├── tabel.egg-info ├── top_level.txt ├── dependency_links.txt ├── requires.txt ├── SOURCES.txt └── PKG-INFO ├── tabel ├── _version.py ├── git_version.py ├── numpy_types.py ├── __init__.py ├── util.py └── hashjoin.py ├── .flake8 ├── docs ├── source │ ├── license.rst │ ├── api │ │ ├── index.rst │ │ ├── module.rst │ │ └── tabel.rst │ ├── readme.rst │ ├── index.rst │ ├── conf.py │ └── performance.rst ├── test.npz ├── build │ ├── html │ │ ├── _sources │ │ │ ├── license.rst.txt │ │ │ ├── api │ │ │ │ ├── index.rst.txt │ │ │ │ ├── module.rst.txt │ │ │ │ └── tabel.rst.txt │ │ │ ├── readme.rst.txt │ │ │ ├── index.rst.txt │ │ │ └── performance.rst.txt │ │ ├── objects.inv │ │ ├── _static │ │ │ ├── up.png │ │ │ ├── down.png │ │ │ ├── file.png │ │ │ ├── minus.png │ │ │ ├── plus.png │ │ │ ├── comment.png │ │ │ ├── up-pressed.png │ │ │ ├── ajax-loader.gif │ │ │ ├── down-pressed.png │ │ │ ├── comment-bright.png │ │ │ ├── comment-close.png │ │ │ ├── fonts │ │ │ │ ├── Lato │ │ │ │ │ ├── lato-bold.eot │ │ │ │ │ ├── lato-bold.ttf │ │ │ │ │ ├── lato-bold.woff │ │ │ │ │ ├── lato-bold.woff2 │ │ │ │ │ ├── lato-italic.eot │ │ │ │ │ ├── lato-italic.ttf │ │ │ │ │ ├── lato-italic.woff │ │ │ │ │ ├── lato-italic.woff2 │ │ │ │ │ ├── lato-regular.eot │ │ │ │ │ ├── lato-regular.ttf │ │ │ │ │ ├── lato-regular.woff │ │ │ │ │ ├── lato-regular.woff2 │ │ │ │ │ ├── lato-bolditalic.eot │ │ │ │ │ ├── lato-bolditalic.ttf │ │ │ │ │ ├── lato-bolditalic.woff │ │ │ │ │ └── lato-bolditalic.woff2 │ │ │ │ ├── fontawesome-webfont.eot │ │ │ │ ├── fontawesome-webfont.ttf │ │ │ │ ├── fontawesome-webfont.woff │ │ │ │ ├── fontawesome-webfont.woff2 │ │ │ │ └── RobotoSlab │ │ │ │ │ ├── roboto-slab-v7-bold.eot │ │ │ │ │ ├── roboto-slab-v7-bold.ttf │ │ │ │ │ ├── roboto-slab-v7-bold.woff │ │ │ │ │ ├── roboto-slab-v7-bold.woff2 │ │ │ │ │ ├── roboto-slab-v7-regular.eot │ │ │ │ │ ├── roboto-slab-v7-regular.ttf │ │ │ │ │ ├── roboto-slab-v7-regular.woff │ │ │ │ │ └── roboto-slab-v7-regular.woff2 │ │ │ ├── documentation_options.js │ │ │ ├── css │ │ │ │ └── badge_only.css │ │ │ ├── js │ │ │ │ └── theme.js │ │ │ ├── pygments.css │ │ │ ├── doctools.js │ │ │ ├── underscore.js │ │ │ └── basic.css │ │ ├── .buildinfo │ │ ├── search.html │ │ ├── py-modindex.html │ │ ├── license.html │ │ ├── searchindex.js │ │ ├── api │ │ │ ├── index.html │ │ │ └── module.html │ │ └── genindex.html │ ├── doctrees │ │ ├── index.doctree │ │ ├── license.doctree │ │ ├── readme.doctree │ │ ├── api │ │ │ ├── index.doctree │ │ │ ├── module.doctree │ │ │ └── tabel.doctree │ │ ├── cookbook.doctree │ │ ├── environment.pickle │ │ └── performance.doctree │ └── doctest │ │ └── output.txt ├── test.csv └── Makefile ├── test ├── test.npz ├── .pytest_cache │ ├── v │ │ └── cache │ │ │ ├── lastfailed │ │ │ └── nodeids │ └── README.md ├── .cache │ └── v │ │ └── cache │ │ └── lastfailed └── test_tabel.py ├── MANIFEST.in ├── .gitignore ├── pylint.sh ├── LICENSE ├── setup.py └── README.rst /tabel.egg-info/top_level.txt: -------------------------------------------------------------------------------- 1 | tabel 2 | -------------------------------------------------------------------------------- /tabel.egg-info/dependency_links.txt: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /tabel/_version.py: -------------------------------------------------------------------------------- 1 | __version__ = "1.2.3" 2 | -------------------------------------------------------------------------------- /.flake8: -------------------------------------------------------------------------------- 1 | [flake8] 2 | max-line-length = 99 3 | -------------------------------------------------------------------------------- /tabel.egg-info/requires.txt: -------------------------------------------------------------------------------- 1 | numpy 2 | tabulate 3 | -------------------------------------------------------------------------------- /docs/source/license.rst: -------------------------------------------------------------------------------- 1 | license 2 | ======= 3 | 4 | .. include:: ../../LICENSE 5 | -------------------------------------------------------------------------------- /docs/test.npz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BastiaanBergman/tabel/HEAD/docs/test.npz -------------------------------------------------------------------------------- /test/test.npz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BastiaanBergman/tabel/HEAD/test/test.npz -------------------------------------------------------------------------------- /MANIFEST.in: -------------------------------------------------------------------------------- 1 | include MANIFEST.in 2 | include LICENSE 3 | include README.rst 4 | include setup.py 5 | -------------------------------------------------------------------------------- /docs/build/html/_sources/license.rst.txt: -------------------------------------------------------------------------------- 1 | license 2 | ======= 3 | 4 | .. include:: ../../LICENSE 5 | -------------------------------------------------------------------------------- /docs/test.csv: -------------------------------------------------------------------------------- 1 | Name,Height,Married 2 | John,1.82,False 3 | Joe,1.65,False 4 | Jane,2.15,True 5 | -------------------------------------------------------------------------------- /test/.pytest_cache/v/cache/lastfailed: -------------------------------------------------------------------------------- 1 | { 2 | "test_tabel.py::TestShape_n_len::()::test_len": true 3 | } -------------------------------------------------------------------------------- /docs/build/html/objects.inv: 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-------------------------------------------------------------------------------- 1 | .. image:: https://readthedocs.org/projects/tabel/badge/?version=latest 2 | :target: https://tabel.readthedocs.io/en/latest/?badge=latest 3 | :alt: Documentation Status 4 | 5 | .. include:: ../../README.rst 6 | -------------------------------------------------------------------------------- /docs/build/html/_sources/readme.rst.txt: -------------------------------------------------------------------------------- 1 | .. image:: https://readthedocs.org/projects/tabel/badge/?version=latest 2 | :target: https://tabel.readthedocs.io/en/latest/?badge=latest 3 | :alt: Documentation Status 4 | 5 | .. include:: ../../README.rst 6 | -------------------------------------------------------------------------------- /docs/build/html/.buildinfo: -------------------------------------------------------------------------------- 1 | # Sphinx build info version 1 2 | # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. 3 | config: 703750249707fe68aac37ef15b1944b9 4 | tags: 645f666f9bcd5a90fca523b33c5a78b7 5 | -------------------------------------------------------------------------------- /pylint.sh: -------------------------------------------------------------------------------- 1 | echo "######## tabel.py" 2 | pylint tabel/tabel.py 3 | echo "######## hashjoin.py" 4 | pylint tabel/hashjoin.py 5 | echo "######## util.py" 6 | pylint tabel/util.py 7 | echo "######## numpy_types.py" 8 | pylint tabel/numpy_types.py 9 | echo "######## __init__.py" 10 | pylint tabel/__init__.py 11 | -------------------------------------------------------------------------------- /docs/source/api/module.rst: -------------------------------------------------------------------------------- 1 | tabel module 2 | ============ 3 | 4 | .. module:: tabel 5 | 6 | transpose 7 | --------- 8 | 9 | .. autofunction:: transpose 10 | 11 | .. autofunction:: T 12 | 13 | read_tabel 14 | ----------- 15 | 16 | .. autofunction:: read_tabel 17 | 18 | first 19 | ------ 20 | 21 | .. autofunction:: first 22 | -------------------------------------------------------------------------------- /docs/build/html/_sources/api/module.rst.txt: -------------------------------------------------------------------------------- 1 | tabel module 2 | ============ 3 | 4 | .. module:: tabel 5 | 6 | transpose 7 | --------- 8 | 9 | .. autofunction:: transpose 10 | 11 | .. autofunction:: T 12 | 13 | read_tabel 14 | ----------- 15 | 16 | .. autofunction:: read_tabel 17 | 18 | first 19 | ------ 20 | 21 | .. autofunction:: first 22 | -------------------------------------------------------------------------------- /docs/build/html/_static/documentation_options.js: -------------------------------------------------------------------------------- 1 | var DOCUMENTATION_OPTIONS = { 2 | URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), 3 | VERSION: 'v1.0', 4 | LANGUAGE: 'None', 5 | COLLAPSE_INDEX: false, 6 | FILE_SUFFIX: '.html', 7 | HAS_SOURCE: true, 8 | SOURCELINK_SUFFIX: '.txt' 9 | }; -------------------------------------------------------------------------------- /test/.pytest_cache/README.md: -------------------------------------------------------------------------------- 1 | # pytest cache directory # 2 | 3 | This directory contains data from the pytest's cache plugin, 4 | which provides the `--lf` and `--ff` options, as well as the `cache` fixture. 5 | 6 | **Do not** commit this to version control. 7 | 8 | See [the docs](https://docs.pytest.org/en/latest/cache.html) for more information. 9 | -------------------------------------------------------------------------------- /tabel.egg-info/SOURCES.txt: -------------------------------------------------------------------------------- 1 | LICENSE 2 | MANIFEST.in 3 | README.rst 4 | setup.py 5 | tabel/__init__.py 6 | tabel/_version.py 7 | tabel/git_version.py 8 | tabel/hashjoin.py 9 | tabel/numpy_types.py 10 | tabel/tabel.py 11 | tabel/util.py 12 | tabel.egg-info/PKG-INFO 13 | tabel.egg-info/SOURCES.txt 14 | tabel.egg-info/dependency_links.txt 15 | tabel.egg-info/requires.txt 16 | tabel.egg-info/top_level.txt 17 | test/test_tabel.py -------------------------------------------------------------------------------- /tabel/numpy_types.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | """ 3 | .. module:: tabel.numpy_types 4 | .. moduleauthor:: Bastiaan Bergman 5 | 6 | """ 7 | from __future__ import (absolute_import, division, print_function, 8 | unicode_literals) 9 | import numpy as np 10 | 11 | NP_FLOAT_TYPES = [np.float, np.float32, np.float64, np.float16] 12 | NP_INT_TYPES = [np.int, np.int32, np.int64] 13 | -------------------------------------------------------------------------------- /tabel/__init__.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | """ 3 | .. module:: tabel 4 | .. moduleauthor:: Bastiaan Bergman 5 | 6 | """ 7 | from .tabel import Tabel, read_tabel, transpose, T 8 | from .hashjoin import first 9 | from ._version import __version__ 10 | __all__ = ["Tabel", "first", "transpose", "T", "read_tabel", "__version__"] 11 | name = "tabel" # pylint: disable=invalid-name 12 | -------------------------------------------------------------------------------- /docs/Makefile: -------------------------------------------------------------------------------- 1 | # Minimal makefile for Sphinx documentation 2 | # 3 | 4 | # You can set these variables from the command line. 5 | SPHINXOPTS = -E -a 6 | SPHINXBUILD = sphinx-build 7 | SPHINXPROJ = tabel 8 | SOURCEDIR = source 9 | BUILDDIR = build 10 | 11 | # Put it first so that "make" without argument is like "make help". 12 | help: 13 | @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 14 | 15 | .PHONY: help Makefile 16 | 17 | # Catch-all target: route all unknown targets to Sphinx using the new 18 | # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). 19 | %: Makefile 20 | @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 21 | -------------------------------------------------------------------------------- /docs/source/index.rst: -------------------------------------------------------------------------------- 1 | .. Tabel documentation master file, created by 2 | sphinx-quickstart on Wed Aug 8 20:31:28 2018. 3 | You can adapt this file completely to your liking, but it should at least 4 | contain the root `toctree` directive. 5 | 6 | Tabel's documentation 7 | ===================== 8 | 9 | Tabel provides a lightweight and intuitive object for data tables, two 10 | dimensional tables with rows and columns. Based on Numpy, therefore fast and 11 | seamless integration with all the popular data tools. 12 | 13 | .. toctree:: 14 | :maxdepth: 6 15 | :caption: Contents: 16 | 17 | 18 | readme 19 | performance 20 | 21 | cookbook 22 | api/index 23 | 24 | license 25 | 26 | 27 | Indices and tables 28 | ================== 29 | 30 | * :ref:`genindex` 31 | * :ref:`modindex` 32 | * :ref:`search` 33 | -------------------------------------------------------------------------------- /docs/build/html/_sources/index.rst.txt: -------------------------------------------------------------------------------- 1 | .. Tabel documentation master file, created by 2 | sphinx-quickstart on Wed Aug 8 20:31:28 2018. 