Version 0.21.0 (October 27, 2017)¶
This is a major release from 0.20.3 and includes a number of API changes, deprecations, 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:
Integration with Apache Parquet, including a new top-level
read_parquet()
function andDataFrame.to_parquet()
method, see here.New user-facing
pandas.api.types.CategoricalDtype
for specifying categoricals independent of the data, see here.The behavior of
sum
andprod
on all-NaN Series/DataFrames is now consistent and no longer depends on whether bottleneck is installed, andsum
andprod
on empty Series now return NaN instead of 0, see here.Compatibility fixes for pypy, see here.
Additions to the
drop
,reindex
andrename
API to make them more consistent, see here.Addition of the new methods
DataFrame.infer_objects
(see here) andGroupBy.pipe
(see here).Indexing with a list of labels, where one or more of the labels is missing, is deprecated and will raise a KeyError in a future version, see here.
Check the API Changes and deprecations before updating.
What’s new in v0.21.0
New features¶
Integration with Apache Parquet file format¶
Integration with Apache Parquet, including a new top-level read_parquet()
and DataFrame.to_parquet()
method, see here (GH15838, GH17438).
Apache Parquet provides a cross-language, binary file format for reading and writing data frames efficiently.
Parquet is designed to faithfully serialize and de-serialize DataFrame
s, supporting all of the pandas
dtypes, including extension dtypes such as datetime with timezones.
This functionality depends on either the pyarrow or fastparquet library. For more details, see the IO docs on Parquet.
Method infer_objects
type conversion¶
The DataFrame.infer_objects()
and Series.infer_objects()
methods have been added to perform dtype inference on object columns, replacing
some of the functionality of the deprecated convert_objects
method. See the documentation here
for more details. (GH11221)
This method only performs soft conversions on object columns, converting Python objects to native types, but not any coercive conversions. For example:
In [1]: df = pd.DataFrame({'A': [1, 2, 3],
...: 'B': np.array([1, 2, 3], dtype='object'),
...: 'C': ['1', '2', '3']})
...:
In [2]: df.dtypes
Out[2]:
A int64
B object
C object
Length: 3, dtype: object
In [3]: df.infer_objects().dtypes
Out[3]:
A int64
B int64
C object
Length: 3, dtype: object
Note that column 'C'
was not converted - only scalar numeric types
will be converted to a new type. Other types of conversion should be accomplished
using the to_numeric()
function (or to_datetime()
, to_timedelta()
).
In [4]: df = df.infer_objects()
In [5]: df['C'] = pd.to_numeric(df['C'], errors='coerce')
In [6]: df.dtypes
Out[6]:
A int64
B int64
C int64
Length: 3, dtype: object
Improved warnings when attempting to create columns¶
New users are often puzzled by the relationship between column operations and
attribute access on DataFrame
instances (GH7175). One specific
instance of this confusion is attempting to create a new column by setting an
attribute on the DataFrame
:
In [1]: df = pd.DataFrame({'one': [1., 2., 3.]})
In [2]: df.two = [4, 5, 6]
This does not raise any obvious exceptions, but also does not create a new column:
In [3]: df
Out[3]:
one
0 1.0
1 2.0
2 3.0
Setting a list-like data structure into a new attribute now raises a UserWarning
about the potential for unexpected behavior. See Attribute Access.
Method drop
now also accepts index/columns keywords¶
The drop()
method has gained index
/columns
keywords as an
alternative to specifying the axis
. This is similar to the behavior of reindex
(GH12392).
For example:
In [7]: df = pd.DataFrame(np.arange(8).reshape(2, 4),
...: columns=['A', 'B', 'C', 'D'])
...:
In [8]: df
Out[8]:
A B C D
0 0 1 2 3
1 4 5 6 7
[2 rows x 4 columns]
In [9]: df.drop(['B', 'C'], axis=1)
Out[9]:
A D
0 0 3
1 4 7
[2 rows x 2 columns]
# the following is now equivalent
In [10]: df.drop(columns=['B', 'C'])
Out[10]:
A D
0 0 3
1 4 7
[2 rows x 2 columns]
Methods rename
, reindex
now also accept axis keyword¶
The DataFrame.rename()
and DataFrame.reindex()
methods have gained
the axis
keyword to specify the axis to target with the operation
(GH12392).
Here’s rename
:
In [11]: df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
In [12]: df.rename(str.lower, axis='columns')
Out[12]:
a b
0 1 4
1 2 5
2 3 6
[3 rows x 2 columns]
In [13]: df.rename(id, axis='index')
Out[13]:
A B
94293413313760 1 4
94293413313792 2 5
94293413313824 3 6
[3 rows x 2 columns]
And reindex
:
In [14]: df.reindex(['A', 'B', 'C'], axis='columns')
Out[14]:
A B C
0 1 4 NaN
1 2 5 NaN
2 3 6 NaN
[3 rows x 3 columns]
In [15]: df.reindex([0, 1, 3], axis='index')
Out[15]:
A B
0 1.0 4.0
1 2.0 5.0
3 NaN NaN
[3 rows x 2 columns]
The “index, columns” style continues to work as before.
In [16]: df.rename(index=id, columns=str.lower)
Out[16]:
a b
94293413313760 1 4
94293413313792 2 5
94293413313824 3 6
[3 rows x 2 columns]
In [17]: df.reindex(index=[0, 1, 3], columns=['A', 'B', 'C'])
Out[17]:
A B C
0 1.0 4.0 NaN
1 2.0 5.0 NaN
3 NaN NaN NaN
[3 rows x 3 columns]
We highly encourage using named arguments to avoid confusion when using either style.
CategoricalDtype
for specifying categoricals¶
pandas.api.types.CategoricalDtype
has been added to the public API and
expanded to include the categories
and ordered
attributes. A
CategoricalDtype
can be used to specify the set of categories and
orderedness of an array, independent of the data. This can be useful for example,
when converting string data to a Categorical
(GH14711,
GH15078, GH16015, GH17643):
In [18]: from pandas.api.types import CategoricalDtype
In [19]: s = pd.Series(['a', 'b', 'c', 'a']) # strings
In [20]: dtype = CategoricalDtype(categories=['a', 'b', 'c', 'd'], ordered=True)
In [21]: s.astype(dtype)
Out[21]:
0 a
1 b
2 c
3 a
Length: 4, dtype: category
Categories (4, object): ['a' < 'b' < 'c' < 'd']
One place that deserves special mention is in read_csv()
. Previously, with
dtype={'col': 'category'}
, the returned values and categories would always
be strings.
