======
eparse
======
.. image:: https://img.shields.io/pypi/v/eparse.svg
:target: https://pypi.python.org/pypi/eparse
.. image:: https://img.shields.io/badge/License-MIT-blue.svg
:target: https://opensource.org/licenses/MIT
:alt: License: MIT
Description
===========
Excel spreadsheet crawler and table parser for data extraction
and querying.
Features
========
* Command-line interface
* Recursive Excel file discovery
* Sub-tabular data extraction (logical tables)
* SQLite and PostgreSQL database interfaces
* CLI query tool
* Summary data metrics
Installation
============
To install eparse, you can use pip and the latest version on PyPI:
.. code-block:: bash
$ pip install eparse
Or you can clone this repo and install from source:
.. code-block:: bash
$ git clone https://github.com/ChrisPappalardo/eparse.git
$ cd eparse
$ pip install .
If you plan to use the postgres interface, you also need to install
the postgres package ``psycopg2``. Instructions can be found
`here <https://www.psycopg.org/docs/install.html#quick-install>`_.
This package is optional, and you can use the other interfaces
such as the ``SQLite3`` interface without having to install
``psycopg2``.
The easiest way to install the ``psycopg2`` package for your
particular environment may be to install the pre-compiled
binary driver as follows:
.. code-block:: bash
$ pip install psycopg2-binary
If you see an error while trying to use a postgres endpoint such
as ``postgres://user:pass@host:port/my_db`` that mentions the
postgres driver is missing, then you know you haven't properly
installed (and compiled) ``psycopg2``.
Usage
=====
eparse can be used as either a python library or from the command-line.
You can view supported CLI commands and usage with ``--help`` as follows:
.. code-block:: bash
$ eparse --help
Usage: eparse [OPTIONS] COMMAND [ARGS]...
excel parser
Options:
-i, --input TEXT input source
-o, --output TEXT output destination
-f, --file TEXT file(s) or dir(s) to target
-d, --debug use debug mode
-l, --loose find tables loosely
-r, --recursive find files recursively
-t, --truncate truncate dataframe output
-v, --verbose increase output verbosity
--help Show this message and exit.
Commands:
migrate migrate eparse table
parse parse table(s) found in sheet for target(s)
query query eparse output
scan scan for excel files in target
You can also use eparse from python like so:
.. code-block:: python
from eparse.core import get_df_from_file
print([table for table in get_df_from_file('myfile.xlsx')])
102 Date Principal Repayment Date Principal Repayment
103 44834 700757.679004 44926 430013.148303
104 44926 71957.776108 45016 100576.127808
105 45016 147578.19262 45107 898008.340095
106 45107 32801.363072 45199 841656.13896
...
Scan
----
To scan one or more directories for Excel files with descriptive
information, you can use the ``scan`` command like so:
.. code-block:: bash
$ eparse -v -f <path_to_files> scan
Increase the verbosity with additional flags, such as ``-vvv``, for
more descriptive information about the file(s), including sheet names.
Parse
-----
Excel files can be parsed as follows:
.. code-block:: bash
$ eparse -v -f <path_to_files> parse
This mode will list each table found in each Excel file to the command-line.
This mode is useful for initial discovery for parseable data.
eparse uses a simple algorithm for identifying tables. Table "corners"
are identified as cells that contain empty cells above and to the right
(or sheet boundaries). A densely or sparsely populated 2x2 table must
follow in order for data to be extracted in relation to that cell.
eparse will automatically adjust for rowspan labels and empty table
corners and the dense vs. sparse criterion can be controlled with
the ``--loose`` flag.
eparse was written to accomodate various types of output formats and
endpoints, including ``null:///``, ``stdout:///``, ``sqlite3:///db_name``,
and ``postgres://user:password@host:port/db_name``.
null
^^^^
This mode is useful for validating files and generating descriptive
info, and is the default. The command above with `-v` is an example
of this mode, which lists out the tables found.
