Section author: Gavin Huttley
The toolkit has a Table object that can be used for manipulating tabular data. It’s properties can be considered like an ordered 2 dimensional dictionary or tuple with flexible output format capabilities of use for exporting statistics for import into external applications. Importantly, via the restructured text format one can generate html or latex formatted tables. The table module is located within cogent.format.
Before using Table, we exercise some formatting code:
>>> from cogent.format.table import formattedCells, drawToPDF, phylipMatrix
>>> from cogent.format.table import latex
Of interest to be able to format an arbitrary 2D list, without a header. We use the formattedCells function directly. We allow for the case that reportlab may not be present.
>>> data = [[230, 'acdef', 1.3], [6, 'cc', 1.9876]]
>>> header, formatted = formattedCells(data, header = ['one', 'two',
... 'three'])
>>> print formatted
[['230', 'acdef', '1.3000'], [' 6', ' cc', '1.9876']]
>>> print header
['one', ' two', ' three']
>>> try:
... drawToPDF(['one', 'two', 'three'], formatted, "junk.pdf")
... except ImportError:
... pass
We directly test the latex formatting.
>>> print latex(formatted, header, justify='lrl', caption='A legend',
... label="table:test")
\begin{longtable}[htp!]{ l r l }
\hline
\bf{one} & \bf{two} & \bf{three} \\
\hline
\hline
230 & acdef & 1.3000 \\
6 & cc & 1.9876 \\
\hline
\caption{A legend}
\label{table:test}
\end{longtable}
Tables can be created directly using the Table object itself, or a convenience function that handles loading from files. We import both here:
>>> from cogent import LoadTable
>>> from cogent.util.table import Table
First, if you try and create a Table without any data, it raises a RuntimeError.
>>> t = Table()
Traceback (most recent call last):
RuntimeError: header and rows must be provided to Table
>>> t = Table(header=[], rows=[])
Traceback (most recent call last):
RuntimeError: header and rows must be provided to Table
Let’s create a very simple, rather nonsensical, table first. To create a table requires a header series, and a 2D series (either of type tuple, list, dict).
>>> column_headings = ['Journal', 'Impact']
The string “Journal” will become the first column heading, “Impact” the second column heading. The data are,
>>> rows = [['INT J PARASITOL', 2.9],
... ['J MED ENTOMOL', 1.4],
... ['Med Vet Entomol', 1.0],
... ['INSECT MOL BIOL', 2.85],
... ['J AM MOSQUITO CONTR', 0.811],
... ['MOL PHYLOGENET EVOL', 2.8],
... ['HEREDITY', 1.99e+0],
... ['AM J TROP MED HYG', 2.105],
... ['MIL MED', 0.605],
... ['MED J AUSTRALIA', 1.736]]
We create the simplest of tables.
>>> t = Table(header = column_headings, rows = rows)
>>> print t
=============================
Journal Impact
-----------------------------
INT J PARASITOL 2.9000
J MED ENTOMOL 1.4000
Med Vet Entomol 1.0000
INSECT MOL BIOL 2.8500
J AM MOSQUITO CONTR 0.8110
MOL PHYLOGENET EVOL 2.8000
HEREDITY 1.9900
AM J TROP MED HYG 2.1050
MIL MED 0.6050
MED J AUSTRALIA 1.7360
-----------------------------
The format above is referred to as ‘simple’ format in the documentation. Notice that the numbers in this table have 4 decimal places, despite the fact the original data were largely strings and had max of 3 decimal places precision. Table converts string representations of numbers to their appropriate form when you do str(table) or print the table.
We have several things we might want to specify when creating a table: the precision and or format of floating point numbers (integer argument - digits), the spacing between columns (integer argument or actual string of whitespace - space), title (argument - title), and legend (argument - legend). Lets modify some of these and provide a title and legend.
>>> t = Table(column_headings, rows, title='Journal impact factors', legend='From ISI',
... digits=2, space=' ')
>>> print t
Journal impact factors
=================================
Journal Impact
---------------------------------
INT J PARASITOL 2.90
J MED ENTOMOL 1.40
Med Vet Entomol 1.00
INSECT MOL BIOL 2.85
J AM MOSQUITO CONTR 0.81
MOL PHYLOGENET EVOL 2.80
HEREDITY 1.99
AM J TROP MED HYG 2.10
MIL MED 0.60
MED J AUSTRALIA 1.74
---------------------------------
From ISI
The Table class cannot handle arbitrary python objects, unless they are passed in as strings. Note in this case we now directly pass in the column headings list and the handling of missing data can be explicitly specified..
>>> t2 = Table(['abcd', 'data'], [[str(range(1,6)), '0'],
... ['x', 5.0], ['y', None]],
... missing_data='*')
>>> print t2
=========================
abcd data
-------------------------
[1, 2, 3, 4, 5] 0
x 5.0000
y *
-------------------------
Table column headings can be assessed from the table.Header property
>>> assert t2.Header == ['abcd', 'data']
and this is immutable (cannot be changed).
>>> t2.Header[1] = 'Data'
Traceback (most recent call last):
RuntimeError: Table Header is immutable, use withNewColumns
If you want to change the Header, use the withNewHeader method. This can be done one column at a time, or as a batch. The returned Table is identical aside from the modified column labels.
>>> mod_header = t2.withNewHeader('abcd', 'ABCD')
>>> assert mod_header.Header == ['ABCD', 'data']
>>> mod_header = t2.withNewHeader(['abcd', 'data'], ['ABCD', 'DATA'])
>>> print mod_header
=========================
ABCD DATA
-------------------------
[1, 2, 3, 4, 5] 0
x 5.0000
y *
-------------------------
Tables may also be created from 2-dimensional dictionaries. In this case, special capabilities are provided to enforce printing rows in a particular order.
