Tag: tutorial

Two mega trends can be observed in the 21st century: (I) the proliferation of data—and (II) the reorganization of the biggest market in the world: the global labor market towards project-based freelancing work.

By positioning yourself as a freelance data scientist, you’ll not only work in an exciting area with massive growth opportunities but you’ll also put yourself into the “blue ocean” of freelancing where there’s still much more demand than supply.

This article shows you six fundamental building blocks (pillars) that will lead you towards success as a freelancer in the data science space.

Pillar 1: Money—How Much Can You Earn as a Data Science Freelancer?

A recent study from O’Reilly found that data science is a wide field with many specializations and job descriptions. However, the average earning of an employed data scientist—45% of all respondents would consider themselves as such—is between $60,000 and $110,000. This means that experienced data scientists over time quite certainly reach six-figure income levels if they keep improving and searching for new opportunities.

There are significant opportunities “down the line” that earn significantly above six-figures by working as an architect, team leader, or manager. Becoming an employed data scientist remains an attractive way to make a great living.

But what about freelance data scientists? Do they earn more?

The best data comes directly from the source: Upwork, the biggest freelancer market in the world. Let’s dive into some profiles from freelance data scientists!

Here’s a table of 24 freelance data scientists incomes from the Upwork results:

Freelancer	Hourly Income	Earned	Job Success
Data Science & Machine Learning	$60	$100.000	100%
Data Science & Machine Learning	$300	$100.000	100%
Data Science Consultant	$50	$10.000	97%
Data Science & Machine Learning	$25	$10.000	91%
Data Science/Analyst, Statistician	$70	$100.000	97%
Applied Machine Learning	$300	$50.000	100%
Chief Technology Officer	$55	$200.000	100%
Computer Vision	$32	$2.000.000	100%
Data Engineer	$50	$10.000	100%
Research Scientist	$150	$700.000	95%
Analytics Expert	$52	$10.000	100%
Deep Learning Expert	$195	$10.000	100%
Data Scientist	$60	$10.000	77%
Scalable Analytics Consultant	$300	$500.000	100%
Machine Learning	$40	$8.000	91%
Machine Learning	$80	$30.000	100%
Tutor	$30	$20.000	92%
Math	$38	$4.000	100%
NLP	$35	$30.000	71%
Machine Learning	$50	$4.000	100%
Big Data Engineer	$50	$10.000	100%
AVERAGE	$96	$186.476	96%

The tabular data is drawn from 100 Upwork freelancer profiles as they appeared in the Upwork search. We randomly chose profiles and filtered them for data availability (e.g., total money earned). The result is that the average freelance data scientist earns $96 per hour. For 1700 working hours per year and a full schedule, this results in an average annual income of $163,200. To accomplish this, you need to join the ranks of relatively high-rated freelancers above 90% job satisfaction.

Let’s have a look at some other data sources: As a data scientist, you’re a programmer—in a way. The demand for programming talent has steadily increased in the preceding decades.

Here’s a quick tabular overview of what you can earn as a data scientist—it shows that as a data scientist, you’re in effect a well-compensated coder with specific skill sets.

Title	Best Programming Languages	Yearly Income (Average US)
Web Developer	JavaScript + HTML + CSS + SQL	$78,088
Mobile Developer Android	Java	$126,154
Mobile Developer Apple	Swift	$123,263
Back End Developer	Python + Django + Flask	$127,913
Front End Developer	JavaScript + HTML + CSS	$109,742
Full-Stack Engineer	Python + JavaScript + HTML + CSS + SQL	$112,098
Data Scientist	Python + Matplotlib + Pandas + NumPy + Dash	$122,700
Machine Learning Engineer	Python + NumPy + Scikit-Learn + TensorFlow	$145,734

Let’s dive into the different freelance developer career choices for maximum success!

Related Articles:

• Best Programming Languages to Start Freelancing in 2020
• Data Science Freelancers – How Much Do They Earn?

Pillar 2: Confidence—Can You Become a Data Science Freelancer?

Before becoming a Python freelancer, you have to learn the very basics of Python. What’s the point of offering your freelancer services when you can not even write Python code?

Having said this, it’s more likely that you live on the other extreme. You do not want to offer your services before you don’t feel 100% confident about your skills. Unfortunately, this moment never arrives. I have met hundreds of advanced coders, who are still not confident in selling their services. They cannot overcome their self-woven system of limiting believes and mental barriers.

May I tell you a harsh truth? You won’t join the top 1% of the Python coders with high probability (a hard statistical fact). But never mind. Your services will still be valuable to clients who either have less programming skills (there are plenty of them) or little time (a big part of the rest).  Most clients are happy to outsource the complex coding work to focus on their key result areas.

Regardless of your skill level, the variety of Python projects is huge. There are simple projects for $10 which an experienced coder can solve in 5 minutes. And there are complex projects that take months and promise you large payments of $100 to $1000 after completing each milestone.

You can be sure that you will find projects in your skill level.

Action steps:

• Take your time to browse all the archived Python freelance projects.
• Select 3 projects that you think you can solve in the price range ($10-$50).
• Solve them. Then, get started on Upwork or Fiverr (best freelancing sites in 2020).

Pillar 3: Learning—What Skills Do You Need as a Data Science Freelancer

Most freelance developers don’t have any experience when they get started on freelancing platforms such as Upwork or Fiverr. You can succeed by follow the three simple steps: (1) get your first gig, (2) learn what’s needed, (3) complete the gig. By repeating this, you’ll learn, grow, and, over time, earn the average hourly rate of $61 per hour for freelance developers.

Teaching many freelancing students, I have come to learn that most don’t believe they have all the skills they need to get started as a freelance developer. And why should they come to that conclusion given that there are so many different skills to be learned?

• Programming
• Marketing
• Sales
• Communication
• Empathy
• Positioning
• Administration
• Business Strategy
• Copy Writing
• Networking

Yet, while all of the listed skills are highly important for your freelancing business, I have yet to meet a single person that is highly skilled in all of those.

Consider each of those skills to be an axis of a multi-dimensional coordinate system. Now, you can assign to each person a score between 0% and 100% for each skill. Here’s the skill score card for two imaginary freelancers Alice and Bob:

Given are two freelancers: Alice and Bob.

• Alice has a talent for marketing and copywriting. She’s an average coder and not very good in administration.
• Bob is a master coder—the classical nerd—but he’s not skilled in marketing, sales, communication. He is a great administrator though.

Here’s the million dollar question: who’s the better freelance developer?

Posed like this, you may find the question ridiculous. Of course, it depends how both position themselves in the marketplace. Alice may have a small edge over Bob due to her people, sales, and marketing skills. However, it will be a close win because Bob’s programming skills are also highly valued by the marketplace.

Both will earn some money between minimum and maximum wage (say, around the average earnings of $51 per hour for freelance developers). The key is to understand that every single person on the planet has some value to the marketplace.

Let’s have a look at a third freelancer: YOU.

Say, Alice earns $55 per hour due to her ability to sell her skills. Bob earns $51 per hour due to his super programming skills.

Suppose you are a beginner in both: sales and programming. Your programming skills are only 30% and your sales skills are even worse with 10%. But you have solid networking, communication, and empathy skills as a human being. That’s all you need—you can offer value to the marketplace! Your skills are worth $23 per hour!

The only thing left for you to do is to sell your skills, keep engaging with the marketplace, and increase your skills over time. You’ll increase your sales and marketing skills. You’ll build confidence. You’ll increase your programming skills over time. By engaging the marketplace, you automatically increase your value to it. Your hourly rate increases with it!

So, do you have enough skills to get started as a freelance developer? Let’s have a look at the following video:

Most people never feel ready to get started with a project. They always want to learn more so that they feel better prepared for the tasks ahead. This may be a result from our modern-day educational system that teaches young people that they have to learn more and more before they can become successful in the real world. Grown ups with 18+ years believe they must learn for 10 more years before they can get started creating value and earning their own income.

