issue_comments

Example output: ``` % sqlite-utils analyze-tables github.db tags
tags.repo: (1/3)

Total rows: 261 Null rows: 0 Blank rows: 0

Distinct values: 14

Most common: 88: 107914493 75: 140912432 27: 206156866 21: 207052882 17: 197431109 8: 197882382 5: 256834907 5: 205429375 4: 248903544 3: 206202864

Least common: 1: 209590345 2: 206649770 2: 303218369 3: 206202864 3: 213286752 4: 248903544 5: 205429375 5: 256834907 8: 197882382 17: 197431109

tags.name: (2/3)

Total rows: 261 Null rows: 0 Blank rows: 0

Distinct values: 175

Most common: 10: 0.2 9: 0.1 7: 0.3 6: 0.4 5: 0.7 5: 0.5 5: 0.1a 4: 0.9 4: 0.8 4: 0.6

Least common: 1: 0.1.1 1: 0.11.1 1: 0.1a2 1: 0.20.1 1: 0.21.1 1: 0.21.2 1: 0.21.3 1: 0.22 1: 0.22.1 1: 0.23

tags.sha: (3/3)

Total rows: 261 Null rows: 0 Blank rows: 0

Distinct values: 261 ```

% sqlite-utils analyze-tables ../datasette/fixtures.db facetable --column pk 1/1: ColumnDetails(table='facetable', column='pk', total_rows=15, num_null=0, num_blank=0, num_distinct=15, most_common=None, least_common=None)

It would be neat if you could optionally specify a subset of columns to analyze, using -c or --column.

If there are less than ten values is it worth outputting them twice, once in most_common and then in reverse in least_common? Feels redundant - I think I should leave least_common empty in that case.

CLI output looks like this at the moment, which is bad: % sqlite-utils analyze-tables ../datasette/fixtures.db facetable 1/10: ColumnDetails(table='facetable', column='pk', total_rows=15, num_null=0, num_blank=0, num_distinct=15, most_common=None, least_common=None) 2/10: ColumnDetails(table='facetable', column='created', total_rows=15, num_null=0, num_blank=0, num_distinct=4, most_common=[('2019-01-17 08:00:00', 4), ('2019-01-15 08:00:00', 4), ('2019-01-14 08:00:00', 4), ('2019-01-16 08:00:00', 3)], least_common=[('2019-01-16 08:00:00', 3), ('2019-01-14 08:00:00', 4), ('2019-01-15 08:00:00', 4), ('2019-01-17 08:00:00', 4)]) 3/10: ColumnDetails(table='facetable', column='planet_int', total_rows=15, num_null=0, num_blank=0, num_distinct=2, most_common=[(1, 14), (2, 1)], least_common=[(2, 1), (1, 14)]) 4/10: ColumnDetails(table='facetable', column='on_earth', total_rows=15, num_null=0, num_blank=0, num_distinct=2, most_common=[(1, 14), (0, 1)], least_common=[(0, 1), (1, 14)]) 5/10: ColumnDetails(table='facetable', column='state', total_rows=15, num_null=0, num_blank=0, num_distinct=3, most_common=[('CA', 10), ('MI', 4), ('MC', 1)], least_common=[('MC', 1), ('MI', 4), ('CA', 10)]) 6/10: ColumnDetails(table='facetable', column='city_id', total_rows=15, num_null=0, num_blank=0, num_distinct=4, most_common=[(1, 6), (3, 4), (2, 4), (4, 1)], least_common=[(4, 1), (2, 4), (3, 4), (1, 6)]) 7/10: ColumnDetails(table='facetable', column='neighborhood', total_rows=15, num_null=0, num_blank=0, num_distinct=14, most_common=[('Downtown', 2), ('Tenderloin', 1), ('SOMA', 1), ('Mission', 1), ('Mexicantown', 1), ('Los Feliz', 1), ('Koreatown', 1), ('Hollywood', 1), ('Hayes Valley', 1), ('Greektown', 1)], least_common=[('Arcadia Planitia', 1), ('Bernal Heights', 1), ('Corktown', 1), ('Dogpatch', 1), ('Greektown', 1), ('Hayes Valley', 1), ('Hollywood', 1), ('Koreatown', 1), ('Los Feliz', 1), ('Mexicantown', 1)]) 8/10: ColumnDetails(table='facetable', column='tags', total_rows=15, num_null=0, num_blank=0, num_distinct=3, most_common=[('[]', 13), ('["tag1", "tag3"]', 1), ('["tag1", "tag2"]', 1)], least_common=[('["tag1", "tag2"]', 1), ('["tag1", "tag3"]', 1), ('[]', 13)]) 9/10: ColumnDetails(table='facetable', column='complex_array', total_rows=15, num_null=0, num_blank=0, num_distinct=2, most_common=[('[]', 14), ('[{"foo": "bar"}]', 1)], least_common=[('[{"foo": "bar"}]', 1), ('[]', 14)]) 10/10: ColumnDetails(table='facetable', column='distinct_some_null', total_rows=15, num_null=13, num_blank=0, num_distinct=2, most_common=[(None, 13), ('two', 1), ('one', 1)], least_common=[('one', 1), ('two', 1), (None, 13)]) (sqlite-utils) sqlite-utils %

Should truncate values in the least/most common JSON array to a sensible length, otherwise you end up with stuff like this: json [ [ "b'\\x00\\x05barry\\x03\\x01\\x02\\x00\\x00\\x03cat\\x03\\x01\\x03\\x00\\x00\\x03dog\\x08\\x01\\x01\\x01\\x03\\x00\\x01\\x03\\x00\\x00\\x07panther\\x05\\x01\\x01\\x02\\x02\\x00\\x01\\x03uma\\x05\\x02\\x01\\x02\\x02\\x00\\x00\\x04sara\\x05\\x02\\x01\\x01\\x02\\x00\\x00\\x05terry\\x08\\x01\\x01\\x01\\x02\\x00\\x01\\x02\\x00\\x00\\x06weasel\\x05\\x02\\x01\\x01\\x03\\x00'", 1 ] ] This example also shows that binary values (like those in _fts tables) look a bit weird, but I think I'm OK with that since binary data can't be represented neatly in JSON anyway.