3 | You can adapt this file completely to your liking, but it should at least 4 | contain the root `toctree` directive. 5 | 6 | Tabel's documentation 7 | ===================== 8 | 9 | Tabel provides a lightweight and intuitive object for data tables, two 10 | dimensional tables with rows and columns. Based on Numpy, therefore fast and 11 | seamless integration with all the popular data tools. 12 | 13 | .. toctree:: 14 | :maxdepth: 6 15 | :caption: Contents: 16 | 17 | 18 | readme 19 | performance 20 | 21 | cookbook 22 | api/index 23 | 24 | license 25 | 26 | 27 | Indices and tables 28 | ================== 29 | 30 | * :ref:`genindex` 31 | * :ref:`modindex` 32 | * :ref:`search` 33 | -------------------------------------------------------------------------------- /tabel/util.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | """ 3 | .. module:: tabel.util 4 | .. moduleauthor:: Bastiaan Bergman 5 | 6 | """ 7 | from __future__ import (absolute_import, division, print_function, 8 | unicode_literals) 9 | import sys 10 | 11 | 12 | if sys.version_info >= (3, 0): 13 | PV = 3 14 | ImpError = ModuleNotFoundError # pylint: disable=invalid-name 15 | elif sys.version_info >= (2, 0): 16 | PV = 2 17 | ImpError = ImportError 18 | else: 19 | print("Forget about it!") 20 | sys.exit(1) 21 | 22 | def isstring(strng): 23 | """ Python version independent string checking """ 24 | if PV == 2: 25 | return isinstance(strng, basestring) # pylint: disable=undefined-variable 26 | if PV == 3: 27 | return isinstance(strng, str) 28 | raise NotImplementedError("Unknown python verison: {}".format(PV)) 29 | -------------------------------------------------------------------------------- /docs/build/doctest/output.txt: -------------------------------------------------------------------------------- 1 | Results of doctest builder run on 2019-08-15 15:39:27 2 | ===================================================== 3 | 4 | Document: api/tabel 5 | ------------------- 6 | 1 items passed all tests: 7 | 30 tests in default 8 | 30 tests in 1 items. 9 | 30 passed and 0 failed. 10 | Test passed. 11 | 12 | Document: cookbook 13 | ------------------ 14 | 1 items passed all tests: 15 | 82 tests in default 16 | 82 tests in 1 items. 17 | 82 passed and 0 failed. 18 | Test passed. 19 | 20 | Document: performance 21 | --------------------- 22 | 1 items passed all tests: 23 | 35 tests in default 24 | 35 tests in 1 items. 25 | 35 passed and 0 failed. 26 | Test passed. 27 | 28 | Document: readme 29 | ---------------- 30 | 1 items passed all tests: 31 | 32 tests in default 32 | 32 tests in 1 items. 33 | 32 passed and 0 failed. 34 | Test passed. 35 | 36 | Doctest summary 37 | =============== 38 | 179 tests 39 | 0 failures in tests 40 | 0 failures in setup code 41 | 0 failures in cleanup code 42 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) [2018] [Bastiaan Bergman] 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | import setuptools 2 | 3 | exec(open("tabel/_version.py").read()) 4 | 5 | with open("README.rst", "r") as fh: 6 | long_description = fh.read() 7 | 8 | setuptools.setup( 9 | name="tabel", 10 | keywords='data table', 11 | version=__version__, 12 | author="Bastiaan Bergman", 13 | author_email="Bastiaan.Bergman@gmail.com", 14 | description="Lightweight, intuitive and fast data-tables.", 15 | long_description=long_description, 16 | long_description_content_type="text/x-rst", 17 | url="https://github.com/BastiaanBergman/tabel", 18 | packages=setuptools.find_packages(), 19 | license='MIT', 20 | python_requires='>=2.6, >=3.0', 21 | install_requires=['numpy', 'tabulate'], 22 | classifiers=[ 23 | "Programming Language :: Python :: 3", 24 | "Programming Language :: Python :: 3.6", 25 | "Programming Language :: Python :: 2", 26 | "Programming Language :: Python :: 2.7", 27 | "License :: OSI Approved :: MIT License", 28 | "Operating System :: OS Independent", 29 | ], 30 | project_urls={ 31 | "Documentation": "https://tabel.readthedocs.io/en/stable/", 32 | "Source Code": "https://github.com/BastiaanBergman/tabel", 33 | } 34 | ) 35 | -------------------------------------------------------------------------------- /test/.pytest_cache/v/cache/nodeids: -------------------------------------------------------------------------------- 1 | [ 2 | "test_tabel.py::TestTabelInit::()::test_tabel_init", 3 | "test_tabel.py::TestAppend::()::test_append_tabel", 4 | "test_tabel.py::TestAppend::()::test_append_column", 5 | "test_tabel.py::TestAppend::()::test_append_combinations", 6 | "test_tabel.py::TestAppend::()::test_row_append_combinations", 7 | "test_tabel.py::TestSlice::()::test_slice_type", 8 | "test_tabel.py::TestSlice::()::test_slice_columns", 9 | "test_tabel.py::TestSlice::()::test_slice_rows", 10 | "test_tabel.py::TestSlice::()::test_slice_boolean", 11 | "test_tabel.py::TestSlice::()::test_slice_permiseable", 12 | "test_tabel.py::TestSetter::()::test_set_individual_elm", 13 | "test_tabel.py::TestSetter::()::test_set_column", 14 | "test_tabel.py::TestSetter::()::test_set_row", 15 | "test_tabel.py::TestSetter::()::test_add_column", 16 | "test_tabel.py::TestSetter::()::test_replace_column", 17 | "test_tabel.py::TestSaveNRead::()::test_save_and_read", 18 | "test_tabel.py::TestGroupBy::()::test_group_by", 19 | "test_tabel.py::TestShapeNLen::()::test_shape_n_len", 20 | "test_tabel.py::TestShapeNLen::()::test_shape", 21 | "test_tabel.py::TestShapeNLen::()::test_len", 22 | "test_tabel.py::TestTranspose::()::test_transpose", 23 | "test_tabel.py::TestJoin::()::test_inner_int_join", 24 | "test_tabel.py::TestJoin::()::test_inner_str_join", 25 | "test_tabel.py::TestJoin::()::test_outer_join", 26 | "test_tabel.py::TestSort::()::test_sort", 27 | "test_tabel.py::TestDTypeProperties::()::test_dtype_properties", 28 | "test_tabel.py::TestPandasBackAndForth::()::test_pandas_b_and_f" 29 | ] -------------------------------------------------------------------------------- /docs/source/api/tabel.rst: -------------------------------------------------------------------------------- 1 | class Tabel 2 | ============ 3 | 4 | .. currentmodule: tabel 5 | 6 | .. autoclass:: tabel.Tabel 7 | 8 | getter 9 | ------ 10 | 11 | .. automethod:: tabel.Tabel.__getitem__ 12 | 13 | setter 14 | ------ 15 | 16 | .. automethod:: tabel.Tabel.__setitem__ 17 | 18 | 19 | delitem 20 | ------- 21 | 22 | .. automethod:: tabel.Tabel.__delitem__ 23 | 24 | 25 | repr 26 | ---- 27 | 28 | .. automethod:: tabel.Tabel.__repr__ 29 | 30 | append 31 | ------ 32 | 33 | .. automethod:: tabel.Tabel.append 34 | 35 | row_append 36 | ---------- 37 | 38 | .. automethod:: tabel.Tabel.row_append 39 | 40 | join 41 | ----- 42 | 43 | .. automethod:: tabel.Tabel.join 44 | 45 | group_by 46 | -------- 47 | 48 | .. automethod:: tabel.Tabel.group_by 49 | 50 | sort 51 | ---- 52 | 53 | .. automethod:: tabel.Tabel.sort 54 | 55 | astype 56 | ------ 57 | 58 | .. automethod:: tabel.Tabel.astype 59 | 60 | save 61 | ---- 62 | 63 | .. automethod:: tabel.Tabel.save 64 | 65 | 66 | 67 | 68 | properties 69 | ----------- 70 | 71 | dict 72 | ~~~~ 73 | 74 | .. autoattribute:: tabel.Tabel.dict 75 | 76 | shape 77 | ~~~~~ 78 | 79 | .. autoattribute:: tabel.Tabel.shape 80 | 81 | len 82 | ~~~ 83 | 84 | .. autoattribute:: tabel.Tabel.__len__ 85 | 86 | dtype 87 | ~~~~~~ 88 | 89 | .. autoattribute:: tabel.Tabel.dtype 90 | 91 | valid 92 | ~~~~~~ 93 | 94 | .. autoattribute:: tabel.Tabel.valid 95 | 96 | 97 | class attributes 98 | ----------------- 99 | 100 | .. autoattribute:: tabel.Tabel.repr_layout 101 | 102 | .. autoattribute:: tabel.Tabel.max_repr_rows 103 | 104 | .. autoattribute:: tabel.Tabel.join_fill_value 105 | -------------------------------------------------------------------------------- /docs/build/html/_sources/api/tabel.rst.txt: -------------------------------------------------------------------------------- 1 | class Tabel 2 | ============ 3 | 4 | .. currentmodule: tabel 5 | 6 | .. autoclass:: tabel.Tabel 7 | 8 | getter 9 | ------ 10 | 11 | .. automethod:: tabel.Tabel.__getitem__ 12 | 13 | setter 14 | ------ 15 | 16 | .. automethod:: tabel.Tabel.__setitem__ 17 | 18 | 19 | delitem 20 | ------- 21 | 22 | .. automethod:: tabel.Tabel.__delitem__ 23 | 24 | 25 | repr 26 | ---- 27 | 28 | .. automethod:: tabel.Tabel.__repr__ 29 | 30 | append 31 | ------ 32 | 33 | .. automethod:: tabel.Tabel.append 34 | 35 | row_append 36 | ---------- 37 | 38 | .. automethod:: tabel.Tabel.row_append 39 | 40 | join 41 | ----- 42 | 43 | .. automethod:: tabel.Tabel.join 44 | 45 | group_by 46 | -------- 47 | 48 | .. automethod:: tabel.Tabel.group_by 49 | 50 | sort 51 | ---- 52 | 53 | .. automethod:: tabel.Tabel.sort 54 | 55 | astype 56 | ------ 57 | 58 | .. automethod:: tabel.Tabel.astype 59 | 60 | save 61 | ---- 62 | 63 | .. automethod:: tabel.Tabel.save 64 | 65 | 66 | 67 | 68 | properties 69 | ----------- 70 | 71 | dict 72 | ~~~~ 73 | 74 | .. autoattribute:: tabel.Tabel.dict 75 | 76 | shape 77 | ~~~~~ 78 | 79 | .. autoattribute:: tabel.Tabel.shape 80 | 81 | len 82 | ~~~ 83 | 84 | .. autoattribute:: tabel.Tabel.__len__ 85 | 86 | dtype 87 | ~~~~~~ 88 | 89 | .. autoattribute:: tabel.Tabel.dtype 90 | 91 | valid 92 | ~~~~~~ 93 | 94 | .. autoattribute:: tabel.Tabel.valid 95 | 96 | 97 | class attributes 98 | ----------------- 99 | 100 | .. autoattribute:: tabel.Tabel.repr_layout 101 | 102 | .. autoattribute:: tabel.Tabel.max_repr_rows 103 | 104 | .. autoattribute:: tabel.Tabel.join_fill_value 105 | 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209 | 210 | 211 | 212 | 213 | 214 | 225 | 226 | 227 | 228 | 229 | 230 | 231 | 232 | 233 | 238 | 239 | 240 | -------------------------------------------------------------------------------- /docs/source/conf.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # 3 | # Configuration file for the Sphinx documentation builder. 4 | # 5 | # This file does only contain a selection of the most common options. For a 6 | # full list see the documentation: 7 | # http://www.sphinx-doc.org/en/master/config 8 | 9 | # -- Path setup -------------------------------------------------------------- 10 | 11 | # If extensions (or modules to document with autodoc) are in another directory, 12 | # add these directories to sys.path here. If the directory is relative to the 13 | # documentation root, use os.path.abspath to make it absolute, like shown here. 14 | # 15 | import os 16 | import sys 17 | # sys.path.insert(0, os.path.abspath('../../')) 18 | # sys.path.insert(0, os.path.abspath('../../tabel')) 19 | # sys.path.insert(0, os.path.abspath('.')) 20 | # sys.path.insert(0, os.path.abspath('../')) 21 | sys.path.insert(0, os.path.abspath('../..')) 22 | # sys.path.insert(0, os.path.abspath('../tabel')) 23 | # APP_DIR = os.path.normpath(os.path.join(os.getcwd(), '../..')) 24 | # sys.path.insert(0, APP_DIR) 25 | # sys.path.insert(0, os.path.abspath('../tabel')) 26 | 27 | # import mock 28 | # 29 | # MOCK_MODULES = ['numpy', 'numpy.lib.recfunctions', 'itertools'] 30 | # for mod_name in MOCK_MODULES: 31 | # sys.modules[mod_name] = mock.Mock() 32 | 33 | # -- Project information ----------------------------------------------------- 34 | 35 | project = 'tabel' 36 | copyright = '2018, Bastiaan Bergman' 37 | author = 'Bastiaan Bergman' 38 | 39 | # The short X.Y version 40 | version = 'v1.0' 41 | # The full version, including alpha/beta/rc tags 42 | release = 'v1.0' 43 | 44 | 45 | 46 | # -- General configuration --------------------------------------------------- 47 | 48 | # If your documentation needs a minimal Sphinx version, state it here. 49 | # 50 | # needs_sphinx = '1.0' 51 | 52 | # Add any Sphinx extension module names here, as strings. They can be 53 | # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom 54 | # ones. 55 | extensions = [ 56 | 'sphinx.ext.napoleon', 57 | # 'sphinx.ext.autodoc', 58 | 'sphinx.ext.doctest', 59 | # 'numpydoc', 60 | # 'sphinx.ext.napoleon', 61 | 'sphinx.ext.autosectionlabel', 62 | ] 63 | 64 | # numpydoc settings: 65 | # numpydoc_show_class_members = True 66 | # numpydoc_show_inherited_class_members = False 67 | # numpydoc_class_members_toctree = True 68 | 69 | # Napoleon settings 70 | napoleon_google_docstring = True 71 | napoleon_numpy_docstring = False 72 | # napoleon_include_init_with_doc = True 73 | # napoleon_include_private_with_doc = False 74 | napoleon_include_special_with_doc = True 75 | napoleon_use_admonition_for_examples = True 76 | napoleon_use_admonition_for_notes = True 77 | # napoleon_use_admonition_for_references = False 78 | napoleon_use_ivar = True 79 | napoleon_use_param = True 80 | # napoleon_use_rtype = True 81 | 82 | doctest_global_setup = """ 83 | from tabel import Tabel 84 | import numpy as np 85 | tbl = Tabel( [ ["John", "Joe", "Jane"],[1.82,1.65,2.15],[False,False,True] ],columns = ["Name", "Height", "Married"]) 86 | """ 87 | # Add any paths that contain templates here, relative to this directory. 88 | templates_path = ['_templates'] 89 | 90 | # The suffix(es) of source filenames. 