Version 0.18.1 (May 3, 2016)#
This is a minor bug-fix release from 0.18.0 and includes a large number of bug fixes along with several new features, enhancements, and performance improvements. We recommend that all users upgrade to this version.
Highlights include:
.groupby(...)
has been enhanced to provide convenient syntax when working with.rolling(..)
,.expanding(..)
and.resample(..)
per group, see herepd.to_datetime()
has gained the ability to assemble dates from aDataFrame
, see hereMethod chaining improvements, see here.
Custom business hour offset, see here.
Many bug fixes in the handling of
sparse
, see hereExpanded the Tutorials section with a feature on modern pandas, courtesy of @TomAugsburger. (GH13045).
What’s new in v0.18.1
New features#
Custom business hour#
The CustomBusinessHour
is a mixture of BusinessHour
and CustomBusinessDay
which
allows you to specify arbitrary holidays. For details,
see Custom Business Hour (GH11514)
In [1]: from pandas.tseries.offsets import CustomBusinessHour
In [2]: from pandas.tseries.holiday import USFederalHolidayCalendar
In [3]: bhour_us = CustomBusinessHour(calendar=USFederalHolidayCalendar())
Friday before MLK Day
In [4]: import datetime
In [5]: dt = datetime.datetime(2014, 1, 17, 15)
In [6]: dt + bhour_us
Out[6]: Timestamp('2014-01-17 16:00:00')
Tuesday after MLK Day (Monday is skipped because it’s a holiday)
In [7]: dt + bhour_us * 2
Out[7]: Timestamp('2014-01-20 09:00:00')
Method .groupby(..)
syntax with window and resample operations#
.groupby(...)
has been enhanced to provide convenient syntax when working with .rolling(..)
, .expanding(..)
and .resample(..)
per group, see (GH12486, GH12738).
You can now use .rolling(..)
and .expanding(..)
as methods on groupbys. These return another deferred object (similar to what .rolling()
and .expanding()
do on ungrouped pandas objects). You can then operate on these RollingGroupby
objects in a similar manner.
Previously you would have to do this to get a rolling window mean per-group:
In [8]: df = pd.DataFrame({"A": [1] * 20 + [2] * 12 + [3] * 8, "B": np.arange(40)})
In [9]: df
Out[9]:
A B
0 1 0
1 1 1
2 1 2
3 1 3
4 1 4
.. .. ..
35 3 35
36 3 36
37 3 37
38 3 38
39 3 39
[40 rows x 2 columns]
In [10]: df.groupby("A").apply(lambda x: x.rolling(4).B.mean())
Out[10]:
A
1 0 NaN
1 NaN
2 NaN
3 1.5
4 2.5
...
3 35 33.5
36 34.5
37 35.5
38 36.5
39 37.5
Name: B, Length: 40, dtype: float64
Now you can do:
In [11]: df.groupby("A").rolling(4).B.mean()
Out[11]:
A
1 0 NaN
1 NaN
2 NaN
3 1.5
4 2.5
...
3 35 33.5
36 34.5
37 35.5
38 36.5
39 37.5
Name: B, Length: 40, dtype: float64
For .resample(..)
type of operations, previously you would have to:
In [12]: df = pd.DataFrame(
....: {
....: "date": pd.date_range(start="2016-01-01", periods=4, freq="W"),
....: "group": [1, 1, 2, 2],
....: "val": [5, 6, 7, 8],
....: }
....: ).set_index("date")
....:
In [13]: df
Out[13]:
group val
date
2016-01-03 1 5
2016-01-10 1 6
2016-01-17 2 7
2016-01-24 2 8
[4 rows x 2 columns]
In [14]: df.groupby("group").apply(lambda x: x.resample("1D").ffill())
Out[14]:
group val
group date
1 2016-01-03 1 5
2016-01-04 1 5
2016-01-05 1 5
2016-01-06 1 5
2016-01-07 1 5
... ... ...
2 2016-01-20 2 7
2016-01-21 2 7
2016-01-22 2 7
2016-01-23 2 7
2016-01-24 2 8
[16 rows x 2 columns]
Now you can do:
In [15]: df.groupby("group").resample("1D").ffill()
Out[15]:
group val
group date
1 2016-01-03 1 5
2016-01-04 1 5
2016-01-05 1 5
2016-01-06 1 5
2016-01-07 1 5
... ... ...
