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, see here
Categorical
New scalar type Timedelta, and a new index type TimedeltaIndex, see here
Timedelta
TimedeltaIndex
New datetimelike properties accessor .dt for Series, see Datetimelike Properties
.dt
New DataFrame default display for df.info() to include memory usage, see Memory Usage
df.info()
read_csv will now by default ignore blank lines when parsing, see here
read_csv
API change in using Indexes in set operations, see here
Enhancements in the handling of timezones, see here
A lot of improvements to the rolling and expanding moment functions, see here
Internal refactoring of the Index class to no longer sub-class ndarray, see Internal Refactoring
Index
ndarray
dropping support for PyTables less than version 3.0.0, and numexpr less than version 2.1 (GH7990)
PyTables
numexpr
Split indexing documentation into Indexing and Selecting Data and MultiIndex / Advanced Indexing
Split out string methods documentation into Working with Text Data
Check the API Changes and deprecations before updating
Other Enhancements
Performance Improvements
Bug Fixes
In 0.15.0 Index has internally been refactored to no longer sub-class ndarray but instead subclass PandasObject, similarly to the rest of the pandas objects. This change allows very easy sub-classing and creation of new index types. This should be a transparent change with only very limited API implications (See the Internal Refactoring)
PandasObject
The refactoring in Categorical changed the two argument constructor from “codes/labels and levels” to “values and levels (now called ‘categories’)”. This can lead to subtle bugs. If you use Categorical directly, please audit your code before updating to this pandas version and change it to use the from_codes() constructor. See more on Categorical here
from_codes()
Categorical can now be included in Series and DataFrames and gained new methods to manipulate. Thanks to Jan Schulz for much of this API/implementation. (GH3943, GH5313, GH5314, GH7444, GH7839, GH7848, GH7864, GH7914, GH7768, GH8006, GH3678, GH8075, GH8076, GH8143, GH8453, GH8518).
For full docs, see the categorical introduction and the API documentation.
In [1]: df = pd.DataFrame({"id": [1, 2, 3, 4, 5, 6], ...: "raw_grade": ['a', 'b', 'b', 'a', 'a', 'e']}) ...: In [2]: df["grade"] = df["raw_grade"].astype("category") In [3]: df["grade"] Out[3]: 0 a 1 b 2 b 3 a 4 a 5 e Name: grade, Length: 6, dtype: category Categories (3, object): [a, b, e] # Rename the categories In [4]: df["grade"].cat.categories = ["very good", "good", "very bad"] # Reorder the categories and simultaneously add the missing categories In [5]: df["grade"] = df["grade"].cat.set_categories(["very bad", "bad", ...: "medium", "good", "very good"]) ...: In [6]: df["grade"] Out[6]: 0 very good 1 good 2 good 3 very good 4 very good 5 very bad Name: grade, Length: 6, dtype: category Categories (5, object): [very bad, bad, medium, good, very good] In [7]: df.sort_values("grade") Out[7]: id raw_grade grade 5 6 e very bad 1 2 b good 2 3 b good 0 1 a very good 3 4 a very good 4 5 a very good [6 rows x 3 columns] In [8]: df.groupby("grade").size() Out[8]: grade very bad 1 bad 0 medium 0 good 2 very good 3 Length: 5, dtype: int64
pandas.core.group_agg and pandas.core.factor_agg were removed. As an alternative, construct a dataframe and use df.groupby(<group>).agg(<func>).
pandas.core.group_agg
pandas.core.factor_agg
df.groupby(<group>).agg(<func>)
Supplying “codes/labels and levels” to the Categorical constructor is not supported anymore. Supplying two arguments to the constructor is now interpreted as “values and levels (now called ‘categories’)”. Please change your code to use the from_codes() constructor.
The Categorical.labels attribute was renamed to Categorical.codes and is read only. If you want to manipulate codes, please use one of the API methods on Categoricals.
Categorical.labels
Categorical.codes
The Categorical.levels attribute is renamed to Categorical.categories.
Categorical.levels
Categorical.categories
We introduce a new scalar type Timedelta, which is a subclass of datetime.timedelta, and behaves in a similar manner, but allows compatibility with np.timedelta64 types as well as a host of custom representation, parsing, and attributes. This type is very similar to how Timestamp works for datetimes. It is a nice-API box for the type. See the docs. (GH3009, GH4533, GH8209, GH8187, GH8190, GH7869, GH7661, GH8345, GH8471)
datetime.timedelta
np.timedelta64
Timestamp
datetimes
Timedelta scalars (and TimedeltaIndex) component fields are not the same as the component fields on a datetime.timedelta object. For example, .seconds on a datetime.timedelta object returns the total number of seconds combined between hours, minutes and seconds. In contrast, the pandas Timedelta breaks out hours, minutes, microseconds and nanoseconds separately.
.seconds
hours
minutes
seconds
# Timedelta accessor In [9]: tds = pd.Timedelta('31 days 5 min 3 sec') In [10]: tds.minutes Out[10]: 5L In [11]: tds.seconds Out[11]: 3L # datetime.timedelta accessor # this is 5 minutes * 60 + 3 seconds In [12]: tds.to_pytimedelta().seconds Out[12]: 303
Note: this is no longer true starting from v0.16.0, where full compatibility with datetime.timedelta is introduced. See the 0.16.0 whatsnew entry
Prior to 0.15.0 pd.to_timedelta would return a Series for list-like/Series input, and a np.timedelta64 for scalar input. It will now return a TimedeltaIndex for list-like input, Series for Series input, and Timedelta for scalar input.
pd.to_timedelta
Series
The arguments to pd.to_timedelta are now (arg,unit='ns',box=True,coerce=False), previously were (arg,box=True,unit='ns') as these are more logical.
(arg,unit='ns',box=True,coerce=False)
(arg,box=True,unit='ns')
Construct a scalar
In [9]: pd.Timedelta('1 days 06:05:01.00003') Out[9]: Timedelta('1 days 06:05:01.000030') In [10]: pd.Timedelta('15.5us') Out[10]: Timedelta('0 days 00:00:00.000015') In [11]: pd.Timedelta('1 hour 15.5us') Out[11]: Timedelta('0 days 01:00:00.000015') # negative Timedeltas have this string repr # to be more consistent with datetime.timedelta conventions In [12]: pd.Timedelta('-1us') Out[12]: Timedelta('-1 days +23:59:59.999999') # a NaT In [13]: pd.Timedelta('nan') Out[13]: NaT
Access fields for a Timedelta
In [14]: td = pd.Timedelta('1 hour 3m 15.5us') In [15]: td.seconds Out[15]: 3780 In [16]: td.microseconds Out[16]: 16 In [17]: td.nanoseconds Out[17]: 500
Construct a TimedeltaIndex
In [18]: pd.TimedeltaIndex(['1 days', '1 days, 00:00:05', ....: np.timedelta64(2, 'D'), ....: datetime.timedelta(days=2, seconds=2)]) ....: Out[18]: TimedeltaIndex(['1 days 00:00:00', '1 days 00:00:05', '2 days 00:00:00', '2 days 00:00:02'], dtype='timedelta64[ns]', freq=None)
Constructing a TimedeltaIndex with a regular range
In [19]: pd.timedelta_range('1 days', periods=5, freq='D') Out[19]: TimedeltaIndex(['1 days', '2 days', '3 days', '4 days', '5 days'], dtype='timedelta64[ns]', freq='D') In [20]: pd.timedelta_range(start='1 days', end='2 days', freq='30T') Out[20]: TimedeltaIndex(['1 days 00:00:00', '1 days 00:30:00', '1 days 01:00:00', '1 days 01:30:00', '1 days 02:00:00', '1 days 02:30:00', '1 days 03:00:00', '1 days 03:30:00', '1 days 04:00:00', '1 days 04:30:00', '1 days 05:00:00', '1 days 05:30:00', '1 days 06:00:00', '1 days 06:30:00', '1 days 07:00:00', '1 days 07:30:00', '1 days 08:00:00', '1 days 08:30:00', '1 days 09:00:00', '1 days 09:30:00', '1 days 10:00:00', '1 days 10:30:00', '1 days 11:00:00', '1 days 11:30:00', '1 days 12:00:00', '1 days 12:30:00', '1 days 13:00:00', '1 days 13:30:00', '1 days 14:00:00', '1 days 14:30:00', '1 days 15:00:00', '1 days 15:30:00', '1 days 16:00:00', '1 days 16:30:00', '1 days 17:00:00', '1 days 17:30:00', '1 days 18:00:00', '1 days 18:30:00', '1 days 19:00:00', '1 days 19:30:00', '1 days 20:00:00', '1 days 20:30:00', '1 days 21:00:00', '1 days 21:30:00', '1 days 22:00:00', '1 days 22:30:00', '1 days 23:00:00', '1 days 23:30:00', '2 days 00:00:00'], dtype='timedelta64[ns]', freq='30T')
You can now use a TimedeltaIndex as the index of a pandas object
In [21]: s = pd.Series(np.arange(5), ....: index=pd.timedelta_range('1 days', periods=5, freq='s')) ....: In [22]: s Out[22]: 1 days 00:00:00 0 1 days 00:00:01 1 1 days 00:00:02 2 1 days 00:00:03 3 1 days 00:00:04 4 Freq: S, Length: 5, dtype: int64
You can select with partial string selections
In [23]: s['1 day 00:00:02'] Out[23]: 2 In [24]: s['1 day':'1 day 00:00:02'] Out[24]: 1 days 00:00:00 0 1 days 00:00:01 1 1 days 00:00:02 2 Freq: S, Length: 3, dtype: int64
Finally, the combination of TimedeltaIndex with DatetimeIndex allow certain combination operations that are NaT preserving:
DatetimeIndex
NaT
In [25]: tdi = pd.TimedeltaIndex(['1 days', pd.NaT, '2 days']) In [26]: tdi.tolist() Out[26]: [Timedelta('1 days 00:00:00'), NaT, Timedelta('2 days 00:00:00')] In [27]: dti = pd.date_range('20130101', periods=3) In [28]: dti.tolist() Out[28]: [Timestamp('2013-01-01 00:00:00', freq='D'), Timestamp('2013-01-02 00:00:00', freq='D'), Timestamp('2013-01-03 00:00:00', freq='D')] In [29]: (dti + tdi).tolist() Out[29]: [Timestamp('2013-01-02 00:00:00'), NaT, Timestamp('2013-01-05 00:00:00')] In [30]: (dti - tdi).tolist() Out[30]: [Timestamp('2012-12-31 00:00:00'), NaT, Timestamp('2013-01-01 00:00:00')]
iteration of a Series e.g. list(Series(...)) of timedelta64[ns] would prior to v0.15.0 return np.timedelta64 for each element. These will now be wrapped in Timedelta.
list(Series(...))
timedelta64[ns]
Implemented methods to find memory usage of a DataFrame. See the FAQ for more. (GH6852).
A new display option display.memory_usage (see Options and settings) sets the default behavior of the memory_usage argument in the df.info() method. By default display.memory_usage is True.
display.memory_usage
memory_usage
True
In [31]: dtypes = ['int64', 'float64', 'datetime64[ns]', 'timedelta64[ns]', ....: 'complex128', 'object', 'bool'] ....: In [32]: n = 5000 In [33]: data = {t: np.random.randint(100, size=n).astype(t) for t in dtypes} In [34]: df = pd.DataFrame(data) In [35]: df['categorical'] = df['object'].astype('category') In [36]: df.info() <class 'pandas.core.frame.DataFrame'> RangeIndex: 5000 entries, 0 to 4999 Data columns (total 8 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 int64 5000 non-null int64 1 float64 5000 non-null float64 2 datetime64[ns] 5000 non-null datetime64[ns] 3 timedelta64[ns] 5000 non-null timedelta64[ns] 4 complex128 5000 non-null complex128 5 object 5000 non-null object 6 bool 5000 non-null bool 7 categorical 5000 non-null category dtypes: bool(1), category(1), complex128(1), datetime64[ns](1), float64(1), int64(1), object(1), timedelta64[ns](1) memory usage: 289.1+ KB
Additionally memory_usage() is an available method for a dataframe object which returns the memory usage of each column.
memory_usage()
In [37]: df.memory_usage(index=True) Out[37]: Index 128 int64 40000 float64 40000 datetime64[ns] 40000 timedelta64[ns] 40000 complex128 80000 object 40000 bool 5000 categorical 10920 Length: 9, dtype: int64
Series has gained an accessor to succinctly return datetime like properties for the values of the Series, if its a datetime/period like Series. (GH7207) This will return a Series, indexed like the existing Series. See the docs
# datetime In [38]: s = pd.Series(pd.date_range('20130101 09:10:12', periods=4)) In [39]: s Out[39]: 0 2013-01-01 09:10:12 1 2013-01-02 09:10:12 2 2013-01-03 09:10:12 3 2013-01-04 09:10:12 Length: 4, dtype: datetime64[ns] In [40]: s.dt.hour Out[40]: 0 9 1 9 2 9 3 9 Length: 4, dtype: int64 In [41]: s.dt.second Out[41]: 0 12 1 12 2 12 3 12 Length: 4, dtype: int64 In [42]: s.dt.day Out[42]: 0 1 1 2 2 3 3 4 Length: 4, dtype: int64 In [43]: s.dt.freq Out[43]: 'D'
This enables nice expressions like this:
In [44]: s[s.dt.day == 2] Out[44]: 1 2013-01-02 09:10:12 Length: 1, dtype: datetime64[ns]
You can easily produce tz aware transformations:
In [45]: stz = s.dt.tz_localize('US/Eastern') In [46]: stz Out[46]: 0 2013-01-01 09:10:12-05:00 1 2013-01-02 09:10:12-05:00 2 2013-01-03 09:10:12-05:00 3 2013-01-04 09:10:12-05:00 Length: 4, dtype: datetime64[ns, US/Eastern] In [47]: stz.dt.tz Out[47]: <DstTzInfo 'US/Eastern' LMT-1 day, 19:04:00 STD>
You can also chain these types of operations:
In [48]: s.dt.tz_localize('UTC').dt.tz_convert('US/Eastern') Out[48]: 0 2013-01-01 04:10:12-05:00 1 2013-01-02 04:10:12-05:00 2 2013-01-03 04:10:12-05:00 3 2013-01-04 04:10:12-05:00 Length: 4, dtype: datetime64[ns, US/Eastern]
The .dt accessor works for period and timedelta dtypes.
