parameter to change the x-axis to log scale (GH2327) • Series arithmetic operators can now handle constant and ndarray input (GH2574) • ExcelFile now takes a kind argument to specify the file type (GH2613) “sparse” versions of the standard data structures for storing data that is mostly missing or mostly constant (some fixed value) • Moving window statistics (rolling mean, rolling standard deviation, etc.) I/O functions. For example, if you wanted to compare the Gross Domestic Products per capita in constant dollars in North America, you would use the search function: In [1]: from pandas.io import wb

parameter to change the x-axis to log scale (GH2327) • Series arithmetic operators can now handle constant and ndarray input (GH2574) • ExcelFile now takes a kind argument to specify the file type (GH2613) “sparse” versions of the standard data structures for storing data that is mostly missing or mostly constant (some fixed value) • Moving window statistics (rolling mean, rolling standard deviation, etc.) I/O functions. For example, if you wanted to compare the Gross Domestic Products per capita in constant dollars in North America, you would use the search function: In [1]: from pandas.io import wb

a dtype that utilizes memory based on the level size. In prior versions, the memory usage was a constant 8 bytes per element in each level. In addition, in prior versions, the reported memory usage was ewmcov() calculation of de-biasing factors when bias=False (the default). Previously an incorrect constant factor was used, based on adjust=True, ignore_na=True, and an infinite number of observations. Now 25 3 1.25 dtype: float64 Note that entry 0 is approximately 0, and the debiasing factors are a constant 1.25. By comparison, the following 0.15.0 results have a NaN for entry 0, and the debiasing factors

a dtype that utilizes memory based on the level size. In prior versions, the memory usage was a constant 8 bytes per element in each level. In addition, in prior versions, the reported memory usage was ewmcov() calculation of de-biasing factors when bias=False (the default). Previously an incorrect constant factor was used, based on adjust=True, ignore_na=True, and an infinite number of observations. Now 25 3 1.25 dtype: float64 Note that entry 0 is approximately 0, and the debiasing factors are a constant 1.25. By comparison, the following 0.15.0 results have a NaN for entry 0, and the debiasing factors

a dtype that utilizes memory based on the level size. In prior versions, the memory usage was a constant 8 bytes per element in each level. In addition, in prior versions, the reported memory usage was ewmcov() calculation of de-biasing factors when bias=False (the default). Previously an incorrect constant factor was used, based on adjust=True, ignore_na=True, and an infinite number of observations. Now analysis toolkit, Release 0.17.0 Note that entry 0 is approximately 0, and the debiasing factors are a constant 1.25. By comparison, the following 0.15.0 results have a NaN for entry 0, and the debiasing factors

“sparse” versions of the standard data structures for storing data that is mostly missing or mostly constant (some fixed value) • Moving window statistics (rolling mean, rolling standard deviation, etc.) substitute a fill_value for missing data in one of the inputs Parameters other : Series, DataFrame, or constant axis : {0, 1, ‘index’, ‘columns’} For Series input, axis to match Series index on fill_value substitute a fill_value for missing data in one of the inputs Parameters other : Series, DataFrame, or constant axis : {0, 1, ‘index’, ‘columns’} For Series input, axis to match Series index on fill_value

“sparse” versions of the standard data structures for storing data that is mostly missing or mostly constant (some fixed value) • Moving window statistics (rolling mean, rolling standard deviation, etc.) substitute a fill_value for missing data in one of the inputs Parameters other : Series, DataFrame, or constant axis : {0, 1, ‘index’, ‘columns’} For Series input, axis to match Series index on fill_value substitute a fill_value for missing data in one of the inputs Parameters other : Series, DataFrame, or constant axis : {0, 1, ‘index’, ‘columns’} For Series input, axis to match Series index on fill_value

“sparse” versions of the standard data structures for storing data that is mostly missing or mostly constant (some fixed value) • Moving window statistics (rolling mean, rolling standard deviation, etc.) substitute a fill_value for missing data in one of the inputs Parameters other : Series, DataFrame, or constant axis : {0, 1, ‘index’, ‘columns’} For Series input, axis to match Series index on fill_value substitute a fill_value for missing data in one of the inputs Parameters other : Series, DataFrame, or constant axis : {0, 1, ‘index’, ‘columns’} For Series input, axis to match Series index on fill_value

parameter to change the x-axis to log scale (GH2327) • Series arithmetic operators can now handle constant and ndarray input (GH2574) • ExcelFile now takes a kind argument to specify the file type (GH2613) “sparse” versions of the standard data structures for storing data that is mostly missing or mostly constant (some fixed value) • Moving window statistics (rolling mean, rolling standard deviation, etc.) substitute a fill_value for missing data in one of the inputs Parameters other : Series, DataFrame, or constant axis : {0, 1, ‘index’, ‘columns’} For Series input, axis to match Series index on fill_value

(GH15125) • Incorrect dtyped Series was returned by comparison methods (e.g., lt, gt, ...) against a constant for an empty DataFrame (GH15077) • Bug in Series.ffill() with mixed dtypes containing tz-aware specified with int (GH12979) • Bug in fill_value is ignored if the argument to a binary operator is a constant (GH12723) • Bug in pd.read_html() when using bs4 flavor and parsing table with a header and only a dtype that utilizes memory based on the level size. In prior versions, the memory usage was a constant 8 bytes per element in each level. In addition, in prior versions, the reported memory usage was