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Top Job Roles With Their Salary Data in the World of Data Science for 2020–2021

Data Science requires the expertise of professionals who possess the skill of collecting, structuring, storing, handling and analyzing data, allowing individuals and organizations to make decisions based on insights generated from the data. Data science is woven into the fabric of our daily lives in myriad ways that we may not even be aware of; starting from the online purchases we make, our social media feeds, the music we listen to or even the movie recommendations that we are shown online.  For several years in a row, the job of a data scientist has been hailed as the “hottest job of the 21st century”. Data scientists are among the highest paid resources in the IT industry. According to Glassdoor, the average data scientist’s salary is $113,436. With the growth of data, the demand for data science job roles in companies has been rising at an accelerated pace.  How Data Science is a powerful career choice The landscape of a data science job is promising and full of opportunities spanning different industries. The nature of the job allows an individual to take on flexible remote jobs and also to be self-employed.  The field of data science has grown exponentially in a very short time, as companies have come to realize the importance of gathering huge volumes of data from websites, devices, social media platforms and other sources, and using them for business benefits. Once the data is made available, data scientists use their analytical skills, evaluate data and extract valuable information that allows organizations to enhance their innovations. A data scientist is responsible for collecting, cleansing, modifying and analyzing data into meaningful insights. In the first phase of their career, a data scientist generally works as a statistician or data analyst. Over many years of experience, they evolve to be data scientists.  The ambit of data has been increasing rapidly which has urged companies to actively recruit data scientists to harness and leverage insights from the huge quantities of valuable data available, enabling efficiency in processes and operations and driving sales and growth.  In the future, data may also emerge as the turning point of the world economy. So, pursuing a career in data science would be very useful for a computer enthusiast, not only because it pays well but also since it is the new trend in IT. According to the Bureau of Labor Statistics (BLS), jobs for computer and information research scientists, as well as data scientists are expected to grow by 15 percent by the year 2028. Who is a Data Scientist & What Do They Do? Data Scientists are people with integral analytical data expertise together with complex problem-solving skills, besides the curiosity to explore a wide range of emerging issues.  They are considered to be the best of both the sectors – IT and business, which makes them extremely skilled individuals whose job roles straddle the worlds of computer science, statistics, and trend analysis. Because of this surging demand for data identification and analysis in various tech fields like AI, Machine Learning, and Data Science, the salary of a data scientist is one of the highest in the world. Requisite skills for a data scientist Before we see the different types of jobs in the data analytics field, we must be aware of the prerequisite skills that make up the foundation of a data scientist: Understanding of data – As the name suggests, Data Science is all about data. You need to understand the language of data and the most important question you must ask yourself is whether you love working with data and crunching numbers. And if your answer is “yes”, then you’re on the right track. Understanding of algorithms or logic – Algorithms are a set of instructions that are given to a computer to perform a particular task. All Machine Learning models are based on algorithms, so it is quite an essential prerequisite for a would-be data scientist to understand the logic behind it.  Understanding of programming – To be an expert in data science, you do not need to be an expert coder. However, you should have the foundational programming knowledge which includes variables, constants, data types, conditional statements, IO functions, client/server, Database, API, hosting, etc. If you feel comfortable working with these and you have your coding skills sorted, then you’re good to go. Understanding of Statistics – Statistics is one of the most significant areas in the field of Data Science. You should be well aware of terminologies such as mean, median, mode, standard deviation, distribution, probability, Bayes’ theorem, and different Statistical tests like hypothesis testing, chi-square, ANOVA, etc. Understanding of Business domain:  If you do not have an in-depth working knowledge of the business domain, it will not really prove to be an obstacle in your journey of being a data scientist. However, if you have the primitive understanding of the specific business area you are working for, it will be an added advantage that can take you ahead. Apart from all the above factors, you need to have good communication skills which will help the entire team to get on the same page and work well together.Data Science Job Roles  Data science experts are in demand in almost every job sector, and are not confined to the IT industry alone.  Let us look at some major job roles, associated responsibilities , and the salary range: 1. Data ScientistsA Data Scientist’s job is as exciting as it is rewarding. With the help of Machine Learning, they handle raw data and analyze it with various algorithms such as regression, clustering, classification, and so on. They are able to arrive at insights that are essential for predicting and addressing complex business problems.  Responsibilities of Data Scientists The responsibilities of Data Scientists are outlined below: Collecting huge amounts of organized and unorganized data and converting them into useful insights. Using analytical skills like text analytics, machine learning, and deep learning to identify potential solutions which will help in the growth of organizations. Following a data-driven approach to solve complex problems.  Enhancing data accuracy and efficiency by cleansing and validating data. Using data visualization to communicate significant observations to the organization’s stakeholders. Data Scientists Salary Range According to Glassdoor, the average Data Scientist salary is $113,436 per annum. The median salary of an entry-level professional can be around $95,000 per annum. However, early level data scientists with 1 to 4 years' experience can get around $128,750 per annum while the median salary for those with more experience ranging around 5 to 9 years  can rise to an average of $165,000 per annum. 2. Data Engineers  A Data Engineer is the one who is responsible for building a specific software infrastructure for data scientists to work. They need to have in-depth knowledge of technologies like Hadoop and Big Data such as MapReduce, Hive, and SQL. Half of the work of Data Engineers is Data Wrangling and it is advantageous if they have a software engineering background. Responsibilities of Data Engineers  The responsibilities of Data Engineers are described below: Collecting data from different sources and then consolidating and cleansing it. Developing essential software for extracting, transforming, and loading data using SQL, AWS, and Big Data. Building data pipelines using machine learning algorithms and statistical techniques. Developing innovative ways to enhance data efficiency and quality. Developing, testing and maintaining data architecture. Required Skills for Data Engineers  There are certain skill sets that data engineers need to have: Strong skills in analytics to manage and work with massive unorganized datasets. Powerful programming skills in trending languages like Python, Java, C++, Ruby, etc. Strong knowledge of database software like SQL and experience in relational databases. Managerial and organizational skills along with fluency in various databases.  Data Engineers’ Salary Range According to Glassdoor, the average salary of a Data Engineer is $102,864 in the USA. Reputed companies like Amazon, Airbnb, Spotify, Netflix, IBM value and pay high salaries to data engineers. Entry-level data and mid-range data engineers get an average salary between $110,000 and $137,770 per annum. However, with experience, a data engineer can get up to $155,000 in a year. 3. Data Analyst As the name suggests, the job of a Data Analyst is to analyze data. A data analyst collects, processes, and executes statistical data analyses which help business users to develop meaningful insights. This process requires creating systems using programming languages like Python, R or SAS. Companies ranging from IT, healthcare, automobile, finance, insurance employ Data Analysts to run their businesses efficiently.  Responsibilities of Data Analysts  The responsibilities of Data Analysts are described below: Identifying correlations and gathering valuable patterns through data mining and analyzing data. Working with customer-centric algorithms and modifying them to suit individual customer demands. Solving certain business problems by mapping data from numerous sources and tracing them. Creating customized models for customer-centric market strategies, customer tastes, and preferences. Conducting consumer data research and analytics by deploying statistical analysis. Data Analyst Salary Range According to Glassdoor, the national average salary of a Data Analyst is $62,453 in the United States. The salaries of an entry-level data analyst start at  $34,5000 per year or $2875 per month.  Glassdoor states that a junior data analyst earns around $70,000 per year and experienced senior data analysts can expect to be paid around $107,000 per year which is roughly $8916 per month. Key Reasons to Become a Data Scientist Becoming a Data Scientist is a dream for many data enthusiasts. There are some basic reasons for this: 1. Highly in-demand field The job of Data Science is hailed as one of the most sought after jobs for 2020 and according to an estimate, it is predicted that this field would generate around 11.5 million jobs by the year 2026. The demand for expertise in data science is increasing while the supply is too low.  This shortage of qualified data scientists has escalated their demand in the market. A survey by the MIT Sloan Management Review indicates that 43 percent of companies report that a major challenge to their growth has been a lack of data analytic skills. 2. Highly Paid & Diverse Roles Since data analytics form the central part of decision-making, companies are willing to hire larger numbers of data scientists who can help them to make the right decisions that will boost business growth. Since it is a less saturated area with a mid-level supply of talents, various opportunities have emerged that require diverse skill sets. According to Glassdoor, in the year 2016,  data science was the highest-paid field across industries. 3. Evolving workplace environments With the arrival of technologies like Artificial Intelligence and Robotics which fall under the umbrella of data science, a vast majority of manual tasks have been replaced with automation.  Machine Learning has made it possible to train machines to perform repetitive tasks , freeing up humans to focus on critical problems that need their attention. Many new and exciting technologies have emerged within this field such as Blockchain, Edge Computing, Serverless Computing, and others.  4. Improving product standards The rigorous use of Machine Learning algorithms for regression, classification recommendation problems like decision trees, random forest, neural networks, naive Bayes etc has boosted the customer experiences that companies desire to have. One of the best examples of such development is the E-commerce sites that use intelligent Recommendation Systems to refer products and provide customer-centric insights depending upon their past purchases. Data Scientists serve as a trusted adviser to such companies by identifying the preferred target audience and handling marketing strategies. 5. Helping the world In today’s world, almost everything revolves around data. Data Scientists extract hidden information from massive lumps of data which helps in decision making across industries ranging from finance and healthcare to manufacturing, pharma and engineering . Organizations are equipped with data driven insights that boost productivity and enhance growth, even as they optimize resources and mitigate potential risks. Data Science catalyzes innovation and research, bringing positive changes across the world we live in. Factors Affecting a Data Scientist’s Salary The salaries of Data Scientists can depend upon several factors. Let us study them one by one and understand their significance: Data Scientist Salary by Location The number of job opportunities and the national data scientist salary for data innovators is the highest in Switzerland in the year 2020, followed by the Netherlands and United Kingdom. However, since Silicon Valley in the United States is the hub of new technological innovations, it is considered to generate the most jobs for startups in the world, followed by Bangalore in India. A data scientist’s salary in Silicon Valley or Bangalore is likely to be higher than in other countries. Below are the highest paying countries for data scientist roles along with their average annual data science salary: Switzerland$115,475Netherlands$68,880Germany$64,024United Kingdom$59,781Spain$30,050Italy$37,785Data Scientist Salary by ExperienceA career in the field of data science is very appealing to young IT professionals. Starting salaries are very lucrative, and there is incremental growth in salary  with  experience. Salaries of a data scientist depend on the expertise, as well as the years of experience: Entry-level data scientist salary – The median entry-level salary for a data scientist is around $95,000 per year which is quite high. Mid-level data scientist salary –   The median salary for a mid-level data scientist having experience of around 1 - 4 years is $128,750 per year. If the data scientist is in a managerial position, the average salary rises upto $185,000 per year. Experienced data scientist salary –  The median salary for an experienced data scientist having experience of around 5 - 9 years is $128,750 per year whereas the median salary of an experienced manager is much higher; around $250,000 per year. Data Scientist Salary by Skills There are some core competencies that will help you to shine in your career as a Data Scientist, and if you want to get the edge over your peers you should consider polishing up these skills: Python is the most crucial and coveted skill which data scientists must be familiar with, followed by R. The average salary in the US for  Python programmers is $120,365 per annum. If you are well versed in both Data Science and Big Data, instead of just one among them, your salary is likely to increase by at least 25 percent . The users of innovative technology like the Statistical Analytical System get a salary of around $77,842. On the other hand, users of software analysis software like SPSS have a pay scale of  $61,452 per year. Machine Learning Engineers on the average earn around $111,855 per year. However, with more experience in Machine Learning along with knowledge in Python, you can earn around $146,085 per annum. A Data Scientist with domain knowledge of Artificial Intelligence can earn an annual salary between $100,000 to $150,000. Extra skills in programming and innovative technologies have always been a value-add that can enhance your employability. Pick skills that are in-demand to see your career graph soar. Data Scientist Salary by Companies Some of the highest paying companies in the field of Data Science are tech giants like Facebook, Amazon, Apple, and service companies like McGuireWoods, Netflix or Airbnb.  Below is a list of top companies with the highest paying salaries: McGuireWoods$165,114Amazon$164,114Airbnb$154,879Netflix$147,617 Apple$144,490Twitter$144,341Walmart$144,198Facebook$143,189eBay$143,005Salaries of Other Related Roles Various other job roles associated with Data Science are also equally exciting and rewarding. Let us look at some of them and their salaries: Machine Learning Engineer$114,826Machine Learning Scientist$114,121Applications Architect$113,757Enterprise Architect$110,663Data Architect$108,278Infrastructure Architect$107,309Business Intelligence Developer$81,514Statistician$76,884Conclusion Let us look at what we have learned in this article so far: What is Data Science? The job of a Data Scientist Pre-requisite skills for a Data Scientist Different job roles Key reasons for becoming a Data Scientist Salary depending upon different factors Salary of other related roles The field of Data Science is ripe in terms of opportunities for Data Scientists, Data Engineers, and Data Analysts. The figures mentioned in this article are not set in stone and may vary depending upon the skills you possess, experience you have and various other factors. With more experience and skills, your salary is bound to increase by a certain percentage every year. Data science is a field that will revolutionize the world in the coming years and you can have a share of this very lucrative pie with the right education qualifications, skills, experience and training.  
Top Job Roles With Their Salary Data in the World of Data Science for 2020–2021
Priyankur
Priyankur

Priyankur Sarkar

Data Science Enthusiast

Priyankur Sarkar loves to play with data and get insightful results out of it, then turn those data insights and results in business growth. He is an electronics engineer with a versatile experience as an individual contributor and leading teams, and has actively worked towards building Machine Learning capabilities for organizations.