stdout
^^^^^^
This mode is good for viewing data extracted from Excel files in the
console. For example, you could view all tables found in `Sheet1`
with the following command:
.. code-block:: bash
$ eparse -f <path_to_files> -o stdout:/// parse -s "Sheet1"
eparse uses `pandas <https://github.com/pandas-dev/pandas>`_
to handle table data. You can view larger tables without truncation
using the ``-t`` flag as follows:
.. code-block:: bash
$ eparse -t -f <path_to_files> -o stdout:/// parse -s "Sheet1"
Data in table format is useful for human viewing, but a serialized
form is better for data interfacing. Serialize your output with
the ``-z`` flag as follows:
.. code-block:: bash
$ eparse -t -f <path_to_files> -o stdout:/// parse -z
Each cell of extracted table data is serialized as follows:
* `row` - 0-indexed table row number
* `column` - 0-indexed table column number
* `value` - the value of the cell as a ``str``
* `type` - the implied python ``type`` of the data found
* `c_header` - the column header
* `r_header` - the row header
* `excel_RC` - the RC reference from the spreadsheet (e.g. B10)
* `sheet` - the name of the sheet
* `f_name` - the name of the file
sqlite3
^^^^^^^
eparse uses the `peewee <https://github.com/coleifer/peewee>`_
package for ORM and database integration. The
`interfaces <eparse/interfaces.py>`_ module contains an
``ExcelParse`` model that provides data persistence and a common
interface.
To create a `SQLite3 <https://github.com/sqlite/sqlite>`_ database
with your parsed Excel data, use the following command:
.. code-block:: bash
$ mkdir .files
$ eparse -f <path_to_files> -o sqlite3:/// parse -z
This command will automatically generate a unique database filename
using the ``uuid`` python package in the ``.files/`` sub-directory
of the working directory. You may need to create this directory
before running this command, as shown.
You can also specify a path and filename of your choosing, like so:
.. code-block:: bash
$ mkdir .files
$ eparse -f <path_to_files> -o sqlite3:///path/filename.db parse -z
postgres
^^^^^^^^
eparse also supports `postgresql` integrations. As mentioned above,
you will need ``psycopg2`` installed for `postgresql` integrations
to work. The eparse ``BaseDatabaseInterface`` abstracts the
implementation details, so you would use this interface the same
way you use the others, with the exception of the endpoint.
To use a ``postgresql`` database as the source and/or destination
of your data, you would supply an ``--input`` and/or ``--output``
endpoint to the tool as follows:
.. code-block:: bash
$ eparse -o postgres://user:password@host:port/db_name ...
Where details like ``user``, ``host``, ``port`` are provided to
you by your db administrator. eparse will create the necessary
table(s) and indexes for you when inserting data into the database.
Query
-----
Once you have stored parsed data, you can begin to query it using the
``peewee`` ORM. This can be done with the tool or directly with
the database.
For example, query distinct column header names from a generated
``SQLite3`` database as follows:
.. code-block:: bash
$ eparse -i sqlite3:///.files/<db_file> -o stdout:/// query -m get_c_header
c_header Total Rows Data Types Distinct Values
0 ABC-col 150 2 76
1 DEF-col 3981 3 15
2 GHI-col 20 1 2
.. ... ... ... ...
This command will give descriptive information of each distinct c_header
found, including total rows, unique data types, and distinct values.