>>> d2D={'edge.parent': {'NineBande': 'root', 'edge.1': 'root',
... 'DogFaced': 'root', 'Human': 'edge.0', 'edge.0': 'edge.1',
... 'Mouse': 'edge.1', 'HowlerMon': 'edge.0'}, 'x': {'NineBande': 1.0,
... 'edge.1': 1.0, 'DogFaced': 1.0, 'Human': 1.0, 'edge.0': 1.0,
... 'Mouse': 1.0, 'HowlerMon': 1.0}, 'length': {'NineBande': 4.0,
... 'edge.1': 4.0, 'DogFaced': 4.0, 'Human': 4.0, 'edge.0': 4.0,
... 'Mouse': 4.0, 'HowlerMon': 4.0}, 'y': {'NineBande': 3.0, 'edge.1': 3.0,
... 'DogFaced': 3.0, 'Human': 3.0, 'edge.0': 3.0, 'Mouse': 3.0,
... 'HowlerMon': 3.0}, 'z': {'NineBande': 6.0, 'edge.1': 6.0,
... 'DogFaced': 6.0, 'Human': 6.0, 'edge.0': 6.0, 'Mouse': 6.0,
... 'HowlerMon': 6.0},
... 'edge.name': ['Human', 'HowlerMon', 'Mouse', 'NineBande', 'DogFaced',
... 'edge.0', 'edge.1']}
>>> row_order = d2D['edge.name']
>>> d2D['edge.name'] = dict(zip(row_order, row_order))
>>> t3 = Table(['edge.name', 'edge.parent', 'length', 'x', 'y', 'z'], d2D,
... row_order = row_order, missing_data='*', space=8, max_width = 50,
... row_ids = True, title = 'My Title',
... legend = 'Legend: this is a nonsense example.')
>>> print t3
My Title
==========================================
edge.name edge.parent length
------------------------------------------
Human edge.0 4.0000
HowlerMon edge.0 4.0000
Mouse edge.1 4.0000
NineBande root 4.0000
DogFaced root 4.0000
edge.0 edge.1 4.0000
edge.1 root 4.0000
------------------------------------------
continued: My Title
=====================================
edge.name x y
-------------------------------------
Human 1.0000 3.0000
HowlerMon 1.0000 3.0000
Mouse 1.0000 3.0000
NineBande 1.0000 3.0000
DogFaced 1.0000 3.0000
edge.0 1.0000 3.0000
edge.1 1.0000 3.0000
-------------------------------------
continued: My Title
=======================
edge.name z
-----------------------
Human 6.0000
HowlerMon 6.0000
Mouse 6.0000
NineBande 6.0000
DogFaced 6.0000
edge.0 6.0000
edge.1 6.0000
-----------------------
Legend: this is a nonsense example.
In the above we specify a maximum width of the table, and also specify row identifiers (using row_ids, the integer corresponding to the column at which data begin, preceding columns are taken as the identifiers). This has the effect of forcing the table to wrap when the simple text format is used, but wrapping does not occur for any other format. The row_ids are keys for slicing the table by row, and as identifiers are presented in each wrapped sub-table.
We can also customise the formatting of individual columns.
>>> rows = (('NP_003077_hs_mm_rn_dna', 'Con', 2.5386013224378985),
... ('NP_004893_hs_mm_rn_dna', 'Con', 0.12135142635634111e+06),
... ('NP_005079_hs_mm_rn_dna', 'Con', 0.95165949788861326e+07),
... ('NP_005500_hs_mm_rn_dna', 'Con', 0.73827030202664901e-07),
... ('NP_055852_hs_mm_rn_dna', 'Con', 1.0933217708952725e+07))
We first create a table and show the default formatting behaviour for Table.
>>> t46 = Table(['Gene', 'Type', 'LR'], rows)
>>> print t46
===============================================
Gene Type LR
-----------------------------------------------
NP_003077_hs_mm_rn_dna Con 2.5386
NP_004893_hs_mm_rn_dna Con 121351.4264
NP_005079_hs_mm_rn_dna Con 9516594.9789
NP_005500_hs_mm_rn_dna Con 0.0000
NP_055852_hs_mm_rn_dna Con 10933217.7090
-----------------------------------------------
We then format the LR column to use a scientific number format.
>>> t46 = Table(['Gene', 'Type', 'LR'], rows)
>>> t46.setColumnFormat('LR', "%.4e")
>>> print t46
============================================
Gene Type LR
--------------------------------------------
NP_003077_hs_mm_rn_dna Con 2.5386e+00
NP_004893_hs_mm_rn_dna Con 1.2135e+05
NP_005079_hs_mm_rn_dna Con 9.5166e+06
NP_005500_hs_mm_rn_dna Con 7.3827e-08
NP_055852_hs_mm_rn_dna Con 1.0933e+07
--------------------------------------------
It is safe to directly modify certain attributes, such as the title, legend and white space separating columns, which we do for the t46.
>>> t46.Title = "A new title"
>>> t46.Legend = "A new legend"
>>> t46.Space = ' '
>>> print t46
A new title
========================================
Gene Type LR
----------------------------------------
NP_003077_hs_mm_rn_dna Con 2.5386e+00
NP_004893_hs_mm_rn_dna Con 1.2135e+05
NP_005079_hs_mm_rn_dna Con 9.5166e+06
NP_005500_hs_mm_rn_dna Con 7.3827e-08
NP_055852_hs_mm_rn_dna Con 1.0933e+07
----------------------------------------
A new legend
We can provide settings for multiple columns.
>>> t3 = Table(['edge.name', 'edge.parent', 'length', 'x', 'y', 'z'], d2D,
... row_order = row_order)
>>> t3.setColumnFormat('x', "%.1e")
>>> t3.setColumnFormat('y', "%.2f")
>>> print t3
===============================================================
edge.name edge.parent length x y z
---------------------------------------------------------------
Human edge.0 4.0000 1.0e+00 3.00 6.0000
HowlerMon edge.0 4.0000 1.0e+00 3.00 6.0000
Mouse edge.1 4.0000 1.0e+00 3.00 6.0000
NineBande root 4.0000 1.0e+00 3.00 6.0000
DogFaced root 4.0000 1.0e+00 3.00 6.0000
edge.0 edge.1 4.0000 1.0e+00 3.00 6.0000
edge.1 root 4.0000 1.0e+00 3.00 6.0000
---------------------------------------------------------------
In some cases, the contents of a column can be of different types. In this instance, rather than passing a column template we pass a reference to a function that will handle this complexity. To illustrate this we will define a function that formats floating point numbers, but returns everything else as is.
>>> def formatcol(value):
... if isinstance(value, float):
... val = "%.2f" % value
... else:
... val = str(value)
... return val
We apply this to a table with mixed string, integer and floating point data.