# period In [49]: s = pd.Series(pd.period_range('20130101', periods=4, freq='D')) In [50]: s Out[50]: 0 2013-01-01 1 2013-01-02 2 2013-01-03 3 2013-01-04 Length: 4, dtype: period[D] In [51]: s.dt.year Out[51]: 0 2013 1 2013 2 2013 3 2013 Length: 4, dtype: int64 In [52]: s.dt.day Out[52]: 0 1 1 2 2 3 3 4 Length: 4, dtype: int64
# timedelta In [53]: s = pd.Series(pd.timedelta_range('1 day 00:00:05', periods=4, freq='s')) In [54]: s Out[54]: 0 1 days 00:00:05 1 1 days 00:00:06 2 1 days 00:00:07 3 1 days 00:00:08 Length: 4, dtype: timedelta64[ns] In [55]: s.dt.days Out[55]: 0 1 1 1 2 1 3 1 Length: 4, dtype: int64 In [56]: s.dt.seconds Out[56]: 0 5 1 6 2 7 3 8 Length: 4, dtype: int64 In [57]: s.dt.components Out[57]: days hours minutes seconds milliseconds microseconds nanoseconds 0 1 0 0 5 0 0 0 1 1 0 0 6 0 0 0 2 1 0 0 7 0 0 0 3 1 0 0 8 0 0 0 [4 rows x 7 columns]
tz_localize(None) for tz-aware Timestamp and DatetimeIndex now removes timezone holding local time, previously this resulted in Exception or TypeError (GH7812)
tz_localize(None)
Exception
TypeError
In [58]: ts = pd.Timestamp('2014-08-01 09:00', tz='US/Eastern') In [59]: ts Out[59]: Timestamp('2014-08-01 09:00:00-0400', tz='US/Eastern') In [60]: ts.tz_localize(None) Out[60]: Timestamp('2014-08-01 09:00:00') In [61]: didx = pd.date_range(start='2014-08-01 09:00', freq='H', ....: periods=10, tz='US/Eastern') ....: In [62]: didx Out[62]: DatetimeIndex(['2014-08-01 09:00:00-04:00', '2014-08-01 10:00:00-04:00', '2014-08-01 11:00:00-04:00', '2014-08-01 12:00:00-04:00', '2014-08-01 13:00:00-04:00', '2014-08-01 14:00:00-04:00', '2014-08-01 15:00:00-04:00', '2014-08-01 16:00:00-04:00', '2014-08-01 17:00:00-04:00', '2014-08-01 18:00:00-04:00'], dtype='datetime64[ns, US/Eastern]', freq='H') In [63]: didx.tz_localize(None) Out[63]: DatetimeIndex(['2014-08-01 09:00:00', '2014-08-01 10:00:00', '2014-08-01 11:00:00', '2014-08-01 12:00:00', '2014-08-01 13:00:00', '2014-08-01 14:00:00', '2014-08-01 15:00:00', '2014-08-01 16:00:00', '2014-08-01 17:00:00', '2014-08-01 18:00:00'], dtype='datetime64[ns]', freq='H')
tz_localize now accepts the ambiguous keyword which allows for passing an array of bools indicating whether the date belongs in DST or not, ‘NaT’ for setting transition times to NaT, ‘infer’ for inferring DST/non-DST, and ‘raise’ (default) for an AmbiguousTimeError to be raised. See the docs for more details (GH7943)
tz_localize
ambiguous
AmbiguousTimeError
DataFrame.tz_localize and DataFrame.tz_convert now accepts an optional level argument for localizing a specific level of a MultiIndex (GH7846)
DataFrame.tz_localize
DataFrame.tz_convert
level
Timestamp.tz_localize and Timestamp.tz_convert now raise TypeError in error cases, rather than Exception (GH8025)
Timestamp.tz_localize
Timestamp.tz_convert
a timeseries/index localized to UTC when inserted into a Series/DataFrame will preserve the UTC timezone (rather than being a naive datetime64[ns]) as object dtype (GH8411)
datetime64[ns]
object
Timestamp.__repr__ displays dateutil.tz.tzoffset info (GH7907)
Timestamp.__repr__
dateutil.tz.tzoffset
rolling_min(), rolling_max(), rolling_cov(), and rolling_corr() now return objects with all NaN when len(arg) < min_periods <= window rather than raising. (This makes all rolling functions consistent in this behavior). (GH7766)
rolling_min()
rolling_max()
rolling_cov()
rolling_corr()
NaN
len(arg) < min_periods <= window
Prior to 0.15.0
In [64]: s = pd.Series([10, 11, 12, 13])
In [15]: pd.rolling_min(s, window=10, min_periods=5) ValueError: min_periods (5) must be <= window (4)
New behavior
In [4]: pd.rolling_min(s, window=10, min_periods=5) Out[4]: 0 NaN 1 NaN 2 NaN 3 NaN dtype: float64
rolling_max(), rolling_min(), rolling_sum(), rolling_mean(), rolling_median(), rolling_std(), rolling_var(), rolling_skew(), rolling_kurt(), rolling_quantile(), rolling_cov(), rolling_corr(), rolling_corr_pairwise(), rolling_window(), and rolling_apply() with center=True previously would return a result of the same structure as the input arg with NaN in the final (window-1)/2 entries.
rolling_sum()
rolling_mean()
rolling_median()
rolling_std()
rolling_var()
rolling_skew()
rolling_kurt()
rolling_quantile()
rolling_corr_pairwise()
rolling_window()
rolling_apply()
center=True
arg
(window-1)/2
Now the final (window-1)/2 entries of the result are calculated as if the input arg were followed by (window-1)/2 NaN values (or with shrinking windows, in the case of rolling_apply()). (GH7925, GH8269)
Prior behavior (note final value is NaN):
In [7]: pd.rolling_sum(Series(range(4)), window=3, min_periods=0, center=True) Out[7]: 0 1 1 3 2 6 3 NaN dtype: float64
New behavior (note final value is 5 = sum([2, 3, NaN])):
5 = sum([2, 3, NaN])
In [7]: pd.rolling_sum(pd.Series(range(4)), window=3, ....: min_periods=0, center=True) Out[7]: 0 1 1 3 2 6 3 5 dtype: float64
rolling_window() now normalizes the weights properly in rolling mean mode (mean=True) so that the calculated weighted means (e.g. ‘triang’, ‘gaussian’) are distributed about the same means as those calculated without weighting (i.e. ‘boxcar’). See the note on normalization for further details. (GH7618)
In [65]: s = pd.Series([10.5, 8.8, 11.4, 9.7, 9.3])
Behavior prior to 0.15.0:
In [39]: pd.rolling_window(s, window=3, win_type='triang', center=True) Out[39]: 0 NaN 1 6.583333 2 6.883333 3 6.683333 4 NaN dtype: float64
In [10]: pd.rolling_window(s, window=3, win_type='triang', center=True) Out[10]: 0 NaN 1 9.875 2 10.325 3 10.025 4 NaN dtype: float64
Removed center argument from all expanding_ functions (see list), as the results produced when center=True did not make much sense. (GH7925)
center
expanding_
Added optional ddof argument to expanding_cov() and rolling_cov(). The default value of 1 is backwards-compatible. (GH8279)
ddof
expanding_cov()
1
Documented the ddof argument to expanding_var(), expanding_std(), rolling_var(), and rolling_std(). These functions’ support of a ddof argument (with a default value of 1) was previously undocumented. (GH8064)
expanding_var()
expanding_std()
ewma(), ewmstd(), ewmvol(), ewmvar(), ewmcov(), and ewmcorr() now interpret min_periods in the same manner that the rolling_*() and expanding_*() functions do: a given result entry will be NaN if the (expanding, in this case) window does not contain at least min_periods values. The previous behavior was to set to NaN the min_periods entries starting with the first non- NaN value. (GH7977)
ewma()
ewmstd()
ewmvol()
ewmvar()
ewmcov()
ewmcorr()
min_periods
rolling_*()
expanding_*()
Prior behavior (note values start at index 2, which is min_periods after index 0 (the index of the first non-empty value)):
2
0
In [66]: s = pd.Series([1, None, None, None, 2, 3])
In [51]: ewma(s, com=3., min_periods=2) Out[51]: 0 NaN 1 NaN 2 1.000000 3 1.000000 4 1.571429 5 2.189189 dtype: float64
New behavior (note values start at index 4, the location of the 2nd (since min_periods=2) non-empty value):
4
min_periods=2
In [2]: pd.ewma(s, com=3., min_periods=2) Out[2]: 0 NaN 1 NaN 2 NaN 3 NaN 4 1.759644 5 2.383784 dtype: float64
ewmstd(), ewmvol(), ewmvar(), ewmcov(), and ewmcorr() now have an optional adjust argument, just like ewma() does, affecting how the weights are calculated. The default value of adjust is True, which is backwards-compatible. See Exponentially weighted moment functions for details. (GH7911)
adjust
ewma(), ewmstd(), ewmvol(), ewmvar(), ewmcov(), and ewmcorr() now have an optional ignore_na argument. When ignore_na=False (the default), missing values are taken into account in the weights calculation. When ignore_na=True (which reproduces the pre-0.15.0 behavior), missing values are ignored in the weights calculation. (GH7543)
ignore_na
ignore_na=False
ignore_na=True
In [7]: pd.ewma(pd.Series([None, 1., 8.]), com=2.) Out[7]: 0 NaN 1 1.0 2 5.2 dtype: float64 In [8]: pd.ewma(pd.Series([1., None, 8.]), com=2., ....: ignore_na=True) # pre-0.15.0 behavior Out[8]: 0 1.0 1 1.0 2 5.2 dtype: float64 In [9]: pd.ewma(pd.Series([1., None, 8.]), com=2., ....: ignore_na=False) # new default Out[9]: 0 1.000000 1 1.000000 2 5.846154 dtype: float64
By default (ignore_na=False) the ewm*() functions’ weights calculation in the presence of missing values is different than in pre-0.15.0 versions. To reproduce the pre-0.15.0 calculation of weights in the presence of missing values one must specify explicitly ignore_na=True.