Posts by Priyankur Sarkar

Top Job Roles With Their Salary Data in the World of Data Science for 2020–2021

Data Science requires the expertise of professionals who possess the skill of collecting, structuring, storing, handling and analyzing data, allowing individuals and organizations to make decisions based on insights generated from the data. Data science is woven into the fabric of our daily lives in myriad ways that we may not even be aware of; starting from the online purchases we make, our social media feeds, the music we listen to or even the movie recommendations that we are shown online.  For several years in a row, the job of a data scientist has been hailed as the “hottest job of the 21st century”. Data scientists are among the highest paid resources in the IT industry. According to Glassdoor, the average data scientist’s salary is $113,436. With the growth of data, the demand for data science job roles in companies has been rising at an accelerated pace.  How Data Science is a powerful career choice The landscape of a data science job is promising and full of opportunities spanning different industries. The nature of the job allows an individual to take on flexible remote jobs and also to be self-employed.  The field of data science has grown exponentially in a very short time, as companies have come to realize the importance of gathering huge volumes of data from websites, devices, social media platforms and other sources, and using them for business benefits. Once the data is made available, data scientists use their analytical skills, evaluate data and extract valuable information that allows organizations to enhance their innovations. A data scientist is responsible for collecting, cleansing, modifying and analyzing data into meaningful insights. In the first phase of their career, a data scientist generally works as a statistician or data analyst. Over many years of experience, they evolve to be data scientists.  The ambit of data has been increasing rapidly which has urged companies to actively recruit data scientists to harness and leverage insights from the huge quantities of valuable data available, enabling efficiency in processes and operations and driving sales and growth.  In the future, data may also emerge as the turning point of the world economy. So, pursuing a career in data science would be very useful for a computer enthusiast, not only because it pays well but also since it is the new trend in IT. According to the Bureau of Labor Statistics (BLS), jobs for computer and information research scientists, as well as data scientists are expected to grow by 15 percent by the year 2028. Who is a Data Scientist & What Do They Do? Data Scientists are people with integral analytical data expertise together with complex problem-solving skills, besides the curiosity to explore a wide range of emerging issues.  They are considered to be the best of both the sectors – IT and business, which makes them extremely skilled individuals whose job roles straddle the worlds of computer science, statistics, and trend analysis. Because of this surging demand for data identification and analysis in various tech fields like AI, Machine Learning, and Data Science, the salary of a data scientist is one of the highest in the world. Requisite skills for a data scientist Before we see the different types of jobs in the data analytics field, we must be aware of the prerequisite skills that make up the foundation of a data scientist: Understanding of data – As the name suggests, Data Science is all about data. You need to understand the language of data and the most important question you must ask yourself is whether you love working with data and crunching numbers. And if your answer is “yes”, then you’re on the right track. Understanding of algorithms or logic – Algorithms are a set of instructions that are given to a computer to perform a particular task. All Machine Learning models are based on algorithms, so it is quite an essential prerequisite for a would-be data scientist to understand the logic behind it.  Understanding of programming – To be an expert in data science, you do not need to be an expert coder. However, you should have the foundational programming knowledge which includes variables, constants, data types, conditional statements, IO functions, client/server, Database, API, hosting, etc. If you feel comfortable working with these and you have your coding skills sorted, then you’re good to go. Understanding of Statistics – Statistics is one of the most significant areas in the field of Data Science. You should be well aware of terminologies such as mean, median, mode, standard deviation, distribution, probability, Bayes’ theorem, and different Statistical tests like hypothesis testing, chi-square, ANOVA, etc. Understanding of Business domain:  If you do not have an in-depth working knowledge of the business domain, it will not really prove to be an obstacle in your journey of being a data scientist. However, if you have the primitive understanding of the specific business area you are working for, it will be an added advantage that can take you ahead. Apart from all the above factors, you need to have good communication skills which will help the entire team to get on the same page and work well together.Data Science Job Roles  Data science experts are in demand in almost every job sector, and are not confined to the IT industry alone.  Let us look at some major job roles, associated responsibilities , and the salary range: 1. Data ScientistsA Data Scientist’s job is as exciting as it is rewarding. With the help of Machine Learning, they handle raw data and analyze it with various algorithms such as regression, clustering, classification, and so on. They are able to arrive at insights that are essential for predicting and addressing complex business problems.  Responsibilities of Data Scientists The responsibilities of Data Scientists are outlined below: Collecting huge amounts of organized and unorganized data and converting them into useful insights. Using analytical skills like text analytics, machine learning, and deep learning to identify potential solutions which will help in the growth of organizations. Following a data-driven approach to solve complex problems.  Enhancing data accuracy and efficiency by cleansing and validating data. Using data visualization to communicate significant observations to the organization’s stakeholders. Data Scientists Salary Range According to Glassdoor, the average Data Scientist salary is $113,436 per annum. The median salary of an entry-level professional can be around $95,000 per annum. However, early level data scientists with 1 to 4 years' experience can get around $128,750 per annum while the median salary for those with more experience ranging around 5 to 9 years  can rise to an average of $165,000 per annum. 2. Data Engineers  A Data Engineer is the one who is responsible for building a specific software infrastructure for data scientists to work. They need to have in-depth knowledge of technologies like Hadoop and Big Data such as MapReduce, Hive, and SQL. Half of the work of Data Engineers is Data Wrangling and it is advantageous if they have a software engineering background. Responsibilities of Data Engineers  The responsibilities of Data Engineers are described below: Collecting data from different sources and then consolidating and cleansing it. Developing essential software for extracting, transforming, and loading data using SQL, AWS, and Big Data. Building data pipelines using machine learning algorithms and statistical techniques. Developing innovative ways to enhance data efficiency and quality. Developing, testing and maintaining data architecture. Required Skills for Data Engineers  There are certain skill sets that data engineers need to have: Strong skills in analytics to manage and work with massive unorganized datasets. Powerful programming skills in trending languages like Python, Java, C++, Ruby, etc. Strong knowledge of database software like SQL and experience in relational databases. Managerial and organizational skills along with fluency in various databases.  Data Engineers’ Salary Range According to Glassdoor, the average salary of a Data Engineer is $102,864 in the USA. Reputed companies like Amazon, Airbnb, Spotify, Netflix, IBM value and pay high salaries to data engineers. Entry-level data and mid-range data engineers get an average salary between $110,000 and $137,770 per annum. However, with experience, a data engineer can get up to $155,000 in a year. 3. Data Analyst As the name suggests, the job of a Data Analyst is to analyze data. A data analyst collects, processes, and executes statistical data analyses which help business users to develop meaningful insights. This process requires creating systems using programming languages like Python, R or SAS. Companies ranging from IT, healthcare, automobile, finance, insurance employ Data Analysts to run their businesses efficiently.  Responsibilities of Data Analysts  The responsibilities of Data Analysts are described below: Identifying correlations and gathering valuable patterns through data mining and analyzing data. Working with customer-centric algorithms and modifying them to suit individual customer demands. Solving certain business problems by mapping data from numerous sources and tracing them. Creating customized models for customer-centric market strategies, customer tastes, and preferences. Conducting consumer data research and analytics by deploying statistical analysis. Data Analyst Salary Range According to Glassdoor, the national average salary of a Data Analyst is $62,453 in the United States. The salaries of an entry-level data analyst start at  $34,5000 per year or $2875 per month.  Glassdoor states that a junior data analyst earns around $70,000 per year and experienced senior data analysts can expect to be paid around $107,000 per year which is roughly $8916 per month. Key Reasons to Become a Data Scientist Becoming a Data Scientist is a dream for many data enthusiasts. There are some basic reasons for this: 1. Highly in-demand field The job of Data Science is hailed as one of the most sought after jobs for 2020 and according to an estimate, it is predicted that this field would generate around 11.5 million jobs by the year 2026. The demand for expertise in data science is increasing while the supply is too low.  This shortage of qualified data scientists has escalated their demand in the market. A survey by the MIT Sloan Management Review indicates that 43 percent of companies report that a major challenge to their growth has been a lack of data analytic skills. 2. Highly Paid & Diverse Roles Since data analytics form the central part of decision-making, companies are willing to hire larger numbers of data scientists who can help them to make the right decisions that will boost business growth. Since it is a less saturated area with a mid-level supply of talents, various opportunities have emerged that require diverse skill sets. According to Glassdoor, in the year 2016,  data science was the highest-paid field across industries. 3. Evolving workplace environments With the arrival of technologies like Artificial Intelligence and Robotics which fall under the umbrella of data science, a vast majority of manual tasks have been replaced with automation.  Machine Learning has made it possible to train machines to perform repetitive tasks , freeing up humans to focus on critical problems that need their attention. Many new and exciting technologies have emerged within this field such as Blockchain, Edge Computing, Serverless Computing, and others.  4. Improving product standards The rigorous use of Machine Learning algorithms for regression, classification recommendation problems like decision trees, random forest, neural networks, naive Bayes etc has boosted the customer experiences that companies desire to have. One of the best examples of such development is the E-commerce sites that use intelligent Recommendation Systems to refer products and provide customer-centric insights depending upon their past purchases. Data Scientists serve as a trusted adviser to such companies by identifying the preferred target audience and handling marketing strategies. 5. Helping the world In today’s world, almost everything revolves around data. Data Scientists extract hidden information from massive lumps of data which helps in decision making across industries ranging from finance and healthcare to manufacturing, pharma and engineering . Organizations are equipped with data driven insights that boost productivity and enhance growth, even as they optimize resources and mitigate potential risks. Data Science catalyzes innovation and research, bringing positive changes across the world we live in. Factors Affecting a Data Scientist’s Salary The salaries of Data Scientists can depend upon several factors. Let us study them one by one and understand their significance: Data Scientist Salary by Location The number of job opportunities and the national data scientist salary for data innovators is the highest in Switzerland in the year 2020, followed by the Netherlands and United Kingdom. However, since Silicon Valley in the United States is the hub of new technological innovations, it is considered to generate the most jobs for startups in the world, followed by Bangalore in India. A data scientist’s salary in Silicon Valley or Bangalore is likely to be higher than in other countries. Below are the highest paying countries for data scientist roles along with their average annual data science salary: Switzerland$115,475Netherlands$68,880Germany$64,024United Kingdom$59,781Spain$30,050Italy$37,785Data Scientist Salary by ExperienceA career in the field of data science is very appealing to young IT professionals. Starting salaries are very lucrative, and there is incremental growth in salary  with  experience. Salaries of a data scientist depend on the expertise, as well as the years of experience: Entry-level data scientist salary – The median entry-level salary for a data scientist is around $95,000 per year which is quite high. Mid-level data scientist salary –   The median salary for a mid-level data scientist having experience of around 1 - 4 years is $128,750 per year. If the data scientist is in a managerial position, the average salary rises upto $185,000 per year. Experienced data scientist salary –  The median salary for an experienced data scientist having experience of around 5 - 9 years is $128,750 per year whereas the median salary of an experienced manager is much higher; around $250,000 per year. Data Scientist Salary by Skills There are some core competencies that will help you to shine in your career as a Data Scientist, and if you want to get the edge over your peers you should consider polishing up these skills: Python is the most crucial and coveted skill which data scientists must be familiar with, followed by R. The average salary in the US for  Python programmers is $120,365 per annum. If you are well versed in both Data Science and Big Data, instead of just one among them, your salary is likely to increase by at least 25 percent . The users of innovative technology like the Statistical Analytical System get a salary of around $77,842. On the other hand, users of software analysis software like SPSS have a pay scale of  $61,452 per year. Machine Learning Engineers on the average earn around $111,855 per year. However, with more experience in Machine Learning along with knowledge in Python, you can earn around $146,085 per annum. A Data Scientist with domain knowledge of Artificial Intelligence can earn an annual salary between $100,000 to $150,000. Extra skills in programming and innovative technologies have always been a value-add that can enhance your employability. Pick skills that are in-demand to see your career graph soar. Data Scientist Salary by Companies Some of the highest paying companies in the field of Data Science are tech giants like Facebook, Amazon, Apple, and service companies like McGuireWoods, Netflix or Airbnb.  Below is a list of top companies with the highest paying salaries: McGuireWoods$165,114Amazon$164,114Airbnb$154,879Netflix$147,617 Apple$144,490Twitter$144,341Walmart$144,198Facebook$143,189eBay$143,005Salaries of Other Related Roles Various other job roles associated with Data Science are also equally exciting and rewarding. Let us look at some of them and their salaries: Machine Learning Engineer$114,826Machine Learning Scientist$114,121Applications Architect$113,757Enterprise Architect$110,663Data Architect$108,278Infrastructure Architect$107,309Business Intelligence Developer$81,514Statistician$76,884Conclusion Let us look at what we have learned in this article so far: What is Data Science? The job of a Data Scientist Pre-requisite skills for a Data Scientist Different job roles Key reasons for becoming a Data Scientist Salary depending upon different factors Salary of other related roles The field of Data Science is ripe in terms of opportunities for Data Scientists, Data Engineers, and Data Analysts. The figures mentioned in this article are not set in stone and may vary depending upon the skills you possess, experience you have and various other factors. With more experience and skills, your salary is bound to increase by a certain percentage every year. Data science is a field that will revolutionize the world in the coming years and you can have a share of this very lucrative pie with the right education qualifications, skills, experience and training.  
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How to use Split in Python

