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How To Become a Data Analyst

With the increase in the generation of data, Data Analysis has become one of the major functions in any organization. Since the past few years, the job of a ‘Data Analyst’ has evolved immensely and is considered to be one of the most sought-after roles after ‘Data Scientist’.  However, there are several questions that you as an aspiring data analyst may have. What is Data Analytics? What are the roles and responsibilities of a Data Analyst? How does one become a Data Analyst and what are the skills required? And many more. Our primary focus, in this article, will be to answer all these questions and take you a step further towards your dream of becoming a Data Analyst.Let us first get a clear understanding of what Data Analytics is.What is Data Analytics?In layman’s terms, the term ‘analyze’ means to examine something in a systematic manner to gain meaningful insights from it. Data Analytics or Data Analysis refers to the process of analyzing the raw data to gather useful information. This data can be in the form of some corporate information, product innovations, or market trends.Let us understand this more precisely with an example. We can compare Data Analytics with a jigsaw puzzle. The first thing is to collect all the pieces of the puzzle and fit them correctly to reveal the final picture. Similarly, in the process of data analytics, we collect the raw data from several sources, analyze it and transform it into some meaningful information that can be interpreted by humans.  Thus, we can define Data Analysis as the process that helps to discover new and significant patterns by cleaning, summarizing, transforming, and modeling data which later can be used to make informative decisions. The collected data can be of any of the three forms – structured, semi-structured, or unstructured and can be represented visually in the form of graphs and charts. The visualized information enhances the precision and allows an individual to have a clearer view of the final analysis.Organizations sniff around to recruit individuals who can perform the task of converting raw data into useful information which in turn helps in their business growth. There are numerous job roles in this field and out of all these, a Data Analyst’s career journey is the most satisfying and amazing.Now, let us understand the role and responsibilities of a Data Analyst in the field of Data Science.What is the role of a Data Analyst?Organizations, in recent times, with the help of huge chunks of data often try to optimize their strategies for efficient business growth. In order to derive useful information from this massive collection of data, they require a highly qualified professional who can make sense of the data and help others understand. That is where a Data Analyst comes in.  A Data Analyst collects, processes, and performs analysis of these large amounts of data. Every business organization, be it small or big generates and collects data which can be in the form of accounts, logistics, marketing research, customer feedback, etc.  A Data Analyst processes the data and generates significant indicators useful for decision making depending upon the customers, or the products, or the performance of the company. These indicators help companies to decide what products should be offered to their customers, what type of marketing strategy is to be implemented, how to reduce transportation costs, or what changes are to be made to enhance the process of production.Mostly data analysts collaborate with IT teams, the management, or data scientists to mine, clean, and then analyze and interpret information with the help of statistical tools. Their prime focus is to determine trends, correlations, and patterns in large and complex data sets which in turn allows companies to identify new ways for process improvement.  Let us now understand the basic responsibilities of a Data Analyst.What are the responsibilities of a Data Analyst?The first step towards becoming a Data Analyst is to understand the numerous responsibilities they need to undertake in their journey. Some of the most common responsibilities are as follows:Understanding the GoalThe first and foremost task of a Data Analyst is to identify the goal of the organization by evaluating the resources, understanding the business problem, and then collecting the proper data.QueryingData Analysts also write SQL queries in order to collect, store, and derive information from databases like Microsoft SQL Server, Oracle, and MySQL.Data MiningData Analysts mine data from multiple sources and structure them to build data models which help in enhancing the system’s efficiency.Data TestingWith the help of analytical and statistical techniques, Data Analysts perform a logical examination of data.Interpretation of Data TrendsUsing different libraries and packages, Data Analysts identify trends and patterns from complex data thereby providing important insights to the organization.Preparation of ReportsThe leading teams of the organization are able to make timely decisions with the help of the summary reports prepared by Data Analysts. They perform this task using Data Visualization tools like Tableau or Google Charts, etc.Let us now take a look at the most popular industries that hire Data Analysts.What are the top industries hiring Data Analysts?There are around 82 thousand job openings worldwide in 2021 that require skills in data analysis, but there is a huge shortage of data talent. Almost every industry requires data to be analyzed and data jobs are diverging into a variety of fields.  