Version 0.16.0 (March 22, 2015)

This is a major release from 0.15.2 and includes a small number of API changes, several new features, enhancements, and performance improvements along with a large number of bug fixes. We recommend that all users upgrade to this version.

Highlights include:

  • DataFrame.assign method, see here

  • Series.to_coo/from_coo methods to interact with scipy.sparse, see here

  • Backwards incompatible change to Timedelta to conform the .seconds attribute with datetime.timedelta, see here

  • Changes to the .loc slicing API to conform with the behavior of .ix see here

  • Changes to the default for ordering in the Categorical constructor, see here

  • Enhancement to the .str accessor to make string operations easier, see here

  • The pandas.tools.rplot, pandas.sandbox.qtpandas and pandas.rpy modules are deprecated. We refer users to external packages like seaborn, pandas-qt and rpy2 for similar or equivalent functionality, see here

Check the API Changes and deprecations before updating.

New features

DataFrame assign

Inspired by dplyr’s mutate verb, DataFrame has a new assign() method. The function signature for assign is simply **kwargs. The keys are the column names for the new fields, and the values are either a value to be inserted (for example, a Series or NumPy array), or a function of one argument to be called on the DataFrame. The new values are inserted, and the entire DataFrame (with all original and new columns) is returned.

In [1]: iris = pd.read_csv('data/iris.data')

In [2]: iris.head()
Out[2]: 
   SepalLength  SepalWidth  PetalLength  PetalWidth         Name
0          5.1         3.5          1.4         0.2  Iris-setosa
1          4.9         3.0          1.4         0.2  Iris-setosa
2          4.7         3.2          1.3         0.2  Iris-setosa
3          4.6         3.1          1.5         0.2  Iris-setosa
4          5.0         3.6          1.4         0.2  Iris-setosa

[5 rows x 5 columns]

In [3]: iris.assign(sepal_ratio=iris['SepalWidth'] / iris['SepalLength']).head()
Out[3]: 
   SepalLength  SepalWidth  PetalLength  PetalWidth         Name  sepal_ratio
0          5.1         3.5          1.4         0.2  Iris-setosa     0.686275
1          4.9         3.0          1.4         0.2  Iris-setosa     0.612245
2          4.7         3.2          1.3         0.2  Iris-setosa     0.680851
3          4.6         3.1          1.5         0.2  Iris-setosa     0.673913
4          5.0         3.6          1.4         0.2  Iris-setosa     0.720000

[5 rows x 6 columns]

Above was an example of inserting a precomputed value. We can also pass in a function to be evaluated.

In [4]: iris.assign(sepal_ratio=lambda x: (x['SepalWidth']
   ...:                                    / x['SepalLength'])).head()
   ...: 
Out[4]: 
   SepalLength  SepalWidth  PetalLength  PetalWidth         Name  sepal_ratio
0          5.1         3.5          1.4         0.2  Iris-setosa     0.686275
1          4.9         3.0          1.4         0.2  Iris-setosa     0.612245
2          4.7         3.2          1.3         0.2  Iris-setosa     0.680851
3          4.6         3.1          1.5         0.2  Iris-setosa     0.673913
4          5.0         3.6          1.4         0.2  Iris-setosa     0.720000

[5 rows x 6 columns]

The power of assign comes when used in chains of operations. For example, we can limit the DataFrame to just those with a Sepal Length greater than 5, calculate the ratio, and plot

In [5]: iris = pd.read_csv('data/iris.data')

In [6]: (iris.query('SepalLength > 5')
   ...:      .assign(SepalRatio=lambda x: x.SepalWidth / x.SepalLength,
   ...:              PetalRatio=lambda x: x.PetalWidth / x.PetalLength)
   ...:      .plot(kind='scatter', x='SepalRatio', y='PetalRatio'))
   ...: 
Out[6]: <AxesSubplot: xlabel='SepalRatio', ylabel='PetalRatio'>
../_images/whatsnew_assign.png

See the documentation for more. (GH9229)

