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 encoding option.

• Column filtering (usecols)

• Dtype specification (dtype argument)

• Ability to specify strings to be recognized as True/False

• Ability to yield NumPy record arrays (as_recarray)

• High performance delim_whitespace option

• Decimal 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]: importpandasaspd
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年01月01日 0.469112 -0.282863 -1.509059 -1.135632
2000年01月02日 1.212112 -0.173215 0.119209 -1.044236
2000年01月03日 -0.861849 -2.104569 -0.494929 1.071804
2000年01月04日 0.721555 -0.706771 -1.039575 0.271860
2000年01月05日 -0.424972 0.567020 0.276232 -1.087401
2000年01月06日 -0.673690 0.113648 -1.478427 0.524988
# deprecated now
In [4]: df - df[0]
Out[4]: 
 0 1 ... 2000年01月05日 00:00:00 2000年01月06日 00:00:00
2000年01月01日 NaN NaN ... NaN NaN
2000年01月02日 NaN NaN ... NaN NaN
2000年01月03日 NaN NaN ... NaN NaN
2000年01月04日 NaN NaN ... NaN NaN
2000年01月05日 NaN NaN ... NaN NaN
2000年01月06日 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年01月01日 0.0 -0.751976 -1.978171 -1.604745
2000年01月02日 0.0 -1.385327 -1.092903 -2.256348
2000年01月03日 0.0 -1.242720 0.366920 1.933653
2000年01月04日 0.0 -1.428326 -1.761130 -0.449695
2000年01月05日 0.0 0.991993 0.701204 -0.662428
2000年01月06日 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年01月01日 00:00:00 0
2000年01月01日 04:00:00 1
2000年01月01日 08:00:00 2
2000年01月01日 12:00:00 3
2000年01月01日 16:00:00 4
2000年01月01日 20:00:00 5
2000年01月02日 00:00:00 6
2000年01月02日 04:00:00 7
2000年01月02日 08:00:00 8
2000年01月02日 12:00:00 9
2000年01月02日 16:00:00 10
2000年01月02日 20:00:00 11
2000年01月03日 00:00:00 12
2000年01月03日 04:00:00 13
2000年01月03日 08:00:00 14
2000年01月03日 12:00:00 15
2000年01月03日 16:00:00 16
2000年01月03日 20:00:00 17
2000年01月04日 00:00:00 18
2000年01月04日 04:00:00 19
2000年01月04日 08:00:00 20
2000年01月04日 12:00:00 21
2000年01月04日 16:00:00 22
2000年01月04日 20:00:00 23
2000年01月05日 00:00:00 24
Freq: 4H, dtype: int64
In [4]: series.resample('D', how='sum')
Out[4]:
2000年01月01日 15
2000年01月02日 51
2000年01月03日 87
2000年01月04日 123
2000年01月05日 24
Freq: D, dtype: int64
In [5]: # old behavior
In [6]: series.resample('D', how='sum', closed='right', label='right')
Out[6]:
2000年01月01日 0
2000年01月02日 21
2000年01月03日 57
2000年01月04日 93
2000年01月05日 129
Freq: D, dtype: int64

• Infinity and negative infinity are no longer treated as NA by isnull and notnull. That they ever were was a relic of early pandas. This behavior can be re-enabled globally by the mode.use_inf_as_null option:

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 inplace option now all return None instead of the calling object. E.g. code written like df = df.fillna(0, inplace=True) may stop working. To fix, simply delete the unnecessary variable assignment.

• pandas.merge no 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 0 through N - 1. This is to create consistency with the DataFrame constructor with no columns specified. The v0.9.0 behavior (names X0, X1, ...) can be reproduced by specifying prefix='X':

In [6]: importio
In [7]: data = """
 ...: a,b,c
 ...: 1,Yes,2
 ...: 3,No,4
 ...: """
 ...:
In [8]: print(data)
 a,b,c
 1,Yes,2
 3,No,4
In [9]: pd.read_csv(io.StringIO(data), header=None)
Out[9]:
 0 1 2
0 a b c
1 1 Yes 2
2 3 No 4
In [10]: pd.read_csv(io.StringIO(data), header=None, prefix="X")
Out[10]:
 X0 X1 X2
0 a b c
1 1 Yes 2
2 3 No 4

• Values like 'Yes' and 'No' are not interpreted as boolean by default, though this can be controlled by new true_values and false_values arguments:

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_values argument. It’s better to do post-processing using the replace function instead.

