Series.fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, **kwargs) [source] Fill NA/NaN values using the specified method Parameters: value : scalar, dict, Series, or DataFrame Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column (for a DataFrame). (values not in the dict/Series/DataFrame will not be filled). This value cannot be a list. method : {?backfi

Series.count(level=None) [source] Return number of non-NA/null observations in the Series Parameters: level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a smaller Series Returns: nobs : int or Series (if level specified)

TimedeltaIndex.freqstr Return the frequency object as a string if its set, otherwise None

CategoricalIndex.intersection(other) [source] Form the intersection of two Index objects. This returns a new Index with elements common to the index and other. Sortedness of the result is not guaranteed. Parameters: other : Index or array-like Returns: intersection : Index Examples >>> idx1 = pd.Index([1, 2, 3, 4]) >>> idx2 = pd.Index([3, 4, 5, 6]) >>> idx1.intersection(idx2) Int64Index([3, 4], dtype='int64')

Series.truediv(other, level=None, fill_value=None, axis=0) [source] Floating division of series and other, element-wise (binary operator truediv). Equivalent to series / other, but with support to substitute a fill_value for missing data in one of the inputs. Parameters: other: Series or scalar value fill_value : None or float value, default None (NaN) Fill missing (NaN) values with this value. If both Series are missing, the result will be missing level : int or name Broadcast across a

DatetimeIndex.take(indices, axis=0, allow_fill=True, fill_value=None, **kwargs) [source] return a new %(klass)s of the values selected by the indices For internal compatibility with numpy arrays. Parameters: indices : list Indices to be taken axis : int, optional The axis over which to select values, always 0. allow_fill : bool, default True fill_value : bool, default None If allow_fill=True and fill_value is not None, indices specified by -1 is regarded as NA. If Index doesn?t hold N

Series.str.repeat(repeats) [source] Duplicate each string in the Series/Index by indicated number of times. Parameters: repeats : int or array Same value for all (int) or different value per (array) Returns: repeated : Series/Index of objects

DataFrame.to_hdf(path_or_buf, key, **kwargs) [source] Write the contained data to an HDF5 file using HDFStore. Parameters: path_or_buf : the path (string) or HDFStore object key : string indentifier for the group in the store mode : optional, {?a?, ?w?, ?r+?}, default ?a? 'w' Write; a new file is created (an existing file with the same name would be deleted). 'a' Append; an existing file is opened for reading and writing, and if the file does not exist it is created. 'r+' It

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 :

MultiIndex.factorize(sort=False, na_sentinel=-1) [source] Encode the object as an enumerated type or categorical variable Parameters: sort : boolean, default False Sort by values na_sentinel: int, default -1 Value to mark ?not found? Returns: labels : the indexer to the original array uniques : the unique Index