Interaction with scipy.sparse

Added SparseSeries.to_coo() and SparseSeries.from_coo() methods (GH8048) for converting to and from scipy.sparse.coo_matrix instances (see here). For example, given a SparseSeries with MultiIndex we can convert to a scipy.sparse.coo_matrix by specifying the row and column labels as index levels:

s = pd.Series([3.0, np.nan, 1.0, 3.0, np.nan, np.nan])
s.index = pd.MultiIndex.from_tuples([(1, 2, 'a', 0),
                                     (1, 2, 'a', 1),
                                     (1, 1, 'b', 0),
                                     (1, 1, 'b', 1),
                                     (2, 1, 'b', 0),
                                     (2, 1, 'b', 1)],
                                    names=['A', 'B', 'C', 'D'])

s

# SparseSeries
ss = s.to_sparse()
ss

A, rows, columns = ss.to_coo(row_levels=['A', 'B'],
                             column_levels=['C', 'D'],
                             sort_labels=False)

A
A.todense()
rows
columns

The from_coo method is a convenience method for creating a SparseSeries from a scipy.sparse.coo_matrix:

from scipy import sparse
A = sparse.coo_matrix(([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])),
                      shape=(3, 4))
A
A.todense()

ss = pd.SparseSeries.from_coo(A)
ss

String methods enhancements

  • Following new methods are accessible via .str accessor to apply the function to each values. This is intended to make it more consistent with standard methods on strings. (GH9282, GH9352, GH9386, GH9387, GH9439)

    Methods

    isalnum()

    isalpha()

    isdigit()

    isdigit()

    isspace()

    islower()

    isupper()

    istitle()

    isnumeric()

    isdecimal()

    find()

    rfind()

    ljust()

    rjust()

    zfill()

    		 
    In [7]: s = pd.Series(['abcd', '3456', 'EFGH'])

In [8]: s.str.isalpha()
Out[8]: 
0     True
1    False
2     True
Length: 3, dtype: bool

In [9]: s.str.find('ab')
Out[9]: 
0    0
1   -1
2   -1
Length: 3, dtype: int64

  • Series.str.pad() and Series.str.center() now accept fillchar option to specify filling character (GH9352)

    In [10]: s = pd.Series(['12', '300', '25'])

In [11]: s.str.pad(5, fillchar='_')
Out[11]: 
0    ___12
1    __300
2    ___25
Length: 3, dtype: object

  • Added Series.str.slice_replace(), which previously raised NotImplementedError (GH8888)

    In [12]: s = pd.Series(['ABCD', 'EFGH', 'IJK'])

In [13]: s.str.slice_replace(1, 3, 'X')
Out[13]: 
0    AXD
1    EXH
2     IX
Length: 3, dtype: object

# replaced with empty char
In [14]: s.str.slice_replace(0, 1)
Out[14]: 
0    BCD
1    FGH
2     JK
Length: 3, dtype: object

Other enhancements

  • Reindex now supports method='nearest' for frames or series with a monotonic increasing or decreasing index (GH9258):

    In [15]: df = pd.DataFrame({'x': range(5)})

In [16]: df.reindex([0.2, 1.8, 3.5], method='nearest')
Out[16]: 
     x
0.2  0
1.8  2
3.5  4

[3 rows x 1 columns]

    This method is also exposed by the lower level Index.get_indexer and Index.get_loc methods.

  • The read_excel() function’s sheetname argument now accepts a list and None, to get multiple or all sheets respectively. If more than one sheet is specified, a dictionary is returned. (GH9450)

    # Returns the 1st and 4th sheet, as a dictionary of DataFrames.
pd.read_excel('path_to_file.xls', sheetname=['Sheet1', 3])

  • Allow Stata files to be read incrementally with an iterator; support for long strings in Stata files. See the docs here (GH9493:).

  • Paths beginning with ~ will now be expanded to begin with the user’s home directory (GH9066)

  • Added time interval selection in get_data_yahoo (GH9071)

  • Added Timestamp.to_datetime64() to complement Timedelta.to_timedelta64() (GH9255)

  • tseries.frequencies.to_offset() now accepts Timedelta as input (GH9064)

  • Lag parameter was added to the autocorrelation method of Series, defaults to lag-1 autocorrelation (GH9192)

  • Timedelta will now accept nanoseconds keyword in constructor (GH9273)

  • SQL code now safely escapes table and column names (GH8986)

  • Added auto-complete for Series.str.<tab>, Series.dt.<tab> and Series.cat.<tab> (GH9322)

  • Index.get_indexer now supports method='pad' and method='backfill' even for any target array, not just monotonic targets. These methods also work for monotonic decreasing as well as monotonic increasing indexes (GH9258).

