Version 0.17.0 (October 9, 2015)#
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.
Warning
pandas >= 0.17.0 will no longer support compatibility with Python version 3.2 (GH 9118)
Warning
The pandas.io.data
package is deprecated and will be replaced by the
pandas-datareader package.
This will allow the data modules to be independently updated to your pandas
installation. The API for pandas-datareader v0.1.1
is exactly the same
as in pandas v0.17.0
(GH 8961, GH 10861).
After installing pandas-datareader, you can easily change your imports:
from pandas.io import data, wb
becomes
from pandas_datareader import data, wb
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 hereThe 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 hereThe default for
to_datetime
will now be toraise
when presented with unparsable formats, previously this would return the original input. Also, date parse functions now return consistent results. See hereThe default for
dropna
inHDFStore
has changed toFalse
, to store by default all rows even if they are allNaN
, see hereDatetime accessor (
dt
) now supportsSeries.dt.strftime
to generate formatted strings for datetime-likes, andSeries.dt.total_seconds
to generate each duration of the timedelta in seconds. See herePeriod
andPeriodIndex
can handle multiplied freq like3D
, which corresponding to 3 days span. See hereDevelopment installed versions of pandas will now have
PEP440
compliant version strings (GH 9518)Development support for benchmarking with the Air Speed Velocity library (GH 8361)
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 (GH 11097)
Compatibility with matplotlib 1.5.0 (GH 11111)
Check the API Changes and deprecations before updating.
What’s new in v0.17.0
New features#
Datetime with TZ#
We are adding an implementation that natively supports datetime with timezones. A Series
or a DataFrame
column previously
could be assigned a datetime with timezones, and would work as an object
dtype. This had performance issues with a large
number rows. See the docs for more details. (GH 8260, GH 10763, GH 11034).
The new implementation allows for having a single-timezone across all rows, with operations in a performant manner.
In [1]: df = pd.DataFrame(
...: {
...: "A": pd.date_range("20130101", periods=3),
...: "B": pd.date_range("20130101", periods=3, tz="US/Eastern"),
...: "C": pd.date_range("20130101", periods=3, tz="CET"),
...: }
...: )
...:
In [2]: df
Out[2]:
A B C
0 2013-01-01 2013-01-01 00:00:00-05:00 2013-01-01 00:00:00+01:00
1 2013-01-02 2013-01-02 00:00:00-05:00 2013-01-02 00:00:00+01:00
2 2013-01-03 2013-01-03 00:00:00-05:00 2013-01-03 00:00:00+01:00
[3 rows x 3 columns]
In [3]: df.dtypes
Out[3]:
A datetime64[ns]
B datetime64[ns, US/Eastern]
C datetime64[ns, CET]
Length: 3, dtype: object
In [4]: df.B
Out[4]:
0 2013-01-01 00:00:00-05:00
1 2013-01-02 00:00:00-05:00
2 2013-01-03 00:00:00-05:00
Name: B, Length: 3, dtype: datetime64[ns, US/Eastern]
In [5]: df.B.dt.tz_localize(None)
Out[5]:
0 2013-01-01
1 2013-01-02
2 2013-01-03
Name: B, Length: 3, dtype: datetime64[ns]
This uses a new-dtype representation as well, that is very similar in look-and-feel to its numpy cousin datetime64[ns]
In [6]: df["B"].dtype
Out[6]: datetime64[ns, US/Eastern]
In [7]: type(df["B"].dtype)
Out[7]: pandas.core.dtypes.dtypes.DatetimeTZDtype
Note
There is a slightly different string repr for the underlying DatetimeIndex
as a result of the dtype changes, but
functionally these are the same.
Previous behavior:
In [1]: pd.date_range('20130101', periods=3, tz='US/Eastern')
Out[1]: DatetimeIndex(['2013-01-01 00:00:00-05:00', '2013-01-02 00:00:00-05:00',
'2013-01-03 00:00:00-05:00'],
dtype='datetime64[ns]', freq='D', tz='US/Eastern')
In [2]: pd.date_range('20130101', periods=3, tz='US/Eastern').dtype
Out[2]: dtype('<M8[ns]')
New behavior:
In [8]: pd.date_range("20130101", periods=3, tz="US/Eastern")
Out[8]:
DatetimeIndex(['2013-01-01 00:00:00-05:00', '2013-01-02 00:00:00-05:00',
'2013-01-03 00:00:00-05:00'],
dtype='datetime64[ns, US/Eastern]', freq='D')
In [9]: pd.date_range("20130101", periods=3, tz="US/Eastern").dtype
Out[9]: datetime64[ns, US/Eastern]
Releasing the GIL#
We are releasing the global-interpreter-lock (GIL) on some cython operations.
This will allow other threads to run simultaneously during computation, potentially allowing performance improvements
from multi-threading. Notably groupby
, nsmallest
, value_counts
and some indexing operations benefit from this. (GH 8882)
For example the groupby expression in the following code will have the GIL released during the factorization step, e.g. df.groupby('key')
as well as the .sum()
operation.
N = 1000000
ngroups = 10
df = DataFrame(
{"key": np.random.randint(0, ngroups, size=N), "data": np.random.randn(N)}
)
df.groupby("key")["data"].sum()
Releasing of the GIL could benefit an application that uses threads for user interactions (e.g. QT), or performing multi-threaded computations. A nice example of a library that can handle these types of computation-in-parallel is the dask library.
Plot submethods#
The Series and DataFrame .plot()
method allows for customizing plot types by supplying the kind
keyword arguments. Unfortunately, many of these kinds of plots use different required and optional keyword arguments, which makes it difficult to discover what any given plot kind uses out of the dozens of possible arguments.
To alleviate this issue, we have added a new, optional plotting interface, which exposes each kind of plot as a method of the .plot
attribute. Instead of writing series.plot(kind=<kind>, ...)
, you can now also use series.plot.<kind>(...)
:
In [10]: df = pd.DataFrame(np.random.rand(10, 2), columns=['a', 'b'])
In [11]: df.plot.bar()
![../_images/whatsnew_plot_submethods.png](../_images/whatsnew_plot_submethods.png)
As a result of this change, these methods are now all discoverable via tab-completion:
In [12]: df.plot.<TAB> # noqa: E225, E999
df.plot.area df.plot.barh df.plot.density df.plot.hist df.plot.line df.plot.scatter
df.plot.bar df.plot.box df.plot.hexbin df.plot.kde df.plot.pie
Each method signature only includes relevant arguments. Currently, these are limited to required arguments, but in the future these will include optional arguments, as well. For an overview, see the new Plotting API documentation.
