Version 0.10.0 (December 17, 2012)¶
This is a major release from 0.9.1 and includes many new features and enhancements along with a large number of bug fixes. There are also a number of important API changes that long-time pandas users should pay close attention to.
File parsing new features¶
The delimited file parsing engine (the guts of read_csv and read_table)
has been rewritten from the ground up and now uses a fraction the amount of
memory while parsing, while being 40% or more faster in most use cases (in some
cases much faster).
There are also many new features:
Much-improved Unicode handling via the
encodingoption.Column filtering (
usecols)Dtype specification (
dtypeargument)Ability to specify strings to be recognized as True/False
Ability to yield NumPy record arrays (
as_recarray)High performance
delim_whitespaceoptionDecimal format (e.g. European format) specification
Easier CSV dialect options:
escapechar,lineterminator,quotechar, etc.More robust handling of many exceptional kinds of files observed in the wild
API changes¶
Deprecated DataFrame BINOP TimeSeries special case behavior
The default behavior of binary operations between a DataFrame and a Series has always been to align on the DataFrame’s columns and broadcast down the rows, except in the special case that the DataFrame contains time series. Since there are now method for each binary operator enabling you to specify how you want to broadcast, we are phasing out this special case (Zen of Python: Special cases aren’t special enough to break the rules). Here’s what I’m talking about:
In [1]: import pandas as pd
In [2]: df = pd.DataFrame(np.random.randn(6, 4), index=pd.date_range("1/1/2000", periods=6))
In [3]: df
Out[3]:
0 1 2 3
2000-01-01 0.469112 -0.282863 -1.509059 -1.135632
2000-01-02 1.212112 -0.173215 0.119209 -1.044236
2000-01-03 -0.861849 -2.104569 -0.494929 1.071804
2000-01-04 0.721555 -0.706771 -1.039575 0.271860
2000-01-05 -0.424972 0.567020 0.276232 -1.087401
2000-01-06 -0.673690 0.113648 -1.478427 0.524988
# deprecated now
In [4]: df - df[0]
Out[4]:
2000-01-01 00:00:00 2000-01-02 00:00:00 2000-01-03 00:00:00 2000-01-04 00:00:00 ... 0 1 2 3
2000-01-01 NaN NaN NaN NaN ... NaN NaN NaN NaN
2000-01-02 NaN NaN NaN NaN ... NaN NaN NaN NaN
2000-01-03 NaN NaN NaN NaN ... NaN NaN NaN NaN
2000-01-04 NaN NaN NaN NaN ... NaN NaN NaN NaN
2000-01-05 NaN NaN NaN NaN ... NaN NaN NaN NaN
2000-01-06 NaN NaN NaN NaN ... NaN NaN NaN NaN
[6 rows x 10 columns]
# Change your code to
In [5]: df.sub(df[0], axis=0) # align on axis 0 (rows)
Out[5]:
0 1 2 3
2000-01-01 0.0 -0.751976 -1.978171 -1.604745
2000-01-02 0.0 -1.385327 -1.092903 -2.256348
2000-01-03 0.0 -1.242720 0.366920 1.933653
2000-01-04 0.0 -1.428326 -1.761130 -0.449695
2000-01-05 0.0 0.991993 0.701204 -0.662428
2000-01-06 0.0 0.787338 -0.804737 1.198677
You will get a deprecation warning in the 0.10.x series, and the deprecated functionality will be removed in 0.11 or later.
Altered resample default behavior
The default time series resample binning behavior of daily D and
higher frequencies has been changed to closed='left', label='left'. Lower
nfrequencies are unaffected. The prior defaults were causing a great deal of
confusion for users, especially resampling data to daily frequency (which
labeled the aggregated group with the end of the interval: the next day).