The problem is that you’ll never feel ready no matter how much you learn. This is inherent in knowledge acquisition. The more you learn, the more you realize how much you don’t know, and the less ready you will feel to get started.

Therefore, a much better model will be proposed next. Most people understand this model rationally but they don’t internalize it—they don’t really get it.

So, what is it?

BIAS TOWARDS ACTION!

Your value to the marketplace is already larger than zero. If you start as a freelance developer, your hourly rate will be larger than $0. I don’t know what it is but you can already give value to clients. Say, you are a complete beginner and a client can hire you for $1 per hour. They will probably do it. Why? Because even as a complete beginner, you can create, say, $3 on their $1-spent, so you help them increase their business and they purchase as many of your services as they can afford. After all—how often would you buy $3 for a buck?

No matter what your current value, no matter where you start, the strategy is always the same: know your hourly rate, work for it, and increase it over time.

And what’s the best way to increase your hourly value? The answer is simple: create value for clients. Get started now. You have an actual value to contribute to clients no matter your current value. Just select any start hourly rate that you feel comfortable with. And then commit on the path to learning and improving your hourly rate by doing practical work for clients.

There’s no better way. If you want to improve your chess game, you better play chess a lot. If you want to improve your golf games, you better practice golf every day. If you want to become a more successful freelance developer earning a higher hourly rate—which is one of the key success metric of freelance developers—you better be out there on a freelancing platform doing the work and actually increase your hourly rate.

So, you go out there, create an account at Fiverr or Upwork, and get started today, now!

To commit on a quest to continuous improvement of your hourly rate, you can also check out the detailed FINXTER Python freelancer course.

Pillar 4: Clients—How Can You Get Clients and Deliver Value to Them?

Many people struggle with finding clients on a freelancer platform. They apply for one or two freelancer projects and wait for a few days until they get a response. The response is usually negative because the probability of getting accepted for a gig is maybe 5-10% — even if you underbid people. Oftentimes, clients want to have freelancers who have a lot of experience with past projects. If you are just starting out, you cannot showcase your experience.

So they apply for one or two projects and get rejected. If they are motivated, they try the same thing again. Only the super-committed ones repeat the same thing a third time. But after this fails too, they are out of the game. They are frustrated, argue that it’s not possible to earn money on freelancing platforms and go on with the next idea to make money online (on which they’ll fail, too).

I recently read the “The 10x Rule” by Grant Cardone. In his book, he invented the concept of taking massive action towards a goal.

Solution—Massive action.

• Not a timid amount of action.
• Not thinking in small numbers like “1” or “2”.

Massive action creates a new level of problems where you have too much instead of too little response from the real world.

It’s a simple idea but it’s really powerful. Applying this idea to finding clients on a freelancer platform is very effective and usually leads to success.

Yet, it’s so simple to find clients. It’s a numbers game.

Just realize that the acceptance rate of getting a freelancer gig is 10%. What’s the result? It means that on average, you need to apply for 10 projects to get one gig. If you apply for two projects, you have to be very lucky to get a gig — but most likely, you’ll fail. Even if you are serious and did everything right.

Before working as a self-employed Python coder, I was an academic computer science researcher. During my Ph.D. program, my goal was to get at least four high-quality research papers accepted. The acceptance rate was very low at 10-15% — even if you wrote a very good paper. So how to solve this problem? The only answer is massive action. Just submit the paper 10 times, improving it on the way. Then, you have a good chance of getting it accepted.

Realizing this early, I just committed to submitting a lot of papers. Because if I only submitted four times to a conference, it would have been virtually impossible to get accepted on four quality conferences. Instead, I submitted to maybe 15 conferences. Most papers got rejected but over time, more and more papers got accepted.

The only way of controlling your success in a competitive research environment is to submit papers regularly.

The same applies to get freelancing clients as a Python freelancer. I just want to encourage you to apply for 10 projects at once. If you do this, you’ll get accepted by maybe one or two.

Many people fear too much work when applying for 10 projects. But think about it: wouldn’t it be great if you got accepted for all 10 projects? This means that you can focus on the most interesting ones and simply write a nice email to the remaining clients telling them that you need a bit more time finishing their projects. It’s better to have too many clients than too few. Actually, you want this problem of having too many clients. Only this way, you can increase your hourly rate over time.

A fundamental law of economics is that if demand exceeds supply, prices rise. Your prices.

This is how you will break through your ceiling. Applying for two projects and waiting is not massive action. Ask yourself whether you really want success or whether you manipulate your own success. Massive action is applying for 10, 20, or even 50 projects. And creating yourself a new level of problems (having too many projects rather than too few).

This way, you’ll create your first experiences and a lot of profitable work for yourself.

Related Article: Massive Action — A Foolproof Way to Find Clients as a Freelance Programmer

Here’s a quick overview of all places fo find great gigs—ordered by relevance for data science freelancers:

1. TopTal Developers
2. StackOverflow Jobs
3. Hacker News Jobs
4. GitHub Jobs
5. Finxter Freelancer
6. PeoplePerHour Developer Jobs
7. Authentic Jobs
8. Vue Jobs
9. Remote Leads
10. Redditors For Hire
11. WeWorkRemotely
12. Upwork
13. Fiverr
14. Twitter Company Remote Jobs

ALL LINKS OPEN IN A NEW TAB!

Related Article: Top 14 Places to Find Remote Freelance Developer Gigs and Work From Home

Pillar 5: Business—How to Build Your Business as a Freelance Data Scientist?

As a freelance data scientist, you’re first and foremost a business person. Only second you’re a data scientist. You need to have solid data science skills but there’s so much more to creating a business system that throws lots of cash at you.

Everyone can create better burgers than McDonalds. But who can create a better business system? If you’re reading this article, chances are that you’re a far better coder than business person (the Finxter community consists of far more coders than business persons). So, stop learning tech-related stuff now and focus on building a great business system. How?

Here are my top tips:

• Give More Value Than You Take in Payment
• Eat Your Customers Complexity
• Perform From Your Strengths
• Position Yourself as a Specialist
• Be Hyper-Responsive
• Be Positive and Upbeat
• Create a Client List
• Create a Simple Ad Funnel
• Lead Acquisition: Contact One Potential Lead Per Day
• Lead Conversion: Implement Strategy Sessions
• Join Freelancing Platforms
• Use Testimonial Videos on Your Website
• Get the Referral Engine Rolling
• Leave Freelancing Platforms
• Use Systems and Templates
• Know Your Hourly Rate
• Increase Your Hourly Rate
• Contribute to Open-Source Projects
• Market Yourself on LinkedIn, Not Facebook
• Create Your Own Blog
• Give, Give, Give, Right Hook
• Befriend Colleagues
• Be a Coding Consultant, Not a Freelance Developer
• Read More Programming Books
• Read More Business Books
• Seek Expert Advice

You can find a detailed explanation on all of those points on my in-depth blog article.

Related Article: 26 Freelance Developer Tips to Double, Triple, Even Quadruple Your Income

Freelance Developer LLC

“A limited liability company (LLC) is a business structure in the United States whereby the owners are not personally liable for the company’s debts or liabilities. Limited liability companies are hybrid entities that combine the characteristics of a corporation with those of a partnership or sole proprietorship.” (source)

So, if you create an LLC, you are generally not liable for any debt or liabilities of your freelancing business. Most likely, your freelancing business doesn’t need a lot of debt—after all, you’re selling your time for money—however, there may still be liabilities!

For example, you may have signed a contract that requires you to pay for all damages incurred by your software. Yes, you shouldn’t have done it—but assuming you have, if you signed in the name of the LLC, you personally cannot be hold accountable for the potentially devastating liabilities.

What are some advantages and disadvantages of a liability?

