Skip to main content
`Ctrl`+`K`

# Version 0.13.1 (February 3, 2014)

This is a minor release from 0.13.0 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:

- Added `infer_datetime_format` keyword to `read_csv/to_datetime` to allow speedups for homogeneously formatted datetimes.

- Will intelligently limit display precision for datetime/timedelta formats.

- Enhanced Panel `Panel.apply` method.

- Suggested tutorials in new Tutorials section.

- Our pandas ecosystem is growing, We now feature related projects in a new ecosystem page section.

- Much work has been taking place on improving the docs, and a new Contributing section has been added.

- Even though it may only be of interest to devs, we <3 our new CI status page: ScatterCI.

Warning

0.13.1 fixes a bug that was caused by a combination of having numpy < 1.8, and doing chained assignment on a string-like array. Chained indexing can have unexpected results and should generally be avoided.

This would previously segfault:

    df = pd.DataFrame({"A": np.array(["foo", "bar", "bah", "foo", "bar"])})
    df["A"].iloc[0] = np.nan

The recommended way to do this type of assignment is:

    In [1]: df = pd.DataFrame({"A": np.array(["foo", "bar", "bah", "foo", "bar"])})

    In [2]: df.loc[0, "A"] = np.nan

    In [3]: df
    Out[3]:
         A
    0  NaN
    1  bar
    2  bah
    3  foo
    4  bar

## Output formatting enhancements

- df.info() view now display dtype info per column (GH 5682)

- df.info() now honors the option `max_info_rows`, to disable null counts for large frames (GH 5974)

      In [4]: max_info_rows = pd.get_option("max_info_rows")

      In [5]: df = pd.DataFrame(
         ...:     {
         ...:         "A": np.random.randn(10),
         ...:         "B": np.random.randn(10),
         ...:         "C": pd.date_range("20130101", periods=10),
         ...:     }
         ...: )
         ...:

      In [6]: df.iloc[3:6, [0, 2]] = np.nan

      # set to not display the null counts
      In [7]: pd.set_option("max_info_rows", 0)

      In [8]: df.info()
      <class 'pandas.DataFrame'>
      RangeIndex: 10 entries, 0 to 9
      Data columns (total 3 columns):
       #   Column  Dtype
      ---  ------  -----
       0   A       float64
       1   B       float64
       2   C       datetime64[us]
      dtypes: datetime64[us](1), float64(2)
      memory usage: 372.0 bytes

      # this is the default (same as in 0.13.0)
      In [9]: pd.set_option("max_info_rows", max_info_rows)

      In [10]: df.info()
      <class 'pandas.DataFrame'>
      RangeIndex: 10 entries, 0 to 9
      Data columns (total 3 columns):
       #   Column  Non-Null Count  Dtype
      ---  ------  --------------  -----
       0   A       7 non-null      float64
       1   B       10 non-null     float64
       2   C       7 non-null      datetime64[us]
      dtypes: datetime64[us](1), float64(2)
      memory usage: 372.0 bytes

- Add `show_dimensions` display option for the new DataFrame repr to control whether the dimensions print.

      In [11]: df = pd.DataFrame([[1, 2], [3, 4]])

      In [12]: pd.set_option("show_dimensions", False)

      In [13]: df
      Out[13]:
         0  1
      0  1  2
      1  3  4

      In [14]: pd.set_option("show_dimensions", True)

      In [15]: df
      Out[15]:
         0  1
      0  1  2
      1  3  4

      [2 rows x 2 columns]

- The `ArrayFormatter` for `datetime` and `timedelta64` now intelligently limit precision based on the values in the array (GH 3401)

  Previously output might look like:

        age                 today               diff
      0 2001-01-01 00:00:00 2013-04-19 00:00:00 4491 days, 00:00:00
      1 2004-06-01 00:00:00 2013-04-19 00:00:00 3244 days, 00:00:00

  Now the output looks like:

      In [16]: df = pd.DataFrame(
         ....:     [pd.Timestamp("20010101"), pd.Timestamp("20040601")], columns=["age"]
         ....: )
         ....:

      In [17]: df["today"] = pd.Timestamp("20130419")

      In [18]: df["diff"] = df["today"] - df["age"]

      In [19]: df
      Out[19]:
               age      today      diff
      0 2001-01-01 2013-04-19 4491 days
      1 2004-06-01 2013-04-19 3244 days

      [2 rows x 3 columns]

## API changes

- Add `-NaN` and `-nan` to the default set of NA values (GH 5952). See NA Values.