In [1]: dates = pd.date_range('1/1/2000', '1/5/2000', freq='4h')
In [2]: series = pd.Series(np.arange(len(dates)), index=dates)
In [3]: series
Out[3]:
2000-01-01 00:00:00 0
2000-01-01 04:00:00 1
2000-01-01 08:00:00 2
2000-01-01 12:00:00 3
2000-01-01 16:00:00 4
2000-01-01 20:00:00 5
2000-01-02 00:00:00 6
2000-01-02 04:00:00 7
2000-01-02 08:00:00 8
2000-01-02 12:00:00 9
2000-01-02 16:00:00 10
2000-01-02 20:00:00 11
2000-01-03 00:00:00 12
2000-01-03 04:00:00 13
2000-01-03 08:00:00 14
2000-01-03 12:00:00 15
2000-01-03 16:00:00 16
2000-01-03 20:00:00 17
2000-01-04 00:00:00 18
2000-01-04 04:00:00 19
2000-01-04 08:00:00 20
2000-01-04 12:00:00 21
2000-01-04 16:00:00 22
2000-01-04 20:00:00 23
2000-01-05 00:00:00 24
Freq: 4H, dtype: int64
In [4]: series.resample('D', how='sum')
Out[4]:
2000-01-01 15
2000-01-02 51
2000-01-03 87
2000-01-04 123
2000-01-05 24
Freq: D, dtype: int64
In [5]: # old behavior
In [6]: series.resample('D', how='sum', closed='right', label='right')
Out[6]:
2000-01-01 0
2000-01-02 21
2000-01-03 57
2000-01-04 93
2000-01-05 129
Freq: D, dtype: int64
Infinity and negative infinity are no longer treated as NA by
isnullandnotnull. That they ever were was a relic of early pandas. This behavior can be re-enabled globally by themode.use_inf_as_nulloption:
In [6]: s = pd.Series([1.5, np.inf, 3.4, -np.inf])
In [7]: pd.isnull(s)
Out[7]:
0 False
1 False
2 False
3 False
Length: 4, dtype: bool
In [8]: s.fillna(0)
Out[8]:
0 1.500000
1 inf
2 3.400000
3 -inf
Length: 4, dtype: float64
In [9]: pd.set_option('use_inf_as_null', True)
In [10]: pd.isnull(s)
Out[10]:
0 False
1 True
2 False
3 True
Length: 4, dtype: bool
In [11]: s.fillna(0)
Out[11]:
0 1.5
1 0.0
2 3.4
3 0.0
Length: 4, dtype: float64
In [12]: pd.reset_option('use_inf_as_null')
Methods with the
inplaceoption now all returnNoneinstead of the calling object. E.g. code written likedf = df.fillna(0, inplace=True)may stop working. To fix, simply delete the unnecessary variable assignment.pandas.mergeno longer sorts the group keys (sort=False) by default. This was done for performance reasons: the group-key sorting is often one of the more expensive parts of the computation and is often unnecessary.The default column names for a file with no header have been changed to the integers
0throughN - 1. This is to create consistency with the DataFrame constructor with no columns specified. The v0.9.0 behavior (namesX0,X1, …) can be reproduced by specifyingprefix='X':
In [6]: import io
In [7]: data = """
...: a,b,c
...: 1,Yes,2
...: 3,No,4
...: """
...: print(data)
...: pd.read_csv(io.StringIO(data), header=None)
...: pd.read_csv(io.StringIO(data), header=None, prefix="X")
...:
Input In [7]
print(data)
^
IndentationError: unexpected indent
Values like
'Yes'and'No'are not interpreted as boolean by default, though this can be controlled by newtrue_valuesandfalse_valuesarguments:
In [4]: print(data)
a,b,c
1,Yes,2
3,No,4
In [5]: pd.read_csv(io.StringIO(data))
Out[5]:
a b c
0 1 Yes 2
1 3 No 4
In [6]: pd.read_csv(io.StringIO(data), true_values=["Yes"], false_values=["No"])
Out[6]:
a b c
0 1 True 2
1 3 False 4
The file parsers will not recognize non-string values arising from a converter function as NA if passed in the
na_valuesargument. It’s better to do post-processing using thereplacefunction instead.Calling
fillnaon Series or DataFrame with no arguments is no longer valid code. You must either specify a fill value or an interpolation method:
In [8]: s = pd.Series([np.nan, 1.0, 2.0, np.nan, 4])
In [9]: s
Out[9]:
0 NaN
1 1.0
2 2.0
3 NaN
4 4.0
dtype: float64
In [10]: s.fillna(0)
Out[10]:
0 0.0
1 1.0
2 2.0
3 0.0
