DatetimeIndex.weekday_name The name of day in a week (ex: Friday) New in version 0.18.1.

Index.nlevels

Series.str.swapcase() [source] Convert strings in the Series/Index to be swapcased. Equivalent to str.swapcase(). Returns: converted : Series/Index of objects

Index.factorize(sort=False, na_sentinel=-1) [source] Encode the object as an enumerated type or categorical variable Parameters: sort : boolean, default False Sort by values na_sentinel: int, default -1 Value to mark ?not found? Returns: labels : the indexer to the original array uniques : the unique Index

pandas.get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False, columns=None, sparse=False, drop_first=False) [source] Convert categorical variable into dummy/indicator variables Parameters: data : array-like, Series, or DataFrame prefix : string, list of strings, or dict of strings, default None String to append DataFrame column names Pass a list with length equal to the number of columns when calling get_dummies on a DataFrame. Alternativly, prefix can be a dictionary mapping colu

TimedeltaIndex.nunique(dropna=True) [source] Return number of unique elements in the object. Excludes NA values by default. Parameters: dropna : boolean, default True Don?t include NaN in the count. Returns: nunique : int

Panel.to_long(*args, **kwargs) [source]

The axis labeling information in pandas objects serves many purposes: Identifies data (i.e. provides metadata) using known indicators, important for analysis, visualization, and interactive console display Enables automatic and explicit data alignment Allows intuitive getting and setting of subsets of the data set In this section, we will focus on the final point: namely, how to slice, dice, and generally get and set subsets of pandas objects. The primary focus will be on Series and DataFra

DataFrame.values Numpy representation of NDFrame Notes The dtype will be a lower-common-denominator dtype (implicit upcasting); that is to say if the dtypes (even of numeric types) are mixed, the one that accommodates all will be chosen. Use this with care if you are not dealing with the blocks. e.g. If the dtypes are float16 and float32, dtype will be upcast to float32. If dtypes are int32 and uint8, dtype will be upcast to int32. By numpy.find_common_type convention, mixing int64 and uint

DatetimeIndex.nlevels