- Added `Series.str.get_dummies` vectorized string method (GH 6021), to extract dummy/indicator variables for separated string columns:

      In [20]: s = pd.Series(["a", "a|b", np.nan, "a|c"])

      In [21]: s.str.get_dummies(sep="|")
      Out[21]:
         a  b  c
      0  1  0  0
      1  1  1  0
      2  0  0  0
      3  1  0  1

      [4 rows x 3 columns]

- Added the `NDFrame.equals()` method to compare if two NDFrames are equal have equal axes, dtypes, and values. Added the `array_equivalent` function to compare if two ndarrays are equal. NaNs in identical locations are treated as equal. (GH 5283) See also the docs for a motivating example.

      df = pd.DataFrame({"col": ["foo", 0, np.nan]})
      df2 = pd.DataFrame({"col": [np.nan, 0, "foo"]}, index=[2, 1, 0])
      df.equals(df2)
      df.equals(df2.sort_index())

- `DataFrame.apply` will use the `reduce` argument to determine whether a `Series` or a `DataFrame` should be returned when the `DataFrame` is empty (GH 6007).

  Previously, calling `DataFrame.apply` an empty `DataFrame` would return either a `DataFrame` if there were no columns, or the function being applied would be called with an empty `Series` to guess whether a `Series` or `DataFrame` should be returned:

      In [32]: def applied_func(col):
        ....:    print("Apply function being called with: ", col)
        ....:    return col.sum()
        ....:

      In [33]: empty = DataFrame(columns=['a', 'b'])

      In [34]: empty.apply(applied_func)
      Apply function being called with:  Series([], Length: 0, dtype: float64)
      Out[34]:
      a   NaN
      b   NaN
      Length: 2, dtype: float64

  Now, when `apply` is called on an empty `DataFrame`: if the `reduce` argument is `True` a `Series` will returned, if it is `False` a `DataFrame` will be returned, and if it is `None` (the default) the function being applied will be called with an empty series to try and guess the return type.

      In [35]: empty.apply(applied_func, reduce=True)
      Out[35]:
      a   NaN
      b   NaN
      Length: 2, dtype: float64

      In [36]: empty.apply(applied_func, reduce=False)
      Out[36]:
      Empty DataFrame
      Columns: [a, b]
      Index: []

      [0 rows x 2 columns]

## Prior version deprecations/changes

There are no announced changes in 0.13 or prior that are taking effect as of 0.13.1

## Deprecations

There are no deprecations of prior behavior in 0.13.1

## Enhancements

- `pd.read_csv` and `pd.to_datetime` learned a new `infer_datetime_format` keyword which greatly improves parsing perf in many cases. Thanks to @lexual for suggesting and @danbirken for rapidly implementing. (GH 5490, GH 6021)

  If `parse_dates` is enabled and this flag is set, pandas will attempt to infer the format of the datetime strings in the columns, and if it can be inferred, switch to a faster method of parsing them. In some cases this can increase the parsing speed by ~5-10x.

      # Try to infer the format for the index column
      df = pd.read_csv(
          "foo.csv", index_col=0, parse_dates=True, infer_datetime_format=True
      )

- `date_format` and `datetime_format` keywords can now be specified when writing to `excel` files (GH 4133)

- `MultiIndex.from_product` convenience function for creating a MultiIndex from the cartesian product of a set of iterables (GH 6055):

      In [22]: shades = ["light", "dark"]

      In [23]: colors = ["red", "green", "blue"]

      In [24]: pd.MultiIndex.from_product([shades, colors], names=["shade", "color"])
      Out[24]:
      MultiIndex([('light',   'red'),
                  ('light', 'green'),
                  ('light',  'blue'),
                  ( 'dark',   'red'),
                  ( 'dark', 'green'),
                  ( 'dark',  'blue')],
                 names=['shade', 'color'])

- Panel `Panel.apply` will work on non-ufuncs. See the docs.