2 2016-01-20 2 7
2016-01-21 2 7
2016-01-22 2 7
2016-01-23 2 7
2016-01-24 2 8
[16 rows x 2 columns]
Method chaining improvements#
The following methods / indexers now accept a callable
. It is intended to make
these more useful in method chains, see the documentation.
(GH11485, GH12533)
.where()
and.mask()
.loc[]
,iloc[]
and.ix[]
[]
indexing
Methods .where()
and .mask()
#
These can accept a callable for the condition and other
arguments.
In [16]: df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
In [17]: df.where(lambda x: x > 4, lambda x: x + 10)
Out[17]:
A B C
0 11 14 7
1 12 5 8
2 13 6 9
[3 rows x 3 columns]
Methods .loc[]
, .iloc[]
, .ix[]
#
These can accept a callable, and a tuple of callable as a slicer. The callable can return a valid boolean indexer or anything which is valid for these indexer’s input.
# callable returns bool indexer
In [18]: df.loc[lambda x: x.A >= 2, lambda x: x.sum() > 10]
Out[18]:
B C
1 5 8
2 6 9
[2 rows x 2 columns]
# callable returns list of labels
In [19]: df.loc[lambda x: [1, 2], lambda x: ["A", "B"]]
Out[19]:
A B
1 2 5
2 3 6
[2 rows x 2 columns]
Indexing with []
#
Finally, you can use a callable in []
indexing of Series, DataFrame and Panel.
The callable must return a valid input for []
indexing depending on its
class and index type.
In [20]: df[lambda x: "A"]
Out[20]:
0 1
1 2
2 3
Name: A, Length: 3, dtype: int64
Using these methods / indexers, you can chain data selection operations without using temporary variable.
In [21]: bb = pd.read_csv("data/baseball.csv", index_col="id")
In [22]: (bb.groupby(["year", "team"]).sum(numeric_only=True).loc[lambda df: df.r > 100])
Out[22]:
stint g ab r h X2b ... so ibb hbp sh sf gidp
year team ...
2007 CIN 6 379 745 101 203 35 ... 127.0 14.0 1.0 1.0 15.0 18.0
DET 5 301 1062 162 283 54 ... 176.0 3.0 10.0 4.0 8.0 28.0
HOU 4 311 926 109 218 47 ... 212.0 3.0 9.0 16.0 6.0 17.0
LAN 11 413 1021 153 293 61 ... 141.0 8.0 9.0 3.0 8.0 29.0
NYN 13 622 1854 240 509 101 ... 310.0 24.0 23.0 18.0 15.0 48.0
SFN 5 482 1305 198 337 67 ... 188.0 51.0 8.0 16.0 6.0 41.0
TEX 2 198 729 115 200 40 ... 140.0 4.0 5.0 2.0 8.0 16.0
TOR 4 459 1408 187 378 96 ... 265.0 16.0 12.0 4.0 16.0 38.0
[8 rows x 18 columns]
Partial string indexing on DatetimeIndex
when part of a MultiIndex
#
Partial string indexing now matches on DateTimeIndex
when part of a MultiIndex
(GH10331)
In [23]: dft2 = pd.DataFrame(
....: np.random.randn(20, 1),
....: columns=["A"],
....: index=pd.MultiIndex.from_product(
....: [pd.date_range("20130101", periods=10, freq="12H"), ["a", "b"]]
....: ),
....: )
....:
In [24]: dft2
Out[24]:
A
2013-01-01 00:00:00 a 0.469112
b -0.282863
2013-01-01 12:00:00 a -1.509059
b -1.135632
2013-01-02 00:00:00 a 1.212112
... ...