91 | # You can specify multiple suffix as a list of string: 92 | # 93 | # source_suffix = ['.rst', '.md'] 94 | source_suffix = '.rst' 95 | 96 | # The master toctree document. 97 | master_doc = 'index' 98 | 99 | # The language for content autogenerated by Sphinx. Refer to documentation 100 | # for a list of supported languages. 101 | # 102 | # This is also used if you do content translation via gettext catalogs. 103 | # Usually you set "language" from the command line for these cases. 104 | language = None 105 | 106 | # List of patterns, relative to source directory, that match files and 107 | # directories to ignore when looking for source files. 108 | # This pattern also affects html_static_path and html_extra_path . 109 | exclude_patterns = [] 110 | 111 | # The name of the Pygments (syntax highlighting) style to use. 112 | pygments_style = 'sphinx' 113 | 114 | 115 | # -- Options for HTML output ------------------------------------------------- 116 | 117 | # The theme to use for HTML and HTML Help pages. See the documentation for 118 | # a list of builtin themes. 119 | # 120 | html_theme = 'sphinx_rtd_theme' 121 | 122 | # Theme options are theme-specific and customize the look and feel of a theme 123 | # further. For a list of options available for each theme, see the 124 | # documentation. 125 | # 126 | # html_theme_options = {} 127 | 128 | # Add any paths that contain custom static files (such as style sheets) here, 129 | # relative to this directory. They are copied after the builtin static files, 130 | # so a file named "default.css" will overwrite the builtin "default.css". 131 | html_static_path = ['_static'] 132 | 133 | # Custom sidebar templates, must be a dictionary that maps document names 134 | # to template names. 135 | # 136 | # The default sidebars (for documents that don't match any pattern) are 137 | # defined by theme itself. Builtin themes are using these templates by 138 | # default: ``['localtoc.html', 'relations.html', 'sourcelink.html', 139 | # 'searchbox.html']``. 140 | # 141 | # html_sidebars = {} 142 | 143 | 144 | # -- Options for HTMLHelp output --------------------------------------------- 145 | 146 | # Output file base name for HTML help builder. 147 | htmlhelp_basename = 'tabeldoc' 148 | 149 | 150 | # -- Options for LaTeX output ------------------------------------------------ 151 | 152 | latex_elements = { 153 | # The paper size ('letterpaper' or 'a4paper'). 154 | # 155 | # 'papersize': 'letterpaper', 156 | 157 | # The font size ('10pt', '11pt' or '12pt'). 158 | # 159 | # 'pointsize': '10pt', 160 | 161 | # Additional stuff for the LaTeX preamble. 162 | # 163 | # 'preamble': '', 164 | 165 | # Latex figure (float) alignment 166 | # 167 | # 'figure_align': 'htbp', 168 | } 169 | 170 | # Grouping the document tree into LaTeX files. List of tuples 171 | # (source start file, target name, title, 172 | # author, documentclass [howto, manual, or own class]). 173 | latex_documents = [ 174 | (master_doc, 'tabel.tex', 'tabel Documentation', 175 | 'Bastiaan Bergman', 'manual'), 176 | ] 177 | 178 | 179 | # -- Options for manual page output ------------------------------------------ 180 | 181 | # One entry per manual page. List of tuples 182 | # (source start file, name, description, authors, manual section). 183 | man_pages = [ 184 | (master_doc, 'tabel', 'tabel Documentation', 185 | [author], 1) 186 | ] 187 | 188 | 189 | # -- Options for Texinfo output ---------------------------------------------- 190 | 191 | # Grouping the document tree into Texinfo files. 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217 | 218 | 219 | 220 | 221 | 222 | 233 | 234 | 235 | 236 | 237 | 238 | 239 | 240 | 241 | 246 | 247 | 248 | -------------------------------------------------------------------------------- /docs/source/performance.rst: -------------------------------------------------------------------------------- 1 | Performance 2 | ============ 3 | 4 | Tabel is designed for those cases where you have data of multiple types, like 5 | records in a database, where the volume is small enough to fit it all in memory 6 | and high freedom of data manipulation is needed. Use Tabel when the following 7 | conditions are met: 8 | 9 | * Multiple columns with different datatyes each 10 | * Fields in a row have a shared relation (e.g. records in a database) 11 | * All data fits in memory 12 | * Only few and relatively simple SQL queries are needed 13 | * A large proportion of the data is numeric and would need column-wise 14 | arithmetic 15 | * Data manipulation requires complex (computed) slicing across both dimensions 16 | (rows and columns) 17 | 18 | If the above conditions are not met, consider these alternatives: 19 | 20 | * numpy - if your data is purely numerical or when fields in a row have no relation. 21 | * HDF5 - if your data does not fit in memory 22 | * SQLite or MySQL - if you need complex DB queries (e.g. transactional) 23 | 24 | That said, good general performance is paramount. Here below are some tests to 25 | give an rough overview of where Tabel stands relative to pandas. 26 | 27 | To do the tests, a few tools are needed: 28 | 29 | >>> import numpy as np 30 | >>> import pandas as pd 31 | >>> from tabel import Tabel 32 | >>> from timeit import default_timer 33 | 34 | 35 | slicing tables 36 | --------------- 37 | 38 | As Tabel is based on numpy, this is where it is good at. I think slicing syntax 39 | is both clean and fast (about 15x faster than pandas in the below tests). 40 | 41 | First slicing test is to get a whole row from a large table. The return 42 | value should be a tuple from Tabel, pandas produces an object array which I 43 | believe is not very efficient as columns usually have different datatypes. 44 | Tabel is abut 43x faster in this particular case: 45 | 46 | >>> n = 1000000 47 | >>> data_dict = {'a':[1,2,3,4] * n, 'b':['a1','a2']*2*n, 'c':np.arange(4*n)} 48 | >>> df = pd.DataFrame(data_dict) 49 | >>> tbl = Tabel(data_dict) 50 | >>> def test_pandas_slice(n): 51 | ... t0 = default_timer() 52 | ... for i in range(n): 53 | ... _ = df.iloc[100].values 54 | ... return (default_timer() - t0)/n*1e6, "micro-sec" 55 | >>> test_pandas_slice(100000) # doctest: +SKIP 56 | (130.62096348032355, 'micro-sec') 57 | >>> def test_tabel_slice(n): 58 | ... t0 = default_timer() 59 | ... for i in range(n): 60 | ... _ = tbl[100] 61 | ... return (default_timer() - t0)/n*1e6, "micro-sec" 62 | >>> test_tabel_slice(100000) # doctest: +SKIP 63 | (3.045091559179127, 'micro-sec') 64 | 65 | 66 | Second slicing test is to get a number of rows from one particular column, the 67 | return value should be an array. Tabel is abut 15x faster in this case: 68 | 69 | >>> def test_pandas_slice(n): 70 | ... t0 = default_timer() 71 | ... for i in range(n): 72 | ... _ = df['a'][1:10].values 73 | ... return (default_timer() - t0)/n*1e6, "micro-sec" 74 | >>> test_pandas_slice(100000) # doctest: +SKIP 75 | (63.41531826881692, 'micro-sec') 76 | >>> def test_tabel_slice(n): 77 | ... t0 = default_timer() 78 | ... for i in range(n): 79 | ... _ = tbl[1:10, 'a'] 80 | ... return (default_timer() - t0)/n*1e6, "micro-sec" 81 | >>> test_tabel_slice(100000) # doctest: +SKIP 82 | (4.007692479062825, 'micro-sec') 83 | 84 | 85 | appending rows 86 | -------------- 87 | 88 | I find myself often doing something similar to the following procedure: 89 | 90 | >>> for i in large_number: # doctest: +SKIP 91 | ... row = calculate_something() 92 | ... tbl.append(row) 93 | 94 | where "do something" could be reading some tricky formatted ascii file or 95 | calculating the position of the stars, it doesn't matter. The point is, 96 | appending data is never a very efficient thing to do, memory needs to be 97 | reserved, all values need to be re-evaluated for type setting and then all data 98 | gets copied over to the new location. Still I prefer this over more efficient 99 | methods as it keeps my code clean and simple. 100 | 101 | I expect bad performance for both Tabel and pandas, but a simple test shows that 102 | pandas is doing a bit worse. If we repeatedly add a simple row of two integers 103 | to a pandas DataFrame we'll use about 300 micro-second per row: 104 | 105 | >>> import pandas as pd 106 | >>> from tabel import Tabel 107 | >>> from timeit import default_timer 108 | >>> df = pd.DataFrame({'1':[],'2':[]}) 109 | >>> row = pd.DataFrame({'1':[1],'2':[2]}) 110 | >>> def test_pandas(n): 111 | ... t0 = default_timer() 112 | ... for i in range(n): 113 | ... df.append(row, ignore_index=True) 114 | ... return (default_timer() - t0)/n*1e6, "micro-sec" 115 | >>> test_pandas(100000) # doctest: +SKIP 116 | (299.0699630905874, 'micro-sec') 117 | 118 | The same exercise with Tabel's :mod:`tabel.Tabel.row_append` method takes only a 119 | quarter of that time: 120 | 121 | >>> tbl = Tabel() 122 | >>> row = (1, 2) 123 | >>> def test_tabel(n): 124 | ... t0 = default_timer() 125 | ... for i in range(n): 126 | ... tbl.row_append(row) 127 | ... return (default_timer() - t0)/n*1e6, "micro-sec" 128 | >>> test_tabel(100000) # doctest: +SKIP 129 | (79.83603572938591, 'micro-sec') 130 | 131 | Granted, there are very many different scenarios thinkable and there probably 132 | are scenarios where pandas would outperform Tabel. If you come across one of 133 | those please let me know and I happily add it here. 134 | 135 | 136 | grouping tables 137 | --------------- 138 | 139 | A big Tabel with a million rows can be grouped by multiple columns. Both pandas 140 | and Tabel take a good amount of time on this, but then this is typically done 141 | once on a an individual Tabel or DataFrame. pandas is about 10x faster than 142 | Tabel on this simple test. The good news is that this is independent of n, 143 | meaning they're both equally scalable. 144 | 145 | >>> n = 100000 146 | >>> data_dict = {'a':[1,2,3,4] * n, 'b':['a1','a2']*2*n, 'c':np.arange(4*n)} 147 | >>> df = pd.DataFrame(data_dict) 148 | >>> def test_pandas_groupby(n): 149 | ... t0 = default_timer() 150 | ... for i in range(n): 151 | ... _ = df.groupby(('a', 'b')).sum() 152 | ... return (default_timer() - t0)/n*1e3, "mili-sec" 153 | >>> test_pandas_groupby(10) # doctest: +SKIP 154 | (34.32465291116387, 'mili-sec') 155 | >>> tbl = Tabel(data_dict) 156 | >>> def test_tabel_groupby(n): 157 | ... t0 = default_timer() 158 | ... for i in range(n): 159 | ... _ = tbl.group_by(('b', 'a'),[(np.sum, 'c')]) 160 | ... return (default_timer() - t0)/n*1e3, "mili-sec" 161 | >>> test_tabel_groupby(10) # doctest: +SKIP 162 | (322.54316059406847, 'mili-sec') 163 | 164 | 165 | joining tables 166 | -------------- 167 | 168 | Two Tabels can be joined together by some common key present in both Tabels. Two 169 | table with a million rows takes about 2.5 second to be joined with pandas and 5 170 | times that with Tabel, this ratio reduces somewhat with n. See the codeblock 171 | below for the specific case tested here. 172 | 173 | >>> n = 1000000 174 | >>> data_dict = {'a':[1,2,3,4] * n, 'b':['a1','a2']*2*n, 'c':np.arange(4*n)} 175 | >>> df_1 = pd.DataFrame(data_dict) 176 | >>> df_2 = pd.DataFrame(data_dict) 177 | >>> def test_pandas_join(): 178 | ... t0 = default_timer() 179 | ... _ = df_1.join(df_2, on='c', how='inner', lsuffix='l', rsuffix='r') 180 | ... return (default_timer() - t0)*1e3, "mili-sec" 181 | >>> test_pandas_join() # doctest: +SKIP 182 | (2462.8482228145003, 'mili-sec') 183 | >>> tbl_1 = Tabel(data_dict) 184 | >>> tbl_2 = Tabel(data_dict) 185 | >>> def test_tabel_join(): 186 | ... t0 = default_timer() 187 | ... _ = tbl_1.join(tbl_2, key='c', jointype='inner') 188 | ... return (default_timer() - t0)*1e3, "mili-sec" 189 | >>> test_tabel_join() # doctest: +SKIP 190 | (10393.40596087277, 'mili-sec') 191 | -------------------------------------------------------------------------------- /docs/build/html/_sources/performance.rst.txt: -------------------------------------------------------------------------------- 1 | Performance 2 | ============ 3 | 4 | Tabel is designed for those cases where you have data of multiple types, like 5 | records in a database, where the volume is small enough to fit it all in memory 6 | and high freedom of data manipulation is needed. Use Tabel when the following 7 | conditions are met: 8 | 9 | * Multiple columns with different datatyes each 10 | * Fields in a row have a shared relation (e.g. records in a database) 11 | * All data fits in memory 12 | * Only few and relatively simple SQL queries are needed 13 | * A large proportion of the data is numeric and would need column-wise 14 | arithmetic 15 | * Data manipulation requires complex (computed) slicing across both dimensions 16 | (rows and columns) 17 | 18 | If the above conditions are not met, consider these alternatives: 19 | 20 | * numpy - if your data is purely numerical or when fields in a row have no relation. 