  • Index.asof now works on all index types (GH9258).

  • A verbose argument has been augmented in io.read_excel(), defaults to False. Set to True to print sheet names as they are parsed. (GH9450)

  • Added days_in_month (compatibility alias daysinmonth) property to Timestamp, DatetimeIndex, Period, PeriodIndex, and Series.dt (GH9572)

  • Added decimal option in to_csv to provide formatting for non-‘.’ decimal separators (GH781)

  • Added normalize option for Timestamp to normalized to midnight (GH8794)

  • Added example for DataFrame import to R using HDF5 file and rhdf5 library. See the documentation for more (GH9636).

Backwards incompatible API changes

Changes in timedelta

In v0.15.0 a new scalar type Timedelta was introduced, that is a sub-class of datetime.timedelta. Mentioned here was a notice of an API change w.r.t. the .seconds accessor. The intent was to provide a user-friendly set of accessors that give the ‘natural’ value for that unit, e.g. if you had a Timedelta('1 day, 10:11:12'), then .seconds would return 12. However, this is at odds with the definition of datetime.timedelta, which defines .seconds as 10 * 3600 + 11 * 60 + 12 == 36672.

So in v0.16.0, we are restoring the API to match that of datetime.timedelta. Further, the component values are still available through the .components accessor. This affects the .seconds and .microseconds accessors, and removes the .hours, .minutes, .milliseconds accessors. These changes affect TimedeltaIndex and the Series .dt accessor as well. (GH9185, GH9139)

Previous behavior

In [2]: t = pd.Timedelta('1 day, 10:11:12.100123')

In [3]: t.days
Out[3]: 1

In [4]: t.seconds
Out[4]: 12

In [5]: t.microseconds
Out[5]: 123

New behavior

In [17]: t = pd.Timedelta('1 day, 10:11:12.100123')

In [18]: t.days
Out[18]: 1

In [19]: t.seconds
Out[19]: 36672

In [20]: t.microseconds
Out[20]: 100123

Using .components allows the full component access

In [21]: t.components
Out[21]: Components(days=1, hours=10, minutes=11, seconds=12, milliseconds=100, microseconds=123, nanoseconds=0)

In [22]: t.components.seconds
Out[22]: 12

Indexing changes

The behavior of a small sub-set of edge cases for using .loc have changed (GH8613). Furthermore we have improved the content of the error messages that are raised:

  • Slicing with .loc where the start and/or stop bound is not found in the index is now allowed; this previously would raise a KeyError. This makes the behavior the same as .ix in this case. This change is only for slicing, not when indexing with a single label.

    In [23]: df = pd.DataFrame(np.random.randn(5, 4),
   ....:                   columns=list('ABCD'),
   ....:                   index=pd.date_range('20130101', periods=5))
   ....: 

In [24]: df
Out[24]: 
                   A         B         C         D
2013-01-01  0.469112 -0.282863 -1.509059 -1.135632
2013-01-02  1.212112 -0.173215  0.119209 -1.044236
2013-01-03 -0.861849 -2.104569 -0.494929  1.071804
2013-01-04  0.721555 -0.706771 -1.039575  0.271860
2013-01-05 -0.424972  0.567020  0.276232 -1.087401

[5 rows x 4 columns]

In [25]: s = pd.Series(range(5), [-2, -1, 1, 2, 3])

In [26]: s
Out[26]: 
-2    0
-1    1
 1    2
 2    3
 3    4
Length: 5, dtype: int64

    Previous behavior

    In [4]: df.loc['2013-01-02':'2013-01-10']
KeyError: 'stop bound [2013-01-10] is not in the [index]'

In [6]: s.loc[-10:3]
KeyError: 'start bound [-10] is not the [index]'

