haven’t used NumPy much or at all, do invest some time in learning about NumPy first. See the package overview for more detail about what’s in the library. 2 CONTENTS CHAPTER ONE WHAT’S NEW These boolean indexer. (Their distinction is strictness: match relies on re.match while contains relies on re.search.) In this release, the deprecated behavior is the default, but the new behavior is available through methods re- quire scipy. Consult the Scipy reference guide and documentation for more information about when the various methods are appropriate. See the docs. Interpolate now also accepts a limit keyword

haven’t used NumPy much or at all, do invest some time in learning about NumPy first. See the package overview for more detail about what’s in the library. 2 CONTENTS CHAPTER ONE WHAT’S NEW These was underreported in prior versions In [1]: dfi.memory_usage(index=True) Out[1]: Index 8000 # took about 24008 bytes in < 0.15.1 A 8000 dtype: int64 current behavior: In [27]: dfi.memory_usage(index=True) mode (mean=True) so that the calculated weighted means (e.g. ‘triang’, ‘gaussian’) are distributed about the same means as those calculated without weighting (i.e. ‘boxcar’). See the note on normalization

haven’t used NumPy much or at all, do invest some time in learning about NumPy first. See the package overview for more detail about what’s in the library. 2 CONTENTS CHAPTER ONE WHAT’S NEW These ’@’ prefix. – You can have an expression like df.query(’@a < a’) with no complaints from pandas about am- biguity of the name a. – The top-level pandas.eval() function does not allow you use the ’@’ boolean indexer. (Their distinction is strictness: match relies on re.match while contains relies on re.search.) In this release, the deprecated behavior is the default, but the new behavior is available through

haven’t used NumPy much or at all, do invest some time in learning about NumPy first. See the package overview for more detail about what’s in the library. 2 CONTENTS CHAPTER ONE WHAT’S NEW These was underreported in prior versions In [1]: dfi.memory_usage(index=True) Out[1]: Index 8000 # took about 24008 bytes in < 0.15.1 A 8000 dtype: int64 current behavior: In [27]: dfi.memory_usage(index=True) mode (mean=True) so that the calculated weighted means (e.g. ‘triang’, ‘gaussian’) are distributed about the same means as those calculated without weighting (i.e. ‘boxcar’). See the note on normalization

haven’t used NumPy much or at all, do invest some time in learning about NumPy first. See the package overview for more detail about what’s in the library. 2 CONTENTS CHAPTER ONE WHAT’S NEW These Warning: Enabling this option will affect the performance for printing of DataFrame and Series (about 2 times slower). Use only when it is actually required. Some East Asian countries use Unicode characters was underreported in prior versions In [1]: dfi.memory_usage(index=True) Out[1]: Index 8000 # took about 24008 bytes in < 0.15.1 A 8000 dtype: int64 current behavior: In [27]: dfi.memory_usage(index=True)

the documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 382 3.4.1 About the pandas documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 382 3.4 haven’t used NumPy much or at all, do invest some time in learning about NumPy first. See the package overview for more detail about what’s in the library. 2 CONTENTS CHAPTER ONE WHAT’S NEW These remove_unused_levels() (GH16556) 1.2.3 Bug Fixes • Silenced a warning on some Windows environments about “tput: terminal attributes: No such device or address” when detecting the terminal size. This fix

the documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332 3.4.1 About the pandas documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332 3.4 haven’t used NumPy much or at all, do invest some time in learning about NumPy first. See the package overview for more detail about what’s in the library. 2 CONTENTS CHAPTER ONE WHAT’S NEW These respect the random seed set via numpy.random.seed(n) (GH13161) • Styler.apply is now more strict about the outputs your function must return. For axis=0 or axis=1, the output shape must be identical. For

the documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334 3.4.1 About the pandas documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334 3.4 haven’t used NumPy much or at all, do invest some time in learning about NumPy first. See the package overview for more detail about what’s in the library. 2 CONTENTS CHAPTER ONE WHAT’S NEW These respect the random seed set via numpy.random.seed(n) (GH13161) • Styler.apply is now more strict about the outputs your function must return. For axis=0 or axis=1, the output shape must be identical. For

the documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 410 3.4.1 About the pandas documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 410 3.4 haven’t used NumPy much or at all, do invest some time in learning about NumPy first. See the package overview for more detail about what’s in the library. 2 CONTENTS CHAPTER ONE WHAT’S NEW These 0 1 2.0 2 3.0 Setting a list-like data structure into a new attribute now raises a UserWarning about the potential for unexpected behavior. See Attribute Access. 1.2.1.4 drop now also accepts index/columns

the documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380 3.4.1 About the pandas documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380 3.4 haven’t used NumPy much or at all, do invest some time in learning about NumPy first. See the package overview for more detail about what’s in the library. 2 CONTENTS CHAPTER ONE WHAT’S NEW These remove_unused_levels() (GH16556) 1.1.3 Bug Fixes • Silenced a warning on some Windows environments about “tput: terminal attributes: No such device or address” when detecting the terminal size. This fix