King Arthur's finest collection of table-like containers
Project description
- Table
Think of this as a list of namedtuples, except the elements of the namedtuples can be edited. The table is initialized with a list of column header names. Appending to the table adds new rows. Rows are created using a list (positional) or dict (column names). Unspecified column cells default to None.
Other list methods (insert, pop, remove, extend, reverse) work as expected. sort allows a string key to indicate a sort-by-column. Search methods (index, count, __contains__) accept either a Row object or a list or dict.
Additional methods useful for tables:
take(indexes_or_func) returns a new table based on passed in indexes Alternatively, applies a function to every row in the table and builds a new table out of rows that evaluate to True
column(colname) returns an iterator over values in a column
- LookupTable
A lookup function or column is added to a basic Table. The lookup function is similar to a hash function, taking a Row object and returning a value. If a column is provided, the value in that column is the lookup value. A dict keeps track of these “lookup” values, allowing fast lookups on all seach methods (index, count, __contains__). These search methods accept either a Row object (which is run through the lookup function) or the lookup value. The lookup values do not need to be unique.
Example
from datetime import datetime, date, timedelta
from roundtable import Table
# Create empty table with column headers
tbl = Table(['Timestamp', 'Event', 'Root Cause', 'Due Date'])
# Add rows to the table
tbl.append((datetime(2013,1,2,12,30), 'Error code 129',
'Short on board', date(2013,1,8)))
tbl.append({'Event': 'Pairwise testing',
'Due Date': date(2013,1,7)}) # other columns default to None
# Build a sorted list of tasks due in the next week
task_list = tbl.take(lambda row: row['Due Date'] - date.today() < timedelta(days=7))
task_list.sort(col='Due Date', reverse=True)
Table access methods:
Code |
Return Type |
|---|---|
mytable[0] |
Row object |
mytable[-1] |
Row object |
mytable[0:10:2] |
new Table object |
mytable[0][0] |
value in cell |
mytable[0][-1] |
value in cell |
mytable[0, 0] |
value in cell |
mytable[0]['a column'] |
value in cell |
mytable[0, 'a column'] |
value in cell |
mytable[0].Col2 |
value in cell* |
mytable.column(1) |
iterator over values in column |
mytable.column('a column') |
iterator over values in column |
* As a special case, if a column name is a valid Python variable name and starts with a capital letter then an attribute will be added to the Row object allowing access. The requirement to start with a capital letter avoids conflicts with other attributes and functions of the Row object.
Table objects can be transformed into other table-like objects if modules are available:
NumPy Array : mytable.as_array()
Pandas DataFrame: mytable.as_dataframe()
Note
Works for Python 2.6+, 3.1+
No dependencies except when using the as_array() or as_dataframe() methods.
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