LLC Pros	LLC Cons
Limited Liability – If you keep your finances separate and fullfil your duties as a business owner, you cannot be personally held liable. Your personal assets like real estate, stocks, bonds, mutual funds will remain protected even if your business fails.	Limitations of Limited Liability – this is called “piercing the corporate veil” and it means that if you don’t follow the rules of the LLC, a judge may decide that your liability protection will be removed and you, personally, can be held liable.
Pass-Through Federal Taxation on Profits – Per default, the profits are not taxed on the company level but are passed through to its owners who then tax them individually. This is an advantage if you have a relatively lower tax rate and it avoids double taxation on the corporate and individual level.	Self-Employment Tax – Per default, you must pay self-employment taxes on the profits of an LLC because it is a pass-through entity.
Management Flexibility – The LLC can be managed by one or more owners. This is a perfect structure for partnerships where ownership percentages can be divided in a flexible way.	Turnover – If an LLC partner dies, goes bankrupt, or leaves the company, the company will be dissolved. You need to create a new one and you take over all the leaving partners’ obligations that result in dissolving the LLC.
Easy Startup Overhead – It’s relatively simple and cheap—a few hundred dollars—to start an LLC. For the amount of protection it offers, it’s a very cheap way to organize your freelancing business.	Investments – It’s difficult to raise outside capital. This is usually not a problem for you as a freelance developer because freelance developing has only minimal capital requirements.
Unproportional Profit Distribution – Members can receive profits that are not proportional to the ownership percentage they hold. This allows you to reinforce members for great work.
Credibility – Being an LLC gives you more credibility as a freelance developer. Clients tend to trust you more, as a freelance developer organized in an LLC, for two reasons: you’re an US-based business and you’re a serious business.

Related Article: Freelance Developer LLC — Is It Smart For You?

Pillar 6: Platform—What is a Good Place to Start Data Science Freelancing?

Freelance Developer Course Link

There are three major freelancing platforms for coders: Upwork, Fiverr, Toptal.

Upwork

Upwork places a great focus on quality. This is great for clients because it ensures that their work will get delivered—without compromising quality.

For freelancers just starting out, Upwork poses a significant barrier of entry—oftentimes, new profiles will get rejected by the Upwork team. They want to ensure that only clients who take their freelancing jobs seriously will start out on their platform.

However, the relatively high barrier of entry also protects established freelancers on the Upwork platform from too much competition. There is no price dumping because of low-quality offers which ultimately benefits all market participants.

Fiverr

Fiverr initially started out as a platform where you could buy and sell small gigs worth five bucks. However, in the meantime it grew to a full-fledged freelancing platform where people earn six-figure incomes.

Many jobs earn hundreds of Dollars per hour and many freelancers make a killing—especially in attractive industries such as programming, machine learning, and data science.

If you want to start earning money as a freelance developer with the hot Python programming language, check out my free webinar:

How to build your high-income skill Python [Webinar]

Toptal

Toptal has a strong market proposition: it’s the platform with the top 3% of freelancers. Hence, it connects high-quality freelancers with high-quality clients.

It’s extremely hard to become a freelancer at Toptal: 97% of the applicants will not enter the platform. However, if you manage to join Toptal, you can greatly benefit with the best-in-class hourly rates. You can easily earn $100 per hour and beyond.

Also, the high barrier of entry ensures that the freelancer stays the valuable resource—he or she doesn’t become a commodity like on other freelancer platforms.

If you are an upcoming freelancer, you should aim for joining Toptal one day. Here’s a great freelancer course that shows you a crystal-clear path towards becoming a highly-paid freelancer.

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You can find out about more freelancing sites at the following resource on this Finxter blog with more than 60 links sorted by the size of the freelancing sites.

Related Article: What Are the Best Freelancing Sites for Coders?

There are many different ways of starting your Python freelancing adventures. Many freelancing platforms compete for your time, attention, and a share of your value creation. These platforms are a great way to start your freelancing career as a Python coder and gain some experience in business and coding, as well as get some testimonial to kick off your freelancing business. But keep in mind that they are only the first step and in the mid-term, you should strive to become independent of those platforms if you want to avoid global competition for each project in the future.

Where to Go From Here?

Enough theory, let’s get some practice!

To become successful in coding, you need to get out there and solve real problems for real people. That’s how you can become a six-figure earner easily. And 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?

Practice projects is how you sharpen your saw in coding!

Do you want to become a code master by focusing on practical code projects that actually earn you money and solve problems for people?

Then become 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.

Join my free webinar “How to Build Your High-Income Skill Python” and watch how I grew my coding business online and how you can, too—from the comfort of your own home.

Join the free webinar now!

The post [Ultimate Guide] Freelancing as a Data Scientist first appeared on Finxter.

Ready, Steady, Go —-> Data Science by setting up Conda in your Computer

It has never been easier from the early python data tools invention than now, to set up an user environment in our own computer. Conda brings that easiness with it.

Conda as it defines itself is an “OS-agnostic, system-level binary package manager and ecosystem.”

The guiding principles of python are written by Tim Peters in  PEP 20 — The Zen of Python. One of the aphorisms mentioned in it is, “There should be one– and preferably only one –obvious way to do it.” Conda is an effort towards it not only for Python but other languages like R, Ruby, etc.

If you wish to read more about conda, read an excellent blog post written by Travis Oliphant. He’s the creator of Numpy and Scipy.

The best way to install Conda package manager for python is through any one of the two distributions,

• Anaconda
• Miniconda

Choosing Between Anaconda and Miniconda

Miniconda is a small, bootstrap version of Anaconda that includes only conda, Python, the packages they depend on. If you’re ready to allocate more space (around 3 GB), Anaconda is the best option. Anaconda will install a wide range of packages that you might need to deal with the data. Otherwise, Miniconda will serve the purpose and you can install any package as required.

To get an up to date and stable version of the software, installing from the official documentation is the best way to do so. We’ll provide you the links to the docs. The steps mentioned in the docs are very easy to follow like a cakewalk. Also, by the time whenever any of the links are dead, We’ll update them.

Installing Conda through Anaconda:

• Installing on Windows
• Installing on macOS
• Installing on Linux

Installing Conda through Miniconda:

• Installing on Windows
• Installing on macOS
• Installing on Linux

We didn’t provide all the instructions directly here on this page because it’s very easy and clearly mentioned in the docs. The theme of this article is only to provide you with the necessary information to choose between Miniconda and Anaconda.

The post How to Set Up Conda first appeared on Finxter.

Do you want to enrich your Python script with powerful text-generation capabilities? You’re in the right place!

What does it do? I just discovered DeepAI’s API that automatically generates a body of text, given a sentence fragment or topic keyword.

How can it be used? You can use this as a basis to generate text automatically.

My opinion: The generated text makes sense (kind of) but you may need to further process it or guide it to generate longer meaningful content. The biggest opportunity, in my opinion, is to use it as a step in a more complex pipeline towards the automatic generation of valuable content. On its own, it wouldn’t generate too much meaning (apart from the entertainment value of reading machine-generated text).

Python Deep API Call

Ready? So, let’s have a look at the short Python script that asks the machine learning model to generate text for you—given a certain keyword such as ‘intelligence’, ‘Donald Trump’, or ‘Learn Python’.

import requests
r = requests.post( "https://api.deepai.org/api/text-generator", data={ 'text': 'intelligence', }, headers={'api-key': 'quickstart-QUdJIGlzIGNvbWluZy4uLi4K'}
)
print(r.json()['output'])

You import Python’s standard library requests to issue web requests and access the DeepAI API that is hosted at the URL "https://api.deepai.org/api/text-generator".

Here’s the output the code snippet generated in my Python shell:

Automatically-Generated Text Example

Intelligence officials were able to confirm the existence of at least some Russian hacking operations, including the one apparent aimed at the White House.

The CIA brief, the statement said, “was made official by the Russian Government on the third assessment of a U.S. official that Russian Government officials had interfered in the 2016 presidential election. In my opinion, assertions that were made in the clear and unequivocal testimony of the public as well as in the public release on any of the Russian accounts were grossly improper, misleading, and should be seen to be completely absent from any official documents as well.”