The top industries that hire data analyst are as follows:Business Intelligence It is one of the leading industries that hire data analysts and according to a survey by Indeed, 20% of Data Analysts are from this sector. The most posted job vacancies for data analysts in the US and Europe are primarily from the Business Intelligence sector.Finance It is one of the earliest industries to be associated with data science and takes advantage of big data to make business ventures more efficient. Finance organizations such as investment banks, consumer banks, capital firms are responsible for generating a large number of data analytics jobs.Healthcare It is one of those industries that were dependent on paper data systems for many centuries. However, the importance of Data Analysis in this field is growing at a good pace and it is certain that they benefit the most. Most, Data Analysts in this sector are termed healthcare data analysts.Sharing-based economyThis industry has blossomed in recent years. Nearly every urban area be it small or big hires data talent. eBay is considered to be the first global marketplace that successfully launched the service economy services. Data analytics can be a game changer in this field.EntertainmentThis sector is evolving very fast. Global streaming networks like Netflix and Amazon are major players in this market and are using data insights to boost their growth. Data Analysts along with messaging analytics engineers or marketing intelligence analysts are very common positions in this industry. What are the technical skills required to master as a Data AnalystsThe most essential task of the data analyst is to parse through a good quantity of raw information and then develop meaningful insights in the entire process. The other tasks also include removing corrupted data, understanding the quality of data, and preparing various reports. All of these tasks involve knowledge of certain technologies and technical skills. Let us focus on a few of them.1. Data VisualizationData Visualizations revolve around a person’s ability to present data findings via graphics or other illustrations. It allows a data analyst to understand data-driven insights and helps the business decision-makers (who may lack advanced analytical training) to identify patterns and understand complex ideas at a glance.Data visualization may even allow you to accomplish more than data analysts traditionally have. As one writer for SAS Insights notes, “Data visualization is going to change the way our analysts work with data. They’re going to be expected to respond to issues more rapidly. And they’ll need to be able to dig for more insights — look at data differently, more imaginatively. Data visualization will promote creative data exploration.”Already, data visualization has become a necessary skill. According to a recent study conducted by LinkedIn Learning, “recent graduates are much more likely to learn hard skills when they first enter the workforce. And these hard skills revolve around analyzing data and telling stories with insights gleaned from the data.” Get yourself enrolled for the Data Visualization course offered by KnowledgeHut.2. Data CleaningIt is believed that cleaning is an invaluable part of achieving success. Similarly, data cleaning is one of the most critical steps in assembling a functional machine learning model and consumes a good amount of time in a data analyst’s day. Any uncleaned data may result in misleading patterns and incorrect conclusions. However, a thoroughly cleaned dataset is capable of generating remarkable insights. Data Analysts should necessarily have proper data cleaning skills.3. RR is one of the most pervasive and well-used languages in data analytics. The structure and syntax were specifically created in order to support analytical work. It comprises several built-in, easy-to-use commands. R can easily handle large and complex quantities of data. As an aspiring data analyst, considering the popularity and functionality of R, it is very essential to learn R. Learn more about R programming language from the course offered by KnowledgeHut.4. PythonPython is among the most popular programming languages for data analysis. It is an essential language to be learnt by would-be analysts. It offers a large number of specialized libraries and built-in functions.  Python is a cross-functional, maximally interpreted language that has lots of advantages to offer. It is easy to learn, well supported, flexible- a fantastic option for data processing, scalable, and has a huge collection of libraries. Python Certification course offered by KnowledgeHut will assist you in mastering the concepts of Python and its libraries like SciPy, Matplotlib, Scikit-Learn, Pandas, NumPy, Lambda functions, and Web Scraping. You will also learn how to write Python Programming for Data Analytics.5. Linear Algebra and CalculusIn data analytics, one thing that is non-negotiable is having advanced mathematical skills. Some data analysts even choose to major in mathematics or statistics during their undergraduate years just to gain a better understanding of the theory that underpins real-world analytical practice!  