The split function is a string manipulation tool in Python.  A string is a collection or array of characters in a sequence that is written inside single quotes, double quotes, or triple quotes; a character ‘a’ in Python is also considered a string value with length 1. The split function is used when we need to break down a large string into smaller strings.Strings represent Unicode character values and are mutable in nature which means the value of a string cannot be altered after it has been declared.  An example of declaring and displaying a string in Python: name = “Python”  print(name[0]) Although we cannot change a string after the declaration, we can split a string into different strings using a variety of different ways in Python.  In this article, we will take a deeper dive and understand how to use Split is in Python. We will begin by understanding what the Split function does, what the need is for such a function and how we work with this function. We will then take a look at Split parameters in Python and the different ways of using the Split function. What is Split in Python? If you have worked on the concatenation of strings that are used to merge or combine different strings into one, the split function performs just the opposite of it. The function scans through a string and separates it when it encounters a separator which has been specified before.  However, if the function does not find any defined separator, it uses white space by default.  The syntax of the Split function is as follows: string.split(separator,max) The separator is a character that has been pre-defined and it gets placed between each variable in the output. The split function depends on the value of the separator variable. What is the need for Split function? The Split function returns a list of words after separating the string or line with the help of a delimiter string such as the comma ( , ) character. Some of the merits of using Split function in Python are listed as follows: It is useful in situations where you need to break down a large string into smaller strings. If the separator is not present within the split function, the white spaces are considered as separators. The split function helps to analyze and deduce conclusions easily. It is also useful in decoding strings encrypted in some manner.  How to work with Split function? Strings variables in Python contain numeric and alphanumeric data which are used to store data directories or display different messages. They are very useful tools for programmers working in Python.  The .split() method is a beneficial tool for manipulating strings. It returns a list of strings after the main string is separated by a delimiter. The method returns one or more new strings and the substrings also get returned in the list datatype.  A simple example of the split function is as follows: x = ‘red,orange,yellow’  x.split(“,”)  [‘red’, ‘orange’, ‘yellow’] Here, we have declared a string variable x with three strings. When the split function is implemented with a comma ( , ) as a separator, the strings get separated with commas in between them.  What are Split parameters in Python? The Split function analyses through a string and separates it whenever the program comes across a pre-defined separator. It depends on mainly three different parameters to optimize the execution of the program: Separator - It instructs Python where to break the string. It works as a delimiter and the string is separated depending upon the pre-defined separator. It is optional which means if the separator is not specified in split, the function uses white space as the default separator. However, if the separator is specified as a string value, the output will be an empty string. Maxsplit-  It specifies the number of times the string can be broken up. It is also optional and it’s default value is -1 which denotes that there are no limits on the number of times a string can be split. If the maxsplit is not defined in the split function, the entire string is scanned and Python separates it whenever a delimiter is encountered.    Return - It returns a list of strings after the split function breaks the string by the specified separator. What are the different ways of using the Split function? Python consists of several different ways by which we can implement the Split function. The different techniques are explained below: Python consists of several different ways by which we can implement the Split function. The different techniques are explained below: Splitting String by Space The split() method in Python splits the string on whitespace if no argument is specified in the function. An example of splitting a string without an argument is shown below: str = “Python is cool”  print(str.split()) The output of the above code is as follows: [‘Python’, ‘is’, ‘cool’] In the example above, we have declared variable str with a string value. You can see that we have not defined any arguments in the Split function, so the string gets split with whitespaces.  Splitting String on first occurrence When we split a string based on the first occurrence of a character, it results in two substrings – the first substring contains the characters before the separator and the second substring contains the character after the separator.  An example of splitting a string on the first occurrence of a character is shown below: str = “abcabc”  print(str.split(c)) The output of the above code is as follows: [‘ab’, ‘abc’] Here, we have declared str with a string value “abcabc”. The split function is implemented with separator as “c” and maxsplit value is taken as 1. Whenever the program encounters “c” in the string, it separates the string into two substrings  – the first string contains characters before “c” and the second one contains characters after “c”.  Splitting a file into a list When you want to split a file into a list, the result turns out to be another list wherein each of the elements is a line of your file. Consider you have a file that contains two lines “First line\nSecond Line”. The resulting output of the split function will be [ “First Line”, “Second line”]. You can perform a file split using the Python in-built function splitlines(). Consider you have a file named “sample.txt” which contains two lines with two strings in each line respectively – “Hi there”, “You are learning Python”. An example of splitting “sample.txt” into a list is shown below: f = open(“sample.txt”, “r”)  info = f.read()  print(info.splitlines())  f.close() The output of the above code is as follows: [‘Hi there’, ‘You are learning Python’] We have a file “sample.txt” which is opened in read (“r”) mode using the open() function. Then, we have called f.read() which returns the entire file as a string. The splitlines() function is implemented and it splits the file into two different substrings which are the two lines contained in “sample.txt”. Splitting a String by newline character (\n) You can split a string using the newline character (\n) in Python. We will take a string which will be separated by the newline character and then split the string. The newline character will act as the separator in the Split function.  An example of splitting a string by newline character is shown below: str = “Welcome\nto\nPython\nSplit”  print(str.split(‘\n’)) The output of the above code is as follows: [‘Welcome’, ‘to’, ‘Python’, ‘Split’] Here, we have declared a variable str with a string that contains newline characters (\n) in between the original string.The Split function is implemented with “\n”  as the separator. Whenever the function sees a newline character, it separates the string into substrings.  You can also perform split by newline character with the help of the splitlines() function. Splitting a String by tab (\t) Tabs are considered as escape characters “\t” in text (.txt) files. When we split a string by tabs, the Split function separates the string at each tab and the result is a list of substrings. The escape character “\t” is used as the separator in the Split function. An example of splitting a string by tab is shown below: str = “Python\tis\ta\tscripting\tlanguage”  print(str.split(“\t”)) The output of the above code is as follows: ['Python', 'is', 'a', 'scripting', 'language'] Here, the variable str is declared with a string with tabs (“\t”). The Split function is executed with “\t” as the separator. Whenever the function finds an escape character, it splits the string and the output comes out to be a list of substrings. Splitting a String by comma (,) We can also split a string by commas (“,”) where commas act as the delimiter in the Split function. The result is a list of strings that are contained in between the commas in the original string.  An example of splitting a string by commas is shown below: str = “Python,was,released,in,1991”  print(str.split(“,”)) The output of the above code is as follows: ['Python', 'was', 'released', 'in', '1991'] Here, the variable str is declared with a string with commas (“,”)  in between them. The Split function is implemented with “,”  as the separator. Whenever the function sees a comma character, it separates the string and the output is a list of substrings between the commas in str. Splitting a String with multiple delimiters You can split a string using multiple delimiters by putting different characters as separator in the Split function. A delimiter is one or more characters in a sequence that are used to denote the bounds between regions in a text. A comma character (“,”) or a colon (“:”) is an example of a delimiter. A string with multiple delimiters can be split using the re.split() function. An example of splitting a string with multiple delimiters is shown below: import re  str = 'Python\nis; an*easy\nlanguage'  print(re.split('; |, |\*|\n',str)) The output of the above code is as follows: ['Python', 'is', 'an', 'easy', 'language'] In the example above, we import the built-in module re which imports the libraries and functions of Regular Expressions. The variable str is declared with a string with multiple delimiters like newline (\n), semicolon (;), or an asterisk (*). There.split() function is implemented with different delimiters as separator and the output is a list of strings excluding the delimiters.  Splitting a String into a list When you split a string into a list around a delimiter, the output comes out to be a partitioned list of substrings. You can take any delimiter as a separator in the Split function to separate the string into a list. An example of splitting a string into a list is shown below: str = “New York-Texas-Colombia”  print(str.split(“-”)) The output of the above code is as follows: ['New York', 'Texas', 'Colombia'] The variable str is declared with a string with dash characters( - ) in between and the Split function is executed with a dash ( - )  as the separator. The function splits the string whenever it encounters a dash and the result is a list of substrings. Splitting a String by hash (#) You can also split any string with a hash character (#) as the delimiter. The Split function takes a hash (#) as the separator and then splits the string at the point where a hash is found. The result is a list of substrings.  An example of splitting a string using a hash is shown below: str = “Python#isa#multi-purpose#language”  print(str.split(“#”)) The output of the above code is as follows: ['Python', 'is a', 'multi-purpose', 'language'] The variable str is declared with a string with hash characters( # ) in between them. The Split function is executed with a hash as the separator. The function splits the string wherever it finds a hash  ( # ) and the result is a list of substrings excluding the hash character. Splitting a String using maxsplit parameter The maxsplit parameter defines the maximum number of splits the function can do. You can perform split by defining a value to the maxsplit parameter. If you put whitespaces as separator and the maxsplit value to be 2, the Split function splits the string into a list with maximum two items.  An example of splitting a string using the maxsplit parameter is shown below: subjects = “Maths Science English History Geography”  print(subjects.split(“ ”,2)) The output of the above code is as follows: ['Maths', 'Science', 'English History Geography']  Here, you can see the variable str is declared with a string of different subject names. The Split function takes whitespace (“ ”) as a separator and the maximum number of splits or maxsplit is 2. The first two strings “Maths” and “Science” are split and the rest of them are in a single string. Splitting a String into an array of characters You can separate a string into an array of characters with the help of the list() function. The result is a list where each of the element is a specific character.  An example of splitting a string into an array of characters  is shown below: str = “PYTHON”  print(list(str)) The output of the above code is as follows: ['P', 'Y', 'T', 'H', 'O', 'N'] Here, the variable str is a string. The string is separated into individual characters using the list() function and the result is a list of elements with each character of the string. Splitting a String using substring You can obtain a string after or before a specific substring with the split() function. A specific string is given as the separator in the Split function and the result comes out to be the strings before and after that particular string.   An example of splitting a string using substring  is shown below: fruits = “Orange Banana Mango Apple Cherry”  print(fruits.split(“Mango”)) The output of the above code is as follows: ['Orange Banana ', ' Apple Cherry'] Here, the variable fruits is a string with names of different fruits. We take the string “Mango” as the separator in the Split function. Whenever the function finds the string “Mango”, it splits the whole string into two substrings – one substring before “Mango” and another substring after “Mango”.  Miscellaneous tips on Split function Since we have now reached at the end of the article, let me give you some useful tips on the Split function: If the maxsplit is not defined in the function and there are enough delimiters in the string, the result will have a length of maxsplit +1.If you want to recombine a string that has been already split in Python, you can perform the concatenation of strings.The Python Split function only works on string variables. If you come across any problem with working with split, you can force the compiler to treat the variable as a string with str(x).Conclusion The .split() function in Python is a very useful tool to split strings into chunks depending upon a delimiter which could be anything starting from characters or numbers or even text. You can also specify the number of splits you want the function to perform using maxsplit, which is used to extract a specific value or text from any given string using list or Arrays. Here are the key areas you should have gained a good understanding on by reading this article: What is a String. What is Split and why is it needed. How does a Python Split function work. What are the Split parameters. What are the many different ways of Splitting strings in Python Important tips on Split You have learned about the Python split function and the different ways to implement in your program. With this, you can begin to work on any project which requires the use of the Split.  If you wish to extend your knowledge about Strings and Split function in Python, you can refer to the official documentation of Python. Also, don’t forget to check out the remaining tutorials made freely available to you. 
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How to use Split in Python

