What is the best way to handle missing data when web scraping?

1 Why missing data matters

Missing data can introduce bias, noise, or distortion to your data set, and affect the accuracy and validity of your results. For example, if you are scraping product reviews, and some reviews are missing ratings, you might end up with a skewed distribution of ratings. Or, if you are scraping stock prices, and some prices are missing due to market closure, you might end up with gaps or outliers in your time series. Therefore, it is important to identify and handle missing data properly before performing any analysis or modeling.

2 How to identify missing data

Different types of missing data can exist, depending on the nature and mechanism of the missingness. For instance, some data might be missing completely at random (MCAR), meaning that the missingness is independent of any other variable. Other data might be missing at random (MAR), meaning that the missingness is related to some observed variables, but not to the missing variable itself. In contrast, some data might be missing not at random (MNAR), indicating that the missingness is related to the missing variable itself. To identify the type and pattern of missing data, you can employ various methods, such as visualizing the data using charts, summarizing it with descriptive statistics, or testing it with statistical tests. Visualizing the data can help you spot any gaps or anomalies in its distribution and correlation. Summarizing the data can provide insight into its central tendency and variability with or without missing values. And testing the data can reveal whether or not the missing values are significantly different from observed values, and if they affect any hypothesis or inference.

3 How to handle missing data

When dealing with missing data, there are various approaches that can be taken, depending on the type and amount of missingness, and the goal and context of the analysis. Generally, these include deletion, imputation, and modeling. Deletion involves removing the missing values or the entire rows or columns that contain them; this is a straightforward and fast method, but it can reduce the sample size and the information content of the data, and introduce bias or error if the missing values are not MCAR. Imputation involves replacing the missing values with some estimated values, based on observed values or assumptions; while this is more complex and flexible, it can introduce noise or uncertainty to the data, and affect the distribution or relationship of variables. There are various types of imputation methods available, such as mean, median, mode, regression, interpolation, or machine learning. Lastly, modeling involves using the missing values as part of the analysis or modeling process. This is a more advanced and robust method; however, it requires more computational resources and technical skills, and may not be compatible with some standard methods or tools. Examples of modeling methods include maximum likelihood, multiple imputation, or Bayesian inference.