Dropping Invalid Rows

Dropping invalid rows refers to the process of removing rows from your dataset that do not meet certain validation rules or conditions.

Imagine you have an array of JavaScript objects, each representing a row of data:

let data = [
  { name: "John", email: "john@email.com", age: 30 },
  { name: "Jane", email: "jane@email", age: 25 },
  { name: "Bob", email: "bob@email.com", age: 40 },
  { name: "Alice", email: "alice@email.com", age: "forty" },
];

You want to drop the rows where the email is invalid or the age is not a number. You could do so with the following JavaScript function:

data = data.filter((row) => {
  let emailRegex = /\S+@\S+\.\S+/;
  return emailRegex.test(row.email) && typeof row.age === "number";
});

Before

After

In this example, the "Jane" row was dropped because the email field did not pass the validation check, and the "Alice" row was dropped because the age field was not a number.

When dropping invalid rows, consider the following:

1. Validation Rules: Your validation rules define what constitutes an "invalid" row. These rules can be as simple or as complex as your data requires. Be aware that overly strict rules might cause you to lose valuable data.
2. Data Loss: Dropping rows is a destructive operation – once a row is removed, it can't be recovered unless you have a backup of your original data. Always create backups before starting the data cleaning process.
3. Data Size: If you're working with a large dataset, dropping rows can help to reduce the size and improve the speed of your data processing. However, if you're dropping a significant number of rows, you may need to reassess your data collection or validation methods.

• Dynamic Data Validation: Before you can drop invalid rows, you first need to validate your data. This process can involve checking for correct data types, ensuring values fall within an expected range, verifying that dates are correctly formatted, and more. Ideally, it is easy to configure these validation rules dynamically.
• Imputing Missing Values: If a row is only considered "invalid" because it has a missing value, you might choose to impute (fill in) the missing value instead of dropping the entire row. This can help to preserve as much data as possible.
• Flagging Invalid Rows: An alternative to dropping invalid rows is to flag them. This could involve adding a new column to your dataset that indicates whether each row passed your validation rules. This allows you to keep all of your data while still being aware of which rows are potentially unreliable.