id,node_id,number,title,user,user_label,state,locked,assignee,assignee_label,milestone,milestone_label,comments,created_at,updated_at,closed_at,author_association,pull_request,body,repo,repo_label,type,active_lock_reason,performed_via_github_app,reactions,draft,state_reason 403922644,MDU6SXNzdWU0MDM5MjI2NDQ=,8,Problems handling column names containing spaces or - ,82988,psychemedia,closed,0,,,,,3,2019-01-28T17:23:28Z,2019-04-14T15:29:33Z,2019-02-23T21:09:03Z,NONE,,"Irrrespective of whether using column names containing a space or - character is good practice, SQLite does allow it, but `sqlite-utils` throws an error in the following cases: ```python from sqlite_utils import Database dbname = 'test.db' DB = Database(sqlite3.connect(dbname)) import pandas as pd df = pd.DataFrame({'col1':range(3), 'col2':range(3)}) #Convert pandas dataframe to appropriate list/dict format DB['test1'].insert_all( df.to_dict(orient='records') ) #Works fine ``` However: ```python df = pd.DataFrame({'col 1':range(3), 'col2':range(3)}) DB['test1'].insert_all(df.to_dict(orient='records')) ``` throws: ``` --------------------------------------------------------------------------- OperationalError Traceback (most recent call last) in () 1 import pandas as pd 2 df = pd.DataFrame({'col 1':range(3), 'col2':range(3)}) ----> 3 DB['test1'].insert_all(df.to_dict(orient='records')) /usr/local/lib/python3.7/site-packages/sqlite_utils/db.py in insert_all(self, records, pk, foreign_keys, upsert, batch_size, column_order) 327 jsonify_if_needed(record.get(key, None)) for key in all_columns 328 ) --> 329 result = self.db.conn.execute(sql, values) 330 self.db.conn.commit() 331 self.last_id = result.lastrowid OperationalError: near ""1"": syntax error ``` and: ```python df = pd.DataFrame({'col-1':range(3), 'col2':range(3)}) DB['test1'].upsert_all(df.to_dict(orient='records')) ``` results in: ``` --------------------------------------------------------------------------- OperationalError Traceback (most recent call last) in () 1 import pandas as pd 2 df = pd.DataFrame({'col-1':range(3), 'col2':range(3)}) ----> 3 DB['test1'].insert_all(df.to_dict(orient='records')) /usr/local/lib/python3.7/site-packages/sqlite_utils/db.py in insert_all(self, records, pk, foreign_keys, upsert, batch_size, column_order) 327 jsonify_if_needed(record.get(key, None)) for key in all_columns 328 ) --> 329 result = self.db.conn.execute(sql, values) 330 self.db.conn.commit() 331 self.last_id = result.lastrowid OperationalError: near ""-"": syntax error ```",140912432,sqlite-utils,issue,,,"{""url"": ""https://api.github.com/repos/simonw/sqlite-utils/issues/8/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed 403625674,MDU6SXNzdWU0MDM2MjU2NzQ=,7,.insert_all() should accept a generator and process it efficiently,9599,simonw,closed,0,,,,,3,2019-01-28T02:11:58Z,2019-01-28T06:26:53Z,2019-01-28T06:26:53Z,OWNER,,"Right now you have to load every record into memory before passing the list to `.insert_all()` and friends. If you want to process millions of rows, this is inefficient. Python has generators - we should use them! The only catch here is that part of the magic of `sqlite-utils` is that it guesses the column types and creates the table for you. This code will need to be updated to notice if the table needs creating and, if it does, create it using the first X (where x=1,000 but can be customized) records. If a record outside of those first 1,000 has a rogue column, we can crash with an error. This will free us up to make the `--nl` option added in #6 much more efficient.",140912432,sqlite-utils,issue,,,"{""url"": ""https://api.github.com/repos/simonw/sqlite-utils/issues/7/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed 403624090,MDU6SXNzdWU0MDM2MjQwOTA=,6,"""sqlite-utils insert"" should support newline-delimited JSON",9599,simonw,closed,0,,,,,1,2019-01-28T02:00:02Z,2019-01-28T02:17:45Z,2019-01-28T02:17:45Z,OWNER,,"We can already export newline delimited JSON. We should learn to import it as well. The neat thing about importing it is that you can import GBs of data without having to read the whole lot into memory in order to decode the wrapping JSON array. Datasette can export it now: https://github.com/simonw/datasette/issues/405 Demo: https://latest.datasette.io/fixtures/facetable.json?_shape=array&_nl=on It should be possible to do this: $ curl ""https://latest.datasette.io/fixtures/facetable.json?_shape=array&_nl=on"" \ | sqlite-utils insert data.db facetable - --nl ",140912432,sqlite-utils,issue,,,"{""url"": ""https://api.github.com/repos/simonw/sqlite-utils/issues/6/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed