meza 0.28.0

A Python toolkit for processing tabular data

meza: A Python toolkit for processing tabular data
======================================================

|travis| |versions| |pypi|

Index
-----

`Introduction`_ | `Requirements`_ | `Motivation`_ | `Usage`_ | `Interoperability`_ |
`Installation`_ | `Project Structure`_ | `Design Principles`_ | `Readers`_ |
`Scripts`_ | `Contributing`_ | `Credits`_ | `License`_

Introduction
------------

**meza** is a Python `library`_ for reading and processing tabular data.
It has a functional programming style API, excels at reading, large files,
and can process 10+ file types.

With meza, you can

- Read csv/xls/xlsx/mdb/dbf files, and more!
- Type cast records (date, float, text...)
- Process Uñicôdë text
- Lazily stream files by default
- and much more...

Requirements
------------

meza has been tested and is known to work on Python 2.7, 3.4, and 3.5;
PyPy 4.0; and PyPy3 2.4

Optional Dependencies
^^^^^^^^^^^^^^^^^^^^^

=============================== ============== ============================== =======================
Function Dependency Installation File type / extension
=============================== ============== ============================== =======================
``meza.io.read_mdb`` `mdbtools`_ ``sudo port install mdbtools`` Microsoft Access / mdb
``meza.io.read_html`` `lxml`_ [#]_ ``pip install lxml`` HTML / html
``meza.convert.records2array`` `NumPy`_ [#]_ ``pip install numpy`` n/a
``meza.convert.records2df`` `pandas`_ ``pip install pandas`` n/a
=============================== ============== ============================== =======================

Notes
^^^^^

.. [#] If ``lxml`` isn't present, ``read_html`` will default to the builtin Python html reader

.. [#] ``records2array`` can be used without ``numpy`` by passing ``native=True`` in the function call. This will convert ``records`` into a list of native ``array.array`` objects.

Motivation
----------

pandas is great, but installing it isn't exactly a `walk in the park`_. It also
doesn't play nice with `PyPy`_. `csvkit`_ is an equally useful project, but it
doesn't expose the same API when used as `a library`_ as it does via the command
line. I designed **meza** to provide much of same functionality as
pandas and csvkit, while using functional programming methods.

A simple data processing example is shown below:

First create a simple csv file (in bash)

.. code-block:: bash

echo 'col1,col2,col3\nhello,5/4/82,1\none,1/1/15,2\nhappy,7/1/92,3\n' > data.csv

Now we can read the file, manipulate the data a bit, and write the manipulated
data back to a new file.

.. code-block:: python

from meza import io, process as pr, convert as cv
from io import open

# Load the csv file
records = io.read_csv('data.csv')

# `records` are iterators over the rows
row = next(records)
row
>>> {'col1': 'hello', 'col2': '5/4/82', 'col3': '1'}

# Let's replace the first row so as not to loose any data
records = pr.prepend(records, row)

# Guess column types. Note: `detect_types` returns a new `records`
# generator since it consumes rows during type detection
records, result = pr.detect_types(records)
{t['id']: t['type'] for t in result['types']}
>>> {'col1': 'text', 'col2': 'date', 'col3': 'int'}

# Now type cast the records. Note: most `meza.process` functions return
# generators, so lets wrap the result in a list to view the data
casted = list(pr.type_cast(records, result['types']))
casted[0]
>>> {'col1': 'hello', 'col2': datetime.date(1982, 5, 4), 'col3': 1}

# Cut out the first column of data and merge the rows to get the max value
# of the remaining columns. Note: since `merge` (by definition) will always
# contain just one row, it is returned as is (not wrapped in a generator)
cut_recs = pr.cut(casted, ['col1'], exclude=True)
merged = pr.merge(cut_recs, pred=bool, op=max)
merged
>>> {'col2': datetime.date(2015, 1, 1), 'col3': 3}

# Now write merged data back to a new csv file.
io.write('out.csv', cv.records2csv(merged))

# View the result
with open('out.csv', 'utf-8') as f:
f.read()
>>> 'col2,col3\n2015-01-01,3\n'

Usage
-----

meza is intended to be used directly as a Python library.