You can also get raw un-truncated data as follows:
.. code-block:: bash
$ eparse -t -i sqlite3:///.files/<db_file> -o stdout:/// query
Filtering data on content is easy. Use the ``--filter`` option as
follows:
.. code-block:: bash
$ eparse -i sqlite3:///.files/<db_file> -o stdout:/// query --filter f_name "somefile.xlsx"
The above command will filter all rows from an Excel file named
`somefile.xlsx`. You can use any of the following ``django``-style
filters:
* ``__eq`` equals X
* ``__lt`` less than X
* ``__lte`` less than or equal to X
* ``__gt`` greater than X
* ``__gte`` greater than or equal to X
* ``__ne`` not equal to X
* ``__in`` X is in
* ``__is`` is X
* ``__like`` like expression, such as ``%somestr%``, case sensitive
* ``__ilike`` like expression, such as ``%somestr%``, case insensitive
* ``__regexp`` regular expression matching such as ``^.*?foo.*?$``
Filters are applied to the ORM fields like so:
* ``--filter row__gte 4`` all extracted table rows `>= 5`
* ``--filter f_name__ilike "%foo%"`` all data from filenames with `foo`
* ``--filter value__ne 100`` all data with values other than `100`
Queried data can even be stored into a new database for creating
curated data subsets, as follows:
.. code-block:: bash
$ eparse -i sqlite3:///.files/<db_file> \
-o sqlite3:///.files/<subq_db_file> \
query --filter f_name "somefile.xlsx"
Since database files the tool generates when using `sqlite3:///` are
``SQLite`` native, you can also use `SQLite` database client tools
and execute raw SQL like so:
.. code-block:: bash
$ sudo apt-get install -y sqlite3-tools
$ sqlite3 .files/<db_file>
SQLite version 3.37.2 2022-01-06 13:25:41
Enter ".help" for usage hints.
sqlite> .schema
CREATE TABLE IF NOT EXISTS "excelparse" ("id" INTEGER NOT NULL PRIMARY KEY, "row" INTEGER NOT NULL, "column" INTEGER NOT NULL, "value" VARCHAR(255) NOT NULL, "type" VARCHAR(255) NOT NULL, "c_header" VARCHAR(255) NOT NULL, "r_header" VARCHAR(255) NOT NULL, "excel_RC" VARCHAR(255) NOT NULL, "name" VARCHAR(255) NOT NULL, "sheet" VARCHAR(255) NOT NULL, "f_name" VARCHAR(255) NOT NULL);
sqlite> .header on
sqlite> SELECT * FROM excelparse LIMIT 1;
id|row|column|value|type|c_header|r_header|excel_RC|name|sheet|f_name
1|0|0|ABC|<class 'str'>|SomeCol|SomeRow|B2|MyTable|Sheet1|myfile.xlsm
Migrate
-------
eparse wouldn't be a solid tool without the ability to migrate your
eparse databases for future code changes. You can apply migrations
that ship with future versions of eparse as follows:
.. code-block:: bash
$ eparse -i sqlite3:///.files/<db_file> migrate -m <migration>
applied <migration>
It is up to you to determine the migrations you need based on the
eparse version you are upgrading from and to. Migrations can be
found in `eparse/migrations.py <eparse/migrations.py>`_
Unstructured
============
If you would like to use eparse to partition xls[x] files alongside unstructured, you can do so with our contributed `partition` and `partition_xlsx` modules. Simply import the `partition` function from `eparse.contrib.unstructured.partition` and use it instead of `partition` from `unstructured.partition.auto` like so:
.. code-block:: python
from eparse.contrib.unstructured.partition import partition
elements = partition(filename='some_file.xlsx', eparse_mode='...')
Valid `eparse_mode` settings are available in `eparse.contrib.unstructured.xlsx._eparse_modes`.
Contributing
============
As an open-source project, contributions are always welcome. Please see `Contributing <CONTRIBUTING.rst>`_ for more information.
License
=======
eparse is licensed under the `MIT License <https://opensource.org/licenses/MIT>`_. See the `LICENSE <LICENSE>`_ file for more details.
Contact
=======
Thanks for your support of eparse. Feel free to contact me at `cpappala@gmail.com <mailto:cpappala@gmail.com>`_ or connect with me on `Github <https://www.linkedin.com/in/chris-a-pappalardo/>`_.
