Introduction
Python, a well-known programming language, wasn't named after the snake. Instead, it was inspired by a comedy group known for creating Monty Python’s Flying Circus. It's recognized for its user-friendly nature, though what's considered "easy" depends on your existing programming experience. If you're new to it and find it perplexing, that's normal. Exploring opportunities for a Python Course in Chennai can provide structured learning and hands-on experience to master the intricacies of Pandas and other essential libraries.
Learning to read and write Python code becomes more manageable with time. Just like how SQL might have seemed confusing initially, it eventually became more understandable.
While not every data analyst uses python in analytics, it offers unique advantages over other tools like Excel, Tableau, and low-code integration software. Its capabilities include reproducibility, speed, efficiency, and the ability to handle complex data views while remaining accessible to collaborators.
Python's utility extends to many data-centric job roles, but becoming a Kaggle Grandmaster, a prestigious title in the field, might not be a realistic goal for most of us. Nonetheless, Python simplifies our lives and handles various tasks proficiently.
Automation
The Boring Stuff
Both within and beyond the world of data, python in analytics is used to automate the mundane, from renaming files all the way to clicking the Reroll button in Baldur’s Gate’s character creation until your character rolls a total stat over 95. (I spent more time writing that admittedly unimpressive code than I did playing Baldur’s Gate that day. Eventually I’ll get around to refactoring it. Maybe.)
A non-exhaustive list of boring stuff Python can automate for you:
- Filling out PDFs
- Converting files in bulk
- Parsing difficult-to-read text formats
- Dynamically formatting spreadsheets
- Organizing local and remote directories
- Clicking one spot on your screen over and over and over and over
Automating repetitive tasks is one of Python’s main selling points—or would be, if Python cost anything. Actually, it’s probably fair to say that the more repetitive a task is, the more likely Python can automate it (and the more likely someone already has).
There are over 300,000 Python libraries and packages (code that you can import and use in your own programs) on PyPi, the official Python Package Index. It’s more likely than not that something out there can help provide a solution to your monotony. This guy wrote a Python script to shoot a water gun at pigeons “voiding their excrements” on his balcony. Not necessarily boring, but hey, you’re not even limited to the digital world.
Reporting
Nowadays, a significant portion of condensed reporting occurs within BI tools like Tableau or Excel, as well as directly within SaaS products. While this is usually sufficient, there are situations where stakeholders lack the time to locate the specific report they need or prefer data presented in a particular format. Additionally, the limitations of these tools can sometimes restrict functionality, particularly when addressing unique requirements.
In such cases, Python can streamline the process, sparing you the effort of tracking down, exporting, reformatting, and distributing reports. If this routine feels familiar, that's because it essentially involves a chain of mundane tasks.
For a prepared data analyst, compiling routine reports should be relatively unexciting, only becoming noteworthy when the incoming data experiences significant shifts, either positive or negative. Let's consider a practical example: every Monday, the executive team requests a simple CSV summarising the previous week's revenue by region. Your responsibility is to query the company database, compile the data, and send it to the executives.
Assuming this task takes 15 minutes each week for 48 weeks a year (accounting for 4 weeks of approved leave), you're expected to manage this recurring report.
Using Python, you could script this entire process in about an hour. Even a beginner can accomplish this task with relative ease, which is what makes Python a compelling option.
By doing so, you would save approximately 11 hours annually. This calculation applies to just one report, while in some data analyst roles, this type of automation might be required frequently. Automating your reporting not only saves time but also leaves a positive impression on your superiors and allows you to concentrate on more meaningful and impactful tasks.
Data Pipelines/Web Scraping
Although this delves into data engineering territory, data analysts and scientists are occasionally tasked with constructing data pipelines through APIs and tools such as AWS Glue and Lambda, in addition to web scraping for third-party data.
They might also be expected to carry out Extract, Transform, Load (ETL) operations. ETL involves taking data from a source, cleaning and altering it to meet stakeholder requirements, and storing it in another location. While ETL is typically considered a data engineering task, analysts, especially in smaller companies, often handle it.