Usage Index
^^^^^^^^^^^

- `Reading data`_
- `Processing data`_

+ `Numerical analysis (à la pandas)`_
+ `Text processing (à la csvkit)`_
+ `Geo processing (à la mapbox)`_

- `Writing data`_
- `Cookbook`_

Reading data
^^^^^^^^^^^^

meza can read both filepaths and file-like objects. Additionally, all readers
return equivalent `records` iterators, i.e., a generator of dictionaries with
keys corresponding to the column names.

.. code-block:: python

from io import open, StringIO
from meza import io

"""Read a filepath"""
records = io.read_json('path/to/file.json')

"""Read a file like object and de-duplicate the header"""
f = StringIO('col,col\nhello,world\n')
records = io.read_csv(f, dedupe=True)

"""View the first row"""
next(records)
>>> {'col': 'hello', 'col_2': 'world'}

"""Read the 1st sheet of an xls file object opened in text mode."""
# Also, santize the header names by converting them to lowercase and
# replacing whitespace and invalid characters with `_`.
with open('path/to/file.xls', 'utf-8') as f:
for row in io.read_xls(f, sanitize=True):
# do something with the `row`
pass

"""Read the 2nd sheet of an xlsx file object opened in binary mode"""
# Note: sheets are zero indexed
with open('path/to/file.xlsx') as f:
records = io.read_xls(f, encoding='utf-8', sheet=1)
first_row = next(records)
# do something with the `first_row`

"""Read any recognized file"""
records = io.read('path/to/file.geojson')
f.seek(0)
records = io.read(f, ext='csv', dedupe=True)

Please see `Readers`_ for a complete list of available readers and recognized
file types.

Processing data
^^^^^^^^^^^^^^^

Numerical analysis (à la pandas) [#]_
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

In the following example, ``pandas`` equivalent methods are preceded by ``-->``,
and command output is preceded by ``>>>``.

.. code-block:: python

import itertools as it
import random

from io import StringIO
from meza import io, process as pr, convert as cv, stats

# Create some data in the same structure as what the various `read...`
# functions output
header = ['A', 'B', 'C', 'D']
data = [(random.random() for _ in range(4)) for x in range(7)]
df = [dict(zip(header, d)) for d in data]
df[0]
>>> {'A': 0.53908..., 'B': 0.28919..., 'C': 0.03003..., 'D': 0.65363...}

"""Sort records by the value of column `B` --> df.sort_values(by='B')"""
next(pr.sort(df, 'B'))
>>> {'A': 0.53520..., 'B': 0.06763..., 'C': 0.02351..., 'D': 0.80529...}

"""Select column `A` --> df['A']"""
next(pr.cut(df, ['A']))
>>> {'A': 0.53908170489952006}

"""Select the first the rows of data --> df[0:3]"""
len(list(it.islice(df, 3)))
>>> 3

"""Select all data whose value for column `A` is less than 0.5
--> df[df.A < 0.5]
"""
next(pr.tfilter(df, 'A', lambda x: x < 0.5))
>>> {'A': 0.21000..., 'B': 0.25727..., 'C': 0.39719..., 'D': 0.64157...}

# Note: since `aggregate` and `merge` (by definition) return just one row,
# they return them as is (not wrapped in a generator).
"""Calculate the mean of column `A` across all data --> df.mean()['A']"""
pr.aggregate(df, 'A', stats.mean)['A']
>>> 0.5410437473067938

"""Calculate the sum of each column across all data --> df.sum()"""
pr.merge(df, pred=bool, op=sum)
>>> {'A': 3.78730..., 'C': 2.82875..., 'B': 3.14195..., 'D': 5.26330...}

Text processing (à la csvkit) [#]_
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

In the following example, ``csvkit`` equivalent commands are preceded by ``-->``,
and command output is preceded by ``>>>``.

First create a few simple csv files (in bash)

.. code-block:: bash

echo 'col_1,col_2,col_3\n1,dill,male\n2,bob,male\n3,jane,female' > file1.csv
echo 'col_1,col_2,col_3\n4,tom,male\n5,dick,male\n6,jill,female' > file2.csv

Now we can read the files, manipulate the data, convert the manipulated data to
json, and write the json back to a new file. Also, note that since all readers
return equivalent `records` iterators, you can use them interchangeably (in
place of ``read_csv``) to open any supported file. E.g., ``read_xls``,
``read_sqlite``, etc.

