Series.str.wrap(width, **kwargs) [source] Wrap long strings in the Series/Index to be formatted in paragraphs with length less than a given width. This method has the same keyword parameters and defaults as textwrap.TextWrapper. Parameters: width : int Maximum line-width expand_tabs : bool, optional If true, tab characters will be expanded to spaces (default: True) replace_whitespace : bool, optional If true, each whitespace character (as defined by string.whitespace) remaining after

Panel.conform(frame, axis='items') [source] Conform input DataFrame to align with chosen axis pair. Parameters: frame : DataFrame axis : {?items?, ?major?, ?minor?} Axis the input corresponds to. E.g., if axis=?major?, then the frame?s columns would be items, and the index would be values of the minor axis Returns: DataFrame

Series.ravel(order='C') [source] Return the flattened underlying data as an ndarray See also numpy.ndarray.ravel

DataFrame.isin(values) [source] Return boolean DataFrame showing whether each element in the DataFrame is contained in values. Parameters: values : iterable, Series, DataFrame or dictionary The result will only be true at a location if all the labels match. If values is a Series, that?s the index. If values is a dictionary, the keys must be the column names, which must match. If values is a DataFrame, then both the index and column labels must match. Returns: DataFrame of booleans Exa

Series.reset_index(level=None, drop=False, name=None, inplace=False) [source] Analogous to the pandas.DataFrame.reset_index() function, see docstring there. Parameters: level : int, str, tuple, or list, default None Only remove the given levels from the index. Removes all levels by default drop : boolean, default False Do not try to insert index into dataframe columns name : object, default None The name of the column corresponding to the Series values inplace : boolean, default Fals

Series.is_time_series

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

SeriesGroupBy.nlargest(*args, **kwargs) [source] Return the largest n elements. Parameters: n : int Return this many descending sorted values keep : {?first?, ?last?, False}, default ?first? Where there are duplicate values: - first : take the first occurrence. - last : take the last occurrence. take_last : deprecated Returns: top_n : Series The n largest values in the Series, in sorted order See also Series.nsmallest Notes Faster than .sort_values(ascending=False).head(n) for s

Series.rank(axis=0, method='average', numeric_only=None, na_option='keep', ascending=True, pct=False) [source] Compute numerical data ranks (1 through n) along axis. Equal values are assigned a rank that is the average of the ranks of those values Parameters: axis: {0 or ?index?, 1 or ?columns?}, default 0 index to direct ranking method : {?average?, ?min?, ?max?, ?first?, ?dense?} average: average rank of group min: lowest rank in group max: highest rank in group first: ranks assigned

DataFrame.reindex_axis(labels, axis=0, method=None, level=None, copy=True, limit=None, fill_value=nan) [source] Conform input object to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and copy=False Parameters: labels : array-like New labels / index to conform to. Preferably an Index object to avoid duplicating data axis : {0 or ?index?, 1 or ?columns?}