Resampler.std(ddof=1, *args, **kwargs) [source] Compute standard deviation of groups, excluding missing values Parameters: ddof : integer, default 1 degrees of freedom

CategoricalIndex.is_lexsorted_for_tuple(tup) [source]

Index.sort(*args, **kwargs) [source]

Panel.pop(item) [source] Return item and drop from frame. Raise KeyError if not found.

pandas.read_sql_query(sql, con, index_col=None, coerce_float=True, params=None, parse_dates=None, chunksize=None) [source] Read SQL query into a DataFrame. Returns a DataFrame corresponding to the result set of the query string. Optionally provide an index_col parameter to use one of the columns as the index, otherwise default integer index will be used. Parameters: sql : string SQL query or SQLAlchemy Selectable (select or text object) to be executed. con : SQLAlchemy connectable(engine

Series.dt.days Number of days for each element.

DatetimeIndex.name = None

DataFrame.rpow(other, axis='columns', level=None, fill_value=None) [source] Exponential power of dataframe and other, element-wise (binary operator rpow). Equivalent to other ** dataframe, but with support to substitute a fill_value for missing data in one of the inputs. Parameters: other : Series, DataFrame, or constant axis : {0, 1, ?index?, ?columns?} For Series input, axis to match Series index on fill_value : None or float value, default None Fill missing (NaN) values with this val

Series.imag

CategoricalIndex.ravel(order='C') [source] return an ndarray of the flattened values of the underlying data See also numpy.ndarray.ravel