Missing values, as the name suggests, refer to data points that are absent in a dataset.
How to Handle Missing Values: Dealing with missing values involves different strategies depending on whether the data is categorical or numerical. Let’s address both types separately.
1) Categorical Data
To handle missing values in a categorical column, one effective approach is to replace them with the most frequently occurring category {use ‘value_counts()’ to get the frequency of occurrence of each category}
2) Numerical Data
Handling missing numerical data requires careful consideration, particularly in two scenarios.
Outliers are Present:
If our dataset contains outliers, it is advisable to replace missing values with the median of the numerical column. The median is less influenced by outliers and provides a robust measure of central tendency.
Outliers are Absent:
When there are no outliers in the data, using the mean as a replacement for missing values is a suitable option.
Additional Functions and Tips: In addition to the above strategies, there are other techniques and considerations for handling missing values:
1.Utilize functions like ‘bfill()’ and ‘ffill()’ to replace null values by propagating non-null values forward or backward in the dataset.
2.If a significant portion (approximately 80% or more) of values in a column are missing, it may be more appropriate to remove the entire column rather than attempting to replace the missing values.
By employing these strategies, you can effectively manage missing values in your dataset, ensuring the quality and reliability of your data analysis.