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

Important Python Libraries for Data Science

Python is the most widely used programming language today. When it comes to solving data science tasks and challenges, Python never ceases to surprise its users. Most data scientists are already leveraging the power of Python programming every day. Python is an easy-to-learn, easy-to-debug, widely used, object-oriented, open-source, high-performance language, and there are many more benefits to Python programming.People in Data Science definitely know about the Python libraries that can be used in Data Science but when asked in an interview to name them or state its function, we often fumble up or probably not remember more than 5 libraries. Important Python Libraries for Data Science: Pandas NumPy SciPy Matplotlib TensorFlow Seaborn Scikit Learn Keras 1. Pandas Pandas (Python data analysis) is a must in the data science life cycle. It is the most popular and widely used Python library for data science, along with NumPy in matplotlib. With around 17,00 comments on GitH...
Post a Comment
Read more

R vs Python: Who is the Winner according to me...!!

As a data scientist, you probably want and need to learn Structured Query Language, or SQL. SQL is the de-facto language of relational databases, where most corporate information still resides. But that only gives you the ability to retrieve the data — not to clean it up or run models against it — and that’s where Python and R come in.R and Python both share similar features and are the most popular tools used by data scientists. Both are open-source and henceforth free yet Python is structured as a broadly useful programming language while R is created for statistical analysis. A little background on R R was created by Ross Ihaka and Robert Gentleman — two statisticians from the University of Auckland in New Zealand. It was initially released in 1995 and they launched a stable beta version in 2000. It’s an interpreted language (you don’t need to run it through a compiler before running the code) and has an extremely powerful suite of tools for statistical modeling and graphing...
Post a Comment
Read more

Differentiate between univariate, bivariate and multivariate analysis.

Univariate analysis are descriptive statistical analysis techniques which can be differentiated based on one variable involved at a given point of time. For example, the pie charts of sales based on territory involve only one variable and can the analysis can be referred to as univariate analysis. The bivariate analysis attempts to understand the difference between two variables at a time as in a scatterplot. For example, analyzing the volume of sale and spending can be considered as an example of bivariate analysis. Multivariate analysis deals with the study of more than two variables to understand the effect of variables on the responses.
Post a Comment
Read more