DataFrame.tshift(periods=1, freq=None, axis=0) [source] Shift the time index, using the index?s frequency if available. Parameters: periods : int Number of periods to move, can be positive or negative freq : DateOffset, timedelta, or time rule string, default None Increment to use from the tseries module or time rule (e.g. ?EOM?) axis : int or basestring Corresponds to the axis that contains the Index Returns: shifted : NDFrame Notes If freq is not specified then tries to use the

Series.ndim return the number of dimensions of the underlying data, by definition 1

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

Series.str.get(i) [source] Extract element from lists, tuples, or strings in each element in the Series/Index. Parameters: i : int Integer index (location) Returns: items : Series/Index of objects

DataFrame.multiply(other, axis='columns', level=None, fill_value=None) [source] Multiplication of dataframe and other, element-wise (binary operator mul). Equivalent to dataframe * other, 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 valu

DataFrame.between_time(start_time, end_time, include_start=True, include_end=True) [source] Select values between particular times of the day (e.g., 9:00-9:30 AM). Parameters: start_time : datetime.time or string end_time : datetime.time or string include_start : boolean, default True include_end : boolean, default True Returns: values_between_time : type of caller

Categorical.__array__(dtype=None) [source] The numpy array interface. Returns: values : numpy array A numpy array of either the specified dtype or, if dtype==None (default), the same dtype as categorical.categories.dtype

pandas.melt(frame, id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None) [source] ?Unpivots? a DataFrame from wide format to long format, optionally leaving identifier variables set. This function is useful to massage a DataFrame into a format where one or more columns are identifier variables (id_vars), while all other columns, considered measured variables (value_vars), are ?unpivoted? to the row axis, leaving just two non-identifier columns, ?variable? and ?va

DataFrame.memory_usage(index=True, deep=False) [source] Memory usage of DataFrame columns. Parameters: index : bool Specifies whether to include memory usage of DataFrame?s index in returned Series. If index=True (default is False) the first index of the Series is Index. deep : bool Introspect the data deeply, interrogate object dtypes for system-level memory consumption Returns: sizes : Series A series with column names as index and memory usage of columns with units of bytes. S

MultiIndex.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 returned.