Two specific fields of mathematical study rise to the forefront in analytics: linear algebra and calculus. Linear algebra has applications in machine and deep learning, where it supports vector, matrix, and tensor operations. Calculus is similarly used to build the objective/cost/loss functions that teach algorithms to achieve their objectives.  6. Microsoft ExcelWhile Excel is a great application to learn, it must be noted that the operations Excel can perform, other programming languages like R and Python can perform much faster. Excel is clunky in comparison to other platforms. However, Spreadsheets are still relevant and a great tool to learn about data. While it’s not the only or most fitting solution for all data projects, but it remains a reliable and affordable tool for analytics. It’s a foundational structure for intelligent data because it deepens your understanding of the analytics process. Many industries and businesses continue to emphasize the importance of Excel skills because it remains an intelligent way to extract actionable insights. Revenue patterns, operations, marketing trends, and more can be analyzed through Excel spreadsheets, but the real advantage is the process.7. Critical ThinkingIt’s not enough to simply look at data; you need to understand it and expand its implications beyond the numbers alone. As a critical thinker, you can think analytically about data, identifying patterns and extracting actionable insights from the information you have at hand. It requires you to go above and beyond and apply yourself to thinking, as opposed to only processing.8. CommunicationAt the end of the day, you need to be able to explain your findings to others. It doesn’t matter if you’re the most talented, insightful data analyst on the planet — if you can’t communicate the patterns you see to those without technical expertise, you’ve fallen short.  Being a good data analyst effectively means becoming “bilingual.” You should have the capability to address highly technical points with your trained peers, as well as provide clear, high-level explanations in a way that supports — rather than confuses — business-centered decision-makers. If you can’t do so, you may still need to build your skill set as a data analyst.What are the Data Analysts’ salaries around the world?According to a report by Forbes, around 92 percent of organizations worldwide gain effective marketing insights by analyzing data. As an individual in the technical field, becoming a Data Analyst is a pretty amazing career opportunity.  In recent times, every business organization extracts information from sales or marketing campaigns and uses this data to gather insights. These insights allow the business to answer questions like what worked well, what did not, and what to do differently in the future. Thus, businesses can make more informed decisions with the right and organized data.  The salaries of Data Analysts depend on several factors like which industry they are working in, how many years of experience they have, what is the size of the organization, and so on. However, one big advantage of being a Data Analyst is they are always in demand globally and if you get bored of working in a particular city or a particular country, you always have the option of moving somewhere else because of the freedom and flexibility that this role offers.  Let us now look at the highest paying countries and their average annual salary of a Data Analyst:CountryAverage Annual SalaryIndiaThe average annual Data Analyst salary in India is over INR 4,45,000USAThe average annual Data Analyst salary in the USA is around USD 65,000GermanyThe average annual Data Analyst salary in Germany is around €44,330United KingdomThe average annual Data Analyst salary in the UK is around £26933CanadaThe average annual Data Analyst salary in Canada is around CAD 55000AustraliaThe average annual Data Analyst salary in Australia is over AUD 82,000DenmarkThe average annual Data Analyst salary in Denmark is around DKK 881,794SingaporeThe average annual Data Analyst salary in Singapore is around SGD 55,000What factors affect the salary of a Data Analyst in India?According to Payscale, around 78 percent of Data Analysts in India have a salary ranging between 0 – 6 Lakhs. A Data Analyst in India with experience between 1 – 4 years has net earnings of around 3,96,125 INR. On the other hand, an individual with experience of 5 – 9 years makes up to 6,00,000 INR per annum and someone with more experience than that can earn up to 9 Lakhs INR per annum. However, there are several factors that are also associated while deciding the salary of a Data Analyst.Every company, big or small, around the world now considers data analytics as an important sector and looks upon its potential to change the market trends. The decision-making authorities of the companies are focusing more on technology and people.  