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How do you deal with NA in categorical data?
How to handle missing values of categorical variables?
- Ignore these observations.
- Replace with general average.
- Replace with similar type of averages.
- Build model to predict missing values.
What are levels in categorical data?
Categorical variables (also known as factor or qualitative variables) are variables that classify observations into groups. They have a limited number of different values, called levels. For example the gender of individuals are a categorical variable that can take two levels: Male or Female.
How to fill NaN values in categorical data?
Real-world data is full of missing values. In order to work on them, we need to impute these missing values and draw meaningful conclusions from them. In this article, we will discuss how to fill NaN values in Categorical Data.
How to fill missing values in categorical data?
Let’s first create a sample dataset to understand methods of filling missing values: To fill missing values in Categorical features, we can follow either of the approaches mentioned below – One approach to fill these missing values can be to replace them with the most common or occurring class.
Which is an example of a categorical data structure?
Internally, the data structure consists of a categories array and an integer array of codes which point to the real value in the categories array. The categorical data type is useful in the following cases: A string variable consisting of only a few different values.
How to handle NaNs in categorical data in pandas?
In [132]: df Out [132]: col1 col2 0 5 cloudy 1 3 windy 2 6 rainy 3 7 rainy 4 10 cloudy 5 5 cloudy 6 10 cloudy 7 7 rainy I know your asking for linear interpolation but this is just another way if you want to do this easier.As converting categories to Numbers isn’t such a good idea I suggest this one.