4 4.0
dtype: float64
In [11]: s.fillna(method="pad")
Out[11]:
0 NaN
1 1.0
2 2.0
3 2.0
4 4.0
dtype: float64
Convenience methods ffill and bfill have been added:
In [12]: s.ffill()
Out[12]:
0 NaN
1 1.0
2 2.0
3 2.0
4 4.0
dtype: float64
Series.applywill now operate on a returned value from the applied function, that is itself a series, and possibly upcast the result to a DataFrameIn [13]: def f(x): ....: return pd.Series([x, x ** 2], index=["x", "x^2"]) ....: In [14]: s = pd.Series(np.random.rand(5)) In [15]: s Out[15]: 0 0.340445 1 0.984729 2 0.919540 3 0.037772 4 0.861549 dtype: float64 In [16]: s.apply(f) Out[16]: x x^2 0 0.340445 0.115903 1 0.984729 0.969691 2 0.919540 0.845555 3 0.037772 0.001427 4 0.861549 0.742267
New API functions for working with pandas options (GH2097):
get_option/set_option- get/set the value of an option. Partial names are accepted. -reset_option- reset one or more options to their default value. Partial names are accepted. -describe_option- print a description of one or more options. When called with no arguments. print all registered options.
Note:
set_printoptions/reset_printoptionsare now deprecated (but functioning), the print options now live under “display.XYZ”. For example:In [17]: pd.get_option("display.max_rows") Out[17]: 15
to_string() methods now always return unicode strings (GH2224).
New features¶
Wide DataFrame printing¶
Instead of printing the summary information, pandas now splits the string representation across multiple rows by default:
In [18]: wide_frame = pd.DataFrame(np.random.randn(5, 16))
In [19]: wide_frame
Out[19]:
0 1 2 3 4 5 6 ... 9 10 11 12 13 14 15
0 -0.548702 1.467327 -1.015962 -0.483075 1.637550 -1.217659 -0.291519 ... 0.991460 -0.919069 0.266046 -0.709661 1.669052 1.037882 -1.705775
1 -0.919854 -0.042379 1.247642 -0.009920 0.290213 0.495767 0.362949 ... -0.089329 0.337863 -0.945867 -0.932132 1.956030 0.017587 -0.016692
2 -0.575247 0.254161 -1.143704 0.215897 1.193555 -0.077118 -0.408530 ... 1.511763 1.627081 -0.990582 -0.441652 1.211526 0.268520 0.024580
3 -1.577585 0.396823 -0.105381 -0.532532 1.453749 1.208843 -0.080952 ... -0.589346 0.339969 -0.693205 -0.339355 0.593616 0.884345 1.591431
4 0.141809 0.220390 0.435589 0.192451 -0.096701 0.803351 1.715071 ... -1.814470 1.018601 -0.595447 1.395433 -0.392670 0.007207 1.928123
[5 rows x 16 columns]
The old behavior of printing out summary information can be achieved via the ‘expand_frame_repr’ print option:
In [20]: pd.set_option("expand_frame_repr", False)
In [21]: wide_frame
Out[21]:
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
0 -0.548702 1.467327 -1.015962 -0.483075 1.637550 -1.217659 -0.291519 -1.745505 -0.263952 0.991460 -0.919069 0.266046 -0.709661 1.669052 1.037882 -1.705775
1 -0.919854 -0.042379 1.247642 -0.009920 0.290213 0.495767 0.362949 1.548106 -1.131345 -0.089329 0.337863 -0.945867 -0.932132 1.956030 0.017587 -0.016692
2 -0.575247 0.254161 -1.143704 0.215897 1.193555 -0.077118 -0.408530 -0.862495 1.346061 1.511763 1.627081 -0.990582 -0.441652 1.211526 0.268520 0.024580
3 -1.577585 0.396823 -0.105381 -0.532532 1.453749 1.208843 -0.080952 -0.264610 -0.727965 -0.589346 0.339969 -0.693205 -0.339355 0.593616 0.884345 1.591431
4 0.141809 0.220390 0.435589 0.192451 -0.096701 0.803351 1.715071 -0.708758 -1.202872 -1.814470 1.018601 -0.595447 1.395433 -0.392670 0.007207 1.928123
The width of each line can be changed via ‘line_width’ (80 by default):
pd.set_option("line_width", 40)
wide_frame
Updated PyTables support¶
Docs for PyTables Table format & several enhancements to the api. Here is a taste of what to expect.