>>> t6 = Table(['ColHead'], [['a'], [1], [0.3], ['cc']],
... column_templates = dict(ColHead=formatcol))
>>> print t6
=======
ColHead
-------
a
1
0.30
cc
-------
Other formats are also possible, including restructured text or ‘rest’ and delimited. These can be obtained using the tostring method and format argument as follows. Using table t from above,
>>> print t.tostring(format='rest')
+------------------------------+
| Journal impact factors |
+---------------------+--------+
| Journal | Impact |
+=====================+========+
| INT J PARASITOL | 2.90 |
+---------------------+--------+
| J MED ENTOMOL | 1.40 |
+---------------------+--------+
| Med Vet Entomol | 1.00 |
+---------------------+--------+
| INSECT MOL BIOL | 2.85 |
+---------------------+--------+
| J AM MOSQUITO CONTR | 0.81 |
+---------------------+--------+
| MOL PHYLOGENET EVOL | 2.80 |
+---------------------+--------+
| HEREDITY | 1.99 |
+---------------------+--------+
| AM J TROP MED HYG | 2.10 |
+---------------------+--------+
| MIL MED | 0.60 |
+---------------------+--------+
| MED J AUSTRALIA | 1.74 |
+---------------------+--------+
| From ISI |
+------------------------------+
Arguments such as space have no effect in this case. The table may also be written to file in any of the available formats (latex, simple text, html, pickle) or using a custom separator (such as a comma or tab). This makes it convenient to get data into other applications (such as R or excel).
Here is the latex format, note how the title and legend are joined into the latex table caption. We also provide optional arguments for the column alignment (fist column left aligned, second column right aligned and remaining columns centred) and a label for table referencing.
>>> print t3.tostring(format='tex', justify="lrcccc", label="table:example")
\begin{longtable}[htp!]{ l r c c c c }
\hline
\bf{edge.name} & \bf{edge.parent} & \bf{length} & \bf{x} & \bf{y} & \bf{z} \\
\hline
\hline
Human & edge.0 & 4.0000 & 1.0e+00 & 3.00 & 6.0000 \\
HowlerMon & edge.0 & 4.0000 & 1.0e+00 & 3.00 & 6.0000 \\
Mouse & edge.1 & 4.0000 & 1.0e+00 & 3.00 & 6.0000 \\
NineBande & root & 4.0000 & 1.0e+00 & 3.00 & 6.0000 \\
DogFaced & root & 4.0000 & 1.0e+00 & 3.00 & 6.0000 \\
edge.0 & edge.1 & 4.0000 & 1.0e+00 & 3.00 & 6.0000 \\
edge.1 & root & 4.0000 & 1.0e+00 & 3.00 & 6.0000 \\
\hline
\label{table:example}
\end{longtable}
More complex latex table justifying is also possible. Specifying the width of individual columns requires passing in a series (list or tuple) of justification commands. In the following we introduce the command for specific columns widths.
>>> print t3.tostring(format='tex', justify=["l","p{3cm}","c","c","c","c"])
\begin{longtable}[htp!]{ l p{3cm} c c c c }
\hline
\bf{edge.name} & \bf{edge.parent} & \bf{length} & \bf{x} & \bf{y} & \bf{z} \\
\hline
\hline
Human & edge.0 & 4.0000 & 1.0e+00 & 3.00 & 6.0000 \\
HowlerMon & edge.0 & 4.0000 & 1.0e+00 & 3.00 & 6.0000 \\
Mouse & edge.1 & 4.0000 & 1.0e+00 & 3.00 & 6.0000 \\
NineBande & root & 4.0000 & 1.0e+00 & 3.00 & 6.0000 \\
DogFaced & root & 4.0000 & 1.0e+00 & 3.00 & 6.0000 \\
edge.0 & edge.1 & 4.0000 & 1.0e+00 & 3.00 & 6.0000 \\
edge.1 & root & 4.0000 & 1.0e+00 & 3.00 & 6.0000 \\
\hline
\end{longtable}
>>> print t3.tostring(sep=',')
edge.name,edge.parent,length, x, y, z
Human, edge.0,4.0000,1.0e+00,3.00,6.0000
HowlerMon, edge.0,4.0000,1.0e+00,3.00,6.0000
Mouse, edge.1,4.0000,1.0e+00,3.00,6.0000
NineBande, root,4.0000,1.0e+00,3.00,6.0000
DogFaced, root,4.0000,1.0e+00,3.00,6.0000
edge.0, edge.1,4.0000,1.0e+00,3.00,6.0000
edge.1, root,4.0000,1.0e+00,3.00,6.0000
You can specify any standard text character that will work with your desired target. Useful separators are tabs (‘\t’), or pipes (‘|’). If Table encounters any of these characters within a cell, it wraps the cell in quotes – a standard approach to facilitate import by other applications. We will illustrate this with t2.
>>> print t2.tostring(sep=', ')
abcd, data
"[1, 2, 3, 4, 5]", 0
x, 5.0000
y, *
Note that I introduced an extra space after the column just to make the result more readable in this example.
Test the writing of phylip distance matrix format.
>>> rows = [['a', '', 0.088337278874079342, 0.18848582712597683,
... 0.44084000179091454], ['c', 0.088337278874079342, '',
... 0.088337278874079342, 0.44083999937417828], ['b', 0.18848582712597683,
... 0.088337278874079342, '', 0.44084000179090932], ['e',
... 0.44084000179091454, 0.44083999937417828, 0.44084000179090932, '']]
>>> dist = Table(rows = rows, header = ['seq1/2', 'a', 'c', 'b', 'e'],
... row_ids = True)
>>> print dist.tostring(format = 'phylip')
4
a 0.0000 0.0883 0.1885 0.4408
c 0.0883 0.0000 0.0883 0.4408
b 0.1885 0.0883 0.0000 0.4408
e 0.4408 0.4408 0.4408 0.0000
Saving a table object to file for later reloading can be done using the standard writeToFile method and filename argument to the Table constructor and either the pickle or a delimited format (eg ‘,’, ‘|’). The writeToFile saves the raw data in the appropriate format, the constructor recreates a table from raw data located at filename. We first write out the table t3 in pickle format and then the table t2 in a csv (comma separated values format).
>>> t3 = Table(['edge.name', 'edge.parent', 'length', 'x', 'y', 'z'], d2D,
... row_order = row_order, missing_data='*', space=8, max_width = 50,
... row_ids = True, title = 'My Title',
... legend = 'Legend: this is a nonsense example.')
>>> t3.writeToFile("t3.pickle")
>>> t3_loaded = LoadTable(filename = "t3.pickle")
>>> print t3_loaded
My Title
==========================================
edge.name edge.parent length
------------------------------------------
Human edge.0 4.0000
HowlerMon edge.0 4.0000
Mouse edge.1 4.0000
NineBande root 4.0000
DogFaced root 4.0000
edge.0 edge.1 4.0000
edge.1 root 4.0000
------------------------------------------
continued: My Title
=====================================
edge.name x y
-------------------------------------
Human 1.0000 3.0000
HowlerMon 1.0000 3.0000
Mouse 1.0000 3.0000
NineBande 1.0000 3.0000
DogFaced 1.0000 3.0000
edge.0 1.0000 3.0000
edge.1 1.0000 3.0000
-------------------------------------
continued: My Title
=======================
edge.name z
-----------------------
Human 6.0000
HowlerMon 6.0000
Mouse 6.0000
NineBande 6.0000
DogFaced 6.0000
edge.0 6.0000
edge.1 6.0000
-----------------------
Legend: this is a nonsense example.
