DataFrame.nsmallest(n, columns, keep='first') [source] Get the rows of a DataFrame sorted by the n smallest values of columns. New in version 0.17.0. Parameters: n : int Number of items to retrieve columns : list or str Column name or names to order by keep : {?first?, ?last?, False}, default ?first? Where there are duplicate values: - first : take the first occurrence. - last : take the last occurrence. Returns: DataFrame Examples >>> df = DataFrame({'a': [1, 10, 8, 11

Series.add(other, level=None, fill_value=None, axis=0) [source] Addition of series and other, element-wise (binary operator add). 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 level, matching

Series.str.rpartition(pat=' ', expand=True) [source] Split the string at the last occurrence of sep, and return 3 elements containing the part before the separator, the separator itself, and the part after the separator. If the separator is not found, return 3 elements containing two empty strings, followed by the string itself. Parameters: pat : string, default whitespace String to split on. expand : bool, default True If True, return DataFrame/MultiIndex expanding dimensionality. If F

MultiIndex.nunique(dropna=True) [source] Return number of unique elements in the object. Excludes NA values by default. Parameters: dropna : boolean, default True Don?t include NaN in the count. Returns: nunique : int

Series.tz_convert(tz, axis=0, level=None, copy=True) [source] Convert tz-aware axis to target time zone. Parameters: tz : string or pytz.timezone object axis : the axis to convert level : int, str, default None If axis ia a MultiIndex, convert a specific level. Otherwise must be None copy : boolean, default True Also make a copy of the underlying data Raises: TypeError If the axis is tz-naive.

ExcelFile.parse(sheetname=0, header=0, skiprows=None, skip_footer=0, names=None, index_col=None, parse_cols=None, parse_dates=False, date_parser=None, na_values=None, thousands=None, convert_float=True, has_index_names=None, converters=None, true_values=None, false_values=None, squeeze=False, **kwds) [source] Parse specified sheet(s) into a DataFrame Equivalent to read_excel(ExcelFile, ...) See the read_excel docstring for more info on accepted parameters

Series.min(axis=None, skipna=None, level=None, numeric_only=None, **kwargs) [source] This method returns the minimum of the values in the object. If you want the index of the minimum, use idxmin. This is the equivalent of the numpy.ndarray method argmin. Parameters: axis : {index (0)} skipna : boolean, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a

DataFrame.update(other, join='left', overwrite=True, filter_func=None, raise_conflict=False) [source] Modify DataFrame in place using non-NA values from passed DataFrame. Aligns on indices Parameters: other : DataFrame, or object coercible into a DataFrame join : {?left?}, default ?left? overwrite : boolean, default True If True then overwrite values for common keys in the calling frame filter_func : callable(1d-array) -> 1d-array<boolean>, default None Can choose to replace va

DataFrameGroupBy.cov(min_periods=None) Compute pairwise covariance of columns, excluding NA/null values Parameters: min_periods : int, optional Minimum number of observations required per pair of columns to have a valid result. Returns: y : DataFrame Notes y contains the covariance matrix of the DataFrame?s time series. The covariance is normalized by N-1 (unbiased estimator).

DataFrame.compound(axis=None, skipna=None, level=None) [source] Return the compound percentage of the values for the requested axis Parameters: axis : {index (0), columns (1)} skipna : boolean, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series numeric_only : boolean, default None Include only float, int, bo