It added: “In addition, the CIA’s assessment asserted the Russian Government’s claims were grossly misleading, misleading and misleading in their assertions. The CIA has concluded to date, including publicly, it has given credible facts to support the Russia’s claims, and there would be no justification for further claims, if such assertions are to be proven to be false.”

The CIA brief also claimed the CIA “should not” have “repeated, misleading details of Russian officials’ conduct.”

CIA spokeswoman Jane Harman told Fox News: “CIA Director John Brennan fully agrees that Director Brennan’s testimony confirms what we have heard from various intelligence agencies.

“He is clear that Director Brennan gave additional testimony to Congress on the subject, in the first instance in which he spoke frankly about the role of Russian intelligence.”

But Harman also said Brennan should have given “more proof” of “firm Russian intervention into the U.S. political environment” if such claims were to be believed about the same thing.

Brennan told me the U.S would “make public its best communications in Russia for all time.”

Brennan’s testimony was confirmed by CIA Director Gina Haspel.

Brennan’s public admission that the Clinton campaign was hacked “was, in fact, rejected by the CIA by the president,” the CIA brief said.

In a memo prepared to be published early Friday, CIA Director R. John Brennan outlined an intelligence assessment that a Russian national and political operative in the United States was responsible for leaking classified information to the Russian media and opposition leaders.

The CIA has denied the CIA’s assessment. But Brennan argued that the Russian “public claims are simply false assertions that are misleading, unconfirmed and improper.”

In the report, US intelligence provided more details to the National Security Council than a year ago, the first time in the United States that Russia was involved in the hack.

The CIA concluded that the Russian government hacked the DNC to help Trump and was trying to influence the 2016 election.

Russia has denied US attempts to influence the campaign. But President Obama, in a letter to US Secretary of State Rex Tillerson, said the US acted in “complete coordination with the Russian government” and “with Russian military officials, whom we have discussed with other countries.”

Russian election meddling and hacking of DNC were part of an international pattern of human rights violations in which countries have accused their governments of using disinformation to advance a political candidate and undermine a democratic election.

Dmitry Peskov, Russia’s president, said in December that the hacking was meant to interfere with the presidential election and may violate the election regulations.

“The cyberwar waged in this new Russia will not be stopped, the threats and security of the country will be the only legitimate measure,” he said, adding that the hackers “will have no chance to stop.”

The Russian state-sponsored cybercrime group F-Secure, which was based in Moscow, has claimed responsibility for a number of Russian cyber incidents that have been claimed by the United States, the Associated Press reports.

The Russian state-sponsored hacking groups include the computer firms Kaspersky Lab and DigiPG, both known for their malware and research programs, and the Moscow-based anti-virus firm Elemental.

The hacking groups have also said that as part of the election, they were targeting an array of Democratic political candidates.

The Russian military is responsible for the attack, according to Kremlin spokesman Dmitry Peskov.

Russia is often accused of using cyber hacks for its own interests. In December 2016, an Obama administration official stated that “every step is worth watching carefully” in the war in Afghanistan in 2014.

But Russia’s President Vladimir Putin, a Russian citizen and former head of the Communist Party, has denied that Russian state-backed separatists used hacking to support the presidential elections.

“I call on all government officials and political parties to avoid interference and the international community to take up arms for the political and economic purposes of Russia. We are not engaged, and should not be used,” Putin said in a speech in Moscow in February.

He said the Kremlin is not to blame for a country’s cyber crimes against the country during the presidential election.

Putin also said that “there is nothing new or wrong with the election result.”

Short Discussion

You can see that the generated text is quite detailed and looks professional. But is it correct? And does it contain plagiarism? To check these questions, I checked it with Grammarly. Here’s the result:

The text has high writing quality and is original!

But it’s obviously fake news—otherwise, Grammarly should have found the quotes of “CIA officials”. That’s why I think that the powerful text-generation ability should be used in a pipeline or system that ensures to create some real value-add—rather than using it as a stand-alone tool.

Try It Yourself (Interactive Shell)

Challenge

Challenge: Find ways to create real value using the Python API call in a more advanced code snippet and share it with the Finxter community. You can contact me by signing up on the Finxter Email Academy:

I’ll share the results of this poll in a follow-up blog article—so, stay tuned!

The post How to Generate Text Automatically With Python? A Guide to the DeepAI API first appeared on Finxter.

Quick Fix: Python throws the “ImportError: No module named pandas” when it cannot find the Pandas installation. The most frequent source of this error is that you haven’t installed Pandas explicitly with pip install pandas. Alternatively, you may have different Python versions on your computer and Pandas is not installed for the particular version you’re using. To fix it, run pip install pandas in your Linux/MacOS/Windows terminal.

Problem: You’ve just learned about the awesome capabilities of the Pandas library and you want to try it out, so you start with the following import statement you found on the web:

import pandas as pd

This is supposed to import the Pandas library into your (virtual) environment. However, it only throws the following import error: no module named pandas!

>>> import pandas as pd
ImportError: No module named pandas on line 1 in main.py

You can reproduce this error in the following interactive Python shell:

Why did this error occur?

The reason is that Python doesn’t provide Pandas in its standard library. You need to install Python first!

Before being able to import the Pandas module, you need to install it using Python’s package manager pip. You can run the following command in your Windows shell:

$ pip install pandas

Here’s the screenshot on my Windows machine:

This simple command installs Pandas in your virtual environment on Windows, Linux, and MacOS. It assumes that you know that your pip version is updated. If it isn’t, use the following two commands (there’s no harm in doing it anyways):

$ python -m pip install --upgrade pip
...
$ pip install pandas

Here’s how this plays out on my Windows command line:

The warning message disappeared!

If you need to refresh your Pandas skills, check out the following Pandas cheat sheets—I’ve compiled the best 5 in this article.

Related article: Top 5 Pandas Cheat Sheets

How to Fix “ImportError: No module named pandas” in PyCharm

If you create a new Python project in PyCharm and try to import the Pandas library, it’ll throw the following error:

Traceback (most recent call last): File "C:/Users/xcent/Desktop/Finxter/Books/book_dash/pythonProject/main.py", line 1, in <module> import pandas as pd
ModuleNotFoundError: No module named 'pandas' Process finished with exit code 1

The reason is that each PyCharm project, per default, creates a virtual environment in which you can install custom Python modules. But the virtual environment is initially empty—even if you’ve already installed Pandas on your computer!

Here’s a screenshot:

The fix is simple: Use the PyCharm installation tooltips to install Pandas in your virtual environment—two clicks and you’re good to go!

First, right-click on the pandas text in your editor:

Second, click “Show Context Actions” in your context menu. In the new menu that arises, click “Install Pandas” and wait for PyCharm to finish the installation.

The code will run after your installation completes successfully.

Here’s a complete introduction to PyCharm:

Related Article: PyCharm—A Helpful Illustrated Guide

The post How to Fix “ImportError: No module named pandas” [Mac/Linux/Windows/PyCharm] first appeared on Finxter.

Rapid Answer: The following one-liner calculates the MD5 from the string 'hello world':

import hashlib as h;print(h.md5(b'hello world').hexdigest())

Background: MD5 message-digest is a vulnerable cryptographic algorithm to map a string to a 128-bit hash value. You can use it as a checksum on a given text to ensure that the message hasn’t been corrupted. However, you shouldn’t use it as a protection against malicious corruption due to its vulnerability. With modern hardware and algorithms, it’s easy to crack!

Problem: How to generate an MD5 sum from a string?

Example: Say, you have the following string text:

text = 'hello world'

And you want to convert it to the MD5 hash value:

5eb63bbbe01eeed093cb22bb8f5acdc3

We’ll discuss some methods to accomplish this next.

Method 1: hashlib.md5() — Multi-Liner

The hashlib library provides a function md5() that creates an object that can calculate the hash value of a given text for you via the method update():

# Method 1: hashlib.md5()
import hashlib m = hashlib.md5()
text = 'hello world'
m.update(text.encode('utf-8')) print(m.hexdigest())
# 5eb63bbbe01eeed093cb22bb8f5acdc3

Make sure to encode the string as a Unicode string with the string.encode('utf-8') method. Otherwise, Python will throw an error.