>>> t2 = Table(['abcd', 'data'], [[str(range(1,6)), '0'], ['x', 5.0],
... ['y', None]], missing_data='*', title = 'A \ntitle')
>>> t2.writeToFile('t2.csv', sep=',')
>>> t2_loaded = LoadTable(filename = 't2.csv', header = True, with_title = True,
... sep = ',')
>>> print t2_loaded
A
title
=========================
abcd data
-------------------------
[1, 2, 3, 4, 5] 0
x 5.0000
y
-------------------------
Note the missing_data attribute is not saved in the delimited format, but is in the pickle format. In the next case, I’m going to override the digits format on reloading of the table.
>>> t2 = Table(['abcd', 'data'], [[str(range(1,6)), '0'], ['x', 5.0],
... ['y', None]], missing_data='*', title = 'A \ntitle',
... legend = "And\na legend too")
>>> t2.writeToFile('t2.csv', sep=',')
>>> t2_loaded = LoadTable(filename = 't2.csv', header = True,
... with_title = True, with_legend = True, sep = ',', digits = 2)
>>> print t2_loaded
A
title
=======================
abcd data
-----------------------
[1, 2, 3, 4, 5] 0
x 5.00
y
-----------------------
And
a legend too
A few things to note about the delimited file saving: formatting arguments are lost in saving to a delimited format; the header argument specifies whether the first line of file should be treated as the header; the with_title and with_legend arguments are necessary if the file contains them, otherwise the become the header or part of the file. Importantly, if you wish to preserve numerical precision use the pickle format.
cPickle should be able to load a useful object from the pickled Table alone.
>>> import cPickle
>>> f = file("t3.pickle")
>>> pickled = cPickle.load(f)
>>> f.close()
>>> pickled.keys()
['digits', 'row_ids', 'rows', 'title', 'space', 'max_width', 'header',...
>>> pickled['rows'][0]
['Human', 'edge.0', 4.0, 1.0, 3.0, 6.0]
We can read in a delimited format using a custom reader, which we’ll now import. We convert columns 2-5 to floats by specifying a field convertor. We then create a reader, specifying the data (below a list but can be a file) properties. Note that if no convertor is provided all data are returned as strings. We can also provide this reader to the Table constructor for a more direct way of opening such files. In this case, Table assumes there is a header row and nothing else.
>>> from cogent.parse.table import ConvertFields, SeparatorFormatParser
>>> t3.Title = t3.Legend = None
>>> comma_sep = t3.tostring(sep=",").splitlines()
>>> print comma_sep
['edge.name,edge.parent,length, x, y, z', ' Human, ...
>>> converter = ConvertFields([(2,float), (3,float), (4,float), (5, float)])
>>> reader = SeparatorFormatParser(with_header=True,converter=converter,
... sep=",")
>>> comma_sep = [line for line in reader(comma_sep)]
>>> print comma_sep
[['edge.name', 'edge.parent', 'length', 'x', 'y', 'z'], ['Human',...
>>> t3.writeToFile("t3.tab", sep="\t")
>>> reader = SeparatorFormatParser(with_header=True,converter=converter,
... sep="\t")
>>> t3a = LoadTable(filename="t3.tab", reader=reader, title="new title",
... space=2)
>>> print t3a
new title
======================================================
edge.name edge.parent length x y z
------------------------------------------------------
Human edge.0 4.0000 1.0000 3.0000 6.0000
HowlerMon edge.0 4.0000 1.0000 3.0000 6.0000
Mouse edge.1 4.0000 1.0000 3.0000 6.0000
NineBande root 4.0000 1.0000 3.0000 6.0000
DogFaced root 4.0000 1.0000 3.0000 6.0000
edge.0 edge.1 4.0000 1.0000 3.0000 6.0000
edge.1 root 4.0000 1.0000 3.0000 6.0000
------------------------------------------------------
We also test the reader function for a ‘t’ delimited format with missing data at the end.
>>> data = ['ab\tcd\t', 'ab\tcd\tef']
>>> tab_reader = SeparatorFormatParser(sep='\t')
>>> for line in tab_reader(data):
... assert len(line) == 3, line
We can likewise specify a writer, using a custom field formatter and provide this to the Table directly for writing. We first illustrate how the writer works to generate output. We then use it to escape some text fields in quotes. In order to read that back in, we define a custom reader that strips these quotes off.
>>> from cogent.format.table import FormatFields, SeparatorFormatWriter
>>> formatter = FormatFields([(0,'"%s"'), (1,'"%s"')])
>>> writer = SeparatorFormatWriter(formatter=formatter, sep=" | ")
>>> for formatted in writer(comma_sep, has_header=True):
... print formatted
edge.name | edge.parent | length | x | y | z
"Human" | "edge.0" | 4.0 | 1.0 | 3.0 | 6.0
"HowlerMon" | "edge.0" | 4.0 | 1.0 | 3.0 | 6.0
"Mouse" | "edge.1" | 4.0 | 1.0 | 3.0 | 6.0
"NineBande" | "root" | 4.0 | 1.0 | 3.0 | 6.0
"DogFaced" | "root" | 4.0 | 1.0 | 3.0 | 6.0
"edge.0" | "edge.1" | 4.0 | 1.0 | 3.0 | 6.0
"edge.1" | "root" | 4.0 | 1.0 | 3.0 | 6.0
>>> t3.writeToFile(filename="t3.tab", writer=writer)
>>> strip = lambda x: x.replace('"', '')
>>> converter = ConvertFields([(0,strip), (1, strip)])
>>> reader = SeparatorFormatParser(with_header=True, converter=converter,
... sep="|", strip_wspace=True)
>>> t3a = LoadTable(filename="t3.tab", reader=reader, title="new title",
... space=2)
>>> print t3a
new title
=============================================
edge.name edge.parent length x y z
---------------------------------------------
Human edge.0 4.0 1.0 3.0 6.0
HowlerMon edge.0 4.0 1.0 3.0 6.0
Mouse edge.1 4.0 1.0 3.0 6.0
NineBande root 4.0 1.0 3.0 6.0
DogFaced root 4.0 1.0 3.0 6.0
edge.0 edge.1 4.0 1.0 3.0 6.0
edge.1 root 4.0 1.0 3.0 6.0
---------------------------------------------
Note
There are performance issues for large files. Pickling has proven very slow for saving very large files and introduces significant file size bloat. A simple delimited format is much more efficient both storage wise and, if you use a custom reader, to generate and read. A custom reader was approximately 6 fold faster than the standard delimited file reader.
