Categorical and non-categorical data in the same column I have a unique dataset that has many columns and most columns contain both categorical and non-categorical data.  For example, let's say that one column is attribute_1 and for observations that have data for attribute_1 the value can be between 100 and 1000.  If an observation does not have data for attribute_1 then they are given a value between -4 and -1, where the value describes why they don't have data for this attribute.  

How can I encode the categorical part of the columns while also applying feature scaling to the non-categorical part of the column?  Would it make sense to split the column into two where one is the categorical and another column just for the non-categorical?
If you provide more information about the detail of why there is no data for some data points that would make it easier. 

That being said, I would split it into three columns as follows:

col_1: it includes 0 and 1 >>> 1 for those samples that have a value of -4 in the original column. For the rest of the samples: 0 

col_2: it includes 0 and 1 >>> 1 for those samples that have a value of -1 in the original column. For the rest of the samples: 0 

col_3: the real values of the original columns. If the value is -4 or -1, there are two options as follows:
1. Use 0 for those samples
2. If they are missing values replace them using one of the methodologies used for replacing the missing values.