v0.22.0:
This is a major release from 0.21.1 and includes a single, API-breaking change. We recommend that all users upgrade to this version after carefully reading the release note.
The only changes are:
* The sum of an empty or all-NA Series is now 0
* The product of an empty or all-NA Series is now 1
* We’ve added a min_count parameter to .sum() and .prod() controlling the minimum number of valid values for the result to be valid. If fewer than min_count non-NA values are present, the result is NA. The default is 0. To return NaN, the 0.21 behavior, use min_count=1.
v0.21.0 Final:
This is a major release from 0.20.3 and includes a number of API changes, deprecations, new features, enhancements, and performance improvements along with a large number of bug fixes. We recommend that all users upgrade to this version.
Highlights include:
* Integration with Apache Parquet, including a new top-level read_parquet function and DataFrame.to_parquet method, see here.
* New user-facing dtype pandas.api.types.CategoricalDtype for specifying categoricals independent of the data, see here.
* The behavior of sum and prod on all-NaN Series/DataFrames is now consistent and no longer depends on whether bottleneck is installed, see here.
* Compatibility fixes for pypy, see here.
* Additions to the drop, reindex and rename API to make them more consistent, see here.
* Addition of the new methods DataFrame.infer_objects (see here) and GroupBy.pipe (see here).
* Indexing with a list of labels, where one or more of the labels is missing, is deprecated and will raise a KeyError in a future version
Bug Fixes
* Fixed a bug in failing to compute rolling computations of a column-MultiIndexed DataFrame
* Fixed a pytest marker failing downstream packages’ tests suites
Conversion
* Bug in pickle compat prior to the v0.20.x series, when UTC is a timezone in a Series/DataFrame/Index
* Bug in Series construction when passing a Series with dtype='category'.
* Bug in DataFrame.astype() when passing a Series as the dtype kwarg..
Indexing
* Bug in Float64Index causing an empty array instead of None to be returned from .get(np.nan) on a Series whose index did not contain any NaN s
* Bug in MultiIndex.isin causing an error when passing an empty iterable
* Fixed a bug in a slicing DataFrame/Series that have a TimedeltaIndex
I/O
* Bug in read_csv() in which files weren’t opened as binary files by the C engine on Windows, causing EOF characters mid-field, which would fail
* Bug in read_hdf() in which reading a Series saved to an HDF file in ‘fixed’ format fails when an explicit mode='r' argument is supplied
* Bug in DataFrame.to_latex() where bold_rows was wrongly specified to be True by default, whereas in reality row labels remained non-bold whatever parameter provided.
* Fixed an issue with DataFrame.style() where generated element ids were not unique
* Fixed loading a DataFrame with a PeriodIndex, from a format='fixed' HDFStore, in Python 3, that was written in Python 2
Plotting
* Fixed regression that prevented RGB and RGBA tuples from being used as color arguments
* Fixed an issue with DataFrame.plot.scatter() that incorrectly raised a KeyError when categorical data is used for plotting
Reshaping
* PeriodIndex / TimedeltaIndex.join was missing the sort= kwarg
* Bug in joining on a MultiIndex with a category dtype for a level.
* Bug in merge() when merging/joining with multiple categorical columns
Categorical
* Bug in DataFrame.sort_values not respecting the kind parameter with categorical data
This is a minor bug-fix release in the 0.20.x series and includes some small regression fixes, bug fixes and performance improvements. We recommend that all users upgrade to this version.
New .agg() API for Series/DataFrame similar to the groupby-rolling-resample API’s, see here
Integration with the feather-format, including a new top-level pd.read_feather() and DataFrame.to_feather() method, see here.
The .ix indexer has been deprecated, see here
Panel has been deprecated, see here
Addition of an IntervalIndex and Interval scalar type, see here
Improved user API when grouping by index levels in .groupby(), see here
Improved support for UInt64 dtypes, see here
A new orient for JSON serialization, orient='table', that uses the Table Schema spec and that gives the possibility for a more interactive repr in the Jupyter Notebook, see here
Experimental support for exporting styled DataFrames (DataFrame.style) to Excel, see here
Window binary corr/cov operations now return a MultiIndexed DataFrame rather than a Panel, as Panel is now deprecated, see here
Support for S3 handling now uses s3fs, see here
Google BigQuery support now uses the pandas-gbq library, see here
py-sqlite3. Add missing test dependency on py-nose.
Add comments with links to bug reports about test failures.
Bump PKGREVISION for dependency change.
Highlights in changelog:
v0.18.1:
.groupby(...) has been enhanced to provide convenient syntax when working with .rolling(..), .expanding(..) and .resample(..) per group, see here
pd.to_datetime() has gained the ability to assemble dates from a DataFrame, see here
Method chaining improvements, see here.