Now, let us understand what are the significant factors that affect the salary of a Data Analyst in India.1. Based on ExperienceAccording to a survey by Zippia, an entry-level Data Analyst in the USA having a bachelor’s degree and 2 years of experience, has an average annual salary of $54,000. A couple more years of experience can help them earn up to $70,000. A senior analyst gets an annual salary of $88,000 with experience of 6 years. However, someone with a specialization in the field can get a salary of around $100,000.Let’s see how experience affects the salary of a Data Analyst in India:The average annual salary of an Entry-Level Data Analyst in India is ₹325,616.The average annual salary of a mid-Level Data Analyst in India is ₹635,379The average annual salary of an experienced Data Analyst in India is ₹852,516.2. Based on IndustrySince every industry around the world recruits Data Analysts, there has been a significant increase in individuals who are choosing this career path, which in turn adds a lot of value to this field.  In an organization, the Data Analysts are directly responsible for some of the decision-making process and they perform this task with the help of the analyzed data using statistical tools like Excel, Tableau, and SQL. The progress impacts the salaries of these Data Analysts, which range between $54,000 to $70,000 for entry-level professionals.  Financial accounting companies hire financial analysts to predict the company’s performance and study the macro and microeconomic trends. The analysts in this industry are responsible for creating economic models and forecasts using the data. In 2017, Robert Half made a survey on the salary of entry-level financial analysts. The survey showed that their average annual salary ranges between $52,700 to $66,000.Source: Data Analysts  Salary Trends in India By IndustryMarketing research analysts use sales data, customer surveys, and competitor research to optimize the targeting and positioning efforts of their products. This industry has a pay scale ranging from $51,000 to $65,000 at the entry-level.Similarly, the Data Analysts working in the healthcare industry whose job is to maintain the daily administrative advancements and operations get an average annual salary of $46,000 to $80,000.3. Based on LocationThe number of Data Analysts and the average annual data salary in India is the highest in the Silicon Valley of India, that is Bangalore.Source: Data Analysts  Salary Trends in India By LocationBangalore, Pune, and Gurgaon offer 19.2%, 9.8%, and 9.5% more than the average annual salary in India respectively. On the other hand, Data Analysts working in Mumbai get 5.2% lesser than the national average. Hyderabad and New Delhi receive 4.85 and 2.8% lesser than the national average respectively.4. Based on CompanyThe top recruiters of Data Analysts in India are tech giants like Tata Consultancy Services, Accenture, and Earnest & Young whereas, according to reports, salaries offered are highest at HSBC which is around 7 Lakhs.Source: Data Analyst Salary Based on Company5. Based on SkillsSkill is an important factor while deciding the salary of a Data Analyst in India. You need to go beyond the qualifications of a Master’s degree and gather more knowledge of the respective languages and software. Some useful insights are as follows:The most important skill is to have a clear understanding of Python. A python programmer in India alone earns around 10 Lakhs per annum.  There is an increase of around 25 percent in the salary of a Data Analyst in India when you get familiar with Big Data and Data Science.  Experts in Statistical Package for Social Sciences or SPSS get an average salary of 7.3  Lakhs whereas experts in Statistical Analysis Software or SAS have an earning of around 9 Lakhs to 10.8 Lakhs.A Machine Learning expert in India alone can earn around 17 Lakhs per year. Along with being a Data Analyst, if you also have Machine Learning and Python skills, you can reach the highest pay in this field.How KnowledgeHut can helpAll these free resources are a great place to start your Data Analytics journey. Beside these there are many other free resources on the internet, but they may not be organized and may not have a structured approach.  This is where KnowledgeHut can make a difference and serve as a one stop shop alternative with its comprehensive Instructor-led live classes. The courses are taught by Industry experts and are perfect for aspirants who wish to become Data Analyst.Links for some of the popular courses by KnowledgeHut are appended below-Big Data Analytics CertificationR Programming Language TrainingPython Certification TrainingData Visualization with Tableau TrainingIn this article we attempt to understand about Data Analytics and the major roles of a Data Analyst. We also learnt about the responsibilities of a Data Analyst, the various industries offering jobs to Data Analysts and also the technical skills required to master to be a Data Analyst.  If you are inspired by the opportunities provided by Data Analytics, enroll in our  Data Analytics Courses for more lucrative career options in this field.
