I get an error saying something along the lines of 'cannot use nlargest because of wrong datatype' and I can't seem to figure out what's wrong. If anyone could help, that'd be wonderful. Thanks!
The error occurs when the pipeline dataframe is empty. The nlargest method doesn't play well with empty dataframes. So, maybe check that the dataframe isn't empty something like this
secs = df['rank'].nlargest(20).index if not df.empty else []
Also, note that there may be times where the nlargest method returns less than 20 stocks. This would happen if the dataframe has less than 20 to begin with.
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The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian.
In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.