sparse-numeric-table-sebastian-achim-mueller 0.0.6

Read, write, and query sparse tables

Sparse-Numeric-Table

Query, write, and read sparse, numeric tables.

I love pandas.DataFrame and numpy.recarray, but with large and sparse tables I run out of memory or struggle to represent empty integer fields with the float's NaN.

Here I use a dict of numpy.recarrays to represent large and sparse tables. Writing into tarfiles (.tar) preserves the table's hirachy and makes it easy to explore in the file-system. I use pandas.merge to query.

Restictions

• Only numeric fields
• Index is unsigned integer

Pros

• Fast read / write with numpy binaries (explicit endianness).
• Just a dict of numpy.recarrays. No classes. No stateful functions.
• Easy to explore files in the tapearchive .tar.

Features

• Read from file / write to file.
• Create from 'records' (A list of dicts, each representing one row in the table)
• Query, cut, and merge on row-indices (columns can be omitted for speed)
• Concatenate files.

Usage

See ./sparse_numeric_table/tests.

1st) You create a dict representing the structure and dtype of your table. Columns which only appear together are bundeled into a level. Each level has an index to merge and join with other levels.

my_table_structure = {
    "A": {
        "a": {"dtype": "<u8"},
        "b": {"dtype": "<f8"},
        "c": {"dtype": "<f4"},
    },
    "B": {
        "g": {"dtype": "<i8"},
    },
    "C": {
        "m": {"dtype": "<i2"},
        "n": {"dtype": "<u8", "comment": "Some comment related to 'n'."},
    },
}

Here A, B, and C are the level-keys. a, ... , n are the column-keys. You can add comments for yourself, but sparse_numeric_table will ignore these.

2nd) You create/read/write the table.

     A             B         C

     idx a b c     idx g     idx m n
     ___ _ _ _     ___ _
    |_0_|_|_|_|   |_0_|_|
    |_1_|_|_|_|
    |_2_|_|_|_|    ___ _
    |_3_|_|_|_|   |_3_|_|
    |_4_|_|_|_|   |_4_|_|    ___ _ _
    |_5_|_|_|_|   |_5_|_|   |_5_|_|_|
    |_6_|_|_|_|
    |_7_|_|_|_|
    |_8_|_|_|_|    ___ _
    |_9_|_|_|_|   |_9_|_|
    |10_|_|_|_|   |10_|_|
    |11_|_|_|_|    ___ _     ___ _ _
    |12_|_|_|_|   |12_|_|   |12_|_|_|
    |13_|_|_|_|    ___ _
    |14_|_|_|_|   |14_|_|