2013-01-04 12:00:00 b 0.271860
2013-01-05 00:00:00 a -0.424972
b 0.567020
2013-01-05 12:00:00 a 0.276232
b -1.087401
[20 rows x 1 columns]
In [25]: dft2.loc["2013-01-05"]
Out[25]:
A
2013-01-05 00:00:00 a -0.424972
b 0.567020
2013-01-05 12:00:00 a 0.276232
b -1.087401
[4 rows x 1 columns]
On other levels
In [26]: idx = pd.IndexSlice
In [27]: dft2 = dft2.swaplevel(0, 1).sort_index()
In [28]: dft2
Out[28]:
A
a 2013-01-01 00:00:00 0.469112
2013-01-01 12:00:00 -1.509059
2013-01-02 00:00:00 1.212112
2013-01-02 12:00:00 0.119209
2013-01-03 00:00:00 -0.861849
... ...
b 2013-01-03 12:00:00 1.071804
2013-01-04 00:00:00 -0.706771
2013-01-04 12:00:00 0.271860
2013-01-05 00:00:00 0.567020
2013-01-05 12:00:00 -1.087401
[20 rows x 1 columns]
In [29]: dft2.loc[idx[:, "2013-01-05"], :]
Out[29]:
A
a 2013-01-05 00:00:00 -0.424972
2013-01-05 12:00:00 0.276232
b 2013-01-05 00:00:00 0.567020
2013-01-05 12:00:00 -1.087401
[4 rows x 1 columns]
Assembling datetimes#
pd.to_datetime()
has gained the ability to assemble datetimes from a passed in DataFrame
or a dict. (GH8158).
In [30]: df = pd.DataFrame(
....: {"year": [2015, 2016], "month": [2, 3], "day": [4, 5], "hour": [2, 3]}
....: )
....:
In [31]: df
Out[31]:
year month day hour
0 2015 2 4 2
1 2016 3 5 3
[2 rows x 4 columns]
Assembling using the passed frame.
In [32]: pd.to_datetime(df)
Out[32]:
0 2015-02-04 02:00:00
1 2016-03-05 03:00:00
Length: 2, dtype: datetime64[ns]
You can pass only the columns that you need to assemble.
In [33]: pd.to_datetime(df[["year", "month", "day"]])
Out[33]:
0 2015-02-04
1 2016-03-05
Length: 2, dtype: datetime64[ns]
Other enhancements#
pd.read_csv()
now supportsdelim_whitespace=True
for the Python engine (GH12958)pd.read_csv()
now supports opening ZIP files that contains a single CSV, via extension inference or explicitcompression='zip'
(GH12175)pd.read_csv()
now supports opening files using xz compression, via extension inference or explicitcompression='xz'
is specified;xz
compressions is also supported byDataFrame.to_csv
in the same way (GH11852)pd.read_msgpack()
now always gives writeable ndarrays even when compression is used (GH12359).pd.read_msgpack()
now supports serializing and de-serializing categoricals with msgpack (GH12573).to_json()
now supportsNDFrames
that contain categorical and sparse data (GH10778)interpolate()
now supportsmethod='akima'
(GH7588).pd.read_excel()
now accepts path objects (e.g.pathlib.Path
,py.path.local
) for the file path, in line with otherread_*
functions (GH12655)Added
.weekday_name
property as a component toDatetimeIndex
and the.dt
accessor. (GH11128)Index.take
now handlesallow_fill
andfill_value
consistently (GH12631)In [34]: idx = pd.Index([1.0, 2.0, 3.0, 4.0], dtype="float") # default, allow_fill=True, fill_value=None In [35]: idx.take([2, -1]) Out[35]: Float64Index([3.0, 4.0], dtype='float64') In [36]: idx.take([2, -1], fill_value=True) Out[36]: Float64Index([3.0, nan], dtype='float64')
Index
now supports.str.get_dummies()
which returnsMultiIndex
, see Creating Indicator Variables (GH10008, GH10103)In [37]: idx = pd.Index(["a|b", "a|c", "b|c"]) In [38]: idx.str.get_dummies("|") Out[38]: MultiIndex([(1, 1, 0), (1, 0, 1), (0, 1, 1)], names=['a', 'b', 'c'])
pd.crosstab()
has gained anormalize
argument for normalizing frequency tables (GH12569). Examples in the updated docs here..resample(..).interpolate()
is now supported (GH12925).isin()
now accepts passedsets
(GH12988)
Sparse changes#
These changes conform sparse handling to return the correct types and work to make a smoother experience with indexing.