21 | * HDF5 - if your data does not fit in memory 22 | * SQLite or MySQL - if you need complex DB queries (e.g. transactional) 23 | 24 | That said, good general performance is paramount. Here below are some tests to 25 | give an rough overview of where Tabel stands relative to pandas. 26 | 27 | To do the tests, a few tools are needed: 28 | 29 | >>> import numpy as np 30 | >>> import pandas as pd 31 | >>> from tabel import Tabel 32 | >>> from timeit import default_timer 33 | 34 | 35 | slicing tables 36 | --------------- 37 | 38 | As Tabel is based on numpy, this is where it is good at. I think slicing syntax 39 | is both clean and fast (about 15x faster than pandas in the below tests). 40 | 41 | First slicing test is to get a whole row from a large table. The return 42 | value should be a tuple from Tabel, pandas produces an object array which I 43 | believe is not very efficient as columns usually have different datatypes. 44 | Tabel is abut 43x faster in this particular case: 45 | 46 | >>> n = 1000000 47 | >>> data_dict = {'a':[1,2,3,4] * n, 'b':['a1','a2']*2*n, 'c':np.arange(4*n)} 48 | >>> df = pd.DataFrame(data_dict) 49 | >>> tbl = Tabel(data_dict) 50 | >>> def test_pandas_slice(n): 51 | ... t0 = default_timer() 52 | ... for i in range(n): 53 | ... _ = df.iloc[100].values 54 | ... return (default_timer() - t0)/n*1e6, "micro-sec" 55 | >>> test_pandas_slice(100000) # doctest: +SKIP 56 | (130.62096348032355, 'micro-sec') 57 | >>> def test_tabel_slice(n): 58 | ... t0 = default_timer() 59 | ... for i in range(n): 60 | ... _ = tbl[100] 61 | ... return (default_timer() - t0)/n*1e6, "micro-sec" 62 | >>> test_tabel_slice(100000) # doctest: +SKIP 63 | (3.045091559179127, 'micro-sec') 64 | 65 | 66 | Second slicing test is to get a number of rows from one particular column, the 67 | return value should be an array. Tabel is abut 15x faster in this case: 68 | 69 | >>> def test_pandas_slice(n): 70 | ... t0 = default_timer() 71 | ... for i in range(n): 72 | ... _ = df['a'][1:10].values 73 | ... return (default_timer() - t0)/n*1e6, "micro-sec" 74 | >>> test_pandas_slice(100000) # doctest: +SKIP 75 | (63.41531826881692, 'micro-sec') 76 | >>> def test_tabel_slice(n): 77 | ... t0 = default_timer() 78 | ... for i in range(n): 79 | ... _ = tbl[1:10, 'a'] 80 | ... return (default_timer() - t0)/n*1e6, "micro-sec" 81 | >>> test_tabel_slice(100000) # doctest: +SKIP 82 | (4.007692479062825, 'micro-sec') 83 | 84 | 85 | appending rows 86 | -------------- 87 | 88 | I find myself often doing something similar to the following procedure: 89 | 90 | >>> for i in large_number: # doctest: +SKIP 91 | ... row = calculate_something() 92 | ... tbl.append(row) 93 | 94 | where "do something" could be reading some tricky formatted ascii file or 95 | calculating the position of the stars, it doesn't matter. The point is, 96 | appending data is never a very efficient thing to do, memory needs to be 97 | reserved, all values need to be re-evaluated for type setting and then all data 98 | gets copied over to the new location. Still I prefer this over more efficient 99 | methods as it keeps my code clean and simple. 100 | 101 | I expect bad performance for both Tabel and pandas, but a simple test shows that 102 | pandas is doing a bit worse. If we repeatedly add a simple row of two integers 103 | to a pandas DataFrame we'll use about 300 micro-second per row: 104 | 105 | >>> import pandas as pd 106 | >>> from tabel import Tabel 107 | >>> from timeit import default_timer 108 | >>> df = pd.DataFrame({'1':[],'2':[]}) 109 | >>> row = pd.DataFrame({'1':[1],'2':[2]}) 110 | >>> def test_pandas(n): 111 | ... t0 = default_timer() 112 | ... for i in range(n): 113 | ... df.append(row, ignore_index=True) 114 | ... return (default_timer() - t0)/n*1e6, "micro-sec" 115 | >>> test_pandas(100000) # doctest: +SKIP 116 | (299.0699630905874, 'micro-sec') 117 | 118 | The same exercise with Tabel's :mod:`tabel.Tabel.row_append` method takes only a 119 | quarter of that time: 120 | 121 | >>> tbl = Tabel() 122 | >>> row = (1, 2) 123 | >>> def test_tabel(n): 124 | ... t0 = default_timer() 125 | ... for i in range(n): 126 | ... tbl.row_append(row) 127 | ... return (default_timer() - t0)/n*1e6, "micro-sec" 128 | >>> test_tabel(100000) # doctest: +SKIP 129 | (79.83603572938591, 'micro-sec') 130 | 131 | Granted, there are very many different scenarios thinkable and there probably 132 | are scenarios where pandas would outperform Tabel. If you come across one of 133 | those please let me know and I happily add it here. 134 | 135 | 136 | grouping tables 137 | --------------- 138 | 139 | A big Tabel with a million rows can be grouped by multiple columns. Both pandas 140 | and Tabel take a good amount of time on this, but then this is typically done 141 | once on a an individual Tabel or DataFrame. pandas is about 10x faster than 142 | Tabel on this simple test. The good news is that this is independent of n, 143 | meaning they're both equally scalable. 144 | 145 | >>> n = 100000 146 | >>> data_dict = {'a':[1,2,3,4] * n, 'b':['a1','a2']*2*n, 'c':np.arange(4*n)} 147 | >>> df = pd.DataFrame(data_dict) 148 | >>> def test_pandas_groupby(n): 149 | ... t0 = default_timer() 150 | ... for i in range(n): 151 | ... _ = df.groupby(('a', 'b')).sum() 152 | ... return (default_timer() - t0)/n*1e3, "mili-sec" 153 | >>> test_pandas_groupby(10) # doctest: +SKIP 154 | (34.32465291116387, 'mili-sec') 155 | >>> tbl = Tabel(data_dict) 156 | >>> def test_tabel_groupby(n): 157 | ... t0 = default_timer() 158 | ... for i in range(n): 159 | ... _ = tbl.group_by(('b', 'a'),[(np.sum, 'c')]) 160 | ... return (default_timer() - t0)/n*1e3, "mili-sec" 161 | >>> test_tabel_groupby(10) # doctest: +SKIP 162 | (322.54316059406847, 'mili-sec') 163 | 164 | 165 | joining tables 166 | -------------- 167 | 168 | Two Tabels can be joined together by some common key present in both Tabels. Two 169 | table with a million rows takes about 2.5 second to be joined with pandas and 5 170 | times that with Tabel, this ratio reduces somewhat with n. See the codeblock 171 | below for the specific case tested here. 172 | 173 | >>> n = 1000000 174 | >>> data_dict = {'a':[1,2,3,4] * n, 'b':['a1','a2']*2*n, 'c':np.arange(4*n)} 175 | >>> df_1 = pd.DataFrame(data_dict) 176 | >>> df_2 = pd.DataFrame(data_dict) 177 | >>> def test_pandas_join(): 178 | ... t0 = default_timer() 179 | ... _ = df_1.join(df_2, on='c', how='inner', lsuffix='l', rsuffix='r') 180 | ... return (default_timer() - t0)*1e3, "mili-sec" 181 | >>> test_pandas_join() # doctest: +SKIP 182 | (2462.8482228145003, 'mili-sec') 183 | >>> tbl_1 = Tabel(data_dict) 184 | >>> tbl_2 = Tabel(data_dict) 185 | >>> def test_tabel_join(): 186 | ... t0 = default_timer() 187 | ... _ = tbl_1.join(tbl_2, key='c', jointype='inner') 188 | ... return (default_timer() - t0)*1e3, "mili-sec" 189 | >>> test_tabel_join() # doctest: +SKIP 190 | (10393.40596087277, 'mili-sec') 191 | -------------------------------------------------------------------------------- /docs/build/html/searchindex.js: 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Reference","tabel module","class Tabel","Cookbook","Tabel\u2019s documentation","license","Performance","About Tabel"],titleterms:{"class":2,about:7,api:0,append:[2,3,6,7],astyp:2,attribut:2,chang:3,column:3,contribut:7,contributor:7,cookbook:3,delitem:2,depend:7,design:7,dict:2,document:4,dtype:2,exampl:2,first:1,get:7,getter:2,group:[3,6],group_bi:2,help:7,indic:4,init:7,initi:3,instal:7,instanc:7,join:[2,3,6],len:2,licens:5,modul:1,name:[3,7],note:2,object:7,panda:7,perform:6,properti:[2,7],quickstart:7,read:3,read_tabel:1,refer:0,referenc:3,releas:7,repr:2,resourc:7,row:[3,6,7],row_append:2,save:[2,3],set:[3,7],setter:2,shape:2,slice:[3,6,7],sort:[2,3],stabl:7,tabel:[1,2,3,4,7],tabl:[4,6],test:7,todo:7,transpos:[1,3],valid:2,what:7}}) -------------------------------------------------------------------------------- /docs/build/html/api/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | API 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268 | }, 269 | 270 | /** 271 | * get the current relative url 272 | */ 273 | getCurrentURL : function() { 274 | var path = document.location.pathname; 275 | var parts = path.split(/\//); 276 | $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() { 277 | if (this === '..') 278 | parts.pop(); 279 | }); 280 | var url = parts.join('/'); 281 | return path.substring(url.lastIndexOf('/') + 1, path.length - 1); 282 | }, 283 | 284 | initOnKeyListeners: function() { 285 | $(document).keyup(function(event) { 286 | var activeElementType = document.activeElement.tagName; 287 | // don't navigate when in search box or textarea 288 | if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT') { 289 | switch (event.keyCode) { 290 | case 37: // left 291 | var prevHref = $('link[rel="prev"]').prop('href'); 292 | if (prevHref) { 293 | window.location.href = prevHref; 294 | return false; 295 | } 296 | case 39: // right 297 | var nextHref = $('link[rel="next"]').prop('href'); 298 | if (nextHref) { 299 | window.location.href = nextHref; 300 | return false; 301 | } 302 | } 303 | } 304 | }); 305 | } 306 | }; 307 | 308 | // quick alias for translations 309 | _ = Documentation.gettext; 310 | 311 | $(document).ready(function() { 312 | Documentation.init(); 313 | }); -------------------------------------------------------------------------------- /tabel/hashjoin.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | """ 3 | .. module:: tabel.hashjoin 4 | .. moduleauthor:: Bastiaan Bergman 5 | 6 | """ 7 | from __future__ import (absolute_import, division, print_function, 8 | unicode_literals) 9 | from collections import defaultdict 10 | import numpy as np 11 | from .numpy_types import * # pylint: disable=wildcard-import 12 | from .util import isstring 13 | 14 | def arg_hash(arlst): 15 | """Return defaultdict with list of rownumbers 16 | """ 17 | keylist = defaultdict(list) 18 | for i, elem in enumerate(zip(*(arlst))): 19 | keylist[elem].append(i) 20 | return keylist 21 | 22 | def arg_left_join(tbl_l, index1, tbl_r, index2): 23 | """Join and return _column_indices 24 | """ 25 | hash_r = arg_hash(tbl_r[:, index2].data) 26 | idx = np.array([(s, i) for i, r in enumerate(zip(*tbl_l[:, index1].data)) 27 | for s in hash_r.get(r, [-1])]) 28 | return idx 29 | 30 | def add_empty_row(tbl): 31 | """Add an empty row using the fill values to the end of an Tabel 32 | """ 33 | empty_row = [] 34 | for c in tbl.dtype.names: 35 | if tbl.dtype[c] in NP_FLOAT_TYPES: 36 | empty_row.append(tbl.join_fill_value['float']) 37 | if tbl.dtype[c].kind in {'U', 'S'}: 38 | empty_row.append(tbl.join_fill_value['string']) 39 | if tbl.dtype[c] in NP_INT_TYPES: 40 | empty_row.append(tbl.join_fill_value['integer']) 41 | try: 42 | tbl.row_append(empty_row) 43 | except ValueError: 44 | raise ValueError("Outer join cannot be fullfilled when there are other ", 45 | "columns than float, string or integer because Tabel ", 46 | "doesn't know what to fill it up with.") 47 | 48 | def remove_empty_row(tbl): 49 | """Remove the last (emtpy) row of a Tabel, used for outer joins 50 | """ 51 | tbl.data = [dt[0:-1] for dt in tbl.data] 52 | 53 | 54 | class HashJoinMixin(object): 55 | """Mixin to add to the Tabel class, providing join and group_by methods 56 | """ 57 | 58 | def _union(self, idx, key, tbl_r, key_r, jointype="inner", suffixes=("_l", "_r")): 59 | """Put together the final output based on indices. 60 | """ 61 | col_l = [c for c in self.columns if c not in key] 62 | col_r = [c for c in tbl_r.columns if c not in key_r] 63 | 64 | if jointype in ("inner", "left"): 65 | columns = (key + [c+suffixes[0] for c in col_l] + 66 | [c+suffixes[1] for c in col_r]) 67 | col_l = key + col_l 68 | elif jointype == "outer": 69 | col_l = key + col_l 70 | col_r = key_r + col_r 71 | columns = ([c+suffixes[0] for c in col_l] + 72 | [c+suffixes[1] for c in col_r]) 73 | elif jointype == "right": 74 | columns = ([c+suffixes[0] for c in col_l] + 75 | key_r + [c+suffixes[1] for c in col_r]) 76 | col_r = key_r + col_r 77 | 78 | data = self[idx[:, 1], col_l].data + tbl_r[idx[:, 0], col_r].data 79 | return self.__class__(data, columns=columns) 80 | 81 | def _arg_inner_join(self, index1, tbl_r, index2): 82 | """Perform inner join and return row indices 83 | """ 84 | hash_r = arg_hash(tbl_r[:, index2].data) 85 | return np.array([(s, i) for i, r in enumerate(zip(*self[:, index1].data)) 86 | for s in hash_r[r]]) 87 | 88 | def _arg_outer_join(self, index1, tbl_r, index2): 89 | """Perform outer join and return indices 90 | """ 91 | hash_l = arg_hash(self[:, index1].data) 92 | hash_r = arg_hash(tbl_r[:, index2].data) 93 | 94 | keys = set(hash_l.keys()).union(set(hash_r.keys())) 95 | 96 | def iter_join(h_l, h_r, join_keys): 97 | for join_key in join_keys: 98 | for elem in h_l.get(join_key, [-1]): 99 | for other in h_r.get(join_key, [-1]): 100 | yield (elem, other) 101 | idx = np.array(list(iter_join(hash_l, hash_r, keys))) 102 | return idx[:, [1, 0]] 103 | 104 | def join(self, tbl_r, key, key_r=None, jointype="inner", suffixes=('_l', '_r')): 105 | """dbase join tables with ind column(s) as the keys. 106 | 107 | Performs a database style joins on the two tables, the current instance 108 | and the provided tabel 'tbl_r' on the columns listed in 'key'. 