pandas.read_html(io, match='.+', flavor=None, header=None, index_col=None, skiprows=None, attrs=None, parse_dates=False, tupleize_cols=False, thousands=', ', encoding=None, decimal='.', converters=None, na_values=None, keep_default_na=True) [source] Read HTML tables into a list of DataFrame objects. Parameters: io : str or file-like A URL, a file-like object, or a raw string containing HTML. Note that lxml only accepts the http, ftp and file url protocols. If you have a URL that starts wi

Index.max() [source] The maximum value of the object

pandas.read_json(path_or_buf=None, orient=None, typ='frame', dtype=True, convert_axes=True, convert_dates=True, keep_default_dates=True, numpy=False, precise_float=False, date_unit=None, encoding=None, lines=False) [source] Convert a JSON string to pandas object Parameters: path_or_buf : a valid JSON string or file-like, default: None The string could be a URL. Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is expected. For instance, a local file could be file://

DatetimeIndex.hasnans = None

Panel.to_long(*args, **kwargs) [source]

The axis labeling information in pandas objects serves many purposes: Identifies data (i.e. provides metadata) using known indicators, important for analysis, visualization, and interactive console display Enables automatic and explicit data alignment Allows intuitive getting and setting of subsets of the data set In this section, we will focus on the final point: namely, how to slice, dice, and generally get and set subsets of pandas objects. The primary focus will be on Series and DataFra

Panel.iat Fast integer location scalar accessor. Similarly to iloc, iat provides integer based lookups. You can also set using these indexers.

Series.consolidate(inplace=False) [source] Compute NDFrame with ?consolidated? internals (data of each dtype grouped together in a single ndarray). Mainly an internal API function, but available here to the savvy user Parameters: inplace : boolean, default False If False return new object, otherwise modify existing object Returns: consolidated : type of caller

pandas.get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False, columns=None, sparse=False, drop_first=False) [source] Convert categorical variable into dummy/indicator variables Parameters: data : array-like, Series, or DataFrame prefix : string, list of strings, or dict of strings, default None String to append DataFrame column names Pass a list with length equal to the number of columns when calling get_dummies on a DataFrame. Alternativly, prefix can be a dictionary mapping colu

TimedeltaIndex.nunique(dropna=True) [source] Return number of unique elements in the object. Excludes NA values by default. Parameters: dropna : boolean, default True Don?t include NaN in the count. Returns: nunique : int