In [22]: data = 'A,B\na,1\nb,2\nc,3'
In [23]: pd.read_csv(StringIO(data), dtype={'B': 'category'}).B.cat.categories
Out[23]: Index(['1', '2', '3'], dtype='object')
Notice the “object” dtype.
With a CategoricalDtype
of all numerics, datetimes, or
timedeltas, we can automatically convert to the correct type
In [24]: dtype = {'B': CategoricalDtype([1, 2, 3])}
In [25]: pd.read_csv(StringIO(data), dtype=dtype).B.cat.categories
Out[25]: Int64Index([1, 2, 3], dtype='int64')
The values have been correctly interpreted as integers.
The .dtype
property of a Categorical
, CategoricalIndex
or a
Series
with categorical type will now return an instance of
CategoricalDtype
. While the repr has changed, str(CategoricalDtype())
is
still the string 'category'
. We’ll take this moment to remind users that the
preferred way to detect categorical data is to use
pandas.api.types.is_categorical_dtype()
, and not str(dtype) == 'category'
.
See the CategoricalDtype docs for more.
GroupBy
objects now have a pipe
method¶
GroupBy
objects now have a pipe
method, similar to the one on
DataFrame
and Series
, that allow for functions that take a
GroupBy
to be composed in a clean, readable syntax. (GH17871)
For a concrete example on combining .groupby
and .pipe
, imagine having a
DataFrame with columns for stores, products, revenue and sold quantity. We’d like to
do a groupwise calculation of prices (i.e. revenue/quantity) per store and per product.
We could do this in a multi-step operation, but expressing it in terms of piping can make the
code more readable.
First we set the data:
In [26]: import numpy as np
In [27]: n = 1000
In [28]: df = pd.DataFrame({'Store': np.random.choice(['Store_1', 'Store_2'], n),
....: 'Product': np.random.choice(['Product_1',
....: 'Product_2',
....: 'Product_3'
....: ], n),
....: 'Revenue': (np.random.random(n) * 50 + 10).round(2),
....: 'Quantity': np.random.randint(1, 10, size=n)})
....:
In [29]: df.head(2)
Out[29]:
Store Product Revenue Quantity
0 Store_2 Product_2 32.09 7
1 Store_1 Product_3 14.20 1
[2 rows x 4 columns]
Now, to find prices per store/product, we can simply do:
In [30]: (df.groupby(['Store', 'Product'])
....: .pipe(lambda grp: grp.Revenue.sum() / grp.Quantity.sum())
....: .unstack().round(2))
....:
Out[30]:
Product Product_1 Product_2 Product_3
Store
Store_1 6.73 6.72 7.14
Store_2 7.59 6.98 7.23
[2 rows x 3 columns]
See the documentation for more.
Categorical.rename_categories
accepts a dict-like¶
rename_categories()
now accepts a dict-like argument for
new_categories
. The previous categories are looked up in the dictionary’s
keys and replaced if found. The behavior of missing and extra keys is the same
as in DataFrame.rename()
.
In [31]: c = pd.Categorical(['a', 'a', 'b'])
In [32]: c.rename_categories({"a": "eh", "b": "bee"})
Out[32]:
['eh', 'eh', 'bee']
Categories (2, object): ['eh', 'bee']
Warning
To assist with upgrading pandas, rename_categories
treats Series
as
list-like. Typically, Series are considered to be dict-like (e.g. in
.rename
, .map
). In a future version of pandas rename_categories
will change to treat them as dict-like. Follow the warning message’s
recommendations for writing future-proof code.
In [33]: c.rename_categories(pd.Series([0, 1], index=['a', 'c']))
FutureWarning: Treating Series 'new_categories' as a list-like and using the values.
In a future version, 'rename_categories' will treat Series like a dictionary.
For dict-like, use 'new_categories.to_dict()'
For list-like, use 'new_categories.values'.
Out[33]:
[0, 0, 1]
Categories (2, int64): [0, 1]
Other enhancements¶
New functions or methods¶
New keywords¶
Added a
skipna
parameter toinfer_dtype()
to support type inference in the presence of missing values (GH17059).Series.to_dict()
andDataFrame.to_dict()
now support aninto
keyword which allows you to specify thecollections.Mapping
subclass that you would like returned. The default isdict
, which is backwards compatible. (GH16122)Series.set_axis()
andDataFrame.set_axis()
now support theinplace
parameter. (GH14636)Series.to_pickle()
andDataFrame.to_pickle()
have gained aprotocol
parameter (GH16252). By default, this parameter is set to HIGHEST_PROTOCOLread_feather()
has gained thenthreads
parameter for multi-threaded operations (GH16359)DataFrame.clip()
andSeries.clip()
have gained aninplace
argument. (GH15388)crosstab()
has gained amargins_name
parameter to define the name of the row / column that will contain the totals whenmargins=True
. (GH15972)read_json()
now accepts achunksize
parameter that can be used whenlines=True
. Ifchunksize
is passed, read_json now returns an iterator which reads inchunksize
lines with each iteration. (GH17048)read_json()
andto_json()
now accept acompression
argument which allows them to transparently handle compressed files. (GH17798)
Various enhancements¶
Improved the import time of pandas by about 2.25x. (GH16764)
Support for PEP 519 – Adding a file system path protocol on most readers (e.g.