Additional methods for dt
accessor#
Series.dt.strftime#
We are now supporting a Series.dt.strftime
method for datetime-likes to generate a formatted string (GH 10110). Examples:
# DatetimeIndex
In [13]: s = pd.Series(pd.date_range("20130101", periods=4))
In [14]: s
Out[14]:
0 2013-01-01
1 2013-01-02
2 2013-01-03
3 2013-01-04
Length: 4, dtype: datetime64[ns]
In [15]: s.dt.strftime("%Y/%m/%d")
Out[15]:
0 2013/01/01
1 2013/01/02
2 2013/01/03
3 2013/01/04
Length: 4, dtype: object
# PeriodIndex
In [16]: s = pd.Series(pd.period_range("20130101", periods=4))
In [17]: s
Out[17]:
0 2013-01-01
1 2013-01-02
2 2013-01-03
3 2013-01-04
Length: 4, dtype: period[D]
In [18]: s.dt.strftime("%Y/%m/%d")
Out[18]:
0 2013/01/01
1 2013/01/02
2 2013/01/03
3 2013/01/04
Length: 4, dtype: object
The string format is as the python standard library and details can be found here
Series.dt.total_seconds#
pd.Series
of type timedelta64
has new method .dt.total_seconds()
returning the duration of the timedelta in seconds (GH 10817)
# TimedeltaIndex
In [19]: s = pd.Series(pd.timedelta_range("1 minutes", periods=4))
In [20]: s
Out[20]:
0 0 days 00:01:00
1 1 days 00:01:00
2 2 days 00:01:00
3 3 days 00:01:00
Length: 4, dtype: timedelta64[ns]
In [21]: s.dt.total_seconds()
Out[21]:
0 60.0
1 86460.0
2 172860.0
3 259260.0
Length: 4, dtype: float64
Period frequency enhancement#
Period
, PeriodIndex
and period_range
can now accept multiplied freq. Also, Period.freq
and PeriodIndex.freq
are now stored as a DateOffset
instance like DatetimeIndex
, and not as str
(GH 7811)
A multiplied freq represents a span of corresponding length. The example below creates a period of 3 days. Addition and subtraction will shift the period by its span.
In [22]: p = pd.Period("2015-08-01", freq="3D")
In [23]: p
Out[23]: Period('2015-08-01', '3D')
In [24]: p + 1
Out[24]: Period('2015-08-04', '3D')
In [25]: p - 2
Out[25]: Period('2015-07-26', '3D')
In [26]: p.to_timestamp()
Out[26]: Timestamp('2015-08-01 00:00:00')
In [27]: p.to_timestamp(how="E")
Out[27]: Timestamp('2015-08-03 23:59:59.999999999')
You can use the multiplied freq in PeriodIndex
and period_range
.
In [28]: idx = pd.period_range("2015-08-01", periods=4, freq="2D")
In [29]: idx
Out[29]: PeriodIndex(['2015-08-01', '2015-08-03', '2015-08-05', '2015-08-07'], dtype='period[2D]')
In [30]: idx + 1
Out[30]: PeriodIndex(['2015-08-03', '2015-08-05', '2015-08-07', '2015-08-09'], dtype='period[2D]')
Support for SAS XPORT files#
read_sas()
provides support for reading SAS XPORT format files. (GH 4052).
df = pd.read_sas("sas_xport.xpt")
It is also possible to obtain an iterator and read an XPORT file incrementally.
for df in pd.read_sas("sas_xport.xpt", chunksize=10000):
do_something(df)
See the docs for more details.
Support for math functions in .eval()#
eval()
now supports calling math functions (GH 4893)
df = pd.DataFrame({"a": np.random.randn(10)})
df.eval("b = sin(a)")
The support math functions are sin
, cos
, exp
, log
, expm1
, log1p
,
sqrt
, sinh
, cosh
, tanh
, arcsin
, arccos
, arctan
, arccosh
,
arcsinh
, arctanh
, abs
and arctan2
.
These functions map to the intrinsics for the NumExpr
engine. For the Python
engine, they are mapped to NumPy
calls.
Changes to Excel with MultiIndex
#
In version 0.16.2 a DataFrame
with MultiIndex
columns could not be written to Excel via to_excel
.
That functionality has been added (GH 10564), along with updating read_excel
so that the data can
be read back with, no loss of information, by specifying which columns/rows make up the MultiIndex
in the header
and index_col
parameters (GH 4679)
See the documentation for more details.
In [31]: df = pd.DataFrame(
....: [[1, 2, 3, 4], [5, 6, 7, 8]],
....: columns=pd.MultiIndex.from_product(
....: [["foo", "bar"], ["a", "b"]], names=["col1", "col2"]
....: ),
....: index=pd.MultiIndex.from_product([["j"], ["l", "k"]], names=["i1", "i2"]),
....: )
....:
In [32]: df
Out[32]:
col1 foo bar
col2 a b a b
i1 i2
j l 1 2 3 4
k 5 6 7 8
[2 rows x 4 columns]
In [33]: df.to_excel("test.xlsx")
In [34]: df = pd.read_excel("test.xlsx", header=[0, 1], index_col=[0, 1])
In [35]: df
Out[35]:
col1 foo bar
col2 a b a b
i1 i2
j l 1 2 3 4
k 5 6 7 8
[2 rows x 4 columns]
Previously, it was necessary to specify the has_index_names
argument in read_excel
,
if the serialized data had index names. For version 0.17.0 the output format of to_excel
has been changed to make this keyword unnecessary - the change is shown below.
Old
![../_images/old-excel-index.png](../_images/old-excel-index.png)
New
![../_images/new-excel-index.png](../_images/new-excel-index.png)
Warning
Excel files saved in version 0.16.2 or prior that had index names will still able to be read in,
but the has_index_names
argument must specified to True
.
Google BigQuery enhancements#
Added ability to automatically create a table/dataset using the
pandas.io.gbq.to_gbq()
function if the destination table/dataset does not exist. (GH 8325, GH 11121).Added ability to replace an existing table and schema when calling the
pandas.io.gbq.to_gbq()
function via theif_exists
argument. See the docs for more details (GH 8325).InvalidColumnOrder
andInvalidPageToken
in the gbq module will raiseValueError
instead ofIOError
.The
generate_bq_schema()
function is now deprecated and will be removed in a future version (GH 11121)The gbq module will now support Python 3 (GH 11094).
Display alignment with Unicode East Asian width#
Warning
Enabling this option will affect the performance for printing of DataFrame
and Series
(about 2 times slower).
