Problem: Given a CSV file, how to read only specific column(s) from the csv file? (Reading a specific column from a csv file will yield all the row values pertaining to that column.)

Example: Consier the following csv file (countries.csv):

Country,Capital,Population,Area

Germany,Berlin,"84,267,549","348,560"

France,Paris,"65,534,239","547,557"

Spain,Madrid,"46,787,468","498,800"

Italy,Rome,"60,301,346","294,140"

India,Delhi,"1,404,495,187","2,973,190"

USA,Washington,"334,506,463","9,147,420"

China,Beijing,"1,449,357,022","9,388,211"

Poland,Warsaw,"37,771,789","306,230"

Russia,Moscow,"146,047,418","16,376,870"

England,London,"68,529,747","241,930"

Question: How will you read the above csv file and display the following columns

1. Country column along with the Capital column?
2. All values in the population column?

Method 1: Using Pandas

Using the Pandas library is probably the best option if you are dealing with csv files. You can easily read a csv file and store an entire column within a variable.

Code:

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

country = df['Country']

# or

# country = df.Country

capital = df['Capital']

# or

# capital = df.Capital # displaying selected columns (Country and Capital)

for x, y in zip(country, capital): print(f"{x} {y}") # displaying a single column (Country)

print()

print(df['Population'])

Output:

Germany Berlin

France Paris

Spain Madrid

Italy Rome

India Delhi

USA Washington

China Beijing

Poland Warsaw

Russia Moscow

England London 0 84,267,549

1 65,534,239

2 46,787,468

3 60,301,346

4 1,404,495,187

5 334,506,463

6 1,449,357,022

7 37,771,789

8 146,047,418

9 68,529,747

Name: Population, dtype: object

Explanation:

• Read the csv file using pd.read_csv() Pandas function.
• Save all the information of the columns Country and Capital within independent variables using
  
  • country = df['Country']
    
    • Alternatively, you can also use country = df.Country
  • capital = df['Capital']
    
    • Alternatively, you can also use capital = df.Capital

• To display the country names and their capitals simultaneously, you can bind the two columns, country and capital, using the zip() function and then display each country along with its capital using a for loop upon the zipped object.
• To display all the values in the population column, you can simply use df['Population'].

TRIVIA
zip() is a built-in function in Python that takes an arbitrary number of iterables and binds them into a single iterable, a zip object. It combines the n-th value of each iterable argument into a tuple.
Read more about zip() here.

➤ List-Based Indexing of a DataFrame

In case you are not comfortable with using zip() to display multiple columns at once, you have another option. You can simply use list-based indexing to accomplish your goal.

List-based indexing is a technique that allows you to pass multiple column names as a list within the square-bracket selector.

Example:

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

print()

print(df[['Country', 'Capital']])

Output:

 Country Capital

0 Germany Berlin

1 France Paris

2 Spain Madrid

3 Italy Rome

4 India Delhi

5 USA Washington

6 China Beijing

7 Poland Warsaw

8 Russia Moscow

9 England London

Method 2: Integer Based Indexing with iloc

Approach: The idea here is to use the df.iloc[rows, columns].values to access individual columns from the DataFrame using indexing. Note that the first column always has the index 0, while the second column has index 1, and so on.

• rows is used to select individual rows. Use the slicing colon: to ensure all rows have been selected.
• columns is used to select individual columns.
  
  • Use country = data.iloc[:, 0].values to save the values of the Country column.
  • capital = data.iloc[:, 1].values to save the values of the Capital column.
  • population = data.iloc[:, 2].values to save the values of the Population column.

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

country = data.iloc[:, 0].values

capital = data.iloc[:, 1].values

population = data.iloc[:, 2].values

# displaying selected columns

print(data[['Country', 'Capital']])

print()

# displaying a single column (Population)

print(population)

Output:

Country Capital

0 Germany Berlin

1 France Paris

2 Spain Madrid

3 Italy Rome

4 India Delhi

5 USA Washington

6 China Beijing

7 Poland Warsaw

8 Russia Moscow

9 England London ['84,267,549' '65,534,239' '46,787,468' '60,301,346' '1,404,495,187' '334,506,463' '1,449,357,022' '37,771,789' '146,047,418' '68,529,747']

Method 3: Name-Based Indexing with loc()

Instead of selecting the columns by their index, you can also select them by their name using the df.loc[] selecter.

The following example shows how to select the columns Country and Capital from the given DataFrame.