A typical ETL pipeline might involve extracting data from an analytics platform like Google Analytics, transforming and potentially enhancing it for new and insightful analysis, and then loading it into the company's data warehouse. This process proves valuable when different departments seek to establish correlations or even causations between their efforts and desired outcomes, particularly when such evidence exists in various data sources.
Could you perform all of this manually? Conceivably. However, manual ETL processes not only consume time inefficiently but also introduce a significant margin for error.
With Python libraries like requests and pandas, the entire pipeline can be automated. To enable the automatic running of this pipeline (without the need to manually open and execute the script), a job scheduler like cron or a service such as AWS Glue/Lambda would be necessary. Nevertheless, since "running it yourself" merely involves pressing a button, Python substantially streamlines the process.
Transformation
Data Cleaning
Even if you never engage in end-to-end Extract, Transform, Load (ETL) processes throughout your data analytics career, you will inevitably encounter the transformation part. This is because, as a data analyst, you are also responsible for maintaining data quality.
Data cleaning varies in complexity. It could involve simple tasks like renaming fields or more laborious tasks such as renaming hundreds of fields (particularly frustrating with wide-format data). It could also include challenging operations like identifying leading and trailing spaces or parsing deeply nested key-value pairs for specific information.
In other words, data can be messy in various ways, and typically, as a company's data volume increases, so does the complexity of the mess. It's like a dog in the mud after a rainfall—you shouldn't expect it to be clean and orderly.
For data cleaning (and most tabular data manipulations), the preferred Python library is pandas. If you could only install one library as a data analyst, pandas would be the uncontested choice.
Pandas is built on top of NumPy, known primarily for its numerical computing capabilities. It enables you to perform nearly any type of data cleaning you would do in SQL or Excel, and then some, but in a more reproducible manner. For instance, pandas offer several built-in functions for managing nested lists and dictionaries within fields and can swiftly alter the names of all your columns based on certain conditions. It can also import and export data from various file types, including CSV, JSON, and Parquet.
Despite its name, Pandas is not restricted to numerical manipulations, as it can also handle string, datetime, and boolean data types. When used alongside Jupyter Notebooks (allowing code execution in distinct steps), it facilitates monitoring the transformation process incrementally rather than merely running the script and hoping for the best.
NLP
NLP, or Natural Language Processing, focuses on comprehending the content of unstructured text.
Not all data adheres to a specific structure, and this is particularly evident in language. For instance, consider each paragraph in this essay being loaded into a database as separate records. If you were asked to write a SQL query to determine the quality of each paragraph, it would likely involve complex nested statements evaluating each paragraph against defined criteria for "good" or "bad."
However, this approach would not only be challenging but also prone to oversimplification. Word meanings heavily rely on context, while grammar, subject to continual evolution, cannot always yield precise results independently. Despite proofreading, it's reasonable to expect a few lurking typos within seemingly innocuous sentences. Evaluating such errors adds further complexity.
In essence, when dealing with language, it's incredibly challenging to consider every aspect thoroughly. If you manage to do so without assistance, you would undoubtedly be regarded as exceptional. Your brilliance would likely be celebrated for generations to come.
While NLP still has progress to make, it has made significant advancements. Notably, a Google-developed AI chatbot recently convinced one of its engineers that it had achieved sentience. python in analytics NLP libraries such as NLTK, spaCy, and Gensim are powerful tools, enabling the implementation of NLP techniques in just a few lines of code. Among these techniques, sentiment analysis is commonly used by data analysts to discern the positivity, negativity, or neutrality expressed in text. Sentiment analysis is frequently employed to understand customer feedback, among other applications. For individuals interested in exploring the applications of Python in natural language processing and data analysis, considering opportunities for a Python Course in Bangalore can provide comprehensive training and practical skills to navigate the intricacies of this dynamic field.
Machine Learning
Machine learning is:
- A form of artificial intelligence
- Utilizing historical data to forecast future outcomes
- Not to be implemented without careful consideration
Python is exceptionally well-suited for this field.
Machine learning is commonly linked to data science, regarded as one of the most desirable job fields today, even if your family or immediate circle might not be aware of it. Although data science is distinct from analytics and comes with its own set of challenges, there's often an overlap in real-world job responsibilities. Depending on the industry, the seniority of the role, and the company's stage, data analysts may be tasked with predictive modelling through machine learning techniques.
Why is python in analytics a great fit for machine learning? The answer lies in its libraries. Common use of python in data analysis libraries for machine learning include Scikit-learn and TensorFlow. Scikit-learn provides a more high-level machine learning framework featuring regression, clustering, and classification algorithms, while TensorFlow specialises in deep learning.
Another advantage of building machine learning models in Python is its platform independence, enabling your code to run across various operating systems. Machine learning models developed in python in analytics can operate on different local machines as well as through cloud services.
Although machine learning can be implemented using other programming languages apart from R, these languages are seldom used in data analytics, making them unlikely to be part of an analyst's toolkit. At FITA Academy, the incorporation of Python not only enhances the reproducibility of your machine learning models but also facilitates collaboration with other data analysts and scientists, fostering a conducive environment for valuable human learning experiences. For individuals keen on expanding their skill set in python in analytics and leveraging its capabilities for data analytics in python and machine learning, exploring opportunities for a Python Course in Coimbatore can provide valuable insights and hands-on experience to enhance your proficiency in this versatile programming language.
Visualisation
The intersection of data and storytelling often leads to effective visualization. While this might sound cliché, an alternate viewpoint places visualization at the crossroads of the analytics team and upper management. It's no secret that even the analysts themselves appreciate a well-crafted chart, but the love for insightful visual representation is particularly strong among management.
Compelling visualisation transforms data, facilitating rapid comprehension that raw numbers often fail to achieve. It offers a combination of context and substance that a mere spreadsheet with last year's aggregated sales by day cannot deliver—imagine the difference between interpreting a time series graph and sifting through a mundane spreadsheet that leaves your eyes glazed over.
While Python may seem more suitable for data preparation than data presentation at first glance, it is capable of producing highly customisable visuals. Certain libraries, such as Dash, even enable the creation of interactive dashboards.
python in analytics is rarely a simpler solution for business intelligence compared to tools like Tableau or Power BI. However, it can provide greater flexibility in certain scenarios. Libraries like Seaborn empower data analysts to swiftly generate charts with customisation options that might prove challenging in Excel, for instance. It can also come to the rescue when dealing with data sources not currently integrated with your BI tool.
Initially, you may find yourself diving into the documentation of visualisation libraries, especially when aiming for ultra-specific requirements. Yet, this exploration is intrinsic to the nature of coding and can be quite enjoyable. You might start by searching for how to add labels to your x-axis and end up learning how to transform the dots in your scatter plot into stars. Stars! It's indeed an exhilarating world we live in. For those looking to embark on a learning journey in Python and delve into the exciting realm of data visualisation, exploring opportunities for a Python Course in Madurai can provide a structured path to acquiring the skills and knowledge needed to navigate the dynamic field of programming and data analysis.
After reading this, you might wonder, "why is python used? Why bother with BI tools, automated ETL solutions, or third-party analytics platforms when Python seems capable of handling it all?"
Indeed, for the most part, python in analytics. However, that doesn't mean it's always the best choice. Just as not everyone builds their own house, creating your own data solutions demands substantial time, planning, and execution. It also requires financial resources and considerable physical stamina—hopefully, you catch the analogy. For those aiming to efficiently harness the power of python in analytics for data solutions without reinventing the wheel, exploring opportunities for Python Training in Coimbatore can provide a guided learning path and practical insights to navigate the landscape of data analytics in python and programming.
I've encountered data analysts who view learning of python in analytics as a somewhat obligatory task, particularly because it frequently appears in higher-paying job descriptions. Yet, to me, it's simply about streamlining your tasks when appropriate. Python is a tool. While I personally find it valuable to learn, its worthiness depends on various factors for each individual, such as their industry, their team's preferred tech stack, and the amount of available time for learning.
The positive news is that the use of python in data analysis remains a staple in the realm of data analytics in python and is not likely to disappear anytime soon. For those interested in gaining proficiency of python in analytics and its applications in data analytics, exploring opportunities for a Python Course in Pondicherry can provide structured learning and hands-on experience to navigate the intricacies of this versatile programming language.