Use only when it is actually required.
Some East Asian countries use Unicode characters its width is corresponding to 2 alphabets. If a DataFrame
or Series
contains these characters, the default output cannot be aligned properly. The following options are added to enable precise handling for these characters.
display.unicode.east_asian_width
: Whether to use the Unicode East Asian Width to calculate the display text width. (GH 2612)display.unicode.ambiguous_as_wide
: Whether to handle Unicode characters belong to Ambiguous as Wide. (GH 11102)
In [36]: df = pd.DataFrame({u"国籍": ["UK", u"日本"], u"名前": ["Alice", u"しのぶ"]})
In [37]: df
Out[37]:
国籍 名前
0 UK Alice
1 日本 しのぶ
[2 rows x 2 columns]
In [38]: pd.set_option("display.unicode.east_asian_width", True)
In [39]: df
Out[39]:
国籍 名前
0 UK Alice
1 日本 しのぶ
[2 rows x 2 columns]
For further details, see here
Other enhancements#
Support for
openpyxl
>= 2.2. The API for style support is now stable (GH 10125)merge
now accepts the argumentindicator
which adds a Categorical-type column (by default called_merge
) to the output object that takes on the values (GH 8790)Observation Origin
_merge
valueMerge key only in
'left'
frameleft_only
Merge key only in
'right'
frameright_only
Merge key in both frames
both
In [40]: df1 = pd.DataFrame({"col1": [0, 1], "col_left": ["a", "b"]}) In [41]: df2 = pd.DataFrame({"col1": [1, 2, 2], "col_right": [2, 2, 2]}) In [42]: pd.merge(df1, df2, on="col1", how="outer", indicator=True) Out[42]: col1 col_left col_right _merge 0 0 a NaN left_only 1 1 b 2.0 both 2 2 NaN 2.0 right_only 3 2 NaN 2.0 right_only [4 rows x 4 columns]
For more, see the updated docs
pd.to_numeric
is a new function to coerce strings to numbers (possibly with coercion) (GH 11133)pd.merge
will now allow duplicate column names if they are not merged upon (GH 10639).pd.pivot
will now allow passing index asNone
(GH 3962).pd.concat
will now use existing Series names if provided (GH 10698).In [43]: foo = pd.Series([1, 2], name="foo") In [44]: bar = pd.Series([1, 2]) In [45]: baz = pd.Series([4, 5])
Previous behavior:
In [1]: pd.concat([foo, bar, baz], axis=1) Out[1]: 0 1 2 0 1 1 4 1 2 2 5
New behavior:
In [46]: pd.concat([foo, bar, baz], axis=1) Out[46]: foo 0 1 0 1 1 4 1 2 2 5 [2 rows x 3 columns]
DataFrame
has gained thenlargest
andnsmallest
methods (GH 10393)Add a
limit_direction
keyword argument that works withlimit
to enableinterpolate
to fillNaN
values forward, backward, or both (GH 9218, GH 10420, GH 11115)In [47]: ser = pd.Series([np.nan, np.nan, 5, np.nan, np.nan, np.nan, 13]) In [48]: ser.interpolate(limit=1, limit_direction="both") Out[48]: 0 NaN 1 5.0 2 5.0 3 7.0 4 NaN 5 11.0 6 13.0 Length: 7, dtype: float64
Added a
DataFrame.round
method to round the values to a variable number of decimal places (GH 10568).In [49]: df = pd.DataFrame( ....: np.random.random([3, 3]), ....: columns=["A", "B", "C"], ....: index=["first", "second", "third"], ....: ) ....: In [50]: df Out[50]: A B C first 0.126970 0.966718 0.260476 second 0.897237 0.376750 0.336222 third 0.451376 0.840255 0.123102 [3 rows x 3 columns] In [51]: df.round(2) Out[51]: A B C first 0.13 0.97 0.26 second 0.90 0.38 0.34 third 0.45 0.84 0.12 [3 rows x 3 columns] In [52]: df.round({"A": 0, "C": 2}) Out[52]: A B C first 0.0 0.966718 0.26 second 1.0 0.376750 0.34 third 0.0 0.840255 0.12 [3 rows x 3 columns]
drop_duplicates
andduplicated
now accept akeep
keyword to target first, last, and all duplicates. Thetake_last
keyword is deprecated, see here (GH 6511, GH 8505)In [53]: s = pd.Series(["A", "B", "C", "A", "B", "D"]) In [54]: s.drop_duplicates() Out[54]: 0 A 1 B 2 C 5 D Length: 4, dtype: object In [55]: s.drop_duplicates(keep="last") Out[55]: 2 C 3 A 4 B 5 D Length: 4, dtype: object In [56]: s.drop_duplicates(keep=False) Out[56]: 2 C 5 D Length: 2, dtype: object
Reindex now has a
tolerance
argument that allows for finer control of Limits on filling while reindexing (GH 10411):In [57]: df = pd.DataFrame({"x": range(5), "t": pd.date_range("2000-01-01", periods=5)}) In [58]: df.reindex([0.1, 1.9, 3.5], method="nearest", tolerance=0.2) Out[58]: x t 0.1 0.0 2000-01-01 1.9 2.0 2000-01-03 3.5 NaN NaT [3 rows x 2 columns]
When used on a
DatetimeIndex
,TimedeltaIndex
orPeriodIndex
,tolerance
will coerced into aTimedelta
if possible. This allows you to specify tolerance with a string:In [59]: df = df.set_index("t") In [60]: df.reindex(pd.to_datetime(["1999-12-31"]), method="nearest", tolerance="1 day") Out[60]: x 1999-12-31 0 [1 rows x 1 columns]
tolerance
is also exposed by the lower levelIndex.get_indexer
andIndex.get_loc
methods.Added functionality to use the
base
argument when resampling aTimeDeltaIndex
(GH 10530)DatetimeIndex
can be instantiated using strings containsNaT
(GH 7599)to_datetime
can now accept theyearfirst
keyword (GH 7599)pandas.tseries.offsets
larger than theDay
offset can now be used with aSeries
for addition/subtraction (GH 10699). See the docs for more details.pd.Timedelta.total_seconds()
now returns Timedelta duration to ns precision (previously microsecond precision) (GH 10939)PeriodIndex
now supports arithmetic withnp.ndarray
(GH 10638)Support pickling of
Period
objects (GH 10439).as_blocks
will now take acopy
optional argument to return a copy of the data, default is to copy (no change in behavior from prior versions), (GH 9607)regex
argument toDataFrame.filter
now handles numeric column names instead of raisingValueError
(GH 10384).Enable reading gzip compressed files via URL, either by explicitly setting the compression parameter or by inferring from the presence of the HTTP Content-Encoding header in the response (GH 8685)
Enable writing Excel files in memory using StringIO/BytesIO (GH 7074)
Enable serialization of lists and dicts to strings in
ExcelWriter
(GH 8188)SQL io functions now accept a SQLAlchemy connectable. (GH 7877)
pd.read_sql
andto_sql
can accept database URI ascon
parameter (GH 10214)read_sql_table
will now allow reading from views (GH 10750).Enable writing complex values to
HDFStores
when using thetable
format (GH 10447)Enable
pd.read_hdf
to be used without specifying a key when the HDF file contains a single dataset (GH 10443)pd.read_stata
will now read Stata 118 type files. (GH 9882)msgpack
submodule has been updated to 0.4.6 with backward compatibility (GH 10581)DataFrame.to_dict
now acceptsorient='index'
keyword argument (GH 10844).DataFrame.apply
will return a Series of dicts if the passed function returns a dict andreduce=True
(GH 8735).Allow passing
kwargs
to the interpolation methods (GH 10378).Improved error message when concatenating an empty iterable of
Dataframe
objects (GH 9157)pd.read_csv
can now read bz2-compressed files incrementally, and the C parser can read bz2-compressed files from AWS S3 (GH 11070, GH 11072).In
pd.read_csv
, recognizes3n://
ands3a://
URLs as designating S3 file storage (GH 11070, GH 11071).Read CSV files from AWS S3 incrementally, instead of first downloading the entire file. (Full file download still required for compressed files in Python 2.) (GH 11070, GH 11073)
pd.read_csv
is now able to infer compression type for files read from AWS S3 storage (GH 11070, GH 11074).
Backwards incompatible API changes#
Changes to sorting API#
The sorting API has had some longtime inconsistencies. (GH 9816, GH 8239).
Here is a summary of the API PRIOR to 0.17.0:
Series.sort
is INPLACE whileDataFrame.sort
returns a new object.Series.order
returns a new objectIt was possible to use
Series/DataFrame.sort_index
to sort by values by passing theby
keyword.Series/DataFrame.sortlevel
worked only on aMultiIndex
for sorting by index.
To address these issues, we have revamped the API:
We have introduced a new method,
DataFrame.sort_values()
, which is the merger ofDataFrame.sort()
,Series.sort()
, andSeries.order()
, to handle sorting of values.The existing methods
Series.sort()
,Series.order()
, andDataFrame.sort()
have been deprecated and will be removed in a future version.The
by
argument ofDataFrame.sort_index()
has been deprecated and will be removed in a future version.The existing method
.sort_index()
will gain thelevel
keyword to enable level sorting.
We now have two distinct and non-overlapping methods of sorting. A *
marks items that
will show a FutureWarning
.
To sort by the values:
Previous |
Replacement |
---|---|
* |
|
* |
|
* |
|
To sort by the index:
Previous |
Replacement |
---|---|
|
|
|
|
|
|
|
|
* |
|
We have also deprecated and changed similar methods in two Series-like classes, Index
and Categorical
.
Previous |
Replacement |
---|---|
* |
|
* |
|
Changes to to_datetime and to_timedelta#
Error handling#
The default for pd.to_datetime
error handling has changed to errors='raise'
.
In prior versions it was errors='ignore'
. Furthermore, the coerce
argument
has been deprecated in favor of errors='coerce'
. This means that invalid parsing
will raise rather that return the original input as in previous versions. (GH 10636)
Previous behavior:
In [2]: pd.to_datetime(['2009-07-31', 'asd'])
Out[2]: array(['2009-07-31', 'asd'], dtype=object)
New behavior:
In [3]: pd.to_datetime(['2009-07-31', 'asd'])
ValueError: Unknown string format
Of course you can coerce this as well.
In [61]: pd.to_datetime(["2009-07-31", "asd"], errors="coerce")
Out[61]: DatetimeIndex(['2009-07-31', 'NaT'], dtype='datetime64[ns]', freq=None)
To keep the previous behavior, you can use errors='ignore'
:
In [4]: pd.to_datetime(["2009-07-31", "asd"], errors="ignore")
Out[4]: Index(['2009-07-31', 'asd'], dtype='object')
Furthermore, pd.to_timedelta
has gained a similar API, of errors='raise'|'ignore'|'coerce'
, and the coerce
keyword
has been deprecated in favor of errors='coerce'
.
Consistent parsing#
The string parsing of to_datetime
, Timestamp
and DatetimeIndex
has
been made consistent. (GH 7599)
Prior to v0.17.0, Timestamp
and to_datetime
may parse year-only datetime-string incorrectly using today’s date, otherwise DatetimeIndex
uses the beginning of the year. Timestamp
and to_datetime
may raise ValueError
in some types of datetime-string which DatetimeIndex
can parse, such as a quarterly string.
Previous behavior:
In [1]: pd.Timestamp('2012Q2')
Traceback
...
ValueError: Unable to parse 2012Q2
# Results in today's date.
In [2]: pd.Timestamp('2014')
Out [2]: 2014-08-12 00:00:00
v0.17.0 can parse them as below. It works on DatetimeIndex
also.
New behavior:
In [62]: pd.Timestamp("2012Q2")
Out[62]: Timestamp('2012-04-01 00:00:00')
In [63]: pd.Timestamp("2014")
Out[63]: Timestamp('2014-01-01 00:00:00')
In [64]: pd.DatetimeIndex(["2012Q2", "2014"])
Out[64]: DatetimeIndex(['2012-04-01', '2014-01-01'], dtype='datetime64[ns]', freq=None)
Note
If you want to perform calculations based on today’s date, use Timestamp.now()
and pandas.tseries.offsets
.
In [65]: import pandas.tseries.offsets as offsets
In [66]: pd.Timestamp.now()
Out[66]: Timestamp('2024-01-20 01:19:19.693403')
In [67]: pd.Timestamp.now() + offsets.DateOffset(years=1)
Out[67]: Timestamp('2025-01-20 01:19:19.694151')
Changes to Index comparisons#
Operator equal on Index
should behavior similarly to Series
(GH 9947, GH 10637)
Starting in v0.17.0, comparing Index
objects of different lengths will raise
a ValueError
. This is to be consistent with the behavior of Series
.