      In [28]: import pandas._testing as tm

      In [29]: panel = tm.makePanel(5)

      In [30]: panel
      Out[30]:
      <class 'pandas.core.panel.Panel'>
      Dimensions: 3 (items) x 5 (major_axis) x 4 (minor_axis)
      Items axis: ItemA to ItemC
      Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
      Minor_axis axis: A to D

      In [31]: panel['ItemA']
      Out[31]:
                         A         B         C         D
      2000-01-03 -0.673690  0.577046 -1.344312 -1.469388
      2000-01-04  0.113648 -1.715002  0.844885  0.357021
      2000-01-05 -1.478427 -1.039268  1.075770 -0.674600
      2000-01-06  0.524988 -0.370647 -0.109050 -1.776904
      2000-01-07  0.404705 -1.157892  1.643563 -0.968914

      [5 rows x 4 columns]

  Specifying an `apply` that operates on a Series (to return a single element)

      In [32]: panel.apply(lambda x: x.dtype, axis='items')
      Out[32]:
                        A        B        C        D
      2000-01-03  float64  float64  float64  float64
      2000-01-04  float64  float64  float64  float64
      2000-01-05  float64  float64  float64  float64
      2000-01-06  float64  float64  float64  float64
      2000-01-07  float64  float64  float64  float64

      [5 rows x 4 columns]

  A similar reduction type operation

      In [33]: panel.apply(lambda x: x.sum(), axis='major_axis')
      Out[33]:
            ItemA     ItemB     ItemC
      A -1.108775 -1.090118 -2.984435
      B -3.705764  0.409204  1.866240
      C  2.110856  2.960500 -0.974967
      D -4.532785  0.303202 -3.685193

      [4 rows x 3 columns]

  This is equivalent to

      In [34]: panel.sum('major_axis')
      Out[34]:
            ItemA     ItemB     ItemC
      A -1.108775 -1.090118 -2.984435
      B -3.705764  0.409204  1.866240
      C  2.110856  2.960500 -0.974967
      D -4.532785  0.303202 -3.685193

      [4 rows x 3 columns]

  A transformation operation that returns a Panel, but is computing the z-score across the major_axis

      In [35]: result = panel.apply(lambda x: (x - x.mean()) / x.std(),
        ....:                      axis='major_axis')
        ....:

      In [36]: result
      Out[36]:
      <class 'pandas.core.panel.Panel'>
      Dimensions: 3 (items) x 5 (major_axis) x 4 (minor_axis)
      Items axis: ItemA to ItemC
      Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
      Minor_axis axis: A to D

      In [37]: result['ItemA']                           # noqa E999
      Out[37]:
                        A         B         C         D
      2000-01-03 -0.535778  1.500802 -1.506416 -0.681456
      2000-01-04  0.397628 -1.108752  0.360481  1.529895
      2000-01-05 -1.489811 -0.339412  0.557374  0.280845
      2000-01-06  0.885279  0.421830 -0.453013 -1.053785
      2000-01-07  0.742682 -0.474468  1.041575 -0.075499

      [5 rows x 4 columns]

- Panel `Panel.apply` operating on cross-sectional slabs. (GH 1148)

      In [38]: def f(x):
         ....:     return ((x.T - x.mean(1)) / x.std(1)).T
         ....:

      In [39]: result = panel.apply(f, axis=['items', 'major_axis'])

      In [40]: result
      Out[40]:
      <class 'pandas.core.panel.Panel'>
      Dimensions: 4 (items) x 5 (major_axis) x 3 (minor_axis)
      Items axis: A to D
      Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
      Minor_axis axis: ItemA to ItemC