=======
History
=======
0.7.3 (2023-08-23)
==================
* Updated partition and partition_xlsx for latest unstructured
0.7.2 (2023-08-22)
==================
* Relaxed unstructured dependency requirement for compatibility
0.7.1 (2023-08-20)
==================
* Added unstructured contrib to core package
* Uopdaed README with instructions
0.7.0 (2023-08-18)
==================
* Added html conversion functions and an html data interface
* Added get_table_digest to core
0.6.3 (2023-06-30)
==================
* Updated get_df_from_file to accept filename or io object
0.6.1 (2023-06-22)
==================
* Added get_df_from_file helper function
* Added contrib with unstructured partition handler
0.5.0 (2023-06-14)
==================
* Optimized database interfaces
0.4.0 (2023-06-14)
==================
* Added postgresql interface
0.3.0 (2023-06-14)
==================
* Added inteface classes
* Added migrations
0.2.0 (2023-06-12)
==================
* Added migrate command
* Added 0.1.2 to 0.2.0 migration
0.1.2 (2023-06-07)
==================
* Updated README
0.1.1 (2023-06-06)
==================
* Updated requirements
* Updated README
0.1.0 (2023-06-06)
==================
* First release on PyPI.
Raw data
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"author": "Chris Pappalardo",
"author_email": "cpappala@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/c3/a4/c3a7cf1f6cfe980ee0c957a629c5e07705d6a94ce06c81b361d4faaa59d8/eparse-0.7.3.tar.gz",
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"description": "======\neparse\n======\n\n\n.. image:: https://img.shields.io/pypi/v/eparse.svg\n :target: https://pypi.python.org/pypi/eparse\n\n.. image:: https://img.shields.io/badge/License-MIT-blue.svg\n :target: https://opensource.org/licenses/MIT\n :alt: License: MIT\n\n\nDescription\n===========\nExcel spreadsheet crawler and table parser for data extraction\nand querying.\n\n\nFeatures\n========\n* Command-line interface\n* Recursive Excel file discovery\n* Sub-tabular data extraction (logical tables)\n* SQLite and PostgreSQL database interfaces\n* CLI query tool\n* Summary data metrics\n\n\nInstallation\n============\nTo install eparse, you can use pip and the latest version on PyPI:\n\n.. code-block:: bash\n\n $ pip install eparse\n\nOr you can clone this repo and install from source:\n\n.. code-block:: bash\n\n $ git clone https://github.com/ChrisPappalardo/eparse.git\n $ cd eparse\n $ pip install .\n\nIf you plan to use the postgres interface, you also need to install\nthe postgres package ``psycopg2``. Instructions can be found\n`here <https://www.psycopg.org/docs/install.html#quick-install>`_.\nThis package is optional, and you can use the other interfaces\nsuch as the ``SQLite3`` interface without having to install\n``psycopg2``.\n\nThe easiest way to install the ``psycopg2`` package for your\nparticular environment may be to install the pre-compiled\nbinary driver as follows:\n\n.. code-block:: bash\n\n $ pip install psycopg2-binary\n\nIf you see an error while trying to use a postgres endpoint such\nas ``postgres://user:pass@host:port/my_db`` that mentions the\npostgres driver is missing, then you know you haven't properly\ninstalled (and compiled) ``psycopg2``.\n\n\nUsage\n=====\neparse can be used as either a python library or from the command-line.\nYou can view supported CLI commands and usage with ``--help`` as follows:\n\n.. code-block:: bash\n\n $ eparse --help\n Usage: eparse [OPTIONS] COMMAND [ARGS]...\n\n excel parser\n\n Options:\n -i, --input TEXT input source\n -o, --output TEXT output destination\n -f, --file TEXT file(s) or dir(s) to target\n -d, --debug use debug mode\n -l, --loose find tables loosely\n -r, --recursive find files recursively\n -t, --truncate truncate dataframe output\n -v, --verbose increase output verbosity\n --help Show this message and exit.