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Learn More About Python

Python is a general-purpose programming language used to develop applications, analyze and visualize data, create machine learning algorithms, automate tasks, and much more. Initially released in 1991 by Guido van Rossum, Python is open-source, emphasizing readable and efficient code while being flexible and scalable.

Through various frameworks and libraries, Python has extensive applications in areas such as data science, software development, machine learning, and scripting. Due to its flexibility and efficiency, Python is the “most wanted” programming language, according to the most recent survey by Stack Overflow.

Python for Data Science

Python is the most commonly used programming language in data science—almost 70% of data scientists report that they use it. It has surpassed R for the number one spot and has maintained this position due to its ease of use, powerful libraries and packages, clear and user-friendly documentation, and abundant community support.

Python is easier to read and write than most general-purpose languages, especially for analytical computing and quantitative data analysis. Data scientists are already handling complex data analyses, so they don’t need their programming language to be complicated, either. Python is known for its simple syntax and ease of use—even for beginners.

Python is open-sourced and has numerous libraries and packages available for data science. While some other languages (like Ruby) have clean and simple syntaxes, they don’t offer the same variety of scientific computing and machine learning libraries as Python.

There are thousands of libraries in the Python Package Index. Some of the most useful libraries and packages are Pandas, NumPy, Matplotlib, and Sci-Kit Learn.

NumPy

NumPy is a powerful linear algebra package for Python. It is primarily used for scientific computing. For example, many other libraries (Pandas, Matplotlib, and Sci-Kit Learn) depend on NumPy. NumPy has extensive documentation and can be installed quickly and easily.

NumPy works with multi-dimensional arrays in Python. Lists can be converted into arrays, random arrays can be created, and numerous operations can be performed on these arrays. This is a crucial feature because operations (addition, subtraction, multiplication, and division, for example) cannot be performed on standalone Python lists, but they can easily be performed on NumPy arrays. Since data scientists often need to perform operations on data sets, NumPy is an invaluable tool.

NumPy allows you to find the min, max, standard deviation, and variance on an array. It allows you to combine different arrays to form a single array.

Overall, NumPy arrays are faster, easier to use, and use less memory than Python lists. When working with massive data sets, convenience and ease of use are two big selling points.

Because arbitrary data types can be defined with NumPy, the package is able to connect with a variety of different databases. This adds to its versatility and makes it an important component of any data scientist’s technical repertoire.

Pandas

Pandas is an open-source library that provides high-performing, user-friendly data analysis tools for Python. It is one of the most popular libraries and, as such, has excellent documentation.

Pandas essentially takes data (from a CSV file or a SQL database, for example) and creates a Python object called a data frame. A data frame organizes data in a format that resembles a table, so it is easy to read, easy to analyze, and easy to work with.

Pandas is dependent on NumPy, and can optionally be used with Matplotlib for data plotting and visualization. Because of this, it can be installed on its own, or it can be installed through a package like Anacondas, which will install all required dependencies.

Pandas is usually used in one of three ways:

• To convert a list, dictionary, or array into a data frame
• To open a local CSV file or a related data file
• To open a remote file (CSV, JSON, SQL database, etc.)
• After opening the file that you’d like to work with, you can perform a number of different commands to analyze the data. You can perform statistical analysis (mean, median, standard deviation, and so on), you can retrieve specific data points, and you can file, sort, or group data as you see fit.

Another important feature is the ability to clean data by checking for null values within the data set. It is difficult to work with data that has not been cleaned; unintentional null values within data sets can skew your results or make the results difficult to analyze. Pandas address this concern by identifying pieces of data that might be missing, incomplete, or otherwise incorrect so that you can get the most accurate results from your analysis.

Matplotlib

Matplotlib is another popular library that allows data scientists to visualize data. Data visualization is a crucial step in making data accessible. It allows you to identify outliers and patterns quickly while making data interpretation easier overall. Research shows that people, in general, are very receptive to visual representations of data, making Matplotlib an invaluable resource in data science.

Matplotlib is free, easy to install, and has robust features. Data can be rendered as a histogram, a pie chart, a line graph, a box plot, and so on. There are enough features to satisfy advanced users, but even entry-level users can create powerful visualizations of data.

Consider an enormous data set that encompasses countless data points over a long period of time. While this data can be displayed in an array or in another numerical format, it would take a while to read and analyze. There is a potential for human error when manually reading and interpreting massive lists of data. Naturally, human error is something that data scientists try to avoid.

Matplotlib allows you to choose the specific data that you’d like to work with and arrange it in any visual format that you can imagine. Data can be rendered and displayed in almost any format with a few quick commands. Because Matplotlib is so easy to use and works seamlessly with other Python libraries and packages, it is a top choice for data scientists who use Python.