    New behavior

    In [27]: df.loc['2013-01-02':'2013-01-10']
Out[27]: 
                   A         B         C         D
2013-01-02  1.212112 -0.173215  0.119209 -1.044236
2013-01-03 -0.861849 -2.104569 -0.494929  1.071804
2013-01-04  0.721555 -0.706771 -1.039575  0.271860
2013-01-05 -0.424972  0.567020  0.276232 -1.087401

[4 rows x 4 columns]

In [28]: s.loc[-10:3]
Out[28]: 
-2    0
-1    1
 1    2
 2    3
 3    4
Length: 5, dtype: int64

  • Allow slicing with float-like values on an integer index for .ix. Previously this was only enabled for .loc:

    Previous behavior

    In [8]: s.ix[-1.0:2]
TypeError: the slice start value [-1.0] is not a proper indexer for this index type (Int64Index)

    New behavior

    In [2]: s.ix[-1.0:2]
Out[2]:
-1    1
 1    2
 2    3
dtype: int64

  • Provide a useful exception for indexing with an invalid type for that index when using .loc. For example trying to use .loc on an index of type DatetimeIndex or PeriodIndex or TimedeltaIndex, with an integer (or a float).

    Previous behavior

    In [4]: df.loc[2:3]
KeyError: 'start bound [2] is not the [index]'

    New behavior

    In [4]: df.loc[2:3]
TypeError: Cannot do slice indexing on <class 'pandas.tseries.index.DatetimeIndex'> with <type 'int'> keys

Categorical changes

In prior versions, Categoricals that had an unspecified ordering (meaning no ordered keyword was passed) were defaulted as ordered Categoricals. Going forward, the ordered keyword in the Categorical constructor will default to False. Ordering must now be explicit.

Furthermore, previously you could change the ordered attribute of a Categorical by just setting the attribute, e.g. cat.ordered=True; This is now deprecated and you should use cat.as_ordered() or cat.as_unordered(). These will by default return a new object and not modify the existing object. (GH9347, GH9190)

Previous behavior

In [3]: s = pd.Series([0, 1, 2], dtype='category')

In [4]: s
Out[4]:
0    0
1    1
2    2
dtype: category
Categories (3, int64): [0 < 1 < 2]

In [5]: s.cat.ordered
Out[5]: True

In [6]: s.cat.ordered = False

In [7]: s
Out[7]:
0    0
1    1
2    2
dtype: category
Categories (3, int64): [0, 1, 2]

New behavior

In [29]: s = pd.Series([0, 1, 2], dtype='category')

In [30]: s
Out[30]: 
0    0
1    1
2    2
Length: 3, dtype: category
Categories (3, int64): [0, 1, 2]

In [31]: s.cat.ordered
Out[31]: False

In [32]: s = s.cat.as_ordered()

In [33]: s
Out[33]: 
0    0
1    1
2    2
Length: 3, dtype: category
Categories (3, int64): [0 < 1 < 2]

In [34]: s.cat.ordered
Out[34]: True

# you can set in the constructor of the Categorical
In [35]: s = pd.Series(pd.Categorical([0, 1, 2], ordered=True))

In [36]: s
Out[36]: 
0    0
1    1
2    2
Length: 3, dtype: category
Categories (3, int64): [0 < 1 < 2]

In [37]: s.cat.ordered
Out[37]: True

For ease of creation of series of categorical data, we have added the ability to pass keywords when calling .astype(). These are passed directly to the constructor.

In [54]: s = pd.Series(["a", "b", "c", "a"]).astype('category', ordered=True)

In [55]: s
Out[55]:
0    a
1    b
2    c
3    a
dtype: category
Categories (3, object): [a < b < c]

In [56]: s = (pd.Series(["a", "b", "c", "a"])
   ....:        .astype('category', categories=list('abcdef'), ordered=False))

In [57]: s
Out[57]:
0    a
1    b
2    c
3    a
dtype: category
Categories (6, object): [a, b, c, d, e, f]

Other API changes

  • Index.duplicated now returns np.array(dtype=bool) rather than Index(dtype=object) containing bool values. (GH8875)

  • DataFrame.to_json now returns accurate type serialisation for each column for frames of mixed dtype (GH9037)

