CODEWITHSUNDEEP
pythonpandasdataframe
Serdia
Serdia

Reputation: 4418

select columns based on multiple conditions in Pandas

How to translate SQL statement to Pandas:

select PolicyNumber, 
       Coverage 
  from  ClaimsData 
  where AccidentState = 'AZ' and Coverage = 'Liability'

I know how select columns for single condition (AccidentState = 'AZ') but how can I add another condition?

ClaimsData[["PolicyNumber","Coverage","AccidentState"]] [ClaimsData["AccidentState"]=="AZ"]  # and here I need to add another condition

Upvotes: 3

Views: 5110

Answers (1)

jezrael
jezrael

Reputation: 862406

You can chain conditions by & for bitwise AND and filter by DataFrame.loc rows by conditions (boolean indexing) and columns by columns names in list:

mask = (ClaimsData["AccidentState"]=="AZ") & (ClaimsData["Coverage"]=="Liability")
df = ClaimsData.loc[mask, ["PolicyNumber","Coverage"]]

Like @Sandeep Kadapa pointed in comments there is alternative with DataFrame.query:

q = "AccidentState == 'AZ' & Coverage == 'Liability'"
df = ClaimsData.query(q)[["PolicyNumber","Coverage"]]

Upvotes: 9

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