read_csv()
) and writers (e.g.DataFrame.to_csv()
) (GH13823).Added a
__fspath__
method topd.HDFStore
,pd.ExcelFile
, andpd.ExcelWriter
to work properly with the file system path protocol (GH13823).The
validate
argument formerge()
now checks whether a merge is one-to-one, one-to-many, many-to-one, or many-to-many. If a merge is found to not be an example of specified merge type, an exception of typeMergeError
will be raised. For more, see here (GH16270)Added support for PEP 518 (
pyproject.toml
) to the build system (GH16745)RangeIndex.append()
now returns aRangeIndex
object when possible (GH16212)Series.rename_axis()
andDataFrame.rename_axis()
withinplace=True
now returnNone
while renaming the axis inplace. (GH15704)api.types.infer_dtype()
now infers decimals. (GH15690)DataFrame.select_dtypes()
now accepts scalar values for include/exclude as well as list-like. (GH16855)date_range()
now accepts ‘YS’ in addition to ‘AS’ as an alias for start of year. (GH9313)date_range()
now accepts ‘Y’ in addition to ‘A’ as an alias for end of year. (GH9313)DataFrame.add_prefix()
andDataFrame.add_suffix()
now accept strings containing the ‘%’ character. (GH17151)Read/write methods that infer compression (
read_csv()
,read_table()
,read_pickle()
, andto_pickle()
) can now infer from path-like objects, such aspathlib.Path
. (GH17206)read_sas()
now recognizes much more of the most frequently used date (datetime) formats in SAS7BDAT files. (GH15871)DataFrame.items()
andSeries.items()
are now present in both Python 2 and 3 and is lazy in all cases. (GH13918, GH17213)pandas.io.formats.style.Styler.where()
has been implemented as a convenience forpandas.io.formats.style.Styler.applymap()
. (GH17474)MultiIndex.is_monotonic_decreasing()
has been implemented. Previously returnedFalse
in all cases. (GH16554)read_excel()
raisesImportError
with a better message ifxlrd
is not installed. (GH17613)DataFrame.assign()
will preserve the original order of**kwargs
for Python 3.6+ users instead of sorting the column names. (GH14207)Series.reindex()
,DataFrame.reindex()
,Index.get_indexer()
now support list-like argument fortolerance
. (GH17367)
Backwards incompatible API changes¶
Dependencies have increased minimum versions¶
We have updated our minimum supported versions of dependencies (GH15206, GH15543, GH15214). If installed, we now require:
Package
Minimum Version
Required
Numpy
1.9.0
X
Matplotlib
1.4.3
Scipy
0.14.0
Bottleneck
1.0.0
Additionally, support has been dropped for Python 3.4 (GH15251).
Sum/prod of all-NaN or empty Series/DataFrames is now consistently NaN¶
Note
The changes described here have been partially reverted. See the v0.22.0 Whatsnew for more.
The behavior of sum
and prod
on all-NaN Series/DataFrames no longer depends on
whether bottleneck is installed, and return value of sum
and prod
on an empty Series has changed (GH9422, GH15507).
Calling sum
or prod
on an empty or all-NaN
Series
, or columns of a DataFrame
, will result in NaN
. See the docs.
In [33]: s = pd.Series([np.nan])
Previously WITHOUT bottleneck
installed:
In [2]: s.sum()
Out[2]: np.nan
Previously WITH bottleneck
:
In [2]: s.sum()
Out[2]: 0.0
New behavior, without regard to the bottleneck installation:
In [34]: s.sum()
Out[34]: 0.0
Note that this also changes the sum of an empty Series
. Previously this always returned 0 regardless of a bottleneck
installation:
In [1]: pd.Series([]).sum()
Out[1]: 0
but for consistency with the all-NaN case, this was changed to return NaN as well:
In [35]: pd.Series([]).sum()
Out[35]: 0.0
Indexing with a list with missing labels is deprecated¶
Previously, selecting with a list of labels, where one or more labels were missing would always succeed, returning NaN
for missing labels.
This will now show a FutureWarning
. In the future this will raise a KeyError
(GH15747).
This warning will trigger on a DataFrame
or a Series
for using .loc[]
or [[]]
when passing a list-of-labels with at least 1 missing label.
See the deprecation docs.
In [36]: s = pd.Series([1, 2, 3])
In [37]: s
Out[37]:
0 1
1 2
2 3
Length: 3, dtype: int64
Previous behavior
In [4]: s.loc[[1, 2, 3]]
Out[4]:
1 2.0
2 3.0
3 NaN
dtype: float64
Current behavior
In [4]: s.loc[[1, 2, 3]]
Passing list-likes to .loc or [] with any missing label will raise
KeyError in the future, you can use .reindex() as an alternative.
See the documentation here:
https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike
Out[4]:
1 2.0
2 3.0
3 NaN
dtype: float64
The idiomatic way to achieve selecting potentially not-found elements is via .reindex()
In [38]: s.reindex([1, 2, 3])
Out[38]:
1 2.0
2 3.0
3 NaN
Length: 3, dtype: float64
Selection with all keys found is unchanged.
In [39]: s.loc[[1, 2]]
Out[39]:
1 2
2 3
Length: 2, dtype: int64
NA naming changes¶
In order to promote more consistency among the pandas API, we have added additional top-level
functions isna()
and notna()
that are aliases for isnull()
and notnull()
.
The naming scheme is now more consistent with methods like .dropna()
and .fillna()
. Furthermore
in all cases where .isnull()
and .notnull()
methods are defined, these have additional methods
named .isna()
and .notna()
, these are included for classes Categorical
,
Index
, Series
, and DataFrame
. (GH15001).
The configuration option pd.options.mode.use_inf_as_null
is deprecated, and pd.options.mode.use_inf_as_na
is added as a replacement.
Iteration of Series/Index will now return Python scalars¶
Previously, when using certain iteration methods for a Series
with dtype int
or float
, you would receive a numpy
scalar, e.g. a np.int64
, rather than a Python int
. Issue (GH10904) corrected this for Series.tolist()
and list(Series)
. This change makes all iteration methods consistent, in particular, for __iter__()
and .map()
; note that this only affects int/float dtypes. (GH13236, GH13258, GH14216).
In [40]: s = pd.Series([1, 2, 3])
In [41]: s
Out[41]:
0 1
1 2
2 3
Length: 3, dtype: int64
Previously:
In [2]: type(list(s)[0])
Out[2]: numpy.int64
New behavior:
In [42]: type(list(s)[0])
Out[42]: int
Furthermore this will now correctly box the results of iteration for DataFrame.to_dict()
as well.
In [43]: d = {'a': [1], 'b': ['b']}
In [44]: df = pd.DataFrame(d)
Previously:
In [8]: type(df.to_dict()['a'][0])
Out[8]: numpy.int64
New behavior:
In [45]: type(df.to_dict()['a'][0])
Out[45]: int
Indexing with a Boolean Index¶
Previously when passing a boolean Index
to .loc
, if the index of the Series/DataFrame
had boolean
labels,
you would get a label based selection, potentially duplicating result labels, rather than a boolean indexing selection
(where True
selects elements), this was inconsistent how a boolean numpy array indexed. The new behavior is to
act like a boolean numpy array indexer. (GH17738)
Previous behavior:
In [46]: s = pd.Series([1, 2, 3], index=[False, True, False])
In [47]: s
Out[47]:
False 1
True 2
False 3
Length: 3, dtype: int64
In [59]: s.loc[pd.Index([True, False, True])]
Out[59]:
True 2
False 1
False 3
True 2
dtype: int64
Current behavior
In [48]: s.loc[pd.Index([True, False, True])]
Out[48]:
False 1
False 3
Length: 2, dtype: int64
Furthermore, previously if you had an index that was non-numeric (e.g. strings), then a boolean Index would raise a KeyError
.
