Skip to main content

How to deal with missing values in data cleaning

The data you inherit for analysis will come from multiple sources and would have been pulled adhoc. So this data will not be immediately ready for you to run any kind of model on. One of the most common issues you will have to deal with is missing values in the dataset. There are many reasons why values might be missing - intentional, user did not fill up, online forms broken, accidentally deleted, legacy issues etc. 

Either way you will need to fix this problem. There are 3 ways to do this - either you will ignore the missing values, delete the missing value rows or fill the missing values with an approximation.

Its easiest to just drop the missing observations but you need to very careful before you do that, because the absence of a value might actually be conveying some information about the data pattern. If you decide to drop missing values :

df_no_missing = df.dropna()

will drop any rows with any value missing. Even if some values are available in a row it will still get dropped even if a single value is missing. 

df_cleaned = df.dropna(how='all')

will only drop rows where all cells are NA or missing values. To drop columns, you will have to add the ‘axis=1’ parameter to the above functions.

The extent of the missing values is identified after identifying the variables with missing values. If any patterns are identified the analyst has to concentrate on them as it could lead to interesting and meaningful business insights. If there are no patterns identified, then the missing values can be substituted with mean or median values (imputation) or they can simply be ignored.There are various factors to be considered when answering this question-

Understand the problem statement, understand the data and then give the answer.Assigning a default value which can be mean, minimum or maximum value. Getting into the data is important.

If it is a categorical variable, the default value is assigned. The missing value is assigned a default value.

If you have a distribution of data coming, for normal distribution give the mean value.

Should we even treat missing values is another important point to consider? If 80% of the values for a variable are missing then you can answer that you would be dropping the variable instead of treating the missing values.

Labels:

Comments

  1. Aaron jhonson10 December 2021 at 02:33

    Thank you for sharing such a useful article. It will be useful to those who are looking for knowledge. Continue to share your knowledge with others through posts like these, and keep posting on
    Data Engineering Services 

    ReplyDelete

Post a Comment

Popular posts from this blog

What is P Value ?

In Data Science interviews, one of the frequently asked questions is ‘What is P-Value?”. According to American Statistical Association, “A p-value is the probability under a specified statistical model that a statistical summary of the data (e.g., the sample mean difference between two compared groups) would be equal to or more extreme than its observed value.”  That’s hard to grasp, yes? Alright, lets understand what really is p value in small meaningful pieces to make it very clear. When and how is p-value used? To understand p-value, you need to understand some background and context behind it. So, let’s start with the basics. p-values are often reported whenever you perform a statistical significance test (like t-test, chi-square test etc). These tests typically return a computed test statistic and the associated p-value. This reported value is used to establish the statistical significance of the relationships being tested. So, whenever you see a p-valu...
Post a Comment
Read more

Introduction to Datascience

Data Science has become one of the most demanded jobs of the 21st century. What is Data Science? “Data Science is about extraction, preparation, analysis, visualization, and maintenance of information. It is a cross-disciplinary field which uses scientific methods and processes to draw insights from data. ” As a data scientist, you take a complex business problem, compile research from it, creating it into data, then use that data to solve the problem. A Data Scientist, specializing in Data Science, not only analyzes the data but also uses machine learning algorithms to predict future occurrences of an event. Therefore, we can understand Data Science as a field that deals with data processing, analysis, and extraction of insights from the data using various statistical methods and computer algorithms. It is a multidisciplinary field that combines mathematics, statistics, and computer science. Why Data Science? So, after knowing what exactly Data Science is, you must explore ...
Post a Comment
Read more