The Table class is capable of slicing by row, range of rows, column or range of columns headings or used to identify a single cell. Slicing using the method getColumns can also be used to reorder columns. In the case of columns, either the string headings or their position integers can be used. For rows, if row_ids was specified as True the 0’th cell in each row can also be used.
>>> t4 = Table(['edge.name', 'edge.parent', 'length', 'x', 'y', 'z'], d2D,
... row_order = row_order, row_ids = True, title = 'My Title')
We subset t4 by column and reorder them.
>>> new = t4.getColumns(['z', 'y'])
>>> print new
My Title
=============================
edge.name z y
-----------------------------
Human 6.0000 3.0000
HowlerMon 6.0000 3.0000
Mouse 6.0000 3.0000
NineBande 6.0000 3.0000
DogFaced 6.0000 3.0000
edge.0 6.0000 3.0000
edge.1 6.0000 3.0000
-----------------------------
We use the column position indexes to do get the same table.
>>> new = t4.getColumns([5, 4])
>>> print new
My Title
=============================
edge.name z y
-----------------------------
Human 6.0000 3.0000
HowlerMon 6.0000 3.0000
Mouse 6.0000 3.0000
NineBande 6.0000 3.0000
DogFaced 6.0000 3.0000
edge.0 6.0000 3.0000
edge.1 6.0000 3.0000
-----------------------------
We can also using more general slicing, by both rows and columns. The following returns all rows from 4 on, and columns up to (but excluding) ‘y’:
>>> k = t4[4:, :'y']
>>> print k
My Title
============================================
edge.name edge.parent length x
--------------------------------------------
DogFaced root 4.0000 1.0000
edge.0 edge.1 4.0000 1.0000
edge.1 root 4.0000 1.0000
--------------------------------------------
We can explicitly reference individual cells, in this case using both row and column keys.
>>> val = t4['HowlerMon', 'y']
>>> print val
3.0
We slice a single row,
>>> new = t4[3]
>>> print new
My Title
================================================================
edge.name edge.parent length x y z
----------------------------------------------------------------
NineBande root 4.0000 1.0000 3.0000 6.0000
----------------------------------------------------------------
and range of rows.
>>> new = t4[3:6]
>>> print new
My Title
================================================================
edge.name edge.parent length x y z
----------------------------------------------------------------
NineBande root 4.0000 1.0000 3.0000 6.0000
DogFaced root 4.0000 1.0000 3.0000 6.0000
edge.0 edge.1 4.0000 1.0000 3.0000 6.0000
----------------------------------------------------------------
You can get disjoint rows.
>>> print t4.getDisjointRows(['Human', 'Mouse', 'DogFaced'])
My Title
================================================================
edge.name edge.parent length x y z
----------------------------------------------------------------
Human edge.0 4.0000 1.0000 3.0000 6.0000
Mouse edge.1 4.0000 1.0000 3.0000 6.0000
DogFaced root 4.0000 1.0000 3.0000 6.0000
----------------------------------------------------------------
You can iterate over the table one row at a time and slice the rows. We illustrate this slicing a single column,
>>> for row in t:
... print row['Journal']
INT J PARASITOL
J MED ENTOMOL
Med Vet Entomol
INSECT MOL BIOL
J AM MOSQUITO CONTR
MOL PHYLOGENET EVOL
HEREDITY
AM J TROP MED HYG
MIL MED
MED J AUSTRALIA
and for multiple columns.
>>> for row in t:
... print row['Journal'], row['Impact']
INT J PARASITOL 2.9
J MED ENTOMOL 1.4
Med Vet Entomol 1.0
INSECT MOL BIOL 2.85
J AM MOSQUITO CONTR 0.811
MOL PHYLOGENET EVOL 2.8
HEREDITY 1.99
AM J TROP MED HYG 2.105
MIL MED 0.605
MED J AUSTRALIA 1.736
The numerical slice equivalent to the first case above would be row[0], to the second case either row[:], row[:2].
We want to be able to slice a table, based on some condition(s), to produce a new subset table. For instance, we construct a table with type and probability values.
>>> header = ['Gene', 'type', 'LR', 'df', 'Prob']
>>> rows = (('NP_003077_hs_mm_rn_dna', 'Con', 2.5386, 1, 0.1111),
... ('NP_004893_hs_mm_rn_dna', 'Con', 0.1214, 1, 0.7276),
... ('NP_005079_hs_mm_rn_dna', 'Con', 0.9517, 1, 0.3293),
... ('NP_005500_hs_mm_rn_dna', 'Con', 0.7383, 1, 0.3902),
... ('NP_055852_hs_mm_rn_dna', 'Con', 0.0000, 1, 0.9997),
... ('NP_057012_hs_mm_rn_dna', 'Unco', 34.3081, 1, 0.0000),
... ('NP_061130_hs_mm_rn_dna', 'Unco', 3.7986, 1, 0.0513),
... ('NP_065168_hs_mm_rn_dna', 'Con', 89.9766, 1, 0.0000),
... ('NP_065396_hs_mm_rn_dna', 'Unco', 11.8912, 1, 0.0006),
... ('NP_109590_hs_mm_rn_dna', 'Con', 0.2121, 1, 0.6451),
... ('NP_116116_hs_mm_rn_dna', 'Unco', 9.7474, 1, 0.0018))
>>> t5 = Table(header, rows)
>>> print t5
=========================================================
Gene type LR df Prob
---------------------------------------------------------
NP_003077_hs_mm_rn_dna Con 2.5386 1 0.1111
NP_004893_hs_mm_rn_dna Con 0.1214 1 0.7276
NP_005079_hs_mm_rn_dna Con 0.9517 1 0.3293
NP_005500_hs_mm_rn_dna Con 0.7383 1 0.3902
NP_055852_hs_mm_rn_dna Con 0.0000 1 0.9997
NP_057012_hs_mm_rn_dna Unco 34.3081 1 0.0000
NP_061130_hs_mm_rn_dna Unco 3.7986 1 0.0513
NP_065168_hs_mm_rn_dna Con 89.9766 1 0.0000
NP_065396_hs_mm_rn_dna Unco 11.8912 1 0.0006
NP_109590_hs_mm_rn_dna Con 0.2121 1 0.6451
NP_116116_hs_mm_rn_dna Unco 9.7474 1 0.0018
---------------------------------------------------------
We then seek to obtain only those rows that contain probabilities < 0.05. We use valid python code within a string. Note: Make sure your column headings could be valid python variable names or the string based approach will fail (you could use an external function instead, see below).