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How to concatenate strings using Python

The string data type in Python is a very important building block of programming. It is basically a sequence of one or more characters that represent Unicode characters. The characters could be letters, numbers, or symbols. The strings are immutable in nature, which means they are unchanging.  You can implement string formatting techniques in Python like merging or splitting strings in Python. When you merge or combine two or more strings in Python, it is called string concatenation. In this article, we will understand what concatenation is and its importance. We will delve into different ways of concatenating strings including the + operator, * operator and % operator and take you through various concatenation methods including the join() method, format() function, the f-string and StringIO methods. What is concatenation in Python? String Concatenation is the operation of joining character strings end-to-end. If you have just started working on Python, you might come through a time when you will need to merge or combine the contents of two or more strings together. In technical terms, this merging or combining of strings together into a single string is called String concatenation. The simplest way to explain concatenation in Python is when you take two separate strings stored in the Interpreter and combine them so that they become one single string. For example, if you take one string as “foot” and another string as “ball” and then merge them into using concatenation technique it comes out to be a single string “football”. There are several ways in which you can perform string concatenation in Python. However, the simplest method is using the “+” operator. What is the need for String formatting in Python? String formatting in Python is a robust and important part of the toolkit of any Python programmer. String formatting techniques have greatly evolved since the time Python was developed. Almost every piece of production software created has its advantage in some way or the other.  Formatted strings in Python are evaluated at run time which acts as a basic capability of any high-level language. At a basic level, String concatenation using the “+” operator might seem inefficient and also difficult to make expressive. This is where Python’s string formatting starting from the “%” formatting to the format() method comes into action. They exhibit great potential when it comes to crafting strings. How can we concatenate strings in Python? Python comprises of a number of ways when it comes to concatenate or combine strings together. Since Python is an object-oriented programming language, everything in Python is an object. So, the new string that is created after concatenation is also referred to as a string object in Python.  Let us see what are the different ways by which we can concatenate strings in Python. Using the + operator The simplest and most common method of concatenating a string is using the plus symbol (“+”). Let us see an example to understand it better: a = “Python”  b = “is”  c = “cool”  print(a + b + c) PythoniscoolHere, we have declared three string variables “a”, “b” and “c” with three different string values. Then, we concatenate the three strings with the help of the “+” operator and display the output using the print statement. The output is the combination of the three strings together.  You might use the “+” operator when you have few strings to concatenate. This is because strings are immutable i.e. they cannot be changed once created. So, for each concatenating statement, the interpreter creates a new object. Thus, it will be quite inefficient if you try to concatenate many strings using the “+” operator. Another disadvantage of the “+” operator is that it does not allow any separator or delimiter between the strings. If you want to concatenate “Hello” and “World” with whitespace as a separator, you need to fo something like this “Hello” + “ ” + “World” and the output will be “Hello World”. Using the * operator The asterisk (*) operator is used when you want to concatenate the same string repeatedly. For example, if you have a string “red” and you want the same string to be concatenated three times, you use the * operator. The result will be “redredred”.  An example to illustrate concatenation of string using “*” operator: a = "Python"  print(a * 3) PythonPythonPython Here, we have declared a single string variable “a” with a string value. Then, we concatenate the string with the help of the “*” operator and display the output using the print statement. The output combines the string with the same string three times repeatedly. Using the join() method The join() method is the most flexible way of concatenating strings in Python. If you have many strings and you want to combine them together, use thejoin() method. It is a string method and the most interesting thing about join() is that you can combine strings using a separator. It works on iterators like lists, tuples, string, dictionaries, etc.  An example to illustrate concatenation of string using “*” operator: a = "Welcome"  b = "to"  c = "Python"  print(“-”.join([a,b,c])) Welcome-to-Python Here, we have declared three string variables “a”, “b” and “c” with three different string values. Then, we concatenate the three strings with the help of the join() method with “-” as a separator and display the output using the print statement. The output is the combination of the three strings together with dash (“-”) operator in between the strings. Using the % operator The modulus operator (“%”) can be used for both string formatting and string concatenation. It is useful for cases in which you need to combine strings and also perform basic formatting. An example to illustrate concatenation of string using “%” operator: a = "Apple"  b = "Shake"  print(“% s % s” % (a, b)) Apple Shake Here, we have declared two string variables “a”, and “b”with two different string values. Then, we concatenate the two strings with the help of the (“%”) and display the output using the print statement.  The “% s” denotes the string data type in Python and the modulus (“%”) operator combines the string stored in the two variables “a” and “b”. The string value in the variables is passed to the string data type and the output is displayed as the combination of two strings. Using the format() function The str.format() function is a powerful function in Python which is also used for both String formatting and String Concatenation. This function combines different elements within a string through positional formatting.     An example to illustrate concatenation of string using format() function: a = "Virgin"  b = "Mojito"  print(“{} {}”.format(a, b)) Virgin Mojito Here, we have declared two string variables “a” and “b”with two different string values. Then, we concatenate the two strings with the help of the format() function and display the output using the print statement.  The curly braces (“{}”) used here are used to fix the string position. The first variable is stored in the first curly braces and the second one in the second curly braces. The job of format() function is to concatenate the strings stored in variables “a” and “b” and display the combined string. Using the f-string  Formatted string literals or f-strings, in short, are string literals in Python. They contain an f at the beginning and curly braces that contain the expressions. It calls the str() method when an object argument is used as field replacement. Let us see an example to illustrate the concatenation of string using f-string: a = "Moscow"  b = "Mule"  print(f’{a} {b}‘) Moscow Mule Here, we have declared two string variables “a” and “b”with two different string values. Then, we concatenate the two strings with the help of the f-string and display the output using the print statement.  The f-string expressions are evaluated at runtime and they are being formatted using the __format__ protocol in Python. It is considered to be a cleaner and easier way of concatenating strings in Python when compared to the format() function.Using StringIO String concatenation using StringIO is also a very flexible way for combining different strings in Python. In this method, we have to import the StringIO() function from the IO module.  An example to illustrate the concatenation of string using StringIO: from io import StringIO  a = StringIO()  a.write(“Machine ”)  a.write(“Learning”)  print(a.getvalue()) Machine Learning Here, we have declared two string variables “a” and “b”with two different string values. Then, we concatenate the two strings with the help of the StringIO() imported from the IO module and display the output using the print statement.  Here, the variable “a”acts as a file object in Python. The write() function is used here to write the string to the file and the getvalue() function returns the entire content of the file. Miscellaneous concatenations in Python We have covered all the ways by which we can concatenate different strings in Python. Let us see some few more miscellaneous examples to understand String Concatenation better. Concatenate multiple strings There are various ways by which you can concatenate multiple strings in Python. The most common among them is using the plus (“+”) operator. You can combine both string variables and string literals using the “+” operator. However, there’s another method that allows an easy way of concatenating multiple strings. It is using the in-place (+=) operator. The in-place operator concatenates the sequence with the right operand and the result gets assigned to that sequence. Let us see an example of string concatenation using the (“+=”) operator: a = "Artificial "  b = "Intelligence"  a += b  print(a) Artificial Intelligence Here, two string variables “a” and “b” are declared with two different string values. The string on the right side of the “+=” operator is combined with the string variable on the left side. Then, the output is displayed using the print statement.  You can also add a string to the end of a string variable using the “+=” operator: a = "Basket"  a += "ball"  print(a) Basketball Another way of concatenating multiple strings in Python is just by writing string literals consecutively: a = "Red""Green""Blue"  print(a) RedGreenBlueConcatenate strings and numbers There are numerous ways of concatenating strings in Python. However, not all methods can concatenate strings and numbers. If you use the “+” operator to combine strings and numbers, it will raise errors. This is because strings can hold any recorded characters but numbers like  integers or floats are recorded number value. a = "Rolls Royce "  b = 1948  print(a + b) Traceback (most recent call last):   File "", line 6, in   TypeError: can only concatenate str (not "int") to str The error shows that the interpreter can concatenate a string value with another string value but cannot concatenate a string value with an integer. Although, you can overcome this problem with the help of the str() function in Python. It converts any integer or floating-point number into a string.  Let us see the same example with the str() function: a = "Rolls Royce "  b = str(1948)  print(a + b) Rolls Royce 1948 The str() function converts the integer value 1948 into a string and then it is concatenated with variable “a” and the output is displayed using the print statement. You can also use the format() function when you need to convert a number with decimal places or zero padding. Concatenate a list of strings into one string You can concatenate a list of strings into one string using the join() method. It takes a character as a delimiter string. If you use an empty string as the delimiter, the list of strings will be simply concatenated without any separator.  Let us see an example to concatenate a list of strings using the join() function: a = ["Apple", "Orange", “Banana”, “Mango”]  print(“\n”.join(a)) Apple  Orange  Banana  Mango Here, the variable “a” is a list declaredwith four different string values. We have used newline (“\n”) as the delimiter in the join() method which inserts a newline for each of the strings. The output is the four strings with each string in a newline. You can use any other delimiter like comma (,) or hyphen (-) in the join() method and then perform concatenation. Also, note that thejoin() method can also concatenate other iterators like tuples, sets, dictionaries, etcConcatenate a list of numbers into one string Python does not allow the concatenation of strings with numbers or numbers with numbers. However, you can convert a numeric value into a string using the str() method and then perform concatenation. If you want to combine a list of numbers into one string, the first thing you need to do is convert each integer in a list to a string using the str() function. Then, combine all the converted strings into a single string with the join() method. Let us see an example to understand it better: a = [1, 2, 3, 4, 5]  b = [str(a) for a in a]  print(“;”.join(b)) 1;2;3;4;5 Here, the variable “a” is a list declared with five integer values. We convert each of the integers into a string using the str() function and store it in variable “b”. Then, we combine them together using the join() method with a colon (;) as the delimiter.Here, the variable “a” is a list declared with five integer values. We convert each of the integers into a string using the str() function and store it in variable “b”. Then, we combine them together using the join() method with a colon (;) as the delimiter. Some useful tips on concatenation Now let me give you some useful tips on String concatenation in Python: The string-formatting operator “%” is a potentially fast and suitable operator when you need to concatenate a few pieces of string. Also, you don’t need to call the str() function when combining numbers because this operator does it implicitly. It also enhances the readability of the code. The join() method is the fastest, cleanest, and most elegant and readable method when you need to concatenate many small pieces of string into a larger string. When you have many small pieces of strings that come either from input or computation and are not in a sequence, always use a list to contain the strings. You can use list comprehension or append method in Python to arrange your list in a sequence.  Conclusion Let us summarize what we have learned in this article so far –  Concatenation and its importance. Different ways of concatenating strings. Some miscellaneous concatenation methods. Important tips on concatenating strings. Concatenation is a crucial part of String manipulation in Python. There are numerous ways to perform concatenation. However, some are more useful than others in some cases.  Now that you have quite an experience in concatenating strings, you can look out for other string formatting methods that Python provides or you can check out the PEP article on Advanced String Formatting on Python.org for more information. 
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How to concatenate strings using Python