This will now be treated as a boolean indexer.
Previously behavior:
In [49]: s = pd.Series([1, 2, 3], index=['a', 'b', 'c'])
In [50]: s
Out[50]:
a 1
b 2
c 3
Length: 3, dtype: int64
In [39]: s.loc[pd.Index([True, False, True])]
KeyError: "None of [Index([True, False, True], dtype='object')] are in the [index]"
Current behavior
In [51]: s.loc[pd.Index([True, False, True])]
Out[51]:
a 1
c 3
Length: 2, dtype: int64
PeriodIndex
resampling¶
In previous versions of pandas, resampling a Series
/DataFrame
indexed by a PeriodIndex
returned a DatetimeIndex
in some cases (GH12884). Resampling to a multiplied frequency now returns a PeriodIndex
(GH15944). As a minor enhancement, resampling a PeriodIndex
can now handle NaT
values (GH13224)
Previous behavior:
In [1]: pi = pd.period_range('2017-01', periods=12, freq='M')
In [2]: s = pd.Series(np.arange(12), index=pi)
In [3]: resampled = s.resample('2Q').mean()
In [4]: resampled
Out[4]:
2017-03-31 1.0
2017-09-30 5.5
2018-03-31 10.0
Freq: 2Q-DEC, dtype: float64
In [5]: resampled.index
Out[5]: DatetimeIndex(['2017-03-31', '2017-09-30', '2018-03-31'], dtype='datetime64[ns]', freq='2Q-DEC')
New behavior:
In [52]: pi = pd.period_range('2017-01', periods=12, freq='M')
In [53]: s = pd.Series(np.arange(12), index=pi)
In [54]: resampled = s.resample('2Q').mean()
In [55]: resampled
Out[55]:
2017Q1 2.5
2017Q3 8.5
Freq: 2Q-DEC, Length: 2, dtype: float64
In [56]: resampled.index
Out[56]: PeriodIndex(['2017Q1', '2017Q3'], dtype='period[2Q-DEC]')
Upsampling and calling .ohlc()
previously returned a Series
, basically identical to calling .asfreq()
. OHLC upsampling now returns a DataFrame with columns open
, high
, low
and close
(GH13083). This is consistent with downsampling and DatetimeIndex
behavior.
Previous behavior:
In [1]: pi = pd.period_range(start='2000-01-01', freq='D', periods=10)
In [2]: s = pd.Series(np.arange(10), index=pi)
In [3]: s.resample('H').ohlc()
Out[3]:
2000-01-01 00:00 0.0
...
2000-01-10 23:00 NaN
Freq: H, Length: 240, dtype: float64
In [4]: s.resample('M').ohlc()
Out[4]:
open high low close
2000-01 0 9 0 9
New behavior:
In [57]: pi = pd.period_range(start='2000-01-01', freq='D', periods=10)
In [58]: s = pd.Series(np.arange(10), index=pi)
In [59]: s.resample('H').ohlc()
Out[59]:
open high low close
2000-01-01 00:00 0.0 0.0 0.0 0.0
2000-01-01 01:00 NaN NaN NaN NaN
2000-01-01 02:00 NaN NaN NaN NaN
2000-01-01 03:00 NaN NaN NaN NaN
2000-01-01 04:00 NaN NaN NaN NaN
... ... ... ... ...
2000-01-10 19:00 NaN NaN NaN NaN
2000-01-10 20:00 NaN NaN NaN NaN
2000-01-10 21:00 NaN NaN NaN NaN
2000-01-10 22:00 NaN NaN NaN NaN
2000-01-10 23:00 NaN NaN NaN NaN
[240 rows x 4 columns]
In [60]: s.resample('M').ohlc()
Out[60]:
open high low close
2000-01 0 9 0 9
[1 rows x 4 columns]
Improved error handling during item assignment in pd.eval¶
eval()
will now raise a ValueError
when item assignment malfunctions, or
inplace operations are specified, but there is no item assignment in the expression (GH16732)
In [61]: arr = np.array([1, 2, 3])
Previously, if you attempted the following expression, you would get a not very helpful error message:
In [3]: pd.eval("a = 1 + 2", target=arr, inplace=True)
...
IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`)
and integer or boolean arrays are valid indices
This is a very long way of saying numpy arrays don’t support string-item indexing. With this change, the error message is now this:
In [3]: pd.eval("a = 1 + 2", target=arr, inplace=True)
...
ValueError: Cannot assign expression output to target
It also used to be possible to evaluate expressions inplace, even if there was no item assignment:
In [4]: pd.eval("1 + 2", target=arr, inplace=True)
Out[4]: 3
However, this input does not make much sense because the output is not being assigned to
the target. Now, a ValueError
will be raised when such an input is passed in:
In [4]: pd.eval("1 + 2", target=arr, inplace=True)
...
ValueError: Cannot operate inplace if there is no assignment
Dtype conversions¶
Previously assignments, .where()
and .fillna()
with a bool
assignment, would coerce to same the type (e.g. int / float), or raise for datetimelikes. These will now preserve the bools with object
dtypes. (GH16821).
In [62]: s = pd.Series([1, 2, 3])
In [5]: s[1] = True
In [6]: s
Out[6]:
0 1
1 1
2 3
dtype: int64
New behavior
In [63]: s[1] = True
In [64]: s
Out[64]:
0 1
1 True
2 3
Length: 3, dtype: object
Previously, as assignment to a datetimelike with a non-datetimelike would coerce the non-datetime-like item being assigned (GH14145).
In [65]: s = pd.Series([pd.Timestamp('2011-01-01'), pd.Timestamp('2012-01-01')])
In [1]: s[1] = 1
In [2]: s
Out[2]:
0 2011-01-01 00:00:00.000000000
1 1970-01-01 00:00:00.000000001
dtype: datetime64[ns]
These now coerce to object
dtype.
In [66]: s[1] = 1
In [67]: s
Out[67]:
0 2011-01-01 00:00:00
1 1
Length: 2, dtype: object
MultiIndex constructor with a single level¶
The MultiIndex
constructors no longer squeezes a MultiIndex with all
length-one levels down to a regular Index
. This affects all the
MultiIndex
constructors. (GH17178)
Previous behavior:
In [2]: pd.MultiIndex.from_tuples([('a',), ('b',)])
Out[2]: Index(['a', 'b'], dtype='object')
Length 1 levels are no longer special-cased. They behave exactly as if you had
length 2+ levels, so a MultiIndex
is always returned from all of the
MultiIndex
constructors:
In [68]: pd.MultiIndex.from_tuples([('a',), ('b',)])
Out[68]:
MultiIndex([('a',),
('b',)],
)
UTC localization with Series¶
Previously, to_datetime()
did not localize datetime Series
data when utc=True
was passed. Now, to_datetime()
will correctly localize Series
with a datetime64[ns, UTC]
dtype to be consistent with how list-like and Index
data are handled. (GH6415).