SparseArray.take
now returns a scalar for scalar input, SparseArray
for others. Furthermore, it handles a negative indexer with the same rule as Index
(GH10560, GH12796)
s = pd.SparseArray([np.nan, np.nan, 1, 2, 3, np.nan, 4, 5, np.nan, 6])
s.take(0)
s.take([1, 2, 3])
Bug in
SparseSeries[]
indexing withEllipsis
raisesKeyError
(GH9467)Bug in
SparseArray[]
indexing with tuples are not handled properly (GH12966)Bug in
SparseSeries.loc[]
with list-like input raisesTypeError
(GH10560)Bug in
SparseSeries.iloc[]
with scalar input may raiseIndexError
(GH10560)Bug in
SparseSeries.loc[]
,.iloc[]
withslice
returnsSparseArray
, rather thanSparseSeries
(GH10560)Bug in
SparseDataFrame.loc[]
,.iloc[]
may results in denseSeries
, rather thanSparseSeries
(GH12787)Bug in
SparseArray
addition ignoresfill_value
of right hand side (GH12910)Bug in
SparseArray
mod raisesAttributeError
(GH12910)Bug in
SparseArray
pow calculates1 ** np.nan
asnp.nan
which must be 1 (GH12910)Bug in
SparseArray
comparison output may incorrect result or raiseValueError
(GH12971)Bug in
SparseSeries.__repr__
raisesTypeError
when it is longer thanmax_rows
(GH10560)Bug in
SparseSeries.shape
ignoresfill_value
(GH10452)Bug in
SparseSeries
andSparseArray
may have differentdtype
from its dense values (GH12908)Bug in
SparseSeries.reindex
incorrectly handlefill_value
(GH12797)Bug in
SparseArray.to_frame()
results inDataFrame
, rather thanSparseDataFrame
(GH9850)Bug in
SparseSeries.value_counts()
does not countfill_value
(GH6749)Bug in
SparseArray.to_dense()
does not preservedtype
(GH10648)Bug in
SparseArray.to_dense()
incorrectly handlefill_value
(GH12797)Bug in
pd.concat()
ofSparseSeries
results in dense (GH10536)Bug in
pd.concat()
ofSparseDataFrame
incorrectly handlefill_value
(GH9765)Bug in
pd.concat()
ofSparseDataFrame
may raiseAttributeError
(GH12174)Bug in
SparseArray.shift()
may raiseNameError
orTypeError
(GH12908)
API changes#
Method .groupby(..).nth()
changes#
The index in .groupby(..).nth()
output is now more consistent when the as_index
argument is passed (GH11039):
In [39]: df = pd.DataFrame({"A": ["a", "b", "a"], "B": [1, 2, 3]})
In [40]: df
Out[40]:
A B
0 a 1
1 b 2
2 a 3
[3 rows x 2 columns]
Previous behavior:
In [3]: df.groupby('A', as_index=True)['B'].nth(0)
Out[3]:
0 1
1 2
Name: B, dtype: int64
In [4]: df.groupby('A', as_index=False)['B'].nth(0)
Out[4]:
0 1
1 2
Name: B, dtype: int64
New behavior:
In [41]: df.groupby("A", as_index=True)["B"].nth(0)
Out[41]:
A
a 1
b 2
Name: B, Length: 2, dtype: int64
In [42]: df.groupby("A", as_index=False)["B"].nth(0)
Out[42]:
0 1
1 2
Name: B, Length: 2, dtype: int64
Furthermore, previously, a .groupby
would always sort, regardless if sort=False
was passed with .nth()
.