Previous behavior:
In [2]: pd.Index([1, 2, 3]) == pd.Index([1, 4, 5])
Out[2]: array([ True, False, False], dtype=bool)
In [3]: pd.Index([1, 2, 3]) == pd.Index([2])
Out[3]: array([False, True, False], dtype=bool)
In [4]: pd.Index([1, 2, 3]) == pd.Index([1, 2])
Out[4]: False
New behavior:
In [8]: pd.Index([1, 2, 3]) == pd.Index([1, 4, 5])
Out[8]: array([ True, False, False], dtype=bool)
In [9]: pd.Index([1, 2, 3]) == pd.Index([2])
ValueError: Lengths must match to compare
In [10]: pd.Index([1, 2, 3]) == pd.Index([1, 2])
ValueError: Lengths must match to compare
Note that this is different from the numpy
behavior where a comparison can
be broadcast:
In [68]: np.array([1, 2, 3]) == np.array([1])
Out[68]: array([ True, False, False])
or it can return False if broadcasting can not be done:
In [11]: np.array([1, 2, 3]) == np.array([1, 2])
Out[11]: False
Changes to boolean comparisons vs. None#
Boolean comparisons of a Series
vs None
will now be equivalent to comparing with np.nan
, rather than raise TypeError
. (GH 1079).
In [69]: s = pd.Series(range(3), dtype="float")
In [70]: s.iloc[1] = None
In [71]: s
Out[71]:
0 0.0
1 NaN
2 2.0
Length: 3, dtype: float64
Previous behavior:
In [5]: s == None
TypeError: Could not compare <type 'NoneType'> type with Series
New behavior:
In [72]: s == None
Out[72]:
0 False
1 False
2 False
Length: 3, dtype: bool
Usually you simply want to know which values are null.
In [73]: s.isnull()
Out[73]:
0 False
1 True
2 False
Length: 3, dtype: bool
Warning
You generally will want to use isnull/notnull
for these types of comparisons, as isnull/notnull
tells you which elements are null. One has to be
mindful that nan's
don’t compare equal, but None's
do. Note that pandas/numpy uses the fact that np.nan != np.nan
, and treats None
like np.nan
.
In [74]: None == None
Out[74]: True
In [75]: np.nan == np.nan
Out[75]: False
HDFStore dropna behavior#
The default behavior for HDFStore write functions with format='table'
is now to keep rows that are all missing. Previously, the behavior was to drop rows that were all missing save the index. The previous behavior can be replicated using the dropna=True
option. (GH 9382)
Previous behavior:
In [76]: df_with_missing = pd.DataFrame(
....: {"col1": [0, np.nan, 2], "col2": [1, np.nan, np.nan]}
....: )
....:
In [77]: df_with_missing
Out[77]:
col1 col2
0 0.0 1.0
1 NaN NaN
2 2.0 NaN
[3 rows x 2 columns]
In [27]:
df_with_missing.to_hdf('file.h5',
key='df_with_missing',
format='table',
mode='w')
In [28]: pd.read_hdf('file.h5', 'df_with_missing')
Out [28]:
col1 col2
0 0 1
2 2 NaN
New behavior:
In [78]: df_with_missing.to_hdf("file.h5", key="df_with_missing", format="table", mode="w")
In [79]: pd.read_hdf("file.h5", "df_with_missing")
Out[79]:
col1 col2
0 0.0 1.0
1 NaN NaN
2 2.0 NaN
[3 rows x 2 columns]
See the docs for more details.
Changes to display.precision
option#
The display.precision
option has been clarified to refer to decimal places (GH 10451).
Earlier versions of pandas would format floating point numbers to have one less decimal place than the value in
display.precision
.
In [1]: pd.set_option('display.precision', 2)
In [2]: pd.DataFrame({'x': [123.456789]})
Out[2]:
x
0 123.5
If interpreting precision as “significant figures” this did work for scientific notation but that same interpretation did not work for values with standard formatting. It was also out of step with how numpy handles formatting.
Going forward the value of display.precision
will directly control the number of places after the decimal, for
regular formatting as well as scientific notation, similar to how numpy’s precision
print option works.
In [80]: pd.set_option("display.precision", 2)
In [81]: pd.DataFrame({"x": [123.456789]})
Out[81]:
x
0 123.46
[1 rows x 1 columns]
To preserve output behavior with prior versions the default value of display.precision
has been reduced to 6
from 7
.
Changes to Categorical.unique
#
Categorical.unique
now returns new Categoricals
with categories
and codes
that are unique, rather than returning np.array
(GH 10508)
unordered category: values and categories are sorted by appearance order.
ordered category: values are sorted by appearance order, categories keep existing order.
In [82]: cat = pd.Categorical(["C", "A", "B", "C"], categories=["A", "B", "C"], ordered=True)
In [83]: cat
Out[83]:
['C', 'A', 'B', 'C']
Categories (3, object): ['A' < 'B' < 'C']
In [84]: cat.unique()
Out[84]:
['C', 'A', 'B']
Categories (3, object): ['A' < 'B' < 'C']
In [85]: cat = pd.Categorical(["C", "A", "B", "C"], categories=["A", "B", "C"])
In [86]: cat
Out[86]:
['C', 'A', 'B', 'C']
Categories (3, object): ['A', 'B', 'C']
In [87]: cat.unique()
Out[87]:
['C', 'A', 'B']
Categories (3, object): ['A', 'B', 'C']
Changes to bool
passed as header
in parsers#
In earlier versions of pandas, if a bool was passed the header
argument of
read_csv
, read_excel
, or read_html
it was implicitly converted to
an integer, resulting in header=0
for False
and header=1
for True
(GH 6113)
A bool
input to header
will now raise a TypeError
In [29]: df = pd.read_csv('data.csv', header=False)
TypeError: Passing a bool to header is invalid. Use header=None for no header or
header=int or list-like of ints to specify the row(s) making up the column names
Other API changes#
Line and kde plot with
subplots=True
now uses default colors, not all black. Specifycolor='k'
to draw all lines in black (GH 9894)Calling the
.value_counts()
method on a Series with acategorical
dtype now returns a Series with aCategoricalIndex
(GH 10704)The metadata properties of subclasses of pandas objects will now be serialized (GH 10553).
