Adding Notebook 3 and Function Library #49
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analysis_examples/3._Segmenting_Loan_Application_Data_by_Type_of_Loan.py
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analysis_examples/3._Segmenting_Loan_Application_Data_by_Type_of_Loan.py
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analysis_examples/3._Segmenting_Loan_Application_Data_by_Type_of_Loan.py
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| def lar_select_by_product( | ||
| schema_name="hmda_public", | ||
| table_name="lar_{year}", | ||
| select="*", |
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Do you have logic that unpacks user defined parameters passed to the "select" argument?
I don't see them and without something it is likely that user inputs will result in an error.
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Sure, we can make a change. Would you be referring to how users name variables to be called against the database?
| return var | ||
| #The case for one product code. | ||
| else: | ||
| #Converts to a string with parentheses. |
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Check into using tuples, they're pretty cool.
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I can look into this! I avoided using the tuple function on single values at the moment because the output tended to be ("value", ). If there is a better way to do this I am open to it!
analysis_examples/3._Segmenting_Loan_Application_Data_by_Type_of_Loan.py
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| # A table of LAR data can be transformed into numerics by casting | ||
| # variables individually in the select statement. Non-categorical | ||
| # variables, such as loan amount or income, can be converted to | ||
| # numerics while maintaining non-categorical variables, such as |
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The discussion here should include bit on handling NA values when mixed with numeric data, such as in the income field.
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Sure! We can discuss.
analysis_examples/3._Segmenting_Loan_Application_Data_by_Type_of_Loan.py
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Close #40 and #43