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The self variable in Python explained with Python tips

If you have been working on Python, you might have across the self variable. You can find it in method definitions and in initializing variables. However, before coming to the self variable, let us have an idea about classes and instances in Python.  What are Instance methods and Class methods in Python? You might have heard of instances and classes while working on Python. Class variables are defined within a class and they are shared with all the instances (objects) of the class whereas instance variables are owned by the instances of a class. For different instances, the instance variables are different. Likewise, Python also contains class methods and instance methods. The class methods inform about the status of the class. On the other hand, instance methods are to set or get details about instances or objects.   If you want to define an instance method, the foremost parameter of the method should always have to be self. Let us understand this with the help of example code – class myClass:      def instance_method(self):          return “Instance method is called”, self The method “instance_method” is a regular instance method. The method accepts one single parameter – self. The self variable points to an instance of the class myClass when the method is revoked. Though the method takes only one parameter here, it can also accept more than one parameter. The instance methods can easily access different attributes and other methods on the same object with the help of the self variable. The self variable also has the power to modify the state of an object and using the self.__class__  attribute, instance methods can also access the class. Thus, the instance methods are also able to modify the class state. Now, let us see what happens when we call “instance_method”: >>>obj = myClass()  >>>obj.instance_method()  ('Instance method is called', ) This shows that “instance_method” can access the object instance (printed as ) through the self parameter. What happens is that the self parameter is replaced with the instance object obj when the method is called. However, if you pass the instance object manually, you will get the same result as before: >>>myClass.instance_method(obj)  ('Instance method is called', ) Note that self is actually not a defined keyword in Python but a convention.What is Self in Python? Unlike this variable in C++, self is not a keyword rather it is more of a coding convention. It represents the instance or objects of a class and binds the attributes of a class with specific arguments. The use of self variable in Python helps to differentiate between the instance attributes (and methods) and local variables. If you do not want the variables of your class to be shared by all instances of the class, you can declare variables within your class without self. Let us understand this with an example: class Car:      def __init__(self, model):  self.model = model      def Car_info(self):  print("Model : ", self.model) Here, we have declared a class Car with one instance variable self.model = model. The value of the instance variable will be unique to the instance objects of the class that might be declared. However, if you want the variables to be shared by all instances of the class, you need to declare the instance variables without self. Otherwise, it would be ambiguous since all cars will have the same model. Need for Self in Python The self variable is used to represent the instance of the class which is often used in object-oriented programming. It works as a reference to the object. Python uses the self parameter to refer to instance attributes and methods of the class.  Unlike other programming languages, Python does not use the “@” syntax to access the instance attributes. This is the sole reason why you need to use the self variable in Python. The language contains methods that allow the instance to be passed automatically but not received automatically. Explicit definition of self The Zen of Python says “Explicit is better than Implicit”. Programmers of other languages often ask why self is passed as an explicit parameter every time to define a method. There are a few reasons for this. Firstly, since Python uses a method or instance attribute instead of a local variable when we read self.name or self.age, it makes it absolutely clear you are using an instance variable or method even if you have no knowledge about the class definition. Secondly, if you explicitly refer or call a method from a particular class in Python, you do not need to use any special syntax for that.  Finally, the third reason is that the explicit definition of self helps Python understand whether to assign to an instance variable or to a local variable. In simpler terms, local variables and instances exist in two separate namespaces and we need to inform Python which namespace should be used. What is a Python class self constructor? The self variable in Python can also be used to access a variable field within the class definition. Let us understand this with the help of example code: class Student:     def __init__(self, Alex):          self.name = Alex    #name created in constructor      def get_student_name(self):          return self.name In the example above, self refers to the variable age of the class Student. The variable age is local to the method. While the method is running, the variable age exists within the class. If there is a variable within a method, the self variable will not work. If you wish to define global fields, you need to define variables outside the class methods.   Is self a keyword in Python? There is a question that always hovers among Python programmers. Is self actually a keyword in Python? Unlike other programming languages like C++, where self is considered to be a keyword, in Python it is a convention that programmers tend to follow. It is basically a parameter in a method definition. However, you can use any other name in place of self like another or me or anything else for the first parameter of a method. Another reason why it is suggested by most people is that it enhances the readability of your code. Let us see an example to understand it: class myClass:   def show(another):   print(“another is used in place of self”)  If you compare this code with the code for the Python class self constructor, you will notice that here we have used the name another in place of self. Now let us create an object of this class and see the output: object = myClass()   object.show()another is used in place of selfYou can see that the program works even if we use some other name in place of the self variable. It works exactly the same way as the self variable does. Why "self" should be used as the first parameter of instance methods in python  This can be understood by the below example. We have a Rectangle class which defines a method area to calculate the area : class Rectangle ():      def __init__(self,x = 0,y = 0):          self.x = x          self.y = y      def area (self):          """Find area of rectangle"""          return (self.x * self.y)  rec1=Rectangle(6,10)  print ("Area is:", rec1.area()) Output:  Area is: 60    In the above example, __init__() defines three parameters but only 2 arguments are passed (6 and 10). Similarly, area () requires one but no arguments are passed.  Rectangle.area and rec1.area in the above example are different and not exactly the same. >>> type(Rectangle.area )    >>> type(rec1.area)  Here, the first one is a function and the second one is a method. A unique feature of Python is that the object itself is passed as the first argument to the corresponding function. In the above example, the method call:  rec1.area()is equivalent to:  Rectangle.area(rec1). Generally, when the method is called with some arguments, the corresponding class function is called by placing the method's object before the first argument.  Therefore: obj.method(args) becomes Class.method(obj, args). This is the reason the first parameter of a function in a class must be the object itself. Writing this parameter as self is merely a convention and not a keyword so it has no special meaning in Python. We could use other names (like this, that) but it is not preferred as it degrades code readability.Should we pass self to a method? Since we can use any other name instead of using the self variable, then what will happen if we just pass self to a method definition. Let us consider the class myClass we have used earlier.  A method named something is defined within the class with a parameter another and two arguments: class myClass:   def something(another, argument1, argument2):   pass Now, let us declare an instance obj of myClass and call the method something with the help of the instance object: obj = myClass()  obj.something(argument1, argument2) Python performs an internal work on the method call and converts it into something like this: myClass.something(obj, argument1, argument2)  This shows that another variable (used in place of self) refers to the instance object of the class. Note that the pass keyword used in the method something does nothing. It is used as a dummy in situations where you do not want any operation to be performed but there is a syntax requirement of a certain programming element. How can we skip self in Python? Consider a situation where the instance method does not need to have access to the instance variables. In such cases, we can consider skipping the self variable in defining methods. Let us have a clear understanding of the fact with example code: class Vehicle:  def Car():  print(“Rolls Royce 1948”)  obj = Vehicle()  print(“Complete”) If you run the following code, the output will be as follows: Complete We have not declared the self variable here but there is still no error in the program and the output comes out fine. However, what will be the case if we call the Car() method: obj = Vehicle()  obj.Car() When we compile the code after calling the Car() method, it shows an error like this: Traceback (most recent call last):   File "", line 11, in   TypeError: Car() takes 0 positional arguments but 1 was given The output shows an error since the method Car() takes 0 positional arguments but we have given 1 positional argument to it. This is because when the instance obj is created, it is automatically passed as the first argument to the method Car() even if we have not declared the self variable. However, if you try to access the instance method Car() with the help of the class reference, there will be no errors and the program will work fine: class Vehicle:  def Car():  print("Rolls Royce 1948")  obj = Vehicle()  Vehicle.Car()  Rolls Royce 1948 Difference between self and __init__  self : self represents the instance of the class. By using the "self" keyword all the attributes and methods of the python class can be accessed. __init__ : "__init__" is a reserved method in python classes. It is known as a constructor in object oriented concepts. This method is called when an object is created from the class and allows the class to initialize class attributes .. Usage of "self" in class to access the methods and attributes: class Rectangle:     def __init__(self, length, breadth, cost_per_unit =0):         self.length = length         self.breadth = breadth         self.cost_per_unit = cost_per_unit     def perimeter(self):         return 2 * (self.length + self.breadth)     def area(self):         return self.length * self.breadth     def calculate_cost(self):         area = self.area()         return area * self.cost_per_unit  # length = 40 cm, breadth = 30 cm and 1 cm^2 = Rs 100  r = Rectangle(40, 30, 100)  print("Area of Rectangle:",r.area())  print("Cost of rectangular field is : Rs ",r.calculate_cost()) Output:  Area of Rectangle: 1200  Cost of rectangular field is : Rs  120000 We have created an object of Rectangle class. While creating the Rectangle object, we passed 3 arguments – 40,30,100; all these arguments are passed to "__init__"method to initialize the object. Here, the keyword "self” represents the instance of the class. It binds the attributes with the given arguments. Self represents the same object or instance of the class. If you see, inside the method "area” , self.length" is used to get the value of the attribute "length".  attribute "length" is bind to the object (instance of the class) at the time of object creation. "self" represents the object inside the class. "self" works just like "r" in the statement “r = Rectangle(40,30, 100)".  If you see the method definition "def area(self): ” , here "self" is used as a parameter in the method because whenever we call the method,  the object (instance of class) is automatically passed as a first argument along with other arguments of the method. If no other arguments are provided only "self" is passed to the method. That's the reason "self" is used to call the method inside the class("self.area()").  We used object (instance of class) to call the method outside of the class definition("r.area()").  "r" is the instance of the class when the method "r.area()” is called; the instance "r" is passed as first argument in the place of self. Miscellaneous Implementations of self Let us now discuss some of the miscellaneous implementations of the self variable. Similar variables for Class Method and Static Method A class method is a method that is bound to the class. Let us understand a class method with an example – class myClass:  @classmethod  def classmethod(cls):  return “Class Method is called”  obj.classmethod() The same behavior of the self variable is present with the Class methods too but the only difference is that for class methods, the convention is to use cls as the variable name instead of self. The class methods take a cls parameter instead of the self parameter. When the method is called, it points to the class. The class method cannot modify the object state but it can modify the class state of all the class instances. On the other hand, static methods are self-sufficient functions and this type of method takes neither a self nor a cls parameter. Let us see an example of a static method – class myClass:  @staticmethod  def staticmethod():  return “Static Method is called”  obj.staticmethod() Since a static method does not accept any parameter, they cannot modify object state or even class state. They are primarily used to namespace different methods and Python restricts them in the data they can access. Note that both the methods here are marked with @classmethod and @staticmethod decorators to flag it as a class method and static method respectively. The self variable is bound to the current instance The self variable allows us to access the properties of the current instance. Let us understand this with an example – class Person:  def __init__(self, n):  self.name = n  def walk(self):  print(f“{self.name} is walking”)  obj1 = Person(“Alex”)  obj2 = Person(“Charles”)  obj1.walk()  obj2.walk()  Alex is walking Charles is walking Here, we have a class Person with two methods __init__ and walk declared with the self parameter. We have created two different instances of the class – obj1 and obj2. When the first instance is revoked, “Alex” is printed with the method walk() whereas when the second instance is revoked, “Charles” gets printed with the properties of the instance method walk(). Tips about the Python self variable Since we have now reached the end of the article, let me give you some tips about when to use and when not to use the self variable in Python. Use self when:you define an instance method, since it is passed automatically as the first parameter when the method is called;you reference a class or an instance attribute from inside an instance method;you want to refer to instance variables and methods from other instance methods. Don’t use self when: you want to call an instance method normally;referencing a class attribute inside the class definition but outside an instance method;you are inside a static method.  Conclusion Let us recap the key points we have covered in this article, namely:Instances and Classes in Python. Self variable and its importance. The explicitness of the self variable. Python class self constructor. Passing self as a method. Skipping self in Python. Variables used for Class methods and Static methods. Bounding of self to the current instance. When to use and when not to use self in Python. With good knowledge about the self variable in Python and its internal working in Python, it is now time for some practice. If you, however, wish to know more about Python self, you can head right on to the official documentation of Python.Happy coding! 
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Top 10 Python IDEs and Code Editors