Method 2: hashlib.md5() — Trivial One-Liner

As a one-liner, the code looks unreadable:

# Method 2: One-Liner
import hashlib; m = hashlib.md5(); m.update(text.encode('utf-8'));print(m.hexdigest())
# 5eb63bbbe01eeed093cb22bb8f5acdc3

We used the standard technique to one-linerize flat code snippets without indented code blocks. Learn more in our related tutorial.

Related Tutorial: How to One-Linerize Code?

Method 3: Improved One-Liner

You can slightly improve the code by using the b'...' string instead of the encode() function to make it a Unicode string:

# Method 3: One-Liner
import hashlib as h;print(h.md5(b'hello world').hexdigest())
# 5eb63bbbe01eeed093cb22bb8f5acdc3

I also initialized the md5 object with the Unicode string directly rather than using the update() method. The one-liner now has minimum number of characters—I don’t think it can be made even more concise!

Python One-Liners Book

Python programmers will improve their computer science skills with these useful one-liners.

Python One-Liners will teach you how to read and write “one-liners”: concise statements of useful functionality packed into a single line of code. You’ll learn how to systematically unpack and understand any line of Python code, and write eloquent, powerfully compressed Python like an expert.

The book’s five chapters cover tips and tricks, regular expressions, machine learning, core data science topics, and useful algorithms. Detailed explanations of one-liners introduce key computer science concepts and boost your coding and analytical skills. You’ll learn about advanced Python features such as list comprehension, slicing, lambda functions, regular expressions, map and reduce functions, and slice assignments. You’ll also learn how to:

•  Leverage data structures to solve real-world problems, like using Boolean indexing to find cities with above-average pollution
•  Use NumPy basics such as array, shape, axis, type, broadcasting, advanced indexing, slicing, sorting, searching, aggregating, and statistics
•  Calculate basic statistics of multidimensional data arrays and the K-Means algorithms for unsupervised learning
•  Create more advanced regular expressions using grouping and named groups, negative lookaheads, escaped characters, whitespaces, character sets (and negative characters sets), and greedy/nongreedy operators
•  Understand a wide range of computer science topics, including anagrams, palindromes, supersets, permutations, factorials, prime numbers, Fibonacci numbers, obfuscation, searching, and algorithmic sorting

By the end of the book, you’ll know how to write Python at its most refined, and create concise, beautiful pieces of “Python art” in merely a single line.

Get your Python One-Liners Now!!

The post How to Get MD5 of a String? A Python One-Liner first appeared on Finxter.

Do you use the awesome WordPress plugin “Enlighter” to embed code in your WordPress site like this?

print('hello world!')

If you’re like me, you want to be able to customize the style (such as font size) globally—not locally for each individual code snippet.

Problem: How to increase the font size of the Enlighter WordPress plugin?

To increase the font size globally, you need to complete the following steps:

• Open your WordPress editor.
• Go to Appearance > Customize > CSS in your WordPress editor.
• Copy the following snippet into your CSS file:

.enlighter-t-bootstrap4 .enlighter span{
font-size: 16px;
}

Note that you can change the font-size to 15px, 17px, or even 20px—as you like! Also note that if you use another theme/style for your embedded code than bootstrap4, you need to set the CSS selector accordingly.

For example:

.enlighter-t-wpcustom .enlighter span{
font-size: 20px;
}

This would change the font size of all Enlighter code environments that use the custom theme.

Here’s how this may look in practice:

Ah, yes—if you want to learn Python, don’t forget to download our free cheat sheets:

The post How to Increase the Font Size on the WordPress Plugin Enlighter? first appeared on Finxter.

Pandas is Excel on steroids—the powerful Python library allows you to analyze structured and tabular data with surprising efficiency and ease. Pandas is one of the reasons why master coders reach 100x the efficiency of average coders. In today’s article, you’ll learn how to work with missing data—in particular, how to handle NaN values in Pandas DataFrames.

You’ll learn about all the different reasons why NaNs appear in your DataFrames—and how to handle them. Let’s get started!

Checking Series for NaN Values

Problem: How to check a series for NaN values?

Have a look at the following code:

import pandas as pd
import numpy as np data = pd.Series([0, np.NaN, 2])
result = data.hasnans print(result)
# True

Series can contain NaN-values—an abbreviation for Not-A-Number—that describe undefined values.

To check if a Series contains one or more NaN value, use the attribute hasnans. The attribute returns True if there is at least one NaN value and False otherwise.

There’s a NaN value in the Series, so the output is True.

Filtering Series Generates NaN

Problem: When filtering a Series with where() and no element passes the filtering condition, what’s the result?

import pandas as pd xs = pd.Series([5, 1, 4, 2, 3])
xs.where(xs > 2, inplace=True)
result = xs.hasnans print(result)
# True

The method where() filters a Series by a condition. Only the elements that satisfy the condition remain in the resulting Series. And what happens if a value doesn’t satisfy the condition? Per default, all rows not satisfying the condition are filled with NaN-values.

This is why our Series contains NaN-values after filtering it with the method where().

Working with Multiple Series of Different Lengths

Problem: If you element-wise add two Series objects with a different number of elements—what happens with the remaining elements?

import pandas as pd s = pd.Series(range(0, 10))
t = pd.Series(range(0, 20))
result = (s + t)[1] print(result)
# 2

To add two Series element-wise, use the default addition operator +. The Series do not need to have the same size because once the first Series ends, the subsequent element-wise results are NaN values.

At index 1 in the resulting Series, you get the result of 1 + 1 = 2.

Create a DataFrame From a List of Dictionaries with Unequal Keys

Problem: How to create a DataFrame from a list of dictionaries if the dictionaries have unequal keys? A DataFrame expects the same columns to be available for each row!

import pandas as pd data = [{'Car':'Mercedes', 'Driver':'Hamilton, Lewis'}, {'Car':'Ferrari', 'Driver':'Schumacher, Michael'}, {'Car':'Lamborghini'}] df = pd.DataFrame(data, index=['Rank 2', 'Rank 1', 'Rank 3'])
df.sort_index(inplace=True)
result = df['Car'].iloc[0] print(result)
# Ferrari

You can create a DataFrame from a list of dictionaries. The dictionaries’ keys define the column labels, and the values define the columns’ entries. Not all dictionaries must contain the same keys. If a dictionary doesn’t contain a particular key, this will be interpreted as a NaN-value.

This code snippet uses string labels as index values to sort the DataFrame. After sorting the DataFrame, the row with index label Rank 1 is at location 0 in the DataFrame and the value in the column Car is Ferrari.

Sorting a DataFrame by Column with NaN Values

Problem: What happens if you sort a DataFrame by column if the column contains a NaN value?

import pandas as pd df = pd.read_csv("Cars.csv") # Dataframe "df"
# ----------
# make fuel aspiration body-style price engine-size
# 0 audi gas turbo sedan 30000 2.0
# 1 dodge gas std sedan 17000 1.8
# 2 mazda diesel std sedan 17000 NaN
# 3 porsche gas turbo convertible 120000 6.0
# 4 volvo diesel std sedan 25000 2.0
# ---------- selection = df.sort_values(by="engine-size")
result = selection.index.to_list()[0]
print(result)
# 1

In this code snippet, you sort the rows of the DataFrame by the values of the column engine-size.

The main point is that NaN values are always moved to the end in Pandas sorting. Thus, the first value is 1.8, which belongs to the row with index value 1.

Count Non-NaN Values

Problem: How to count the number of elements in a dataframe column that are not Nan?

import pandas as pd df = pd.read_csv("Cars.csv") # Dataframe "df"
# ----------
# make fuel aspiration body-style price engine-size
# 0 audi gas turbo sedan 30000 2.0
# 1 dodge gas std sedan 17000 1.8
# 2 mazda diesel std sedan 17000 NaN
# 3 porsche gas turbo convertible 120000 6.0
# 4 volvo diesel std sedan 25000 2.0
# ---------- df.count()[5]
print(result)
# 4

The method count() returns the number of non-NaN values for each column. The DataFrame df has five rows. The fifth column
contains one NaN value. Therefore, the count of the fifth column is 4.