ewm*()
Bug in expanding_cov(), expanding_corr(), rolling_cov(), rolling_cor(), ewmcov(), and ewmcorr() returning results with columns sorted by name and producing an error for non-unique columns; now handles non-unique columns and returns columns in original order (except for the case of two DataFrames with pairwise=False, where behavior is unchanged) (GH7542)
expanding_corr()
rolling_cor()
pairwise=False
Bug in rolling_count() and expanding_*() functions unnecessarily producing error message for zero-length data (GH8056)
rolling_count()
Bug in rolling_apply() and expanding_apply() interpreting min_periods=0 as min_periods=1 (GH8080)
expanding_apply()
min_periods=0
min_periods=1
Bug in expanding_std() and expanding_var() for a single value producing a confusing error message (GH7900)
Bug in rolling_std() and rolling_var() for a single value producing 0 rather than NaN (GH7900)
Bug in ewmstd(), ewmvol(), ewmvar(), and ewmcov() calculation of de-biasing factors when bias=False (the default). Previously an incorrect constant factor was used, based on adjust=True, ignore_na=True, and an infinite number of observations. Now a different factor is used for each entry, based on the actual weights (analogous to the usual N/(N-1) factor). In particular, for a single point a value of NaN is returned when bias=False, whereas previously a value of (approximately) 0 was returned.
bias=False
adjust=True
N/(N-1)
For example, consider the following pre-0.15.0 results for ewmvar(..., bias=False), and the corresponding debiasing factors:
ewmvar(..., bias=False)
In [67]: s = pd.Series([1., 2., 0., 4.])
In [89]: ewmvar(s, com=2., bias=False) Out[89]: 0 -2.775558e-16 1 3.000000e-01 2 9.556787e-01 3 3.585799e+00 dtype: float64 In [90]: ewmvar(s, com=2., bias=False) / ewmvar(s, com=2., bias=True) Out[90]: 0 1.25 1 1.25 2 1.25 3 1.25 dtype: float64
Note that entry 0 is approximately 0, and the debiasing factors are a constant 1.25. By comparison, the following 0.15.0 results have a NaN for entry 0, and the debiasing factors are decreasing (towards 1.25):
In [14]: pd.ewmvar(s, com=2., bias=False) Out[14]: 0 NaN 1 0.500000 2 1.210526 3 4.089069 dtype: float64 In [15]: pd.ewmvar(s, com=2., bias=False) / pd.ewmvar(s, com=2., bias=True) Out[15]: 0 NaN 1 2.083333 2 1.583333 3 1.425439 dtype: float64
See Exponentially weighted moment functions for details. (GH7912)
Added support for a chunksize parameter to to_sql function. This allows DataFrame to be written in chunks and avoid packet-size overflow errors (GH8062).
chunksize
to_sql
Added support for a chunksize parameter to read_sql function. Specifying this argument will return an iterator through chunks of the query result (GH2908).
read_sql
Added support for writing datetime.date and datetime.time object columns with to_sql (GH6932).
datetime.date
datetime.time
Added support for specifying a schema to read from/write to with read_sql_table and to_sql (GH7441, GH7952). For example:
schema
read_sql_table
df.to_sql('table', engine, schema='other_schema') # noqa F821 pd.read_sql_table('table', engine, schema='other_schema') # noqa F821
Added support for writing NaN values with to_sql (GH2754).
Added support for writing datetime64 columns with to_sql for all database flavors (GH7103).
API changes related to Categorical (see here for more details):
The Categorical constructor with two arguments changed from “codes/labels and levels” to “values and levels (now called ‘categories’)”. This can lead to subtle bugs. If you use Categorical directly, please audit your code by changing it to use the from_codes() constructor.
An old function call like (prior to 0.15.0):
pd.Categorical([0,1,0,2,1], levels=['a', 'b', 'c'])
will have to adapted to the following to keep the same behaviour:
In [2]: pd.Categorical.from_codes([0,1,0,2,1], categories=['a', 'b', 'c']) Out[2]: [a, b, a, c, b] Categories (3, object): [a, b, c]
API changes related to the introduction of the Timedelta scalar (see above for more details):
Prior to 0.15.0 to_timedelta() would return a Series for list-like/Series input, and a np.timedelta64 for scalar input. It will now return a TimedeltaIndex for list-like input, Series for Series input, and Timedelta for scalar input.
to_timedelta()
For API changes related to the rolling and expanding functions, see detailed overview above.
Other notable API changes:
Consistency when indexing with .loc and a list-like indexer when no values are found.
.loc
In [68]: df = pd.DataFrame([['a'], ['b']], index=[1, 2]) In [69]: df Out[69]: 0 1 a 2 b [2 rows x 1 columns]
In prior versions there was a difference in these two constructs:
df.loc[[3]] would return a frame reindexed by 3 (with all np.nan values)
df.loc[[3]]
np.nan
df.loc[[3],:] would raise KeyError.
df.loc[[3],:]
KeyError
Both will now raise a KeyError. The rule is that at least 1 indexer must be found when using a list-like and .loc (GH7999)
Furthermore in prior versions these were also different:
df.loc[[1,3]] would return a frame reindexed by [1,3]
df.loc[[1,3]]
df.loc[[1,3],:] would raise KeyError.
df.loc[[1,3],:]
Both will now return a frame reindex by [1,3]. E.g.
In [3]: df.loc[[1, 3]] Out[3]: 0 1 a 3 NaN In [4]: df.loc[[1, 3], :] Out[4]: 0 1 a 3 NaN
This can also be seen in multi-axis indexing with a Panel.
Panel
>>> p = pd.Panel(np.arange(2 * 3 * 4).reshape(2, 3, 4), ... items=['ItemA', 'ItemB'], ... major_axis=[1, 2, 3], ... minor_axis=['A', 'B', 'C', 'D']) >>> p <class 'pandas.core.panel.Panel'> Dimensions: 2 (items) x 3 (major_axis) x 4 (minor_axis) Items axis: ItemA to ItemB Major_axis axis: 1 to 3 Minor_axis axis: A to D
The following would raise KeyError prior to 0.15.0:
In [5]: Out[5]: ItemA ItemD 1 3 NaN 2 7 NaN 3 11 NaN
Furthermore, .loc will raise If no values are found in a MultiIndex with a list-like indexer:
In [70]: s = pd.Series(np.arange(3, dtype='int64'), ....: index=pd.MultiIndex.from_product([['A'], ....: ['foo', 'bar', 'baz']], ....: names=['one', 'two']) ....: ).sort_index() ....: In [71]: s Out[71]: one two A bar 1 baz 2 foo 0 Length: 3, dtype: int64 In [72]: try: ....: s.loc[['D']] ....: except KeyError as e: ....: print("KeyError: " + str(e)) ....:
Assigning values to None now considers the dtype when choosing an ‘empty’ value (GH7941).
None
Previously, assigning to None in numeric containers changed the dtype to object (or errored, depending on the call). It now uses NaN:
In [73]: s = pd.Series([1, 2, 3]) In [74]: s.loc[0] = None In [75]: s Out[75]: 0 NaN 1 2.0 2 3.0 Length: 3, dtype: float64
NaT is now used similarly for datetime containers.
For object containers, we now preserve None values (previously these were converted to NaN values).
In [76]: s = pd.Series(["a", "b", "c"]) In [77]: s.loc[0] = None In [78]: s Out[78]: 0 None 1 b 2 c Length: 3, dtype: object
To insert a NaN, you must explicitly use np.nan. See the docs.
In prior versions, updating a pandas object inplace would not reflect in other python references to this object. (GH8511, GH5104)
In [79]: s = pd.Series([1, 2, 3]) In [80]: s2 = s In [81]: s += 1.5
Behavior prior to v0.15.0
# the original object In [5]: s Out[5]: 0 2.5 1 3.5 2 4.5 dtype: float64 # a reference to the original object In [7]: s2 Out[7]: 0 1 1 2 2 3 dtype: int64
This is now the correct behavior
# the original object In [82]: s Out[82]: 0 2.5 1 3.5 2 4.5 Length: 3, dtype: float64 # a reference to the original object In [83]: s2 Out[83]: 0 2.5 1 3.5 2 4.5 Length: 3, dtype: float64
Made both the C-based and Python engines for read_csv and read_table ignore empty lines in input as well as white space-filled lines, as long as sep is not white space. This is an API change that can be controlled by the keyword parameter skip_blank_lines. See the docs (GH4466)
sep
skip_blank_lines
A timeseries/index localized to UTC when inserted into a Series/DataFrame will preserve the UTC timezone and inserted as object dtype rather than being converted to a naive datetime64[ns] (GH8411).
Bug in passing a DatetimeIndex with a timezone that was not being retained in DataFrame construction from a dict (GH7822)
In prior versions this would drop the timezone, now it retains the timezone, but gives a column of object dtype:
In [84]: i = pd.date_range('1/1/2011', periods=3, freq='10s', tz='US/Eastern') In [85]: i Out[85]: DatetimeIndex(['2011-01-01 00:00:00-05:00', '2011-01-01 00:00:10-05:00', '2011-01-01 00:00:20-05:00'], dtype='datetime64[ns, US/Eastern]', freq='10S') In [86]: df = pd.DataFrame({'a': i}) In [87]: df Out[87]: a 0 2011-01-01 00:00:00-05:00 1 2011-01-01 00:00:10-05:00 2 2011-01-01 00:00:20-05:00 [3 rows x 1 columns] In [88]: df.dtypes Out[88]: a datetime64[ns, US/Eastern] Length: 1, dtype: object
Previously this would have yielded a column of datetime64 dtype, but without timezone info.
datetime64
The behaviour of assigning a column to an existing dataframe as df[‘a’] = i remains unchanged (this already returned an object column with a timezone).
When passing multiple levels to stack(), it will now raise a ValueError when the levels aren’t all level names or all level numbers (GH7660). See Reshaping by stacking and unstacking.
stack()
ValueError
Raise a ValueError in df.to_hdf with ‘fixed’ format, if df has non-unique columns as the resulting file will be broken (GH7761)
df.to_hdf
df
SettingWithCopy raise/warnings (according to the option mode.chained_assignment) will now be issued when setting a value on a sliced mixed-dtype DataFrame using chained-assignment. (GH7845, GH7950)
SettingWithCopy
mode.chained_assignment
In [1]: df = pd.DataFrame(np.arange(0, 9), columns=['count']) In [2]: df['group'] = 'b' In [3]: df.iloc[0:5]['group'] = 'a' /usr/local/bin/ipython:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
merge, DataFrame.merge, and ordered_merge now return the same type as the left argument (GH7737).
merge
DataFrame.merge
ordered_merge
left
Previously an enlargement with a mixed-dtype frame would act unlike .append which will preserve dtypes (related GH2578, GH8176):
.append
In [89]: df = pd.DataFrame([[True, 1], [False, 2]], ....: columns=["female", "fitness"]) ....: In [90]: df Out[90]: female fitness 0 True 1 1 False 2 [2 rows x 2 columns] In [91]: df.dtypes Out[91]: female bool fitness int64 Length: 2, dtype: object # dtypes are now preserved In [92]: df.loc[2] = df.loc[1] In [93]: df Out[93]: female fitness 0 True 1 1 False 2 2 False 2 [3 rows x 2 columns] In [94]: df.dtypes Out[94]: female bool fitness int64 Length: 2, dtype: object
Series.to_csv() now returns a string when path=None, matching the behaviour of DataFrame.to_csv() (GH8215).
Series.to_csv()
path=None
DataFrame.to_csv()
read_hdf now raises IOError when a file that doesn’t exist is passed in. Previously, a new, empty file was created, and a KeyError raised (GH7715).
read_hdf
IOError
DataFrame.info() now ends its output with a newline character (GH8114)
DataFrame.info()
Concatenating no objects will now raise a ValueError rather than a bare Exception.
Merge errors will now be sub-classes of ValueError rather than raw Exception (GH8501)
DataFrame.plot and Series.plot keywords are now have consistent orders (GH8037)
DataFrame.plot
Series.plot
In 0.15.0 Index has internally been refactored to no longer sub-class ndarray but instead subclass PandasObject, similarly to the rest of the pandas objects. This change allows very easy sub-classing and creation of new index types. This should be a transparent change with only very limited API implications (GH5080, GH7439, GH7796, GH8024, GH8367, GH7997, GH8522):
you may need to unpickle pandas version < 0.15.0 pickles using pd.read_pickle rather than pickle.load. See pickle docs
pd.read_pickle
pickle.load
when plotting with a PeriodIndex, the matplotlib internal axes will now be arrays of Period rather than a PeriodIndex (this is similar to how a DatetimeIndex passes arrays of datetimes now)
PeriodIndex
Period
MultiIndexes will now raise similarly to other pandas objects w.r.t. truth testing, see here (GH7897).
When plotting a DatetimeIndex directly with matplotlib’s plot function, the axis labels will no longer be formatted as dates but as integers (the internal representation of a datetime64). UPDATE This is fixed in 0.15.1, see here.
The attributes Categorical labels and levels attributes are deprecated and renamed to codes and categories.
labels
levels
codes
categories
The outtype argument to pd.DataFrame.to_dict has been deprecated in favor of orient. (GH7840)
outtype
pd.DataFrame.to_dict
orient
The convert_dummies method has been deprecated in favor of get_dummies (GH8140)
convert_dummies
get_dummies
The infer_dst argument in tz_localize will be deprecated in favor of ambiguous to allow for more flexibility in dealing with DST transitions. Replace infer_dst=True with ambiguous='infer' for the same behavior (GH7943). See the docs for more details.
infer_dst
infer_dst=True
ambiguous='infer'
The top-level pd.value_range has been deprecated and can be replaced by .describe() (GH8481)
pd.value_range
.describe()
The Index set operations + and - were deprecated in order to provide these for numeric type operations on certain index types. + can be replaced by .union() or |, and - by .difference(). Further the method name Index.diff() is deprecated and can be replaced by Index.difference() (GH8226)
+
-
.union()
|
.difference()
Index.diff()
Index.difference()
# + pd.Index(['a', 'b', 'c']) + pd.Index(['b', 'c', 'd']) # should be replaced by pd.Index(['a', 'b', 'c']).union(pd.Index(['b', 'c', 'd']))
# - pd.Index(['a', 'b', 'c']) - pd.Index(['b', 'c', 'd']) # should be replaced by pd.Index(['a', 'b', 'c']).difference(pd.Index(['b', 'c', 'd']))
The infer_types argument to read_html() now has no effect and is deprecated (GH7762, GH7032).
infer_types
read_html()
Remove DataFrame.delevel method in favor of DataFrame.reset_index
DataFrame.delevel
DataFrame.reset_index
Enhancements in the importing/exporting of Stata files:
Added support for bool, uint8, uint16 and uint32 data types in to_stata (GH7097, GH7365)
to_stata
Added conversion option when importing Stata files (GH8527)
DataFrame.to_stata and StataWriter check string length for compatibility with limitations imposed in dta files where fixed-width strings must contain 244 or fewer characters. Attempting to write Stata dta files with strings longer than 244 characters raises a ValueError. (GH7858)
DataFrame.to_stata
StataWriter
read_stata and StataReader can import missing data information into a DataFrame by setting the argument convert_missing to True. When using this options, missing values are returned as StataMissingValue objects and columns containing missing values have object data type. (GH8045)
read_stata
StataReader
DataFrame
convert_missing
StataMissingValue
Enhancements in the plotting functions:
Added layout keyword to DataFrame.plot. You can pass a tuple of (rows, columns), one of which can be -1 to automatically infer (GH6667, GH8071).
layout
(rows, columns)
-1
Allow to pass multiple axes to DataFrame.plot, hist and boxplot (GH5353, GH6970, GH7069)
hist
boxplot
Added support for c, colormap and colorbar arguments for DataFrame.plot with kind='scatter' (GH7780)
c
colormap
colorbar
kind='scatter'
Histogram from DataFrame.plot with kind='hist' (GH7809), See the docs.
kind='hist'
Boxplot from DataFrame.plot with kind='box' (GH7998), See the docs.
kind='box'
Other:
read_csv now has a keyword parameter float_precision which specifies which floating-point converter the C engine should use during parsing, see here (GH8002, GH8044)
float_precision
Added searchsorted method to Series objects (GH7447)
searchsorted
describe() on mixed-types DataFrames is more flexible. Type-based column filtering is now possible via the include/exclude arguments. See the docs (GH8164).
describe()
include
exclude
In [95]: df = pd.DataFrame({'catA': ['foo', 'foo', 'bar'] * 8, ....: 'catB': ['a', 'b', 'c', 'd'] * 6, ....: 'numC': np.arange(24), ....: 'numD': np.arange(24.) + .5}) ....: In [96]: df.describe(include=["object"]) Out[96]: catA catB count 24 24 unique 2 4 top foo d freq 16 6 [4 rows x 2 columns] In [97]: df.describe(include=["number", "object"], exclude=["float"]) Out[97]: catA catB numC count 24 24 24.000000 unique 2 4 NaN top foo d NaN freq 16 6 NaN mean NaN NaN 11.500000 std NaN NaN 7.071068 min NaN NaN 0.000000 25% NaN NaN 5.750000 50% NaN NaN 11.500000 75% NaN NaN 17.250000 max NaN NaN 23.000000 [11 rows x 3 columns]
Requesting all columns is possible with the shorthand ‘all’
In [98]: df.describe(include='all') Out[98]: catA catB numC numD count 24 24 24.000000 24.000000 unique 2 4 NaN NaN top foo d NaN NaN freq 16 6 NaN NaN mean NaN NaN 11.500000 12.000000 std NaN NaN 7.071068 7.071068 min NaN NaN 0.000000 0.500000 25% NaN NaN 5.750000 6.250000 50% NaN NaN 11.500000 12.000000 75% NaN NaN 17.250000 17.750000 max NaN NaN 23.000000 23.500000 [11 rows x 4 columns]
Without those arguments, describe will behave as before, including only numerical columns or, if none are, only categorical columns. See also the docs
describe
Added split as an option to the orient argument in pd.DataFrame.to_dict. (GH7840)
split
The get_dummies method can now be used on DataFrames. By default only categorical columns are encoded as 0’s and 1’s, while other columns are left untouched.
In [99]: df = pd.DataFrame({'A': ['a', 'b', 'a'], 'B': ['c', 'c', 'b'], ....: 'C': [1, 2, 3]}) ....: In [100]: pd.get_dummies(df) Out[100]: C A_a A_b B_b B_c 0 1 1 0 0 1 1 2 0 1 0 1 2 3 1 0 1 0 [3 rows x 5 columns]
PeriodIndex supports resolution as the same as DatetimeIndex (GH7708)
resolution
pandas.tseries.holiday has added support for additional holidays and ways to observe holidays (GH7070)
pandas.tseries.holiday
pandas.tseries.holiday.Holiday now supports a list of offsets in Python3 (GH7070)
pandas.tseries.holiday.Holiday
pandas.tseries.holiday.Holiday now supports a days_of_week parameter (GH7070)
GroupBy.nth() now supports selecting multiple nth values (GH7910)
GroupBy.nth()
In [101]: business_dates = pd.date_range(start='4/1/2014', end='6/30/2014', freq='B') In [102]: df = pd.DataFrame(1, index=business_dates, columns=['a', 'b']) # get the first, 4th, and last date index for each month In [103]: df.groupby([df.index.year, df.index.month]).nth([0, 3, -1]) Out[103]: a b 2014 4 1 1 4 1 1 4 1 1 5 1 1 5 1 1 5 1 1 6 1 1 6 1 1 6 1 1 [9 rows x 2 columns]
Period and PeriodIndex supports addition/subtraction with timedelta-likes (GH7966)
timedelta
If Period freq is D, H, T, S, L, U, N, Timedelta-like can be added if the result can have same freq. Otherwise, only the same offsets can be added.
D
H
T
S
L
U
N
offsets
In [104]: idx = pd.period_range('2014-07-01 09:00', periods=5, freq='H') In [105]: idx Out[105]: PeriodIndex(['2014-07-01 09:00', '2014-07-01 10:00', '2014-07-01 11:00', '2014-07-01 12:00', '2014-07-01 13:00'], dtype='period[H]', freq='H') In [106]: idx + pd.offsets.Hour(2) Out[106]: PeriodIndex(['2014-07-01 11:00', '2014-07-01 12:00', '2014-07-01 13:00', '2014-07-01 14:00', '2014-07-01 15:00'], dtype='period[H]', freq='H') In [107]: idx + pd.Timedelta('120m') Out[107]: PeriodIndex(['2014-07-01 11:00', '2014-07-01 12:00', '2014-07-01 13:00', '2014-07-01 14:00', '2014-07-01 15:00'], dtype='period[H]', freq='H') In [108]: idx = pd.period_range('2014-07', periods=5, freq='M') In [109]: idx Out[109]: PeriodIndex(['2014-07', '2014-08', '2014-09', '2014-10', '2014-11'], dtype='period[M]', freq='M') In [110]: idx + pd.offsets.MonthEnd(3) Out[110]: PeriodIndex(['2014-10', '2014-11', '2014-12', '2015-01', '2015-02'], dtype='period[M]', freq='M')
Added experimental compatibility with openpyxl for versions >= 2.0. The DataFrame.to_excel method engine keyword now recognizes openpyxl1 and openpyxl2 which will explicitly require openpyxl v1 and v2 respectively, failing if the requested version is not available. The openpyxl engine is a now a meta-engine that automatically uses whichever version of openpyxl is installed. (GH7177)
openpyxl
DataFrame.to_excel
engine
openpyxl1
openpyxl2
DataFrame.fillna can now accept a DataFrame as a fill value (GH8377)
DataFrame.fillna
Passing multiple levels to stack() will now work when multiple level numbers are passed (GH7660). See Reshaping by stacking and unstacking.