Custom business hour offset, see here.
Many bug fixes in the handling of sparse, see here
Expanded the Tutorials section with a feature on modern pandas, courtesy of @TomAugsburger. (GH13045).
v0.18.0:
Moving and expanding window functions are now methods on Series and DataFrame, similar to .groupby, see here.
Adding support for a RangeIndex as a specialized form of the Int64Index for memory savings, see here.
API breaking change to the .resample method to make it more .groupby like, see here.
Removal of support for positional indexing with floats, which was deprecated since 0.14.0. This will now raise a TypeError, see here.
The .to_xarray() function has been added for compatibility with the xarray package, see here.
The read_sas function has been enhanced to read sas7bdat files, see here.
Addition of the .str.extractall() method, and API changes to the .str.extract() method and .str.cat() method.
pd.test() top-level nose test runner is available (GH4327).
Update by K.I.A.Derouiche in PR pkg/51272
Slightly modified.
0.17.1
This is a minor bug-fix release from 0.17.0 and includes a large
number of bug fixes along several new features, enhancements, and
performance improvements. We recommend that all users upgrade to
this version.
Highlights include:
Support for Conditional HTML Formatting, see here
Releasing the GIL on the csv reader & other ops, see here
Fixed regression in DataFrame.drop_duplicates from 0.16.2,
causing incorrect results on integer values (GH11376)
0.17.0
This is a major release from 0.16.2 and includes a small number of
API changes, several new features, enhancements, and performance
improvements along with a large number of bug fixes. We recommend
that all users upgrade to this version.
Highlights include:
Release the Global Interpreter Lock (GIL) on some cython
operations, see here
Plotting methods are now available as attributes of the .plot
accessor, see here
The sorting API has been revamped to remove some long-time
inconsistencies, see here
Support for a datetime64[ns] with timezones as a first-class
dtype, see here
The default for to_datetime will now be to raise when presented
with unparseable formats, previously this would return the
original input. Also, date parse functions now return consistent
results. See here
The default for dropna in HDFStore has changed to False, to
store by default all rows even if they are all NaN, see here
Datetime accessor (dt) now supports Series.dt.strftime to
generate formatted strings for datetime-likes, and
Series.dt.total_seconds to generate each duration of the
timedelta in seconds. See here
Period and PeriodIndex can handle multiplied freq like 3D,
which corresponding to 3 days span. See here
Development installed versions of pandas will now have PEP440
compliant version strings (GH9518)
Development support for benchmarking with the Air Speed Velocity
library (GH8361)
Support for reading SAS xport files, see here
Documentation comparing SAS to pandas, see here
Removal of the automatic TimeSeries broadcasting, deprecated
since 0.8.0, see here
Display format with plain text can optionally align with Unicode
East Asian Width, see here
Compatibility with Python 3.5 (GH11097)
Compatibility with matplotlib 1.5.0 (GH11111)
Closes PR pkg/49958 by matthewd.
Changes since 0.14.1 for a full list see
http://pandas.pydata.org/pandas-docs/stable/whatsnew.html:
v 0.16.2
This is a minor bug-fix release from 0.16.1 and includes a a large
number of bug fixes along some new features (pipe() method),
enhancements, and performance improvements.
We recommend that all users upgrade to this version.
Highlights include:
A new pipe method
Documentation on how to use numba with pandas,
v 0.16.1
This is a minor bug-fix release from 0.16.0 and includes a a large
number of bug fixes along several new features, enhancements, and
performance improvements. We recommend that all users upgrade to this
version.
Highlights include:
Support for a CategoricalIndex, a category based index
New section on how-to-contribute to pandas
Revised “Merge, join, and concatenate” documentation, including
graphical examples to make it easier to understand each operations
New method sample for drawing random samples from Series, DataFrames
and Panels.
The default Index printing has changed to a more uniform format
BusinessHour datetime-offset is now supported
Further enhancement to the .str accessor to make string operations easier
v0.16.0 (March 22, 2015)
This is a major release from 0.15.2 and includes a small number of
API changes, several new features, enhancements, and performance
improvements along with a large number of bug fixes. We recommend that
all users upgrade to this version.
Highlights include:
DataFrame.assign method
Series.to_coo/from_coo methods to interact with scipy.sparse
Backwards incompatible change to Timedelta to conform the .seconds
attribute with datetime.timedelta
Changes to the .loc slicing API to conform with the behavior of .ix
Changes to the default for ordering in the Categorical constructor
Enhancement to the .str accessor to make string operations easier
The pandas.tools.rplot, pandas.sandbox.qtpandas and pandas.rpy
modules are deprecated. We refer users to external packages like
seaborn, pandas-qt and rpy2 for similar or equivalent functionality,
see here
v0.15.0 (October 18, 2014)
This is a major release from 0.14.1 and includes a small number of
API changes, several new features, enhancements, and performance
improvements along with a large number of bug fixes. We recommend that
all users upgrade to this version.