Drop NaN-Values

Problem: How to drop all rows that contain a NaN value in any of its columns—and how to restrict this to certain columns?

import pandas as pd df = pd.read_csv("Cars.csv") # Dataframe "df"
# ----------
# make fuel aspiration body-style price engine-size
# 0 audi gas turbo sedan 30000 2.0
# 1 dodge gas std sedan 17000 1.8
# 2 mazda diesel std sedan 17000 NaN
# 3 porsche gas turbo convertible 120000 6.0
# 4 volvo diesel std sedan 25000 2.0
# ---------- selection1 = df.dropna(subset=["price"])
selection2 = df.dropna()
print(len(selection1), len(selection2))
# 5 4

The DataFrame’s dropna() method drops all rows that contain a NaN value in any of its columns. But how to restrict the columns to be scanned for NaN values?

By passing a list of column labels to the optional parameter subset, you can define which columns you want to consider.

The call of dropna() without restriction, drops line 2 because of the NaN value in the column engine-size. When you restrict the columns only to price, no rows will be dropped, because no NaN value is present.

Drop Nan and Reset Index

Problem: What happens to indices after dropping certain rows?

import pandas as pd df = pd.read_csv("Cars.csv") # Dataframe "df"
# ----------
# make fuel aspiration body-style price engine-size
# 0 audi gas turbo sedan 30000 2.0
# 1 dodge gas std sedan 17000 1.8
# 2 mazda diesel std sedan 17000 NaN
# 3 porsche gas turbo convertible 120000 6.0
# 4 volvo diesel std sedan 25000 2.0
# ---------- df.drop([0, 1, 2], inplace=True)
df.reset_index(inplace=True)
result = df.index.to_list()
print(result)
# [0, 1]

The method drop() on a DataFrame deletes rows or columns by index. You can either pass a single value or a list of values.

By default the inplace parameter is set to False, so that modifications won’t affect the initial DataFrame object. Instead, the method returns a modified copy of the DataFrame. In the puzzle, you set inplace to True, so the deletions are performed directly on the DataFrame.

After deleting the first three rows, the first two index labels are 3 and 4. You can reset the default indexing by calling the method reset_index() on the DataFrame, so that the index starts at 0 again. As there are only two rows left in the DataFrame, the result is [0, 1].

Concatenation of Dissimilar DataFrames Filled With NaN

Problem: How to concatenate two DataFrames if they have different columns?

import pandas as pd df = pd.read_csv("Cars.csv")
df2 = pd.read_csv("Cars2.csv") # Dataframe "df"
# ----------
# make fuel aspiration body-style price engine-size
# 0 audi gas turbo sedan 30000 2.0
# 1 dodge gas std sedan 17000 1.8
# 2 mazda diesel std sedan 17000 NaN
# 3 porsche gas turbo convertible 120000 6.0
# 4 volvo diesel std sedan 25000 2.0
# ---------- # Additional Dataframe "df2"
# ----------
# make origin
# 0 skoda Czechia
# 1 toyota Japan
# 2 ford USA
# ---------- try: result = pd.concat([df, df2], axis=0, ignore_index=True) print("Y")
except Exception: print ("N") # Y

Even if DataFrames have different columns, you can concatenate them.

If DataFrame 1 has columns A and B and DataFrame 2 has columns C and D, the result of concatenating DataFrames 1 and 2 is a DataFrame with columns A, B, C, and D. Missing values in the rows are filled with NaN.

Outer Merge

Problem: When merging (=joining) two DataFrames—what happens if there are missing values?

import pandas as pd df = pd.read_csv("Cars.csv")
df2 = pd.read_csv("Cars2.csv") # Dataframe "df"
# ----------
# make fuel aspiration body-style price engine-size
# 0 audi gas turbo sedan 30000 2.0
# 1 dodge gas std sedan 17000 1.8
# 2 mazda diesel std sedan 17000 NaN
# 3 porsche gas turbo convertible 120000 6.0
# 4 volvo diesel std sedan 25000 2.0
# ---------- # Additional dataframe "df2"
# ----------
# make origin
# 0 skoda Czechia
# 1 mazda Japan
# 2 ford USA
# ---------- result = pd.merge(df, df2, how="outer", left_on="make", right_on="make")
print(len(result["fuel"]))
print(result["fuel"].count())
# 7
# 5

With Panda’s function merge() and the parameter how set to outer, you can perform an outer join.

The resulting DataFrame of an outer join contains all values from both input DataFrames; missing values are filled with NaN.

In addition, this puzzle shows how NaN values are counted by the len() function whereas the method count() does not include NaN values.

Replacing NaN

Problem: How to Replace all NaN values in a DataFrame with a given value?

import pandas as pd df = pd.read_csv("Cars.csv") # Dataframe "df"
# ----------
# make fuel aspiration body-style price engine-size
# 0 audi gas turbo sedan 30000 2.0
# 1 dodge gas std sedan 17000 1.8
# 2 mazda diesel std sedan 17000 NaN
# 3 porsche gas turbo convertible 120000 6.0
# 4 volvo diesel std sedan 25000 2.0
# ---------- df.fillna(2.0, inplace=True)
result = df["engine-size"].sum()
print(result)
# 13.8

The method fillna() replaces NaN values with a new value. Thus, the sum of all values in the column engine-size is 13.8.

Length vs. Count Difference — It’s NaN!

Problem: What’s the difference between the len() and the count() functions?

import pandas as pd df = pd.read_csv("Cars.csv")
df2 = pd.read_csv("Cars2.csv") # Dataframe "df"
# ----------
# make fuel aspiration body-style price engine-size
# 0 audi gas turbo sedan 30000 2.0
# 1 dodge gas std sedan 17000 1.8
# 2 mazda diesel std sedan 17000 NaN
# 3 porsche gas turbo convertible 120000 6.0
# 4 volvo diesel std sedan 25000 2.0
# ---------- # Additional dataframe "df2"
# ----------
# make origin
# 0 skoda Czechia
# 1 mazda Japan
# 2 ford USA
# ---------- result = pd.merge(df2, df, how="left", left_on="make", right_on="make")
print(len(result["fuel"]))
print(result["fuel"].count())
# 3
# 1

In a left join, the left DataFrame is the master, and all its values are included in the resulting DataFrame.

Therefore, the result DataFrame contains three rows, yet, since skoda and ford don’t appear in DataFrame df, only one the row for mazda contains value.

Again, we see the difference between using the function len() which also includes NaN values and the method count() which does not count NaN values.

Equals() vs. == When Comparing NaN

Problem:

import pandas as pd df = pd.read_csv("Cars.csv") # Dataframe "df"
# ----------
# make fuel aspiration body-style price engine-size
# 0 audi gas turbo sedan 30000 2.0
# 1 dodge gas std sedan 17000 1.8
# 2 mazda diesel std sedan 17000 NaN
# 3 porsche gas turbo convertible 120000 6.0
# 4 volvo diesel std sedan 25000 2.0
# ---------- df["engine-size_copy"] = df["engine-size"]
check1 = (df["engine-size_copy"] == df["engine-size"]).all()
check2 = df["engine-size_copy"].equals(df["engine-size"])
print(check1 == check2)
# False

This code snippet shows how to compare columns or entire DataFrames regarding the shape and the elements.

The comparison using the operator == returns False for our DataFrame because the comparing NaN-values with == always yields False.

On the other hand, df.equals() allows comparing two Series or DataFrames. In this case, NaN-values in the same location are considered to be equal.

The column headers do not need to have the same type, but the elements within the columns must be of the same dtype.

Since the result of check1 is False and the result of check2 yields True, the final output is False.

