tablite 2022.10.1

multiprocessing enabled out-of-memory data analysis library for tabular data.

Tablite

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Overview

Tablite seeks to be the go-to library for tabular data with an api that is as close in synxtax to pure python as possible.

Even smaller memory footprint

Tablite uses HDF5 as a backend with strong abstraction, so that copy, append & repetition of data is handled in pages. This is imperative for incremental data processing.

Tablite tests for memory footprint. One test compares the memory footprint of 10,000,000 integers where tablite will use < 1 Mb RAM in contrast to python which will require around 133.7 Mb of RAM (1M lists with 10 integers). Tablite also tests to assure that working with 1Tb of data is tolerable.

Tablite achieves this by using HDF5 as storage which is faster than mmap'ed files for the average case [1, 2 ] and stores all data in /tmp/tablite.hdf5 so if your OS (windows/linux/mac) sits on a SSD it will benefit from high IOPS and permit slices of 9,000,000,000 rows in less than a second.

Multiprocessing enabled by default

Tablite uses multiprocessing for bypassing the GIL on all major operations. CSV import is tested with 96M fields that are imported and type-mapped to native python types in 120 secs.

All algorithms have been reworked to respect memory limits

Tablite respects the limits of free memory by tagging the free memory and defining task size before each memory intensive task is initiated (join, groupby, data import, etc)

100% support for all python datatypes

Tablite wants to make it easy for you to work with data. tablite.Table's behave like a dict with lists:

my_table[column name] = [... data ...].

Tablite uses datatype mapping to HDF5 native types where possible and uses type mapping for non-native types such as timedelta, None, date, time… e.g. what you put in, is what you get out. This is inspired by bank python.

Light weight

Tablite is ~200 kB.

Helpful

Tablite wants you to be productive, so a number of helpers are available.

• Table.import_file to import csv*, tsv, txt, xls, xlsx, xlsm, ods, zip and logs. There is automatic type detection (see tutorial.ipynb)
• To peek into any supported file use get_headers which shows the first 10 rows.
• Use mytable.rows and mytable.columns to iterate over rows or columns.
• Create multi-key .index for quick lookups.
• Perform multi-key .sort,
• Filter using .any and .all to select specific rows.
• use multi-key .lookup and .join to find data across tables.
• Perform .groupby and reorganise data as a .pivot table with max, min, sum, first, last, count, unique, average, st.deviation, median and mode
• Append / concatenate tables with += which automatically sorts out the columns - even if they're not in perfect order.
• Should you tables be similar but not the identical you can use .stack to "stack" tables on top of each other

If you're still missing something add it to the wishlist

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Installation

Tablite

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

General overview

want to...	this way...
loop over rows	[ row for row in table.rows ]
loop over columns	[ table[col_name] for col_name in table.columns ]
slice	myslice = table['A', 'B', slice(0,None,15)]
get column by name	my_table['A']
get row by index	my_table[9_000_000_001]
value update	mytable['A'][2] = new value
update w. list comprehension	mytable['A'] = [ x*x for x in mytable['A'] if x % 2 != 0 ]
join	a_join = numbers.join(letters, left_keys=['colour'], right_keys=['color'], left_columns=['number'], right_columns=['letter'], kind='left')
lookup	travel_plan = friends.lookup(bustable, (DataTypes.time(21, 10), "<=", 'time'), ('stop', "==", 'stop'))
groupby	group_by = table.groupby(keys=['C', 'B'], functions=[('A', gb.count)])
pivot table	my_pivot = t.pivot(rows=['C'], columns=['A'], functions=[('B', gb.sum), ('B', gb.count)], values_as_rows=False)
index	indices = old_table.index(*old_table.columns)
sort	lookup1_sorted = lookup_1.sort(**{'time': True, 'name':False, "sort_mode":'unix'})
filter	true, false = unfiltered.filter( [{"column1": 'a', "criteria":">=", 'value2':3}, ... more criteria ... ], filter_type='all' )
find any	any_even_rows = mytable.any('A': lambda x : x%2==0, 'B': lambda x > 0)
find all	all_even_rows = mytable.all('A': lambda x : x%2==0, 'B': lambda x > 0)
to json	json_str = my_table.to_json()
from json	Table.from_json(json_str)

Tutorial

To learn more see the tutorial.ipynb (Jupyter notebook)

Credits

• Martynas Kaunas - GroupBy functionality.
• Audrius Kulikajevas - Edge case testing / various bugs.
• realratchet - Jupyter notebook integration.