indexing and selection, via data_columns keyword in append – support write chunking to reduce memory footprint, via chunksize keyword to append – support automagic indexing via index keyword to append – support klib/khash-based hash tables in Index classes for better performance in many cases and lower memory footprint • Shipping some functions from scipy.stats to reduce dependency, e.g. Series.describe and DataFrame Series.unique problem with NAs (GH714) • Memoize objects when reading from file to reduce memory footprint • Can get and set a column of a DataFrame with hierarchical columns containing “empty” (‘’) lower

indexing and selection, via data_columns keyword in append – support write chunking to reduce memory footprint, via chunksize keyword to append – support automagic indexing via index keyword to append – support klib/khash-based hash tables in Index classes for better performance in many cases and lower memory footprint • Shipping some functions from scipy.stats to reduce dependency, e.g. Series.describe and DataFrame Series.unique problem with NAs (GH714) • Memoize objects when reading from file to reduce memory footprint • Can get and set a column of a DataFrame with hierarchical columns containing “empty” (‘’) lower

indexing and selection, via data_columns keyword in append – support write chunking to reduce memory footprint, via chunksize keyword to append – support automagic indexing via index keyword to append – support klib/khash-based hash tables in Index classes for better performance in many cases and lower memory footprint • Shipping some functions from scipy.stats to reduce dependency, e.g. Series.describe and DataFrame Series.unique problem with NAs (GH714) • Memoize objects when reading from file to reduce memory footprint • Can get and set a column of a DataFrame with hierarchical columns containing “empty” (‘’) lower

columns as small integers, rather than floats (GH8725). This should provide an improved memory footprint. Previous behavior: In [1]: pd.get_dummies(['a', 'b', 'a', 'c']).dtypes Out[1]: a float64 b indexing and selection, via data_columns keyword in append – support write chunking to reduce memory footprint, via chunksize keyword to append – support automagic indexing via index keyword to append – support klib/khash-based hash tables in Index classes for better performance in many cases and lower memory footprint • Shipping some functions from scipy.stats to reduce dependency, e.g. Series.describe and DataFrame

columns as small integers, rather than floats (GH8725). This should provide an improved memory footprint. Previous behavior: In [1]: pd.get_dummies(['a', 'b', 'a', 'c']).dtypes Out[1]: a float64 b indexing and selection, via data_columns keyword in append – support write chunking to reduce memory footprint, via chunksize keyword to append – support automagic indexing via index keyword to append – support klib/khash-based hash tables in Index classes for better performance in many cases and lower memory footprint • Shipping some functions from scipy.stats to reduce dependency, e.g. Series.describe and DataFrame

columns as small integers, rather than floats (GH8725). This should provide an improved memory footprint. Previous behavior: In [1]: pd.get_dummies(['a', 'b', 'a', 'c']).dtypes Out[1]: a float64 b indexing and selection, via data_columns keyword in append – support write chunking to reduce memory footprint, via chunksize keyword to append – support automagic indexing via index keyword to append – support klib/khash-based hash tables in Index classes for better performance in many cases and lower memory footprint • Shipping some functions from scipy.stats to reduce dependency, e.g. Series.describe and DataFrame

columns as small integers, rather than floats (GH8725). This should provide an improved memory footprint. 60 Chapter 1. What’s New pandas: powerful Python data analysis toolkit, Release 0.20.2 Previous indexing and selection, via data_columns keyword in append – support write chunking to reduce memory footprint, via chunksize keyword to append – support automagic indexing via index keyword to append – support klib/khash-based hash tables in Index classes for better performance in many cases and lower memory footprint • Shipping some functions from scipy.stats to reduce dependency, e.g. Series.describe and DataFrame

indexing and selection, via data_columns keyword in append – support write chunking to reduce memory footprint, via chunksize keyword to append – support automagic indexing via index keyword to append – support klib/khash-based hash tables in Index classes for better performance in many cases and lower memory footprint • Shipping some functions from scipy.stats to reduce dependency, e.g. Series.describe and DataFrame Series.unique problem with NAs (GH714) • Memoize objects when reading from file to reduce memory footprint • Can get and set a column of a DataFrame with hierarchical columns containing “empty” (‘’) lower

indexing and selection, via data_columns keyword in append – support write chunking to reduce memory footprint, via chunksize keyword to append – support automagic indexing via index keyword to append – support klib/khash-based hash tables in Index classes for better performance in many cases and lower memory footprint • Shipping some functions from scipy.stats to reduce dependency, e.g. Series.describe and DataFrame Series.unique problem with NAs (GH714) • Memoize objects when reading from file to reduce memory footprint • Can get and set a column of a DataFrame with hierarchical columns containing “empty” (‘’) lower

columns as small integers, rather than floats (GH8725). This should provide an improved memory footprint. Previous behavior: In [1]: pd.get_dummies(['a', 'b', 'a', 'c']).dtypes Out[1]: a float64 b indexing and selection, via data_columns keyword in append – support write chunking to reduce memory footprint, via chunksize keyword to append – support automagic indexing via index keyword to append – support klib/khash-based hash tables in Index classes for better performance in many cases and lower memory footprint • Shipping some functions from scipy.stats to reduce dependency, e.g. Series.describe and DataFrame