>>> sub_table1 = t5.filtered(callback = "Prob < 0.05")
>>> print sub_table1
=========================================================
Gene type LR df Prob
---------------------------------------------------------
NP_057012_hs_mm_rn_dna Unco 34.3081 1 0.0000
NP_065168_hs_mm_rn_dna Con 89.9766 1 0.0000
NP_065396_hs_mm_rn_dna Unco 11.8912 1 0.0006
NP_116116_hs_mm_rn_dna Unco 9.7474 1 0.0018
---------------------------------------------------------
Using the above table we test the function to extract the raw data for a single column,
>>> raw = sub_table1.getRawData('LR')
>>> raw
[34.308100000000003, 89.976600000000005, 11.8912, 9.7474000000000007]
and from multiple columns.
>>> raw = sub_table1.getRawData(columns = ['LR', 'df', 'Prob'])
>>> raw
[[34.308100000000003, 1, 0.0], [89.976600000000005, 1, 0.0],...
We can also do filtering using an external function, in this case we use a lambda to obtain only those rows of type ‘Unco’ that contain probabilities < 0.05, modifying our callback function.
>>> func = lambda (ty, pr): ty == 'Unco' and pr < 0.05
>>> sub_table2 = t5.filtered(columns = ('type', 'Prob'), callback = func)
>>> print sub_table2
=========================================================
Gene type LR df Prob
---------------------------------------------------------
NP_057012_hs_mm_rn_dna Unco 34.3081 1 0.0000
NP_065396_hs_mm_rn_dna Unco 11.8912 1 0.0006
NP_116116_hs_mm_rn_dna Unco 9.7474 1 0.0018
---------------------------------------------------------
This can also be done using the string approach.
>>> sub_table2 = t5.filtered(callback = "type == 'Unco' and Prob < 0.05")
>>> print sub_table2
=========================================================
Gene type LR df Prob
---------------------------------------------------------
NP_057012_hs_mm_rn_dna Unco 34.3081 1 0.0000
NP_065396_hs_mm_rn_dna Unco 11.8912 1 0.0006
NP_116116_hs_mm_rn_dna Unco 9.7474 1 0.0018
---------------------------------------------------------
Tables may also be appended to each other, to make larger tables. We’ll construct two simple tables to illustrate this.
>>> geneA = Table(['edge.name', 'edge.parent', 'z'], [['Human','root',
... 6.0],['Mouse','root', 6.0], ['Rat','root', 6.0]],
... title='Gene A')
>>> geneB = Table(['edge.name', 'edge.parent', 'z'], [['Human','root',
... 7.0],['Mouse','root', 7.0], ['Rat','root', 7.0]],
... title='Gene B')
>>> print geneB
Gene B
==================================
edge.name edge.parent z
----------------------------------
Human root 7.0000
Mouse root 7.0000
Rat root 7.0000
----------------------------------
we now use the appended Table method to create a new table, specifying that we want a new column created (by passing the new_column argument a heading) in which the table titles will be placed.
>>> new = geneA.appended('Gene', geneB, title='Appended tables')
>>> print new
Appended tables
============================================
Gene edge.name edge.parent z
--------------------------------------------
Gene A Human root 6.0000
Gene A Mouse root 6.0000
Gene A Rat root 6.0000
Gene B Human root 7.0000
Gene B Mouse root 7.0000
Gene B Rat root 7.0000
--------------------------------------------
We repeat this without adding a new column.
>>> new = geneA.appended(None, geneB, title="Appended, no new column")
>>> print new
Appended, no new column
==================================
edge.name edge.parent z
----------------------------------
Human root 6.0000
Mouse root 6.0000
Rat root 6.0000
Human root 7.0000
Mouse root 7.0000
Rat root 7.0000
----------------------------------
Tables have a Shape attribute, which specifies x (number of columns) and y (number of rows). The attribute is a tuple and we illustrate it for the above sub_table tables. Combined with the filtered method, this attribute can tell you how many rows satisfy a specific condition.
>>> t5.Shape
(11, 5)
>>> sub_table1.Shape
(4, 5)
>>> sub_table2.Shape
(3, 5)
For instance, 3 of the 11 rows in t were significant and belonged to the Unco type.
For completeness, we generate a table with no rows and assess its shape.
>>> func = lambda (ty, pr): ty == 'Unco' and pr > 0.1
>>> sub_table3 = t5.filtered(columns = ('type', 'Prob'), callback = func)
>>> sub_table3.Shape
(0, 5)
The distinct values can be obtained for a single column,
>>> distinct = new.getDistinctValues("edge.name")
>>> assert distinct == set(['Rat', 'Mouse', 'Human'])
or multiple columns
>>> distinct = new.getDistinctValues(["edge.parent", "z"])
>>> assert distinct == set([('root', 6.0), ('root', 7.0)]), distinct
We can compute column sums. This will fail if there are non-numerical values in a column.
>>> assert new.summed('z') == 39., new.summed('z')
In some cases it is desirable to compute an additional column from existing column values. This is done using the withNewColumn method. We’ll use t4 from above, adding two of the columns to create an additional column.
>>> t7 = t4.withNewColumn('Sum', callback="z+x", digits=2)
>>> print t7
My Title
==================================================================
edge.name edge.parent length x y z Sum
------------------------------------------------------------------
Human edge.0 4.00 1.00 3.00 6.00 7.00
HowlerMon edge.0 4.00 1.00 3.00 6.00 7.00
Mouse edge.1 4.00 1.00 3.00 6.00 7.00
NineBande root 4.00 1.00 3.00 6.00 7.00
DogFaced root 4.00 1.00 3.00 6.00 7.00
edge.0 edge.1 4.00 1.00 3.00 6.00 7.00
edge.1 root 4.00 1.00 3.00 6.00 7.00
------------------------------------------------------------------
We test this with an externally defined function.
