Don't get mad, get
Tabular data and SQL for people who don't have time to faff about.
Move between xlsx, xls, csv, python, postgres and back with ease.
- Zero-boilerplate database creating, connecting and querying.
- Loading/tidying/transforming csv and excel data.
- Autodetect column types, load your data with little or no manual specification.
- Powerful multi-column, multi-order keyset paging of database results.
- Schema syncing.
- Python 3.6+, PostgreSQL 10+ only. Many features will work with other databases, but many won't. Just use Postgres!
results is on PyPI. Install it with
pip or any of the (many) Python package managers.
Somebody gives you a messy csv or excel file. You need to load it, clean it up, put it into a database, query it, make a pivot table from it, then send the pivot table to somebody as a csv.
results is here to get this sort of thing done quickly and with minimum possible fuss.
First, load and clean:
import results # load a csv (in this example, some airport data) sheet = results.from_file("airports.csv") # do general cleanup sheet.standardize_spaces() sheet.set_blanks_to_none() # give the keys lowercase-with-underscore names to keep the database happy cleaned = sheet.with_standardized_keys()
Then, create a database:
# create a database DB = "postgresql:///example" db = results.db(DB) # create it if it doesn't exist db.create_database()
Then create a table for the data, automatically guessing the columns and creating a table to match.
# guess the column types guessed = cleaned.guessed_sql_column_types() # create a table for the data create_table_statement = results.create_table_statement("data", guessed) # create or auto-update the table structure in the database # syncing requires a copy of postgres running locally with your current user set up as superuser db.sync_db_structure_to_definition(create_table_statement, confirm=False)
Then insert the data and freely query it.
# insert the data. you can also do upserts with upsert_on! db.insert("data", cleaned) # show recent airfreight numbers from the top 5 airports # ss means "single statement" query_result = db.ss( """ with top5 as ( select foreignport, sum(freight_in_tonnes) from data where year >= 2010 group by foreignport order by 2 desc limit 5 ) select year, foreignport, sum(freight_in_tonnes) from data where year >= 2010 and foreignport in (select foreignport from top5) group by 1, 2 order by 1, 2 """ )
Create a pivot table, then print it as markdown or save it as csv.
# create a pivot table pivot = query_result.pivoted() # print the pivot table in markdown format print(pivot.md)
| year | Auckland | Dubai | Hong Kong | Kuala Lumpur | Singapore | |-------:|-----------:|---------:|------------:|---------------:|------------:| | 2010 | 288997 | 145527 | 404735 | 226787 | 529407 | | 2011 | 304628 | 169868 | 428990 | 244053 | 583921 | | 2012 | 312828 | 259444 | 400596 | 272093 | 614155 | | 2013 | 306783 | 257263 | 353895 | 272804 | 592886 | | 2014 | 309318 | 244776 | 330521 | 261438 | 620419 | | 2015 | 286202 | 263378 | 290292 | 252906 | 633862 | | 2016 | 285973 | 236419 | 309556 | 175858 | 614172 | | 2017 | 314405 | 226048 | 340216 | 199868 | 662505 | | 2018 | 126712 | 91611.2 | 134540 | 74667.5 | 250653 |
Save the table as a csv:
Avoid boilerplate at all costs. Make it as simple as possible but no simpler.
Don't reinvent the wheel:
resultsuses sqlalchemy for database connections, existing excel parsing libraries for excel parsing, etc etc.
resultsbrings it all together, sprinkles some sugar on top, and puts it at your fingertips.
Eat your own dogfood: We use this ourselves every day.
This README.md is currently all there is :( But we'll add more soon, we promise!