Version 0.15.1 (November 9, 2014)#
This is a minor bug-fix release from 0.15.0 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.
API changes#
s.dt.hour
and other.dt
accessors will now returnnp.nan
for missing values (rather than previously -1), (GH8689)In [1]: s = pd.Series(pd.date_range("20130101", periods=5, freq="D")) In [2]: s.iloc[2] = np.nan In [3]: s Out[3]: 0 2013-01-01 1 2013-01-02 2 NaT 3 2013-01-04 4 2013-01-05 Length: 5, dtype: datetime64[ns]
previous behavior:
In [6]: s.dt.hour Out[6]: 0 0 1 0 2 -1 3 0 4 0 dtype: int64
current behavior:
In [4]: s.dt.hour Out[4]: 0 0.0 1 0.0 2 NaN 3 0.0 4 0.0 Length: 5, dtype: float64
groupby
withas_index=False
will not add erroneous extra columns to result (GH8582):In [5]: np.random.seed(2718281) In [6]: df = pd.DataFrame(np.random.randint(0, 100, (10, 2)), columns=["jim", "joe"]) In [7]: df.head() Out[7]: jim joe 0 61 81 1 96 49 2 55 65 3 72 51 4 77 12 [5 rows x 2 columns] In [8]: ts = pd.Series(5 * np.random.randint(0, 3, 10))
previous behavior:
In [4]: df.groupby(ts, as_index=False).max() Out[4]: NaN jim joe 0 0 72 83 1 5 77 84 2 10 96 65
current behavior:
In [9]: df.groupby(ts, as_index=False).max() Out[9]: jim joe 0 72 83 1 77 84 2 96 65 [3 rows x 2 columns]
groupby
will not erroneously exclude columns if the column name conflicts with the grouper name (GH8112):In [10]: df = pd.DataFrame({"jim": range(5), "joe": range(5, 10)}) In [11]: df Out[11]: jim joe 0 0 5 1 1 6 2 2 7 3 3 8 4 4 9 [5 rows x 2 columns] In [12]: gr = df.groupby(df["jim"] < 2)
previous behavior (excludes 1st column from output):
In [4]: gr.apply(sum) Out[4]: joe jim False 24 True 11
current behavior:
In [13]: gr.apply(sum) Out[13]: jim joe jim False 9 24 True 1 11 [2 rows x 2 columns]
Support for slicing with monotonic decreasing indexes, even if
start
orstop
is not found in the index (GH7860):In [14]: s = pd.Series(["a", "b", "c", "d"], [4, 3, 2, 1]) In [15]: s Out[15]: 4 a 3 b 2 c 1 d Length: 4, dtype: object
previous behavior:
In [8]: s.loc[3.5:1.5] KeyError: 3.5
current behavior:
In [16]: s.loc[3.5:1.5] Out[16]: 3 b 2 c Length: 2, dtype: object
io.data.Options
has been fixed for a change in the format of the Yahoo Options page (GH8612), (GH8741)Note
As a result of a change in Yahoo’s option page layout, when an expiry date is given,
Options
methods now return data for a single expiry date. Previously, methods returned all data for the selected month.The
month
andyear
parameters have been undeprecated and can be used to get all options data for a given month.If an expiry date that is not valid is given, data for the next expiry after the given date is returned.
Option data frames are now saved on the instance as
callsYYMMDD
orputsYYMMDD
. Previously they were saved ascallsMMYY
andputsMMYY
. The next expiry is saved ascalls
andputs
.New features:
The expiry parameter can now be a single date or a list-like object containing dates.
A new property
expiry_dates
was added, which returns all available expiry dates.
Current behavior:
In [17]: from pandas.io.data import Options In [18]: aapl = Options('aapl', 'yahoo') In [19]: aapl.get_call_data().iloc[0:5, 0:1] Out[19]: Last Strike Expiry Type Symbol 80 2014-11-14 call AAPL141114C00080000 29.05 84 2014-11-14 call AAPL141114C00084000 24.80 85 2014-11-14 call AAPL141114C00085000 24.05 86 2014-11-14 call AAPL141114C00086000 22.76 87 2014-11-14 call AAPL141114C00087000 21.74 In [20]: aapl.expiry_dates Out[20]: [datetime.date(2014, 11, 14), datetime.date(2014, 11, 22), datetime.date(2014, 11, 28), datetime.date(2014, 12, 5), datetime.date(2014, 12, 12), datetime.date(2014, 12, 20), datetime.date(2015, 1, 17), datetime.date(2015, 2, 20), datetime.date(2015, 4, 17), datetime.date(2015, 7, 17), datetime.date(2016, 1, 15), datetime.date(2017, 1, 20)] In [21]: aapl.get_near_stock_price(expiry=aapl.expiry_dates[0:3]).iloc[0:5, 0:1] Out[21]: Last Strike Expiry Type Symbol 109 2014-11-22 call AAPL141122C00109000 1.48 2014-11-28 call AAPL141128C00109000 1.79 110 2014-11-14 call AAPL141114C00110000 0.55 2014-11-22 call AAPL141122C00110000 1.02 2014-11-28 call AAPL141128C00110000 1.32
pandas now also registers the
datetime64
dtype in matplotlib’s units registry to plot such values as datetimes. This is activated once pandas is imported. In previous versions, plotting an array ofdatetime64
values will have resulted in plotted integer values. To keep the previous behaviour, you can dodel matplotlib.units.registry[np.datetime64]
(GH8614).
Enhancements#
concat
permits a wider variety of iterables of pandas objects to be passed as the first parameter (GH8645):In [17]: from collections import deque In [18]: df1 = pd.DataFrame([1, 2, 3]) In [19]: df2 = pd.DataFrame([4, 5, 6])
previous behavior:
In [7]: pd.concat(deque((df1, df2))) TypeError: first argument must be a list-like of pandas objects, you passed an object of type "deque"
current behavior:
In [20]: pd.concat(deque((df1, df2))) Out[20]: 0 0 1 1 2 2 3 0 4 1 5 2 6 [6 rows x 1 columns]
Represent
MultiIndex
labels with a dtype that utilizes memory based on the level size. In prior versions, the memory usage was a constant 8 bytes per element in each level. In addition, in prior versions, the reported memory usage was incorrect as it didn’t show the usage for the memory occupied by the underling data array. (GH8456)In [21]: dfi = pd.DataFrame( ....: 1, index=pd.MultiIndex.from_product([["a"], range(1000)]), columns=["A"] ....: ) ....:
previous behavior:
# this was underreported in prior versions In [1]: dfi.memory_usage(index=True) Out[1]: Index 8000 # took about 24008 bytes in < 0.15.1 A 8000 dtype: int64
current behavior:
In [22]: dfi.memory_usage(index=True) Out[22]: Index 44212 A 8000 Length: 2, dtype: int64
Added Index properties
is_monotonic_increasing
andis_monotonic_decreasing
(GH8680).Added option to select columns when importing Stata files (GH7935)
Qualify memory usage in
DataFrame.info()
by adding+
if it is a lower bound (GH8578)Raise errors in certain aggregation cases where an argument such as
numeric_only
is not handled (GH8592).Added support for 3-character ISO and non-standard country codes in
io.wb.download()
(GH8482)World Bank data requests now will warn/raise based on an
errors
argument, as well as a list of hard-coded country codes and the World Bank’s JSON response. In prior versions, the error messages didn’t look at the World Bank’s JSON response. Problem-inducing input were simply dropped prior to the request. The issue was that many good countries were cropped in the hard-coded approach. All countries will work now, but some bad countries will raise exceptions because some edge cases break the entire response. (GH8482)Added option to
Series.str.split()
to return aDataFrame
rather than aSeries
(GH8428)Added option to
df.info(null_counts=None|True|False)
to override the default display options and force showing of the null-counts (GH8701)
Bug fixes#
Bug in unpickling of a
CustomBusinessDay
object (GH8591)Bug in coercing
Categorical
to a records array, e.g.df.to_records()
(GH8626)Bug in
Categorical
not created properly withSeries.to_frame()
(GH8626)Bug in coercing in astype of a
Categorical
of a passedpd.Categorical
(this now raisesTypeError
correctly), (GH8626)Bug in
cut
/qcut
when usingSeries
andretbins=True
(GH8589)Bug in writing Categorical columns to an SQL database with
to_sql
(GH8624).Bug in comparing
Categorical
of datetime raising when being compared to a scalar datetime (GH8687)Bug in selecting from a
Categorical
with.iloc
(GH8623)Bug in groupby-transform with a Categorical (GH8623)
Bug in duplicated/drop_duplicates with a Categorical (GH8623)
Bug in
Categorical
reflected comparison operator raising if the first argument was a numpy array scalar (e.g. np.int64) (GH8658)Bug in Panel indexing with a list-like (GH8710)
Compat issue is
DataFrame.dtypes
whenoptions.mode.use_inf_as_null
is True (GH8722)Bug in
read_csv
,dialect
parameter would not take a string (GH8703)Bug in slicing a MultiIndex level with an empty-list (GH8737)
Bug in numeric index operations of add/sub with Float/Index Index with numpy arrays (GH8608)
Bug in setitem with empty indexer and unwanted coercion of dtypes (GH8669)
Bug in ix/loc block splitting on setitem (manifests with integer-like dtypes, e.g. datetime64) (GH8607)
Bug when doing label based indexing with integers not found in the index for non-unique but monotonic indexes (GH8680).
Bug when indexing a Float64Index with
np.nan
on numpy 1.7 (GH8980).Fix
shape
attribute forMultiIndex
(GH8609)Bug in
GroupBy
where a name conflict between the grouper and columns would breakgroupby
operations (GH7115, GH8112)Fixed a bug where plotting a column
y
and specifying a label would mutate the index name of the original DataFrame (GH8494)Fix regression in plotting of a DatetimeIndex directly with matplotlib (GH8614).
Bug in
date_range
where partially-specified dates would incorporate current date (GH6961)Bug in Setting by indexer to a scalar value with a mixed-dtype
Panel4d
was failing (GH8702)Bug where
DataReader
’s would fail if one of the symbols passed was invalid. Now returns data for valid symbols and np.nan for invalid (GH8494)Bug in
get_quote_yahoo
that wouldn’t allow non-float return values (GH5229).
Contributors#
A total of 23 people contributed patches to this release. People with a “+” by their names contributed a patch for the first time.
Aaron Staple +
Andrew Rosenfeld
Anton I. Sipos
Artemy Kolchinsky
Bill Letson +
Dave Hughes +
David Stephens
Guillaume Horel +
Jeff Reback
Joris Van den Bossche
Kevin Sheppard
Nick Stahl +
Sanghee Kim +
Stephan Hoyer
Tom Augspurger
TomAugspurger
WANG Aiyong +
behzad nouri
immerrr
jnmclarty
jreback
pallav-fdsi +
unutbu