>>> func = lambda (x, y): x * y
>>> t7 = t4.withNewColumn('Sum', callback=func, columns=("y","z"),
... digits=2)
>>> print t7
My Title
===================================================================
edge.name edge.parent length x y z Sum
-------------------------------------------------------------------
Human edge.0 4.00 1.00 3.00 6.00 18.00
HowlerMon edge.0 4.00 1.00 3.00 6.00 18.00
Mouse edge.1 4.00 1.00 3.00 6.00 18.00
NineBande root 4.00 1.00 3.00 6.00 18.00
DogFaced root 4.00 1.00 3.00 6.00 18.00
edge.0 edge.1 4.00 1.00 3.00 6.00 18.00
edge.1 root 4.00 1.00 3.00 6.00 18.00
-------------------------------------------------------------------
>>> func = lambda x: x**3
>>> t7 = t4.withNewColumn('Sum', callback=func, columns="y", digits=2)
>>> print t7
My Title
===================================================================
edge.name edge.parent length x y z Sum
-------------------------------------------------------------------
Human edge.0 4.00 1.00 3.00 6.00 27.00
HowlerMon edge.0 4.00 1.00 3.00 6.00 27.00
Mouse edge.1 4.00 1.00 3.00 6.00 27.00
NineBande root 4.00 1.00 3.00 6.00 27.00
DogFaced root 4.00 1.00 3.00 6.00 27.00
edge.0 edge.1 4.00 1.00 3.00 6.00 27.00
edge.1 root 4.00 1.00 3.00 6.00 27.00
-------------------------------------------------------------------
We want a table sorted according to values in a column.
>>> sorted = t5.sorted(columns = 'LR')
>>> print sorted
=========================================================
Gene type LR df Prob
---------------------------------------------------------
NP_055852_hs_mm_rn_dna Con 0.0000 1 0.9997
NP_004893_hs_mm_rn_dna Con 0.1214 1 0.7276
NP_109590_hs_mm_rn_dna Con 0.2121 1 0.6451
NP_005500_hs_mm_rn_dna Con 0.7383 1 0.3902
NP_005079_hs_mm_rn_dna Con 0.9517 1 0.3293
NP_003077_hs_mm_rn_dna Con 2.5386 1 0.1111
NP_061130_hs_mm_rn_dna Unco 3.7986 1 0.0513
NP_116116_hs_mm_rn_dna Unco 9.7474 1 0.0018
NP_065396_hs_mm_rn_dna Unco 11.8912 1 0.0006
NP_057012_hs_mm_rn_dna Unco 34.3081 1 0.0000
NP_065168_hs_mm_rn_dna Con 89.9766 1 0.0000
---------------------------------------------------------
We want a table sorted according to values in a subset of columns, note the order of columns determines the sort order.
>>> sorted = t5.sorted(columns=('LR', 'type'))
>>> print sorted
=========================================================
Gene type LR df Prob
---------------------------------------------------------
NP_055852_hs_mm_rn_dna Con 0.0000 1 0.9997
NP_004893_hs_mm_rn_dna Con 0.1214 1 0.7276
NP_109590_hs_mm_rn_dna Con 0.2121 1 0.6451
NP_005500_hs_mm_rn_dna Con 0.7383 1 0.3902
NP_005079_hs_mm_rn_dna Con 0.9517 1 0.3293
NP_003077_hs_mm_rn_dna Con 2.5386 1 0.1111
NP_061130_hs_mm_rn_dna Unco 3.7986 1 0.0513
NP_116116_hs_mm_rn_dna Unco 9.7474 1 0.0018
NP_065396_hs_mm_rn_dna Unco 11.8912 1 0.0006
NP_057012_hs_mm_rn_dna Unco 34.3081 1 0.0000
NP_065168_hs_mm_rn_dna Con 89.9766 1 0.0000
---------------------------------------------------------
We now do a sort based on 2 columns.
>>> sorted = t5.sorted(columns=('type', 'LR'))
>>> print sorted
=========================================================
Gene type LR df Prob
---------------------------------------------------------
NP_055852_hs_mm_rn_dna Con 0.0000 1 0.9997
NP_004893_hs_mm_rn_dna Con 0.1214 1 0.7276
NP_109590_hs_mm_rn_dna Con 0.2121 1 0.6451
NP_005500_hs_mm_rn_dna Con 0.7383 1 0.3902
NP_005079_hs_mm_rn_dna Con 0.9517 1 0.3293
NP_003077_hs_mm_rn_dna Con 2.5386 1 0.1111
NP_065168_hs_mm_rn_dna Con 89.9766 1 0.0000
NP_061130_hs_mm_rn_dna Unco 3.7986 1 0.0513
NP_116116_hs_mm_rn_dna Unco 9.7474 1 0.0018
NP_065396_hs_mm_rn_dna Unco 11.8912 1 0.0006
NP_057012_hs_mm_rn_dna Unco 34.3081 1 0.0000
---------------------------------------------------------
Reverse sort a single column
>>> sorted = t5.sorted('LR', reverse = 'LR')
>>> print sorted
=========================================================
Gene type LR df Prob
---------------------------------------------------------
NP_065168_hs_mm_rn_dna Con 89.9766 1 0.0000
NP_057012_hs_mm_rn_dna Unco 34.3081 1 0.0000
NP_065396_hs_mm_rn_dna Unco 11.8912 1 0.0006
NP_116116_hs_mm_rn_dna Unco 9.7474 1 0.0018
NP_061130_hs_mm_rn_dna Unco 3.7986 1 0.0513
NP_003077_hs_mm_rn_dna Con 2.5386 1 0.1111
NP_005079_hs_mm_rn_dna Con 0.9517 1 0.3293
NP_005500_hs_mm_rn_dna Con 0.7383 1 0.3902
NP_109590_hs_mm_rn_dna Con 0.2121 1 0.6451
NP_004893_hs_mm_rn_dna Con 0.1214 1 0.7276
NP_055852_hs_mm_rn_dna Con 0.0000 1 0.9997
---------------------------------------------------------
Reverse sort one column but not another
>>> sorted = t5.sorted(columns=('type', 'LR'), reverse = 'LR')
>>> print sorted
=========================================================
Gene type LR df Prob
---------------------------------------------------------
NP_065168_hs_mm_rn_dna Con 89.9766 1 0.0000
NP_003077_hs_mm_rn_dna Con 2.5386 1 0.1111
NP_005079_hs_mm_rn_dna Con 0.9517 1 0.3293
NP_005500_hs_mm_rn_dna Con 0.7383 1 0.3902
NP_109590_hs_mm_rn_dna Con 0.2121 1 0.6451
NP_004893_hs_mm_rn_dna Con 0.1214 1 0.7276
NP_055852_hs_mm_rn_dna Con 0.0000 1 0.9997
NP_057012_hs_mm_rn_dna Unco 34.3081 1 0.0000
NP_065396_hs_mm_rn_dna Unco 11.8912 1 0.0006
NP_116116_hs_mm_rn_dna Unco 9.7474 1 0.0018
NP_061130_hs_mm_rn_dna Unco 3.7986 1 0.0513
---------------------------------------------------------
Reverse sort both columns.