How To Become a Data Analyst
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

How To Become a Data Analyst

With the increase in the generation of data, Data Analysis has become one of the major functions in any organization. Since the past few years, the job of a ‘Data Analyst’ has evolved immensely and is considered to be one of the most sought-after roles after ‘Data Scientist’.  However, there are several questions that you as an aspiring data analyst may have. What is Data Analytics? What are the roles and responsibilities of a Data Analyst? How does one become a Data Analyst and what are the skills required? And many more. Our primary focus, in this article, will be to answer all these questions and take you a step further towards your dream of becoming a Data Analyst.Let us first get a clear understanding of what Data Analytics is.What is Data Analytics?In layman’s terms, the term ‘analyze’ means to examine something in a systematic manner to gain meaningful insights from it. Data Analytics or Data Analysis refers to the process of analyzing the raw data to gather useful information. This data can be in the form of some corporate information, product innovations, or market trends.Let us understand this more precisely with an example. We can compare Data Analytics with a jigsaw puzzle. The first thing is to collect all the pieces of the puzzle and fit them correctly to reveal the final picture. Similarly, in the process of data analytics, we collect the raw data from several sources, analyze it and transform it into some meaningful information that can be interpreted by humans.  Thus, we can define Data Analysis as the process that helps to discover new and significant patterns by cleaning, summarizing, transforming, and modeling data which later can be used to make informative decisions. The collected data can be of any of the three forms – structured, semi-structured, or unstructured and can be represented visually in the form of graphs and charts. The visualized information enhances the precision and allows an individual to have a clearer view of the final analysis.Organizations sniff around to recruit individuals who can perform the task of converting raw data into useful information which in turn helps in their business growth. There are numerous job roles in this field and out of all these, a Data Analyst’s career journey is the most satisfying and amazing.Now, let us understand the role and responsibilities of a Data Analyst in the field of Data Science.What is the role of a Data Analyst?Organizations, in recent times, with the help of huge chunks of data often try to optimize their strategies for efficient business growth. In order to derive useful information from this massive collection of data, they require a highly qualified professional who can make sense of the data and help others understand. That is where a Data Analyst comes in.  A Data Analyst collects, processes, and performs analysis of these large amounts of data. Every business organization, be it small or big generates and collects data which can be in the form of accounts, logistics, marketing research, customer feedback, etc.  A Data Analyst processes the data and generates significant indicators useful for decision making depending upon the customers, or the products, or the performance of the company. These indicators help companies to decide what products should be offered to their customers, what type of marketing strategy is to be implemented, how to reduce transportation costs, or what changes are to be made to enhance the process of production.Mostly data analysts collaborate with IT teams, the management, or data scientists to mine, clean, and then analyze and interpret information with the help of statistical tools. Their prime focus is to determine trends, correlations, and patterns in large and complex data sets which in turn allows companies to identify new ways for process improvement.  Let us now understand the basic responsibilities of a Data Analyst.What are the responsibilities of a Data Analyst?The first step towards becoming a Data Analyst is to understand the numerous responsibilities they need to undertake in their journey. Some of the most common responsibilities are as follows:Understanding the GoalThe first and foremost task of a Data Analyst is to identify the goal of the organization by evaluating the resources, understanding the business problem, and then collecting the proper data.QueryingData Analysts also write SQL queries in order to collect, store, and derive information from databases like Microsoft SQL Server, Oracle, and MySQL.Data MiningData Analysts mine data from multiple sources and structure them to build data models which help in enhancing the system’s efficiency.Data TestingWith the help of analytical and statistical techniques, Data Analysts perform a logical examination of data.Interpretation of Data TrendsUsing different libraries and packages, Data Analysts identify trends and patterns from complex data thereby providing important insights to the organization.Preparation of ReportsThe leading teams of the organization are able to make timely decisions with the help of the summary reports prepared by Data Analysts. They perform this task using Data Visualization tools like Tableau or Google Charts, etc.Let us now take a look at the most popular industries that hire Data Analysts.What are the top industries hiring Data Analysts?There are around 82 thousand job openings worldwide in 2021 that require skills in data analysis, but there is a huge shortage of data talent. Almost every industry requires data to be analyzed and data jobs are diverging into a variety of fields.  