groupby
usingCategorical
follows the same rule asCategorical.unique
described above (GH 10508)When constructing
DataFrame
with an array ofcomplex64
dtype previously meant the corresponding column was automatically promoted to thecomplex128
dtype. pandas will now preserve the itemsize of the input for complex data (GH 10952)some numeric reduction operators would return
ValueError
, rather thanTypeError
on object types that includes strings and numbers (GH 11131)Passing currently unsupported
chunksize
argument toread_excel
orExcelFile.parse
will now raiseNotImplementedError
(GH 8011)Allow an
ExcelFile
object to be passed intoread_excel
(GH 11198)DatetimeIndex.union
does not inferfreq
ifself
and the input haveNone
asfreq
(GH 11086)NaT
’s methods now either raiseValueError
, or returnnp.nan
orNaT
(GH 9513)Behavior
Methods
return
np.nan
weekday
,isoweekday
return
NaT
date
,now
,replace
,to_datetime
,today
return
np.datetime64('NaT')
to_datetime64
(unchanged)raise
ValueError
All other public methods (names not beginning with underscores)
Deprecations#
For
Series
the following indexing functions are deprecated (GH 10177).Deprecated Function
Replacement
.irow(i)
.iloc[i]
or.iat[i]
.iget(i)
.iloc[i]
or.iat[i]
.iget_value(i)
.iloc[i]
or.iat[i]
For
DataFrame
the following indexing functions are deprecated (GH 10177).Deprecated Function
Replacement
.irow(i)
.iloc[i]
.iget_value(i, j)
.iloc[i, j]
or.iat[i, j]
.icol(j)
.iloc[:, j]
Note
These indexing function have been deprecated in the documentation since 0.11.0.
Categorical.name
was deprecated to makeCategorical
morenumpy.ndarray
like. UseSeries(cat, name="whatever")
instead (GH 10482).Setting missing values (NaN) in a
Categorical
’scategories
will issue a warning (GH 10748). You can still have missing values in thevalues
.drop_duplicates
andduplicated
’stake_last
keyword was deprecated in favor ofkeep
. (GH 6511, GH 8505)Series.nsmallest
andnlargest
’stake_last
keyword was deprecated in favor ofkeep
. (GH 10792)DataFrame.combineAdd
andDataFrame.combineMult
are deprecated. They can easily be replaced by using theadd
andmul
methods:DataFrame.add(other, fill_value=0)
andDataFrame.mul(other, fill_value=1.)
(GH 10735).TimeSeries
deprecated in favor ofSeries
(note that this has been an alias since 0.13.0), (GH 10890)SparsePanel
deprecated and will be removed in a future version (GH 11157).Series.is_time_series
deprecated in favor ofSeries.index.is_all_dates
(GH 11135)Legacy offsets (like
'A@JAN'
) are deprecated (note that this has been alias since 0.8.0) (GH 10878)WidePanel
deprecated in favor ofPanel
,LongPanel
in favor ofDataFrame
(note these have been aliases since < 0.11.0), (GH 10892)DataFrame.convert_objects
has been deprecated in favor of type-specific functionspd.to_datetime
,pd.to_timestamp
andpd.to_numeric
(new in 0.17.0) (GH 11133).
Removal of prior version deprecations/changes#
Removal of
na_last
parameters fromSeries.order()
andSeries.sort()
, in favor ofna_position
. (GH 5231)Remove of
percentile_width
from.describe()
, in favor ofpercentiles
. (GH 7088)Removal of
colSpace
parameter fromDataFrame.to_string()
, in favor ofcol_space
, circa 0.8.0 version.Removal of automatic time-series broadcasting (GH 2304)
In [88]: np.random.seed(1234) In [89]: df = pd.DataFrame( ....: np.random.randn(5, 2), ....: columns=list("AB"), ....: index=pd.date_range("2013-01-01", periods=5), ....: ) ....: In [90]: df Out[90]: A B 2013-01-01 0.471435 -1.190976 2013-01-02 1.432707 -0.312652 2013-01-03 -0.720589 0.887163 2013-01-04 0.859588 -0.636524 2013-01-05 0.015696 -2.242685 [5 rows x 2 columns]
Previously
In [3]: df + df.A FutureWarning: TimeSeries broadcasting along DataFrame index by default is deprecated. Please use DataFrame.<op> to explicitly broadcast arithmetic operations along the index Out[3]: A B 2013-01-01 0.942870 -0.719541 2013-01-02 2.865414 1.120055 2013-01-03 -1.441177 0.166574 2013-01-04 1.719177 0.223065 2013-01-05 0.031393 -2.226989
Current
In [91]: df.add(df.A, axis="index") Out[91]: A B 2013-01-01 0.942870 -0.719541 2013-01-02 2.865414 1.120055 2013-01-03 -1.441177 0.166574 2013-01-04 1.719177 0.223065 2013-01-05 0.031393 -2.226989 [5 rows x 2 columns]
Remove
table
keyword inHDFStore.put/append
, in favor of usingformat=
(GH 4645)Remove
kind
inread_excel/ExcelFile
as its unused (GH 4712)Remove
infer_type
keyword frompd.read_html
as its unused (GH 4770, GH 7032)Remove
offset
andtimeRule
keywords fromSeries.tshift/shift
, in favor offreq
(GH 4853, GH 4864)Remove
pd.load/pd.save
aliases in favor ofpd.to_pickle/pd.read_pickle
(GH 3787)
Performance improvements#
Development support for benchmarking with the Air Speed Velocity library (GH 8361)
Added vbench benchmarks for alternative ExcelWriter engines and reading Excel files (GH 7171)
Performance improvements in
Categorical.value_counts
(GH 10804)Performance improvements in
SeriesGroupBy.nunique
andSeriesGroupBy.value_counts
andSeriesGroupby.transform
(GH 10820, GH 11077)Performance improvements in
DataFrame.drop_duplicates
with integer dtypes (GH 10917)Performance improvements in
DataFrame.duplicated
with wide frames. (GH 10161, GH 11180)4x improvement in
timedelta
string parsing (GH 6755, GH 10426)8x improvement in
timedelta64
anddatetime64
ops (GH 6755)Significantly improved performance of indexing
MultiIndex
with slicers (GH 10287)8x improvement in
iloc
using list-like input (GH 10791)Improved performance of
Series.isin
for datetimelike/integer Series (GH 10287)20x improvement in
concat
of Categoricals when categories are identical (GH 10587)Improved performance of
to_datetime
when specified format string is ISO8601 (GH 10178)2x improvement of
Series.value_counts
for float dtype (GH 10821)Enable
infer_datetime_format
into_datetime
