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# Version 0.16.2 (June 12, 2015)

This is a minor bug-fix release from 0.16.1 and includes a large number of bug fixes along some new features (`pipe()` method), enhancements, and performance improvements.

We recommend that all users upgrade to this version.

Highlights include:

- A new `pipe` method, see here

- Documentation on how to use numba with pandas, see here

## New features

### Pipe

We’ve introduced a new method `DataFrame.pipe()`. As suggested by the name, `pipe` should be used to pipe data through a chain of function calls. The goal is to avoid confusing nested function calls like

    # df is a DataFrame
    # f, g, and h are functions that take and return DataFrames
    f(g(h(df), arg1=1), arg2=2, arg3=3)  # noqa F821

The logic flows from inside out, and function names are separated from their keyword arguments. This can be rewritten as

    (
        df.pipe(h)  # noqa F821
        .pipe(g, arg1=1)  # noqa F821
        .pipe(f, arg2=2, arg3=3)  # noqa F821
    )

Now both the code and the logic flow from top to bottom. Keyword arguments are next to their functions. Overall the code is much more readable.

In the example above, the functions `f`, `g`, and `h` each expected the DataFrame as the first positional argument. When the function you wish to apply takes its data anywhere other than the first argument, pass a tuple of `(function, keyword)` indicating where the DataFrame should flow. For example:

    In [1]: import statsmodels.formula.api as sm

    In [2]: bb = pd.read_csv("data/baseball.csv", index_col="id")

    # sm.ols takes (formula, data)
    In [3]: (
    ...:     bb.query("h > 0")
    ...:     .assign(ln_h=lambda df: np.log(df.h))
    ...:     .pipe((sm.ols, "data"), "hr ~ ln_h + year + g + C(lg)")
    ...:     .fit()
    ...:     .summary()
    ...: )
    ...:
    Out[3]:
    <class 'statsmodels.iolib.summary.Summary'>
    """
                                OLS Regression Results
    ==============================================================================
    Dep. Variable:                     hr   R-squared:                       0.685
    Model:                            OLS   Adj. R-squared:                  0.665
    Method:                 Least Squares   F-statistic:                     34.28
    Date:                Tue, 04 Mar 2025   Prob (F-statistic):           3.48e-15
    Time:                        20:01:00   Log-Likelihood:                -205.92
    No. Observations:                  68   AIC:                             421.8
    Df Residuals:                      63   BIC:                             432.9
    Df Model:                           4
    Covariance Type:            nonrobust
    ===============================================================================
                    coef    std err          t      P>|t|      [0.025      0.975]
    -------------------------------------------------------------------------------
    Intercept   -8484.7720   4664.146     -1.819      0.074   -1.78e+04     835.780
    C(lg)[T.NL]    -2.2736      1.325     -1.716      0.091      -4.922       0.375
    ln_h           -1.3542      0.875     -1.547      0.127      -3.103       0.395
    year            4.2277      2.324      1.819      0.074      -0.417       8.872
    g               0.1841      0.029      6.258      0.000       0.125       0.243
    ==============================================================================
    Omnibus:                       10.875   Durbin-Watson:                   1.999
    Prob(Omnibus):                  0.004   Jarque-Bera (JB):               17.298
    Skew:                           0.537   Prob(JB):                     0.000175
    Kurtosis:                       5.225   Cond. No.                     1.49e+07
    ==============================================================================

    Notes:
    [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
    [2] The condition number is large, 1.49e+07. This might indicate that there are
    strong multicollinearity or other numerical problems.
    """

The pipe method is inspired by unix pipes, which stream text through processes. More recently dplyr and magrittr have introduced the popular `(%>%)` pipe operator for R.

See the documentation for more. (GH 10129)

### Other enhancements

- Added `rsplit` to Index/Series StringMethods (GH 10303)

- Removed the hard-coded size limits on the `DataFrame` HTML representation in the IPython notebook, and leave this to IPython itself (only for IPython v3.0 or greater). This eliminates the duplicate scroll bars that appeared in the notebook with large frames (GH 10231).

  Note that the notebook has a `toggle output scrolling` feature to limit the display of very large frames (by clicking left of the output). You can also configure the way DataFrames are displayed using the pandas options, see here here.

- `axis` parameter of `DataFrame.quantile` now accepts also `index` and `column`. (GH 9543)

## API changes

- `Holiday` now raises `NotImplementedError` if both `offset` and `observance` are used in the constructor instead of returning an incorrect result (GH 10217).