      In [41]: result.loc[:, :, 'ItemA']
      Out[41]:
                         A         B         C         D
      2000-01-03  0.012922 -0.030874 -0.629546 -0.757034
      2000-01-04  0.392053 -1.071665  0.163228  0.548188
      2000-01-05 -1.093650 -0.640898  0.385734 -1.154310
      2000-01-06  1.005446 -1.154593 -0.595615 -0.809185
      2000-01-07  0.783051 -0.198053  0.919339 -1.052721

      [5 rows x 4 columns]

  This is equivalent to the following

      In [42]: result = pd.Panel({ax: f(panel.loc[:, :, ax]) for ax in panel.minor_axis})

      In [43]: result
      Out[43]:
      <class 'pandas.core.panel.Panel'>
      Dimensions: 4 (items) x 5 (major_axis) x 3 (minor_axis)
      Items axis: A to D
      Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
      Minor_axis axis: ItemA to ItemC

      In [44]: result.loc[:, :, 'ItemA']
      Out[44]:
                         A         B         C         D
      2000-01-03  0.012922 -0.030874 -0.629546 -0.757034
      2000-01-04  0.392053 -1.071665  0.163228  0.548188
      2000-01-05 -1.093650 -0.640898  0.385734 -1.154310
      2000-01-06  1.005446 -1.154593 -0.595615 -0.809185
      2000-01-07  0.783051 -0.198053  0.919339 -1.052721

      [5 rows x 4 columns]

## Performance

Performance improvements for 0.13.1

- Series datetime/timedelta binary operations (GH 5801)

- DataFrame `count/dropna` for `axis=1`

- Series.str.contains now has a `regex=False` keyword which can be faster for plain (non-regex) string patterns. (GH 5879)

- Series.str.extract (GH 5944)

- `dtypes/ftypes` methods (GH 5968)

- indexing with object dtypes (GH 5968)

- `DataFrame.apply` (GH 6013)

- Regression in JSON IO (GH 5765)

- Index construction from Series (GH 6150)

## Experimental

There are no experimental changes in 0.13.1

## Bug fixes

- Bug in `io.wb.get_countries` not including all countries (GH 6008)

- Bug in Series replace with timestamp dict (GH 5797)

- read_csv/read_table now respects the `prefix` kwarg (GH 5732).

- Bug in selection with missing values via `.ix` from a duplicate indexed DataFrame failing (GH 5835)

- Fix issue of boolean comparison on empty DataFrames (GH 5808)

- Bug in isnull handling `NaT` in an object array (GH 5443)

- Bug in `to_datetime` when passed a `np.nan` or integer datelike and a format string (GH 5863)

- Bug in groupby dtype conversion with datetimelike (GH 5869)

- Regression in handling of empty Series as indexers to Series (GH 5877)

- Bug in internal caching, related to (GH 5727)

- Testing bug in reading JSON/msgpack from a non-filepath on windows under py3 (GH 5874)

- Bug when assigning to .ix[tuple(…)] (GH 5896)

- Bug in fully reindexing a Panel (GH 5905)

- Bug in idxmin/max with object dtypes (GH 5914)

- Bug in `BusinessDay` when adding n days to a date not on offset when n>5 and n%5==0 (GH 5890)

- Bug in assigning to chained series with a series via ix (GH 5928)

- Bug in creating an empty DataFrame, copying, then assigning (GH 5932)

- Bug in DataFrame.tail with empty frame (GH 5846)

- Bug in propagating metadata on `resample` (GH 5862)

- Fixed string-representation of `NaT` to be “NaT” (GH 5708)

- Fixed string-representation for Timestamp to show nanoseconds if present (GH 5912)

- `pd.match` not returning passed sentinel

- `Panel.to_frame()` no longer fails when `major_axis` is a `MultiIndex` (GH 5402).

- Bug in `pd.read_msgpack` with inferring a `DateTimeIndex` frequency incorrectly (GH 5947)

- Fixed `to_datetime` for array with both Tz-aware datetimes and `NaT`’s (GH 5961)

- Bug in rolling skew/kurtosis when passed a Series with bad data (GH 5749)

- Bug in scipy `interpolate` methods with a datetime index (GH 5975)

- Bug in NaT comparison if a mixed datetime/np.datetime64 with NaT were passed (GH 5968)

- Fixed bug with `pd.concat` losing dtype information if all inputs are empty (GH 5742)

- Recent changes in IPython cause warnings to be emitted when using previous versions of pandas in QTConsole, now fixed. If you’re using an older version and need to suppress the warnings, see (GH 5922).

