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Manipulating dataframes on notebook
Lahiru Fernando
edited

Hello all,

I would like to get a better understanding of manipulating multi-index dataframes. Given a df created by pipeline for a period I would like to extract the equities and it's related data by date. Meaning I want all of the equities and its pipeline data for one date at a time, I would then like to assign this to a new df_1 and manipulate and generate scores based on my own ideas. Any ideas as to do this without unstacking? I personally don't want to unstack because it takes quite along time in which the full dataframe is needed to run and generate daily symbols. I will try to attach a unstack example which I got the results I needed but it took way to long and it got to messy. I would like the notebook to run like the algorithm in which only daily data is given rather than all data. Help would be much appreciated, Thank you in advance!

2 responses
Lahiru Fernando

If anyone is interested I was able to iterate through using the groupby function.

df_group = df.groupby(level=1)

Then iterate through the dates with each date for all stocks by:

for i in range(# of dates available):
print df_group.nth(i) #prints all data available for stocks for that date

Dan Whitnable

A simple way to get a 'slice' of a multi indexed dataframe is to use the pandas xs method (https://pandas.pydata.org/pandas-docs/version/0.18/generated/pandas.DataFrame.xs.html ) Given a date, it will return a new single index dataframe with data just for that date. This will look identical to the pipeline dataframe in an algo. Something like this

df = run_pipeline(make_pipeline(), start, end)  
df_1 = df.xs('1-2-2018')

One can also slice by equity. Instead of a single date and all equities, one can get a single equity and all dates. Like this

df = run_pipeline(make_pipeline(), start, end)  
df_1 = df.xs(symbols('AAPL'), level=1)

That will fetch just the data for AAPL. Note the index level needs to be specified if not level=0.

Another option is to use the ubiquitous loc method. 

df = run_pipeline(make_pipeline(), start, end)  
df.loc['1-2-2018']

This will keep the multi index dataframe but just fetch data for single date.

See the attached notebook.

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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.