109 | 110 | arguments: 111 | tbl_r (Tabel) : 112 | The right tabel to be joined. 113 | key (string or list) : 114 | Name of the column(s) to be used as the key(s). 115 | key_r (list) : 116 | A list of columnnames of the right tabel matching the left 117 | tabel. Defaults to the list provided in `ind`. 118 | jointype (str) : 119 | One of: `inner`, `left`, `right`, `outer`. If `inner`, returns 120 | the elements common to both tabels. If `outer`, returns the 121 | common elements as well as the elements of the left tabel not in 122 | the right tabel and the elements of the right tabel not in the 123 | left tabel. If `left`, returns the common elements and the 124 | elements of the left tabel not in the right tabel. If `right`, 125 | returns the common elements and the elements of the right tabel 126 | not in the left tabel. 127 | suffixes (tuple) : 128 | Strings to be added to the left and right tabel column names. 129 | 130 | returns: 131 | The joined tabel 132 | 133 | notes: 134 | The order and suffixes of the returned Tabel depend on the jointype. 135 | For all types, all but the key columns are suffixed with the left 136 | and the right suffix respectively. The left Tabel columns come first 137 | followed by the right Tabel columns, with the key column placed 138 | first of its Tabel columns. For `inner` and `left` jointypes the 139 | right key column is left out. for `right` jointype the left key 140 | column is left out. For the `outer` jointype both keys are present 141 | and suffixed. 142 | 143 | Examples: 144 | Join a Tabel into the current Tabel matching on column 'a': 145 | 146 | >>> tbl = Tabel({"a":list(range(4)), "b": ['a', 'b'] *2}) 147 | >>> tbl_b = Tabel({"a":list(range(4)), "c": ['d', 'e'] *2}) 148 | >>> tbl.join(tbl_b, "a") 149 | a | b_l | c_r 150 | -----+-------+------- 151 | 0 | a | d 152 | 1 | b | e 153 | 2 | a | d 154 | 3 | b | e 155 | 4 rows ['>> tbl = Tabel({'a':[10, 20, 30, 40]*3, 'b':["100", "200"]*6, 'c':[100, 200]*6}) 211 | >>> from tabel import first 212 | >>> tbl.group_by(['b', 'a'], [ (np.sum, 'a'), (first, 'c')]) 213 | b | a | a_sum | c_first 214 | -----+-----+---------+----------- 215 | 100 | 10 | 30 | 100 216 | 200 | 20 | 60 | 200 217 | 100 | 30 | 90 | 100 218 | 200 | 40 | 120 | 200 219 | 4 rows [' 0 else None 252 | -------------------------------------------------------------------------------- /README.rst: -------------------------------------------------------------------------------- 1 | About Tabel 2 | ============ 3 | 4 | Lightweight, intuitive and fast data-tables. 5 | 6 | *Tabel* data-tables are tables with columns and column names, rows and row 7 | numbers. Indexing and slicing your data is analogous to numpy array's. The 8 | only real difference is that each column can have its own data type. 9 | 10 | 11 | Design objectives 12 | ----------------- 13 | 14 | I got frustrated with pandas: it's complicated slicing syntax (.loc, .x, 15 | .iloc, .. etc), it's enforced index column and the Series objects I get when I 16 | want a numpy array. With Tabel I created the simplified pandas I need for many 17 | of my data-jobs. Just focussing on simple slicing of multi-datatype tables and 18 | basic table tools. 19 | 20 | * Intuitive simple slicing. 21 | 22 | * Using numpy machinery, for best performance, integration with other tools 23 | and future support. 24 | 25 | * Store data by column numpy arrays (column store). 26 | 27 | * No particular index column, all columns can be used as the index, the choice 28 | is up to the user. 29 | 30 | * Fundamental necessities for sorting, grouping, joining and appending tables. 31 | 32 | 33 | Install 34 | ======== 35 | 36 | pip install tabel 37 | 38 | Quickstart 39 | =========== 40 | 41 | init 42 | ---- 43 | 44 | To setup a Tabel: 45 | 46 | >>> from tabel import Tabel 47 | >>> tbl = Tabel([ ["John", "Joe", "Jane"], 48 | ... [1.82,1.65,2.15], 49 | ... [False,False,True]], columns = ["Name", "Height", "Married"]) 50 | >>> tbl 51 | Name | Height | Married 52 | --------+----------+----------- 53 | John | 1.82 | 0 54 | Joe | 1.65 | 0 55 | Jane | 2.15 | 1 56 | 3 rows ['>> tbl[1:3,[0,2]] 69 | Name | Married 70 | --------+----------- 71 | Joe | 0 72 | Jane | 1 73 | 2 rows ['>> tbl[1:3,'Name'] # doctest: +SKIP 80 | array(['Joe', 'Jane'], 81 | dtype='>> tbl[0,:] 86 | ('John', 1.82, False) 87 | 88 | 3. explicitly one element is requested: 89 | 90 | >>> tbl[0,'Name'] 91 | 'John' 92 | 93 | In general, slicing is intuitive and does not deviate from what would expect 94 | from numpy. With the one addition that columns can be referred to by names as 95 | well as numbers. 96 | 97 | set 98 | ---- 99 | 100 | Setting elements works the same as slicing: 101 | 102 | >>> tbl = Tabel({'Name' : ["John", "Joe", "Jane"], 'Height' : [1.82,1.65,2.15], 'Married': [False,False,True]}) 103 | >>> tbl[0,"Name"] = "Jos" 104 | >>> tbl 105 | Name | Height | Married 106 | --------+----------+----------- 107 | Jos | 1.82 | 0 108 | Joe | 1.65 | 0 109 | Jane | 2.15 | 1 110 | 3 rows ['>> tbl = Tabel({'Name' : ["John", "Joe", "Jane"], 'Height' : [1.82,1.65,2.15], 'Married': [False,False,True]}) 118 | >>> tbl['new'] = [1,2,3] 119 | >>> tbl 120 | Name | Height | Married | new 121 | --------+----------+-----------+------- 122 | John | 1.82 | 0 | 1 123 | Joe | 1.65 | 0 | 2 124 | Jane | 2.15 | 1 | 3 125 | 3 rows ['>> tbl = Tabel({'Name' : ["John", "Joe", "Jane"], 'Height' : [1.82,1.65,2.15], 'Married': [False,False,True]}) 130 | >>> tbl['new'] = 13 131 | >>> tbl 132 | Name | Height | Married | new 133 | --------+----------+-----------+------- 134 | John | 1.82 | 0 | 13 135 | Joe | 1.65 | 0 | 13 136 | Jane | 2.15 | 1 | 13 137 | 3 rows ['>> tbl = Tabel({'Name' : ["John", "Joe", "Jane"], 'Height' : [1.82,1.65,2.15], 'Married': [False,False,True]}) 148 | >>> tbl += tbl 149 | >>> tbl 150 | Name | Height | Married 151 | --------+----------+----------- 152 | John | 1.82 | 0 153 | Joe | 1.65 | 0 154 | Jane | 2.15 | 1 155 | John | 1.82 | 0 156 | Joe | 1.65 | 0 157 | Jane | 2.15 | 1 158 | 6 rows ['>> tbl = Tabel({'Name' : ["John", "Joe", "Jane"], 'Height' : [1.82,1.65,2.15], 'Married': [False,False,True]}) 163 | >>> tbl.row_append({'Height':1.81, 'Name':"Jack", 'Married':True}) 164 | >>> tbl 165 | Name | Height | Married 166 | --------+----------+----------- 167 | John | 1.82 | 0 168 | Joe | 1.65 | 0 169 | Jane | 2.15 | 1 170 | Jack | 1.81 | 1 171 | 4 rows ['>> tbl = Tabel({'Name' : ["John", "Joe", "Jane"], 'Height' : [1.82,1.65,2.15], 'Married': [False,False,True]}) 181 | >>> tbl.columns 182 | ['Name', 'Height', 'Married'] 183 | >>> tbl.data # doctest: +SKIP 184 | [array(['John', 'Joe', 'Jane'], 185 | dtype='>> tbl.shape 190 | (3, 3) 191 | >>> len(tbl) 192 | 3 193 | 194 | pandas 195 | ------- 196 | 197 | For for interfacing with the popular datatable framework, going back and forth 198 | is easy: 199 | 200 | >>> import pandas as pd 201 | >>> df = pd.DataFrame({'a':range(3),'b':range(10,13)}) 202 | >>> df 203 | a b 204 | 0 0 10 205 | 1 1 11 206 | 2 2 12 207 | 208 | To make a Tabel from a DataFrame, just supply it to the initialize: 209 | 210 | >>> tbl = Tabel(df) 211 | >>> tbl 212 | a | b 213 | -----+----- 214 | 0 | 10 215 | 1 | 11 216 | 2 | 12 217 | 3 rows ['>> df = pd.DataFrame(tbl.dict) 222 | >>> df 223 | a b 224 | 0 0 10 225 | 1 1 11 226 | 2 2 12 227 | 228 | 229 | Resources & getting help 230 | ========================== 231 | 232 | * See for the full API and more examples the documentation on `RTD `_. 233 | * The repository on `Github `_. 234 | * Installables on `pip `_. 235 | * Questions and answers on `StackOverflow `_, I 236 | will try to monitor for it. 237 | 238 | Stable releases 239 | ================ 240 | * tabel 1.2.3 241 | 242 | * Added __delitem__ feature to delete row(s) or a column. 243 | 244 | * tabel 1.2.2 245 | 246 | * Added argument to save and read methods for csv and gz formats to specify 247 | whether or not to write/read a header with the column names. For reading 248 | header can be left to None for automatic sniffing of the header. Default is 249 | True for both read and save methods. 250 | 251 | * tabel 1.2.1 252 | 253 | * Removed unicode characters from description to fix pip install 254 | `issue `. 255 | 256 | * tabel 1.2.0 257 | 258 | * Fix for numpy 1.15.5 "warnings" 259 | * Fix for outerjoin to raise an error in case of unsupported datatypes 260 | 261 | * tabel 1.1 262 | 263 | * Added join and group_by methods 264 | * September 27, 2018 265 | 266 | * tabel 1.0 267 | 268 | * First release 269 | * September 8, 2018 270 | 271 | 272 | Dependencies 273 | ============ 274 | 275 | * numpy 276 | * tabulate (optional, recommended) 277 | * pandas (optional, for converting back and forth to DataFrames) 278 | 279 | Tested on: 280 | ---------- 281 | 282 | * Python 3.6.4; numpy 1.15.4 283 | * Python 3.6.4; numpy 1.14.3 284 | * Python 2.7.14; numpy 1.14.0 285 | 286 | 287 | Contributing to Tabel 288 | ===================== 289 | Tabel is perfect already, no more contributions needed. Just kidding! 290 | 291 | See the repository for filing issues and proposing enhancements. 292 | 293 | git: 294 | ---- 295 | 296 | * Using master as the development branch 297 | * Every new version is branched of of master (after its finished) into its own 298 | "v1.2.3" named branch. Subsequent version specific fixes can be done in the 299 | version branches. 300 | 301 | 302 | I'm using pytest, pylint, doctest, sphynx and setuptools. 303 | 304 | - git :: 305 | 306 | git checkout master 307 | git pull 308 | 309 | - pytest :: 310 | 311 | cd tabel/test 312 | conda activate py3_6 313 | pytest 314 | conda activate py2_7 315 | pytest 316 | 317 | - pylint :: 318 | 319 | cd tabel/ 320 | ./pylint.sh 321 | 322 | - doctest :: 323 | 324 | cd tabel/docs 325 | make doctest 326 | 327 | - sphynx :: 328 | 329 | cd tabel/docs 330 | make html 331 | 332 | - setuptools/pypi :: 333 | 334 | python setup.py sdist bdist_wheel 335 | twine upload dist/tabel-1.1.0.* 336 | 337 | - git :: 338 | 339 | git add . 340 | git commit -m 341 | git push 342 | git checkout v1.2.3 -b 343 | git push --set-upstream origin v1.2.3 344 | 345 | 346 | 347 | Contributors 348 | ============ 349 | Just me, Bastiaan Bergman [Bastiaan.Bergman@gmail.com]. 350 | 351 | 352 | What's in the name? 353 | =================== 354 | 355 | *Tabel* is Dutch for table (two-dimensional enlisting), `wiktionary 356 | `_. The english word table, as in "dinner 357 | table", translates in Dutch to *tafel*. The Dutch word *tafel* is an old 358 | fashioned word for data-table, mostly used for calculation tables which itself 359 | is old fashioned as well. 360 | 361 | 362 | ToDo 363 | ===== 364 | 365 | * polish error messages and validity checking and add testing for it. 366 | * cache buffers for faster appending: store temp in list and concatenate to 367 | array only once we use another method 368 | * allow for (sparse) numpy arrays as an element 369 | * adjust & limit __repr__ width for very wide Tabels in jupyter cell 370 | * items() and row_items() and keys() and values() method 371 | * pop_column method 372 | * tox - environment testing 373 | * set subsets of tabels with (subsets) of other tabels, seems logic as __setitem__ is 374 | allowed to provide the datatype that should have come from a __getitem__ 375 | * datetime column support 376 | * add disk datalogger 377 | -------------------------------------------------------------------------------- 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a=i.call(arguments);H.call(a,this._wrapped);return v(c.apply(b,a),this._chain)}};b.mixin(b);j("pop,push,reverse,shift,sort,splice,unshift".split(","),function(a){var b=k[a];m.prototype[a]=function(){var d=this._wrapped;b.apply(d,arguments);var e=d.length;(a=="shift"||a=="splice")&&e===0&&delete d[0];return v(d,this._chain)}});j(["concat","join","slice"],function(a){var b=k[a];m.prototype[a]=function(){return v(b.apply(this._wrapped,arguments),this._chain)}});m.prototype.chain=function(){this._chain= 31 | true;return this};m.prototype.value=function(){return this._wrapped}}).call(this); 32 | -------------------------------------------------------------------------------- /docs/build/html/genindex.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | Index — tabel v1.0 documentation 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 |
42 | 43 | 44 | 107 | 108 |
109 | 110 | 111 | 117 | 118 | 119 |
120 | 121 |
122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 |
140 | 141 |
    142 | 143 |
  • Docs »
    • 144 | 145 |
  • Index
    • 146 | 147 | 148 |
  • 149 | 150 | 151 | 152 |
    • 153 | 154 |
155 | 156 | 157 |
158 |
159 |
160 |
161 | 162 | 163 |