\n\n Commands:\n migrate migrate eparse table\n parse parse table(s) found in sheet for target(s)\n query query eparse output\n scan scan for excel files in target\n\nYou can also use eparse from python like so:\n\n.. code-block:: python\n\n from eparse.core import get_df_from_file\n\n print([table for table in get_df_from_file('myfile.xlsx')])\n 102 Date Principal Repayment Date Principal Repayment\n 103 44834 700757.679004 44926 430013.148303\n 104 44926 71957.776108 45016 100576.127808\n 105 45016 147578.19262 45107 898008.340095\n 106 45107 32801.363072 45199 841656.13896\n ...\n\n\nScan\n----\nTo scan one or more directories for Excel files with descriptive\ninformation, you can use the ``scan`` command like so:\n\n.. code-block:: bash\n\n $ eparse -v -f <path_to_files> scan\n\nIncrease the verbosity with additional flags, such as ``-vvv``, for\nmore descriptive information about the file(s), including sheet names.\n\n\nParse\n-----\nExcel files can be parsed as follows:\n\n.. code-block:: bash\n\n $ eparse -v -f <path_to_files> parse\n\nThis mode will list each table found in each Excel file to the command-line.\nThis mode is useful for initial discovery for parseable data.\n\neparse uses a simple algorithm for identifying tables. Table \"corners\"\nare identified as cells that contain empty cells above and to the right\n(or sheet boundaries). A densely or sparsely populated 2x2 table must\nfollow in order for data to be extracted in relation to that cell.\neparse will automatically adjust for rowspan labels and empty table\ncorners and the dense vs. sparse criterion can be controlled with\nthe ``--loose`` flag.\n\neparse was written to accomodate various types of output formats and\nendpoints, including ``null:///``, ``stdout:///``, ``sqlite3:///db_name``,\nand ``postgres://user:password@host:port/db_name``.\n\nnull\n^^^^\nThis mode is useful for validating files and generating descriptive\ninfo, and is the default. The command above with `-v` is an example\nof this mode, which lists out the tables found.\n\nstdout\n^^^^^^\nThis mode is good for viewing data extracted from Excel files in the\nconsole. For example, you could view all tables found in `Sheet1`\nwith the following command:\n\n.. code-block:: bash\n\n $ eparse -f <path_to_files> -o stdout:/// parse -s \"Sheet1\"\n\neparse uses `pandas <https://github.com/pandas-dev/pandas>`_\nto handle table data. You can view larger tables without truncation\nusing the ``-t`` flag as follows:\n\n.. code-block:: bash\n\n $ eparse -t -f <path_to_files> -o stdout:/// parse -s \"Sheet1\"\n\nData in table format is useful for human viewing, but a serialized\nform is better for data interfacing. Serialize your output with\nthe ``-z`` flag as follows:\n\n.. code-block:: bash\n\n $ eparse -t -f <path_to_files> -o stdout:/// parse -z\n\nEach cell of extracted table data is serialized as follows:\n\n* `row` - 0-indexed table row number\n* `column` - 0-indexed table column number\n* `value` - the value of the cell as a ``str``\n* `type` - the implied python ``type`` of the data found\n* `c_header` - the column header\n* `r_header` - the row header\n* `excel_RC` - the RC reference from the spreadsheet (e.g. B10)\n* `sheet` - the name of the sheet\n* `f_name` - the name of the file\n\nsqlite3\n^^^^^^^\neparse uses the `peewee <https://github.com/coleifer/peewee>`_\npackage for ORM and database integration. The\n`interfaces <eparse/interfaces.py>`_ module contains an\n``ExcelParse`` model that provides data persistence and a common\ninterface.