>>> sorted = t5.sorted(columns=('type', 'LR'), reverse = ('type', 'LR'))
>>> print sorted
=========================================================
Gene type LR df Prob
---------------------------------------------------------
NP_057012_hs_mm_rn_dna Unco 34.3081 1 0.0000
NP_065396_hs_mm_rn_dna Unco 11.8912 1 0.0006
NP_116116_hs_mm_rn_dna Unco 9.7474 1 0.0018
NP_061130_hs_mm_rn_dna Unco 3.7986 1 0.0513
NP_065168_hs_mm_rn_dna Con 89.9766 1 0.0000
NP_003077_hs_mm_rn_dna Con 2.5386 1 0.1111
NP_005079_hs_mm_rn_dna Con 0.9517 1 0.3293
NP_005500_hs_mm_rn_dna Con 0.7383 1 0.3902
NP_109590_hs_mm_rn_dna Con 0.2121 1 0.6451
NP_004893_hs_mm_rn_dna Con 0.1214 1 0.7276
NP_055852_hs_mm_rn_dna Con 0.0000 1 0.9997
---------------------------------------------------------
The Table object is capable of joins or merging of records in two tables. There are two fundamental types of joins – inner and outer – with there being different sub-types. We demonstrate these first constructing some simple tables.
>>> a=Table(header=["index", "col2","col3"],
... rows=[[1,2,3],[2,3,1],[2,6,5]], title="A")
>>> print a
A
=====================
index col2 col3
---------------------
1 2 3
2 3 1
2 6 5
---------------------
>>> b=Table(header=["index", "col2","col3"],
... rows=[[1,2,3],[2,2,1],[3,6,3]], title="B")
>>> print b
B
=====================
index col2 col3
---------------------
1 2 3
2 2 1
3 6 3
---------------------
>>> c=Table(header=["index","col_c2"],rows=[[1,2],[3,2],[3,5]],title="C")
>>> print c
C
===============
index col_c2
---------------
1 2
3 2
3 5
---------------
For a natural inner join, only 1 copy of columns with the same name are retained. So we expect the headings to be identical between the table a/b and the result of a.joined(b) or b.joined(a).
>>> assert a.joined(b).Header == b.Header
>>> assert b.joined(a).Header == a.Header
For a standard inner join, the joined table should contain all columns from a and b excepting the index column(s). Simply providing a column name (or index) selects this behaviour. Note that in this case, column names from the second table are made unique by prefixing them with that tables title. If the provided tables do not have a title, a RuntimeError is raised.
>>> b.Title = None
>>> try:
... a.joined(b)
... except RuntimeError:
... pass
>>> b.Title = 'B'
>>> assert a.joined(b, "index").Header == ["index", "col2", "col3",
... "B_col2", "B_col3"]
...
Note that the table title’s were used to prefix the column headings from the second table. We further test this using table c which has different dimensions.
>>> assert a.joined(c,"index").Header == ["index","col2","col3",
... "C_col_c2"]
It’s also possible to specify index columns using numerical values, the results of which should be the same.
>>> assert a.joined(b,[0, 2]).getRawData() ==\
... a.joined(b,["index","col3"]).getRawData()
Additionally, it’s possible to provide two series of indices for the two tables. Here, they have identical values.
>>> assert a.joined(b, ["index", "col3"],["index", "col3"]).getRawData()\
... == a.joined(b,["index","col3"]).getRawData()
The results of a standard join between tables a and b are
>>> print a.joined(b, ["index"], title='A&B')
A&B
=========================================
index col2 col3 B_col2 B_col3
-----------------------------------------
1 2 3 2 3
2 3 1 2 1
2 6 5 2 1
-----------------------------------------
We demo the table specific indices.
>>> print a.joined(c, ["col2"], ["index"], title='A&C by "col2/index"')
A&C by "col2/index"
=================================
index col2 col3 C_col_c2
---------------------------------
2 3 1 2
2 3 1 5
---------------------------------
Tables a and c share a single row with the same value in the index column, hence a join by that index should return a table with just that row.
>>> print a.joined(c, "index", title='A&C by "index"')
A&C by "index"
=================================
index col2 col3 C_col_c2
---------------------------------
1 2 3 2
---------------------------------
A natural join of tables a and b results in a table with only rows that were identical between the two parents.
>>> print a.joined(b, title='A&B Natural Join')
A&B Natural Join
=====================
index col2 col3
---------------------
1 2 3
---------------------
We test the outer join by defining an additional table with different dimensions, and conducting a join specifying inner_join=False.
>>> d=Table(header=["index", "col_c2"], rows=[[5,42],[6,23]], title="D")
>>> print d
D
===============
index col_c2
---------------
5 42
6 23
---------------
>>> print c.joined(d,inner_join=False, title='C&D Outer join')
C&D Outer join
======================================
index col_c2 D_index D_col_c2
--------------------------------------
1 2 5 42
1 2 6 23
3 2 5 42
3 2 6 23
3 5 5 42
3 5 6 23
--------------------------------------
We establish the joined method works for mixtures of character and numerical data, setting some indices and some cell values to be strings.
>>> a=Table(header=["index", "col2","col3"],
... rows=[[1,2,"3"],["2",3,1],[2,6,5]], title="A")
>>> b=Table(header=["index", "col2","col3"],
... rows=[[1,2,"3"],["2",2,1],[3,6,3]], title="B")
>>> assert a.joined(b, ["index", "col3"],["index", "col3"]).getRawData()\
... == a.joined(b,["index","col3"]).getRawData()
We test that the joined method works when the column index orders differ.
>>> t1_header = ['a', 'b']
>>> t1_rows = [(1,2),(3,4)]
>>> t2_header = ['b', 'c']
>>> t2_rows = [(3,6),(4,8)]
>>> t1 = Table(header = t1_header, rows = t1_rows, title='t1')
>>> t2 = Table(header = t2_header, rows = t2_rows, title='t2')
>>> t3 = t1.joined(t2, columns_self = ["b"], columns_other = ["b"])
>>> print t3
==============
a b t2_c
--------------
3 4 8
--------------
We then establish that a join with no values does not cause a failure, just returns an empty Table.
>>> t4_header = ['b', 'c']
>>> t4_rows = [(5,6),(7,8)]
>>> t4 = LoadTable(header = t4_header, rows = t4_rows)
>>> t4.Title = 't4'
>>> t5 = t1.joined(t4, columns_self = ["b"], columns_other = ["b"])
We can count the number of rows for which a condition holds. This method uses the same arguments as filtered but returns an integer result only.
>>> print c.count("col_c2 == 2")
2
>>> print c.joined(d,inner_join=False).count("index==3 and D_index==5")
2