    Previously data was coerced to a common dtype before serialisation, which for example resulted in integers being serialised to floats:

    In [2]: pd.DataFrame({'i': [1,2], 'f': [3.0, 4.2]}).to_json()
Out[2]: '{"f":{"0":3.0,"1":4.2},"i":{"0":1.0,"1":2.0}}'

    Now each column is serialised using its correct dtype:

    In [2]:  pd.DataFrame({'i': [1,2], 'f': [3.0, 4.2]}).to_json()
Out[2]: '{"f":{"0":3.0,"1":4.2},"i":{"0":1,"1":2}}'

  • DatetimeIndex, PeriodIndex and TimedeltaIndex.summary now output the same format. (GH9116)

  • TimedeltaIndex.freqstr now output the same string format as DatetimeIndex. (GH9116)

  • Bar and horizontal bar plots no longer add a dashed line along the info axis. The prior style can be achieved with matplotlib’s axhline or axvline methods (GH9088).

  • Series accessors .dt, .cat and .str now raise AttributeError instead of TypeError if the series does not contain the appropriate type of data (GH9617). This follows Python’s built-in exception hierarchy more closely and ensures that tests like hasattr(s, 'cat') are consistent on both Python 2 and 3.

  • Series now supports bitwise operation for integral types (GH9016). Previously even if the input dtypes were integral, the output dtype was coerced to bool.

    Previous behavior

    In [2]: pd.Series([0, 1, 2, 3], list('abcd')) | pd.Series([4, 4, 4, 4], list('abcd'))
Out[2]:
a    True
b    True
c    True
d    True
dtype: bool

    New behavior. If the input dtypes are integral, the output dtype is also integral and the output values are the result of the bitwise operation.

    In [2]: pd.Series([0, 1, 2, 3], list('abcd')) | pd.Series([4, 4, 4, 4], list('abcd'))
Out[2]:
a    4
b    5
c    6
d    7
dtype: int64

  • During division involving a Series or DataFrame, 0/0 and 0//0 now give np.nan instead of np.inf. (GH9144, GH8445)

    Previous behavior

    In [2]: p = pd.Series([0, 1])

In [3]: p / 0
Out[3]:
0    inf
1    inf
dtype: float64

In [4]: p // 0
Out[4]:
0    inf
1    inf
dtype: float64

    New behavior

    In [38]: p = pd.Series([0, 1])

In [39]: p / 0
Out[39]: 
0    NaN
1    inf
Length: 2, dtype: float64

In [40]: p // 0
Out[40]: 
0    NaN
1    inf
Length: 2, dtype: float64

  • Series.values_counts and Series.describe for categorical data will now put NaN entries at the end. (GH9443)

  • Series.describe for categorical data will now give counts and frequencies of 0, not NaN, for unused categories (GH9443)

  • Due to a bug fix, looking up a partial string label with DatetimeIndex.asof now includes values that match the string, even if they are after the start of the partial string label (GH9258).

    Old behavior:

    In [4]: pd.to_datetime(['2000-01-31', '2000-02-28']).asof('2000-02')
Out[4]: Timestamp('2000-01-31 00:00:00')

    Fixed behavior:

    In [41]: pd.to_datetime(['2000-01-31', '2000-02-28']).asof('2000-02')
Out[41]: Timestamp('2000-02-28 00:00:00')

    To reproduce the old behavior, simply add more precision to the label (e.g., use 2000-02-01 instead of 2000-02).

Deprecations

  • The rplot trellis plotting interface is deprecated and will be removed in a future version. We refer to external packages like seaborn for similar but more refined functionality (GH3445). The documentation includes some examples how to convert your existing code from rplot to seaborn here.

