Series.axes Return a list of the row axis labels

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

Series.at_time(time, asof=False) [source] Select values at particular time of day (e.g. 9:30AM). Parameters: time : datetime.time or string Returns: values_at_time : type of caller

Series.at Fast label-based scalar accessor Similarly to loc, at provides label based scalar lookups. You can also set using these indexers.

Series.autocorr(lag=1) [source] Lag-N autocorrelation Parameters: lag : int, default 1 Number of lags to apply before performing autocorrelation. Returns: autocorr : float

Series.astype(dtype, copy=True, raise_on_error=True, **kwargs) [source] Cast object to input numpy.dtype Return a copy when copy = True (be really careful with this!) Parameters: dtype : data type, or dict of column name -> data type Use a numpy.dtype or Python type to cast entire pandas object to the same type. Alternatively, use {col: dtype, ...}, where col is a column label and dtype is a numpy.dtype or Python type to cast one or more of the DataFrame?s columns to column-specific ty

Series.as_matrix(columns=None) [source] Convert the frame to its Numpy-array representation. Parameters: columns: list, optional, default:None If None, return all columns, otherwise, returns specified columns. Returns: values : ndarray If the caller is heterogeneous and contains booleans or objects, the result will be of dtype=object. See Notes. See also pandas.DataFrame.values Notes Return is NOT a Numpy-matrix, rather, a Numpy-array. The dtype will be a lower-common-denominator

Series.as_blocks(copy=True) [source] Convert the frame to a dict of dtype -> Constructor Types that each has a homogeneous dtype. NOTE: the dtypes of the blocks WILL BE PRESERVED HERE (unlike in as_matrix) Parameters: copy : boolean, default True Returns: values : a dict of dtype -> Constructor Types

Series.asfreq(freq, method=None, how=None, normalize=False) [source] Convert TimeSeries to specified frequency. Optionally provide filling method to pad/backfill missing values. Parameters: freq : DateOffset object, or string method : {?backfill?/?bfill?, ?pad?/?ffill?}, default None Method to use for filling holes in reindexed Series (note this does not fill NaNs that already were present): ?pad? / ?ffill?: propagate last valid observation forward to next valid ?backfill? / ?bfill?: use

Series.asof(where, subset=None) [source] The last row without any NaN is taken (or the last row without NaN considering only the subset of columns in the case of a DataFrame) New in version 0.19.0: For DataFrame If there is no good value, NaN is returned. Parameters: where : date or array of dates subset : string or list of strings, default None if not None use these columns for NaN propagation Returns: where is scalar value or NaN if input is Series Series if input is DataFrame whe