News file for torch-dataframe

Version: development

Nothing for the moment

Version: 1.7

Added faster torch.Tensor functions to fill/stat functions for speed
Added mutate function to Dataseries
__index__ access for Df_Array
More complete documentation for Df_Array and specs
Df_Dict elements can be accessed using myDict[index] or myDict["$colname"]
Df_Dict key property available. It list the Df_Dict's keys
Df_Dict length property available. It list by key, the length of its content
Df_Dict check_length() checks if all elements have the same length
Df_Dict set_keys(table) replaces every keys by the given table (must be the same size)
More complete documentation for Df_Dict and specs
More complete documentation for Df_Tbl and specs
Internal methods _infer_csvigo_schema() and _infer_data_schema() renamed to _infer_schema()
Type inference is now based on type frequences but if it encounter a single double/float in a integer column it will consider the column as double/float
it is now possible to directly set a schema for a Dataframe without any checks with set_schema(). Use it wisely
Possibility to init a Dataframe with a schema, a column order and a number of rows with internal method _init_with_schema()
Added bulk_load_csv() method wich loads large CSVs files using threads but without checking missing values or data integrity. To use with caution. See #28
Added load_threadcsv()
Added the possiblity to create empty Dataseries
Added Dataseries load() method to directly load a tensor or tds.Vec in memory without any check
Added iris dataset in /specs/data
New specs structure
Fixed csv loading when no header and test case according to it
Changed assert_is_index return value to true on success instead of self

Version: 1.6.1

The get_max_value/get_min_value use torch.max/min when no missing data is present in the column
Fixed upgrade_frame bug
Fixed bug with saving CSV-files when they contain boolean values

Version: 1.6

The data is now stored in Dataseries that handles all the manipulations, statistics, categoricals, etc internally. The data backend is either a tensor or a tds.Vec in order to better accomodate large datasets.
The self.columns has been dropped and there is now only self.column_order that keeps track of column order.
Most functions now use either tds.Hash or tds.Vec for returning values instead of regular tables.
The data types are now more sophisticate with boolean, integer, long, double, and string. The first and the last are internally stored as tds.Vec while the remaining are in the form of torch tensors.
Since conversions are more restricted with the new column types the is a boolean2tensor and boolean2categorical that help converting boolean columns into numerical.
The Dataframe.schema property has been removed as it now resides in the series. The same information can be retrieved using get_schema().
There is now a custom busted assertion that can compare tensors, tds, and Dataseries.
The csv data is entered using csvigo's large mode thus circumventing the memory limit for large csv's.
The to_/from_categorical now always return a single value when a single value is entered.
Add column now takes a Dataseries instead of a Df_Array
Generalized the argcheck by adding string.split for | separated arguments
Multiple minor bug-fixes with non-local variables

Version: 1.5

Added new subset and batch loading functionality (issue #22)
Added metatable functionality (issue #18)
The as_categorical can now receive levels, labels and exclusions (issue #23)
The unique sorts the results before returning, thereby preventing the order to depend on any irrelevant changes in the original table order. IMPORTANT: This will affect the numbers corresponding to categoricals (part of issue #23)
Added compatibility with the torchnet infrastructure via inheritance and a custom iterator that allows utilizing the internal permutation logistic (issue #24)
The Batchframe can now have default data/load options allowing a simpler to_tensor call
The Batchframe now supports common transformations that may be required for the label via the label_shape. See the ntwrk_implementation_spec file that contains basic examples.
Added so that __init parameters can be passed along the subset line primary for allowing default Batchframe load/data arguments
Insert now takes an index argument allowing insertion of rows. Backward compatibility retained.
Added append that does the same as index previously did, i.e. adding a row at the bottom of the table
Added rbind (row-bind) as an append alias
Added cbind (column-bind)
Added wide2long for converting wide datasets to long
Added a version function
Added upgrade_frame that handles upgrades from previous Dataframe versions
The statistics can now return dataframe that is also the default (allows nicer printing)
The add_column can now take a position argument and updates the schema + columns
The init-constructor for with a table argument now also accepts column_order argument
The column order can now be specified using the pos_column_order and manipulated using swap_column_order.
The tostring now has a more advanced printing that aims at total table width instead of just making sure that certain columns didn't end up too wide. The previous Dataframe.print default arguments for printing have been moved to Dataframe.tostring_defaults
The helper classes Df_Array, Df_Dict and Df_Tbl now have a metatable __len__ option
The as_batchframe has been renamed to frame_type that defaults to current frame type
The set now changes all matching values instead of only the first occurrence
Fixed bug with outputting categorical columns
Fixed bug related to boolean columns. Note: columns that are created using the csv-option are currently not being converted to boolean columns but will remain as strings with 'true' and 'false'

Version: 1.1

Converted all functions to use argcheck
API-changes due to above

Version_ 1.0

Full test suite
Load batch support
Categorical data support

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Uh oh!