  • The pandas.sandbox.qtpandas interface is deprecated and will be removed in a future version. We refer users to the external package pandas-qt. (GH9615)

  • The pandas.rpy interface is deprecated and will be removed in a future version. Similar functionality can be accessed through the rpy2 project (GH9602)

  • Adding DatetimeIndex/PeriodIndex to another DatetimeIndex/PeriodIndex is being deprecated as a set-operation. This will be changed to a TypeError in a future version. .union() should be used for the union set operation. (GH9094)

  • Subtracting DatetimeIndex/PeriodIndex from another DatetimeIndex/PeriodIndex is being deprecated as a set-operation. This will be changed to an actual numeric subtraction yielding a TimeDeltaIndex in a future version. .difference() should be used for the differencing set operation. (GH9094)

Removal of prior version deprecations/changes

  • DataFrame.pivot_table and crosstab’s rows and cols keyword arguments were removed in favor of index and columns (GH6581)

  • DataFrame.to_excel and DataFrame.to_csv cols keyword argument was removed in favor of columns (GH6581)

  • Removed convert_dummies in favor of get_dummies (GH6581)

  • Removed value_range in favor of describe (GH6581)

Performance improvements

  • Fixed a performance regression for .loc indexing with an array or list-like (GH9126:).

  • DataFrame.to_json 30x performance improvement for mixed dtype frames. (GH9037)

  • Performance improvements in MultiIndex.duplicated by working with labels instead of values (GH9125)

  • Improved the speed of nunique by calling unique instead of value_counts (GH9129, GH7771)

  • Performance improvement of up to 10x in DataFrame.count and DataFrame.dropna by taking advantage of homogeneous/heterogeneous dtypes appropriately (GH9136)

  • Performance improvement of up to 20x in DataFrame.count when using a MultiIndex and the level keyword argument (GH9163)

  • Performance and memory usage improvements in merge when key space exceeds int64 bounds (GH9151)

  • Performance improvements in multi-key groupby (GH9429)

  • Performance improvements in MultiIndex.sortlevel (GH9445)

  • Performance and memory usage improvements in DataFrame.duplicated (GH9398)

  • Cythonized Period (GH9440)

  • Decreased memory usage on to_hdf (GH9648)

Bug fixes

  • Changed .to_html to remove leading/trailing spaces in table body (GH4987)

  • Fixed issue using read_csv on s3 with Python 3 (GH9452)

  • Fixed compatibility issue in DatetimeIndex affecting architectures where numpy.int_ defaults to numpy.int32 (GH8943)

  • Bug in Panel indexing with an object-like (GH9140)

  • Bug in the returned Series.dt.components index was reset to the default index (GH9247)

  • Bug in Categorical.__getitem__/__setitem__ with listlike input getting incorrect results from indexer coercion (GH9469)

  • Bug in partial setting with a DatetimeIndex (GH9478)

  • Bug in groupby for integer and datetime64 columns when applying an aggregator that caused the value to be changed when the number was sufficiently large (GH9311, GH6620)

  • Fixed bug in to_sql when mapping a Timestamp object column (datetime column with timezone info) to the appropriate sqlalchemy type (GH9085).

  • Fixed bug in to_sql dtype argument not accepting an instantiated SQLAlchemy type (GH9083).

  • Bug in .loc partial setting with a np.datetime64 (GH9516)

  • Incorrect dtypes inferred on datetimelike looking Series & on .xs slices (GH9477)

  • Items in Categorical.unique() (and s.unique() if s is of dtype category) now appear in the order in which they are originally found, not in sorted order (GH9331). This is now consistent with the behavior for other dtypes in pandas.

  • Fixed bug on big endian platforms which produced incorrect results in StataReader (GH8688).

  • Bug in MultiIndex.has_duplicates when having many levels causes an indexer overflow (GH9075, GH5873)

  • Bug in pivot and unstack where nan values would break index alignment (GH4862, GH7401, GH7403, GH7405, GH7466, GH9497)

  • Bug in left join on MultiIndex with sort=True or null values (GH9210).

  • Bug in MultiIndex where inserting new keys would fail (GH9250).

  • Bug in groupby when key space exceeds int64 bounds (GH9096).

  • Bug in unstack with TimedeltaIndex or DatetimeIndex and nulls (GH9491).

  • Bug in rank where comparing floats with tolerance will cause inconsistent behaviour (GH8365).

  • Fixed character encoding bug in read_stata and StataReader when loading data from a URL (GH9231).

