I have a classification case study where I am using Logistic Regression model. I want to use One Hot Encoding to convert my categorical column (SalStat) values into 0 and 1. This is my code:
data2["SalStat"] = data2["SalStat"].map({"less than or equal to 50,000":0, "greater than 50,000":1})
print(data2["SalStat"])
Above code does not convert the values to 0
and 1
but instead converts them to nan!
Where am I going wrong?
PS: The SalStat column classifies rows as "less than or equal to 50,000" or "greater than 50,000"
I guess it throws error because of the values in SalStat column. It is better to assign them to a variable instead of typing manually.
val_1 = data2["SalStat"].unique()[0]
val_2 = data2["SalStat"].unique()[1]
data2["SalStat"] = data2["SalStat"].map({val_1 :0, val_2 :1})
print(data2["SalStat"])