set_names(), set_labels(), and set_levels() methods now take an optional level keyword argument to all modification of specific level(s) of a MultiIndex. Additionally set_names() now accepts a scalar string value when operating on an Index or on a specific level of a MultiIndex (GH7792)
set_names()
set_labels()
set_levels()
MultiIndex
In [111]: idx = pd.MultiIndex.from_product([['a'], range(3), list("pqr")], .....: names=['foo', 'bar', 'baz']) .....: In [112]: idx.set_names('qux', level=0) Out[112]: MultiIndex([('a', 0, 'p'), ('a', 0, 'q'), ('a', 0, 'r'), ('a', 1, 'p'), ('a', 1, 'q'), ('a', 1, 'r'), ('a', 2, 'p'), ('a', 2, 'q'), ('a', 2, 'r')], names=['qux', 'bar', 'baz']) In [113]: idx.set_names(['qux', 'corge'], level=[0, 1]) Out[113]: MultiIndex([('a', 0, 'p'), ('a', 0, 'q'), ('a', 0, 'r'), ('a', 1, 'p'), ('a', 1, 'q'), ('a', 1, 'r'), ('a', 2, 'p'), ('a', 2, 'q'), ('a', 2, 'r')], names=['qux', 'corge', 'baz']) In [114]: idx.set_levels(['a', 'b', 'c'], level='bar') Out[114]: MultiIndex([('a', 'a', 'p'), ('a', 'a', 'q'), ('a', 'a', 'r'), ('a', 'b', 'p'), ('a', 'b', 'q'), ('a', 'b', 'r'), ('a', 'c', 'p'), ('a', 'c', 'q'), ('a', 'c', 'r')], names=['foo', 'bar', 'baz']) In [115]: idx.set_levels([['a', 'b', 'c'], [1, 2, 3]], level=[1, 2]) Out[115]: MultiIndex([('a', 'a', 1), ('a', 'a', 2), ('a', 'a', 3), ('a', 'b', 1), ('a', 'b', 2), ('a', 'b', 3), ('a', 'c', 1), ('a', 'c', 2), ('a', 'c', 3)], names=['foo', 'bar', 'baz'])
Index.isin now supports a level argument to specify which index level to use for membership tests (GH7892, GH7890)
Index.isin
In [1]: idx = pd.MultiIndex.from_product([[0, 1], ['a', 'b', 'c']]) In [2]: idx.values Out[2]: array([(0, 'a'), (0, 'b'), (0, 'c'), (1, 'a'), (1, 'b'), (1, 'c')], dtype=object) In [3]: idx.isin(['a', 'c', 'e'], level=1) Out[3]: array([ True, False, True, True, False, True], dtype=bool)
Index now supports duplicated and drop_duplicates. (GH4060)
duplicated
drop_duplicates
In [116]: idx = pd.Index([1, 2, 3, 4, 1, 2]) In [117]: idx Out[117]: Int64Index([1, 2, 3, 4, 1, 2], dtype='int64') In [118]: idx.duplicated() Out[118]: array([False, False, False, False, True, True]) In [119]: idx.drop_duplicates() Out[119]: Int64Index([1, 2, 3, 4], dtype='int64')
add copy=True argument to pd.concat to enable pass through of complete blocks (GH8252)
copy=True
pd.concat
Added support for numpy 1.8+ data types (bool_, int_, float_, string_) for conversion to R dataframe (GH8400)
bool_
int_
float_
string_
Performance improvements in DatetimeIndex.__iter__ to allow faster iteration (GH7683)
DatetimeIndex.__iter__
Performance improvements in Period creation (and PeriodIndex setitem) (GH5155)
Improvements in Series.transform for significant performance gains (revised) (GH6496)
Performance improvements in StataReader when reading large files (GH8040, GH8073)
Performance improvements in StataWriter when writing large files (GH8079)
Performance and memory usage improvements in multi-key groupby (GH8128)
groupby
Performance improvements in groupby .agg and .apply where builtins max/min were not mapped to numpy/cythonized versions (GH7722)
.agg
.apply
Performance improvement in writing to sql (to_sql) of up to 50% (GH8208).
Performance benchmarking of groupby for large value of ngroups (GH6787)
Performance improvement in CustomBusinessDay, CustomBusinessMonth (GH8236)
CustomBusinessDay
CustomBusinessMonth
Performance improvement for MultiIndex.values for multi-level indexes containing datetimes (GH8543)
MultiIndex.values
Bug in pivot_table, when using margins and a dict aggfunc (GH8349)
Bug in read_csv where squeeze=True would return a view (GH8217)
squeeze=True
Bug in checking of table name in read_sql in certain cases (GH7826).
Bug in DataFrame.groupby where Grouper does not recognize level when frequency is specified (GH7885)
DataFrame.groupby
Grouper
Bug in multiindexes dtypes getting mixed up when DataFrame is saved to SQL table (GH8021)
Bug in Series 0-division with a float and integer operand dtypes (GH7785)
Bug in Series.astype("unicode") not calling unicode on the values correctly (GH7758)
Series.astype("unicode")
unicode
Bug in DataFrame.as_matrix() with mixed datetime64[ns] and timedelta64[ns] dtypes (GH7778)
DataFrame.as_matrix()
Bug in HDFStore.select_column() not preserving UTC timezone info when selecting a DatetimeIndex (GH7777)
HDFStore.select_column()
Bug in to_datetime when format='%Y%m%d' and coerce=True are specified, where previously an object array was returned (rather than a coerced time-series with NaT), (GH7930)
to_datetime
format='%Y%m%d'
coerce=True
Bug in DatetimeIndex and PeriodIndex in-place addition and subtraction cause different result from normal one (GH6527)
Bug in adding and subtracting PeriodIndex with PeriodIndex raise TypeError (GH7741)
Bug in combine_first with PeriodIndex data raises TypeError (GH3367)
combine_first
Bug in MultiIndex slicing with missing indexers (GH7866)
Bug in MultiIndex slicing with various edge cases (GH8132)
Regression in MultiIndex indexing with a non-scalar type object (GH7914)
Bug in Timestamp comparisons with == and int64 dtype (GH8058)
==
int64
Bug in pickles contains DateOffset may raise AttributeError when normalize attribute is referred internally (GH7748)
DateOffset
AttributeError
normalize
Bug in Panel when using major_xs and copy=False is passed (deprecation warning fails because of missing warnings) (GH8152).
major_xs
copy=False
warnings
Bug in pickle deserialization that failed for pre-0.14.1 containers with dup items trying to avoid ambiguity when matching block and manager items, when there’s only one block there’s no ambiguity (GH7794)
Bug in putting a PeriodIndex into a Series would convert to int64 dtype, rather than object of Periods (GH7932)
Periods
Bug in HDFStore iteration when passing a where (GH8014)
HDFStore
Bug in DataFrameGroupby.transform when transforming with a passed non-sorted key (GH8046, GH8430)
DataFrameGroupby.transform
Bug in repeated timeseries line and area plot may result in ValueError or incorrect kind (GH7733)
Bug in inference in a MultiIndex with datetime.date inputs (GH7888)
Bug in get where an IndexError would not cause the default value to be returned (GH7725)
get
IndexError
Bug in offsets.apply, rollforward and rollback may reset nanosecond (GH7697)
offsets.apply
rollforward
rollback
Bug in offsets.apply, rollforward and rollback may raise AttributeError if Timestamp has dateutil tzinfo (GH7697)
dateutil
Bug in sorting a MultiIndex frame with a Float64Index (GH8017)
Float64Index
Bug in inconsistent panel setitem with a rhs of a DataFrame for alignment (GH7763)
Bug in is_superperiod and is_subperiod cannot handle higher frequencies than S (GH7760, GH7772, GH7803)
is_superperiod
is_subperiod
Bug in 32-bit platforms with Series.shift (GH8129)
Series.shift
Bug in PeriodIndex.unique returns int64 np.ndarray (GH7540)
PeriodIndex.unique
np.ndarray
Bug in groupby.apply with a non-affecting mutation in the function (GH8467)
groupby.apply
Bug in DataFrame.reset_index which has MultiIndex contains PeriodIndex or DatetimeIndex with tz raises ValueError (GH7746, GH7793)
Bug in DataFrame.plot with subplots=True may draw unnecessary minor xticks and yticks (GH7801)
subplots=True
Bug in StataReader which did not read variable labels in 117 files due to difference between Stata documentation and implementation (GH7816)
Bug in StataReader where strings were always converted to 244 characters-fixed width irrespective of underlying string size (GH7858)
Bug in DataFrame.plot and Series.plot may ignore rot and fontsize keywords (GH7844)
rot
fontsize
Bug in DatetimeIndex.value_counts doesn’t preserve tz (GH7735)
DatetimeIndex.value_counts
Bug in PeriodIndex.value_counts results in Int64Index (GH7735)
PeriodIndex.value_counts
Int64Index
Bug in DataFrame.join when doing left join on index and there are multiple matches (GH5391)
DataFrame.join
Bug in GroupBy.transform() where int groups with a transform that didn’t preserve the index were incorrectly truncated (GH7972).
