4 min read

Data wrangling is the process of cleaning and structuring complex data sets for easy analysis and making speedy decisions in less time. Due to the internet explosion and the huge trove of IoT devices there is a massive availability of data, at present.
However, this data is most often in its raw form and includes a lot of noise in the form of unnecessary data, broken data, and so on. Clean up of this data is essential in order to use it for analysis by organizations. Data wrangling plays a very important role here by cleaning this data and making it fit for analysis. Also, Python language has built-in features to apply any wrangling methods to various data sets to achieve the analytical goal.

Here are 5 best practices that will help you out in your data wrangling journey with the help of Python. And at the end, all you’ll have is a clean and ready to use data for your business needs.

5 best practices for data wrangling with Python

Learn the data structures in Python really well

  • Designed to be a very high-level language, Python offers an array of amazing data structures with great built-in methods. Having a solid grasp of all the capabilities will be a potent weapon in your repertoire for handling data wrangling task.
  • For example, dictionary in Python can act almost like a mini in-memory database with key-value pairs. It supports extremely fast retrieval and search by utilizing a hash table underneath.
  • Explore other built-in libraries related to these data structures e.g. ordered dict, string library for advanced functions.
  • Build your own version of essential data structures like stack, queues, heaps, and trees, using classes and basic structures and keep them handy for quick data retrieval and traversal.

Learn and practice file and OS handling in Python

  • How to open and manipulate files
  • How to manipulate and navigate directory structure

Have a solid understanding of core data types and capabilities of Numpy and Pandas

  • How to create, access, sort, and search a Numpy array.
  • Always think if you can replace a conventional list traversal (for loop) with a vectorized operation. This will increase speed of your data operation.
  • Explore special file types like .npy (Numpy’s native storage) to access/read large data set with much higher speed than usual list.
  • Know in details all the file types you can read using built-in Pandas methods. This will simplify to a great extent your data scraping. Almost all of these methods have great data cleaning and other checks built in. Try to use such optimized routines instead of writing your own to speed up the process.

Build a good understanding of basic statistical tests and a panache for visualization

  • Running some standard statistical tests can quickly give you an idea about the quality of the data you need to wrangle with.
  • Plot data often even if it is multi-dimensional. Do not try to create fancy 3D plots. Learn to explore simple set of pairwise scatter plots.
  • Use boxplots often to see the spread and range of the data and detect outliers.
  • For time-series data, learn basic concepts of ARIMA modeling to check the sanity of the data

Apart from Python, if you want to master one language, go for SQL

  • As a data engineer, you will inevitably run across situations where you have to read from a large, conventional database storage.
  • Even if you use Python interface to access such database, it is always a good idea to know basic concepts of database management and relational algebra.
  • This knowledge will help you build on later and move into the world of Big Data and Massive Data Mining (technologies like Hadoop/Pig/Hive/Impala) easily. Your basic data wrangling knowledge will surely help you deal with such scenarios.

Although Data wrangling may be the most time-consuming process, it is the most important part of the data management. Data collected by businesses on a daily basis can help them make decisions on the latest information available. It also allows businesses to find the hidden insights and use it in the decision-making processes and provide them with new analytic initiatives, improved reporting efficiency and much more.

About the authors

Dr. Tirthajyoti Sarkar works in San Francisco Bay area as a senior semiconductor technologist where he designs state-of-the-art power management products and applies cutting-edge data science/ machine learning techniques for design automation and predictive analytics. He has 15+ years of R&D experience and is a senior member of IEEE.

Shubhadeep Roychowdhury works as a Sr. Software Engineer at a Paris based Cyber Security startup where he is applying the state-of-the-art Computer Vision and Data Engineering algorithms and tools to develop cutting edge product.

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