Previous behavior
In [69]: s = pd.Series(['20130101 00:00:00'] * 3)
In [12]: pd.to_datetime(s, utc=True)
Out[12]:
0 2013-01-01
1 2013-01-01
2 2013-01-01
dtype: datetime64[ns]
New behavior
In [70]: pd.to_datetime(s, utc=True)
Out[70]:
0 2013-01-01 00:00:00+00:00
1 2013-01-01 00:00:00+00:00
2 2013-01-01 00:00:00+00:00
Length: 3, dtype: datetime64[ns, UTC]
Additionally, DataFrames with datetime columns that were parsed by read_sql_table()
and read_sql_query()
will also be localized to UTC only if the original SQL columns were timezone aware datetime columns.
Consistency of range functions¶
In previous versions, there were some inconsistencies between the various range functions: date_range()
, bdate_range()
, period_range()
, timedelta_range()
, and interval_range()
. (GH17471).
One of the inconsistent behaviors occurred when the start
, end
and period
parameters were all specified, potentially leading to ambiguous ranges. When all three parameters were passed, interval_range
ignored the period
parameter, period_range
ignored the end
parameter, and the other range functions raised. To promote consistency among the range functions, and avoid potentially ambiguous ranges, interval_range
and period_range
will now raise when all three parameters are passed.
Previous behavior:
In [2]: pd.interval_range(start=0, end=4, periods=6)
Out[2]:
IntervalIndex([(0, 1], (1, 2], (2, 3]]
closed='right',
dtype='interval[int64]')
In [3]: pd.period_range(start='2017Q1', end='2017Q4', periods=6, freq='Q')
Out[3]: PeriodIndex(['2017Q1', '2017Q2', '2017Q3', '2017Q4', '2018Q1', '2018Q2'], dtype='period[Q-DEC]', freq='Q-DEC')
New behavior:
In [2]: pd.interval_range(start=0, end=4, periods=6)
---------------------------------------------------------------------------
ValueError: Of the three parameters: start, end, and periods, exactly two must be specified
In [3]: pd.period_range(start='2017Q1', end='2017Q4', periods=6, freq='Q')
---------------------------------------------------------------------------
ValueError: Of the three parameters: start, end, and periods, exactly two must be specified
Additionally, the endpoint parameter end
was not included in the intervals produced by interval_range
. However, all other range functions include end
in their output. To promote consistency among the range functions, interval_range
will now include end
as the right endpoint of the final interval, except if freq
is specified in a way which skips end
.
Previous behavior:
In [4]: pd.interval_range(start=0, end=4)
Out[4]:
IntervalIndex([(0, 1], (1, 2], (2, 3]]
closed='right',
dtype='interval[int64]')
New behavior:
In [71]: pd.interval_range(start=0, end=4)
Out[71]: IntervalIndex([(0, 1], (1, 2], (2, 3], (3, 4]], dtype='interval[int64, right]')
No automatic Matplotlib converters¶
pandas no longer registers our date
, time
, datetime
,
datetime64
, and Period
converters with matplotlib when pandas is
imported. Matplotlib plot methods (plt.plot
, ax.plot
, …), will not
nicely format the x-axis for DatetimeIndex
or PeriodIndex
values. You
must explicitly register these methods:
pandas built-in Series.plot
and DataFrame.plot
will register these
converters on first-use (GH17710).
Note
This change has been temporarily reverted in pandas 0.21.1, for more details see here.
Other API changes¶
The Categorical constructor no longer accepts a scalar for the
categories
keyword. (GH16022)Accessing a non-existent attribute on a closed
HDFStore
will now raise anAttributeError
rather than aClosedFileError
(GH16301)read_csv()
now issues aUserWarning
if thenames
parameter contains duplicates (GH17095)read_csv()
now treats'null'
and'n/a'
strings as missing values by default (GH16471, GH16078)pandas.HDFStore
’s string representation is now faster and less detailed. For the previous behavior, usepandas.HDFStore.info()
. (GH16503).Compression defaults in HDF stores now follow pytables standards. Default is no compression and if
complib
is missing andcomplevel
> 0zlib
is used (GH15943)Index.get_indexer_non_unique()
now returns a ndarray indexer rather than anIndex
; this is consistent withIndex.get_indexer()
(GH16819)Removed the
@slow
decorator frompandas._testing
, which caused issues for some downstream packages’ test suites. Use@pytest.mark.slow
instead, which achieves the same thing (GH16850)Moved definition of
MergeError
to thepandas.errors
module.The signature of
Series.set_axis()
andDataFrame.set_axis()
has been changed fromset_axis(axis, labels)
toset_axis(labels, axis=0)
, for consistency with the rest of the API. The old signature is deprecated and will show aFutureWarning
(GH14636)Series.argmin()
andSeries.argmax()
will now raise aTypeError
when used withobject
dtypes, instead of aValueError
(GH13595)Period
is now immutable, and will now raise anAttributeError
when a user tries to assign a new value to theordinal
orfreq
attributes (GH17116).to_datetime()
when passed a tz-awareorigin=
kwarg will now raise a more informativeValueError
rather than aTypeError
(GH16842)to_datetime()
now raises aValueError
when format includes%W
or%U
without also including day of the week and calendar year (GH16774)Renamed non-functional
index
toindex_col
inread_stata()
to improve API consistency (GH16342)Bug in
DataFrame.drop()
caused boolean labelsFalse
andTrue
to be treated as labels 0 and 1 respectively when dropping indices from a numeric index. This will now raise a ValueError (GH16877)Restricted DateOffset keyword arguments. Previously,
DateOffset
subclasses allowed arbitrary keyword arguments which could lead to unexpected behavior. Now, only valid arguments will be accepted. (GH17176).
