Category: Programming

Problem Formulation

Question: Given a Python list stored in a variable lst. How to remove the first and last elements from the list lst?

Example: The list ['Alice', 'Bob', 'Carl', 'Dave'] stored in variable lst becomes ['Bob', 'Carl'].

Method 1: Slicing List[1:-1]

To remove the first and last elements from a Python list, use the expression lst = lst[1:-1] that uses a Python feature called slicing with the syntax variable[start:stop] to iterate over a sequence starting from the start index (included) and ending in the element at stop index (excluded). If stop is a negative integer such as -i, Python takes the i-th right-most element.

Here’s an example:

lst = ['Alice', 'Bob', 'Carl', 'Dave']
lst = lst[1:-1]
print(lst)
# ['Bob', 'Carl']

This code snippet removes the first element 'Alice' and the last element 'Dave' from the list.

Note that this approach also works for lists with one element:

lst = ['Alice']
lst = lst[1:-1]
print(lst)
# []

… and also for an empty list with zero elements:

lst = []
lst = lst[1:-1]
print(lst)
# []

Feel free to get some background on slicing by watching the following tutorial:

Learn More: An Introduction to Python Slicing

Method 2: Slicing List Without Negative Index

To remove the first and last elements from a Python list, you can also use the slightly more complex expression lst = lst[1:len(lst)-1] that assigns the result of the slicing operation to the list and, thereby, overwrites the original longer list. We decrement the len() function result to obtain the index of the last element that is excluded from the slice.

Here’s an example:

lst = ['Alice', 'Bob', 'Carl', 'Dave']
lst = lst[1:len(lst)-1]
print(lst)
# ['Bob', 'Carl']

You can watch my related tutorial video:

Learn More: The Python len() function

Method 3: list.pop() and list.pop(0)

To remove the first element of a Python list, you can use the list.pop(0) method. To remove the last element of a Python list, you can use the list.pop() method without argument. You can call both to remove the first and the last elements if the list has at least two elements.

Here’s a minimal example:

lst = ['Alice', 'Bob', 'Carl', 'Dave']
lst.pop() # remove last
lst.pop(0) # remove first
print(lst)
# ['Bob', 'Carl']

However, for a list with less than two elements, this will raise an IndexError: pop from empty list. A simple if check can make sure that the list has at least two elements—and otherwise simply override it with the empty list.

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Here’s some background info in case you want to dive deeper into this approach:

The list.pop() method removes and returns the last element from an existing list. The list.pop(index) method with the optional argument index removes and returns the element at the position index.

Learn More: The Python list.pop() method

Summary

You’ve learned the three easy ways to remove both the first and last elements from a Python list:

• Method 1: Slicing list[1:-1]
• Method 2: Slicing List Without Negative Index list[1:len(list)-1]
• Method 3: list.pop() and list.pop(0)

Thanks for your interest in learning with Finxter! You can check out our free email academy here:

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This article will go over the top 21 most attractive developer jobs in the decade to come. Note that the purpose of this article is to look forward to the future rather than looking backward into the past. The future is inherently uncertain but we did everything we could (as you’ll see) to remain objective and use data to guide our predictions.

Here’s a quick tabular overview on the most relevant career paths and jobs you could pursue in the decade to come – if you’re a developer optimizing for the future rather than the past:

Career Path (Job)	Annual Income (Low)	Annual Income (High)
Blockchain Developer	$105,180	$108,560
Data Scientist	$97,294	$135,924
Machine Learning Engineer	$112,000	$157,000
Deep Learning Engineer	$124,000	$148,000
Computer Science Researcher	$74,210	$208,000
Distributed Systems Developer	$97,000	$169,656
AWS Developer	$100,000	$130,000
Android App Developer	$85,000	$126,577
C++ Developer	$45,000	$140,000
Full-Stack Developer	$79,584	$108,984
Solidity Developer	$60,000	$180,000
Python Developer	$65,000	$82,000
Crypto Bot Developer	$70,000	$110,000
Azure Developer	$107,000	$127,000
Game Developer	$64,053	$115,846
JavaScript Developer	$62,000	$118,000
Quant Developer	$86,528	$170,000
Security Engineer	$75,732	$144,874
Test Automation Engineer	$74,821	$120,000
VHDL Developer	$32,000	$169,500
Frontend Developer	$61,194	$119,224

Okay, let’s dive into the meat—the most promising developer role for the upcoming decade(s)!

Blockchain Developer

A blockchain engineer operates, designs, develops, analyzes, implements, and supports a distributed blockchain network. Blockchain engineers manage specific business models dealing with blockchain technology.

The average annual income of a Blockchain engineer is between $105,180 and $108,560 according to Glassdoor (source):

But is there enough demand? Let’s have a look at Google trends to find out how interest evolves over time (source):

Do you want to become a Blockchain engineer? Here’s a learning path I’d propose in five steps to get started:

• Step 1: Introduction to Computer Science (~40 hours)
• Step 2: Introduction to Distributed Systems (~20 hours)
• Step 3: Introduction to Solidity and Ethereum (~10 hours)
• Step 4: Advanced Solidity (~10 hours)
• Step 5: Build Your Own NFT Marketplace (~20 hours)

Learn More: You can find out more about this career path in my full article on the Finxter blog.

Data Scientist

Data scientists use data to find quantifiable answers to questions that often need to be found as well!

For example, they not only find the answer to the question “How can company ABC make more money?” Instead, they may find that a better question to ask would be: “Who are the top 20% of the clients that bring 80% of the revenue, and what do they want in the first place?”

How much does a Data Scientist make per year?

The average annual income of a Data Scientist in the United States is between $97,294 and $135,924 with an average of $116,505 and a median of $119,413 per year.

But is there enough demand? Let’s have a look at Google trends to find out how interest evolves over time (source):

Clearly, this is a long-term trend you can build your whole career on!

Do you want to become a Data Scientist? Here’s a step-by-step learning path I’d propose to get started with Data :

• Step 1: Introduction to Computer Science (~40 hours)
• Step 2: Introduction to Statistics (~40 hours)
• Step 3: Introduction to Data Science (~40 hours)
• Step 4: Introduction to Data Visualization (~20 hours)
• Step 4: Introduction to Machine Learning (~40 hours)

Learn More: You can find out more about this career path in my full article on the Finxter blog.

Machine Learning Engineer

A Machine Learning Engineer creates, edits, analyzes, debugs, models, and supervises the development of machine learning models using programming languages such as Python or C++ and machine learning libraries such as Keras or TensorFlow.

How much does a Machine Learning Engineer make per year?

The average annual income of a Machine Learning Engineer in the United States is between $112,000 and $157,000 with a median of $131,000 per year according to multiple data sources such as Indeed, Glassdoor, Salary.com, and Payscale.

Learn More: You can find out more about this career path in my full article on the Finxter blog.

Deep Learning Engineer

Deep learning is a subset of machine learning using artificial neural network (ANN) models with more than three layers. ANNs are inspired by the behavior of the human brain to enable machines to learn — with the idea to connect neurons with each other via artificial “synapses” and learning is modeled as the collective weights and magnitude of the neural connections.

The average annual income of a Deep Learning Engineer in the United States is between $124,000 and $148,000 based on multiple sources such as Indeed, Ziprecruiter, and Salary.com.

A Deep Learning Engineer creates, edits, analyzes, debugs, and supervises the development of artificial neural networks (ANN) with multiple layers written in programming environments such as Python, TensorFlow, or Keras.

Do you want to become a Deep Learning Engineer? Here’s a step-by-step learning path I’d propose to get started with Deep Learning :

• Step 1: Introduction to Computer Science (~40 hours)
• Step 2: Introduction to Algorithms (~40 hours)
• Step 3: Introduction to Python (~10 hours)
• Step 4: Introduction to Machine Learning (~40 hours)
• Step 5: Introduction to Deep Learning (~20 hours)
• Step 6: Introduction to Deep Learning (Free Video Courses) (~10 hours)
• Step 7: Introduction to Machine Learning (~20 hours)
• Step 8: Introduction to Machine Learning (Free Video Courses) (~10 hours)
• Step 9: Introduction to TensorFlow (Free Video Courses) (~10 hours)
• Step 10: Introduction to Keras (~20 hours)
• Step 11: Introduction to Keras (Free Video Courses) (~10 hours)

Learn More: You can find out more about this career path in my full article on the Finxter blog.

Computer Science Researcher

A computer science researcher and scientist identifies and answers open research questions in computer science. They apply scientific reasoning and research techniques to push the state-of-the-art forward in various fields such as machine learning, distributed systems, databases, algorithms, and data science.

Six of the most common activities of computer science researchers, based on my own experience:

• reading research papers,
• thinking about research questions and problems,
• identifying research gaps and discussing them with their peers,
• creating code and software systems for evaluation purposes,
• writing research papers, and
• presenting those scientific results at conferences and in journals.

The median annual income (=50th percentile) of a computer science researcher was $131,490 in May 2021. The bottom 10% (=10th percentile) of computer science researchers earned less than $74,210 and the top 10% (=90th percentile) earned more than $208,000.

Learn More: You can find out more about this career path in my full article on the Finxter blog.

Distributed Systems Developer

A distributed system is a computer system spread across multiple computing devices connected via a communication network. Each participating device takes over part of the overall work performed by the system. By means of the collaboration of individual units, the system can provide services that each individual system component couldn’t provide on its own.

Some examples of distributed systems are:

• Ethereum is a distributed system of Ethereum nodes connected via the Internet and a specific communication protocol.
• Bitcoin is a distributed system of Bitcoin nodes connected via the Internet and a specific communication protocol as defined by the open-source Bitcoin protocol.
• The World Wide Web is a distributed system of servers connected via IP to provide a coherent web experience via browsers and an HTML-like web experience.

A distributed systems engineer designs, implements, and debugs distributed systems for data storage, crypto & web3, or analytics. The idea is to design a distributed system that can provide a service to users that couldn’t be provided by a centralized system (e.g., providing a decentralized, censorship-free monetary network).

The average annual income of a Distributed Systems Engineer in the United States is between $97,000 and $169,656, with an average of $126,894 and a statistical median of $130,000 per year.

Learn More: You can find out more about this career path in my full article on the Finxter blog.

AWS Developer

An AWS Developer creates, edits, analyzes, debugs, and supervises the development of software written for the AWS cloud services that support many programming languages such as Python or Java.

Learning AWS is one of the most important, most sought-after, and most profitable things you can do as a developer!

The average annual income of an AWS Developer in the United States is $122,799 per year according to ZipRecruiter. Another estimate is provided by Glassdoor that provides data supporting an annual income of AWS Developers of $115,000 per year.

Learn More: You can find out more about this career path in my full article on the Finxter blog.

Android App Developer

An Android app developer is a programmer who focuses on software creation for mobile devices such as smartphones or wearables using the Android operating system.

As an Android app developer, your skill set varies depending on the concrete set of applications you’re working on. However, these skills will proof useful no matter what, and most successful Android app developers have these seven skills:

1. General programming skills (e.g., Java, C++, Python)
2. Specific app framework skills (e.g., Flutter)
3. Distributed systems skills
4. Web development skills (e.g., HTML, CSS, JavaScript)
5. Design skills (e.g., Photoshop)
6. Security skills (e.g., SSL encryption)
7. Soft skills (e.g., communication, presentation, marketing)

The average annual income of an Android App Developer in the United States is between $85,000 and $126,577 with an average of $106,923 and a statistical median of $107,343 per year.

Learn More: You can find out more about this career path in my full article on the Finxter blog.

C++ Developer

As a C++ developer, you create software in the programming language C++ which is among the most widely used programming languages. For example, Google, Amazon, Facebook all employ a large number of C++ developers.

The average annual income of a C++ Developer is between $45,000 and $140,000 according to PayScale with an average of $67,473 in the US based on 31 salary reports (source). But Indeed.com reports an even higher annual C++ developer income of $116,925 based on 480 salaries reported (source).

Do you want to become a C++ Developer? Here’s a step-by-step learning path I’d propose to get started with C++:

• Step 1: Introduction to Computer Science (~40 hours)
• Step 2: MIT Introduction to C++ (~20 hours)
• Step 3: C++ Language Tutorial (~20 hours)

Learn More: You can find out more about this career path in my full article on the Finxter blog.

Full-Stack Developer

A full-stack web developer works both with back-end and front-end web technologies.

• The back-end consists of the webserver infrastructure, databases, and code function integration to facilitate a smooth and secure serving of user requests.
• The front-end focuses on the graphical user interface (GUI) of the website using HTML, CSS, and JavaScript with the goal of setting up the whole technology stack to enable users to view and interact with the website.

Full-stack developers have skills in all those fields so they often take crucial roles in overseeing the technical implementation of large web projects.

The average annual income of a Full-Stack Web Developer in the United States is between $79,584 and $108,984 with an average income of $98,454 and a median income of $99,274 per year according to our meta-study of 7 aggregated data sources such as Glassdoor and Indeed.

Learn More: You can find out more about this career path in my full article on the Finxter blog.

Solidity Developer

The average annual income of a Solidity Developer is between $60,000 and $180,000 with an average of $100,000 per year (source).

Here are a couple of example jobs for Solidity engineers:

Let’s have a look at Google trends to find out how interest evolves over time (source):

A Solidity developer creates, edits, analyzes, and debugs code in the Solidity programming language used to develop smart contracts for modern Blockchain ecosystems such as Ethereum.

Do you want to become a Solidity Developer? Here’s a step-by-step learning path I’d propose to get started with Solidity:

• Step 1: Introduction to Computer Science (~40 hours)
• Step 2: Introduction to Distributed Systems (~20 hours)
• Step 3: Introduction to Solidity and Ethereum (~10 hours)
• Step 4: Advanced Solidity (~10 hours)
• Step 5: Build Your Own NFT Marketplace (~20 hours)

Learn More: You can find out more about this career path in my full article on the Finxter blog.

Python Developer

A Python developer is a programmer who creates software in the Python programming language. Python developers are often involved in data science, web development, and machine learning applications.

A Python developer earns $65,000 (entry-level), $82,000 (mid-level), or $114,000 (experienced) per year in the US according to Indeed. (source)

Do you want to become a Python Developer? Here’s a step-by-step learning path I’d propose to get started with Python:

• Step 1: Introduction to Computer Science (~40 hours)
• Step 2: Introduction to Python (~10 hours)
• Step 3: Python Object-Orientation (~10 hours)

Learn More: You can find out more about this career path in my full article on the Finxter blog.

Crypto Bot Developer

The average annual income of a Crypto Trading Bot Developer is similar to algorithmic traders of $104,422 (source). However, due to the novelty of the industry, there’s little official data. If you assume an hourly rate of $50 and an annual 2000 hours worked, the annual income of a crypto trading bot developer would be $100,000.

Let’s have a look at Google trends to find out how interest evolves over time (source):

Yes, this definitely is an interesting industry for programmers to make $50 per hour and more!

Trading bots are software programs that talk directly to financial exchanges. Crypto trading bots are programs that talk to crypto exchanges. A crypto bot developer develops those programs. Crypto trading bot developers tend to be very proficient in trading, financial algorithms, APIs, and web services.

Do you want to become a Crypto Trading Bot Developer? Here’s a learning path I’d propose in five steps to get started:

• Step 1: Introduction to Computer Science (~40 hours)
• Step 2: Create Your First Crypto Trading Bot (~20 hours)
• Step 3: Introduction to Solidity and Ethereum (~10 hours)
• Step 4: Advanced Solidity (~10 hours)
• Step 5: Mastery Crypto Trading Bot Strategies (~20 hours)

Learn More: You can find out more about this career path in my full article on the Finxter blog.

Azure Developer

An Azure Developer creates, designs, edits, analyzes, debugs, deploys, and supervises the development of cloud applications written for the Azure cloud and development ecosystem.

The average annual income of an Azure Developer in the United States is $122,031 per year according to Talent.com. Entry-level Azure Developers start with $107,250 per year. Top earners make $127,000 and more in the US according to Glassdoor!

Let’s have a look at Google trends to find out how interest evolves over time (source):

Learn More: You can find out more about this career path in my full article on the Finxter blog.

Game Developer

Game Development is the art of creating games that involves multiple development stages such as game concept generation, game design, game development, game testing, game building, game deployment, and game release.

Game developers and video game developers create code for games in a variety of formats such as desktop-based games (Windows, macOS, Linux), consoles (PS2), web browsers (Chrome, Safari), crypto (Ethereum, Polygon/Optimism, Solana), and mobile phone (iOS, Android).

The average annual income of a Game Developer in the United States is between $64,053 and $115,846 with an average of $89,889 and a median of $92,061 per year.

Learn More: You can find out more about this career path in my full article on the Finxter blog.

JavaScript Developer

A JavaScript developer creates dynamic web applications focusing mostly on the front-end logic—but recently some back-end JavaScript frameworks emerged as well. If you like web development and programming user interfaces, you’ll love the work as a JavaScript developer.

The average annual income of a JavaScript Developer is between $62,000 and $118,000 with an average of $84,000 per year according to Daxx.com and PayScale (source).

Do you want to become a JavaScript Developer? Here’s a step-by-step learning path I’d propose to get started with JavaScript and web development:

• Step 1: Introduction to HTML (~20 hours)
• Step 2: Introduction to CSS (~20 hours)
• Step 3: Introduction to JavaScript (~20 hours)

Learn More: You can find out more about this career path in my full article on the Finxter blog.

Quant Developer

A quantitative developer (i.e., Quant) is a financial programmer focused on financial modeling and quantitative finance and trading.

Quants use their profound knowledge of

• statistics and math,
• finance,
• data structures,
• algorithms,
• machine learning,
• scientific computing,
• data science,
• chart technique, and
• data visualization

to create models for financial prediction, backtesting, analysis, and implementation of trading and financial applications (e.g., for risk management).

The expected annual income of a Quantitative Developer (Quant) in the United States is between $86,528 and $170,000 per year, with an average annual income of $127,375 per year and a median income of $136,321 per year.

Learn More: You can find out more about this career path in my full article on the Finxter blog.

Security Engineer

A security engineer is a “white-hat hacker”, i.e., an IT professional who analyzes computer systems and computer networks to ensure they are running securely. This involves proactive analysis and understanding of possible security threats and attack vectors and designing the system to minimize the exposure to these threats.

The average annual income of a Security Engineer in the United States is between $75,732 and $144,874, with an average of $108,851 and a statistical median of $105,928 per year.

Learn More: You can find out more about this career path in my full article on the Finxter blog.

Test Automation Engineer

A Test Automation Engineer is a software developer who creates automated software tests for existing or new applications. Testing is a crucial phase in the software development cycle to learn about bugs, usability, and security issues and fix them before deploying an application in the real world.

The expected annual income of a Test Automation Engineer in the United States is between $74,821 and $120,000 per year, with an average annual income of $95,285 per year and a median income of $93,657 per year.

Learn More: You can find out more about this career path in my full article on the Finxter blog.

VHDL Developer

A VHDL Developer developer creates, edits, analyzes, debugs, and supervises the development of software written in the VHDL programming language. VHDL is the other popular hardware description language. In fact, most professionals who know VHDL also know Verilog.

The annual income of a VHDL developer in the US can be as high as $169,500 and as low as $32,000 according to Ziprecruiter (source). Most VHDL developer make between $65,000 (25th percentile) and $135,000 (75th percentile) with top earners (90th percentile) making $157,500 annually across the United States.

Learn More: You can find out more about this career path in my full article on the Finxter blog.

Frontend Developer

A web developer is a programmer who specializes in the development of websites or applications viewed on web browsers, mobile devices, and large desktop screens that are transported over private or public networks such as the Internet.

A front-end web developer focuses on the graphical user interface (GUI) of the website using HTML, CSS, and JavaScript with the goal of setting up the whole technology stack to enable users to view and interact with the website.

The average annual income of a Front-end Web Developer in the United States is between $61,194 and $119,224 with an average income of $89,683 and a median income of $90,499 per year according to our meta-study of 8 aggregated data sources such as Glassdoor and Indeed.

The following graphic shows the individual data sources, as well as the average and median income level of a web developer in the US:

Learn More: You can find out more about this career path in my full article on the Finxter blog.

Summary

This article has shown you the top career choices for developers in the upcoming decade:

• Blockchain Developer
• Data Scientist
• Machine Learning Engineer
• Deep Learning Engineer
• Computer Science Researcher
• Distributed Systems Developer
• AWS Developer
• Android App Developer
• C++ Developer
• Full-Stack Developer
• Solidity Developer
• Python Developer
• Crypto Bot Developer
• Azure Developer
• Game Developer
• JavaScript Developer
• Quant Developer
• Security Engineer
• Test Automation Engineer
• VHDL Developer
• Frontend Developer

Feel free to join our free email academy to stay tuned and informed in many of those fields!

Programmer Humor – Blockchain

Where to Go From Here?

Enough theory. Let’s get some practice!

Coders get paid six figures and more because they can solve problems more effectively using machine intelligence and automation.

To become more successful in coding, solve more real problems for real people. That’s how you polish the skills you really need in practice. After all, what’s the use of learning theory that nobody ever needs?

You build high-value coding skills by working on practical coding projects!

Do you want to stop learning with toy projects and focus on practical code projects that earn you money and solve real problems for people?

If your answer is YES!, consider becoming a Python freelance developer! It’s the best way of approaching the task of improving your Python skills—even if you are a complete beginner.

If you just want to learn about the freelancing opportunity, feel free to watch my free webinar “How to Build Your High-Income Skill Python” and learn how I grew my coding business online and how you can, too—from the comfort of your own home.

Join the free webinar now!

Problem Formulation and Solution Overview

In this article, you’ll learn how to multiply List Elements by a Number in Python.

This example multiples the first five (5) Prime Numbers by two (2) and return the result.

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Question: How would we write Python code to multiply the list elements?

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

• Method 1: Use List Comprehension
• Method 2: Use Pandas tolist()
• Method 3: Use map() and a lambda()
• Method 4: Use NumPy Array()
• Method 5: Use Slicing

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Method 1: Use List Comprehension

This method uses List Comprehension to apply a mathematical operation to each element and return the result.

prime_nums = [2, 3, 5, 7, 11]
mult_result = [x * 2 for x in prime_nums]
print(mult_result)

Above declares the first (5) Prime Numbers and saves this List to prime_nums. Next, List Comprehension loops through each element and applies the multiplication operation to each. The output saves to mult_result and is output to the terminal.

[4, 6, 10, 14, 22]

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Method 2: Use Pandas tolist()

This method requires an additional library to be imported, Pandas, to use the tolist() function.

import pandas as pd prime_nums = [2, 3, 5, 7, 11]
mult_result = pd.Series(prime_nums)
mult_result = (mult_result*2).tolist()
print(mult_result)

Above, imports the Pandas Library. Click here if this requires installation. Then, the first (5) Prime Numbers are declared and saved to prime_nums.

Next, prime_nums is passed as an argument to the pd.Series() function and returns mult_result. The output of mult_result at this point is shown below.

0 2 1 3 2 5 3 7 4 11 dtype: int64

Now, we need to convert this output to a list (tolist()) and apply the multiplication operation to each element. The results save to mult_result and are output to the terminal.

[4, 6, 10, 14, 22]

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Method 3: Use map and lambda Functions

This method wraps the map(), and lambda functions inside a Python List and calculates the results.

prime_nums = [2, 3, 5, 7, 11]
mult_result = list(map(lambda x: x*2, prime_nums))
print(mult_result)

Above declares the first (5) Prime Numbers and saves them to prime_nums. The next line does the following:

• The map() function is passed the lambda() function as an argument (map(lambda x: x*2, prime_nums)).
• The lambda performs the multiplication operation to each element of prime_nums and saves it to map() as an object similar to below.
  <map object at 0x000001DC99CBBBB0>
• The map() object is then converted to a List.
• The results save to mult_result.

Then, mult_result is output to the terminal.

[4, 6, 10, 14, 22]

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Method 4: Use Numpy Array()

This method requires an additional library to be imported, NumPy, to use the np.array() function.

import numpy as np prime_nums = [2, 3, 5, 7, 11]
the_result = list(np.array(prime_nums) * 2)
print(the_result)

Above, imports the NumPy Library. Click here if this requires installation. Then the first (5) Prime Numbers are declared and saved to prime_nums.

Next, prime_nums is passed as an argument to np.array() where the multiplication operation is applied to each element. Then, this is converted to a List, saved to the_result and output to the terminal.

[4, 6, 10, 14, 22]

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Method 5: Use Slicing

This method uses Python’s infamous Slicing! No overhead, and a very pythonic way to resolve the issue.

prime_nums = [2, 3, 5, 7, 11]
prime_nums[:] = [x * 2 for x in prime_nums]
print(prime_nums)

Above declares the first (5) Prime Numbers and saves them to prime_nums.

Then slicing is applied and used in conjunction with List Comprehension to apply the multiplication operation to each element. The results save back to prime_nums and are output to the terminal.

[4, 6, 10, 14, 22]

A Finxter Favorite!

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Summary

These methods of multiplying list elements by a number should give you enough information to select the best one for your coding requirements.

Good Luck & Happy Coding!

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Programmer Humor

Programmer 1: We have a problem
Programmer 2: Let’s use RegEx!
Programmer 1: Now we have two problems

… yet – you can easily reduce the two problems to zero as you polish your “RegEx Superpower in Python“.

The TypeError: unhashable type: 'list' usually occurs when you try to use a list object as a set element or dictionary key and Python internally passes the unhashable list into the hash() function. But as lists are mutable objects, they do not have a fixed hash value. The easiest way to fix this error is to use a hashable tuple instead of a non-hashable list as a dictionary key or set element.

We’ll show how this is done in the remaining article. The last method is a unique way to still use lists in sets or dictionary keys that you likely won’t find anywhere else, so keep reading and learn something new!

Problem Formulation and Explanation

Question: How to fix the TypeError: unhashable type: 'list' in your Python script?

There are two common reasons for this error:

• You try to use a list as a dictionary key, or
• You try to use a list as a set element.

This is not a trivial problem because Python lists are mutable and, therefore, not hashable.

Here’s the first example:

my_list = [1, 2, 3]
my_dict = {}
my_dict[my_list] = 'hello Finxters!' '''
Traceback (most recent call last): File "C:\Users\xcent\Desktop\code.py", line 3, in <module> my_dict[my_list] = 'hello Finxters!'
TypeError: unhashable type: 'list' '''

And here’s the second example:

my_list = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
my_set = set(my_list) '''
Traceback (most recent call last): File "C:\Users\xcent\Desktop\code.py", line 2, in <module> my_set = set(my_list)
TypeError: unhashable type: 'list' '''

As you’ve seen in the previous two code snippets, the TypeError: unhashable type: 'list' usually occurs when you try to use a list object as a set element or dictionary key.

But let’s dive deeper to find the real reason for the error:

Minimal Reproducible Error Example: Lists are mutable objects so they do not have a fixed hash value. In fact, the error can be reproduced most easily when calling hash(lst) on a list object lst.

This is shown in the following minimal example that causes the error:

hash([1, 2, 3])

The output is the error message:

Traceback (most recent call last): File "C:\Users\xcent\Desktop\code.py", line 1, in <module> hash([1, 2, 3])
TypeError: unhashable type: 'list'

Because you cannot successfully pass a list into the hash() function, you cannot directly use lists as set elements or dictionary keys.

But let’s dive into some solutions to this problem!

Method 1: Use Tuple Instead of List as Dictionary Key

The easiest way to fix the TypeError: unhashable type: 'list' is to use a hashable tuple instead of a non-hashable list as a dictionary key. For example, whereas d[my_list] will raise the error, you can simply use d[tuple(my_list)] to fix the error.

Here’s an example of what you can do instead:

my_list = [1, 2, 3]
my_dict = {}
my_dict[tuple(my_list)] = 'hello Finxters!' print(my_dict)
# {(1, 2, 3): 'hello Finxters!'}

The error may also occur when you try to use a list as a set element. Next, you’ll learn what to do in that case:

Method 2: Use Tuple Instead of List as Set Element

To fix the TypeError: unhashable type: 'list' when trying to use a list as a set element is to use a hashable tuple instead of a non-hashable list. For example, whereas set.add([1, 2]) will raise the error, you can simply use set.add((1, 2)) or set.add(tuple([1, 2])) to fix the error.

Here’s a minimal example:

my_set = set() # Error: my_set.add([1, 2]) # This is how to resolve the error:
my_set.add((1, 2))
# Or: my_set.add(tuple([1, 2])) print(my_set)
# {(1, 2)}

If you want to convert a list of lists to a set, you can check out my detailed tutorial on the Finxter blog:

Related Tutorial: How to convert a list of lists to a set?

Method 3: Use String Representation of List as Set Element or Dict Key

To fix the TypeError: unhashable type: 'list', you can also use a string representation of the list obtained with str(my_list) as a set element or dictionary key. Strings are hashable and immutable, so Python won’t raise the error when using this approach.

Here’s an example:

my_list = [1, 2, 3] # 1. Use str repr of list as dict key:
d = {}
d[str(my_list)] = 'hello Finxters' # 2. Use str repr of list as set element:
s = set()
s.add(str(my_list))

In both cases, we used the string representation of the list instead of the list itself. The string is immutable and hashable and it fixes the error.

But what if you really need a mutable set or dictionary key? Well, you shouldn’t but you can by using this approach:

Method 4: Create Hashable Wrapper List Class

You can still use a mutable list as a dictionary key, set element, or argument of the hash() function by defining a wrapper class, say HackedList, that overrides the __hash__() dunder method.

Python’s built-in hash(object) function takes one object as an argument and returns its hash value as an integer. You can view this hash value as a unique fingerprint of this object.

The Python __hash__() method implements the built-in hash() function.

Here’s the minimal code example that creates a wrapper class HackedList that overrides the __hash__() dunder method so you can use an instance of HackedList as a dictionary key, set element, or just as input to the hash() function:

my_list = [1, 2, 3] class HackedList: def __init__(self, lst): self.lst = lst def __hash__(self): return len(self.lst) my_hacked_list = HackedList(my_list) # 1. Pass hacked list into hash() function:
print(hash(my_hacked_list)) # Output: 3 # 2. Use hacked list as dictionary key:
d = dict()
d[my_hacked_list] = 'hello Finxters' # 3: Use hacked list as set element:
s = set()
s.add(my_hacked_list)

Here’s the content of the dictionary and set defined previously:

{<__main__.HackedList object at 0x0000016CFB0BDFA0>: 'hello Finxters'}
{<__main__.HackedList object at 0x0000016CFB0BDFA0>}

If you want to fix the ugly output, you can additionally define the __str__() and __repr__() magic methods like so:

my_list = [1, 2, 3] class HackedList: def __init__(self, lst): self.lst = lst def __hash__(self): return len(self.lst) def __str__(self): return str(self.lst) def __repr__(self): return str(self.lst) my_hacked_list = HackedList(my_list) # 1. Pass hacked list into hash() function:
print(hash(my_hacked_list)) # Output: 3 # 2. Use hacked list as dictionary key:
d = dict()
d[my_hacked_list] = 'hello Finxters' # 3: Use hacked list as set element:
s = set()
s.add(my_hacked_list) print(d)
print(s)

Beautiful output:

{[1, 2, 3]: 'hello Finxters'}
{[1, 2, 3]}

Summary

The five most Pythonic ways to convert a list of lists to a set in Python are:

• Method 1: Use Tuple Instead of List as Dictionary Key
• Method 2: Use Tuple Instead of List as Set Element
• Method 3: Use String Representation of List as Set Element or Dict Key
• Method 4: Create Hashable Wrapper List Class

Feel free to check out more free tutorials and cheat sheets for learning and improving your Python skills in our free email academy:

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Nerd Humor

Full Course: Check out the full beginner course on Forex trading on this Finxter page (5 video lessons).

As a Python beginner, or anything else new that we dive into, everything is fresh and exciting for a while and we have no problem staying motivated to do the work and move ahead.

It’s no wonder you can stay fired up when you are learning the most popular language, in a field that looks promising for years to come, and its innovations will shape the future. That’s exciting!

There’s a book that summarizes the next step in your journey, whether it be Python, Forex, business, freelancing, or anything else. It deals with what most people call, “being at the intermediate level.”

It’s called “The Dip”, by Seth Godin. Like most “self-help” type books, even though this one is only around 100 pages, it could have been done in 10 or 15. In this case though, the author gets an “A+” for the concept.

The idea that after the honeymoon, there will be a period of uncertain struggle on where to go next. Python has the mother of all dips.

To wrap up this beginner’s guide, I want to help you find your way through the dip and come out the other side a success.  That “way” is in the title – “Practical Projects.”

Freelance and Get Some Work that Uses Your Python Skills

Doing a project for someone who doesn’t know how, or have the time to do it themselves, is a great way to put your Python skills to the test.

The great thing about freelancing your skills, is you never know what someone is going to need, and this can give you a great variety of projects.

PRO TIP: Don’t wait until you “feel” ready.  You will never feel ready – what you need is confidence – by doing some real work, learning from your mistakes, and not making them again.

Getting started on a platform like Upwork is simple and you will know which projects you can handle, and which ones you can’t – besides, it’s good for you to take a couple that will push your skills and require you to learn how to complete them.

Here are a few more Ideas for some real-world projects:

Data Analysis Projects with Python and Its Libraries

We went through some simple examples of what you can do with data earlier in the series.  Let’s break down a sample in detail:

• Think about a subject that interests you, and where you can find data collections for that topic.
• Do a search and find some downloadable files from their collections.
• Pick a file that suits your project, download the CSV, (I hope you’re using Anaconda and Jupyter), clean it up and organize it, then see what types of patterns, if any, you can identify.  I grabbed historical data on interest rates from the Fed’s website for my last analysis.  There is so much information out there for free that we will never be able to cover a tiny percentage of it.  So narrow it down to your specific needs.

Projects and Tests for Forex Trading and Python

• Form a hypothesis – “Is there a correlation between the EUR/USD and WTI?”  In light of recent global events, one would be safe in questioning crude oil’s affect on the entire world.
• Do a comparison – Do you remember in a previous lesson when I demonstrated how to overlay one instrument with another on your charts? This is a simple way to look for correlation. Remember, correlation can be positive or negative.
• Look to see if one or the other seems to “lead” its partner.  This can be a great way to see into the future – so to speak.
• If your theory looks promising, question if there is a way to quantify and automate the information using Python.  This would also be a good time to start digging into machine learning.  Use Python to streamline the process and set alerts.

This is a hypothetical situation I created as an example.  Do not trade any theory from anyone without thoroughly testing it yourself.

Sources for Datasets

• Governments collect data and make it available to the public on their websites.  Records of everything from NFP to GDP, and weather events can be found with a little effort.
• Central banks, the IMF, and the World Bank also issue reports and data on a variety of economic indicators and predictions created by their own experts.
• Be wary of “advice” sites that are trying to sell you something – look for facts gleaned from statistics and research instead.

Get on Board with a Broker and Get a Robot

We have already discussed how to choose a broker, and did some analysis together on the subject.  With the regulations in place these days, it’s really easy to find one that is legit.  It will boil down to personal preference in the end.  Make sure you feel comfortable with your choice, and that they have responsive customer service so you can communicate easily.

As a beginner, just like with Python, it’s important to start getting some experience while you’re learning to code your own bots. Using a ready-made bot on a demo account is the best way to get going and see if automated trading is right for you.

REMEMBER: Don’t make it all about the money just yet – the knowledge you’re getting in the process is the real value.  If you have followed the steps in this series, you should already be on your way to safely making money with Python.

Bonus for Finishing the Beginner Series on Forex Bots and Python

For all of you who have stuck it out until the end of our beginner series, I’m going to give you some analysis that will demonstrate the many different ways to go about your trade planning – they’re endless, which is what makes Forex so interesting.  No matter your style, you can find a system that fits.

In the accompanying video, I’m going to give some high-level tips and analysis on the EUR/USD pair that we have been using in the series, and explain what actually makes currency values change.

Check out the video, and it has been a pleasure sharing this information with you.

Here’s to good trading and good luck!

In this tutorial we will unearth the solutions to three commonly asked questions that users come across while dealing with huge sets of data.

Problem Formulation

Given: Consider the following csv file (Note: You need to use it as a Pandas DataFrame).

import pandas as pd df = pd.read_csv('countries.csv')
print(df)

 Country Capital Population Area
0 Germany Berlin 84,267,549 348,560
1 France Paris 65,534,239 547,557
2 Spain Madrid 46,787,468 498,800
3 Italy Rome 60,301,346 294,140
4 India Delhi 1,404,495,187 2,973,190
5 USA Washington 334,506,463 9,147,420
6 China Beijing 1,449,357,022 9,388,211
7 Poland Warsaw 37,771,789 306,230
8 Russia Moscow 146,047,418 16,376,870
9 England London 68,529,747 241,930

Here’s the list of the questions that we will be focusing upon in this article:

• How to get the last N rows of a Pandas DataFrame?
• How to get last N rows from last N columns of a Pandas DataFrame?
• How to read last N rows of a large csv file in Pandas?

Recommended Read: How to Select Rows From a DataFrame Based on Column Values?

Without further delay, let us dive into the solutions to the first question and learn how to get the last N rows of a Pandas DataFrame.

Method 1: Using iloc

Approach: Use the iloc property as pandas.DataFrame.iloc[-n:].

The iloc property is used to get or set the values of specified indices. Select the last n rows using the square bracket notation syntax [-n:] with the iloc property. Here, -n represents the index of the last n rows of the given pandas DataFrame.

Code:

import pandas as pd df = pd.read_csv('countries.csv')
rows = df.iloc[-5:]
print(rows)

Output:

 Country Capital Population Area
5 USA Washington 334,506,463 9,147,420
6 China Beijing 1,449,357,022 9,388,211
7 Poland Warsaw 37,771,789 306,230
8 Russia Moscow 146,047,418 16,376,870
9 England London 68,529,747 241,930

Method 2: Using tail()

Approach: Use the pandas.DataFrame.tail(n) to select the last n rows of the given DataFrame.

The tail(n) method returns n number of methods from the bottom end of the DataFrame. Here, n represents an integer that denotes the number of rows you want to fetch from the bottom end of the DataFrame.

Code:

import pandas as pd df = pd.read_csv('countries.csv')
rows = df.tail(5)
print(rows)

Output:

 Country Capital Population Area
5 USA Washington 334,506,463 9,147,420
6 China Beijing 1,449,357,022 9,388,211
7 Poland Warsaw 37,771,789 306,230
8 Russia Moscow 146,047,418 16,376,870
9 England London 68,529,747 241,930

Well, that brings us to the next question in line – “How to get the last N rows from last N columns of a Pandas DataFrame?”

Method 1: Integer Based Indexing

Approach: Call pandas.DataFrame.iloc[-n:, -m:] to display last n rows from the last m columns of the given DataFrame.

Code: In the following code snippet we will fetch the last 5 rows from the last 2 columns, i.e., Population and Area.

import pandas as pd df = pd.read_csv('countries.csv')
rows = df.iloc[-5:, -2:]
print(rows)

Output:

 Population Area
5 334,506,463 9,147,420
6 1,449,357,022 9,388,211
7 37,771,789 306,230
8 146,047,418 16,376,870
9 68,529,747 241,930

Method 2: Name Based Indexing

In case, you happen to know the names of the specific columns and you want to get the last N records from the DataFrame from those columns then you can follow a two step process.

• Call the Pandas.DataFrame.loc(:, 'start_column_name':'end_column_name') selector. It allows you to use slicing on column names instead of integer identifiers which can be more comfortable.
• .loc is for label based indexing. Hence, the negative indices are not found and reindexed to NaN. Thus, to deal with this you have to use the tail() method to extract the last N records from the selected columns.

Code: The following code snippet shows how you can use the column names and fetch the corresponding values from the last 5 rows of the given Dataframe.

import pandas as pd df = pd.read_csv('countries.csv')
rows = df.loc[:, 'Population':'Area']
print(rows.tail(5))

Output:

 Population Area
5 334,506,463 9,147,420
6 1,449,357,022 9,388,211
7 37,771,789 306,230
8 146,047,418 16,376,870
9 68,529,747 241,930

Last but not least, let us solve the third and final problem of today’s tutorial – “How to read last N rows of a large csv file in Pandas?”

Unfortunately, read_csv() does not facilitate us with any parameter that allows you to directly read the last N lines from a file. This can be a troublesome issue to handle when you are dealing with large datasets.

Thus, a workaround to this problem is to first find out the total number of lines/records in the file. Then use the skiprows parameter to directly jump to the row/line from which you want to select the records.

Code: In the following code snippet we will fetch the first 5 rows from the csv file into our DataFrame.

import pandas as pd def num_of_lines(fname): with open(fname) as f: for i, _ in enumerate(f): pass return i + 1 num_lines = num_of_lines("countries.csv")
n = 5
df = pd.read_csv("countries.csv", skiprows=range(1, num_lines - n))
print(df)

Output:

 Country Capital Population Area
0 USA Washington 334,506,463 9,147,420
1 China Beijing 1,449,357,022 9,388,211
2 Poland Warsaw 37,771,789 306,230
3 Russia Moscow 146,047,418 16,376,870
4 England London 68,529,747 241,930

Conclusion

Phew! We have successfully solved all the problems that were presented to us in this tutorial.  I hope this tutorial helped you to sharpen your coding skills. Please stay tuned and subscribe for more interesting coding problems.

Recommended Reads:

• Pandas DataFrame head() and tail() Method
• Delete Column from Pandas DataFrame
• Change Column Type in Pandas

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Learn Pandas the Fun Way by Solving Code Puzzles

If you want to boost your Pandas skills, consider checking out my puzzle-based learning book Coffee Break Pandas (Amazon Link).

It contains 74 hand-crafted Pandas puzzles including explanations. By solving each puzzle, you’ll get a score representing your skill level in Pandas. Can you become a Pandas Grandmaster?

Coffee Break Pandas offers a fun-based approach to data science mastery—and a truly gamified learning experience.

Problem Formulation

How to convert .blf from a CAN bus to .csv in Python?

What is BLF? The Binary Logging Format (BLF) is a proprietary
CAN log format from the automative company Vector Informatik GmbH.

What is CAN? The Controller Area Network (CAN bus) is a message-based protocol standard for microcontrollers in vehicles to communicate without a host computer.

Method 1: Using BLF Reader and CSV Writer

To convert the BLF file 'my_file.blf' to the CSV file 'my_file.csv', you can first iterate over the bus messages using can.BLFReader('my_file.csv') and add the data to a list of lists. Then, you can use the csv.writer() approach to write the list of lists to a CSV file.

Here’s an example that improves upon this SO thread:

import can
import csv log = [] for msg in list(can.BLFReader("my_file.blf")): msg = str(msg) row = [msg[18:26], msg[38:40], msg[40:42], msg[46], msg[62], msg[67:90]] log.append(row) with open("my_file.csv", "w", newline='') as f: writer = csv.writer(f, delimiter=',', quotechar='\"', quoting=csv.QUOTE_ALL) writer.writerows(log)

A more sophisticated version of this code is provided in this Github repository. Here’s a screenshot of the code — notice the more advanced processing of a single message compared to our solution:

Method 2: Using the candas Library

The candas library provides utility functions to work with .blf files and the CAN bus. Among other things, it helps you with the conversion from BLF to CSV as outlined here.

This is the provided example:

import candas as cd db = cd.load_dbc("dbc_folder") # This is the BLF file 'my_file.blf':
log = cd.from_file("my_file") # This prints a signal from the messages in the BLF:
print(log["AVGcellTemperature"])

Method 3: Using Custom Solution from python-can Library

You can use your tailor-made solutions by combining the Readers and Writers provided in the python-can library.

It provides multiple utility functions such as:

• Listener
• BufferedReader
• RedirectReader
• Logger
• Printer
• CSVWriter
• SqliteWriter
• ASC
• Log
• BLF

Chances are you’ll find what you’re looking for when going over those functions!

Related Video

Still not satisfied? I found the following relevant video when searching for a solution to this problem. I think you’ll find some nice tricks in the video!

Problem Formulation

Given a CSV file (e.g., stored in the file with name 'my_file.csv').

INPUT: file 'my_file.csv'
9,8,7
6,5,4
3,2,1

Challenge: How to convert it to a NumPy Array?

OUTPUT: 2D NumPy Array
[[9. 8. 7.] [6. 5. 4.] [3. 2. 1.]]

Method 1: np.loadtxt()

You can convert a CSV file to a NumPy array simply by calling np.loadtxt() with two arguments: the filename and the delimiter string. For example, the expression np.loadtxt('my_file.csv', delimiter=',') returns a NumPy array from the 'my_file.csv' with delimiter symbols ','.

Here’s an example:

import numpy as np array = np.loadtxt('my_file.csv', delimiter=',')
print(array)

Output:

[[9. 8. 7.] [6. 5. 4.] [3. 2. 1.]]

Method 2: np.loadtxt() with Header

You can convert a CSV file with first-line header to a NumPy array by calling np.loadtxt() with three arguments: the filename, skiprows=1 to skip the first line (header), and the delimiter string. For example, the expression np.loadtxt('my_file.csv', skiprows=1, delimiter=',') returns a NumPy array from the 'my_file.csv' with delimiter symbols ',' while skipping the first line.

Here’s an example:

import numpy as np array = np.loadtxt('my_file.csv', skiprows=1, delimiter=',')
print(array)

Output:

[[9. 8. 7.] [6. 5. 4.] [3. 2. 1.]]

Method 3: CSV Reader

To convert a CSV file 'my_file.csv' into a list of lists in Python, use the csv.reader(file_obj) method to create a CSV file reader. Then convert the resulting object to a list using the list() constructor. As a final step, you can convert the nested list to a NumPy array by using the np.array(list) constructor.

Here’s an example:

import numpy as np
import csv csv_filename = 'my_file.csv' with open(csv_filename) as f: reader = csv.reader(f) lst = list(reader) print(lst)

The output is the list of lists:

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

Now, if you need to convert it to a NumPy array, you can simply use the np.array() function on the newly-created list like so:

array = np.array(lst)
print(array)

Output:

[['9' '8' '7'] ['6' '5' '4'] ['3' '2' '1']]

Related Tutorial: How to Convert CSV to List of Lists in Python

Method 4: np.genfromtxt()

You can convert a CSV file to a NumPy array simply by calling np.genfromtxt() with two arguments: the filename and the delimiter string. For example, the expression np.genfromtxt('my_file.csv', delimiter=',') returns a NumPy array from the 'my_file.csv' with delimiter symbol ','.

Here’s an example:

import numpy as np array = np.loadtxt('my_file.csv', delimiter=',')
print(array)

Output:

[[9. 8. 7.] [6. 5. 4.] [3. 2. 1.]]

Method 5: Pandas read_csv() and df.to_numpy()

A quick and efficient way to read a CSV to a NumPy array is to combine Pandas’ pd.read_csv() function to read a given CSV file to a DataFrame with the df.to_numpy() function to convert the Pandas DataFrame to a NumPy array.

Here’s an example:

import pandas as pd df = pd.read_csv('my_file.csv', header=None)
array = df.to_numpy() print(array)

Output:

[[9 8 7] [6 5 4] [3 2 1]]

Related Tutorial: 17 Ways to Read a CSV File to a Pandas DataFrame

Summary

We have seen five ways to convert a CSV file to a 2D NumPy array:

• Method 1: np.loadtxt()
• Method 2: np.loadtxt() with Header
• Method 3: CSV Reader
• Method 4: np.genfromtxt()
• Method 5: Pandas read_csv() and df.to_numpy()

Our preferred way is np.loadtxt() for its simplicity and Pandas for its extensibility.

pip install psycopg2

The Python psycopg2 library is among the top 100 Python libraries, with more than 15,749,750 downloads. This article will show you everything you need to get this installed in your Python environment.

• Library Link

How to Install psycopg2 on Windows?

1. Type "cmd" in the search bar and hit Enter to open the command line.
2. Type “pip install psycopg2” (without quotes) in the command line and hit Enter again. This installs psycopg2 for your default Python installation.
3. The previous command may not work if you have both Python versions 2 and 3 on your computer. In this case, try "pip3 install psycopg2" or “python -m pip install psycopg2“.
4. Wait for the installation to terminate successfully. It is now installed on your Windows machine.

Here’s how to open the command line on a (German) Windows machine:

First, try the following command to install psycopg2 on your system:

pip install psycopg2

Second, if this leads to an error message, try this command to install psycopg2 on your system:

pip3 install psycopg2

Third, if both do not work, use the following long-form command:

python -m pip install psycopg2

The difference between pip and pip3 is that pip3 is an updated version of pip for Python version 3. Depending on what’s first in the PATH variable, pip will refer to your Python 2 or Python 3 installation—and you cannot know which without checking the environment variables. To resolve this uncertainty, you can use pip3, which will always refer to your default Python 3 installation.

How to Install psycopg2 on Linux?

You can install psycopg2 on Linux in four steps:

1. Open your Linux terminal or shell
2. Type “pip install psycopg2” (without quotes), hit Enter.
3. If it doesn’t work, try "pip3 install psycopg2" or “python -m pip install psycopg2“.
4. Wait for the installation to terminate successfully.

The package is now installed on your Linux operating system.

How to Install psycopg2 on macOS?

Similarly, you can install psycopg2 on macOS in four steps:

1. Open your macOS terminal.
2. Type “pip install psycopg2” without quotes and hit Enter.
3. If it doesn’t work, try "pip3 install psycopg2" or “python -m pip install psycopg2“.
4. Wait for the installation to terminate successfully.

The package is now installed on your macOS.

How to Install psycopg2 in PyCharm?

Given a PyCharm project. How to install the psycopg2 library in your project within a virtual environment or globally? Here’s a solution that always works:

• Open File > Settings > Project from the PyCharm menu.
• Select your current project.
• Click the Python Interpreter tab within your project tab.
• Click the small + symbol to add a new library to the project.
• Now type in the library to be installed, in your example "psycopg2" without quotes, and click Install Package.
• Wait for the installation to terminate and close all pop-ups.

Here’s the general package installation process as a short animated video—it works analogously for psycopg2 if you type in “psycopg2” in the search field instead:

Make sure to select only “psycopg2” because there may be other packages that are not required but also contain the same term (false positives):

How to Install psycopg2 in a Jupyter Notebook?

To install any package in a Jupyter notebook, you can prefix the !pip install my_package statement with the exclamation mark "!". This works for the psycopg2 library too:

!pip install my_package

This automatically installs the psycopg2 library when the cell is first executed.

How to Resolve ModuleNotFoundError: No module named ‘psycopg2’?

Say you try to import the psycopg2 package into your Python script without installing it first:

import psycopg2
# ... ModuleNotFoundError: No module named 'psycopg2'

Because you haven’t installed the package, Python raises a ModuleNotFoundError: No module named 'psycopg2'.

To fix the error, install the psycopg2 library using “pip install psycopg2” or “pip3 install psycopg2” in your operating system’s shell or terminal first.

See above for the different ways to install psycopg2 in your environment.

Improve Your Python Skills

If you want to keep improving your Python skills and learn about new and exciting technologies such as Blockchain development, machine learning, and data science, check out the Finxter free email academy with cheat sheets, regular tutorials, and programming puzzles.

Join us, it’s fun!