What Python is
Python is a programming language. This means that you can write computer instructions with it that the computer can understand. In addition, it it practically considered an interpreted language. This means that it is executed at the time the code is running. Computers dont understand human language. So they need the instruction is a format they can understand. Python code is converted into machine language when the program executes.
In contrast, compiled languages convert the whole code into computer readable format first, and then it is executed once in wholesale. Think of it like two human interpreters, one tries to interpret the conversation you are having to a different speaker as you speak, while the other wait for your whole speech before they can convey your message.
You can already begin to see the nuances in both types of processing. For instance, when someone is interpreting your speech as you speak, you might change your mind along the conversation. If you had offered to deliver an order on Thursday, you change that to Wednesday. Python instructions or variables can be changed along the way.
Why Python is popular in data analytics
Python is ubiquitous in data analytics for several reasons. The main reason is probably its simplicity. It is not difficult to have a general grasp of the concepts as a new beginner. This makes it more preferable than other languages.
Python has an extensive ecosystem of packages, additional tool-sets that achieve specialized tasks. This makes Python the Swiss-knife of programming languages for data-oriented tasks. From visualization, machine learning and even data manipulation, there is always a python package for the job. Tools like Notebooks allow for live code and visualization which is very convenient for data analytics.
Documentation is also very vast and due to its large user-base, there is almost always a documented solution to any problem you may encounter. This makes trouble shooting even for very technical setups easier.
Python allows for a workflow to be fully integrated. Data Engineers find this particularly useful. This ability to integrate with other environments also makes python a tool of choice.
Python libraries used in data analytics
The most popular data analytics libraries used in python are the following:
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Pandas: data analysis and manipulation that works well with structured data like tables -
Numpy: numerical python (numpy) acts a base for numerical manipulation in python from where other libraries borrow heavily for their own use. -
Tensorflow: a machine learning library developed by google -
Plotly: plotly is mainly for visualizing data -
Seaborn: visualization library that focuses on statistical graphs -
Scikit-Learn: machine learning library
How Python is used to clean, analyze, and visualize data
Python libraries like pandas are used to clean data. This involves finding and removing duplicates from data columns where none should exist, eg. unique IDs in a customers' table. Mixed column data types are also identified and standardization done.
Clean data is used to feature engineer business metrics. This may include identifying patterns to detect fraud or even segment customer groups in marketing data. The python libraries also come with many useful advanced statistical techniques that make it easy to compute for things like percentiles on very large datasets. Machine learning models also make heavy use of python and this makes it very intuitive even for beginners.
Visualization libraries such as plotly, seaborn allow for display of metric in graph or charts that make them more friendly to non-technical users. This also allows for trends to be cleaned quickly from a general visual presentation of the data.
Real-world examples of Python in data analytics
Google search engine algorithms use python. This goes to show just how powerful python is for analysis. It is further integrated into their machine learning models to further refine their data.
Amazon's customer hyperpersonalization is due to its use of python to analyze very large datasets to detect trends. This confirms that python is very powerful especially in analyzing big data.
Why beginners should learn Python
Beginners should learn python because it will be around for a long time. And since under the hood it also makes use of C-compiler, it is proving to be incredibly fast for demanding data analytics tasks.
Python has a friendly learning curve due to its object oriented nature. Object oriented means that variables are assigned to objects. eg. name = 'Jane'. When you want to get the value for name, it will always be Jane, unless changed.
Coupled with its gentle path for learning, a beginner is exposed to industry level work. The basic use of python for learning as well as for actual work is very similar. This makes it easy for one to be job-ready in a shorter time.
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