Data cleaning is a crucial step in any data science project, but how do you make sure your code is doing what you expect it to do? In this article, you will learn some practical tips and techniques to check your data cleaning code and avoid common errors and pitfalls.
Unit tests are small pieces of code that check if a specific function or operation works as intended. They can help you catch bugs, typos, and logical errors in your data cleaning code. For example, you can use a unit test to verify that a function that removes missing values from a column does not affect other columns or rows. You can use frameworks like pytest or unittest to write and run unit tests in Python.
Summary statistics are numerical measures that describe the main features of a dataset, such as mean, median, standard deviation, min, max, and count. They can help you check if your data cleaning code has altered the distribution, shape, or range of your data in an unexpected way. For example, you can compare the summary statistics of your raw data and your cleaned data to see if there are any significant changes in the mean or standard deviation of a variable. You can use libraries like pandas or numpy to calculate summary statistics in Python.
Visualizing your data is a powerful way to check your data cleaning code and spot any anomalies, outliers, or patterns in your data. You can use different types of plots, such as histograms, boxplots, scatterplots, or heatmaps, to explore your data and see how it changes after applying your data cleaning code. For example, you can use a histogram to check if your data cleaning code has created any gaps or spikes in the frequency distribution of a variable. You can use libraries like matplotlib or seaborn to create visualizations in Python.
Using sample data is a smart way to check your data cleaning code and save time and resources. Instead of running your data cleaning code on the entire dataset, you can use a smaller subset of data that represents the main characteristics and variability of your data. This way, you can test your data cleaning code faster and easier, and identify any issues or errors before applying it to the full dataset. You can use methods like random sampling or stratified sampling to create sample data in Python.
Reviewing your code is a good practice to check your data cleaning code and improve its quality and readability. You can review your code yourself or ask a colleague or a peer to review it for you. Reviewing your code can help you find and fix any mistakes, inconsistencies, or redundancies in your data cleaning code. It can also help you document your code and follow the best coding standards and conventions. You can use tools like pylint or flake8 to check your code quality and style in Python.