No clean way to count duplicate items in a Table, with Table API about datascience HOT 3 CLOSED

We removed this operation from the class because it is pretty easy to get
with numpy.unique. You need to ask it for both unique values and the map.
Absolutely important so we should have the example.
On Sep 14, 2015 3:06 PM, "davidwagner" [email protected] wrote:

Goal: Given a table T and a column C, build a new table that has one row
for each unique value in T.C along with a count of the number of times that
value appears in T.C.

I was not able to find any clean way to do this within the Table API.
Should this be doable using Tables, without leaving Table space and going
back to arrays and raw Python?

Here is the solution I came up with:

from collections import Counter
c = Counter(origtbl['column_label'])
t = Table.from_rows(c.items(), ['column_label', 'count'])

Not so terrible if you know Python idioms, but also probably not so
discoverable for students. Should there be an API in Table that's
accessible to students that allows performing this kind of task? Or some
suitable generalized primitive, which is enough to solve this problem?

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#68.

from datascience.

Cool. Thank you. Note to future self: np.unique(x, return_counts=True)

from datascience.

Here's the example

T = Table([['A','A','B','C','A']],['letters'])
labels, counts = np.unique(T['letters'],return_counts=True)
R = Table(list(counts),list(labels))

from datascience.