Series.idxmax(axis=None, skipna=True, *args, **kwargs) [source] Index of first occurrence of maximum of values. Parameters: skipna : boolean, default True Exclude NA/null values Returns: idxmax : Index of maximum of values See also DataFrame.idxmax, numpy.ndarray.argmax Notes This method is the Series version of ndarray.argmax.

Series.update(other) [source] Modify Series in place using non-NA values from passed Series. Aligns on index Parameters: other : Series

HDFStore.append(key, value, format=None, append=True, columns=None, dropna=None, **kwargs) [source] Append to Table in file. Node must already exist and be Table format. Parameters: key : object value : {Series, DataFrame, Panel, Panel4D} format: ?table? is the default table(t) : table format Write as a PyTables Table structure which may perform worse but allow more flexible operations like searching / selecting subsets of the data append : boolean, default True, append the input data

Series.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 is

Series.astype(dtype, copy=True, raise_on_error=True, **kwargs) [source] Cast object to input numpy.dtype Return a copy when copy = True (be really careful with this!) Parameters: dtype : data type, or dict of column name -> data type Use a numpy.dtype or Python type to cast entire pandas object to the same type. Alternatively, use {col: dtype, ...}, where col is a column label and dtype is a numpy.dtype or Python type to cast one or more of the DataFrame?s columns to column-specific ty

Resampler.pad(limit=None) [source] Forward fill the values Parameters: limit : integer, optional limit of how many values to fill See also Series.fillna, DataFrame.fillna

Series.tshift(periods=1, freq=None, axis=0) [source] Shift the time index, using the index?s frequency if available. Parameters: periods : int Number of periods to move, can be positive or negative freq : DateOffset, timedelta, or time rule string, default None Increment to use from the tseries module or time rule (e.g. ?EOM?) axis : int or basestring Corresponds to the axis that contains the Index Returns: shifted : NDFrame Notes If freq is not specified then tries to use the fre

Series.append(to_append, ignore_index=False, verify_integrity=False) [source] Concatenate two or more Series. Parameters: to_append : Series or list/tuple of Series ignore_index : boolean, default False If True, do not use the index labels. verify_integrity : boolean, default False If True, raise Exception on creating index with duplicates Returns: appended : Series Examples >>> s1 = pd.Series([1, 2, 3]) >>> s2 = pd.Series([4, 5, 6]) >>> s3 = pd.Series([4,

Series.filter(items=None, like=None, regex=None, axis=None) [source] Subset rows or columns of dataframe according to labels in the specified index. Note that this routine does not filter a dataframe on its contents. The filter is applied to the labels of the index. Parameters: items : list-like List of info axis to restrict to (must not all be present) like : string Keep info axis where ?arg in col == True? regex : string (regular expression) Keep info axis with re.search(regex, col)

DataFrame.rmod(other, axis='columns', level=None, fill_value=None) [source] Modulo of dataframe and other, element-wise (binary operator rmod). Equivalent to other % dataframe, but with support to substitute a fill_value for missing data in one of the inputs. Parameters: other : Series, DataFrame, or constant axis : {0, 1, ?index?, ?columns?} For Series input, axis to match Series index on fill_value : None or float value, default None Fill missing (NaN) values with this value. If both