In [43]: np.random.seed(1234)
In [44]: df = pd.DataFrame(np.random.randn(100, 2), columns=["a", "b"])
In [45]: df["c"] = np.random.randint(0, 4, 100)
Previous behavior:
In [4]: df.groupby('c', sort=True).nth(1)
Out[4]:
a b
c
0 -0.334077 0.002118
1 0.036142 -2.074978
2 -0.720589 0.887163
3 0.859588 -0.636524
In [5]: df.groupby('c', sort=False).nth(1)
Out[5]:
a b
c
0 -0.334077 0.002118
1 0.036142 -2.074978
2 -0.720589 0.887163
3 0.859588 -0.636524
New behavior:
In [46]: df.groupby("c", sort=True).nth(1)
Out[46]:
a b
c
0 -0.334077 0.002118
1 0.036142 -2.074978
2 -0.720589 0.887163
3 0.859588 -0.636524
[4 rows x 2 columns]
In [47]: df.groupby("c", sort=False).nth(1)
Out[47]:
a b
c
2 -0.720589 0.887163
3 0.859588 -0.636524
0 -0.334077 0.002118
1 0.036142 -2.074978
[4 rows x 2 columns]
NumPy function compatibility#
Compatibility between pandas array-like methods (e.g. sum
and take
) and their numpy
counterparts has been greatly increased by augmenting the signatures of the pandas
methods so
as to accept arguments that can be passed in from numpy
, even if they are not necessarily
used in the pandas
implementation (GH12644, GH12638, GH12687)
.searchsorted()
forIndex
andTimedeltaIndex
now accept asorter
argument to maintain compatibility with numpy’ssearchsorted
function (GH12238)Bug in numpy compatibility of
np.round()
on aSeries
(GH12600)
An example of this signature augmentation is illustrated below:
sp = pd.SparseDataFrame([1, 2, 3])
sp
Previous behaviour:
In [2]: np.cumsum(sp, axis=0)
...
TypeError: cumsum() takes at most 2 arguments (4 given)
New behaviour:
np.cumsum(sp, axis=0)
Using .apply
on GroupBy resampling#
Using apply
on resampling groupby operations (using a pd.TimeGrouper
) now has the same output types as similar apply
calls on other groupby operations. (GH11742).
In [48]: df = pd.DataFrame(
....: {"date": pd.to_datetime(["10/10/2000", "11/10/2000"]), "value": [10, 13]}
....: )
....:
In [49]: df
Out[49]:
date value
0 2000-10-10 10
1 2000-11-10 13
[2 rows x 2 columns]
Previous behavior:
In [1]: df.groupby(pd.TimeGrouper(key='date',
...: freq='M')).apply(lambda x: x.value.sum())
Out[1]:
...
TypeError: cannot concatenate a non-NDFrame object
# Output is a Series
In [2]: df.groupby(pd.TimeGrouper(key='date',
...: freq='M')).apply(lambda x: x[['value']].sum())
Out[2]:
date
2000-10-31 value 10
2000-11-30 value 13
dtype: int64
New behavior:
# Output is a Series
In [55]: df.groupby(pd.TimeGrouper(key='date',
...: freq='M')).apply(lambda x: x.value.sum())
Out[55]:
date
2000-10-31 10
2000-11-30 13
Freq: M, dtype: int64
# Output is a DataFrame
In [56]: df.groupby(pd.TimeGrouper(key='date',
...: freq='M')).apply(lambda x: x[['value']].sum())
Out[56]:
value
date
2000-10-31 10
2000-11-30 13
Changes in read_csv
exceptions#
In order to standardize the read_csv
API for both the c
and python
engines, both will now raise an
EmptyDataError
, a subclass of ValueError
, in response to empty columns or header (GH12493, GH12506)
Previous behaviour:
In [1]: import io
In [2]: df = pd.read_csv(io.StringIO(''), engine='c')
...
ValueError: No columns to parse from file
In [3]: df = pd.read_csv(io.StringIO(''), engine='python')
...
StopIteration
New behaviour:
In [1]: df = pd.read_csv(io.StringIO(''), engine='c')
...
pandas.io.common.EmptyDataError: No columns to parse from file
In [2]: df = pd.read_csv(io.StringIO(''), engine='python')
...