Warning
pandas >= 0.15.0 will no longer support compatibility with NumPy
versions < 1.7.0. If you want to use the latest versions of pandas,
please upgrade to NumPy >= 1.7.0 (GH7711)
Highlights include:
The Categorical type was integrated as a first-class pandas type
New scalar type Timedelta, and a new index type TimedeltaIndex
New datetimelike properties accessor .dt for Series, see
Datetimelike Properties
New DataFrame default display for df.info() to include memory
usage, see Memory Usage
read_csv will now by default ignore blank lines when parsing
API change in using Indexes in set operations
Enhancements in the handling of timezones
A lot of improvements to the rolling and expanding moment funtions
Internal refactoring of the Index class to no longer sub-class
ndarray, see Internal Refactoring
dropping support for PyTables less than version 3.0.0, and
numexpr less than version 2.1 (GH7990)
Split indexing documentation into Indexing and Selecting Data
and MultiIndex / Advanced Indexing
Split out string methods documentation into Working with Text Data
This is two major releases since 0.12.0. Changes include API changes, new
features, enhancements, and performance improvements along with a large
number of bug fixes.
For the detailed list of changes see
http://pandas.pydata.org/pandas-docs/stable/whatsnew.html
This is a major release from 0.11.0 and includes several new features
and enhancements along with a large number of bug fixes.
Highlites include a consistent I/O API naming scheme, routines to read
html, write multi-indexes to csv files, read & write STATA data files,
read & write JSON format files, Python 3 support for HDFStore, filtering
of groupby expressions via filter, and a revamped replace routine that
accepts regular expressions.
For detailed changes see:
http://pandas.pydata.org/pandas-docs/stable/whatsnew.html
Summary of changes since 0.10.1:
This is a major release from 0.10.1 and includes many new features and
enhancements along with a large number of bug fixes. The methods of
Selecting Data have had quite a number of additions, and Dtype support
is now full-fledged. There are also a number of important API changes
that long-time pandas users should pay close attention to.
* New precision indexing fields loc, iloc, at, and iat, to reduce
occasional ambiguity in the catch-all hitherto ix method.
* Expanded support for NumPy data types in DataFrame.
* NumExpr integration to accelerate various operator evaluation.
* Improved DataFrame to CSV exporting performance.
For a full list refer to the "what's new" page.
Also fixes PLIST errors introduced in last update.
Release date: 2013-01-22
New features:
Add data inferface to World Bank WDI pandas.io.wb (GH2592)
API Changes:
Restored inplace=True behavior returning self (same object) with
deprecation warning until 0.11 (GH1893)
HDFStore
refactored HFDStore to deal with non-table stores as objects, will
allow future enhancements
removed keyword compression from put (replaced by keyword complib
to be consistent across library)
warn PerformanceWarning if you are attempting to store types that
will be pickled by PyTables
Improvements to existing features:
HDFStore
enables storing of multi-index dataframes (closes GH1277)
support data column indexing and selection, via data_columns
keyword in append
support write chunking to reduce memory footprint, via chunksize
keyword to append
support automagic indexing via index keyword to append
support expectedrows keyword in append to inform PyTables about
the expected tablesize
support start and stop keywords in select to limit the row
selection space
added get_store context manager to automatically import with pandas
added column filtering via columns keyword in select
added methods append_to_multiple/select_as_multiple/
select_as_coordinates to do multiple-table append/selection
added support for datetime64 in columns
added method unique to select the unique values in an indexable
or data column
added method copy to copy an existing store (and possibly upgrade)
show the shape of the data on disk for non-table stores when
printing the store
added ability to read PyTables flavor tables (allows compatiblity
to other HDF5 systems)
Add logx option to DataFrame/Series.plot (GH2327, GH2565)
Support reading gzipped data from file-like object
pivot_table aggfunc can be anything used in GroupBy.aggregate (GH2643)
Implement DataFrame merges in case where set cardinalities might
overflow 64-bit integer (GH2690)
Raise exception in C file parser if integer dtype specified and have
NA values. (GH2631)
Attempt to parse ISO8601 format dates when parse_dates=True in read_csv
for major performance boost in such cases (GH2698)
Add methods neg and inv to Series
Implement kind option in ExcelFile to indicate whether it's an XLS
or XLSX file (GH2613)
Bug fixes:
Fix read_csv/read_table multithreading issues (GH2608)
HDFStore