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

val = data.loc[:, ['Country', 'Capital']]

print(val)

Output:

Country Capital

0 Germany Berlin

1 France Paris

2 Spain Madrid

3 Italy Rome

4 India Delhi

5 USA Washington

6 China Beijing

7 Poland Warsaw

8 Russia Moscow

9 England London

Related Tutorial: Slicing Data from a Pandas DataFrame using .loc and .iloc

Method 4: Using csv Module

csv module is yet another spectacular option in Python that allows you to play with csv files. Let us have a look at the code that helps us to read the given csv file and then read specific columns from it:

import csv population = []

with open('countries.csv', newline='', encoding='utf-8-sig') as csvfile: data = csv.DictReader(csvfile) for r in data: print("Country", ":", "Capital") # append values from population column to population list population.append(r['Population']) # displaying specific columns (Country and Capital) print(r['Country'], ":", r['Capital']) # display the population list print(population)

Output:

Country : Capital

Germany : Berlin

Country : Capital

France : Paris

Country : Capital

Spain : Madrid

Country : Capital

Italy : Rome

Country : Capital

India : Delhi

Country : Capital

USA : Washington

Country : Capital

China : Beijing

Country : Capital

Poland : Warsaw

Country : Capital

Russia : Moscow

Country : Capital

England : London

['84,267,549', '65,534,239', '46,787,468', '60,301,346', '1,404,495,187', '334,506,463', '1,449,357,022', '37,771,789', '146,047,418', '68,529,747']

Explanation:

• Import the csv module and open up the csv file. Ensure that you feed in the encoding argument as it helps to eliminate any unreadable characters that may occur in the given csv file.
  
  • with open('countries.csv', newline='', encoding='utf-8-sig') as csvfile
• Allow Python to read the csv file as a dictionary using csv.Dictreader object.
• Once the file has been read in the form of a dictionary, you can easily fetch the values from respective columns by using the keys within square bracket notation from the dictionary. Here each column represents the key within the given dictionary.

Bonus: Here’s a quick look at how the DictReader() class looks like:

import csv population = []

with open('countries.csv', newline='', encoding='utf-8-sig') as csvfile: data = csv.DictReader(csvfile) for row in data: print(row)

Output:

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

It is evident from the output that csv.DictReader() returns a dictionary for each row such that the column header is the key while the value in the row is the associated value in the dictionary.

Conclusion

To sum things up, there are majorly four different ways of accessing specific columns from a given csv file:

• List-Based Indexing.
• Integer-Based Indexing.
• Name-Based Indexing.
• Using csv modules DictReader class.

Feel free to use the one that suits you best. I hope this tutorial helped you. Please subscribe and stay tuned for more interesting tutorials. Happy learning!

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Before we learn about the money, let’s get this question out of the way:

What Does a Computer Science Research Scientist Do?

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.

These are some of the fields that are relevant for you as a computer science researcher:

Six of the most common daily activities of computer science researchers are:

• 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.

How to Become a Computer Science Researcher?

The following section is based on my own experience and communications with hundreds of computer science researchers at various international conferences and various stages in their careers—from first year PhD students to prestigious computer science professors. My field of study is distributed systems.

A CS research scientist holds an advanced academic degree that’s formally required to work for a research institution such as a University. Thus, you should complete both the bachelor of science (BSc) and the master of science (MSc) in computer science or a related field to become a computer science researcher.

While the previous two steps, i.e., obtaining the BSc and MSc degrees, will be crucial on your path towards a computer science researcher, the following steps are a bit less strict.

First, it doesn’t harm to have good grades in your BSc and MSc degrees. However, you don’t need to have excellent grades for many reasons:

• Income Potential in the Real World: Due to the high income potential of computer scientists — for example, as freelancers or highly-paid employees in Big Tech corporations — obtaining a research position in computer science is not as competitive as in other fields. When working as a PhD researcher myself a couple of years ago, I have seen many computer science researchers with average grades.
• Variance of Quality Requirements in Universities: There are many top-notch Universities for computer science research such as Stanford or MIT with strong brands and outstanding researchers. It is very tough and potentially expensive to enter those Universities — even as a student. However, there are many 2nd and 3rd tier Universities that have much more demand for computer science researchers than supply. Getting a research position in a 2nd tier University may be as simple as applying for it after your successful completion of MSc in a computer science related field.
• MSc Grades may not be available yet. When applying for a CS researching position, you often do so as a master of science student. This means that you don’t even know your final grade for the MSc degree which makes this grade irrelevant for your application as a CS researcher. Even if the grade may be available in your particular case, it is not for many other researchers so it’s not a must-have generally.

Second, any academic project (e.g., a published research paper) or even a writing project (e.g., having written a couple of online articles as a freelancer) can help you land the research position you desire. But again, that’s not a requirement and it will only increase your odds — all things being equal.

You can find many computer science courses on the Finxter Computer Science Academy (flatrate model).

But don’t wait too long to acquire practical experience!

Even if you have little skills, it’s best to get started as a freelance developer and learn as you work on real projects for clients — earning income as you learn and gaining motivation through real-world feedback.

Tip: An excellent start to turbo-charge your freelancing career (earning more in less time) is our Finxter Freelancer Course. The goal of the course is to pay for itself!