Over the years, Python language has evolved enormously with the contribution of developers. Python is one of the most popular programming languages. It was designed primarily for server-side web development, software development, evaluation, scripting and artificial intelligence. For this feature Python encloses certain code editors and IDEs that are used for software development say, Python itself. If you are new to programming, learning Python is highly recommended as it is fast, efficient and easy to learn. Python interpreters are available on various operating systems such as Windows, Linux, Mac OS. This article provides a look into code editors and IDEs along with their features, pros and cons and talks about which are the best suited for writing Python codes. But first let us see what are code editors and IDEs. What is a Code Editor? A code editor is built for editing and modifying source code. A standalone text editor is used for writing and editing computer programs. Excellent ones can execute code as well as control a debugger as well as interact with source control systems. Compared to an IDE, a good dedicated code editor is usually smaller and quicker, but is less functional. Typically they are optimized for programming languages. One major feature of a text editor is that they are designed to modify various files and work with whatever language or framework you choose. What is IDE? IDE (Integrated Development Environment) understands the code significantly better than a text editor. It is a program exclusively built for software development. It is designed with a set of tools that all work together:  Text editor  Compiler Build automation Debugging Libraries, and many more to speed up the work.  These tools integrate: An editor designed to frame codes with text formatting, auto-completionetc., build, execution, debugging tools, file management and source and version control. It reduces manual efforts and combines all the equipment in a typical framework. IDE comes with heavy files. Hence, the downloads and installation is quite tedious. IDE requires expertise along with a lot of patience.  How does an IDE and Code editor differ from each other? An IDE is distinctive from code editors in the following ways: Integrated build process:The user does not have to write his own scripts to build apps in an IDE.  File management: IDE has an integrated file management system and deployment tool. It provides support to other framework as well. On the other hand, a Text editor is a simple editor where source code can be edited and it has no other formatting or compiling options. Development Environment: An IDE is mainly used for development purposes as it provides comparatively better features than a text editor. It allows you to write, compile and debug the entire script.  Syntax Highlighting:The editor displays the text message and puts the source code in different colours to improve its readability. Even error messages are displayed in different colours so that the user understands where he has written the wrong code.  Auto completion:It identifies and inserts a common code for the user instantly. This feature acts as an assistance for the programmer. The code suggestion automatically gets displayed.  Debugger: This tool helps the programmer to test and debug the source code of the main program.  Although IDEs have far better features than a Text editor one major significance of Text editor is that it allows modifying all types of files rather than specifying any definite language or types. Features For a good software development, we need code editors and IDEs which help the developer to automate the process of editing, compiling, testing, debugging and much more. Some of the features of these editors are listed below: Good user interface: They allow users to interact and run programs easily. Incredibly fast: Although these IDEs need to import heavy libraries, compile and debug, they offer fast compilation and run time.  Syntax stylizing: Codes are colorized automatically and syntax is highlighted.    Debugging tool: Itruns the code, set breakpoints, examine the variables. Provides good language syntax: IDEs usually work on a specific language but the others are designed for multi-language support. Code editors are designed with multi-language support.  Good source and version control environment: IDEs come with source control feature to keep a track of changes made in source code and other text files during the development of any software. Intelligent code completion:This feature speeds up the coding process by automatically suggesting for incomplete codes. It reduces typos and other common mistakes. Why do we need a good coding environment? For a good software development one seeks a better coding environment. Although features vary from app to app, a definite set of features is required for one. There are many other things involved such as source code control, extension tools, language support etc. Listed below are the core features which make a good coding environment : Retrieve files: All the codes written in an IDE get saved. Also, the programmer can retrieve his code file at the same state where the work is left off. Run within the environment: It should be able to compile and run within the environment where the codes are written. No external file shall be needed to be downloaded for the execution of the programs.  Good Debugging Tool: An IDE or editor should be able to diagnose and  troubleshoot the programmer’s works and highlight the lines with errors if any. A pop-up window should display the error message. This way the programmer can keep a track of his errands and diagnose them.   Automatic formatting tool: Indentation is done automatically as soon as the programmer moves onto the next line. It keeps the code clean and readable. Quick highlighting: keywords, variables and symbols are highlighted. This feature keeps the code clean and easy to understand. Also, pops up the variables making them easy to spot. This makes it a whole lot easier to pick out portions of code than simply looking at a wall of undifferentiated text. Some of the IDEs and code editors There are various Python IDEs and text editors. Some of the IDEs and text editors along with their features and pros and cons are mentioned below: IDLEKey Features: It is an open source IDE entirely written in Python. It is mainly supported by WINDOWS, LINUX, MAC OS etc.. IDLE is a decent IDE for learning because it is lightweight and quite simple to use. IDLE is installed by default as soon as installation of Python is complete. This makes it easier to get started in Python. IDLE features include the Python shell window(interactive interpreter), auto-completion, syntax highlighting, smart indentation, and a basic integrated debugger. It is however not suitable for the completion of larger projects and best suitable for educational purposes only.  Pros A cross-platform where a developer can search within any window, search through multiple files and replace within the windows editor  Supports syntax highlighting, auto code completion, smart indentation and editable configurations Includes Python shell with highlighter Powerful Integrated Debugger with continuous breakpoints, global view, and local spaces Improves the performance  Call stack visibility Increases the flexibility for developers Cons Used for programming just for beginners Limited to handle normal usage issues. Supports basic design  Large software development cannot be handled  Sublime text Key Features: It is a source code editor, supported on all platforms. It is a very popular cross-platform  and a better text editor. It possesses a built-in support for Python for code editing and packages to extend the syntax and editing features. All Sublime Text packages are written in Python and also a Python API. Installation of the packages often requires you to execute scripts directly in Sublime Text. it is designed to support huge programming and markup languages. Additional functions can be applied by the user with the help of plugins.  Pros More reliable for developers and is cross-platform Supports GOTO anything to access files  Generates wide index of each method, class, and function. AllowsUser interface toolkit Easy navigation to words or symbols Multiple selections to change things at one time Offers command palette to sort, edit and modify the syntax and maintain the indentation.  Offers powerful API and package ecosystem Great performance Highly customizable Allows split editing and instant project switch  Better compatibility with language grammar Custom selection on specific projects Cons Not free Installation of extensions is quite tricky Does not support for direct executing or debugging code from within the editor Less active GIT plugin AtomKey Features: It is an open source code editor developed by Github. It is supported on all platforms. It has features similar to that of Python. It has a framework based on atom shells which help to achieve cross platform functionality. With a sleek interface, file system browser, and marketplace for extensions, it offers a framework for creating desktop applications using JavaScript, HTML, CSS . Extensions can be installed when Atom is running.It enables support for third party packages. Its major feature is that although it is a code editor,it can also be used as an IDE. It is also used for educational purposes. Atom is being improvised day by day, striving to make the user experience rewarding and not remain confined to beginners use only.  Pros Cross-platform  Smooth editing Improves performance of its users Offers built-in package manager and file system browser Faster scripting  Offers smart auto-completion  Smart and flexible Supports multiple pane features Easy navigation across an application Simple to use Allows user interface customization Full support from GitHub Quick access to data and information Cons For beginners only Tedious for sorting configurations and plugins Clumsy tabs reduce performance  Slow loading Runs on JavaScript process  Built on Electron, does not run as a native application VimKey Features: Categorized as a stable open source code editor, VI and VIM are modal editors. As it is supported on almost every platform such as: Windows, LINUX, MAC OS, IOS, Android, UNIX, AmigaOS, MorphOS etc. it is highly configurable. Because of its modal mode of operation, it differs from most other text editors. It possesses three basic modes: insert mode, normal or command mode and command line mode. It is easily customized by the addition of extensions and configuration which makes it easily adaptable for Python development.  Pros Free and easily accessible Customizable and persistent  Has a multi-level undo tree  Extensions are added manually Configuration file is modified Multi-buffers support simultaneous file editing Automated indentation  Good user interface Recognition and conversion of file formats Exclusive libraries including wide range of languages Comes with own scripting language with powerful integration, search and replace functionality Extensive system of plugins Allows debugging and refactoring  Provides two different modes to work: normal and editing mode Strings in VIM can be saved and reused  Cons Used as a text editor only No different color for the pop-up option Not good for beginners PyDev Key Features: It is also categorized as an open source IDE mainly written with JAVA.Since it is an eclipse plugin, the Java IDE is transformed into Python IDE. Its integration with Django gives a Python framework. It also has keyword auto-completion, good debugging tool, syntax highlighting and indentation. Pros Free open source Robust IDE feature set Auto-completion of codes and analysis Smart indentation Interactive console shortcuts Integrated with Django configuration  Platform independent Cons: User interface is not great  Visual studioKey Features: It is categorized as an IDE, is a full-featured IDE developed by Microsoft. It is compatible with Windows and Mac OS only and comes with free as well as paid versions. It has its own marketplace for extensions. PTVS(Python Tools for Visual Studio) offers various features as in coding for Python development, IntelliSense, debugging, refactoring etc. Pros Easy and less tedious installation for development purposes Cons Spacious files  Not supported on Linux Visual studio code Key Features: VS code is a code editor and is way more different from VS. It is a free open source code editor developed by Microsoft can be run on platforms such as Windows, Linux and Mac OS X.  It has a full-featured editor that is highly configurable with Python compatibility for software development. Python tools can be added to enable coding in Python.VS code is integrated with Git which promotes it to perform operations like push, commit directly from the editor itself. It also has electron framework for Node JS applications running on the Blink browser engine. It is enclosed with smart code completion with function definition, imported modules and variable types. Apart from these, VS code also comes with syntax highlighting, a debugging console and proprietary IntelliSense code auto completion. After installing Python, VS code recognizes Python files and libraries immediately.  Pros Free and available on every platform  Small, light-weight but highly extensible Huge compatibility Has a powerful code management system Enables debugging from the editor Multi-language support  Extensive libraries Smart user interface and an acceptable layout Cons Slow search engine Tedious launch time Not a native app just like Atom WingKey Features: Wing is also one of the powerful IDEs today and comes with a lot of good features. It is an open source IDE used commercially. It also is constituted with a strong framework and has a strong debugger and smart editor for Python development making it fast, accurate and fun to perform. It comes with a 30 day trial version. It supports text driven development with unit test, PyTest and Django testing framework.  Pros Open source Find and go-to definition Customizable and extensible Auto-code completion Quick Troubleshoot  Source browser shows all the variables used in the script Powerful debugger  Good refactoring  Cons Not capable of supporting dark themes Wing interface is quite intimidating Commercial version is expensive Python-specific IDEs and Editors Anaconda - Jupyter NotebooksKey Features: It is also an open source IDE with a server-client structure, used to create and edit the codes of a Python. Once it is saved, you can share live code equations, visualizations and text. It has anaconda distribution i.e., libraries are preinstalled so downloading the anaconda itself does the task. It supports Python and R language which are installed by default at installation.  This IDE is again used for data science learning. Quite easy to use, it is not just used as an editor but also as an educational tool or presentation. It supports numerical simulation, machine  learning visualization and statistical modelling. Pros Free Open source  Good user interface Server-client structure Educational tool- Data science, Machine learning  Supports numerical simulation  Enables to create, write, edit and insert images Combines code, text and images Integrated libraries - Matplotlib, NumPy, Pandas Multi-language support Auto code completion Cons Sometimes slow loading is experienced Google Colaboratory Key Features: It is the simplest web IDE used for Python. It gives a free GPU access. Instead of downloading heavy files and tedious launch time, one can directly update the files from Colab to the drive. All you need to do is log in to your google account and open Colab. There is no need for extra setup. Unlike other IDEs no files are required to download. Google provides free computation resources with Colaboratory. It is designed for creating machine learning models. For compilation and execution, all you need to do is to update Python package and get started.   Pros Available to all Code can be run without any interruption Highly user interactive No heavy file downloads Integrated libraries Multi-language support Updated in google drive Update the Python package for execution  Runs on cloud Comments can be added in cells Can import Jupiter or IPython notebooks Cons  All colaboratory files are to be stored in google drive Install all specific libraries No access to unsaved files once the session is over Pycharm Key Features: Developed by Jet Brains and one of the widely used full-featured Python IDE, this is a cross-platform IDE for Python programming and  is well-integrated with Python console and IPython Notebook. It is supported by Windows, Linux, Mac OS and other platforms as well. It has massive productivity and saves ample amount of time. It comes with smart code navigation, code editor, good debugging tool, quick refactoring etc. and supports Python web development frameworks such as Angular JS, JavaScript, CSS, HTML  and live editing functions. The paid version offers advanced features such as full database management and a multitude Framework than the community version such as Django, Flask, Google App, Engine, Pyramid and web2py. Pros Great supportive community Brilliant performance. Amazing editing tools Robust debugging tool Smart code navigation Quick and safe refactoring  Built in developer tools Error detection and fix up suggestions Customizable interface Available in free and paid version Cons Slow loading  Installation is quite difficult and may hang up in between SpyderKey Features: It is an open source IDE supported on all platforms. Ranked as one of the best Python compilers, it supports syntax highlighting, auto completion of codes just like Pycharm. It offers an advanced level of editing, debugging, quick diagnose, troubleshoot and many data exploration features. To get started with Spyder, one needs to install anaconda distribution which is basically used in data science and machine learning. Just like Pycharm it has IntelliSense auto-completion of code. Spyder is built on a structured and powerful framework which makes it one of the best IDE used so far. It is most commonly used for scientific development. Pros Free open source IDE Quick troubleshoot Active framework Smart editing and debugging Syntax is automatically highlighted Auto completion of codes Good for data science and machine learning Structured framework Integrates common Python data science libraries like SciPy, NumPy, and Matplotlib Finds and eliminates bottlenecks Explores and edits variables directly from GUI  Performs well in multi-language editor and auto completion mode Cons Spyder is not capable to configure a specific warning Too many plugins degrades its performance ThonnyKey Features: Thonny is another IDE best suited for beginners for Python development and provides a good virtual environment. It is supported on all platforms. It gives a simple debugger with F5, F6 and F7 keys for debugging. Also, Thonny supports highlighting errors, good representation of function calls, auto code completion and smart indentation. It even allows the developers to configure their code and shell commands. by default,  in Thonny Python is pre-installed as it downloads with its own version of Python.  Pros Simple Graphical user interface.  Free open source IDE Best for beginners Simple debugger with F5, F6, F7 Keys Tackles issues with Python interpreters Highlights syntax error Auto-completion of code Good representation of function calls User can change reference mode easily Step through expression evaluation Reply and resolve to comments Cons Interface is not that good for developers Confined to text editing No template support Slow plugin creation Too basic IDE for software development Which Python IDE is right for you? Requirements vary from programmer to programmer. It is one’s own choice to pick the right tool that is best suited for the task at hand. Beginners need to use a simple tool with few customizations whereas experts require tools with advanced features to bring new updates. Few suggestions are listed below:- Beginners should start with IDLE and Thonny as they do not have complex features and are pretty easy to learn. For data science learners Jupyter Notebooks and Google Colaboratory is preferred. Generally, large scale enterprises prefer the paid versions of IDEs like PyCharm, Atom, Sublime Text etc. in order to get extensive service support from the company. Also, they provide easy finance options and manpower. On the other hand, middle and small scale enterprises tend to look for open source tools which provides them with excellent features. Some of such IDEs are Spyder, Pydev, IDLE and Visual Studio. Conclusion Today, Python stands out as one of the most popular programming languages worldwide. IDE being a program dedicated to software development has made it easier for developers to build, execute, and debug their codes. Code editors can only be used for editing codes whereas an IDE is a feature rich editor which has inbuilt text editor, compiler, debugging tool and libraries. Different IDEs and code editors are detailed in this article along with their merits and demerits. Some are suitable for beginners because of their lightweight nature and simplicity like IDLE, Thonny whereas experts require advance featured ones for building software.  For learning purposes say data science, machine learning Jupyter and Google Colaboratory are strongly recommended. Again there are large scale enterprises who prefer PyCharm, Atom, Sublime Text for software development. On the other hand, small scale enterprises prefer Spyder, Pydev, IDLE and Visual Studio. Hence,the type of IDE or code editor that should be used completely depends upon the requirement of the programmer . To gain more knowledge about Python tips and tricks, check our Python tutorial and get a good hold over coding in Python by joining the Python certification course. 
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Top 10 Python IDEs and Code Editors