GroupBy.transform()
Bug in groupby where callable objects without name attributes would take the wrong path, and produce a DataFrame instead of a Series (GH7929)
Bug in groupby error message when a DataFrame grouping column is duplicated (GH7511)
Bug in read_html where the infer_types argument forced coercion of date-likes incorrectly (GH7762, GH7032).
read_html
Bug in Series.str.cat with an index which was filtered as to not include the first item (GH7857)
Series.str.cat
Bug in Timestamp cannot parse nanosecond from string (GH7878)
nanosecond
Bug in Timestamp with string offset and tz results incorrect (GH7833)
tz
Bug in tslib.tz_convert and tslib.tz_convert_single may return different results (GH7798)
tslib.tz_convert
tslib.tz_convert_single
Bug in DatetimeIndex.intersection of non-overlapping timestamps with tz raises IndexError (GH7880)
DatetimeIndex.intersection
Bug in alignment with TimeOps and non-unique indexes (GH8363)
Bug in GroupBy.filter() where fast path vs. slow path made the filter return a non scalar value that appeared valid but wasn’t (GH7870).
GroupBy.filter()
Bug in date_range()/DatetimeIndex() when the timezone was inferred from input dates yet incorrect times were returned when crossing DST boundaries (GH7835, GH7901).
date_range()
DatetimeIndex()
Bug in to_excel() where a negative sign was being prepended to positive infinity and was absent for negative infinity (GH7949)
to_excel()
Bug in area plot draws legend with incorrect alpha when stacked=True (GH8027)
alpha
stacked=True
Period and PeriodIndex addition/subtraction with np.timedelta64 results in incorrect internal representations (GH7740)
Bug in Holiday with no offset or observance (GH7987)
Holiday
Bug in DataFrame.to_latex formatting when columns or index is a MultiIndex (GH7982).
DataFrame.to_latex
Bug in DateOffset around Daylight Savings Time produces unexpected results (GH5175).
Bug in DataFrame.shift where empty columns would throw ZeroDivisionError on numpy 1.7 (GH8019)
DataFrame.shift
ZeroDivisionError
Bug in installation where html_encoding/*.html wasn’t installed and therefore some tests were not running correctly (GH7927).
html_encoding/*.html
Bug in read_html where bytes objects were not tested for in _read (GH7927).
bytes
_read
Bug in DataFrame.stack() when one of the column levels was a datelike (GH8039)
DataFrame.stack()
Bug in broadcasting numpy scalars with DataFrame (GH8116)
Bug in pivot_table performed with nameless index and columns raises KeyError (GH8103)
pivot_table
index
columns
Bug in DataFrame.plot(kind='scatter') draws points and errorbars with different colors when the color is specified by c keyword (GH8081)
DataFrame.plot(kind='scatter')
Bug in Float64Index where iat and at were not testing and were failing (GH8092).
iat
at
Bug in DataFrame.boxplot() where y-limits were not set correctly when producing multiple axes (GH7528, GH5517).
DataFrame.boxplot()
Bug in read_csv where line comments were not handled correctly given a custom line terminator or delim_whitespace=True (GH8122).
delim_whitespace=True
Bug in read_html where empty tables caused a StopIteration (GH7575)
StopIteration
Bug in casting when setting a column in a same-dtype block (GH7704)
Bug in accessing groups from a GroupBy when the original grouper was a tuple (GH8121).
GroupBy
Bug in .at that would accept integer indexers on a non-integer index and do fallback (GH7814)
.at
Bug with kde plot and NaNs (GH8182)
Bug in GroupBy.count with float32 data type were nan values were not excluded (GH8169).
GroupBy.count
Bug with stacked barplots and NaNs (GH8175).
Bug in resample with non evenly divisible offsets (e.g. ‘7s’) (GH8371)
Bug in interpolation methods with the limit keyword when no values needed interpolating (GH7173).
limit
Bug where col_space was ignored in DataFrame.to_string() when header=False (GH8230).
col_space
DataFrame.to_string()
header=False
Bug with DatetimeIndex.asof incorrectly matching partial strings and returning the wrong date (GH8245).
DatetimeIndex.asof
Bug in plotting methods modifying the global matplotlib rcParams (GH8242).
Bug in DataFrame.__setitem__ that caused errors when setting a dataframe column to a sparse array (GH8131)
DataFrame.__setitem__
Bug where Dataframe.boxplot() failed when entire column was empty (GH8181).
Dataframe.boxplot()
Bug with messed variables in radviz visualization (GH8199).
radviz
Bug in to_clipboard that would clip long column data (GH8305)
to_clipboard
Bug in DataFrame terminal display: Setting max_column/max_rows to zero did not trigger auto-resizing of dfs to fit terminal width/height (GH7180).
Bug in OLS where running with “cluster” and “nw_lags” parameters did not work correctly, but also did not throw an error (GH5884).
Bug in DataFrame.dropna that interpreted non-existent columns in the subset argument as the ‘last column’ (GH8303)
DataFrame.dropna
Bug in Index.intersection on non-monotonic non-unique indexes (GH8362).
Index.intersection
Bug in masked series assignment where mismatching types would break alignment (GH8387)
Bug in NDFrame.equals gives false negatives with dtype=object (GH8437)
NDFrame.equals
Bug in assignment with indexer where type diversity would break alignment (GH8258)
Bug in NDFrame.loc indexing when row/column names were lost when target was a list/ndarray (GH6552)
NDFrame.loc
Regression in NDFrame.loc indexing when rows/columns were converted to Float64Index if target was an empty list/ndarray (GH7774)
Bug in Series that allows it to be indexed by a DataFrame which has unexpected results. Such indexing is no longer permitted (GH8444)
Bug in item assignment of a DataFrame with MultiIndex columns where right-hand-side columns were not aligned (GH7655)
Suppress FutureWarning generated by NumPy when comparing object arrays containing NaN for equality (GH7065)
Bug in DataFrame.eval() where the dtype of the not operator (~) was not correctly inferred as bool.
DataFrame.eval()
not
~
bool
A total of 80 people contributed patches to this release. People with a “+” by their names contributed a patch for the first time.
Aaron Schumacher +
Adam Greenhall
Andy Hayden
Anthony O’Brien +
Artemy Kolchinsky +
Ben Schiller +
Benedikt Sauer
Benjamin Thyreau +
BorisVerk +
Chris Reynolds +
Chris Stoafer +
DSM
Dav Clark +
FragLegs +
German Gomez-Herrero +
Hsiaoming Yang +
Huan Li +
Hyungtae Kim +
Isaac Slavitt +
Jacob Schaer
Jacob Wasserman +
Jan Schulz
Jeff Reback
Jeff Tratner
Jesse Farnham +
Joe Bradish +
Joerg Rittinger +
John W. O’Brien
Joris Van den Bossche
Kevin Sheppard
Kyle Meyer
Max Chang +
Michael Mueller
Michael W Schatzow +
Mike Kelly
Mortada Mehyar
Nathan Sanders +
Nathan Typanski +
Paul Masurel +
Phillip Cloud
Pietro Battiston
RenzoBertocchi +
Ross Petchler +
Shahul Hameed +
Shashank Agarwal +
Stephan Hoyer
Tom Augspurger
TomAugspurger
Tony Lorenzo +
Wes Turner
Wilfred Hughes +
Yevgeniy Grechka +
Yoshiki Vázquez Baeza +
behzad nouri +
benjamin
bjonen +
dlovell +
dsm054
hunterowens +
immerrr
ischwabacher
jmorris0x0 +
jnmclarty +
jreback
klonuo +
lexual
mcjcode +
mtrbean +
onesandzeroes
rockg
seth-p
sinhrks
someben +
stahlous +
stas-sl +
thatneat +
tom-alcorn +
unknown
unutbu
zachcp +