Common mistakes individuals make when dealing with missing values in Python

Missing data is a ubiquitous challenge in data analysis, affecting every domain, for example, healthcare, finance, social sciences, and machine learning. It’s a puzzle that data scientists, analysts, and researchers frequently encounter, and how they handle it can significantly impact the integrity and reliability of their results. In this thread, we’ll dive into the intricate world of missing data and explore the common mistakes individuals often make when dealing with it.

1. Ignoring missing values:

One common mistake, especially among beginners, is proceeding with their analysis or calculations without addressing missing values. This oversight can lead to misleading and inaccurate results. The code below calculates the mean of a column in a sample dataframe after excluding its missing values.

2. Incorrectly identifying missing values:

Failing to recognize different representations of missing values like NA, NaN, None, or empty strings can compromise the accuracy of your analysis. Therefore, it is important to look out for these representations in the data and handle them appropriately. The code below replaces all these different representations of missing values with np.nan which is recognized in Python as missing value.

3. Dropping too many rows or columns:

Removing rows or columns with missing values without considering their impact on the dataset can lead to the loss of critical information and potentially biased results. It’s vital to strike a balance between data preservation and handling missing values effectively. Instead, you can fill in the missing values in important columns or rows, and the code below shows you how to achieve this.

4. Filling missing values with arbitrary values:

Substituting missing values with arbitrary or incorrect data can introduce bias and compromise the accuracy of your analysis or results. One should replace the missing values with the appropriate values such as mean and median for numerical data and mode for categorical data. The code below fills the missing values in each column with the column’s mean.