Over the years, Python language has evolved enormo... Read More

What are List Methods in Python

Sequence is one of the most basic data types in Python. Every element of a sequence is allocated a unique number called its position or index. The first designated index is zero, the second index is one, and so forth. Although Python comes with six types of pre-installed sequences, the most used ones are lists and tuples, and in this article we would be discussing about lists and its methods.Certain tasks can be performed with all types of sequence; these include adding, multiplying, indexing, slicing, and so on. For added convenience, Python provides built-in functions to find the length of a sequence and to find the largest and smallest elements of the sequence. If you are interested to learn more about other functions and features of Python, you may go through our Python tutorial.What is a ListList is the most versatile data-type available in Python that can be written as a collection of comma-separated values or items between square brackets. The items in a list need not necessarily be homogeneous, i.e. of the same type. This property of List makes it one of the most powerful tools in Python eg:[‘HELLO’, 57, “SKY”]. A single list can contain different Data-Types such as integers, strings, as well as Objects. Lists are mutable, and hence can be changed even after their creation.In Python, lists are ordered in a definite manner and have a definite count. Elements of a list are indexed according to a particular sequence and the indexing is done with 0 being the starting index. Even element of a list has its unique place in the list, which allows duplication of the elements in the list, with each element having its own distinct place and credibility. Lists are a useful tool for storing a sequence of data.Creating a list is as simple as putting different types of comma-separated values between square brackets:list1 = ['jack', 'jill', 1998, 2019];[Text Wrapping Break] list2 = [1, 2, 3, 4, 5 ];[Text Wrapping Break] list3 = ["w", "x", "y", "z"]Just like string indices, list indices start with 0, and lists can be sliced, concatenated and so on.Creating a ListPython Lists can be created just by placing the integer, string, or character inside the square brackets[]. Unlike Sets, a list does not require a built-in function for its creation.# Python program to demonstrate the creation of a list [Text Wrapping Break] # Creating a List [Text Wrapping Break] List = [] [Text Wrapping Break] print("Initial blank List: ") [Text Wrapping Break] print(List) [Text Wrapping Break] [Text Wrapping Break] # Creating a List with  [Text Wrapping Break] # the use of a String [Text Wrapping Break] List = ['PythonListDemo'] [Text Wrapping Break] print("\nList with the use of String: ") [Text Wrapping Break] print(List) [Text Wrapping Break][Text Wrapping Break] # Creating a List with [Text Wrapping Break] # the use of multiple values  [Text Wrapping Break] List = ["Python", "List", "Demo"] [Text Wrapping Break] print("\nList containing multiple values: ") [Text Wrapping Break] print(List[0]) [Text Wrapping Break] print(List[2]) [Text Wrapping Break] [Text Wrapping Break] # Creating a Multi-Dimensional List [Text Wrapping Break] # (By Nesting a list inside a List) [Text Wrapping Break] List = [['Python', 'List'] , ['Demo']] [Text Wrapping Break] print("\nMulti-Dimensional List: ") [Text Wrapping Break] print(List)Initial blank List: [Text Wrapping Break] [] [Text Wrapping Break] [Text Wrapping Break] List with the use of String: [Text Wrapping Break] ['PythonListDemo'] [Text Wrapping Break] [Text Wrapping Break] List containing multiple values: [Text Wrapping Break] Python [Text Wrapping Break] Demo[Text Wrapping Break] [Text Wrapping Break] Multi-Dimensional List:  [Text Wrapping Break] [['Python', 'List'], ['Demo']]Creating a list with multiple distinct or duplicate elements:Multiple distinct or duplicate values can be stored as a sequence during creation of list:# Creating a List with [Text Wrapping Break] # the use of  Numbers [Text Wrapping Break] # (Having duplicate values) [Text Wrapping Break] List = [1, 2, 4, 4, 3, 3, 3, 6, 5] [Text Wrapping Break] print("\nList with the use of Numbers: ")  [Text Wrapping Break] print(List) [Text Wrapping Break]  [Text Wrapping Break] # Creating a List with [Text Wrapping Break] # mixed type of values [Text Wrapping Break] #  (Having numbers and strings) [Text Wrapping Break] List = [1, 2, 'Python', 4, 'List', 6, 'Demo'] [Text Wrapping Break] print("\nList with the use of Mixed Values: ") [Text Wrapping Break] print(List)List with the use of Numbers: [Text Wrapping Break][1, 2, 4, 4, 3, 3, 3, 6,  5] [Text Wrapping Break] [Text Wrapping Break] List with the use of Mixed Values: [Text Wrapping Break] [1, 2, 'Python', 4, 'List', 6, 'Demo'] [Text Wrapping Break]Adding Elements to a ListUsing append() methodUsing the built-in append() function elements can be added to the List. At a time only one element can be added to the list by the use of append() method, but for the addition of multiple elements with the method, loops are used. Unlike Sets, a new List can be added to an existing one with the use of the append() method.# Python program to demonstrate addition of elements in a List [Text Wrapping Break] [Text Wrapping Break] # Creating a List [Text Wrapping Break] List = [] [Text Wrapping Break] print("Initial blank List: ") [Text Wrapping Break] print(List) [Text Wrapping Break] [Text Wrapping Break] #  Addition of Elements  [Text Wrapping Break] # in the List [Text Wrapping Break] List.append(1) [Text Wrapping Break] List.append (2) [Text Wrapping Break] List.append(4) [Text Wrapping Break] print("\nList after Addition of Three elements: ") [Text Wrapping Break] print(List) [Text Wrapping Break] [Text Wrapping Break]# Adding elements to the List [Text Wrapping Break] # using Iterator [Text Wrapping Break] for i in range(1, 4): [Text Wrapping Break] List.append(i) [Text Wrapping Break] print("\nList after Addition of elements from 1-3: ") [Text Wrapping Break] print(List) [Text Wrapping Break] [Text Wrapping Break] # Addition of List to a List [Text Wrapping Break] List2 = ['Python', 'List'] [Text Wrapping Break] List.append(List2) [Text Wrapping Break] print ("\nList after Addition of a List: ") [Text Wrapping Break] print(List)Initial blank List: [Text Wrapping Break] [] [Text Wrapping Break]  [Text Wrapping Break] List after Addition of Three elements: [Text Wrapping Break] [1,2,4][Text Wrapping Break][Text Wrapping Break]List after Addition of elements from 1-3: [Text Wrapping Break] [1, 2, 4, 1, 2, 3] [Text Wrapping Break]  [Text Wrapping Break] List after Addition of a List:[Text Wrapping Break] [1, 2,  4, 1, 2, 3, ['Python', 'List']]Using insert() methodAppend() method restricts the addition of elements at the end of the List only. Using the insert() method, elements can be added to the list at your desired position. Unlike append() which requires only one argument, insert() method requires two arguments for defining the position and value of the element to be inserted (position, value).# Python program to demonstrate addition of elements in a List [Text Wrapping Break] [Text Wrapping Break] # Creating a List  [Text Wrapping Break] List = [1,2,3,4] [Text Wrapping Break] print("Initial List: ") [Text Wrapping Break] print(List) [Text Wrapping Break] [Text Wrapping Break] # Addition of Element at  [Text Wrapping Break] # specific Position [Text Wrapping Break] # (using Insert Method) [Text Wrapping Break] List.insert(3, 12) [Text Wrapping Break] List.insert(0, 'Python') [Text Wrapping Break] print("\nList after performing Insert Operation: ") [Text Wrapping Break] print(List)Initial List: [Text Wrapping Break] [1, 2, 3, 4] [Text Wrapping Break] [Text Wrapping Break] List after performing Insert Operation: [Text Wrapping Break] [ 'Python', 1, 2, 3, 12, 4]Using extend() methodApart from append() and insert() methods, there’s another method to add elements by the use of extend() method. This method is used for adding multiple elements to the end of the list at once.# Python program to demonstrate  [Text Wrapping Break] # Addition of elements in a List [Text Wrapping Break] [Text Wrapping Break] # Creating a List  [Text Wrapping Break] List = [1,2,3,4] [Text Wrapping Break] print("Initial List: ") [Text Wrapping Break] print(List) [Text Wrapping Break] [Text Wrapping Break] #  Addition of multiple elements [Text Wrapping Break] # to the List at the end [Text Wrapping Break] # (using Extend Method)  [Text Wrapping Break] List.extend ( [8,  'Python', 'Program']) [Text Wrapping Break] print( "\nList after performing  Extend Operation: " ) [Text Wrapping Break] print(List)Initial List: [Text Wrapping Break] [1, 2, 3, 4] [Text Wrapping Break]  [Text Wrapping Break] List after performing Extend Operation:  [Text Wrapping Break] [1, 2, 3, 4, 8, 'Python', 'Program']Accessing elements from the ListIn order to access the items in a List, the index number is used as reference. The index operator [ ] is used to access the items of a list. The index should be an integer and nested lists are accessed by using nested indexing.# Python program to demonstrate  [Text Wrapping Break] # accessing of element from list [Text Wrapping Break][Text Wrapping Break] # Creating a List with [Text Wrapping Break]# the use of multiple values [Text Wrapping Break] List = ["Access", "List", "Elements"] [Text Wrapping Break]  [Text Wrapping Break]# accessing an element from the  [Text Wrapping Break] # list using index number [Text Wrapping Break] print("Accessing an element from the list") [Text Wrapping Break] print(List[0])  [Text Wrapping Break] print(List[2]) [Text Wrapping Break]  [Text Wrapping Break] # Creating a Multi-Dimensional List [Text Wrapping Break] # (By Nesting a list inside a List) [Text Wrapping Break] List = [['Access', 'List'] , ['Elements']] [Text Wrapping Break]  [Text Wrapping Break] # accessing an element from the [Text Wrapping Break] # Multi-Dimensional List using [Text Wrapping Break] # index number [Text Wrapping Break] print("Accessing an element from a Multi -  Dimensional list") [Text Wrapping Break] print(List[0][1]) [Text Wrapping Break] print(List[1][0])Accessing an element from the list [Text Wrapping Break] Access [Text Wrapping Break] Elements [Text Wrapping Break] [Text Wrapping Break] Accessing an element from a Multi-Dimensional  list [Text Wrapping Break] List [Text Wrapping Break] ElementsNegative indexingIn Python, negative sequence indexing means the representation of positions of the array from the end. Rather than calculating the offset like List[len(List)-3], we can just write it like List[-3]. Here, -1 refers to the last item, -2 refers to the second last item etc. i.e. beginning from the end.List = [1, 2, 'Python', 4, 'Negative', 6, 'Index'] [Text Wrapping Break] [Text Wrapping Break] # Accessing an element using negative indexing [Text Wrapping Break] print("Accessing element using negative indexing") [Text Wrapping Break]  [Text Wrapping Break] # print the last element of list [Text Wrapping Break] print(List[-1]) [Text Wrapping Break] [Text Wrapping Break]# print the third last element of list  [Text Wrapping Break] print(List[-3])Accessing element using negative indexing [Text Wrapping Break] Index [Text Wrapping Break] NegativeRemoving Elements from the ListUsing remove() methodIn Python, using the built-in remove() function, elements can be removed from the List but an Error will arise if the element is not present in the set. Remove() method is only capable of removing one element at a time, to remove a range of elements, an iterator is used. A limitation of this method is that it will only remove the first occurrence of the searched element and would not work if there are multiple occurrences of the searched element.