In [41]: store = pd.HDFStore('store.h5')
In [42]: df = pd.DataFrame(np.random.randn(8, 3),
....: index=pd.date_range('1/1/2000', periods=8),
....: columns=['A', 'B', 'C'])
In [43]: df
Out[43]:
A B C
2000-01-01 -2.036047 0.000830 -0.955697
2000-01-02 -0.898872 -0.725411 0.059904
2000-01-03 -0.449644 1.082900 -1.221265
2000-01-04 0.361078 1.330704 0.855932
2000-01-05 -1.216718 1.488887 0.018993
2000-01-06 -0.877046 0.045976 0.437274
2000-01-07 -0.567182 -0.888657 -0.556383
2000-01-08 0.655457 1.117949 -2.782376
[8 rows x 3 columns]
# appending data frames
In [44]: df1 = df[0:4]
In [45]: df2 = df[4:]
In [46]: store.append('df', df1)
In [47]: store.append('df', df2)
In [48]: store
Out[48]:
<class 'pandas.io.pytables.HDFStore'>
File path: store.h5
/df frame_table (typ->appendable,nrows->8,ncols->3,indexers->[index])
# selecting the entire store
In [49]: store.select('df')
Out[49]:
A B C
2000-01-01 -2.036047 0.000830 -0.955697
2000-01-02 -0.898872 -0.725411 0.059904
2000-01-03 -0.449644 1.082900 -1.221265
2000-01-04 0.361078 1.330704 0.855932
2000-01-05 -1.216718 1.488887 0.018993
2000-01-06 -0.877046 0.045976 0.437274
2000-01-07 -0.567182 -0.888657 -0.556383
2000-01-08 0.655457 1.117949 -2.782376
[8 rows x 3 columns]
In [50]: wp = pd.Panel(np.random.randn(2, 5, 4), items=['Item1', 'Item2'],
....: major_axis=pd.date_range('1/1/2000', periods=5),
....: minor_axis=['A', 'B', 'C', 'D'])
In [51]: wp
Out[51]:
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 5 (major_axis) x 4 (minor_axis)
Items axis: Item1 to Item2
Major_axis axis: 2000-01-01 00:00:00 to 2000-01-05 00:00:00
Minor_axis axis: A to D
# storing a panel
In [52]: store.append('wp', wp)
# selecting via A QUERY
In [53]: store.select('wp', [pd.Term('major_axis>20000102'),
....: pd.Term('minor_axis', '=', ['A', 'B'])])
....:
Out[53]:
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 3 (major_axis) x 2 (minor_axis)
Items axis: Item1 to Item2
Major_axis axis: 2000-01-03 00:00:00 to 2000-01-05 00:00:00
Minor_axis axis: A to B
# removing data from tables
In [54]: store.remove('wp', pd.Term('major_axis>20000103'))
Out[54]: 8
In [55]: store.select('wp')
Out[55]:
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 3 (major_axis) x 4 (minor_axis)
Items axis: Item1 to Item2
Major_axis axis: 2000-01-01 00:00:00 to 2000-01-03 00:00:00
Minor_axis axis: A to D
# deleting a store
In [56]: del store['df']
In [57]: store
Out[57]:
<class 'pandas.io.pytables.HDFStore'>
File path: store.h5
/wp wide_table (typ->appendable,nrows->12,ncols->2,indexers->[major_axis,minor_axis])
Enhancements
added ability to hierarchical keys
In [58]: store.put('foo/bar/bah', df) In [59]: store.append('food/orange', df) In [60]: store.append('food/apple', df) In [61]: store Out[61]: <class 'pandas.io.pytables.HDFStore'> File path: store.h5 /foo/bar/bah frame (shape->[8,3]) /food/apple frame_table (typ->appendable,nrows->8,ncols->3,indexers->[index]) /food/orange frame_table (typ->appendable,nrows->8,ncols->3,indexers->[index]) /wp wide_table (typ->appendable,nrows->12,ncols->2,indexers->[major_axis,minor_axis]) # remove all nodes under this level In [62]: store.remove('food') In [63]: store Out[63]: <class 'pandas.io.pytables.HDFStore'> File path: store.h5 /foo/bar/bah frame (shape->[8,3]) /wp wide_table (typ->appendable,nrows->12,ncols->2,indexers->[major_axis,minor_axis])
added mixed-dtype support!