.. code-block:: python

import itertools as it

from meza import io, process as pr, convert as cv

"""Combine the files into one iterator
--> csvstack file1.csv file2.csv
"""
records = io.join('file1.csv', 'file2.csv')
next(records)
>>> {'col_1': '1', 'col_2': 'dill', 'col_3': 'male'}
next(it.islice(records, 4, None))
>>> {'col_1': '6', 'col_2': 'jill', 'col_3': 'female'}

# Now let's create a persistant records list
records = list(io.read_csv('file1.csv'))

"""Sort records by the value of column `col_2`
--> csvsort -c col_2 file1.csv
"""
next(pr.sort(records, 'col_2'))
>>> {'col_1': '2', 'col_2': 'bob', 'col_3': 'male'

"""Select column `col_2` --> csvcut -c col_2 file1.csv"""
next(pr.cut(records, ['col_2']))
>>> {'col_2': 'dill'}

"""Select all data whose value for column `col_2` contains `jan`
--> csvgrep -c col_2 -m jan file1.csv
"""
next(pr.grep(records, [{'pattern': 'jan'}], ['col_2']))
>>> {'col_1': '3', 'col_2': 'jane', 'col_3': 'female'}

"""Convert a csv file to json --> csvjson -i 4 file1.csv"""
io.write('file.json', cv.records2json(records))

# View the result
with open('file.json', 'utf-8') as f:
f.read()
>>> '[{"col_1": "1", "col_2": "dill", "col_3": "male"}, {"col_1": "2",
... "col_2": "bob", "col_3": "male"}, {"col_1": "3", "col_2": "jane",
... "col_3": "female"}]'

Geo processing (à la mapbox) [#]_
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

In the following example, ``mapbox`` equivalent commands are preceded by ``-->``,
and command output is preceded by ``>>>``.

First create a geojson file (in bash)

.. code-block:: bash

echo '{"type": "FeatureCollection","features": [' > file.geojson
echo '{"type": "Feature", "id": 11, "geometry": {"type": "Point", "coordinates": [10, 20]}},' >> file.geojson
echo '{"type": "Feature", "id": 12, "geometry": {"type": "Point", "coordinates": [5, 15]}}]}' >> file.geojson

Now we can open the file, split the data by id, and finally convert the split data
to a new geojson file-like object.

.. code-block:: python

from meza import io, process as pr, convert as cv

# Load the geojson file and peek at the results
records, peek = pr.peek(io.read_geojson('file.geojson'))
peek[0]
>>> {'lat': 20, 'type': 'Point', 'lon': 10, 'id': 11}

"""Split the records by feature ``id`` and select the first feature
--> geojsplit -k id file.geojson
"""
splits = pr.split(records, 'id')
feature_records, name = next(splits)
name
>>> 11

"""Convert the feature records into a GeoJSON file-like object"""
geojson = cv.records2geojson(feature_records)
geojson.readline()
>>> '{"type": "FeatureCollection", "bbox": [10, 20, 10, 20], "features": '
... '[{"type": "Feature", "id": 11, "geometry": {"type": "Point", '
... '"coordinates": [10, 20]}, "properties": {"id": 11}}], "crs": {"type": '
... '"name", "properties": {"name": "urn:ogc:def:crs:OGC:1.3:CRS84"}}}'

# Note: you can also write back to a file as shown previously
# io.write('file.geojson', geojson)

Writing data
^^^^^^^^^^^^

meza can persist ``records`` to disk via the following functions:

- ``meza.convert.records2csv``
- ``meza.convert.records2json``
- ``meza.convert.records2geojson``

Each function returns a file-like object that you can write to disk via
``meza.io.write('/path/to/file', result)``.

