Multi-index pandas dataframe, how to get list of specific indexes

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Summary: I'm trying to use pandas dataframes to store historical stock option chain information. I have the indexes set up in the following order:

  1. quote_datetime: This represents a specific time/candle that the row is from. There will be many rows of data for a specific quote_datetime.
  2. expiration: Options have an expiration date, and there are many expiration dates available at a given point in time.
  3. strike: The strike price for a given option
  4. option_type: either a P or a C for put or call.

With these 4 indexes, you can select any single row of data.

Problem: The problem isn't getting a row in question, it's trying to look up valid combinations of index values without extra information included. For example, what if I want to know which option expiries are available for trade at a specific date (quote_datetime is known, and I want to return all unique 'expirations' matching the quote_datetime key). Or I want to know all strikes available for a given quote_datetime and expiration. In these examples, I don't care about the data, I'm trying to look up which index keys are valid with only a few of the index values known.

Example: I'm dropping the option_type index and lots of data columns for this example to try and keep it small.

oc = { 'quote_datetime': ['2020-08-01', '2020-08-01', '2020-08-01', '2020-08-01', '2020-08-01', '2020-08-01', '2020-08-01', '2020-08-01', '2020-08-01', '2020-08-02', '2020-08-02', '2020-08-02', '2020-08-02', '2020-08-02', '2020-08-02'],
       'expiration': ['2020-08-01', '2020-08-01', '2020-08-01', '2020-08-03', '2020-08-03', '2020-08-03', '2020-08-05', '2020-08-05', '2020-08-05', '2020-08-03', '2020-08-03', '2020-08-03', '2020-08-05', '2020-08-05', '2020-08-05'],
       'strike': [10, 15, 20, 10, 15, 20, 10, 15, 20, 10, 15, 20, 10, 15, 20],
       'price':[3, 2, 1, 4, 3, 2, 5, 4, 3, 3.5, 2.5, 1.5, 4.5, 3.5, 2.5]}


df = pd.DataFrame(data=oc)
df = df.set_index(['quote_datetime','expiration','strike'])
df = df.sort_index()

This gives us a dataframe that looks like:

                                  price
quote_datetime expiration strike       
2020-08-01     2020-08-01 10        3.0
                          15        2.0
                          20        1.0
               2020-08-03 10        4.0
                          15        3.0
                          20        2.0
               2020-08-05 10        5.0
                          15        4.0
                          20        3.0
2020-08-02     2020-08-03 10        3.5
                          15        2.5
                          20        1.5
               2020-08-05 10        4.5
                          15        3.5
                          20        2.5

Let's say I want to see all expiries available on August 2nd.

df.loc['2020-08-02'].index.levels[0]

I expect to receive ['2020-08-03', '2020-08-05'], but instead get

Index(['2020-08-01', '2020-08-03', '2020-08-05'], dtype='object', name='expiration')

'2020-08-01' isn't a valid expiry for the quote_datetime I used in the .loc[]. It seems that the .levels simply returns every index in the dataframe, ignoring that I filtered rows using .loc.

I've also tried

df.loc['2020-08-02'].index.get_level_values(0)

but it returns every row instead of the unique indexes. This almost works, except that I'd have to run it through a function to get the unique combinations.

Index(['2020-08-03', '2020-08-03', '2020-08-03', '2020-08-05', '2020-08-05',
       '2020-08-05'],
      dtype='object', name='expiration')

This is being done on a 20 GB .csv with a lot of rows, so I'm trying to keep it light and fast if at all possible... Though at this point, it would be nice just to get the data I need. I'm not very well versed in using python for this type of work, but doing so allows me to take advantage of libraries that already exist.

pandas dataframe python
2 Answers

You can combine .loc and index.unique() together and pass the required level to get the indices at other levels. Also, since these are multi-indices, I'd suggest you to use tuple for using .loc even for a single level index to make everything look consistent.

# To get unique expiration indices for given quote date index

>>> df.loc[('2020-08-02',)].index.unique(0)
Index(['2020-08-03', '2020-08-05'], dtype='object', name='expiration')


#To get unique strike indices for a given quote date index

>>> df.loc[('2020-08-02',)].index.unique(1)
Int64Index([10, 15, 20], dtype='int64', name='strike')

#To know the strike indices for given quote and expiration date indices

>>> df.loc[('2020-08-01', '2020-08-01')].index.unique(0)
Int64Index([10, 15, 20], dtype='int64', name='strike')
user_91363480

The main problem (based on the code you provided) is that your date fields are not datetime types.

So, first confirm that 'quote_datetime' and 'expiration' are datetime types. Assuming they are not, you can alter existing fields in-place

df.reset_index(inplace=True)

# make cols datetime
df.quote_datetime = pd.to_datetime(df.quote_datetime)
df.expiration = pd.to_datetime(df.expiration)

df = df.set_index(['quote_datetime','expiration','strike'])

Or, I prefer starting with the initial csv import statement which should look something like:

df = pd.read_csv("file.csv", header=0, parse_dates=['quote_datetime', 'expiration'], keep_date_col=False)

At this point, you can slice based on the dates (as attempted in your post). To return a subsetted dataframe you can work with:

# see https://stackoverflow.com/a/45270356/9249533
df.loc[pd.IndexSlice['2020-08-02':'2020-08-02',:], :]

or to get list of unique dates as you wanted:

df.loc[pd.IndexSlice['2020-08-02':'2020-08-02',:], :].index.unique(level=1).tolist()

which returns a list of timestamps you can reformat as needed.

[Timestamp('2020-08-03 00:00:00'), Timestamp('2020-08-05 00:00:00')]

[i.strftime('%Y-%m-%d') for i in lst]

to get

['2020-08-03', '2020-08-05']

You're right to attempt to use via the Index. Running on regular fields can be done, but is verbose and I suspect suffer performance given your file size. For comparative purposes (assuming you've run my first block without the last line)...

# add logical test to subset data
d_test = '2020-08-02'
df['expiration'].loc[df['quote_datetime'] >= d_test].unique()

returns

array(['2020-08-03T00:00:00.000000000', '2020-08-05T00:00:00.000000000'], dtype='datetime64[ns]')

So, adapting the logic from this post:

[dt.fromtimestamp(t.item() / 10**9).strftime('%Y-%m-%d') for t in df['expiration'].loc[df['quote_datetime'] >= d].unique()]

['2020-08-03', '2020-08-05']

user_92495330
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