Pandas tools you didn’t know you needed, part 1: Apply

Dataframe rows can contain arrays rather than a single value. This is the case with Datajoint's "blobs." Let's examine an instance of such a dataframe I fetched from a datajoint table:

We'd like to get the interspike intervals.
In this case, 
np.diff(df['spike_time'])
will not work as it will attempt to work across rows:

array([array([16.10803333, 35.39643333, 34.54946667, 35.66286667, 24.33606667,
37.98336667, 51.62246667, 52.10143333, 53.9414 , 63.69666667]),
array([-16.2661 , 78.14463333, 77.79163333, 120.67333333,
296.20163333, 285.72446667, 269.20226667, 269.53706667,
265.94483333, 255.13863333]),
array([ 62.5528 , 29.845 , 69.64793333, 27.54353333,
-61.65676667, -41.7106 , -20.2559 , -8.94323333,
-0.19113333, 1.96883333]),
...,
array([29.80516667, 38.70003333, 40.26796667, 53.5446 , 45.7118 ,
55.4116 , 55.9258 , 79.12003333, 77.03993333, 83.48073333]),
array([-11.45576667, -11.35606667, -9.69276667, -23.187 ,
-22.03416667, -31.00213333, -29.06093333, -51.92653333,
-51.33696667, -57.59203333]),
array([ 72.3652 , 92.1018 , 157.1723 , 173.74453333,
185.13646667, 188.13473333, 187.84116667, 187.3681 ,
206.6341 , 206.1082 ])], dtype=object)

You can use a for loop, but there is an easier way.

To get our operation of interest to work row by row without looping, use apply:

df['ISI']=df['spike_time'].apply(np.diff)
df['ISI']

This get the mean without having to resort to looping over rows. If you have a more complex procedure in mind, simply define it before using apply:

def inverse_isi(spikes):
    """
    Inverse mean ISI of spikes is an alternative measure of firing rate
    """
    return 1/np.mean(np.diff(spikes))

and then apply it in the same way

df['ISI-based firing rate']=df['blob_column'].apply(inverse_isi)