\n\nTo create a `SQLite3 <https://github.com/sqlite/sqlite>`_ database\nwith your parsed Excel data, use the following command:\n\n.. code-block:: bash\n\n $ mkdir .files\n $ eparse -f <path_to_files> -o sqlite3:/// parse -z\n\nThis command will automatically generate a unique database filename\nusing the ``uuid`` python package in the ``.files/`` sub-directory\nof the working directory. You may need to create this directory\nbefore running this command, as shown.\n\nYou can also specify a path and filename of your choosing, like so:\n\n.. code-block:: bash\n\n $ mkdir .files\n $ eparse -f <path_to_files> -o sqlite3:///path/filename.db parse -z\n\npostgres\n^^^^^^^^\neparse also supports `postgresql` integrations. As mentioned above,\nyou will need ``psycopg2`` installed for `postgresql` integrations\nto work. The eparse ``BaseDatabaseInterface`` abstracts the\nimplementation details, so you would use this interface the same\nway you use the others, with the exception of the endpoint.\n\nTo use a ``postgresql`` database as the source and/or destination\nof your data, you would supply an ``--input`` and/or ``--output``\nendpoint to the tool as follows:\n\n.. code-block:: bash\n\n $ eparse -o postgres://user:password@host:port/db_name ...\n\nWhere details like ``user``, ``host``, ``port`` are provided to\nyou by your db administrator. eparse will create the necessary\ntable(s) and indexes for you when inserting data into the database.\n\n\nQuery\n-----\nOnce you have stored parsed data, you can begin to query it using the\n``peewee`` ORM. This can be done with the tool or directly with\nthe database.\n\nFor example, query distinct column header names from a generated\n``SQLite3`` database as follows:\n\n.. code-block:: bash\n\n $ eparse -i sqlite3:///.files/<db_file> -o stdout:/// query -m get_c_header\n c_header Total Rows Data Types Distinct Values\n 0 ABC-col 150 2 76\n 1 DEF-col 3981 3 15\n 2 GHI-col 20 1 2\n .. ... ... ... ...\n\nThis command will give descriptive information of each distinct c_header\nfound, including total rows, unique data types, and distinct values.\n\nYou can also get raw un-truncated data as follows:\n\n.. code-block:: bash\n\n $ eparse -t -i sqlite3:///.files/<db_file> -o stdout:/// query\n\nFiltering data on content is easy. Use the ``--filter`` option as\nfollows:\n\n.. code-block:: bash\n\n $ eparse -i sqlite3:///.files/<db_file> -o stdout:/// query --filter f_name \"somefile.xlsx\"\n\nThe above command will filter all rows from an Excel file named\n`somefile.xlsx`. You can use any of the following ``django``-style\nfilters:\n\n* ``__eq`` equals X\n* ``__lt`` less than X\n* ``__lte`` less than or equal to X\n* ``__gt`` greater than X\n* ``__gte`` greater than or equal to X\n* ``__ne`` not equal to X\n* ``__in`` X is in\n* ``__is`` is X\n* ``__like`` like expression, such as ``%somestr%``, case sensitive\n* ``__ilike`` like expression, such as ``%somestr%``, case insensitive\n* ``__regexp`` regular expression matching such as ``^.*?foo.*?$``\n\nFilters are applied to the ORM fields like so:\n\n* ``--filter row__gte 4`` all extracted table rows `>= 5`\n* ``--filter f_name__ilike \"%foo%\"`` all data from filenames with `foo`\n* ``--filter value__ne 100`` all data with values other than `100`\n\nQueried data can even be stored into a new database for creating\ncurated data subsets, as follows:\n\n.. code-block:: bash\n\n $ eparse -i sqlite3:///.files/<db_file> \\\n -o sqlite3:///.files/<subq_db_file> \\\n query --filter f_name \"somefile.xlsx\"\n\nSince database files the tool generates when using `sqlite3:///` are\n``SQLite`` native, you can also use `SQLite` database client tools\nand execute raw SQL like so:\n\n.. code-block:: bash\n\n $ sudo apt-get install -y sqlite3-tools\n $ sqlite3 .files/<db_file>\n SQLite version 3.37.2 2022-01-06 13:25:41\n Enter \".help\" for usage hints.