correctly handle nan elements in string columns; serialize via the
nan_rep keyword to append
raise correctly on non-implemented column types (unicode/date)
handle correctly Term passed types (e.g. index<1000, when index is
Int64), (closes GH512)
handle Timestamp correctly in data_columns (closes GH2637)
contains correctly matches on non-natural names
correctly store float32 dtypes in tables (if not other float types
in the same table)
Fix DataFrame.info bug with UTF8-encoded columns. (GH2576)
Fix DatetimeIndex handling of FixedOffset tz (GH2604)
More robust detection of being in IPython session for wide DataFrame
console formatting (GH2585)
Fix platform issues with file:/// in unit test (GH2564)
Fix bug and possible segfault when grouping by hierarchical level that
contains NA values (GH2616)
Ensure that MultiIndex tuples can be constructed with NAs (GH2616)
Fix int64 overflow issue when unstacking MultiIndex with many levels
(GH2616)
Exclude non-numeric data from DataFrame.quantile by default (GH2625)
Fix a Cython C int64 boxing issue causing read_csv to return incorrect
results (GH2599)
Fix groupby summing performance issue on boolean data (GH2692)
Don't bork Series containing datetime64 values with to_datetime (GH2699)
Fix DataFrame.from_records corner case when passed columns, index
column, but empty record list (GH2633)
Fix C parser-tokenizer bug with trailing fields. (GH2668)
Don't exclude non-numeric data from GroupBy.max/min (GH2700)
Don't lose time zone when calling DatetimeIndex.drop (GH2621)
Fix setitem on a Series with a boolean key and a non-scalar as value
(GH2686)
Box datetime64 values in Series.apply/map (GH2627, GH2689)
Upconvert datetime + datetime64 values when concatenating frames
(GH2624)
Raise a more helpful error message in merge operations when one
DataFrame has duplicate columns (GH2649)
Fix partial date parsing issue occuring only when code is run at EOM
(GH2618)
Prevent MemoryError when using counting sort in sortlevel with
high-cardinality MultiIndex objects (GH2684)
Fix Period resampling bug when all values fall into a single bin
(GH2070)
Fix buggy interaction with usecols argument in read_csv when there is
an implicit first index column (GH2654)
pkgsrc change: depend on math/py-pytables.
Changes since 0.9.1:
* Delimited file parsing engine rewritten to use a fraction of memory while
being 40%+ faster.
- Much-improved Unicode handling via the encoding option.
- Column filtering (usecols)
- Dtype specification (dtype argument)
- Ability to specify strings to be recognized as True/False
- Ability to yield NumPy record arrays (as_recarray)
- High performance delim_whitespace option
- Decimal format (e.g. European format) specification
- Easier CSV dialect options: escapechar, lineterminator, quotechar, etc.
- More robust handling of many exceptional kinds of files observed in the wild
* API changes
- Deprecated DataFrame BINOP TimeSeries special case behavior
- Altered resample default behavior
- Infinity and negative infinity are no longer treated as NA by isnull and
notnull.
- Methods with the inplace option now all return None instead of the calling
object.
- pandas.merge no longer sorts the group keys (sort=False) by default.
- The default column names for a file with no header have been changed.
- Values like 'Yes' and 'No' are not interpreted as boolean by default.
- The file parsers will not recognize non-string values arising from a
converter function as NA.
- Calling fillna on Series or DataFrame with no arguments is no longer valid
code.
- Series.apply will now operate on a returned value from the applied function.
- New API functions for working with pandas options.
* New features
- Wide DataFrame Printing.
- Updated PyTables Support.
* Enhancements
- added ability to hierarchical keys.
- added mixed-dtype support!
- performance improvments on table writing.
- support for arbitrarily indexed dimensions.
- SparseSeries now has a density property.
* Bug fixes
- added Term method of specifying where conditions.
- del store['df'] now call store.remove('df') for store deletion.
- deleting of consecutive rows is much faster than before.
- in_itemsize parameter can be specified in table creation to force a minimum
size for indexing columns.
- indexing support via create_table_index (requires PyTables >= 2.3)
- appending on a store would fail if the table was not first created via put.
- fixed issue with missing attributes after loading a pickled dataframe.
- minor change to select and remove: require a table ONLY if where is also
provided.
* Compatibility
- 0.10 of HDFStore is backwards compatible for reading tables created
in a prior version of pandas, however, query terms using the prior
(undocumented) methodology are unsupported.
* N Dimensional Panels (Experimental)
pandas is an open source, BSD-licensed library providing
high-performance, easy-to-use data structures and data analysis tools
for the Python programming language.
