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

DataFrame.applymap(func) [source] Apply a function to a DataFrame that is intended to operate elementwise, i.e. like doing map(func, series) for each series in the DataFrame Parameters: func : function Python function, returns a single value from a single value Returns: applied : DataFrame See also DataFrame.apply For operations on rows/columns Examples >>> df = pd.DataFrame(np.random.randn(3, 3)) >>> df 0 1 2 0 -0.029638 1.081563 1.2803

MultiIndex.get_values() [source] return the underlying data as an ndarray

pandas.pivot(index, columns, values) [source] Produce ?pivot? table based on 3 columns of this DataFrame. Uses unique values from index / columns and fills with values. Parameters: index : ndarray Labels to use to make new frame?s index columns : ndarray Labels to use to make new frame?s columns values : ndarray Values to use for populating new frame?s values Returns: DataFrame Notes Obviously, all 3 of the input arguments must have the same length

DataFrame.shape Return a tuple representing the dimensionality of the DataFrame.

pandas.read_csv(filepath_or_buffer, sep=', ', delimiter=None, header='infer', names=None, index_col=None, usecols=None, squeeze=False, prefix=None, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=False, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False, i

Resampler.ohlc(_method='ohlc', *args, **kwargs) [source] Compute sum of values, excluding missing values For multiple groupings, the result index will be a MultiIndex See also pandas.Series.groupby, pandas.DataFrame.groupby, pandas.Panel.groupby

DataFrame.iat Fast integer location scalar accessor. Similarly to iloc, iat provides integer based lookups. You can also set using these indexers.

TimedeltaIndex.sym_diff(*args, **kwargs) [source]

Series.reindex(index=None, **kwargs) [source] Conform Series to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and copy=False Parameters: index : array-like, optional (can be specified in order, or as keywords) New labels / index to conform to. Preferably an Index object to avoid duplicating data method : {None, ?backfill?/?bfill?, ?pad?/?ffill?, ?near