.. code-block:: python

from meza import io, convert as cv
from io import StringIO, open

# First let's create a simple tsv file like object
f = StringIO('col1\tcol2\nhello\tworld\n')
f.seek(0)

# Next create a records list so we can reuse it
records = list(io.read_tsv(f))
records[0]
>>> {'col1': 'hello', 'col2': 'world'}

# Now we're ready to write the records data to file

"""Create a csv file like object"""
cv.records2csv(records).readline()
>>> 'col1,col2\n'

"""Create a json file like object"""
cv.records2json(records).readline()
>>> '[{"col1": "hello", "col2": "world"}]'

"""Write back csv to a filepath"""
io.write('file.csv', cv.records2csv(records))
with open('file.csv', 'utf-8') as f_in:
f_in.read()
>>> 'col1,col2\nhello,world\n'

"""Write back json to a filepath"""
io.write('file.json', cv.records2json(records))
with open('file.json', 'utf-8') as f_in:
f_in.readline()
>>> '[{"col1": "hello", "col2": "world"}]'

Cookbook
^^^^^^^^

Please see the `cookbook guide`_ for more examples.

Notes
^^^^^

.. [#] http://pandas.pydata.org/pandas-docs/stable/10min.html#min
.. [#] https://csvkit.readthedocs.org/en/0.9.1/cli.html#processing
.. [#] https://github.com/mapbox?utf8=%E2%9C%93&query=geojson

Interoperability
----------------

meza plays nicely with NumPy and friends out of the box

setup
^^^^^

.. code-block:: python

from meza import process as pr

# First create some records and types. Also, convert the records to a list
# so we can reuse them.
records = [{'a': 'one', 'b': 2}, {'a': 'five', 'b': 10, 'c': 20.1}]
records, result = pr.detect_types(records)
records, types = list(records), result['types']
types
>>> [
... {'type': 'text', 'id': 'a'},
... {'type': 'int', 'id': 'b'},
... {'type': 'float', 'id': 'c'}]


from records to pandas.DataFrame to records
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. code-block:: python

import pandas as pd
from meza import convert as cv

"""Convert the records to a DataFrame"""
df = cv.records2df(records, types)
df
>>> a b c
... 0 one 2 NaN
... 1 five 10 20.1
# Alternatively, you can do `pd.DataFrame(records)`

"""Convert the DataFrame back to records"""
next(cv.df2records(df))
>>> {'a': 'one', 'b': 2, 'c': nan}

from records to arrays to records
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. code-block:: python

import numpy as np

from array import array
from meza import convert as cv

"""Convert records to a structured array"""
recarray = cv.records2array(records, types)
recarray
>>> rec.array([('one', 2, nan), ('five', 10, 20.100000381469727)],
... dtype=[('a', 'O'), ('b', '<i4'), ('c', '<f4')])
recarray.b
>>> array([ 2, 10], dtype=int32)

"""Convert records to a native array"""
narray = cv.records2array(records, types, native=True)
narray
>>> [[array('u', 'a'), array('u', 'b'), array('u', 'c')],
... [array('u', 'one'), array('u', 'five')],
... array('i', [2, 10]),
... array('f', [0.0, 20.100000381469727])]

"""Convert a 2-D NumPy array to a records generator"""
data = np.array([[1, 2, 3], [4, 5, 6]], np.int32)
data
>>> array([[1, 2, 3],
... [4, 5, 6]], dtype=int32)
next(cv.array2records(data))
>>> {'column_1': 1, 'column_2': 2, 'column_3': 3}

"""Convert the structured array back to a records generator"""
next(cv.array2records(recarray))
>>> {'a': 'one', 'b': 2, 'c': nan}

"""Convert the native array back to records generator"""
next(cv.array2records(narray, native=True))
{'a': 'one', 'b': 2, 'c': 0.0}

Installation
------------

(You are using a `virtualenv`_, right?)

At the command line, install meza using either ``pip`` (*recommended*)

.. code-block:: bash

pip install meza

or ``easy_install``

.. code-block:: bash

easy_install meza

Please see the `installation doc`_ for more details.