• Calling fillna on Series or DataFrame with no arguments is no longer valid code. You must either specify a fill value or an interpolation method:

In [6]: s = pd.Series([np.nan, 1.0, 2.0, np.nan, 4])
In [7]: s
Out[7]: 
0 NaN
1 1.0
2 2.0
3 NaN
4 4.0
dtype: float64
In [8]: s.fillna(0)
Out[8]: 
0 0.0
1 1.0
2 2.0
3 0.0
4 4.0
dtype: float64
In [9]: s.fillna(method="pad")
Out[9]: 
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 [10]: s.ffill()
Out[10]: 
0 NaN
1 1.0
2 2.0
3 2.0
4 4.0
dtype: float64

• Series.apply will now operate on a returned value from the applied function, that is itself a series, and possibly upcast the result to a DataFrame

  In [11]: deff(x):
   ....:  return pd.Series([x, x ** 2], index=["x", "x^2"])
   ....: 
  In [12]: s = pd.Series(np.random.rand(5))
  In [13]: s
  Out[13]: 
  0 0.340445
  1 0.984729
  2 0.919540
  3 0.037772
  4 0.861549
  dtype: float64
  In [14]: s.apply(f)
  Out[14]: 
   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 (GH 2097):

  • 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_printoptions are now deprecated (but functioning), the print options now live under "display.XYZ". For example:

  In [15]: pd.get_option("display.max_rows")
  Out[15]: 15
  

• to_string() methods now always return unicode strings (GH 2224).

New features#

Wide DataFrame printing#

Instead of printing the summary information, pandas now splits the string representation across multiple rows by default:

In [16]: wide_frame = pd.DataFrame(np.random.randn(5, 16))
In [17]: wide_frame
Out[17]: 
 0 1 2 ... 13 14 15
0 -0.548702 1.467327 -1.015962 ... 1.669052 1.037882 -1.705775
1 -0.919854 -0.042379 1.247642 ... 1.956030 0.017587 -0.016692
2 -0.575247 0.254161 -1.143704 ... 1.211526 0.268520 0.024580
3 -1.577585 0.396823 -0.105381 ... 0.593616 0.884345 1.591431
4 0.141809 0.220390 0.435589 ... -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 [18]: pd.set_option("expand_frame_repr", False)
In [19]: wide_frame
Out[19]: 
 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年01月01日 -2.036047 0.000830 -0.955697
2000年01月02日 -0.898872 -0.725411 0.059904
2000年01月03日 -0.449644 1.082900 -1.221265
2000年01月04日 0.361078 1.330704 0.855932
2000年01月05日 -1.216718 1.488887 0.018993
2000年01月06日 -0.877046 0.045976 0.437274
2000年01月07日 -0.567182 -0.888657 -0.556383
2000年01月08日 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年01月01日 -2.036047 0.000830 -0.955697
2000年01月02日 -0.898872 -0.725411 0.059904
2000年01月03日 -0.449644 1.082900 -1.221265
2000年01月04日 0.361078 1.330704 0.855932
2000年01月05日 -1.216718 1.488887 0.018993
2000年01月06日 -0.877046 0.045976 0.437274
2000年01月07日 -0.567182 -0.888657 -0.556383
2000年01月08日 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年01月01日 00:00:00 to 2000年01月05日 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年01月03日 00:00:00 to 2000年01月05日 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年01月01日 00:00:00 to 2000年01月03日 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年01月01日 -2.036047 0.000830 -0.955697 string 1
  2000年01月02日 -0.898872 -0.725411 0.059904 string 1
  2000年01月03日 -0.449644 1.082900 -1.221265 string 1
  2000年01月04日 0.361078 1.330704 0.855932 string 1
  2000年01月05日 -1.216718 1.488887 0.018993 string 1
  2000年01月06日 -0.877046 0.045976 0.437274 string 1
  2000年01月07日 -0.567182 -0.888657 -0.556383 string 1
  2000年01月08日 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

• SparseSeries now has a density property (GH 2384)

• enable Series.str.strip/lstrip/rstrip methods to take an input argument to strip arbitrary characters (GH 2411)

• implement value_vars in melt to limit values to certain columns and add melt to pandas namespace (GH 2412)

Bug Fixes

• added Term method of specifying where conditions (GH 1996).

• del store['df'] now call store.remove('df') for store deletion

• deleting of consecutive rows is much faster than before

• min_itemsize parameter 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) (GH 698).

• appending on a store would fail if the table was not first created via put

• fixed 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年01月01日 00:00:00 to 2000年01月05日 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 +