when date components do not have 0 padding (GH 11142)Regression from 0.16.1 in constructing
DataFrame
from nested dictionary (GH 11084)Performance improvements in addition/subtraction operations for
DateOffset
withSeries
orDatetimeIndex
(GH 10744, GH 11205)
Bug fixes#
Bug in incorrect computation of
.mean()
ontimedelta64[ns]
because of overflow (GH 9442)Bug in
.isin
on older numpies (GH 11232)Bug in
DataFrame.to_html(index=False)
renders unnecessaryname
row (GH 10344)Bug in
DataFrame.to_latex()
thecolumn_format
argument could not be passed (GH 9402)Bug in
DatetimeIndex
when localizing withNaT
(GH 10477)Bug in
Series.dt
ops in preserving meta-data (GH 10477)Bug in preserving
NaT
when passed in an otherwise invalidto_datetime
construction (GH 10477)Bug in
DataFrame.apply
when function returns categorical series. (GH 9573)Bug in
to_datetime
with invalid dates and formats supplied (GH 10154)Bug in
Index.drop_duplicates
dropping name(s) (GH 10115)Bug in
Series.quantile
dropping name (GH 10881)Bug in
pd.Series
when setting a value on an emptySeries
whose index has a frequency. (GH 10193)Bug in
pd.Series.interpolate
with invalidorder
keyword values. (GH 10633)Bug in
DataFrame.plot
raisesValueError
when color name is specified by multiple characters (GH 10387)Bug in
Index
construction with a mixed list of tuples (GH 10697)Bug in
DataFrame.reset_index
when index containsNaT
. (GH 10388)Bug in
ExcelReader
when worksheet is empty (GH 6403)Bug in
BinGrouper.group_info
where returned values are not compatible with base class (GH 10914)Bug in clearing the cache on
DataFrame.pop
and a subsequent inplace op (GH 10912)Bug in indexing with a mixed-integer
Index
causing anImportError
(GH 10610)Bug in
Series.count
when index has nulls (GH 10946)Bug in pickling of a non-regular freq
DatetimeIndex
(GH 11002)Bug causing
DataFrame.where
to not respect theaxis
parameter when the frame has a symmetric shape. (GH 9736)Bug in
Table.select_column
where name is not preserved (GH 10392)Bug in
offsets.generate_range
wherestart
andend
have finer precision thanoffset
(GH 9907)Bug in
pd.rolling_*
whereSeries.name
would be lost in the output (GH 10565)Bug in
stack
when index or columns are not unique. (GH 10417)Bug in setting a
Panel
when an axis has a MultiIndex (GH 10360)Bug in
USFederalHolidayCalendar
whereUSMemorialDay
andUSMartinLutherKingJr
were incorrect (GH 10278 and GH 9760 )Bug in
.sample()
where returned object, if set, gives unnecessarySettingWithCopyWarning
(GH 10738)Bug in
.sample()
where weights passed asSeries
were not aligned along axis before being treated positionally, potentially causing problems if weight indices were not aligned with sampled object. (GH 10738)Regression fixed in (GH 9311, GH 6620, GH 9345), where groupby with a datetime-like converting to float with certain aggregators (GH 10979)
Bug in
DataFrame.interpolate
withaxis=1
andinplace=True
(GH 10395)Bug in
io.sql.get_schema
when specifying multiple columns as primary key (GH 10385).Bug in
groupby(sort=False)
with datetime-likeCategorical
raisesValueError
(GH 10505)Bug in
groupby(axis=1)
withfilter()
throwsIndexError
(GH 11041)Bug in
test_categorical
on big-endian builds (GH 10425)Bug in
Series.shift
andDataFrame.shift
not supporting categorical data (GH 9416)Bug in
Series.map
using categoricalSeries
raisesAttributeError
(GH 10324)Bug in
MultiIndex.get_level_values
includingCategorical
raisesAttributeError
(GH 10460)Bug in
pd.get_dummies
withsparse=True
not returningSparseDataFrame
(GH 10531)Bug in
Index
subtypes (such asPeriodIndex
) not returning their own type for.drop
and.insert
methods (GH 10620)Bug in
algos.outer_join_indexer
whenright
array is empty (GH 10618)Bug in
filter
(regression from 0.16.0) andtransform
when grouping on multiple keys, one of which is datetime-like (GH 10114)Bug in
to_datetime
andto_timedelta
causingIndex
name to be lost (GH 10875)Bug in
len(DataFrame.groupby)
causingIndexError
when there’s a column containing only NaNs (GH 11016)Bug that caused segfault when resampling an empty Series (GH 10228)
Bug in
DatetimeIndex
andPeriodIndex.value_counts
resets name from its result, but retains in result’sIndex
. (GH 10150)Bug in
pd.eval
usingnumexpr
engine coerces 1 element numpy array to scalar (GH 10546)Bug in
pd.concat
withaxis=0
when column is of dtypecategory
(GH 10177)Bug in
read_msgpack
where input type is not always checked (GH 10369, GH 10630)Bug in
pd.read_csv
with kwargsindex_col=False
,index_col=['a', 'b']
ordtype
(GH 10413, GH 10467, GH 10577)Bug in
Series.from_csv
withheader
kwarg not setting theSeries.name
or theSeries.index.name
(GH 10483)Bug in
groupby.var
which caused variance to be inaccurate for small float values (GH 10448)Bug in
Series.plot(kind='hist')
Y Label not informative (GH 10485)Bug in
read_csv
when using a converter which generates auint8
type (GH 9266)Bug causes memory leak in time-series line and area plot (GH 9003)
Bug when setting a
Panel
sliced along the major or minor axes when the right-hand side is aDataFrame
(GH 11014)Bug that returns
None
and does not raiseNotImplementedError
when operator functions (e.g..add
) ofPanel
are not implemented (GH 7692)Bug in line and kde plot cannot accept multiple colors when
subplots=True
(GH 9894)Bug in
DataFrame.plot
raisesValueError
when color name is specified by multiple characters (GH 10387)Bug in left and right
align
ofSeries
withMultiIndex
may be inverted (GH 10665)Bug in left and right
join
of withMultiIndex
may be inverted (GH 10741)Bug in
read_stata
when reading a file with a different order set incolumns
(GH 10757)Bug in
Categorical
may not representing properly when category containstz
orPeriod
(GH 10713)Bug in
Categorical.__iter__
may not returning correctdatetime
andPeriod
(GH 10713)Bug in indexing with a