## Performance improvements

- Improved `Series.resample` performance with `dtype=datetime64[ns]` (GH 7754)

- Increase performance of `str.split` when `expand=True` (GH 10081)

## Bug fixes

- Bug in `Series.hist` raises an error when a one row `Series` was given (GH 10214)

- Bug where `HDFStore.select` modifies the passed columns list (GH 7212)

- Bug in `Categorical` repr with `display.width` of `None` in Python 3 (GH 10087)

- Bug in `to_json` with certain orients and a `CategoricalIndex` would segfault (GH 10317)

- Bug where some of the nan functions do not have consistent return dtypes (GH 10251)

- Bug in `DataFrame.quantile` on checking that a valid axis was passed (GH 9543)

- Bug in `groupby.apply` aggregation for `Categorical` not preserving categories (GH 10138)

- Bug in `to_csv` where `date_format` is ignored if the `datetime` is fractional (GH 10209)

- Bug in `DataFrame.to_json` with mixed data types (GH 10289)

- Bug in cache updating when consolidating (GH 10264)

- Bug in `mean()` where integer dtypes can overflow (GH 10172)

- Bug where `Panel.from_dict` does not set dtype when specified (GH 10058)

- Bug in `Index.union` raises `AttributeError` when passing array-likes. (GH 10149)

- Bug in `Timestamp`’s’ `microsecond`, `quarter`, `dayofyear`, `week` and `daysinmonth` properties return `np.int` type, not built-in `int`. (GH 10050)

- Bug in `NaT` raises `AttributeError` when accessing to `daysinmonth`, `dayofweek` properties. (GH 10096)

- Bug in Index repr when using the `max_seq_items=None` setting (GH 10182).

- Bug in getting timezone data with `dateutil` on various platforms ( GH 9059, GH 8639, GH 9663, GH 10121)

- Bug in displaying datetimes with mixed frequencies; display ‘ms’ datetimes to the proper precision. (GH 10170)

- Bug in `setitem` where type promotion is applied to the entire block (GH 10280)

- Bug in `Series` arithmetic methods may incorrectly hold names (GH 10068)

- Bug in `GroupBy.get_group` when grouping on multiple keys, one of which is categorical. (GH 10132)

- Bug in `DatetimeIndex` and `TimedeltaIndex` names are lost after timedelta arithmetic ( GH 9926)

- Bug in `DataFrame` construction from nested `dict` with `datetime64` (GH 10160)

- Bug in `Series` construction from `dict` with `datetime64` keys (GH 9456)

- Bug in `Series.plot(label="LABEL")` not correctly setting the label (GH 10119)

- Bug in `plot` not defaulting to matplotlib `axes.grid` setting (GH 9792)

- Bug causing strings containing an exponent, but no decimal to be parsed as `int` instead of `float` in `engine='python'` for the `read_csv` parser (GH 9565)

- Bug in `Series.align` resets `name` when `fill_value` is specified (GH 10067)

- Bug in `read_csv` causing index name not to be set on an empty DataFrame (GH 10184)

- Bug in `SparseSeries.abs` resets `name` (GH 10241)

- Bug in `TimedeltaIndex` slicing may reset freq (GH 10292)

- Bug in `GroupBy.get_group` raises `ValueError` when group key contains `NaT` (GH 6992)

- Bug in `SparseSeries` constructor ignores input data name (GH 10258)

- Bug in `Categorical.remove_categories` causing a `ValueError` when removing the `NaN` category if underlying dtype is floating-point (GH 10156)

- Bug where infer_freq infers time rule (WOM-5XXX) unsupported by to_offset (GH 9425)

- Bug in `DataFrame.to_hdf()` where table format would raise a seemingly unrelated error for invalid (non-string) column names. This is now explicitly forbidden. (GH 9057)

- Bug to handle masking empty `DataFrame` (GH 10126).

- Bug where MySQL interface could not handle numeric table/column names (GH 10255)

- Bug in `read_csv` with a `date_parser` that returned a `datetime64` array of other time resolution than `[ns]` (GH 10245)

- Bug in `Panel.apply` when the result has ndim=0 (GH 10332)

- Bug in `read_hdf` where `auto_close` could not be passed (GH 9327).

- Bug in `read_hdf` where open stores could not be used (GH 10330).

- Bug in adding empty `DataFrames`, now results in a `DataFrame` that `.equals` an empty `DataFrame` (GH 10181).

- Bug in `to_hdf` and `HDFStore` which did not check that complib choices were valid (GH 4582, GH 8874).

## Contributors

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