Index

164 | 165 |
166 | _ 167 | | A 168 | | D 169 | | F 170 | | G 171 | | J 172 | | M 173 | | R 174 | | S 175 | | T 176 | | V 177 | 178 |
179 |

_

180 | 181 | 187 | 195 |
196 | 197 |

A

198 | 199 | 203 | 207 |
208 | 209 |

D

210 | 211 | 215 | 219 |
220 | 221 |

F

222 | 223 | 227 |
228 | 229 |

G

230 | 231 | 235 |
236 | 237 |

J

238 | 239 | 243 | 247 |
248 | 249 |

M

250 | 251 | 255 |
256 | 257 |

R

258 | 259 | 263 | 269 |
270 | 271 |

S

272 | 273 | 277 | 283 |
284 | 285 |

T

286 | 287 | 293 | 299 |
300 | 301 |

V

302 | 303 | 307 |
308 | 309 | 310 | 311 |
312 | 313 |
314 |
315 | 316 | 317 |
318 | 319 |
320 |

321 | © Copyright 2018, Bastiaan Bergman. 322 | 323 |

324 |
325 | Built with Sphinx using a theme provided by Read the Docs. 326 | 327 |
328 | 329 |
330 |
331 | 332 |
333 | 334 |
335 | 336 | 337 | 338 | 339 | 340 | 351 | 352 | 353 | 354 | 355 | 356 | 357 | 358 | 359 | 364 | 365 | 366 | -------------------------------------------------------------------------------- /docs/build/html/api/module.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | tabel module — tabel v1.0 documentation 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 |
43 | 44 | 45 | 117 | 118 |
119 | 120 | 121 | 127 | 128 | 129 |
130 | 131 |
132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 149 |
150 | 151 | 169 | 170 | 171 |
172 |
173 |
174 |
175 | 176 |
177 |

tabel module

178 |
179 |

transpose

180 |
181 |
182 | tabel.transpose(datastruct)
183 |

Transpose rows and columns.

184 |

Convenience function. Usually DB connectors return data as a list of 185 | records, Tabel takes and internally stores data as a list of columns (column 186 | store). This function will transpose the list of records into a list of 187 | columns without a priori assuming anything about the datatype of each 188 | individual element.

189 | 190 | 191 | 192 | 193 | 194 | 195 | 197 | 198 | 199 |
Parameters:datastruct (list) – list or tuple containing lists or tuples with the data for each row.
Returns:transposed datastruct, list containing lists with the data for each 196 | column.
200 |
201 | 202 |
203 |
204 | tabel.T(datastruct)
205 |

Convenience alias for tabel.transpose.

206 |
207 | 208 |
209 |
210 |

read_tabel

211 |
212 |
213 | tabel.read_tabel(filename, fmt='auto', header=True)
214 |

Read data from disk

215 |

Read data from disk and return a Tabel object.

216 | 217 | 218 | 219 | 220 | 227 | 228 | 230 | 231 | 232 |
Parameters:
    221 |
  • filename (str) – filename sring, including path and extension.
    • 222 |
  • fmt (str) – format specifier, supports: ‘csv’, ‘npz’, ‘gz’.
    • 223 |
  • header (bool) – whether to expect a header (True) or not (False) or try to sniff 224 | (None), only used for csv and gz
    • 225 |
226 |
Returns:

Tabel object containing the data.

229 |
233 |
234 | 235 |
236 |
237 |

first

238 |
239 |
240 | tabel.first(array)
241 |

Get the first element when doing a tabel.Tabel.group_by.

242 |
243 |
Arguments :
244 |
245 |
array (numpy ndarray) :
246 |
numpy 1D array containing the subset of elements
247 |
248 |
249 |
Returns :
250 |
The first element of ar
251 |
Examples :
252 |
See Tabel.group_by for examples
253 |
254 |
255 | 256 |
257 |
258 | 259 | 260 |
261 | 262 |
263 |
264 | 265 | 273 | 274 | 275 |
276 | 277 |
278 |