Sci-Kit Learn

Many data scientists begin analyzing and evaluating data with Pandas before moving over to Sci-Kit Learn for machine learning. Sci-Kit Learn is a machine learning library for Python that works with NumPy arrays and focuses on modeling data, not operating on it (NumPy and Pandas handle this).

Some modeling options include clustering, data sets, parameter tuning, and cross-validation. Sci-Kit Learn comes with standard data sets (for classification and regression of data, for example). Sci-Kit Learn is used in conjunction with stats and linear regression to make predictions based on data sets.

Other Libraries and Packages

These libraries and packages, among others, are one of the main reasons that Python is so popular in data science. The options to import, manipulate, operate on, clean up, visualize, and model data are unmatched by any other programming language’s libraries.

In our Python for Data Science Bootcamp, we cover Python in-depth, and we hone in on NumPy, Pandas, Matplotlib, and Sci-Kit Learn to help you make the most of your data.

Python for Machine Learning

One of the most powerful tools of Python is its machine learning capabilities. Machine learning is a subsection of artificial intelligence that creates programs to automate data analysis and learn from the data. This is a remarkably powerful tool because as data continues to grow and become more complex, machine learning algorithms will be able to produce full-scale automated models that are efficient and reliable.

Python is the number one language used in machine learning projects due to its simplicity, wide usage, and open-source packages, like scikit-learn. Scikit-learn is a machine learning library built for Python that allows programmers to cluster data and run various forms of modeling algorithms on the data.

Sci-kit-learn has much to offer when it comes to machine learning due to its simplicity and flexibility. In as few as two lines of code, an analyst can run a decision tree model on a massive data frame in seconds!

However, the main reason scikit-learn is the gold standard for machine learning is that it’s built on top of several common Python libraries, which allows programmers to input Numpy arrays and Pandas data frames into scikit-learn. Additionally, scikit-learn provides programmers with a full suite of data modeling tools such as Regression, Decision trees, Neural Networks, SVMs, and Naive Bayes.

Python for Software Development

As the number of websites, daily active users, and applications grows, programmers are increasingly turning to Python for software development. Python was designed for server-side web applications for its easy integration with other languages and its flexible frameworks,

One of Python’s benefits is its easy integration with other web languages. Python has third-party packages that enable collaboration with other languages, such as C, Java, Ruby, and Objective-C. This allows for quick development and deployment of particular parts of tools and applications.

Python’s web frameworks, namely Django and Flask, allow programmers to create and scale projects efficiently.

Flask is a relatively new framework and is now the most commonly used web framework for new Python coders. Flask is simple and easy to learn due to its lack of syntax and need for boilerplate code. This minimalistic language allows for a great deal of control and is the ideal choice for websites that provide live updates, for example, a stock ticker, due to its speed and live data fetching abilities.

Django is a “batteries included” framework, which means that Django makes it easy for Python developers to dive into web applications without worrying about the infrastructure upfront. Django is a well-established platform that supports many plug-ins, but it, unfortunately, has a steep learning curve and can feel overwhelming for new programmers.

Web Development is another vertical that displays Python’s flexibility and power. Whether you want to create software, websites, web applications or just learn how to code, Python is the perfect language to choose!

Python for Automation

Python’s power extends beyond data science, machine learning, and web development and makes its way into automation. With the power of Python, programmers can automate tasks that, for decades, had to be manually completed. Python scripts can automate countless types of tasks due to the extensive libraries available in the language.

One of the most popular automated tasks in Python is called web scraping. Using Beautifulsoup, programmers are able to write a Python script that will “scrape” the data off a webpage and store it into a CSV file. This allows researchers to gain all the information they are seeking in a clean, easy-to-analyze format within seconds!

Python can be used to automate hundreds of additional tasks, such as inputting data into a form, searching for files, updating data in Excel, and much more. Through the power of automation, Python enables programmers to complete tens of hours’ worth of tedious tasks in just a few seconds.

Python for Financial Technology

One of the major emerging trends in data analytics is the development of dedicated financial technology (FinTech) tools built to assist in analyzing data, investing, and other important aspects of the financial industry. These tools utilize Python’s data science-related libraries to analyze long-term trends, interpret huge amounts of financial data and make predictions and forecasts based on this information.

Far too much financial information is produced daily for human beings to consume and interpret. While in the past, this meant that financial analysis and investment practices were based on generalized market principles and instincts, this is no longer the case. Python programs and machine learning tools can read and interpret the vast amount of data produced and, in doing so, give investors and financial analysts a clearer picture of the state of the market. Thus, these tools can help eliminate some of the guesswork involved in making significant financial decisions.

The key skills you learn in a class like Noble Desktop’s FinTech Bootcamp involve fine-tuning the algorithms and programs that perform this financial analysis. If you put bad data into a system, you’ll get bad results out of the system, and this can be disastrous if you are making investments or financial decisions based on this output. In addition, you’ll have to learn how to read and interpret this data to make sound decisions based on it.

