Version 1.1.0

Version 1.1.0
Previous release:	Version 1.0.0

Frame

• Frames with columns of type obj64 can now be saved into CSV. The values in the object column will be stringified upon saving. #3064

• .replace() now supports numpy scalars. #3164

• .to_numpy() now has an option to control memory layout of the resulting numpy array. #3275

• column types returned by the method .sum() are now consistent with the ones returned by the function dt.sum(), i.e. int64 for void, boolean and integer columns; float32 for float32 columns; float64 for float64 columns. #2904

• .to_csv() now has an option sep to control the field separator. #3337

• Void columns can now be used with dt.sort() and dt.by(). In addition, datatable will now skip sorting any column that it knows contains constant values. #3088 #3104 #3108 #3109

• Saving a frame with a void column into Jay no longer leads to a crash. #3074 #3099 #3246

• Joining with void columns now works correctly. #3094

• dt.sum() now works correctly when applied to the same column as dt.by(). #3110

• Fixed a crash which could have occurred when sorting very long identical or nearly identical strings. #3134

• It is now possible to sort all columns according to boolean flags in the reverse list #3168

• Fixed support for .max_column_width option when rendering frames in Jupyter notebooks. #3160

• Fixed a crash which in rare situations happened in .to_csv() due to multithreading. #3176

• Fixed a crash in .to_pandas() when called on keyed frames. #3224

• Fixed .to_csv() to quote missing values when quoting=”all” is specified. #3340

• Fixed groupby behaviour on columns that contain missing values. #3331

• Converting a column of void type into pandas now produces a pandas object column filled with Nones. Converting such column back into datatable produces a void column again. #3063

• When creating Frame from a list of values, a floating-point nan value will now be treated as None. In particular, nans can now be safely mixed with values of other types, and a list consisting of only nans will turn into a Column of type void. #3083

• Converting string or object columns to numpy no longer produces a masked array. Instead, we create a regular object array, filled with Nones in place of NAs. Similarly, converting a string or object column to pandas creates a Series with None values (instead of nans as before) in place of NAs. #3083

FExpr

• Class dt.FExpr now has method .as_type(), which behaves exactly as the equivalent base level function dt.as_type().

• Added functions dt.rowargmin() and dt.rowargmin() to find the index of the largest and smallest values among columns of each row. #2998

• Added reducer function dt.prod() and the corresponding .prod() method to calculate product of values in columns. #3140

• Added function dt.cumsum(), as well as .cumsum() method, to calculate the cumulative sum of values per column. #3279

• Added functions dt.cummin() and dt.cummax(), as well as the corresponding .cummin() and .cummax() methods, to calculate the cumulative minimum and maximum of values per column. #3279

• Added function dt.cumprod(), as well as .cumprod() method, to calculate the cumulative product of values per column. #3279

• Added function dt.cumcount() and dt.ngroup(), to return the row number and group number respectively. #3279

• Added reducer functions dt.countna() and dt.nunique(). #2999

• Class dt.FExpr now has method .nunique(), which behaves exactly as the equivalent base level function dt.nunique().

• Class dt.FExpr now has method .countna(), which behaves exactly as the equivalent base level function dt.countna().

• Added function dt.fillna(), as well as .fillna() method, to impute missing values. #3279

• Class dt.FExpr now has method .alias(), to assign new names to the selected columns. #2684

• Added function dt.categories(), as well as the corresponding .categories() method, to retrieve categories for categorical columns. #3367

• Added function dt.codes(), as well as the corresponding .codes() method, to retrieve codes for categorical columns. #3371

• Function dt.re.match() now supports case insensitive matching. #3216

• Function dt.qcut() can now be used in a groupby context. #3165

• dt.qcut() won’t segfault anymore when used as an i-filter. #3061

• Fixed selection of time64 columns by ltype. #3251

• Fixed selection of time64 columns by python class name. #3253

• Fixed dt.shift() behaviour on grouped columns. #3269 #3272

• Reducers and row-wise functions now support void columns. #3284

fread

• When reading Excel files, datetime fields will now be converted into time64 columns in the resulting frame.

• When reading Excel files, forward slash, backslash, and their mix are supported as separators for specifying subpath. #3221

• fread() now supports reading from public S3 buckets, when the source has a format of s3://bucket-name/key-name. #3302

• Header detection heuristics has been improved in the case when some of the column names are missing. #3363

• fread() will no longer fail while reading mostly empty files. #3055

• fread() will no longer fail when reading excel files on Windows. #3178

• Parameter tempdir is now honored for memory limited fread() operation. #3244

• Parameter sep= in fread() will no longer accept values '-', '+', or '.'. Previously, these values were allowed but they produced errors during parsing. #3065

Models

• Fixed a bug in the LinearModel that in some cases resulted in the gradient and model coefficients blow up. #3234

• Fixed undefined behaviour when LinearModel predicted on frames with missing values. #3260

General

• Datatable no longer supports Python 3.6, because Python 3.6 itself has reached its end of life on 2021-12-23 and will no longer be supported. If you are still using Python 3.6, please consider upgrading. #3376

• Added properties .is_array, .is_boolean, .is_categorical, .is_compound, .is_float, .is_integer, .is_numeric, .is_object, .is_string, .is_temporal, .is_void to class dt.Type. #3101 #3149

• Added support for macOS Big Sur. #3175

• Added support for Python 3.10. #3210

• Added support for Python 3.11. #3374

• Parameter force=True in function rbind() (or method dt.Frame.rbind()) will now allow combining columns of incompatible types. #3062

• datatable’s thread pool can now be used to parallelize external C++ applications and will have no specific datatable dependencies, when the code is built with DT_DISABLE variable being defined. #3306