pandas.io.common.EmptyDataError: No columns to parse from file
In addition to this error change, several others have been made as well:
CParserError
now sub-classesValueError
instead of just aException
(GH12551)A
CParserError
is now raised instead of a genericException
inread_csv
when thec
engine cannot parse a column (GH12506)A
ValueError
is now raised instead of a genericException
inread_csv
when thec
engine encounters aNaN
value in an integer column (GH12506)A
ValueError
is now raised instead of a genericException
inread_csv
whentrue_values
is specified, and thec
engine encounters an element in a column containing unencodable bytes (GH12506)pandas.parser.OverflowError
exception has been removed and has been replaced with Python’s built-inOverflowError
exception (GH12506)pd.read_csv()
no longer allows a combination of strings and integers for theusecols
parameter (GH12678)
Method to_datetime
error changes#
Bugs in pd.to_datetime()
when passing a unit
with convertible entries and errors='coerce'
or non-convertible with errors='ignore'
. Furthermore, an OutOfBoundsDateime
exception will be raised when an out-of-range value is encountered for that unit when errors='raise'
. (GH11758, GH13052, GH13059)
Previous behaviour:
In [27]: pd.to_datetime(1420043460, unit='s', errors='coerce')
Out[27]: NaT
In [28]: pd.to_datetime(11111111, unit='D', errors='ignore')
OverflowError: Python int too large to convert to C long
In [29]: pd.to_datetime(11111111, unit='D', errors='raise')
OverflowError: Python int too large to convert to C long
New behaviour:
In [2]: pd.to_datetime(1420043460, unit='s', errors='coerce')
Out[2]: Timestamp('2014-12-31 16:31:00')
In [3]: pd.to_datetime(11111111, unit='D', errors='ignore')
Out[3]: 11111111
In [4]: pd.to_datetime(11111111, unit='D', errors='raise')
OutOfBoundsDatetime: cannot convert input with unit 'D'
Other API changes#
.swaplevel()
forSeries
,DataFrame
,Panel
, andMultiIndex
now features defaults for its first two parametersi
andj
that swap the two innermost levels of the index. (GH12934).searchsorted()
forIndex
andTimedeltaIndex
now accept asorter
argument to maintain compatibility with numpy’ssearchsorted
function (GH12238)Period
andPeriodIndex
now raisesIncompatibleFrequency
error which inheritsValueError
rather than rawValueError
(GH12615)Series.apply
for category dtype now applies the passed function to each of the.categories
(and not the.codes
), and returns acategory
dtype if possible (GH12473)read_csv
will now raise aTypeError
ifparse_dates
is neither a boolean, list, or dictionary (matches the doc-string) (GH5636)The default for
.query()/.eval()
is nowengine=None
, which will usenumexpr
if it’s installed; otherwise it will fallback to thepython
engine. This mimics the pre-0.18.1 behavior ifnumexpr
is installed (and which, previously, if numexpr was not installed,.query()/.eval()
would raise). (GH12749)pd.show_versions()
now includespandas_datareader
version (GH12740)Provide a proper
__name__
and__qualname__
attributes for generic functions (GH12021)pd.concat(ignore_index=True)
now usesRangeIndex
as default (GH12695)pd.merge()
andDataFrame.join()
will show aUserWarning
when merging/joining a single- with a multi-leveled dataframe (GH9455, GH12219)Compat with
scipy
> 0.17 for deprecatedpiecewise_polynomial
interpolation method; support for the replacementfrom_derivatives
method (GH12887)
Deprecations#
Performance improvements#
Performance improvements in
.groupby(..).cumcount()
(GH11039)Improved memory usage in
pd.read_csv()
when usingskiprows=an_integer
(GH13005)Improved performance of
DataFrame.to_sql
when checking case sensitivity for tables. Now only checks if table has been created correctly when table name is not lower case. (GH12876)Improved performance of
Period
construction and time series plotting (GH12903, GH11831).Improved performance of
.str.encode()
and.str.decode()
methods (GH13008)Improved performance of
to_numeric
if input is numeric dtype (GH12777)Improved performance of sparse arithmetic with
IntIndex
(GH13036)
Bug fixes#
usecols
parameter inpd.read_csv
is now respected even when the lines of a CSV file are not even (GH12203)Bug in
groupby.transform(..)
whenaxis=1
is specified with a non-monotonic ordered index (GH12713)Bug in
Period
andPeriodIndex
creation raisesKeyError
iffreq="Minute"
is specified. Note that “Minute” freq is deprecated in v0.17.0, and recommended to usefreq="T"
instead (GH11854)Bug in
.resample(...).count()
with aPeriodIndex
always raising aTypeError
(GH12774)Bug in
.resample(...)