279 | © Copyright 2018, Bastiaan Bergman. 280 | 281 |

282 |
283 | Built with Sphinx using a theme provided by Read the Docs. 284 | 285 |
286 | 287 |
288 |
289 | 290 |
291 | 292 |
293 | 294 | 295 | 296 | 297 | 298 | 309 | 310 | 311 | 312 | 313 | 314 | 315 | 316 | 317 | 322 | 323 | 324 | -------------------------------------------------------------------------------- /docs/build/html/_static/basic.css: -------------------------------------------------------------------------------- 1 | /* 2 | * basic.css 3 | * ~~~~~~~~~ 4 | * 5 | * Sphinx stylesheet -- basic theme. 6 | * 7 | * :copyright: Copyright 2007-2018 by the Sphinx team, see AUTHORS. 8 | * :license: BSD, see LICENSE for details. 9 | * 10 | */ 11 | 12 | /* -- main layout ----------------------------------------------------------- */ 13 | 14 | div.clearer { 15 | clear: both; 16 | } 17 | 18 | /* -- relbar ---------------------------------------------------------------- */ 19 | 20 | div.related { 21 | width: 100%; 22 | font-size: 90%; 23 | } 24 | 25 | div.related h3 { 26 | display: none; 27 | } 28 | 29 | div.related ul { 30 | margin: 0; 31 | padding: 0 0 0 10px; 32 | list-style: none; 33 | } 34 | 35 | div.related li { 36 | display: inline; 37 | } 38 | 39 | div.related li.right { 40 | float: right; 41 | margin-right: 5px; 42 | } 43 | 44 | /* -- sidebar --------------------------------------------------------------- */ 45 | 46 | div.sphinxsidebarwrapper { 47 | padding: 10px 5px 0 10px; 48 | } 49 | 50 | div.sphinxsidebar { 51 | float: left; 52 | width: 230px; 53 | margin-left: -100%; 54 | font-size: 90%; 55 | word-wrap: break-word; 56 | overflow-wrap : break-word; 57 | } 58 | 59 | div.sphinxsidebar ul { 60 | list-style: none; 61 | } 62 | 63 | div.sphinxsidebar ul ul, 64 | div.sphinxsidebar ul.want-points { 65 | margin-left: 20px; 66 | list-style: square; 67 | } 68 | 69 | div.sphinxsidebar ul ul { 70 | margin-top: 0; 71 | margin-bottom: 0; 72 | } 73 | 74 | div.sphinxsidebar form { 75 | margin-top: 10px; 76 | } 77 | 78 | div.sphinxsidebar input { 79 | border: 1px solid #98dbcc; 80 | font-family: sans-serif; 81 | font-size: 1em; 82 | } 83 | 84 | div.sphinxsidebar #searchbox input[type="text"] { 85 | float: left; 86 | width: 80%; 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664 | } 665 | } -------------------------------------------------------------------------------- /tabel.egg-info/PKG-INFO: -------------------------------------------------------------------------------- 1 | Metadata-Version: 2.1 2 | Name: tabel 3 | Version: 1.2.3 4 | Summary: Lightweight, intuitive and fast data-tables. 5 | Home-page: https://github.com/BastiaanBergman/tabel 6 | Author: Bastiaan Bergman 7 | Author-email: Bastiaan.Bergman@gmail.com 8 | License: MIT 9 | Project-URL: Documentation, https://tabel.readthedocs.io/en/stable/ 10 | Project-URL: Source Code, https://github.com/BastiaanBergman/tabel 11 | Description: About Tabel 12 | ============ 13 | 14 | Lightweight, intuitive and fast data-tables. 15 | 16 | *Tabel* data-tables are tables with columns and column names, rows and row 17 | numbers. Indexing and slicing your data is analogous to numpy array's. The 18 | only real difference is that each column can have its own data type. 19 | 20 | 21 | Design objectives 22 | ----------------- 23 | 24 | I got frustrated with pandas: it's complicated slicing syntax (.loc, .x, 25 | .iloc, .. etc), it's enforced index column and the Series objects I get when I 26 | want a numpy array. With Tabel I created the simplified pandas I need for many 27 | of my data-jobs. Just focussing on simple slicing of multi-datatype tables and 28 | basic table tools. 29 | 30 | * Intuitive simple slicing. 31 | 32 | * Using numpy machinery, for best performance, integration with other tools 33 | and future support. 34 | 35 | * Store data by column numpy arrays (column store). 36 | 37 | * No particular index column, all columns can be used as the index, the choice 38 | is up to the user. 39 | 40 | * Fundamental necessities for sorting, grouping, joining and appending tables. 41 | 42 | 43 | Install 44 | ======== 45 | 46 | pip install tabel 47 | 48 | Quickstart 49 | =========== 50 | 51 | init 52 | ---- 53 | 54 | To setup a Tabel: 55 | 56 | >>> from tabel import Tabel 57 | >>> tbl = Tabel([ ["John", "Joe", "Jane"], 58 | ... [1.82,1.65,2.15], 59 | ... [False,False,True]], columns = ["Name", "Height", "Married"]) 60 | >>> tbl 61 | Name | Height | Married 62 | --------+----------+----------- 63 | John | 1.82 | 0 64 | Joe | 1.65 | 0 65 | Jane | 2.15 | 1 66 | 3 rows ['>> tbl[1:3,[0,2]] 79 | Name | Married 80 | --------+----------- 81 | Joe | 0 82 | Jane | 1 83 | 2 rows ['>> tbl[1:3,'Name'] # doctest: +SKIP 90 | array(['Joe', 'Jane'], 91 | dtype='>> tbl[0,:] 96 | ('John', 1.82, False) 97 | 98 | 3. explicitly one element is requested: 99 | 100 | >>> tbl[0,'Name'] 101 | 'John' 102 | 103 | In general, slicing is intuitive and does not deviate from what would expect 104 | from numpy. With the one addition that columns can be referred to by names as 105 | well as numbers. 106 | 107 | set 108 | ---- 109 | 110 | Setting elements works the same as slicing: 111 | 112 | >>> tbl = Tabel({'Name' : ["John", "Joe", "Jane"], 'Height' : [1.82,1.65,2.15], 'Married': [False,False,True]}) 113 | >>> tbl[0,"Name"] = "Jos" 114 | >>> tbl 115 | Name | Height | Married 116 | --------+----------+----------- 117 | Jos | 1.82 | 0 118 | Joe | 1.65 | 0 119 | Jane | 2.15 | 1 120 | 3 rows ['>> tbl = Tabel({'Name' : ["John", "Joe", "Jane"], 'Height' : [1.82,1.65,2.15], 'Married': [False,False,True]}) 128 | >>> tbl['new'] = [1,2,3] 129 | >>> tbl 130 | Name | Height | Married | new 131 | --------+----------+-----------+------- 132 | John | 1.82 | 0 | 1 133 | Joe | 1.65 | 0 | 2 134 | Jane | 2.15 | 1 | 3 135 | 3 rows ['>> tbl = Tabel({'Name' : ["John", "Joe", "Jane"], 'Height' : [1.82,1.65,2.15], 'Married': [False,False,True]}) 140 | >>> tbl['new'] = 13 141 | >>> tbl 142 | Name | Height | Married | new 143 | --------+----------+-----------+------- 144 | John | 1.82 | 0 | 13 145 | Joe | 1.65 | 0 | 13 146 | Jane | 2.15 | 1 | 13 147 | 3 rows ['>> tbl = Tabel({'Name' : ["John", "Joe", "Jane"], 'Height' : [1.82,1.65,2.15], 'Married': [False,False,True]}) 158 | >>> tbl += tbl 159 | >>> tbl 160 | Name | Height | Married 161 | --------+----------+----------- 162 | John | 1.82 | 0 163 | Joe | 1.65 | 0 164 | Jane | 2.15 | 1 165 | John | 1.82 | 0 166 | Joe | 1.65 | 0 167 | Jane | 2.15 | 1 168 | 6 rows ['>> tbl = Tabel({'Name' : ["John", "Joe", "Jane"], 'Height' : [1.82,1.65,2.15], 'Married': [False,False,True]}) 173 | >>> tbl.row_append({'Height':1.81, 'Name':"Jack", 'Married':True}) 174 | >>> tbl 175 | Name | Height | Married 176 | --------+----------+----------- 177 | John | 1.82 | 0 178 | Joe | 1.65 | 0 179 | Jane | 2.15 | 1 180 | Jack | 1.81 | 1 181 | 4 rows ['>> tbl = Tabel({'Name' : ["John", "Joe", "Jane"], 'Height' : [1.82,1.65,2.15], 'Married': [False,False,True]}) 191 | >>> tbl.columns 192 | ['Name', 'Height', 'Married'] 193 | >>> tbl.data # doctest: +SKIP 194 | [array(['John', 'Joe', 'Jane'], 195 | dtype='>> tbl.shape 200 | (3, 3) 201 | >>> len(tbl) 202 | 3 203 | 204 | pandas 205 | ------- 206 | 207 | For for interfacing with the popular datatable framework, going back and forth 208 | is easy: 209 | 210 | >>> import pandas as pd 211 | >>> df = pd.DataFrame({'a':range(3),'b':range(10,13)}) 212 | >>> df 213 | a b 214 | 0 0 10 215 | 1 1 11 216 | 2 2 12 217 | 218 | To make a Tabel from a DataFrame, just supply it to the initialize: 219 | 220 | >>> tbl = Tabel(df) 221 | >>> tbl 222 | a | b 223 | -----+----- 224 | 0 | 10 225 | 1 | 11 226 | 2 | 12 227 | 3 rows ['>> df = pd.DataFrame(tbl.dict) 232 | >>> df 233 | a b 234 | 0 0 10 235 | 1 1 11 236 | 2 2 12 237 | 238 | 239 | Resources & getting help 240 | ========================== 241 | 242 | * See for the full API and more examples the documentation on `RTD `_. 243 | * The repository on `Github `_. 244 | * Installables on `pip `_. 245 | * Questions and answers on `StackOverflow `_, I 246 | will try to monitor for it. 247 | 248 | Stable releases 249 | ================ 250 | * tabel 1.2.3 251 | 252 | * Added __delitem__ feature to delete row(s) or a column. 253 | 254 | * tabel 1.2.2 255 | 256 | * Added argument to save and read methods for csv and gz formats to specify 257 | whether or not to write/read a header with the column names. For reading 258 | header can be left to None for automatic sniffing of the header. Default is 259 | True for both read and save methods. 260 | 261 | * tabel 1.2.1 262 | 263 | * Removed unicode characters from description to fix pip install 264 | `issue `. 265 | 266 | * tabel 1.2.0 267 | 268 | * Fix for numpy 1.15.5 "warnings" 269 | * Fix for outerjoin to raise an error in case of unsupported datatypes 270 | 271 | * tabel 1.1 272 | 273 | * Added join and group_by methods 274 | * September 27, 2018 275 | 276 | * tabel 1.0 277 | 278 | * First release 279 | * September 8, 2018 280 | 281 | 282 | Dependencies 283 | ============ 284 | 285 | * numpy 286 | * tabulate (optional, recommended) 287 | * pandas (optional, for converting back and forth to DataFrames) 288 | 289 | Tested on: 290 | ---------- 291 | 292 | * Python 3.6.4; numpy 1.15.4 293 | * Python 3.6.4; numpy 1.14.3 294 | * Python 2.7.14; numpy 1.14.0 295 | 296 | 297 | Contributing to Tabel 298 | ===================== 299 | Tabel is perfect already, no more contributions needed. Just kidding! 300 | 301 | See the repository for filing issues and proposing enhancements. 302 | 303 | I'm using pytest, pylint, doctest, sphynx and setuptools. 304 | 305 | - pytest :: 306 | 307 | cd tabel/test 308 | conda activate py3_6 309 | pytest 310 | conda activate py2_7 311 | pytest 312 | 313 | - pylint :: 314 | 315 | cd tabel/ 316 | ./pylint.sh 317 | 318 | - doctest :: 319 | 320 | cd tabel/docs 321 | make doctest 322 | 323 | - sphynx :: 324 | 325 | cd tabel/docs 326 | make html 327 | 328 | - setuptools/pypi :: 329 | 330 | python setup.py sdist bdist_wheel 331 | twine upload dist/tabel-1.1.0.* 332 | 333 | 334 | Contributors 335 | ============ 336 | Just me, Bastiaan Bergman [Bastiaan.Bergman@gmail.com]. 337 | 338 | 339 | What's in the name? 340 | =================== 341 | 342 | *Tabel* is Dutch for table (two-dimensional enlisting), `wiktionary 343 | `_. The english word table, as in "dinner 344 | table", translates in Dutch to *tafel*. The Dutch word *tafel* is an old 345 | fashioned word for data-table, mostly used for calculation tables which itself 346 | is old fashioned as well. 347 | 348 | 349 | ToDo 350 | ===== 351 | 352 | * polish error messages and validity checking and add testing for it. 353 | * cache buffers for faster appending: store temp in list and concatenate to 354 | array only once we use another method 355 | * allow for (sparse) numpy arrays as an element 356 | * adjust & limit __repr__ width for very wide Tabels in jupyter cell 357 | * items() and row_items() and keys() and values() method 358 | * pop_column method 359 | * tox - environment testing 360 | * set subsets of tabels with (subsets) of other tabels, seems logic as __setitem__ is 361 | allowed to provide the datatype that should have come from a __getitem__ 362 | * datetime column support 363 | * add disk datalogger 364 | 365 | Keywords: data table 366 | Platform: UNKNOWN 367 | Classifier: Programming Language :: Python :: 3 368 | Classifier: Programming Language :: Python :: 3.6 369 | Classifier: Programming Language :: Python :: 2 370 | Classifier: Programming Language :: Python :: 2.7 371 | Classifier: License :: OSI Approved :: MIT License 372 | Classifier: Operating System :: OS Independent 373 | Requires-Python: >=2.6, >=3.0 374 | Description-Content-Type: text/x-rst 375 | -------------------------------------------------------------------------------- /test/test_tabel.