  • Bug in adding offsets.Nano to other offsets raises TypeError (GH9284)

  • Bug in DatetimeIndex iteration, related to (GH8890), fixed in (GH9100)

  • Bugs in resample around DST transitions. This required fixing offset classes so they behave correctly on DST transitions. (GH5172, GH8744, GH8653, GH9173, GH9468).

  • Bug in binary operator method (eg .mul()) alignment with integer levels (GH9463).

  • Bug in boxplot, scatter and hexbin plot may show an unnecessary warning (GH8877)

  • Bug in subplot with layout kw may show unnecessary warning (GH9464)

  • Bug in using grouper functions that need passed through arguments (e.g. axis), when using wrapped function (e.g. fillna), (GH9221)

  • DataFrame now properly supports simultaneous copy and dtype arguments in constructor (GH9099)

  • Bug in read_csv when using skiprows on a file with CR line endings with the c engine. (GH9079)

  • isnull now detects NaT in PeriodIndex (GH9129)

  • Bug in groupby .nth() with a multiple column groupby (GH8979)

  • Bug in DataFrame.where and Series.where coerce numerics to string incorrectly (GH9280)

  • Bug in DataFrame.where and Series.where raise ValueError when string list-like is passed. (GH9280)

  • Accessing Series.str methods on with non-string values now raises TypeError instead of producing incorrect results (GH9184)

  • Bug in DatetimeIndex.__contains__ when index has duplicates and is not monotonic increasing (GH9512)

  • Fixed division by zero error for Series.kurt() when all values are equal (GH9197)

  • Fixed issue in the xlsxwriter engine where it added a default ‘General’ format to cells if no other format was applied. This prevented other row or column formatting being applied. (GH9167)

  • Fixes issue with index_col=False when usecols is also specified in read_csv. (GH9082)

  • Bug where wide_to_long would modify the input stub names list (GH9204)

  • Bug in to_sql not storing float64 values using double precision. (GH9009)

  • SparseSeries and SparsePanel now accept zero argument constructors (same as their non-sparse counterparts) (GH9272).

  • Regression in merging Categorical and object dtypes (GH9426)

  • Bug in read_csv with buffer overflows with certain malformed input files (GH9205)

  • Bug in groupby MultiIndex with missing pair (GH9049, GH9344)

  • Fixed bug in Series.groupby where grouping on MultiIndex levels would ignore the sort argument (GH9444)

  • Fix bug in DataFrame.Groupby where sort=False is ignored in the case of Categorical columns. (GH8868)

  • Fixed bug with reading CSV files from Amazon S3 on python 3 raising a TypeError (GH9452)

  • Bug in the Google BigQuery reader where the ‘jobComplete’ key may be present but False in the query results (GH8728)

  • Bug in Series.values_counts with excluding NaN for categorical type Series with dropna=True (GH9443)

  • Fixed missing numeric_only option for DataFrame.std/var/sem (GH9201)

  • Support constructing Panel or Panel4D with scalar data (GH8285)

  • Series text representation disconnected from max_rows/max_columns (GH7508).

  • Series number formatting inconsistent when truncated (GH8532).

    Previous behavior

    In [2]: pd.options.display.max_rows = 10
In [3]: s = pd.Series([1,1,1,1,1,1,1,1,1,1,0.9999,1,1]*10)
In [4]: s
Out[4]:
0    1
1    1
2    1
...
127    0.9999
128    1.0000
129    1.0000
Length: 130, dtype: float64

    New behavior

    0      1.0000
1      1.0000
2      1.0000
3      1.0000
4      1.0000
...
125    1.0000
126    1.0000
127    0.9999
128    1.0000
129    1.0000
dtype: float64

  • A Spurious SettingWithCopy Warning was generated when setting a new item in a frame in some cases (GH8730)

    The following would previously report a SettingWithCopy Warning.

    In [42]: df1 = pd.DataFrame({'x': pd.Series(['a', 'b', 'c']),
   ....:                     'y': pd.Series(['d', 'e', 'f'])})
   ....: 

In [43]: df2 = df1[['x']]

In [44]: df2['y'] = ['g', 'h', 'i']

Contributors

For contributors, please see /usr/share/doc/contributors_list.txt or https://github.com/pandas-dev/pandas/graphs/contributors

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