tablite 2022.7.dev0

A library for cleaning tabular data.

Tablite

---

Overview

We're all tired of reinventing the wheel when we need to process a bit of data.

• Pandas has a huge memory overhead when the datatypes are messy (hint: They are!).
• Numpy has become a language of it's own. It just doesn't seem pythonic anymore.
• Arrows isn't ready.
• SQLite is great but just too slow, particularly on disk.
• Protobuffer is just overkill for storing data when I still need to implement all the analytics after that.

So what do we do? We write a custom built class for the problem at hand and discover that we've just spent 3 hours doing something that should have taken 20 minutes. No more please!

Solution: Tablite

A python library for tables that does everything you need in 200kB.

Install: pip install tablite
Usage: >>> from tablite import Table

• it handles all datatypes: str, float, bool, int, date, datetime, time and type checking is automatic when you append or replace values.
• Move fluently between disk and ram using t.use_disk = True/False For 10,000,000 integers python will use 4.2Mb RAM instead of 133.7 Mb.
• it can import csv*, tsv, txt, xls, xlsx, xlsm, ods, zip and log using Table.from_file(...)
• file_reader is a generator of tables, so it doesn't take up memory until the tables are consumed.
• Iterate over rows or columns with for row in table.rows or for column in table.columns.
• Create multikey index, sort, use filter, any and all to select.
• Lookup between tables using custom functions.
• Perform multikey joins with other tables.
• Perform groupby and reorganise data as a pivot table with max, min, sum, first, last, count, unique, average, st.deviation, median and mode
• Update tables with += which automatically sorts out the columns - even if they're not in perfect order.
• Calculate out-of-memory summaries using += on groupby, f.x. groupby += t1
• you can select:
  • all rows in a column as table['A']
  • rows across all columns as table[4:8]
  • or a slice as list(table.filter('A', 'B', slice(4,8))).
• you to update with table['A'][2] = new value
• you can store or send data using json, by:
  • dumping to json: json_str = table.to_json(), or
  • you can load it with Table.from_json(json_str).-
• it automatically deduplicates header names that already are in use.
• you can add any type of metadata to the table as table(some_key='some_value') or as table.metadata['some key'] = 'some value'.
• you can ask column_xyz in Table.columns ?
• load from files with tables = list(Table.from_file('this.csv')) which has automatic datatype detection
• perform inner, outer & left sql join between tables as simple as table_1.inner_join(table2, keys=['A', 'B'])
• summarise using table.groupby( ... )
• create pivot tables using groupby.pivot( ... )
• perform multi-criteria lookup in tables using table1.lookup(table2, criteria=.....
• And everything else a python list can do, plus data type checking.

Tutorial

To learn more see tutorial.ipynb

API

To read the detailed documentation see tablite

Credits

• Martynas Kaunas - GroupBy functionality.
• Audrius Kulikajevas - Edge case testing / various bugs.