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import os 3 | local_path = os.path.abspath(os.path.join(os.getcwd(), "../")) 4 | sys.path.insert(0,local_path) 5 | 6 | from tabel import Tabel, read_tabel, T, first 7 | import os 8 | import pytest 9 | import numpy as np 10 | from itertools import product 11 | from copy import copy, deepcopy 12 | import pandas as pd 13 | 14 | def naneq(a, b): 15 | """ Test wether a and b are equal, counting both nan as being equal. 16 | """ 17 | try: 18 | return np.all(np.isnan(a) == np.isnan(b)) or np.all(a == b) 19 | except TypeError as e: 20 | return np.all(a == b) 21 | 22 | @pytest.fixture 23 | def tbls(): 24 | tbls = [Tabel({'Name' : ["John", "Joe", "Jane"], 'Height' : [1.82,1.65,2.15], 'Married': [False,False,True]}), 25 | Tabel([ np.array(["John", "Joe", "Jane"]),np.array([1.82,1.65,2.15]),np.array([False,False,True])], columns = ["Name", "Height", "Married"]), 26 | Tabel([ ["John", "Joe", "Jane"],[1.82,1.65,2.15],[False,False,True]], columns = ["Name", "Height", "Married"]), 27 | Tabel(), 28 | Tabel([1,2,3]), 29 | Tabel({'a':range(10),'b':range(10)}), 30 | Tabel({'a':range(10),'b':list(map(str, range(10)))}), 31 | Tabel([range(10),range(10),range(10)]), 32 | Tabel(np.array([np.arange(10),np.arange(10,20)])), 33 | Tabel({'a':12,'b':13}), 34 | Tabel(pd.DataFrame({'a':[12]*6, 'b':np.arange(6), 'c':range(6)})), 35 | Tabel({'a':[3,4,5], 'b':1}), 36 | Tabel({'a':1, 'b':[3,4,5]}), 37 | Tabel({'aa':[1,1,1], 'b':[3,4,5]}), 38 | ] 39 | return tbls 40 | 41 | 42 | class TestTabelInit(object): 43 | def test_tabel_init(self, tbls): 44 | for tbl in tbls: 45 | assert tbl.valid 46 | 47 | 48 | class TestAppend(object): 49 | def test_append_tabel(self, tbls): 50 | for tbl in tbls: 51 | length = len(tbl) 52 | tbl += tbl 53 | assert tbl.valid 54 | assert len(tbl) == 2 * length 55 | 56 | def test_append_column(self, tbls): 57 | for tbl in tbls: 58 | width = tbl.shape[1] 59 | ln = len(tbl) 60 | tbl["n1"] = 12 61 | tbl["n2"] = "Foo" 62 | tbl["n3"] = np.arange(len(tbl)) 63 | tbl["n4"] = [12]*len(tbl) 64 | tbl["n5"] = ["Bar"]*len(tbl) 65 | assert tbl.valid 66 | assert tbl.shape[1] == width + 5 67 | if ln > 0: 68 | assert len(tbl) == ln 69 | 70 | def test_append_combinations(self): 71 | first_tbl = [Tabel(), Tabel({'a':2,'b':3}), Tabel({'a':"ff",'b':"ffr"})] 72 | seccond_tbl = [Tabel({'a':22,'b':13}), pd.DataFrame({'a':[22],'b':[13]}), 73 | Tabel({'a':"ff",'b':"ffr"}), 74 | pd.DataFrame({'a':["jdw"],'b':["okdw"]}), 75 | ] 76 | for ftbl, stbl in product(first_tbl, seccond_tbl): 77 | ftbl = deepcopy(ftbl) 78 | fl = len(ftbl) 79 | ftbl.append(stbl) 80 | assert ftbl.valid 81 | assert len(ftbl) == fl + 1 82 | 83 | def test_row_append_combinations(self): 84 | first_tbl = [Tabel(), Tabel({'a':2,'b':3}), Tabel({'a':"ff",'b':"ffr"})] 85 | seccond_tbl = [{'a':22,'b':13}, [22,33], (44,55), np.array([33,33]), 86 | {'a':"ded",'b':"fr"}, ["frd","werf"], 87 | ("efds","dew"), np.array(["frde","frtg"])] 88 | for ftbl, stbl in product(first_tbl, seccond_tbl): 89 | ftbl = deepcopy(ftbl) 90 | fl = len(ftbl) 91 | ftbl.row_append(stbl) 92 | assert ftbl.valid 93 | assert len(ftbl) == fl + 1 94 | 95 | 96 | class TestSlice(object): 97 | def test_slice_type(self, tbls): 98 | for tbl in tbls: 99 | if len(tbl) > 0: 100 | assert type(tbl[0]) == tuple 101 | assert type(tbl[:,0]) == np.ndarray 102 | assert type(tbl[:,:]) == Tabel 103 | assert type(tbl[0,0]) == type(tbl.data[0][0]) 104 | assert type(tbl[[0],[0]]) == Tabel 105 | (r,c) = tbl.shape 106 | assert type(tbl[np.array([True]*r),np.array([True]*c)]) == Tabel 107 | assert type(tbl[list(range(r)),list(range(c))]) == Tabel 108 | assert type(tbl[:,:]) == Tabel 109 | 110 | def test_slice_columns(self, tbls): 111 | for tbl in tbls: 112 | if tbl: 113 | assert np.all(tbl[:,0] == tbl.data[0]) 114 | assert np.all(tbl[:,tbl.columns[0]] == tbl.data[0]) 115 | 116 | def test_slice_rows(self, tbls): 117 | for tbl in tbls: 118 | if tbl: 119 | assert np.all(tbl[0][0] == tbl.data[0][0]) 120 | 121 | def test_slice_boolean(self, tbls): 122 | for tbl in tbls: 123 | if tbl.shape[0] > 0 & tbl.shape[1] > 0: 124 | ind = tbl[:,0] == 0 125 | ind = ind if isinstance(ind, np.ndarray) else np.array([ind]) 126 | assert type(tbl[ind,:]) == Tabel 127 | 128 | def test_slice_permiseable(self, tbls): 129 | row_it = [slice(0,2,None), [0,1], np.array([True,False,True]), 0, np.array([0,1]), 130 | np.array([False, True, False]), slice(0,3,2)] 131 | col_it = row_it + [["Name", "Height"], "Name", ["Height", "Name"]] 132 | keys = list(product(row_it, col_it)) 133 | for tbl in tbls[0:3]: 134 | for key in keys: 135 | _ = tbl[key] 136 | 137 | 138 | class TestSetter(object): 139 | def test_set_individual_elm(self, tbls): 140 | for tbl in tbls[0:3]: 141 | tbl[0,0] = "Bas" 142 | tbl[1,1] = 12 143 | tbl[2,1] = True 144 | 145 | def test_set_column(self, tbls): 146 | for tbl in tbls[:3]: 147 | i = tbl.columns.index("Name") 148 | tbl[0:2,i] = np.array(["Bas", "Jack"]) 149 | tbl[0:2,"Name"] = ["Bas", "Jack"] 150 | tbl[[0], "Name"] = ["Bas"] 151 | length = len(tbl) 152 | tbl["Name"] = "Blanc" 153 | assert np.all(tbl["Name"] == np.array(length*["Blanc"]).astype(tbl["Name"].dtype)) 154 | tbl[[False, True, False], 1] = [13] 155 | tbl["Name"] = ["Noone"] * length 156 | tbl[:, "Name"] = ["Noone"] * length 157 | 158 | def test_set_row(self): 159 | tbl = Tabel({"a":list(range(2,6)), "b": ['1','2'] *2, "c":[1.1,2.2]*2}) 160 | tbl[0,:] = (12, "12", 12.12) 161 | tbl[1,['a','b']] = [12, "12"] 162 | tbl[2] = (13, "13", 13.13) 163 | tbl[3, np.array([True, False, True])] = (14, 14.14) 164 | 165 | def test_add_column(self): 166 | tbl = Tabel({"a":list(range(2,6)), "b": ['1','2'] *2, "c":[1.1,2.2]*2}) 167 | tbl["d"] = np.array([True]*len(tbl)) 168 | tbl["e"] = 100 169 | tbl["f"] = "Blanc" 170 | assert len(tbl.columns) == 6 171 | assert np.all([len(c)==len(tbl) for c in tbl.data]) 172 | 173 | def test_replace_column(self): 174 | tbl = Tabel({"a":list(range(2,6)), "b": ['1','2'] *2, "c":[1.1,2.2]*2}) 175 | tbl[:,"a"] = np.array([1.4,2.4]*2) 176 | assert tbl[0,'a'] == 1 177 | tbl["a"] = np.array([1.1,2.2]*2) 178 | assert tbl['a'].dtype == np.float 179 | tbl[:,"b"] = np.arange(4) 180 | assert tbl[0, "b"] == "0" 181 | tbl["b"] = np.arange(4) 182 | assert tbl[0, "b"] == 0 183 | tbl[:,"c"] = np.arange(4) 184 | assert tbl["c"].dtype == np.float 185 | tbl["c"] = np.arange(4) 186 | assert tbl["c"].dtype == np.int 187 | 188 | 189 | class TestSaveNRead(object): 190 | def test_save_n_read(self, tbls, tmpdir): 191 | for header in [True, False]: 192 | for fmt in ['csv', 'gz', 'npz']: 193 | for tbl in tbls: 194 | if len(tbl) > 0: 195 | fn = os.path.join(str(tmpdir), "test."+fmt) 196 | tbl.save(fn, header=header) 197 | tbl_r = read_tabel(fn, header=header) 198 | assert (set(tbl_r.columns) == set(tbl.columns)) or (not header), (tbl, tbl_r) 199 | assert len(tbl_r) == len(tbl), (tbl, tbl_r) 200 | assert tbl_r.valid, (tbl, tbl_r) 201 | 202 | if not tbl_r.columns[0].isdigit() and len(tbl)>2: 203 | # header sniffer should work 204 | tbl_r = read_tabel(fn, header=None) 205 | assert set(tbl_r.columns) == set(tbl.columns), (tbl, tbl_r) 206 | assert len(tbl_r) == len(tbl), (tbl, tbl_r) 207 | 208 | class TestGroupBy(object): 209 | def test_group_by(self): 210 | tbl = Tabel({'a':[10,20,30, 40]*3, 'b':["100","200"]*6, 'c':[100,200]*6}) 211 | tbl_g = tbl.group_by(['b','a'], [(np.sum, 'a'), (first, 'c')]) 212 | idx = np.argsort(tbl_g['a_sum']) 213 | assert np.all(tbl_g[idx, 'a_sum'] == np.array([30, 60, 90, 120])) 214 | assert np.all(tbl['a'] == [10,20,30, 40]*3) 215 | assert np.all(tbl_g[idx, 'c_first'] == np.array([100, 200, 100, 200])) 216 | tbl_g = tbl.group_by(['b','a'], []) 217 | idx = np.argsort(tbl_g['a']) 218 | assert np.all(tbl_g[idx, 'a'] == [10,20,30,40]) 219 | 220 | 221 | class TestShapeNLen(object): 222 | def test_shape_n_len(self, tbls): 223 | for tbl in tbls: 224 | assert len(tbl) == tbl.shape[0] 225 | 226 | def test_shape(self, tbls): 227 | for tbl in tbls: 228 | assert len(tbl.columns) == tbl.shape[1] 229 | assert len(tbl.data) == tbl.shape[1] 230 | 231 | def test_len(self, tbls): 232 | for tbl in tbls: 233 | for c in tbl.columns: 234 | assert len(tbl[c]) == tbl.shape[0] 235 | 236 | 237 | class TestTranspose(object): 238 | def test_transpose(self, tbls): 239 | for tbl in tbls: 240 | if len(tbl) == 0: 241 | continue 242 | lrec = [tbl[i] for i in range(len(tbl))] 243 | tbl_n = Tabel(T(lrec)) 244 | for i,column in enumerate(tbl.columns): 245 | assert np.all(tbl[column] == tbl_n[str(i)]) 246 | lrec = [list(tbl[i]) for i in range(len(tbl))] 247 | tbl_n = Tabel(T(lrec)) 248 | for i,column in enumerate(tbl.columns): 249 | assert np.all(tbl[column] == tbl_n[str(i)]) 250 | 251 | 252 | class TestJoin(object): 253 | def test_inner_int_join(self): 254 | tbl = Tabel({"a":list(range(4)), "b": ['a','b'] *2}) 255 | tbl_b = Tabel({"a":list(range(2,6)), "c": ['d','e'] *2}) 256 | tbl_j = tbl.join(tbl_b, "a") 257 | assert "c_r" in tbl_j.columns 258 | assert tbl_j[0] == (2,'a','d') 259 | assert len(tbl_j) == 2 260 | assert np.all(tbl[0] == (0,'a')) 261 | 262 | def test_inner_str_join(self): 263 | tbl = Tabel({"a":list(map(str, range(4))), "b": ['a','b'] *2}) 264 | tbl_b = Tabel({"a":list(map(str, range(4))), "c": ['d','e'] *2}) 265 | tbl_j = tbl.join(tbl_b, "a", jointype="inner") 266 | assert "c_r" in tbl_j.columns 267 | assert tbl_j[0] == ('0','a','d') 268 | assert len(tbl_j) == 4 269 | assert np.all(tbl[0] == ('0','a')) 270 | 271 | def test_outer_join(self): 272 | tbl = Tabel({"a":list(range(4)), "b": ['a','b'] *2}) 273 | tbl_b = Tabel({"a":list(range(2,6)), "c": [1,2] *2, "d":[1.1,2.2]*2}) 274 | tbl_j = tbl.join(tbl_b, "a", jointype='outer') 275 | naneq(tbl_j[tbl_j['a_l']==0,'c_r'], Tabel.join_fill_value['integer']) 276 | naneq(tbl_j[tbl_j['a_l']==0, 'd_r'], Tabel.join_fill_value['float']) 277 | naneq(tbl_j[tbl_j['a_r']==4, 'b_l'], Tabel.join_fill_value['string']) 278 | assert "c_r" in tbl_j.columns 279 | assert len(tbl_j) == 6 280 | 281 | 282 | class TestSort(object): 283 | def test_sort(self): 284 | tbl = Tabel({'aa':['b','g','d'], 'b':list(range(3))}) 285 | tbl.sort('aa') 286 | assert tbl[0] == ('b',0) 287 | assert tbl[1] == ('d',2) 288 | tbl.sort(['aa', 'b']) 289 | assert tbl[1] == ('g',1) 290 | 291 | 292 | class TestDTypeProperties(object): 293 | def test_dtype_properties(self): 294 | tbl = Tabel({"a":list(range(2,6)), "c": [u'1',u'2'] *2, "d":[1.1,2.2]*2}) 295 | assert tbl.dtype == np.dtype([('a', '