressing the allowed versions of the compiler, even more, complex rules are available by using the syntax available here.

Note: version pragma just instructs the compiler to self-check if it is compliant with the version required by the source file. In case of a mismatch, the compiler will throw an (in)appropriate error. I mean, who ever saw an appropriate error, anyways?

Conclusion

With this introductory article to the topic of the layout of a Solidity source file, we covered a few very light concepts, including SPDX license identifier, reintroduced the pragma keyword, and retouched the version pragma.

In the next article, we will continue with the next two pragmas and other, very interesting topics.

In the SPDX License Identifier section, we were asking around inconspicuously about the SPDX. We wanted to find out what it is, how and when it is used, and how it can make our developing life easier.

In the Pragmas section, we proudly reminded ourselves of the knowledge from long ago, why do we have to slam a pragma at the beginning of each source file?

If at least they looked nice… Starting our coding masterpieces with a dangling comment seemed like a skewed joke (like most of mine do) – until we learned why

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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.

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

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?