# Python program to demonstrate removal of elements in a List [Text Wrapping Break][Text Wrapping Break] # Creating a List [Text Wrapping Break] List = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] [Text Wrapping Break] print("Initial List: ") [Text Wrapping Break] print(List) [Text Wrapping Break]  [Text Wrapping Break] # Removing elements from List [Text Wrapping Break] # using Remove() method [Text Wrapping Break] List.remove(5) [Text Wrapping Break] List.remove(6) [Text Wrapping Break] print("\nList after removal of two elements: ") [Text Wrapping Break] print(List) [Text Wrapping Break]  [Text Wrapping Break] # Removing elements from List [Text Wrapping Break] # using iterator method [Text Wrapping Break] for i in range(1, 5): [Text Wrapping Break]    List.remove(i) [Text Wrapping Break] print("\nList after removing a range of elements: ") [Text Wrapping Break] print(List)Initial List: [Text Wrapping Break][1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] [Text Wrapping Break][Text Wrapping Break]List after removal of two elements: [Text Wrapping Break][1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12][Text Wrapping Break] [Text Wrapping Break] List after removing a range of elements: [Text Wrapping Break] [7, 8, 9, 10, 11, 12]Using pop() methodIn Python,  we can also remove and return an element from the set using the Pop() function, but it removes  the last element of the set only by default. To remove a specific element from a position of the List, index of the element is passed as an argument to the pop() function.List = [1,2,3,4,5] [Text Wrapping Break] [Text Wrapping Break] # Removing element from the  [Text Wrapping Break] # Set using the pop() method [Text Wrapping Break] List.pop() [Text Wrapping Break] print("\nList after popping an element: ") [Text Wrapping Break] print(List) [Text Wrapping Break]  [Text Wrapping Break] # Removing element at a  [Text Wrapping Break] # specific location from the  [Text Wrapping Break] # Set using the pop() method [Text Wrapping Break] List.pop(2) [Text Wrapping Break] print("\nList after popping a specific element: ") [Text Wrapping Break] print(List)List after popping an element: [Text Wrapping Break] [1, 2, 3, 4]  [Text Wrapping Break][Text Wrapping Break]List after popping a specific  element: [Text Wrapping Break][1, 2, 4]Slicing of a ListAlthough there are several ways to print the whole List with all the elements in Python, there is only one way to print a specific range of elements from the list: by the use of Slice operation. Slice operation is performed on Lists by the use of colon(:). For printing elements from the beginning of the range use [:Index], for printing elements from end use [:-Index], to print elements from a specific index till the end use [Index:], for printing elements within a specific range, use [Start Index: End Index] and to print the entire List by the use of slicing operation, use [:]. Moreover, in order to print entire List in reverse order, use [::-1]. For printing the elements of List from rear end, negative indexes are used.# Python program to demonstrate removal of elements in a List [Text Wrapping Break][Text Wrapping Break]# Creating a List [Text Wrapping Break] List = ['P', 'Y', 'T', 'H','O', 'N', 'P', 'R', 'O', 'G', 'R', 'A', 'M'] [Text Wrapping Break] print("Initial List: ") [Text Wrapping Break] print(List) [Text Wrapping Break]  [Text Wrapping Break] # Print elements of a range [Text Wrapping Break] # using Slice operation [Text Wrapping Break] Sliced_List = List[3:10] [Text Wrapping Break] print("\nSlicing elements in a range 3-10: ") [Text Wrapping Break] print(Sliced_List) [Text Wrapping Break]  [Text Wrapping Break] # Print elements from a  [Text Wrapping Break] # pre-defined point to end [Text Wrapping Break] Sliced_List = List[6:] [Text Wrapping Break] print("\nElements sliced from 6th ""element till the end: ") [Text Wrapping Break] print(Sliced_List) [Text Wrapping Break] [Text Wrapping Break]# Printing elements from [Text Wrapping Break] # beginning till end [Text Wrapping Break] Sliced_List = List[:] [Text Wrapping Break] print("\nPrinting all elements using slice operation: ") [Text Wrapping Break] print(Sliced_List) Initial List: [Text Wrapping Break] ['P', 'Y', 'T', 'H','O', 'N', 'P', 'R', 'O', 'G', 'R', 'A', 'M'] [Text Wrapping Break] [Text Wrapping Break] Slicing elements in a range 3-10: [Text Wrapping Break]['H', 'O', 'N', 'P', 'R','O','G'][Text Wrapping Break][Text Wrapping Break]Elements sliced from 6th element till the end: [Text Wrapping Break]['P', 'R', 'O', 'G', 'R', 'A', 'M'][Text Wrapping Break][Text Wrapping Break]Printing all elements using slice operation: [Text Wrapping Break] ['P', 'Y', 'T', 'H', 'O', 'N', 'P', 'R', 'O', 'G', 'R', 'A', 'M']Negative index List Slicing# Creating a List [Text Wrapping Break] List = ['P', 'Y', 'T', 'H','O', 'N', 'P', 'R', 'O', 'G', 'R', 'A', 'M'] [Text Wrapping Break] print("Initial List: ") [Text Wrapping Break] print(List) [Text Wrapping Break]  [Text Wrapping Break] # Print elements from beginning [Text Wrapping Break] # to a pre-defined point using Slice [Text Wrapping Break] Sliced_List = List[:-7] [Text Wrapping Break] print("\nElements sliced till 7th element from last: ") [Text Wrapping Break] print(Sliced_List) [Text Wrapping Break]  [Text Wrapping Break] #  Print elements of a range [Text Wrapping Break] # using negative index List slicing [Text Wrapping Break] Sliced_List = List[-6:-1] [Text Wrapping Break] print("\nElements sliced from index -6 to -1") [Text Wrapping Break] print(Sliced_List) [Text Wrapping Break]  [Text Wrapping Break] #  Printing elements in reverse [Text Wrapping Break] # using Slice operation [Text Wrapping Break] Sliced_List = List[::-1] [Text Wrapping Break] print("\nPrinting List in reverse: ") [Text Wrapping Break] print(Sliced_List)Initial List: [Text Wrapping Break] ['P', 'Y', 'T', 'H','O', 'N', 'P', 'R', 'O', 'G', 'R', 'A', 'M'][Text Wrapping Break] [Text Wrapping Break]Elements sliced till 7th element from last: [Text Wrapping Break]['P', 'Y', 'T', 'H', 'O', 'N'][Text Wrapping Break] [Text Wrapping Break]Elements sliced from index -6 to -1[Text Wrapping Break] ['R', 'O', 'G', 'R', 'A'][Text Wrapping Break] [Text Wrapping Break]Printing List in reverse:[Text Wrapping Break]['M', 'A', 'R', 'G', 'O', 'R', 'P', 'N', 'O', 'H', 'T', 'Y', 'P']Updating ListsYou can update single or multiple elements of lists by reassigning the values on the lists individually.# Python program to update elements of a list[Text Wrapping Break]list = ['physics', 'chemistry', 1998, 2019];[Text Wrapping Break] print "Value available at index 2 : "[Text Wrapping Break] print list[2] [Text Wrapping Break] list[2] = 2000;[Text Wrapping Break] print "New value available at index 2 : "[Text Wrapping Break] print list[2]Value available at index 2 : [Text Wrapping Break] 1998 [Text Wrapping Break] New value available at index 2 : [Text Wrapping Break] 2000Built-in functionsFUNCTION DESCRIPTIONsum() Addsall numbers.ord() Used for returning an integer which represents the unique Unicode code point of the given Unicode character.cmp() If the first list is “greater” than the second list, the function returns 1.max() It returns the largest element in the list.min() It returns the smallest element in the list.all() It returns true if all elements are true or false if any element in the list is empty.any() It returns true if even one of the elements of the list is true. If one list is empty, it returns false.len() It returns length the list.enumerate() It adds a counter to an enumerate object that can be used directly for loops.accumulate() It makes an iterator that gives the result of a function. It takes a function as an argument.filter() It can individually check if every element is true or not.map() It applies a particular function to each item of an iterable and shows a list of the results.lambda() It is an anonymous function that behaves like a normal function in regard to arguments. While normal functions are defined with def keyword, anonymous functions are defined using lambda keyword.List MethodsFUNCTION DESCRIPTIONAppend() Adds an element at the end of the listExtend() Adds all elements of one list to another listInsert() Inserts an item at a desired indexRemove() Removes an item from the listPop() Removes and returns an element at a desired indexClear() Removes all elements from the listIndex() Returns the index of the first identical itemCount() Returns the number of items passed as argumentSort() Sort items of a list in ascending orderReverse() Reverses the listcopy() Returns a copy of the listSummaryIn this article, we have covered the concept of Lists in Python. You have learned the basics of creating a List, adding value to it, accessing its elements, removing the elements, and various other operations. We have also covered some basic built-in functions of Python and several other methods along with their functions. To gain more knowledge about Python tips and tricks, check our Python tutorial and get a good hold over coding in Python by joining the Python certification course.
5083
What are List Methods in Python

Sequence is one of the most basic data types in Py... Read More