\n sqlite> .schema\n CREATE TABLE IF NOT EXISTS \"excelparse\" (\"id\" INTEGER NOT NULL PRIMARY KEY, \"row\" INTEGER NOT NULL, \"column\" INTEGER NOT NULL, \"value\" VARCHAR(255) NOT NULL, \"type\" VARCHAR(255) NOT NULL, \"c_header\" VARCHAR(255) NOT NULL, \"r_header\" VARCHAR(255) NOT NULL, \"excel_RC\" VARCHAR(255) NOT NULL, \"name\" VARCHAR(255) NOT NULL, \"sheet\" VARCHAR(255) NOT NULL, \"f_name\" VARCHAR(255) NOT NULL);\n sqlite> .header on\n sqlite> SELECT * FROM excelparse LIMIT 1;\n id|row|column|value|type|c_header|r_header|excel_RC|name|sheet|f_name\n 1|0|0|ABC|<class 'str'>|SomeCol|SomeRow|B2|MyTable|Sheet1|myfile.xlsm\n\n\nMigrate\n-------\neparse wouldn't be a solid tool without the ability to migrate your\neparse databases for future code changes. You can apply migrations\nthat ship with future versions of eparse as follows:\n\n.. code-block:: bash\n\n $ eparse -i sqlite3:///.files/<db_file> migrate -m <migration>\n applied <migration>\n\nIt is up to you to determine the migrations you need based on the\neparse version you are upgrading from and to. Migrations can be\nfound in `eparse/migrations.py <eparse/migrations.py>`_\n\n\nUnstructured\n============\nIf you would like to use eparse to partition xls[x] files alongside unstructured, you can do so with our contributed `partition` and `partition_xlsx` modules. Simply import the `partition` function from `eparse.contrib.unstructured.partition` and use it instead of `partition` from `unstructured.partition.auto` like so:\n\n.. code-block:: python\n\n from eparse.contrib.unstructured.partition import partition\n\n elements = partition(filename='some_file.xlsx', eparse_mode='...')\n\nValid `eparse_mode` settings are available in `eparse.contrib.unstructured.xlsx._eparse_modes`.\n\n\nContributing\n============\nAs an open-source project, contributions are always welcome. Please see `Contributing <CONTRIBUTING.rst>`_ for more information.\n\n\nLicense\n=======\neparse is licensed under the `MIT License <https://opensource.org/licenses/MIT>`_. See the `LICENSE <LICENSE>`_ file for more details.\n\n\nContact\n=======\nThanks for your support of eparse. Feel free to contact me at `cpappala@gmail.com <mailto:cpappala@gmail.com>`_ or connect with me on `Github <https://www.linkedin.com/in/chris-a-pappalardo/>`_.\n\n\n=======\nHistory\n=======\n\n0.7.3 (2023-08-23)\n==================\n\n* Updated partition and partition_xlsx for latest unstructured\n\n0.7.2 (2023-08-22)\n==================\n\n* Relaxed unstructured dependency requirement for compatibility\n\n0.7.1 (2023-08-20)\n==================\n\n* Added unstructured contrib to core package\n* Uopdaed README with instructions\n\n0.7.0 (2023-08-18)\n==================\n\n* Added html conversion functions and an html data interface\n* Added get_table_digest to core\n\n0.6.3 (2023-06-30)\n==================\n\n* Updated get_df_from_file to accept filename or io object\n\n0.6.1 (2023-06-22)\n==================\n\n* Added get_df_from_file helper function\n* Added contrib with unstructured partition handler\n\n0.5.0 (2023-06-14)\n==================\n\n* Optimized database interfaces\n\n0.4.0 (2023-06-14)\n==================\n\n* Added postgresql interface\n\n0.3.0 (2023-06-14)\n==================\n\n* Added inteface classes\n* Added migrations\n\n0.2.0 (2023-06-12)\n==================\n\n* Added migrate command\n* Added 0.1.2 to 0.2.0 migration\n\n0.1.2 (2023-06-07)\n==================\n\n* Updated README\n\n0.1.1 (2023-06-06)\n==================\n\n* Updated requirements\n* Updated README\n\n0.1.0 (2023-06-06)\n==================\n\n* First release on PyPI.\n",
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