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Using an imported CSV file, I indexed the DataFrame like this...
rdata.set_index(['race_date', 'track_code', 'race_number', 'horse_name'])
This is what a section of the DataFrame looks like...
race_date track_code race_number horse_name work_date work_track
2007-08-24 BM 8 Count Me Twice 2007-05-31 PLN
Count Me Twice 2007-06-09 PLN
Count Me Twice 2007-06-16 PLN
Count Me Twice 2007-06-23 PLN
Count Me Twice 2007-08-05 PLN
Judge's Choice 2007-06-07 BM
Judge's Choice 2007-06-14 BM
Judge's Choice 2007-07-08 BM
Judge's Choice 2007-08-18 BM
Why isn't the 'horse_name' column being grouped like the date, track and race? Perhaps it's by design, thus how can I slice this larger DataFrame by race to have a new DataFrame with 'horse_name' as its index?
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Pandas set_index() is a method to set a List, Series or Data frame as index of a Data Frame. Index column can be set while making a data frame too. But sometimes a data frame is made out of two or more data frames and hence later index can be changed using this method.
To change the index values we need to use the set_index method which is available in pandas allows specifying the indexes. where, inplace parameter accepts True or False, which specifies that change in index is permanent or temporary. True indicates that change is Permanent.
pandas DataFrame to CSV with no index can be done by using index=False param of to_csv() method. With this, you can specify ignore index while writing/exporting DataFrame to CSV file.
Set index by keeping old indexset_index() is used to set a new index to the DataFrame. It is also used to extend the existing DataFrame, i.e., we can update the index by append to the existing index. We need to use the append parameter of the DataFrame. set_index() function to append the new index to the existing one.
It's not a bug. This is exactly how it's intended to work.
DataFrame has to show show every single item in it's data. So if the index has one level, that level will be fully expanded. If it has two levels, first level will be grouped and the second will be fully expanded, if it has tree levels, first two will be grouped and the third will be expanded, and so on.
So this is why the horse name is not grouped. How would you be able to see all the items in the DataFrame if you group also by the horse name :)
Try doing:
rdata.set_index(['race_date', 'track_code', 'race_number'])
or:
rdata.set_index(['race_date', 'track_code'])
You'll see that the last level of the index is always fully expanded, to enable you to see all the items in the DataFrame.
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