Data visualization is one of the other key skills you’ll pick up in a FinTech course (which is related to Python). A massive database of important financial information will be of minimal use if you can’t communicate your findings to other interested parties. Using tools like Tableau, students will learn how to create charts, maps, and graphs so that they may organize and present financial data to interested shareholders or investors.

Why Learn Python?

Easy to Build & Test

Python code is very similar to English and can, therefore, be learned quickly. We are therefore witnessing an increase in start-up technology companies using Python as their preferred language.

Unlike Java or C++, Python’s syntax is very simple which allows programmers to focus on the product they are trying to build and not the syntax they need to follow. All this and more leads to products in Python being launched faster and smarter. Programmers can launch minimal viable products into the market for customer testing. The result is the creation of more technology products that have a proven and tested market. This ultimately prompts an increase in the flow of venture capital money into products built on Python.

The Language of the People

A language is only as strong and as useful as the number of people who are using it. Python has just surpassed 35 million downloads per year, and an estimated 5 million programmers worldwide are using Python as their preferred language. The massive adoption of Python by programmers is a testament to its strength and speed. This also creates a highly valuable social network of Python developers. From searching for debugging answers on Stack Overflow to finding a job in a new city, the Python social network reigns supreme.

Diverse Network

With Python, programmers can build software for NASA, create data science models for Fortune 500 companies, and scrape data from websites and academic journals. In other words, there is an endlessly diverse group of people who use Python for very different reasons: traditional programmers use it to build software and foster technological innovation, the data scientists will use it to build models to see which marketing strategy is most effective, and the academics use it to retrieve data autonomously using Python web scraping extension such as Beautiful Soup.

Prominent Companies Are Using Python

Instagram, Spotify, Amazon, and Facebook are all examples of companies that currently use Python as their coding language of choice.

Instagram uses Python because it fits with their company philosophy to “do the simple thing first.” Instagram uses the Django web framework, which is written in Python. Another reason engineers at Instagram opted to use Python is that it is simple and effective, which allows them to launch new features with little downtime.

Spotify uses Python mostly for data analysis and backend services, but programmers at Spotify said, “Python has a habit of turning up in other random places, as most of our developers are happy programming in it.” Amazon and Facebook also use Python for features, including recommended friends and products.

Frameworks & Environments & Libraries

Python's frameworks and environments all it to be used for a variety of tasks. 

• Django is a full-stack Python web framework that is open-source and free to all. Django is widely popular amongst developers because it provides programmers with templates that simplify complex code.
• Flask is a Python web framework that allows the use of Python in web development.
• Beautiful Soup is a library for pulling data off of the internet.
• Jupyter is an open-source web application that allows programmers to input, analyze, and visualize data.

Python Career Paths

Owing to the versatility of the language and its expansive collection of libraries, Python is used in a wide range of different professional industries for a huge variety of applications. While it is most commonly thought of as a back end database language, companies are utilizing Python as a data science tool, a financial investment tool, and for its machine learning capabilities. If you are interested in a career in data science, computer programming, or finance, learning Python is a great way to open the door to a great many career opportunities.

• Data Science: As data science tools have become more accessible and easier to use (and with the rise of third-party distributed computing services, primarily Amazon Web Services), more and more industries are realizing the value of data collection and analysis in their regular operations. While it certainly helps traditional advertisers and marketers to understand their audience better, data analytics is being employed in the entertainment industry, the shipping industry, government, healthcare and agriculture, and virtually every industry you can think of. Almost all innovations in any industry emerge when Data Scientists take a deeper look at the systems in which they operate, so learning Python can help you succeed in any field.
• Machine Learning: Machine learning algorithms are at the forefront of people’s imaginations across many different professional industries. While they are currently used primarily in content recommendation algorithms, like those associated with Amazon or Netflix, there is a lot of buzz surrounding their future applications, particularly as tools become more readily available and the cost of running machine learning applications becomes more affordable. Machine learning is poised to improve workflow efficiency and optimize tasks for a huge number of professionals (ranging from programmers and scientists to copywriters and managers) and these technologies have the potential to cause major shifts in fields like healthcare or financial services. Learning how to build and train these algorithms offers long-term career opportunities and financial stability in any industry that ultimately adopts machine learning technologies as a standard tool.
• FinTech: Anyone who wants long-term success in the financial services industry will likely want to learn Python and its associated data science libraries. Success in investing and finance isn’t about hitting big once but about making many small informed decisions that create stability and consistency (particularly when you are working with other people’s money). Python tools help investors and financial institutions make those informed decisions more easily and help to take the guesswork out of these processes. While you may not be required to learn Python, leaving this vital tool on the table will only cause problems in the long term if you hope to succeed in the financial industry.