Series.nonzero() [source] Return the indices of the elements that are non-zero This method is equivalent to calling numpy.nonzero on the series data. For compatability with NumPy, the return value is the same (a tuple with an array of indices for each dimension), but it will always be a one-item tuple because series only have one dimension. See also numpy.nonzero Examples >>> s = pd.Series([0, 3, 0, 4]) >>> s.nonzero() (array([1, 3]),) >>> s.iloc[s.nonzero()[0]]

CategoricalIndex.astype(dtype, copy=True) [source] Create an Index with values cast to dtypes. The class of a new Index is determined by dtype. When conversion is impossible, a ValueError exception is raised. Parameters: dtype : numpy dtype or pandas type copy : bool, default True By default, astype always returns a newly allocated object. If copy is set to False and internal requirements on dtype are satisfied, the original data is used to create a new Index or the original Index is retu

Series.between(left, right, inclusive=True) [source] Return boolean Series equivalent to left <= series <= right. NA values will be treated as False Parameters: left : scalar Left boundary right : scalar Right boundary Returns: is_between : Series

TimedeltaIndex.dtype

HDFStore.put(key, value, format=None, append=False, **kwargs) [source] Store object in HDFStore Parameters: key : object value : {Series, DataFrame, Panel} format : ?fixed(f)|table(t)?, default is ?fixed? fixed(f) : Fixed format Fast writing/reading. Not-appendable, nor searchable table(t) : Table format Write as a PyTables Table structure which may perform worse but allow more flexible operations like searching / selecting subsets of the data append : boolean, default False This w

Panel.align(other, **kwargs) [source]

Panel.select(crit, axis=0) [source] Return data corresponding to axis labels matching criteria Parameters: crit : function To be called on each index (label). Should return True or False axis : int Returns: selection : type of caller

TimedeltaIndex.is_floating() [source]

class pandas.DatetimeIndex [source] Immutable ndarray of datetime64 data, represented internally as int64, and which can be boxed to Timestamp objects that are subclasses of datetime and carry metadata such as frequency information. Parameters: data : array-like (1-dimensional), optional Optional datetime-like data to construct index with copy : bool Make a copy of input ndarray freq : string or pandas offset object, optional One of pandas date offset strings or corresponding objects

Index.is_categorical() [source]