Deprecations¶
DataFrame.from_csv()
andSeries.from_csv()
have been deprecated in favor ofread_csv()
(GH4191)read_excel()
has deprecatedsheetname
in favor ofsheet_name
for consistency with.to_excel()
(GH10559).read_excel()
has deprecatedparse_cols
in favor ofusecols
for consistency withread_csv()
(GH4988)read_csv()
has deprecated thetupleize_cols
argument. Column tuples will always be converted to aMultiIndex
(GH17060)DataFrame.to_csv()
has deprecated thetupleize_cols
argument. MultiIndex columns will be always written as rows in the CSV file (GH17060)The
convert
parameter has been deprecated in the.take()
method, as it was not being respected (GH16948)pd.options.html.border
has been deprecated in favor ofpd.options.display.html.border
(GH15793).SeriesGroupBy.nth()
has deprecatedTrue
in favor of'all'
for its kwargdropna
(GH11038).DataFrame.as_blocks()
is deprecated, as this is exposing the internal implementation (GH17302)pd.TimeGrouper
is deprecated in favor ofpandas.Grouper
(GH16747)cdate_range
has been deprecated in favor ofbdate_range()
, which has gainedweekmask
andholidays
parameters for building custom frequency date ranges. See the documentation for more details (GH17596)passing
categories
orordered
kwargs toSeries.astype()
is deprecated, in favor of passing a CategoricalDtype (GH17636).get_value
and.set_value
onSeries
,DataFrame
,Panel
,SparseSeries
, andSparseDataFrame
are deprecated in favor of using.iat[]
or.at[]
accessors (GH15269)Passing a non-existent column in
.to_excel(..., columns=)
is deprecated and will raise aKeyError
in the future (GH17295)raise_on_error
parameter toSeries.where()
,Series.mask()
,DataFrame.where()
,DataFrame.mask()
is deprecated, in favor oferrors=
(GH14968)Using
DataFrame.rename_axis()
andSeries.rename_axis()
to alter index or column labels is now deprecated in favor of using.rename
.rename_axis
may still be used to alter the name of the index or columns (GH17833).reindex_axis()
has been deprecated in favor ofreindex()
. See here for more (GH17833).
Series.select and DataFrame.select¶
The Series.select()
and DataFrame.select()
methods are deprecated in favor of using df.loc[labels.map(crit)]
(GH12401)
In [72]: df = pd.DataFrame({'A': [1, 2, 3]}, index=['foo', 'bar', 'baz'])
In [3]: df.select(lambda x: x in ['bar', 'baz'])
FutureWarning: select is deprecated and will be removed in a future release. You can use .loc[crit] as a replacement
Out[3]:
A
bar 2
baz 3
In [73]: df.loc[df.index.map(lambda x: x in ['bar', 'baz'])]
Out[73]:
A
bar 2
baz 3
[2 rows x 1 columns]
Series.argmax and Series.argmin¶
The behavior of Series.argmax()
and Series.argmin()
have been deprecated in favor of Series.idxmax()
and Series.idxmin()
, respectively (GH16830).
For compatibility with NumPy arrays, pd.Series
implements argmax
and
argmin
. Since pandas 0.13.0, argmax
has been an alias for
pandas.Series.idxmax()
, and argmin
has been an alias for
pandas.Series.idxmin()
. They return the label of the maximum or minimum,
rather than the position.
We’ve deprecated the current behavior of Series.argmax
and
Series.argmin
. Using either of these will emit a FutureWarning
. Use
Series.idxmax()
if you want the label of the maximum. Use
Series.values.argmax()
if you want the position of the maximum. Likewise for
the minimum. In a future release Series.argmax
and Series.argmin
will
return the position of the maximum or minimum.
Removal of prior version deprecations/changes¶
read_excel()
has dropped thehas_index_names
parameter (GH10967)The
pd.options.display.height
configuration has been dropped (GH3663)The
pd.options.display.line_width
configuration has been dropped (GH2881)The
pd.options.display.mpl_style
configuration has been dropped (GH12190)Index
has dropped the.sym_diff()
method in favor of.symmetric_difference()
(GH12591)Categorical
has dropped the.order()
and.sort()
methods in favor of.sort_values()
(GH12882)eval()
andDataFrame.eval()
have changed the default ofinplace
fromNone
toFalse
(GH11149)The function
get_offset_name
has been dropped in favor of the.freqstr
attribute for an offset (GH11834)pandas no longer tests for compatibility with hdf5-files created with pandas < 0.11 (GH17404).
Performance improvements¶
Improved performance of instantiating
SparseDataFrame
(GH16773)Series.dt
no longer performs frequency inference, yielding a large speedup when accessing the attribute (GH17210)Improved performance of
set_categories()
by not materializing the values (GH17508)Timestamp.microsecond
no longer re-computes on attribute access (GH17331)Improved performance of the
CategoricalIndex
for data that is already categorical dtype (GH17513)Improved performance of
RangeIndex.min()
andRangeIndex.max()
by usingRangeIndex
properties to perform the computations (GH17607)
Documentation changes¶
Bug fixes¶
Conversion¶
Bug in assignment against datetime-like data with
int
may incorrectly convert to datetime-like (GH14145)Bug in assignment against
int64
data withnp.ndarray
withfloat64
dtype may keepint64
dtype (GH14001)Fixed the return type of
IntervalIndex.is_non_overlapping_monotonic
to be a Pythonbool
for consistency with similar attributes/methods. Previously returned anumpy.bool_
. (GH17237)Bug in
IntervalIndex.is_non_overlapping_monotonic
when intervals are closed on both sides and overlap at a point (GH16560)Bug in
Series.fillna()
returns frame wheninplace=True
andvalue
is dict (GH16156)Bug in
Timestamp.weekday_name
returning a UTC-based weekday name when localized to a timezone (GH17354)Bug in
Timestamp.replace
when replacingtzinfo
around DST changes (GH15683)Bug in
Timedelta
construction and arithmetic that would not propagate theOverflow
exception (GH17367)Bug in
astype()
converting to object dtype when passed extension type classes (DatetimeTZDtype
,CategoricalDtype
) rather than instances. Now aTypeError
is raised when a class is passed (GH17780).Bug in
to_numeric()
in which elements were not always being coerced to numeric whenerrors='coerce'
(GH17007, GH17125)Bug in
DataFrame
andSeries
constructors whererange
objects are converted toint32
dtype on Windows instead ofint64
(GH16804)
Indexing¶
When called with a null slice (e.g.