PeriodIndex
on an object with aPeriodIndex
(GH 4125)Bug in
read_csv
withengine='c'
: EOF preceded by a comment, blank line, etc. was not handled correctly (GH 10728, GH 10548)Reading “famafrench” data via
DataReader
results in HTTP 404 error because of the website url is changed (GH 10591).Bug in
read_msgpack
where DataFrame to decode has duplicate column names (GH 9618)Bug in
io.common.get_filepath_or_buffer
which caused reading of valid S3 files to fail if the bucket also contained keys for which the user does not have read permission (GH 10604)Bug in vectorised setting of timestamp columns with python
datetime.date
and numpydatetime64
(GH 10408, GH 10412)Bug in
Index.take
may add unnecessaryfreq
attribute (GH 10791)Bug in
merge
with emptyDataFrame
may raiseIndexError
(GH 10824)Bug in
to_latex
where unexpected keyword argument for some documented arguments (GH 10888)Bug in indexing of large
DataFrame
whereIndexError
is uncaught (GH 10645 and GH 10692)Bug in
read_csv
when using thenrows
orchunksize
parameters if file contains only a header line (GH 9535)Bug in serialization of
category
types in HDF5 in presence of alternate encodings. (GH 10366)Bug in
pd.DataFrame
when constructing an empty DataFrame with a string dtype (GH 9428)Bug in
pd.DataFrame.diff
when DataFrame is not consolidated (GH 10907)Bug in
pd.unique
for arrays with thedatetime64
ortimedelta64
dtype that meant an array with object dtype was returned instead the original dtype (GH 9431)Bug in
Timedelta
raising error when slicing from 0s (GH 10583)Bug in
DatetimeIndex.take
andTimedeltaIndex.take
may not raiseIndexError
against invalid index (GH 10295)Bug in
Series([np.nan]).astype('M8[ms]')
, which now returnsSeries([pd.NaT])
(GH 10747)Bug in
PeriodIndex.order
reset freq (GH 10295)Bug in
date_range
whenfreq
dividesend
as nanos (GH 10885)Bug in
iloc
allowing memory outside bounds of a Series to be accessed with negative integers (GH 10779)Bug in
read_msgpack
where encoding is not respected (GH 10581)Bug preventing access to the first index when using
iloc
with a list containing the appropriate negative integer (GH 10547, GH 10779)Bug in
TimedeltaIndex
formatter causing error while trying to saveDataFrame
withTimedeltaIndex
usingto_csv
(GH 10833)Bug in
DataFrame.where
when handling Series slicing (GH 10218, GH 9558)Bug where
pd.read_gbq
throwsValueError
when Bigquery returns zero rows (GH 10273)Bug in
to_json
which was causing segmentation fault when serializing 0-rank ndarray (GH 9576)Bug in plotting functions may raise
IndexError
when plotted onGridSpec
(GH 10819)Bug in plot result may show unnecessary minor ticklabels (GH 10657)
Bug in
groupby
incorrect computation for aggregation onDataFrame
withNaT
(E.gfirst
,last
,min
). (GH 10590, GH 11010)Bug when constructing
DataFrame
where passing a dictionary with only scalar values and specifying columns did not raise an error (GH 10856)Bug in
.var()
causing roundoff errors for highly similar values (GH 10242)Bug in
DataFrame.plot(subplots=True)
with duplicated columns outputs incorrect result (GH 10962)Bug in
Index
arithmetic may result in incorrect class (GH 10638)Bug in
date_range
results in empty if freq is negative annually, quarterly and monthly (GH 11018)Bug in
DatetimeIndex
cannot infer negative freq (GH 11018)Remove use of some deprecated numpy comparison operations, mainly in tests. (GH 10569)
Bug in
Index
dtype may not applied properly (GH 11017)Bug in
io.gbq
when testing for minimum google api client version (GH 10652)Bug in
DataFrame
construction from nesteddict
withtimedelta
keys (GH 11129)Bug in
.fillna
against may raiseTypeError
when data contains datetime dtype (GH 7095, GH 11153)Bug in
.groupby
when number of keys to group by is same as length of index (GH 11185)Bug in
convert_objects
where converted values might not be returned if all null andcoerce
(GH 9589)Bug in
convert_objects
wherecopy
keyword was not respected (GH 9589)
Contributors#
A total of 112 people contributed patches to this release. People with a “+” by their names contributed a patch for the first time.
Alex Rothberg
Andrea Bedini +
Andrew Rosenfeld
Andy Hayden
Andy Li +
Anthonios Partheniou +
Artemy Kolchinsky
Bernard Willers
Charlie Clark +
Chris +
Chris Whelan
Christoph Gohlke +
Christopher Whelan
Clark Fitzgerald
Clearfield Christopher +
Dan Ringwalt +
Daniel Ni +
Data & Code Expert Experimenting with Code on Data +
David Cottrell
David John Gagne +
David Kelly +
ETF +
Eduardo Schettino +
Egor +
Egor Panfilov +
Evan Wright
Frank Pinter +
Gabriel Araujo +
Garrett-R
Gianluca Rossi +
Guillaume Gay
Guillaume Poulin
Harsh Nisar +
Ian Henriksen +
Ian Hoegen +
Jaidev Deshpande +
Jan Rudolph +
Jan Schulz
Jason Swails +
Jeff Reback
Jonas Buyl +
Joris Van den Bossche
Joris Vankerschaver +
Josh Levy-Kramer +
Julien Danjou
Ka Wo Chen
Karrie Kehoe +
Kelsey Jordahl
Kerby Shedden
Kevin Sheppard
Lars Buitinck
Leif Johnson +
Luis Ortiz +
Mac +
Matt Gambogi +
Matt Savoie +
Matthew Gilbert +
Maximilian Roos +
Michelangelo D’Agostino +
Mortada Mehyar
Nick Eubank
Nipun Batra
Ondřej Čertík
Phillip Cloud
Pratap Vardhan +
Rafal Skolasinski +
Richard Lewis +
Rinoc Johnson +
Rob Levy
Robert Gieseke
Safia Abdalla +
Samuel Denny +
Saumitra Shahapure +
Sebastian Pölsterl +
Sebastian Rubbert +
Sheppard, Kevin +
Sinhrks
Siu Kwan Lam +
Skipper Seabold
Spencer Carrucciu +
Stephan Hoyer
Stephen Hoover +
Stephen Pascoe +
Terry Santegoeds +
Thomas Grainger
Tjerk Santegoeds +
Tom Augspurger
Vincent Davis +
Winterflower +
Yaroslav Halchenko
Yuan Tang (Terry) +
agijsberts
ajcr +
behzad nouri
cel4
chris-b1 +
cyrusmaher +
davidovitch +
ganego +
jreback
juricast +
larvian +
maximilianr +
msund +
rekcahpassyla
robertzk +
scls19fr
seth-p
sinhrks
springcoil +
terrytangyuan +
tzinckgraf +