The top industries that hire data analyst are as follows:Business Intelligence It is one of the leading industries that hire data analysts and according to a survey by Indeed, 20% of Data Analysts are from this sector. The most posted job vacancies for data analysts in the US and Europe are primarily from the Business Intelligence sector.Finance It is one of the earliest industries to be associated with data science and takes advantage of big data to make business ventures more efficient. Finance organizations such as investment banks, consumer banks, capital firms are responsible for generating a large number of data analytics jobs.Healthcare It is one of those industries that were dependent on paper data systems for many centuries. However, the importance of Data Analysis in this field is growing at a good pace and it is certain that they benefit the most. Most, Data Analysts in this sector are termed healthcare data analysts.Sharing-based economyThis industry has blossomed in recent years. Nearly every urban area be it small or big hires data talent. eBay is considered to be the first global marketplace that successfully launched the service economy services. Data analytics can be a game changer in this field.EntertainmentThis sector is evolving very fast. Global streaming networks like Netflix and Amazon are major players in this market and are using data insights to boost their growth. Data Analysts along with messaging analytics engineers or marketing intelligence analysts are very common positions in this industry. What are the technical skills required to master as a Data AnalystsThe most essential task of the data analyst is to parse through a good quantity of raw information and then develop meaningful insights in the entire process. The other tasks also include removing corrupted data, understanding the quality of data, and preparing various reports. All of these tasks involve knowledge of certain technologies and technical skills. Let us focus on a few of them.1. Data VisualizationData Visualizations revolve around a person’s ability to present data findings via graphics or other illustrations. It allows a data analyst to understand data-driven insights and helps the business decision-makers (who may lack advanced analytical training) to identify patterns and understand complex ideas at a glance.Data visualization may even allow you to accomplish more than data analysts traditionally have. As one writer for SAS Insights notes, “Data visualization is going to change the way our analysts work with data. They’re going to be expected to respond to issues more rapidly. And they’ll need to be able to dig for more insights — look at data differently, more imaginatively. Data visualization will promote creative data exploration.”Already, data visualization has become a necessary skill. According to a recent study conducted by LinkedIn Learning, “recent graduates are much more likely to learn hard skills when they first enter the workforce. And these hard skills revolve around analyzing data and telling stories with insights gleaned from the data.” Get yourself enrolled for the Data Visualization course offered by KnowledgeHut.2. Data CleaningIt is believed that cleaning is an invaluable part of achieving success. Similarly, data cleaning is one of the most critical steps in assembling a functional machine learning model and consumes a good amount of time in a data analyst’s day. Any uncleaned data may result in misleading patterns and incorrect conclusions. However, a thoroughly cleaned dataset is capable of generating remarkable insights. Data Analysts should necessarily have proper data cleaning skills.3. RR is one of the most pervasive and well-used languages in data analytics. The structure and syntax were specifically created in order to support analytical work. It comprises several built-in, easy-to-use commands. R can easily handle large and complex quantities of data. As an aspiring data analyst, considering the popularity and functionality of R, it is very essential to learn R. Learn more about R programming language from the course offered by KnowledgeHut.4. PythonPython is among the most popular programming languages for data analysis. It is an essential language to be learnt by would-be analysts. It offers a large number of specialized libraries and built-in functions.  Python is a cross-functional, maximally interpreted language that has lots of advantages to offer. It is easy to learn, well supported, flexible- a fantastic option for data processing, scalable, and has a huge collection of libraries. Python Certification course offered by KnowledgeHut will assist you in mastering the concepts of Python and its libraries like SciPy, Matplotlib, Scikit-Learn, Pandas, NumPy, Lambda functions, and Web Scraping. You will also learn how to write Python Programming for Data Analytics.5. Linear Algebra and CalculusIn data analytics, one thing that is non-negotiable is having advanced mathematical skills. Some data analysts even choose to major in mathematics or statistics during their undergraduate years just to gain a better understanding of the theory that underpins real-world analytical practice!  Two specific fields of mathematical study rise to the forefront in analytics: linear algebra and calculus. Linear algebra has applications in machine and deep learning, where it supports vector, matrix, and tensor operations. Calculus is similarly used to build the objective/cost/loss functions that teach algorithms to achieve their objectives.  6. Microsoft ExcelWhile Excel is a great application to learn, it must be noted that the operations Excel can perform, other programming languages like R and Python can perform much faster. Excel is clunky in comparison to other platforms. However, Spreadsheets are still relevant and a great tool to learn about data. While it’s not the only or most fitting solution for all data projects, but it remains a reliable and affordable tool for analytics. It’s a foundational structure for intelligent data because it deepens your understanding of the analytics process. Many industries and businesses continue to emphasize the importance of Excel skills because it remains an intelligent way to extract actionable insights. Revenue patterns, operations, marketing trends, and more can be analyzed through Excel spreadsheets, but the real advantage is the process.7. Critical ThinkingIt’s not enough to simply look at data; you need to understand it and expand its implications beyond the numbers alone. As a critical thinker, you can think analytically about data, identifying patterns and extracting actionable insights from the information you have at hand. It requires you to go above and beyond and apply yourself to thinking, as opposed to only processing.8. CommunicationAt the end of the day, you need to be able to explain your findings to others. It doesn’t matter if you’re the most talented, insightful data analyst on the planet — if you can’t communicate the patterns you see to those without technical expertise, you’ve fallen short.  Being a good data analyst effectively means becoming “bilingual.” You should have the capability to address highly technical points with your trained peers, as well as provide clear, high-level explanations in a way that supports — rather than confuses — business-centered decision-makers. If you can’t do so, you may still need to build your skill set as a data analyst.What are the Data Analysts’ salaries around the world?According to a report by Forbes, around 92 percent of organizations worldwide gain effective marketing insights by analyzing data. As an individual in the technical field, becoming a Data Analyst is a pretty amazing career opportunity.  In recent times, every business organization extracts information from sales or marketing campaigns and uses this data to gather insights. These insights allow the business to answer questions like what worked well, what did not, and what to do differently in the future. Thus, businesses can make more informed decisions with the right and organized data.  The salaries of Data Analysts depend on several factors like which industry they are working in, how many years of experience they have, what is the size of the organization, and so on. However, one big advantage of being a Data Analyst is they are always in demand globally and if you get bored of working in a particular city or a particular country, you always have the option of moving somewhere else because of the freedom and flexibility that this role offers.  Let us now look at the highest paying countries and their average annual salary of a Data Analyst:CountryAverage Annual SalaryIndiaThe average annual Data Analyst salary in India is over INR 4,45,000USAThe average annual Data Analyst salary in the USA is around USD 65,000GermanyThe average annual Data Analyst salary in Germany is around €44,330United KingdomThe average annual Data Analyst salary in the UK is around £26933CanadaThe average annual Data Analyst salary in Canada is around CAD 55000AustraliaThe average annual Data Analyst salary in Australia is over AUD 82,000DenmarkThe average annual Data Analyst salary in Denmark is around DKK 881,794SingaporeThe average annual Data Analyst salary in Singapore is around SGD 55,000What factors affect the salary of a Data Analyst in India?According to Payscale, around 78 percent of Data Analysts in India have a salary ranging between 0 – 6 Lakhs. A Data Analyst in India with experience between 1 – 4 years has net earnings of around 3,96,125 INR. On the other hand, an individual with experience of 5 – 9 years makes up to 6,00,000 INR per annum and someone with more experience than that can earn up to 9 Lakhs INR per annum. However, there are several factors that are also associated while deciding the salary of a Data Analyst.Every company, big or small, around the world now considers data analytics as an important sector and looks upon its potential to change the market trends. The decision-making authorities of the companies are focusing more on technology and people.  