Project Structure
-----------------

.. code-block:: bash

┌── AUTHORS.rst
├── CHANGES.rst
├── CONTRIBUTING.rst
├── INSTALLATION.rst
├── LICENSE
├── MANIFEST.in
├── Makefile
├── README.rst
├── TODO.rst
├── data
│   ├── converted
│   │   ├── dbf.csv
│   │   ├── fixed.csv
│   │   ├── geo.csv
│   │   ├── geojson.csv
│   │   ├── json.csv
│   │   ├── json_multiline.csv
│   │   └── sheet_2.csv
│   └── test
│   ├── fixed.txt
│   ├── fixed_w_header.txt
│   ├── iris.csv
│   ├── irismeta.csv
│   ├── latin1.csv
│   ├── mac_newlines.csv
│   ├── newline.json
│   ├── no_header_row.csv
│   ├── test.csv
│   ├── test.dbf
│   ├── test.geojson
│   ├── test.html
│   ├── test.json
│   ├── test.mdb
│   ├── test.sqlite
│   ├── test.tsv
│   ├── test.xls
│   ├── test.xlsx
│   ├── test.yml
│   ├── utf16_big.csv
│   ├── utf16_little.csv
│   └── utf8.csv
├── dev-requirements.txt
├── examples.py
├── helpers
│   ├── check-stage
│   ├── clean
│   ├── pippy
│   ├── srcdist
│   └── wheel
├── manage.py
├── py2-requirements.txt
├── requirements.txt
├── setup.cfg
├── setup.py
├── meza
│   ├── __init__.py
│   ├── convert.py
│   ├── dbf.py
│   ├── fntools.py
│   ├── io.py
│   ├── process.py
│   ├── stats.py
│   ├── typetools.py
│   └── unicsv.py
├── tests
│   ├── __init__.py
│   ├── standard.rc
│   ├── test_fntools.py
│   ├── test_io.py
│   └── test_process.py
└── tox.ini

Design Principles
-----------------

- prefer functions over objects
- provide enough functionality out of the box to easily implement the most common data analysis use cases
- make conversion between ``records``, ``arrays``, and ``DataFrames`` dead simple
- whenever possible, lazily read objects and stream the result [#]_

.. [#] Notable exceptions are ``meza.process.group``, ``meza.process.sort``, ``meza.io.read_dbf``, ``meza.io.read_yaml``, and ``meza.io.read_html``. These functions read the entire contents into memory up front.

Readers
-------

meza' available readers are outlined below:

+-----------------------+-------------------------+----------------+
| File type | Recognized extension(s) | Default reader |
+=======================+=========================+================+
| Comma separated file | csv | read_csv |
+-----------------------+-------------------------+----------------+
| dBASE/FoxBASE | dbf | read_dbf |
+-----------------------+-------------------------+----------------+
| Fixed width file | fixed | read_fixed_fmt |
+-----------------------+-------------------------+----------------+
| GeoJSON | geojson, geojson.json | read_geojson |
+-----------------------+-------------------------+----------------+
| HTML table | html | read_html |
+-----------------------+-------------------------+----------------+
| JSON | json | read_json |
+-----------------------+-------------------------+----------------+
| Microsoft Access | mdb | read_mdb |
+-----------------------+-------------------------+----------------+
| SQLite | sqlite | read_sqlite |
+-----------------------+-------------------------+----------------+
| Tab separated file | tsv | read_tsv |
+-----------------------+-------------------------+----------------+
| Microsoft Excel | xls, xlsx | read_xls |
+-----------------------+-------------------------+----------------+
| YAML | yml, yaml | read_yaml |
+-----------------------+-------------------------+----------------+

Alternatively, meza provides a universal reader which will select the
appropriate reader based on the file extension as specified in the above
table.

.. code-block:: python

from io import open
from meza import io

records1 = io.read('path/to/file.csv')
records2 = io.read('path/to/file.xls')

with open('path/to/file.json', encoding='utf-8') as f:
records3 = io.read(f, ext='json')

Args
^^^^

Most readers take as their first argument, either a file path or file like object.
The notable execption is ``read_mdb`` which only accepts a file path.
File like objects should be opened using Python's stdlib ``io.open``. If the file
is opened in binary mode ``io.open('/path/to/file')``, be sure to pass the proper
encoding if it is anything other than ``utf-8``, e.g.,

.. code-block:: python

from io import open
from meza import io

with open('path/to/file.xlsx') as f:
records = io.read_xls(f, encoding='latin-1')

Kwargs
^^^^^^

While each reader has kwargs specific to itself, the following table outlines
the most common ones.

