DataFrame.lookup(row_labels, col_labels) [source] Label-based ?fancy indexing? function for DataFrame. Given equal-length arrays of row and column labels, return an array of the values corresponding to each (row, col) pair. Parameters: row_labels : sequence The row labels to use for lookup col_labels : sequence The column labels to use for lookup Notes Akin to: result = [] for row, col in zip(row_labels, col_labels): result.append(df.get_value(row, col)) Examples values : ndarray

DataFrame.loc Purely label-location based indexer for selection by label. .loc[] is primarily label based, but may also be used with a boolean array. Allowed inputs are: A single label, e.g. 5 or 'a', (note that 5 is interpreted as a label of the index, and never as an integer position along the index). A list or array of labels, e.g. ['a', 'b', 'c']. A slice object with labels, e.g. 'a':'f' (note that contrary to usual python slices, both the start and the stop are included!). A boolean ar

DataFrame.le(other, axis='columns', level=None) [source] Wrapper for flexible comparison methods le

DataFrame.last_valid_index() [source] Return label for last non-NA/null value

DataFrame.last(offset) [source] Convenience method for subsetting final periods of time series data based on a date offset. Parameters: offset : string, DateOffset, dateutil.relativedelta Returns: subset : type of caller Examples ts.last(?5M?) -> Last 5 months

DataFrame.kurtosis(axis=None, skipna=None, level=None, numeric_only=None, **kwargs) [source] Return unbiased kurtosis over requested axis using Fisher?s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1 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,

DataFrame.kurt(axis=None, skipna=None, level=None, numeric_only=None, **kwargs) [source] Return unbiased kurtosis over requested axis using Fisher?s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1 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, coll

DataFrame.keys() [source] Get the ?info axis? (see Indexing for more) This is index for Series, columns for DataFrame and major_axis for Panel.

DataFrame.join(other, on=None, how='left', lsuffix='', rsuffix='', sort=False) [source] Join columns with other DataFrame either on index or on a key column. Efficiently Join multiple DataFrame objects by index at once by passing a list. Parameters: other : DataFrame, Series with name field set, or list of DataFrame Index should be similar to one of the columns in this one. If a Series is passed, its name attribute must be set, and that will be used as the column name in the resulting joi

DataFrame.itertuples(index=True, name='Pandas') [source] Iterate over DataFrame rows as namedtuples, with index value as first element of the tuple. Parameters: index : boolean, default True If True, return the index as the first element of the tuple. name : string, default ?Pandas? The name of the returned namedtuples or None to return regular tuples. See also iterrows Iterate over DataFrame rows as (index, Series) pairs. iteritems Iterate over (column name, Series) pairs. No