GroupBy.ohlc() [source] Compute sum of values, excluding missing values For multiple groupings, the result index will be a MultiIndex See also pandas.Series.groupby, pandas.DataFrame.groupby, pandas.Panel.groupby

GroupBy.std(ddof=1, *args, **kwargs) [source] Compute standard deviation of groups, excluding missing values For multiple groupings, the result index will be a MultiIndex Parameters: ddof : integer, default 1 degrees of freedom See also pandas.Series.groupby, pandas.DataFrame.groupby, pandas.Panel.groupby

Index.get_values() [source] return the underlying data as an ndarray

pandas.io.gbq.read_gbq(query, project_id=None, index_col=None, col_order=None, reauth=False, verbose=True, private_key=None, dialect='legacy') [source] Load data from Google BigQuery. THIS IS AN EXPERIMENTAL LIBRARY The main method a user calls to execute a Query in Google BigQuery and read results into a pandas DataFrame. Google BigQuery API Client Library v2 for Python is used. Documentation is available at https://developers.google.com/api-client-library/python/apis/bigquery/v2 Authentic

DataFrame.set_index(keys, drop=True, append=False, inplace=False, verify_integrity=False) [source] Set the DataFrame index (row labels) using one or more existing columns. By default yields a new object. Parameters: keys : column label or list of column labels / arrays drop : boolean, default True Delete columns to be used as the new index append : boolean, default False Whether to append columns to existing index inplace : boolean, default False Modify the DataFrame in place (do not

Series.to_dict() [source] Convert Series to {label -> value} dict Returns: value_dict : dict

CategoricalIndex.repeat(n, *args, **kwargs) [source] Repeat elements of an Index. Refer to numpy.ndarray.repeat for more information about the n argument. See also numpy.ndarray.repeat

CategoricalIndex.get_slice_bound(label, side, kind) [source] Calculate slice bound that corresponds to given label. Returns leftmost (one-past-the-rightmost if side=='right') position of given label. Parameters: label : object side : {?left?, ?right?} kind : {?ix?, ?loc?, ?getitem?}

CategoricalIndex.difference(other) [source] Return a new Index with elements from the index that are not in other. This is the set difference of two Index objects. It?s sorted if sorting is possible. Parameters: other : Index or array-like Returns: difference : Index Examples >>> idx1 = pd.Index([1, 2, 3, 4]) >>> idx2 = pd.Index([3, 4, 5, 6]) >>> idx1.difference(idx2) Int64Index([1, 2], dtype='int64')

CategoricalIndex.get_indexer_for(target, **kwargs) [source] guaranteed return of an indexer even when non-unique