Now, let us understand what are the significant factors that affect the salary of a Data Analyst in India.1. Based on ExperienceAccording to a survey by Zippia, an entry-level Data Analyst in the USA having a bachelor’s degree and 2 years of experience, has an average annual salary of $54,000. A couple more years of experience can help them earn up to $70,000. A senior analyst gets an annual salary of $88,000 with experience of 6 years. However, someone with a specialization in the field can get a salary of around $100,000.Let’s see how experience affects the salary of a Data Analyst in India:The average annual salary of an Entry-Level Data Analyst in India is ₹325,616.The average annual salary of a mid-Level Data Analyst in India is ₹635,379The average annual salary of an experienced Data Analyst in India is ₹852,516.2. Based on IndustrySince every industry around the world recruits Data Analysts, there has been a significant increase in individuals who are choosing this career path, which in turn adds a lot of value to this field.  In an organization, the Data Analysts are directly responsible for some of the decision-making process and they perform this task with the help of the analyzed data using statistical tools like Excel, Tableau, and SQL. The progress impacts the salaries of these Data Analysts, which range between $54,000 to $70,000 for entry-level professionals.  Financial accounting companies hire financial analysts to predict the company’s performance and study the macro and microeconomic trends. The analysts in this industry are responsible for creating economic models and forecasts using the data. In 2017, Robert Half made a survey on the salary of entry-level financial analysts. The survey showed that their average annual salary ranges between $52,700 to $66,000.Source: Data Analysts  Salary Trends in India By IndustryMarketing research analysts use sales data, customer surveys, and competitor research to optimize the targeting and positioning efforts of their products. This industry has a pay scale ranging from $51,000 to $65,000 at the entry-level.Similarly, the Data Analysts working in the healthcare industry whose job is to maintain the daily administrative advancements and operations get an average annual salary of $46,000 to $80,000.3. Based on LocationThe number of Data Analysts and the average annual data salary in India is the highest in the Silicon Valley of India, that is Bangalore.Source: Data Analysts  Salary Trends in India By LocationBangalore, Pune, and Gurgaon offer 19.2%, 9.8%, and 9.5% more than the average annual salary in India respectively. On the other hand, Data Analysts working in Mumbai get 5.2% lesser than the national average. Hyderabad and New Delhi receive 4.85 and 2.8% lesser than the national average respectively.4. Based on CompanyThe top recruiters of Data Analysts in India are tech giants like Tata Consultancy Services, Accenture, and Earnest & Young whereas, according to reports, salaries offered are highest at HSBC which is around 7 Lakhs.Source: Data Analyst Salary Based on Company5. Based on SkillsSkill is an important factor while deciding the salary of a Data Analyst in India. You need to go beyond the qualifications of a Master’s degree and gather more knowledge of the respective languages and software. Some useful insights are as follows:The most important skill is to have a clear understanding of Python. A python programmer in India alone earns around 10 Lakhs per annum.  There is an increase of around 25 percent in the salary of a Data Analyst in India when you get familiar with Big Data and Data Science.  Experts in Statistical Package for Social Sciences or SPSS get an average salary of 7.3  Lakhs whereas experts in Statistical Analysis Software or SAS have an earning of around 9 Lakhs to 10.8 Lakhs.A Machine Learning expert in India alone can earn around 17 Lakhs per year. Along with being a Data Analyst, if you also have Machine Learning and Python skills, you can reach the highest pay in this field.How KnowledgeHut can helpAll these free resources are a great place to start your Data Analytics journey. Beside these there are many other free resources on the internet, but they may not be organized and may not have a structured approach.  This is where KnowledgeHut can make a difference and serve as a one stop shop alternative with its comprehensive Instructor-led live classes. The courses are taught by Industry experts and are perfect for aspirants who wish to become Data Analyst.Links for some of the popular courses by KnowledgeHut are appended below-Big Data Analytics CertificationR Programming Language TrainingPython Certification TrainingData Visualization with Tableau TrainingIn this article we attempt to understand about Data Analytics and the major roles of a Data Analyst. We also learnt about the responsibilities of a Data Analyst, the various industries offering jobs to Data Analysts and also the technical skills required to master to be a Data Analyst.  If you are inspired by the opportunities provided by Data Analytics, enroll in our  Data Analytics Courses for more lucrative career options in this field.
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How To Become a Data Analyst

With the increase in the generation of data, Data ... Read More

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$76,884ConclusionLet 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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Top Job Roles With Their Salary Data in the World ...

Data Science requires the expertise of professio... Read More

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).ConclusionThe .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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The self variable in Python explained with Python ...

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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