Styler.apply(func, axis=0, subset=None, **kwargs) [source] Apply a function column-wise, row-wise, or table-wase, updating the HTML representation with the result. New in version 0.17.1. Parameters: func : function func should take a Series or DataFrame (depending on axis), and return an object with the same shape. Must return a DataFrame with identical index and column labels when axis=None axis : int, str or None apply to each column (axis=0 or 'index') or to each row (axis=1 or 'co

StataReader.variable_labels() [source] Returns variable labels as a dict, associating each variable name with corresponding label

StataWriter.write_file() [source]

StataReader.value_labels() [source] Returns a dict, associating each variable name a dict, associating each value its corresponding label

StataReader.data(**kwargs) [source] DEPRECATED: Reads observations from Stata file, converting them into a dataframe This is a legacy method. Use read in new code. Parameters: convert_dates : boolean, defaults to True Convert date variables to DataFrame time values convert_categoricals : boolean, defaults to True Read value labels and convert columns to Categorical/Factor variables index : identifier of index column identifier of column that should be used as index of the DataFrame c

StataReader.data_label() [source] Returns data label of Stata file

Note The SparsePanel class has been removed in 0.19.0 We have implemented ?sparse? versions of Series and DataFrame. These are not sparse in the typical ?mostly 0?. Rather, you can view these objects as being ?compressed? where any data matching a specific value (NaN / missing value, though any value can be chosen) is omitted. A special SparseIndex object tracks where data has been ?sparsified?. This will make much more sense in an example. All of the standard pandas data structures have a t

classmethod SparseSeries.from_coo(A, dense_index=False) [source] Create a SparseSeries from a scipy.sparse.coo_matrix. New in version 0.16.0. Parameters: A : scipy.sparse.coo_matrix dense_index : bool, default False If False (default), the SparseSeries index consists of only the coords of the non-null entries of the original coo_matrix. If True, the SparseSeries index consists of the full sorted (row, col) coordinates of the coo_matrix. Returns: s : SparseSeries Examples >>>

SparseSeries.to_coo(row_levels=(0, ), column_levels=(1, ), sort_labels=False) [source] Create a scipy.sparse.coo_matrix from a SparseSeries with MultiIndex. Use row_levels and column_levels to determine the row and column coordinates respectively. row_levels and column_levels are the names (labels) or numbers of the levels. {row_levels, column_levels} must be a partition of the MultiIndex level names (or numbers). New in version 0.16.0. Parameters: row_levels : tuple/list column_levels :

SeriesGroupBy.unique() Return np.ndarray of unique values in the object. Significantly faster than numpy.unique. Includes NA values. The order of the original is preserved. Returns: uniques : np.ndarray