Series.ftypes return if the data is sparse|dense

DatetimeIndex.holds_integer() [source]

DataFrame.keys() [source] Get the ?info axis? (see Indexing for more) This is index for Series, columns for DataFrame and major_axis for Panel.

TimedeltaIndex.data return the data pointer of the underlying data

TimedeltaIndex.sortlevel(level=None, ascending=True, sort_remaining=None) [source] For internal compatibility with with the Index API Sort the Index. This is for compat with MultiIndex Parameters: ascending : boolean, default True False to sort in descending order level, sort_remaining are compat parameters Returns: sorted_index : Index

Series.bool() [source] Return the bool of a single element PandasObject. This must be a boolean scalar value, either True or False. Raise a ValueError if the PandasObject does not have exactly 1 element, or that element is not boolean

pandas.read_excel(io, sheetname=0, header=0, skiprows=None, skip_footer=0, index_col=None, names=None, parse_cols=None, parse_dates=False, date_parser=None, na_values=None, thousands=None, convert_float=True, has_index_names=None, converters=None, true_values=None, false_values=None, engine=None, squeeze=False, **kwds) [source] Read an Excel table into a pandas DataFrame Parameters: io : string, path object (pathlib.Path or py._path.local.LocalPath), file-like object, pandas ExcelFile, or

Series.to_json(path_or_buf=None, orient=None, date_format='epoch', double_precision=10, force_ascii=True, date_unit='ms', default_handler=None, lines=False) [source] Convert the object to a JSON string. Note NaN?s and None will be converted to null and datetime objects will be converted to UNIX timestamps. Parameters: path_or_buf : the path or buffer to write the result string if this is None, return a StringIO of the converted string orient : string Seriesdefault is ?index? allowed val

Series.ftype return if the data is sparse|dense

The easiest way for the majority of users to install pandas is to install it as part of the Anaconda distribution, a cross platform distribution for data analysis and scientific computing. This is the recommended installation method for most users. Instructions for installing from source, PyPI, various Linux distributions, or a development version are also provided. Python version support Officially Python 2.7, 3.4, 3.5, and 3.6 Installing pandas Trying out pandas, no installation required