with aPeriodIndex
casting to aDatetimeIndex
when empty (GH12868)Bug in
.resample(...)
with aPeriodIndex
when resampling to an existing frequency (GH12770)Bug in printing data which contains
Period
with differentfreq
raisesValueError
(GH12615)Bug in
Series
construction withCategorical
anddtype='category'
is specified (GH12574)Bugs in concatenation with a coercible dtype was too aggressive, resulting in different dtypes in output formatting when an object was longer than
display.max_rows
(GH12411, GH12045, GH11594, GH10571, GH12211)Bug in
float_format
option with option not being validated as a callable. (GH12706)Bug in
GroupBy.filter
whendropna=False
and no groups fulfilled the criteria (GH12768)Bug in
__name__
of.cum*
functions (GH12021)Bug in
.astype()
of aFloat64Inde/Int64Index
to anInt64Index
(GH12881)Bug in round tripping an integer based index in
.to_json()/.read_json()
whenorient='index'
(the default) (GH12866)Bug in plotting
Categorical
dtypes cause error when attempting stacked bar plot (GH13019)Compat with >=
numpy
1.11 forNaT
comparisons (GH12969)Bug in
.drop()
with a non-uniqueMultiIndex
. (GH12701)Bug in
.concat
of datetime tz-aware and naive DataFrames (GH12467)Bug in correctly raising a
ValueError
in.resample(..).fillna(..)
when passing a non-string (GH12952)Bug fixes in various encoding and header processing issues in
pd.read_sas()
(GH12659, GH12654, GH12647, GH12809)Bug in
pd.crosstab()
where would silently ignoreaggfunc
ifvalues=None
(GH12569).Potential segfault in
DataFrame.to_json
when serialisingdatetime.time
(GH11473).Potential segfault in
DataFrame.to_json
when attempting to serialise 0d array (GH11299).Segfault in
to_json
when attempting to serialise aDataFrame
orSeries
with non-ndarray values; now supports serialization ofcategory
,sparse
, anddatetime64[ns, tz]
dtypes (GH10778).Bug in
DataFrame.to_json
with unsupported dtype not passed to default handler (GH12554).Bug in
.align
not returning the sub-class (GH12983)Bug in aligning a
Series
with aDataFrame
(GH13037)Bug in
ABCPanel
in whichPanel4D
was not being considered as a valid instance of this generic type (GH12810)Bug in consistency of
.name
on.groupby(..).apply(..)
cases (GH12363)Bug in
Timestamp.__repr__
that causedpprint
to fail in nested structures (GH12622)Bug in
Timedelta.min
andTimedelta.max
, the properties now report the true minimum/maximumtimedeltas
as recognized by pandas. See the documentation. (GH12727)Bug in
.quantile()
with interpolation may coerce tofloat
unexpectedly (GH12772)Bug in
.quantile()
with emptySeries
may return scalar rather than emptySeries
(GH12772)Bug in
.loc
with out-of-bounds in a large indexer would raiseIndexError
rather thanKeyError
(GH12527)Bug in resampling when using a
TimedeltaIndex
and.asfreq()
, would previously not include the final fencepost (GH12926)Bug in equality testing with a
Categorical
in aDataFrame
(GH12564)Bug in
GroupBy.first()
,.last()
returns incorrect row whenTimeGrouper
is used (GH7453)Bug in
pd.read_csv()
with thec
engine when specifyingskiprows
with newlines in quoted items (GH10911, GH12775)Bug in
DataFrame
timezone lost when assigning tz-aware datetimeSeries
with alignment (GH12981)Bug in
.value_counts()
whennormalize=True
anddropna=True
where nulls still contributed to the normalized count (GH12558)Bug in
Series.value_counts()
loses name if its dtype iscategory
(GH12835)Bug in
Series.value_counts()
loses timezone info (GH12835)Bug in
Series.value_counts(normalize=True)
withCategorical
raisesUnboundLocalError
(GH12835)Bug in
Panel.fillna()