df.iloc[:]
), the.iloc
and.loc
indexers return a shallow copy of the original object. Previously they returned the original object. (GH13873).When called on an unsorted
MultiIndex
, theloc
indexer now will raiseUnsortedIndexError
only if proper slicing is used on non-sorted levels (GH16734).Fixes regression in 0.20.3 when indexing with a string on a
TimedeltaIndex
(GH16896).Fixed
TimedeltaIndex.get_loc()
handling ofnp.timedelta64
inputs (GH16909).Fix
MultiIndex.sort_index()
ordering whenascending
argument is a list, but not all levels are specified, or are in a different order (GH16934).Fixes bug where indexing with
np.inf
caused anOverflowError
to be raised (GH16957)Bug in reindexing on an empty
CategoricalIndex
(GH16770)Fixes
DataFrame.loc
for setting with alignment and tz-awareDatetimeIndex
(GH16889)Avoids
IndexError
when passing an Index or Series to.iloc
with older numpy (GH17193)Allow unicode empty strings as placeholders in multilevel columns in Python 2 (GH17099)
Bug in
.iloc
when used with inplace addition or assignment and an int indexer on aMultiIndex
causing the wrong indexes to be read from and written to (GH17148)Bug in
.isin()
in which checking membership in emptySeries
objects raised an error (GH16991)Bug in
CategoricalIndex
reindexing in which specified indices containing duplicates were not being respected (GH17323)Bug in intersection of
RangeIndex
with negative step (GH17296)Bug in
IntervalIndex
where performing a scalar lookup fails for included right endpoints of non-overlapping monotonic decreasing indexes (GH16417, GH17271)Bug in
DataFrame.first_valid_index()
andDataFrame.last_valid_index()
when no valid entry (GH17400)Bug in
Series.rename()
when called with a callable, incorrectly alters the name of theSeries
, rather than the name of theIndex
. (GH17407)Bug in
String.str_get()
raisesIndexError
instead of inserting NaNs when using a negative index. (GH17704)
IO¶
Bug in
read_hdf()
when reading a timezone aware index fromfixed
format HDFStore (GH17618)Bug in
read_csv()
in which columns were not being thoroughly de-duplicated (GH17060)Bug in
read_csv()
in which specified column names were not being thoroughly de-duplicated (GH17095)Bug in
read_csv()
in which non integer values for the header argument generated an unhelpful / unrelated error message (GH16338)Bug in
read_csv()
in which memory management issues in exception handling, under certain conditions, would cause the interpreter to segfault (GH14696, GH16798).Bug in
read_csv()
when called withlow_memory=False
in which a CSV with at least one column > 2GB in size would incorrectly raise aMemoryError
(GH16798).Bug in
read_csv()
when called with a single-element listheader
would return aDataFrame
of all NaN values (GH7757)Bug in
DataFrame.to_csv()
defaulting to ‘ascii’ encoding in Python 3, instead of ‘utf-8’ (GH17097)Bug in
read_stata()
where value labels could not be read when using an iterator (GH16923)Bug in
read_stata()
where the index was not set (GH16342)Bug in
read_html()
where import check fails when run in multiple threads (GH16928)Bug in
read_csv()
where automatic delimiter detection caused aTypeError
to be thrown when a bad line was encountered rather than the correct error message (GH13374)Bug in
DataFrame.to_html()
withnotebook=True
where DataFrames with named indices or non-MultiIndex indices had undesired horizontal or vertical alignment for column or row labels, respectively (GH16792)Bug in
DataFrame.to_html()
in which there was no validation of thejustify
parameter (GH17527)Bug in
HDFStore.select()
when reading a contiguous mixed-data table featuring VLArray (GH17021)Bug in
to_json()
where several conditions (including objects with unprintable symbols, objects with deep recursion, overlong labels) caused segfaults instead of raising the appropriate exception (GH14256)
Plotting¶
Bug in plotting methods using
secondary_y
andfontsize
not setting secondary axis font size (GH12565)Bug when plotting
timedelta
anddatetime
dtypes on y-axis (GH16953)Line plots no longer assume monotonic x data when calculating xlims, they show the entire lines now even for unsorted x data. (GH11310, GH11471)
With matplotlib 2.0.0 and above, calculation of x limits for line plots is left to matplotlib, so that its new default settings are applied. (GH15495)
Bug in
Series.plot.bar
orDataFrame.plot.bar
withy
not respecting user-passedcolor
(GH16822)Bug causing
plotting.parallel_coordinates
to reset the random seed when using random colors (GH17525)
GroupBy/resample/rolling¶
Bug in
DataFrame.resample(...).size()
where an emptyDataFrame
did not return aSeries
(GH14962)Bug in
infer_freq()
causing indices with 2-day gaps during the working week to be wrongly inferred as business daily (GH16624)Bug in
.rolling(...).quantile()
which incorrectly used different defaults thanSeries.quantile()
andDataFrame.quantile()
(GH9413, GH16211)Bug in
groupby.transform()
that would coerce boolean dtypes back to float (GH16875)Bug in
Series.resample(...).apply()
where an emptySeries
modified the source index and did not return the name of aSeries
(GH14313)Bug in
.rolling(...).apply(...)
with aDataFrame
with aDatetimeIndex
, awindow
of a timedelta-convertible andmin_periods >= 1
(GH15305)Bug in
DataFrame.groupby
where index and column keys were not recognized correctly when the number of keys equaled the number of elements on the groupby axis (GH16859)Bug in
groupby.nunique()
withTimeGrouper
which cannot handleNaT
correctly (GH17575)Bug in
DataFrame.groupby
where a single level selection from aMultiIndex
unexpectedly sorts (GH17537)Bug in
DataFrame.groupby
where spurious warning is raised whenGrouper
object is used to override ambiguous column name (GH17383)Bug in
TimeGrouper
differs when passes as a list and as a scalar (GH17530)
Sparse¶
Bug in
SparseSeries
raisesAttributeError
when a dictionary is passed in as data (GH16905)Bug in
SparseDataFrame.fillna()
not filling all NaNs when frame was instantiated from SciPy sparse matrix (GH16112)Bug in
SparseSeries.unstack()
andSparseDataFrame.stack()
(GH16614, GH15045)Bug in
make_sparse()
treating two numeric/boolean data, which have same bits, as same when arraydtype
isobject
(GH17574)SparseArray.all()
andSparseArray.any()
are now implemented to handleSparseArray
, these were used but not implemented (GH17570)
Reshaping¶
Joining/Merging with a non unique
PeriodIndex
raised aTypeError
(GH16871)Bug in
crosstab()
where non-aligned series of integers were casted to float (GH17005)Bug in merging with categorical dtypes with datetimelikes incorrectly raised a
TypeError
(GH16900)Bug when using
isin()
on a large object series and large comparison array (GH16012)Fixes regression from 0.20,
Series.aggregate()
andDataFrame.aggregate()
allow dictionaries as return values again (GH16741)Fixes dtype of result with integer dtype input, from
pivot_table()
when called withmargins=True
(GH17013)Bug in
crosstab()
where passing twoSeries
with the same name raised aKeyError
(GH13279)Series.argmin()
,Series.argmax()
, and their counterparts onDataFrame
and groupby objects work correctly with floating point data that contains infinite values (GH13595).Bug in
unique()
where checking a tuple of strings raised aTypeError
(GH17108)Bug in
concat()
where order of result index was unpredictable if it contained non-comparable elements (GH17344)Fixes regression when sorting by multiple columns on a
datetime64
dtypeSeries
withNaT
values (GH16836)Bug in
pivot_table()
where the result’s columns did not preserve the categorical dtype ofcolumns
whendropna
wasFalse
(GH17842)Bug in
DataFrame.drop_duplicates
where dropping with non-unique column names raised aValueError
(GH17836)Bug in
unstack()
which, when called on a list of levels, would discard thefillna
argument (GH13971)Bug in the alignment of
range
objects and other list-likes withDataFrame
leading to operations being performed row-wise instead of column-wise (GH17901)
Numeric¶
Bug in
.clip()
withaxis=1
and a list-like forthreshold
is passed; previously this raisedValueError
(GH15390)Series.clip()
andDataFrame.clip()
now treat NA values for upper and lower arguments asNone
instead of raisingValueError
(GH17276).