In [64]: df['string'] = 'string' In [65]: df['int'] = 1 In [66]: store.append('df', df) In [67]: df1 = store.select('df') In [68]: df1 Out[68]: A B C string int 2000-01-01 -2.036047 0.000830 -0.955697 string 1 2000-01-02 -0.898872 -0.725411 0.059904 string 1 2000-01-03 -0.449644 1.082900 -1.221265 string 1 2000-01-04 0.361078 1.330704 0.855932 string 1 2000-01-05 -1.216718 1.488887 0.018993 string 1 2000-01-06 -0.877046 0.045976 0.437274 string 1 2000-01-07 -0.567182 -0.888657 -0.556383 string 1 2000-01-08 0.655457 1.117949 -2.782376 string 1 [8 rows x 5 columns] In [69]: df1.get_dtype_counts() Out[69]: float64 3 int64 1 object 1 dtype: int64
performance improvements on table writing
support for arbitrarily indexed dimensions
SparseSeriesnow has adensityproperty (GH2384)enable
Series.str.strip/lstrip/rstripmethods to take an input argument to strip arbitrary characters (GH2411)implement
value_varsinmeltto limit values to certain columns and addmeltto pandas namespace (GH2412)
Bug Fixes
added
Termmethod of specifying where conditions (GH1996).del store['df']now callstore.remove('df')for store deletiondeleting of consecutive rows is much faster than before
min_itemsizeparameter can be specified in table creation to force a minimum size for indexing columns (the previous implementation would set the column size based on the first append)indexing support via
create_table_index(requires PyTables >= 2.3) (GH698).appending on a store would fail if the table was not first created via
putfixed issue with missing attributes after loading a pickled dataframe (GH2431)
minor change to select and remove: require a table ONLY if where is also provided (and not None)
Compatibility
0.10 of HDFStore is backwards compatible for reading tables created in a prior version of pandas,
however, query terms using the prior (undocumented) methodology are unsupported. You must read in the entire
file and write it out using the new format to take advantage of the updates.
N dimensional panels (experimental)¶
Adding experimental support for Panel4D and factory functions to create n-dimensional named panels. Here is a taste of what to expect.
In [58]: p4d = Panel4D(np.random.randn(2, 2, 5, 4),
....: labels=['Label1','Label2'],
....: items=['Item1', 'Item2'],
....: major_axis=date_range('1/1/2000', periods=5),
....: minor_axis=['A', 'B', 'C', 'D'])
....:
In [59]: p4d
Out[59]:
<class 'pandas.core.panelnd.Panel4D'>
Dimensions: 2 (labels) x 2 (items) x 5 (major_axis) x 4 (minor_axis)
Labels axis: Label1 to Label2
Items axis: Item1 to Item2
Major_axis axis: 2000-01-01 00:00:00 to 2000-01-05 00:00:00
Minor_axis axis: A to D
See the full release notes or issue tracker on GitHub for a complete list.
Contributors¶
A total of 26 people contributed patches to this release. People with a “+” by their names contributed a patch for the first time.
A. Flaxman +
Abraham Flaxman
Adam Obeng +
Brenda Moon +
Chang She
Chris Mulligan +
Dieter Vandenbussche
Donald Curtis +
Jay Bourque +
Jeff Reback +
Justin C Johnson +
K.-Michael Aye
Keith Hughitt +
Ken Van Haren +
Laurent Gautier +
Luke Lee +
Martin Blais
Tobias Brandt +
Wes McKinney
Wouter Overmeire
alex arsenovic +
jreback +
locojaydev +
timmie
y-p
zach powers +