ignoringinplace=True
(GH12633)Bug in
pd.read_csv()
when specifyingnames
,usecols
, andparse_dates
simultaneously with thec
engine (GH9755)Bug in
pd.read_csv()
when specifyingdelim_whitespace=True
andlineterminator
simultaneously with thec
engine (GH12912)Bug in
Series.rename
,DataFrame.rename
andDataFrame.rename_axis
not treatingSeries
as mappings to relabel (GH12623).Clean in
.rolling.min
and.rolling.max
to enhance dtype handling (GH12373)Bug in
groupby
where complex types are coerced to float (GH12902)Bug in
Series.map
raisesTypeError
if its dtype iscategory
or tz-awaredatetime
(GH12473)Bugs on 32bit platforms for some test comparisons (GH12972)
Bug in index coercion when falling back from
RangeIndex
construction (GH12893)Better error message in window functions when invalid argument (e.g. a float window) is passed (GH12669)
Bug in slicing subclassed
DataFrame
defined to return subclassedSeries
may return normalSeries
(GH11559)Bug in
.str
accessor methods may raiseValueError
if input hasname
and the result isDataFrame
orMultiIndex
(GH12617)Bug in
DataFrame.last_valid_index()
andDataFrame.first_valid_index()
on empty frames (GH12800)Bug in
CategoricalIndex.get_loc
returns different result from regularIndex
(GH12531)Bug in
PeriodIndex.resample
where name not propagated (GH12769)Bug in
date_range
closed
keyword and timezones (GH12684).Bug in
pd.concat
raisesAttributeError
when input data contains tz-aware datetime and timedelta (GH12620)Bug in
pd.concat
did not handle emptySeries
properly (GH11082)Bug in
.plot.bar
alignment whenwidth
is specified withint
(GH12979)Bug in
fill_value
is ignored if the argument to a binary operator is a constant (GH12723)Bug in
pd.read_html()
when using bs4 flavor and parsing table with a header and only one column (GH9178)Bug in
.pivot_table
whenmargins=True
anddropna=True
where nulls still contributed to margin count (GH12577)Bug in
.pivot_table
whendropna=False
where table index/column names disappear (GH12133)Bug in
pd.crosstab()
whenmargins=True
anddropna=False
which raised (GH12642)Bug in
Series.name
whenname
attribute can be a hashable type (GH12610)Bug in
.describe()
resets categorical columns information (GH11558)Bug where
loffset
argument was not applied when callingresample().count()
on a timeseries (GH12725)pd.read_excel()
now accepts column names associated with keyword argumentnames
(GH12870)Bug in
pd.to_numeric()
withIndex
returnsnp.ndarray
, rather thanIndex
(GH12777)Bug in
pd.to_numeric()
with datetime-like may raiseTypeError
(GH12777)Bug in
pd.to_numeric()
with scalar raisesValueError
(GH12777)
Contributors#
A total of 60 people contributed patches to this release. People with a “+” by their names contributed a patch for the first time.
Andrew Fiore-Gartland +
Bastiaan +
Benoît Vinot +
Brandon Rhodes +
DaCoEx +
Drew Fustin +
Ernesto Freitas +
Filip Ter +
Gregory Livschitz +
Gábor Lipták
Hassan Kibirige +
Iblis Lin
Israel Saeta Pérez +
Jason Wolosonovich +
Jeff Reback
Joe Jevnik
Joris Van den Bossche
Joshua Storck +
Ka Wo Chen
Kerby Shedden
Kieran O’Mahony
Leif Walsh +
Mahmoud Lababidi +
Maoyuan Liu +
Mark Roth +
Matt Wittmann
MaxU +
Maximilian Roos
Michael Droettboom +
Nick Eubank
Nicolas Bonnotte
OXPHOS +
Pauli Virtanen +
Peter Waller +
Pietro Battiston
Prabhjot Singh +
Robin Wilson
Roger Thomas +
Sebastian Bank
Stephen Hoover
Tim Hopper +
Tom Augspurger
WANG Aiyong
Wes Turner
Winand +
Xbar +
Yan Facai +
adneu +
ajenkins-cargometrics +
behzad nouri
chinskiy +
gfyoung
jeps-journal +
jonaslb +
kotrfa +
nileracecrew +
onesandzeroes
rs2 +
sinhrks
tsdlovell +