Let’s have a look at the income potential of a computer science researcher next.

Annual Income

How much does a Computer Science Researcher make per year?

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.

Source: Bureau of Labor Statistics

The source also lists the income for different industries as a computer science researcher:

Computer Science Researcher Industry	Annual Income
Private computer systems design and related services	$161,870
Corporate software publishers and software houses	$152,940
Research and development (R&D) in physical sciences, engineering, and life sciences	$132,810
Federal government	$112,310
Public and private universities, colleges, and professional schools	$79,510

Learnings: You make the least amount of money when working as a researcher at University—even though it can open the door to lucrative six-figure opportunities in the private sector (e.g., working in the R&D team for BigTech corporations such as Google, Facebook, and Netflix).

Let’s have a look at the hourly rate of Computer Science Researchers next!

Hourly Rate

Computer Science Researchers are well-paid on freelancing platforms such as Upwork or Fiverr.

• Related Article: What’s the best freelancing platform?

If you decide to go the route as a freelance Computer Science Researcher, you can expect to make between $30 and $150 per hour on Upwork (source). Assuming an annual workload of 2000 hours, you can expect to make between $60,000 and $300,000 per year.

This data is consistent with the official Bureau of Labor statistics that lists the median pay of a computer science researcher at $63 per hour (annualized income of $126,000).

Note: Do you want to create your own thriving coding business online? Feel free to check out our freelance developer course — the world’s #1 best-selling freelance developer course that specifically shows you how to succeed on Upwork and Fiverr!

Industry Demand

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

After an initial peak in 2004, the search trend has remain stable over the last two decades.

Related Video

You can find more job descriptions for coders, programmers, and computer scientists in our detailed overview guide:

• Get Your Job in IT [Overview]

Related Income of Professional Developers

The following statistic shows the self-reported income from 9,649 US-based professional developers (source).

The average annual income of professional developers in the US is between $70,000 and $177,500 for various programming languages.

Question: What is your current total compensation (salary, bonuses, and perks, before taxes and deductions)? Please enter a whole number in the box below, without any punctuation. If you are paid hourly, please estimate an equivalent weekly, monthly, or yearly salary. (source)

The following statistic compares the self-reported income from 46,693 professional programmers as conducted by StackOverflow.

The average annual income of professional developers worldwide (US and non-US) is between $33,000 and $95,000 for various programming languages.

Here’s a screenshot of a more detailed overview of each programming language considered in the report:

Here’s what different database professionals earn:

Here’s an overview of different cloud solutions experts:

Here’s what professionals in web frameworks earn:

There are many other interesting frameworks—that pay well!

Look at those tools:

Okay, but what do you need to do to get there? What are the skill requirements and qualifications to make you become a professional developer in the area you desire?

Let’s find out next!

General Qualifications of Professionals

StackOverflow performs an annual survey asking professionals, coders, developers, researchers, and engineers various questions about their background and job satisfaction on their website.

Interestingly, when aggregating the data of the developers’ educational background, a good three quarters have an academic background.

Here’s the question asked by StackOverflow (source):

Which of the following best describes the highest level of formal education that you’ve completed?

However, if you don’t have a formal degree, don’t fear! Many of the respondents with degrees don’t have a degree in their field—so it may not be of much value for their coding careers anyways.

Also, about one out of four don’t have a formal degree and still succeeds in their field! You certainly don’t need a degree if you’re committed to your own success!

Freelancing vs Employment Status

The percentage of freelance developers increases steadily. The fraction of freelance developers has already reached 11.21%!

This indicates that more and more work will be done in a more flexible work environment—and fewer and fewer companies and clients want to hire inflexible talent.

Here are the stats from the StackOverflow developer survey (source):

Do you want to become a professional freelance developer and earn some money on the side or as your primary source of income?

Resource: Check out our freelance developer course—it’s the best freelance developer course in the world with the highest student success rate in the industry!

Other Programming Languages Used by Professional Developers

The StackOverflow developer survey collected 58000 responses about the following question (source):

Which programming, scripting, and markup languages have you done extensive development work in over the past year, and which do you want to work in over the next year?

These are the languages you want to focus on when starting out as a coder:

And don’t worry—if you feel stuck or struggle with a nasty bug. We all go through it. Here’s what SO survey respondents and professional developers do when they’re stuck:

What do you do when you get stuck on a problem? Select all that apply. (source)

Related Tutorials

To get started with some of the fundamentals and industry concepts, feel free to check out these articles:

• Freelance Developer – How to Code From Home and Earn Six Figures [Industry Report]
• How to Become a Python Freelancer—and Earn $1,000 on the Side? [A Step-by-Step Tutorial]
• How Adam Earns $5000 per Month as a Python Freelancer on Upwork [Month 4]

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!

Resources

• Bureau of Labor Statistics