Where to Go From Here?

Enough theory, let’s get some practice!

To become successful in coding, you need to get out there and solve real problems for real people. That’s how you can become a six-figure earner easily. And 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?

Practice projects is how you sharpen your saw in coding!

Do you want to become a code master by focusing on practical code projects that actually earn you money and solve problems for people?

Then become 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.

Join my free webinar “How to Build Your High-Income Skill Python” and watch how I grew my coding business online and how you can, too—from the comfort of your own home.

Join the free webinar now!

The post Pandas NaN — Working With Missing Data first appeared on Finxter.

Being a Python freelancer is a new way of living in the 21st century. It’s a path of personal growth, learning new skills, and earning money in the process. But in today’s digital economy, becoming a Python freelancer is – above everything else – a lifestyle choice. It can give you fulfillment, flexibility, and endless growth opportunities. Additionally, it offers you a unique way of connecting with other people, learning about their exciting projects, and finding friends and acquaintances on the way.

Disclaimer: Please don’t take this as legal advice but as tips & tricks from someone who’s been there and done that.

Freelance Developer Germany Pros and Cons

So what are the advantages of being a freelance coder? Let’s dive right into them.

Advantages of Being a Freelance Programmer in Germany

• Flexibility: One big advantage of being a Python freelancer is that you are flexible in time and space. I live in a large German city (Stuttgart) where rent prices are increasing. However, since I am working full-time in the Python industry, being self-employed, and 100% digital, I have the freedom to move to the countryside. Outside large cities, housing is exceptionally cheap and living expenses are truly affordable. I am earning good money matched only by a few employees in my home town — while I am not forced to compete for housing to live close to my employers.  A few cities demand very high prices in Germany while others allow you to get affordable and beautiful houses in the countryside. A clear advantage for freelance developers!
• Independence: Do you hate working for your boss? Does your boss still value old-school values such as working 9-to-5? In Germany, a lot of bosses are that way. Being a freelancer injects a dose of true independence into your life. While you are not totally free (after all, you are still working for clients), you can theoretically get rid of any single client while not losing your profession. Firing your bad clients is even a smart thing to do because they demand more of your time, drain your energy, pay you badly (if at all), and don’t value your work in general. In contrast, good clients will treat you with respect, pay well and on time, come back, refer you to other clients, and make working with them a pleasant and productive experience. As an employee, you don’t have this freedom of firing your boss until you find a good one.
• Tax advantages: As a freelancer, you start your own business. Please note that I’m not an accountant — and tax laws are different in different countries. But in Germany and many other developed nations, your small Python freelancing business usually comes with a lot of tax advantages. You can deduct many expenses from the taxes you pay, such as your Notebook, your car, your living expenses, working environment, eating outside with clients or partners, your smartphone, and so on. At the end of the year, many freelancers enjoy tax benefits worth tens of thousands of Euros. You can find a detailed tax guide here.
• Business expertise: This advantage is maybe the most important one. As a freelancer, you gain a tremendous amount of experience in the business world. You learn to offer and sell your skills in the marketplace, you learn how to acquire clients and keep them happy, you learn how to solve problems, and you learn how to keep your books clean, invest, and manage your money. Being a freelancer gives you a lot of valuable business experiences. And even if you plan to start a more scalable business system, being a freelance developer is truly a great first step towards your goal. The business experience is a clear plus compared to other, more “nerdy” developers working only with code. The business skills will make you a more valuable person—even for established companies.
• Paid learning: While you have to pay to learn at University—living is relatively expensive in Germany—being a freelance developer flips this situation upside down. You are getting paid for learning. As a bonus, the things you are learning are as practical as they can be. Instead of coding toy projects in University, you are coding (more or less) exciting projects with an impact on the real world. In Germany, the pay is relatively good due to the developed nature of the economy.
• Save time in commute: Many Germans are stuck in commute for hours and hours every day. Being in commute is one of the major time killers in modern life. During a 10 year period, you’ll waste 2000-4000 hours — enough to become a master in a new topic of your choice, or writing more than ten full books and sell them on the marketplace. Commute time to work is one of the greatest inefficiencies of our society. And you, as a freelance developer, can completely eliminate it. This will make your life constantly easier, you have an unfair advantage compared to any other employee. You can spend the time on learning, recreation, or building more side businesses. You don’t even need a car (I don’t have one) which will save you hundreds of thousands of Euros throughout your lifetime (the average German employee spends 300,000 € on cars).
• Family time: During the last 12 months being self-employed with Python, I watched my 1-year old son walking his first steps and speaking his first words. I was actually attending every single stage of his development and growth. While this often seems very normal to me, I guess that many fathers who work at big companies as employees may have missed their sons and daughters growing up. In my environment, most fathers do not have time to spend with their kids during their working days. But I have and I’m very grateful for this.
• Competition: In Germany, there’s a seller’s market for freelance developers—demand is much higher than supply. This means that you can charge premium rates and work only on the gigs you want.

Are you already convinced that becoming a Python freelancer is the way to go for you? You are not alone. To help you with your quest, I have created the one and only Python freelancer course on the web which pushes you to Python freelancer level in a few months — starting out as a beginner coder. The course is designed to pay for itself because it will instantly increase your hourly rate on diverse freelancing platforms such as Upwork or Fiverr.

Disadvantages of Being a Freelance Programmer in Germany

• Less stability: It’s hard to reach a stable income as a freelancer. In Germany, many people seek security above freedom. Also, if you want to buy your own home and need credit, banks usually have less trust in your ability to generate income than if you were employed.
• Bad clients: You will get those bad clients for sure. However, in Germany this disadvantage is somehow mitigated as clients are mostly business owners that are able to pay their freelancing fees.
• Legacy code: Germany has a lot of large and established industry players such as Bosch, Daimler, and other manufacturers. These older industries usually have older code bases as well. As a German freelance developer, you may need to handle more legacy code than as a freelancer in a newer economy such as, for example, India.
• Solitude: If you are working as an employee at a company, you always have company, quite literally. In Germany, this culture is especially true—only a small percentage of your IT friends will work as self-employed freelance developers. Most coders work for big companies.

Freelance Developer Germany While Employed

If you’re an employee, you have the freedom to create your own side-hustle in Germany. However, there are some laws that ensure that people don’t work too much. Thus, you need to be careful not to work too many hours per week. In this resource, they recommend not to work more than 18 hours per week on your side business—if you still have a main job. In general, these are the points to consider when creating your own side-business as a freelance developer in Germany:

• Side vs Main Income: Make sure to earn more in your main job than in your side business. This is required so that it still counts as a side-business and not your main income. In that case, your business would be considered your main income which would result in a loss of some benefits paid by your employer.
• Inform Your Employer: You may need to inform your employer that you create your side business. This may be required by contract or even by law (for some type of jobs such as government employees).
• Register Your Business: You need to register your business with the tax office and government. As in most other countries, you cannot just “go for it” but need to register your intent to create a business—even if it is on the side.
• Social Insurance: If you’re creating a business on the side, you’re stilled insured by your employer (e.g., for pension funds and health insurance). That’s why you need to make sure not to work or earn too much for your side business. As soon as you cross this threshold and your “side” business becomes your primary income stream, you need to take care of insurance yourself and you’ll lose access to the benefits provided by the employer. (Well, if you reach this point, you essentially have a double income so you probably wouldn’t care financially.)
• Tax: You need to make sure to pay all taxes (e.g., sales tax and income tax). However, suppose you don’t earn a lot with your side business. In that case, you’re probably eligible to apply for special tax treatment (“Kleinunternehmerregelung”) to free you from the need (partially) to pay sales taxes.

You can read more in this excellent online overview article.

Freelance Developer Germany Hourly Rate

Freelance developers in Germany earn more than their employed colleagues. A recent article from a German magazine states that the average freelance developer earns 84€ per hour. If you work 8 hours per day for a client, you’d earn 640€ per day or 13440€ per month.