- Bug in merging `timedelta` dtypes (GH 5695)

- Bug in plotting.scatter_matrix function. Wrong alignment among diagonal and off-diagonal plots, see (GH 5497).

- Regression in Series with a MultiIndex via ix (GH 6018)

- Bug in Series.xs with a MultiIndex (GH 6018)

- Bug in Series construction of mixed type with datelike and an integer (which should result in object type and not automatic conversion) (GH 6028)

- Possible segfault when chained indexing with an object array under NumPy 1.7.1 (GH 6026, GH 6056)

- Bug in setting using fancy indexing a single element with a non-scalar (e.g. a list), (GH 6043)

- `to_sql` did not respect `if_exists` (GH 4110 GH 4304)

- Regression in `.get(None)` indexing from 0.12 (GH 5652)

- Subtle `iloc` indexing bug, surfaced in (GH 6059)

- Bug with insert of strings into DatetimeIndex (GH 5818)

- Fixed unicode bug in to_html/HTML repr (GH 6098)

- Fixed missing arg validation in get_options_data (GH 6105)

- Bug in assignment with duplicate columns in a frame where the locations are a slice (e.g. next to each other) (GH 6120)

- Bug in propagating _ref_locs during construction of a DataFrame with dups index/columns (GH 6121)

- Bug in `DataFrame.apply` when using mixed datelike reductions (GH 6125)

- Bug in `DataFrame.append` when appending a row with different columns (GH 6129)

- Bug in DataFrame construction with recarray and non-ns datetime dtype (GH 6140)

- Bug in `.loc` setitem indexing with a dataframe on rhs, multiple item setting, and a datetimelike (GH 6152)

- Fixed a bug in `query`/`eval` during lexicographic string comparisons (GH 6155).

- Fixed a bug in `query` where the index of a single-element `Series` was being thrown away (GH 6148).

- Bug in `HDFStore` on appending a dataframe with MultiIndexed columns to an existing table (GH 6167)

- Consistency with dtypes in setting an empty DataFrame (GH 6171)

- Bug in selecting on a MultiIndex `HDFStore` even in the presence of under specified column spec (GH 6169)

- Bug in `nanops.var` with `ddof=1` and 1 elements would sometimes return `inf` rather than `nan` on some platforms (GH 6136)

- Bug in Series and DataFrame bar plots ignoring the `use_index` keyword (GH 6209)

- Bug in groupby with mixed str/int under python3 fixed; `argsort` was failing (GH 6212)

## Contributors

A total of 52 people contributed patches to this release. People with a “+” by their names contributed a patch for the first time.

- Alex Rothberg

- Alok Singhal +

- Andrew Burrows +

- Andy Hayden

- Bjorn Arneson +

- Brad Buran

- Caleb Epstein

- Chapman Siu

- Chase Albert +

- Clark Fitzgerald +

- DSM

- Dan Birken

- Daniel Waeber +

- David Wolever +

- Doran Deluz +

- Douglas McNeil +

- Douglas Rudd +

- Dražen Lučanin

- Elliot S +

- Felix Lawrence +

- George Kuan +

- Guillaume Gay +

- Jacob Schaer

- Jan Wagner +

- Jeff Tratner

- John McNamara

- Joris Van den Bossche

- Julia Evans +

- Kieran O’Mahony

- Michael Schatzow +

- Naveen Michaud-Agrawal +

- Patrick O’Keeffe +

- Phillip Cloud

- Roman Pekar

- Skipper Seabold

- Spencer Lyon

- Tom Augspurger +

- TomAugspurger

- acorbe +

- akittredge +

- bmu +

- bwignall +

- chapman siu

- danielballan

- david +

- davidshinn

- immerrr +

- jreback

- lexual

- mwaskom +

- unutbu

- y-p