========== ==== ======================================= ======= =====================================================================================================
kwarg type description default implementing readers
========== ==== ======================================= ======= =====================================================================================================
mode str File open mode rU read_csv, read_fixed_fmt, read_geojson, read_html, read_json, read_tsv, read_xls, read_yaml
encoding str File encoding utf-8 read_csv, read_dbf, read_fixed_fmt, read_geojson, read_html, read_json, read_tsv, read_xls, read_yaml
has_header bool Data has a header row? True read_csv, read_fixed_fmt, read_tsv, read_xls
first_row int First row to read (zero indexed) 0 read_csv, read_fixed_fmt, read_tsv, read_xls
first_col int First column to read (zero indexed) 0 read_csv, read_fixed_fmt, read_tsv, read_xls
sanitize bool Underscorify and lowercase field names? False read_csv, read_dbf, read_fixed_fmt, read_html, read_mdb, read_tsv, read_xls
dedupe bool Deduplicate field names? False read_csv, read_fixed_fmt, read_html, read_mdb, read_tsv, read_xls
sheet int Sheet to read (zero indexed) 0 read_xls
table int Table to read (zero indexed) 0 read_dbf, read_html, read_mdb, read_sqlite
========== ==== ======================================= ======= =====================================================================================================

Scripts
-------

meza comes with a built in task manager ``manage.py``

Setup
^^^^^

.. code-block:: bash

pip install -r dev-requirements.txt

Examples
^^^^^^^^

*Run python linter and nose tests*

.. code-block:: bash

manage lint
manage test

Contributing
------------

Please mimic the coding style/conventions used in this repo.
If you add new classes or functions, please add the appropriate doc blocks with
examples. Also, make sure the python linter and nose tests pass.

Please see the `contributing doc`_ for more details.

Credits
-------

Shoutouts to `csvkit`_, `messytables`_, and `pandas`_ for heavily inspiring meza.

License
-------

meza is distributed under the `MIT License`_.

.. |travis| image:: https://img.shields.io/travis/reubano/meza/master.svg
:target: https://travis-ci.org/reubano/meza

.. |versions| image:: https://img.shields.io/pypi/pyversions/meza.svg
:target: https://pypi.python.org/pypi/meza

.. |pypi| image:: https://img.shields.io/pypi/v/meza.svg
:target: https://pypi.python.org/pypi/meza

.. _mdbtools: http://sourceforge.net/projects/mdbtools/
.. _lxml: http://www.crummy.com/software/BeautifulSoup/bs4/doc/#installing-a-parser
.. _library: #usage
.. _NumPy: https://github.com/numpy/numpy
.. _a library: https://csvkit.readthedocs.org/en/0.9.1/api/csvkit.py3.html
.. _PyPy: https://github.com/pydata/pandas/issues/9532
.. _walk in the park: http://pandas.pydata.org/pandas-docs/stable/install.html#installing-pandas-with-anaconda
.. _csvkit: https://github.com/onyxfish/csvkit
.. _messytables: https://github.com/okfn/messytables
.. _pandas: https://github.com/pydata/pandas
.. _MIT License: http://opensource.org/licenses/MIT
.. _virtualenv: http://www.virtualenv.org/en/latest/index.html
.. _contributing doc: https://github.com/reubano/meza/blob/master/CONTRIBUTING.rst
.. _installation doc: https://github.com/reubano/meza/blob/master/INSTALLATION.rst
.. _cookbook guide: https://github.com/reubano/meza/blob/master/COOKBOOK.rst


=========
Changelog
=========

Here you can find the recent changes to meza..

.. changelog::
:version: dev
:released: Ongoing

.. change::
:tags: docs

Updated CHANGES.

.. changelog::
:version: 0.1.0
:released: 2015-12-05

.. change::
:tags: docs

First release on PyPi.

.. todo:: vim: set filetype=rst: