Series.ftypes return if the data is sparse|dense

Series.ge(other, level=None, fill_value=None, axis=0) [source] Greater than or equal to of series and other, element-wise (binary operator ge). Equivalent to series >= other, but with support to substitute a fill_value for missing data in one of the inputs. Parameters: other: Series or scalar value fill_value : None or float value, default None (NaN) Fill missing (NaN) values with this value. If both Series are missing, the result will be missing level : int or name Broadcast across

Series.get(key, default=None) [source] Get item from object for given key (DataFrame column, Panel slice, etc.). Returns default value if not found. Parameters: key : object Returns: value : type of items contained in object

Series.ftype return if the data is sparse|dense

classmethod Series.from_csv(path, sep=', ', parse_dates=True, header=None, index_col=0, encoding=None, infer_datetime_format=False) [source] Read CSV file (DISCOURAGED, please use pandas.read_csv() instead). It is preferable to use the more powerful pandas.read_csv() for most general purposes, but from_csv makes for an easy roundtrip to and from a file (the exact counterpart of to_csv), especially with a time Series. This method only differs from pandas.read_csv() in some defaults: index_c

classmethod Series.from_array(arr, index=None, name=None, dtype=None, copy=False, fastpath=False) [source]

Series.floordiv(other, level=None, fill_value=None, axis=0) [source] Integer division of series and other, element-wise (binary operator floordiv). Equivalent to series // other, but with support to substitute a fill_value for missing data in one of the inputs. Parameters: other: Series or scalar value fill_value : None or float value, default None (NaN) Fill missing (NaN) values with this value. If both Series are missing, the result will be missing level : int or name Broadcast across

Series.first_valid_index() [source] Return label for first non-NA/null value

Series.flags

Series.first(offset) [source] Convenience method for subsetting initial periods of time series data based on a date offset. Parameters: offset : string, DateOffset, dateutil.relativedelta Returns: subset : type of caller Examples ts.first(?10D?) -> First 10 days