Data is the lifeblood of data analytics, but it is often messy, incomplete, or inconsistent. How do you use Python to handle these common data quality issues and prepare your data for analysis? In this article, you will learn some practical tips and techniques to deal with missing, incomplete, or inconsistent data using Python.
The first step to handle data quality issues is to identify them. You can use various Python libraries and functions to inspect your data and check for missing, incomplete, or inconsistent values. For example, you can use pandas to read your data into a DataFrame and use methods like .info(), .describe(), and .isna() to get a summary of your data, its statistics, and its null values. You can also use seaborn or matplotlib to visualize your data and spot any outliers, anomalies, or patterns.
These are good points for simple data. In the workplace, I've found the best first step is contacting whoever is in charge of the data to identify any known issues. Data quality can be affected by user-input, what platform it is being hosted by, etc. Inspecting datatypes, using value counts, searching for erroneous whitespace should all be included in initial steps, especially when dealing with non-numeric data.
Missing data can affect your analysis and results, so you need to decide how to handle them. There are two main options: deleting or imputing. Deleting means removing the rows or columns that have missing values, which can reduce the size and complexity of your data, but also lose some information and introduce bias. Imputing means replacing the missing values with some reasonable estimates, which can preserve the size and structure of your data, but also introduce noise and uncertainty. You can use pandas to delete or impute missing data using methods like .dropna(), .fillna(), or .interpolate(). You can also use sklearn to apply more advanced imputation techniques like mean, median, mode, or KNN.
KNN is a great tool. Often when imputing missing financial data, I rely on bucketed means as a good estimator. For example, if you know historical spend of a customer and are perhaps looking to fill a missing spend value for a given month, you could used the pandas.cut() function to bin like-spend customers, and average their spend across that missing month. This is a computationally cheap, good rough estimate.
Incomplete data means that your data does not cover the full range or scope of your analysis. For example, you may have data for only a subset of your population, a sample of your variables, or a period of your time frame. Incomplete data can limit your analysis and conclusions, so you need to decide how to handle them. There are two main options: expanding or reducing. Expanding means adding more data to fill the gaps, which can increase the coverage and validity of your analysis, but also require more resources and effort. Reducing means narrowing down your analysis to focus on the available data, which can simplify your analysis and avoid extrapolation, but also reduce your insights and generalizability. You can use Python to expand or reduce your data by using methods like .concat(), .merge(), or .filter() from pandas, or by using web scraping, APIs, or external sources to collect more data.
Inconsistent data means that your data has errors, conflicts, or variations that affect its accuracy and reliability. For example, you may have data that has typos, duplicates, formatting issues, or different units or scales. Inconsistent data can distort your analysis and results, so you need to decide how to handle them. There are two main options: correcting or transforming. Correcting means fixing the errors, conflicts, or variations in your data, which can improve the quality and consistency of your data, but also require manual intervention and verification. Transforming means changing the format, unit, or scale of your data, which can standardize and normalize your data, but also alter its original meaning and distribution. You can use Python to correct or transform your data by using methods like .replace(), .drop_duplicates(), .str(), or .apply() from pandas, or by using regex, numpy, or scipy to manipulate your data.
Python is a powerful and versatile tool for data analytics, but it also requires you to handle data quality issues carefully and effectively. By using the tips and techniques in this article, you can use Python to handle missing, incomplete, or inconsistent data and prepare your data for analysis.