table-maker

Table Maker

Description

Make simple tables from rows and columns, with values separated into cells and the contents left-justified.

Initially designed for creating map marginalia in QGIS and ArcMap, as well as a susbtitute for the often over-engineered and clunky table wizards that are featured in word processors.

Installation

At some point: pip install table_maker

Usage

Most basic use is to create a simple, cleanly-formatted table:

  >>> from table_maker import make_table
  >>> cols = ('athlete', 'time')
  >>> rows = (('Clint', '16:04'), ('Mitch', '12:12'),('Tommy', '22:57'), ('Zach', '27:56'))
  >>> print(make_table(cols, rows, sort_col=1))
  +--------+--------+
  | athlete| time   |
  +========+========+
  | Mitch  | 12:12  |
  +--------+--------+
  | Clint  | 16:04  |
  +--------+--------+
  | Tommy  | 22:57  |
  +--------+--------+
  | Zach   | 27:56  |
  +--------+--------+

That's mostly it, but it does have a few simple utilities, for example, you can add some row numbers if you like:

  >>> import table_maker as tm
  >>> cols = ('u-boat', 'commissioned', 'sunk')
  >>> rows = (
  ...  ('u-64', '16 Dec 39', '13 Apr 40'), 
  ...  ('u-104', '19 Aug 40', '28 Nov 40'),  
  ...  ('u-107', '08 Oct 40', '08 Aug 44'))
  >>> uboats = tm.insert_row_numbers(tm.make_table(cols, rows))
  >>> print(uboats)
      +--------------+--------------+--------------+
      | u-boat       | commissioned | sunk         |
      +==============+==============+==============+
   1  | u-64         | 16 Dec 39    | 13 Apr 40    |
      +--------------+--------------+--------------+
   2  | u-104        | 19 Aug 40    | 28 Nov 40    |
      +--------------+--------------+--------------+
   3  | u-107        | 08 Oct 40    | 08 Aug 44    |
      +--------------+--------------+--------------+

Or you can remove separators from the rows if you want a more compact table, and convert items to title case if you like:

>>> import table_maker as tm
>>> cols = ('album', 'year')
>>> rows = (('fandango!', '1975'), ('tres hombres', '1973'),('eliminator', '1983'))
>>> x, y = tm.capitalize_inputs(cols, rows)
>>> print(tm.remove_seps(tm.make_table(cols=x, rows=y, scaling=1.1, sort_col=1)))
+--------------+--------------+
| Album        | Year         |
+==============+==============+
| Tres Hombres | 1973         |
| Fandango!    | 1975         |
| Eliminator   | 1983         |
+--------------+--------------+

There are no utilities for selecting rows, you are better off doing that ahead of time. For example let's say you want to make a table like from this string:

>>> csv = '''first,last,GOATscore,rank
Kareem,Abdul-Jabbar,5.600
LeBron,James,5.511
Michael,Jordan,5.219
Tim,Duncan,4.273
Bill,Russell,4.066
Kobe,Bryant,4.021
Wilt,Chamberlain,3.885
Karl,Malone,3.823
Shaquille,O'Neal,3.820
Julius,Erving,3.502
...
'''

Then you only want players whose score is above 4, it's easiet to get that before creating the table:

>>> import table_maker as tm
>>> cols = csv.split()[0].split(',')
>>> rows = [item.split(',') for item in csv.split('\n')[1:] if item]
>>> above_4 = [row for row in rows if float(row[2]) > 4] 
>>> table = tm.make_table(cols, above_4, sort_col=-1, reverse=True, scaling=1.25)
>>> print(tm.insert_title("   the greatest of all time", tm.insert_row_numbers(table)))
   THE GREATEST OF ALL TIME
   +---------------+---------------+---------------+
   | first         | last          | GOATscore     |
   +===============+===============+===============+
1  | Kareem        | Abdul-Jabbar  | 5.600         |
   +---------------+---------------+---------------+
2  | LeBron        | James         | 5.511         |
   +---------------+---------------+---------------+
3  | Michael       | Jordan        | 5.219         |
   +---------------+---------------+---------------+
4  | Tim           | Duncan        | 4.273         |
   +---------------+---------------+---------------+
5  | Bill          | Russell       | 4.066         |
   +---------------+---------------+---------------+
6  | Kobe          | Bryant        | 4.021         |
   +---------------+---------------+---------------+

Alternatively, you could use the transform function to convert the table into a dictionary, which is conveniently designed to be compatible with the pandas.DataFrame.from_dict() method, turn it into a dataframe and process it that way.