Note that this is the average rate of a freelance developer! Most people can significantly increase their income by honing their business and programming skills at the same time—and reach above-average hourly rates over time. If you reach expert status in a certain area, you can charge 100€ per hour which results in a monthly income of 16000€.

Becoming a freelance developer in Germany is, indeed, a profitable endeavor!

Make sure to save some 10% of your income for more calm times to ensure liquidity at all costs. Cash is the lifeblood of any business and the sensible business owner makes sure to always have enough cash on their bank account to pay for at least 6 months of expenses.

To learn how to reach above-average hourly rates, join my Python freelancer course—the world’s most comprehensive and in-depth freelance developer program!

*** The Six-Figure Python Freelance Developer Course ***

Freelance Developer Germany Tax

There are two primary types of taxes for self-employed freelancers in Germany: sales tax and income tax.

• Sales tax is between 16-18% of each transaction volume and if you sell your services to another business, you can usually deduct it again (ask your accountant)!
• Income tax can easily reach 40% of your income if you reach the average earning levels of a German freelancer of a six-figure income.

However, if you’re just starting out and you’ve only a few or zero clients, you don’t have to pay either sales tax and income tax.

There are many ways to optimize your taxes and I recommend you check out our detailed tax guide (for hackers) to learn some smart ways to pay less tax and invest in your future success.

Freelance Developer German Language

My friend and freelancer Lukas is involved in freelancing for German clients. He’s a German guy so he swears on using a German gig description on freelancing platforms such as Fiverr. The big benefit is that, as a person being able to speak the German language, you can shield yourself from international competition and price wars. Many German clients only seek freelancers who can speak German with them because they’re uncomfortable in expressing their needs and gig specifications in a foreign language such as English. Being able to speak German well can make your freelancing business even more profitable and better protected against the competition!

Where to Go From Here?

Enough theory, let’s get some practice!

To become successful in coding, you need to get out there and solve real problems for real people. That’s how you can become a six-figure earner easily. And 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?

Practice projects is how you sharpen your saw in coding!

Do you want to become a code master by focusing on practical code projects that actually earn you money and solve problems for people?

Then become 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.

Join my free webinar “How to Build Your High-Income Skill Python” and watch how I grew my coding business online and how you can, too—from the comfort of your own home.

Join the free webinar now!

The post How to Be a Freelance Developer in Germany first appeared on Finxter.

How To Write Python Code In Pycharm?

Every code or file within Pycharm is written inside a project. This means, everything in PyCharm is written with respect to a Project and the first thing that you need to create before you can write any code is a project. So, let’s see how we can create a project in PyCharm.

➮ After successfully installing PyCharm, the welcome screen comes up. Click Create New Project.

The Create New Project window opens up.

✶ In this window specify the location of the project where you want to save the files.

✶ Expand the Python Interpreter menu. Here, you can specify whether you want to create a new project interpreter or reuse an existing one. From the drop-down list, you can select one of the options: Virtualenv, Pipenv, or Conda. These are the tools that help us to keep dependencies required by different projects and are separated by creating isolated Python environments for each of them. You can also specify the location of the New environment and select a Base interpreter (for example Python2.x or Python3.x) from the options available. Then we have checkboxes to select Inherit global site-packages and Make available to all projects. Usually, it is a good idea to keep the defaults.

✶ Click Create on the bottom right corner of the dialog box to create the new project.

Note: You might be notified that: Projects can either be opened in a new window or you can replace the project in the existing window or be attached to the already opened projects. How would you like to open the project? Select the desired option.

You might also get a small Tip of the Day popup where PyCharm gives you one trick to learn at each startup. Feel free to close this popup.

Now, we are all set to start writing our first Python code in PyCharm.

• Click on File.
• Choose New.

• Choose Python File and provide a name for the new file. In our case we name it add. Press enter on your keyboard and your new file will be ready and you can write your code in it.

Let us write a simple code that adds two numbers and prints the result of the addition as output.

How To Run The Python Code In PyCharm?

Once the code is written, it is time to run the code. There are three ways of running the Python code in PyCharm.

Method 1: Using Shortcuts

• Use the shortcut Ctrl+Shift+R on Mac to run the code.
• Use the shortcut Ctrl+Shift+F10 on Windows or Linux to run the code.

Method 2: Right click the code window and click on Run ‘add’

Method 3: Choose ‘add’  and click on the little green arrow at the top right corner of the screen as shown in the diagram below.

How To Debug The Code Using Breakpoints?

While coding, you are bound to come across bugs especially if you are working with a tedious production code. PyCharm provides an effective way of debugging your code and allows you to debug your code line by line and identify exceptions or errors with ease. Let us have a look at the following example to visualize how to debug your code in PyCharm.

Example:

Output:

Note: This is a very basic example and has been just used for the purpose to guide you through the process of debugging in PyCharm. The example computes the average of two numbers but yields different results in the two print statements. A spoiler: we have not used the brackets properly which results in the wrong result in the first case. Now, we will have a look at how we can identify the same by debugging our Python code in PyCharm.

Debugging our code:

Step 1: Setting The Breakpoint

The first requirement to start debugging our code is to place a breakpoint by clicking on the blank space to the left of line number 1 ( this might vary according to your code and requirements). This is the point where the program will be suspended and the process of debugging can be started from here, one line at a time.

Step 2: Start Debugging

Once the breakpoint is set the next step is to start debugging using one of the following ways:

• Using Shortcuts: Ctrl+Shift+D on Mac or Shift+Alt+F9 on Windows or Linux.
• Right-click on the code and choose Debug ‘add’.
• Choose ‘add’  and click on the icon on the top right corner of the menu bar.

Once you use any one of the above methods to start debugging your code, the Debug Window will open up at the bottom as shown in the figure below. Also, note that the current line is highlighted in blue.

Step 3: Debug line by line and identify the error (logical in our case). Press F8 on your keyboard to execute the current line and step over to the next line. To step into the function in the current line, press F7. As each statement is executed, the changes in the variables are automatically reflected in the Debugger window.

How To Test Code In PyCharm?

For any application or code to be operational, it must undergo unit test and PyCharm facilitates us with numerous testing frameworks for testing our code. The default test runner in Python is unittest, however, PyCharm also supports other testing frameworks such as pytest, nose, doctest, tox, and trial.

Let us create a file with the name currency.py and then test our file using unit testing.

Now, let us begin unit testing. Follow the steps given below:

Step 1: Create The Test File

To begin testing keep the currency.py file open and execute any one of the following steps:

1. Use Shortcut: Press Shift+Cmd+T on Mac or Ctrl+Shift+T on Windows or Linux.
2. Right-click on the class and select Go To ➠ Test. Make sure you right-click on the name of the class to avoid confusion!
3. Go to the main menu ➠ select Navigate ➠ Select Test

Step 2: Select Create New Test and that opens up the Create Test window. Keep the defaults and select all the methods and click on OK.

✶ PyCharm will automatically create a file with the name test_currency.py with the following tests within it.

Step 3: Create the Test Cases

Once our test file is created we need to import the currency class within it and define the test cases as follows:

Step 4: Run the Unit Test

Now, we need to run the test using one of the following methods:

• Using Shortcut: Press Ctrl+R on Mac or Shift+F10 on Windows or Linux.
• Right-click and choose Run ‘Unittests for test_currency.py’.
• Click on the green arrow to the left of the test class name and choose Run ‘Unittests for test_currency.py’.

You will since that two tests are successful while one test fails. To be more specific unit test for test_euro() and test_yen() are successful while the test fails for test_pound().

Output:

That brings us to the end of this section and it is time for us to move on to a very important section of our tutorial where we will be discussing numerous tips and tricks to navigate PyCharm with the help of some interesting shortcuts. We will also discuss in brief about some of the tools like Django that we can integrate with PyCharm. So, without further delay lets dive into to next section.

Please click on the Next link/button given below to move on to the next phase of PyCharm journey!

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The post Writing, Running, Debugging, and Testing Code In PyCharm first appeared on Finxter.