Categorical¶
Bug in
Series.isin()
when called with a categorical (GH16639)Bug in the categorical constructor with empty values and categories causing the
.categories
to be an emptyFloat64Index
rather than an emptyIndex
with object dtype (GH17248)Bug in categorical operations with Series.cat not preserving the original Series’ name (GH17509)
Bug in
DataFrame.merge()
failing for categorical columns with boolean/int data types (GH17187)Bug in constructing a
Categorical
/CategoricalDtype
when the specifiedcategories
are of categorical type (GH17884).
PyPy¶
Compatibility with PyPy in
read_csv()
withusecols=[<unsorted ints>]
andread_json()
(GH17351)Split tests into cases for CPython and PyPy where needed, which highlights the fragility of index matching with
float('nan')
,np.nan
andNAT
(GH17351)Fix
DataFrame.memory_usage()
to support PyPy. Objects on PyPy do not have a fixed size, so an approximation is used instead (GH17228)
Contributors¶
A total of 206 people contributed patches to this release. People with a “+” by their names contributed a patch for the first time.
3553x +
Aaron Barber
Adam Gleave +
Adam Smith +
AdamShamlian +
Adrian Liaw +
Alan Velasco +
Alan Yee +
Alex B +
Alex Lubbock +
Alex Marchenko +
Alex Rychyk +
Amol K +
Andreas Winkler
Andrew +
Andrew 亮
André Jonasson +
Becky Sweger
Berkay +
Bob Haffner +
Bran Yang
Brian Tu +
Brock Mendel +
Carol Willing +
Carter Green +
Chankey Pathak +
Chris
Chris Billington
Chris Filo Gorgolewski +
Chris Kerr
Chris M +
Chris Mazzullo +
Christian Prinoth
Christian Stade-Schuldt
Christoph Moehl +
DSM
Daniel Chen +
Daniel Grady
Daniel Himmelstein
Dave Willmer
David Cook
David Gwynne
David Read +
Dillon Niederhut +
Douglas Rudd
Eric Stein +
Eric Wieser +
Erik Fredriksen
Florian Wilhelm +
Floris Kint +
Forbidden Donut
Gabe F +
Giftlin +
Giftlin Rajaiah +
Giulio Pepe +
Guilherme Beltramini
Guillem Borrell +
Hanmin Qin +
Hendrik Makait +
Hugues Valois
Hussain Tamboli +
Iva Miholic +
Jan Novotný +
Jan Rudolph
Jean Helie +
Jean-Baptiste Schiratti +
Jean-Mathieu Deschenes
Jeff Knupp +
Jeff Reback
Jeff Tratner
JennaVergeynst
JimStearns206
Joel Nothman
John W. O’Brien
Jon Crall +
Jon Mease
Jonathan J. Helmus +
Joris Van den Bossche
JosephWagner
Juarez Bochi
Julian Kuhlmann +
Karel De Brabandere
Kassandra Keeton +
Keiron Pizzey +
Keith Webber
Kernc
Kevin Sheppard
Kirk Hansen +
Licht Takeuchi +
Lucas Kushner +
Mahdi Ben Jelloul +
Makarov Andrey +
Malgorzata Turzanska +
Marc Garcia +
Margaret Sy +
MarsGuy +
Matt Bark +
Matthew Roeschke
Matti Picus
Mehmet Ali “Mali” Akmanalp
Michael Gasvoda +
Michael Penkov +
Milo +
Morgan Stuart +
Morgan243 +
Nathan Ford +
Nick Eubank
Nick Garvey +
Oleg Shteynbuk +
P-Tillmann +
Pankaj Pandey
Patrick Luo
Patrick O’Melveny
Paul Reidy +
Paula +
Peter Quackenbush
Peter Yanovich +
Phillip Cloud
Pierre Haessig
Pietro Battiston
Pradyumna Reddy Chinthala
Prasanjit Prakash
RobinFiveWords
Ryan Hendrickson
Sam Foo
Sangwoong Yoon +
Simon Gibbons +
SimonBaron
Steven Cutting +
Sudeep +
Sylvia +
T N +
Telt
Thomas A Caswell
Tim Swast +
Tom Augspurger
Tong SHEN
Tuan +
Utkarsh Upadhyay +
Vincent La +
Vivek +
WANG Aiyong
WBare
Wes McKinney
XF +
Yi Liu +
Yosuke Nakabayashi +
aaron315 +
abarber4gh +
aernlund +
agustín méndez +
andymaheshw +
ante328 +
aviolov +
bpraggastis
cbertinato +
cclauss +
chernrick
chris-b1
dkamm +
dwkenefick
economy
faic +
fding253 +
gfyoung
guygoldberg +
hhuuggoo +
huashuai +
ian
iulia +
jaredsnyder
jbrockmendel +
jdeschenes
jebob +
jschendel +
keitakurita
kernc +
kiwirob +
kjford
linebp
lloydkirk
louispotok +
majiang +
manikbhandari +
matthiashuschle +
mattip
maxwasserman +
mjlove12 +
nmartensen +
pandas-docs-bot +
parchd-1 +
philipphanemann +
rdk1024 +
reidy-p +
ri938
ruiann +
rvernica +
s-weigand +
scotthavard92 +
skwbc +
step4me +
tobycheese +
topper-123 +
tsdlovell
ysau +
zzgao +