String columns with missing values

Hi guys, I have a question about this screen:

https://app.dataquest.io/m/239/processing-and-transforming-features/6/imputing-missing-values we see how to deal with numerical columns with missing values:

It looks like about half of the columns in df_missing_values are string columns ( object data type), while about half are float64 columns. For numerical columns with missing values, a common strategy is to compute the mean, median, or mode of each column and replace all missing values in that column with that value.

In the case of string columns with missing values what do you recommend to do?

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Hi again!
I’ve been doing some research and I think these are the options for dealing with missing values:

  • Drop columns
  • Drop specific rows with missing values
  • Replace values with the mode

Any other idea?

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Hi @arredocana,

I believe this article will help:

Best,
Sahil

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