pandas.pivot_table(data, values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All') [source] Create a spreadsheet-style pivot table as a DataFrame. The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on the index and columns of the result DataFrame Parameters: data : DataFrame values : column to aggregate, optional index : column, Grouper, array, or list of the previous If an array is passed

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

pandas.period_range(start=None, end=None, periods=None, freq='D', name=None) [source] Return a fixed frequency datetime index, with day (calendar) as the default frequency Parameters: start : starting value, period-like, optional end : ending value, period-like, optional periods : int, default None Number of periods in the index freq : str/DateOffset, default ?D? Frequency alias name : str, default None Name for the resulting PeriodIndex Returns: prng : PeriodIndex

pandas.notnull(obj) [source] Replacement for numpy.isfinite / -numpy.isnan which is suitable for use on object arrays. Parameters: arr : ndarray or object value Object to check for not-null-ness Returns: isnulled : array-like of bool or bool Array or bool indicating whether an object is not null or if an array is given which of the element is not null. See also pandas.isnull boolean inverse of pandas.notnull

pandas.merge_ordered(left, right, on=None, left_on=None, right_on=None, left_by=None, right_by=None, fill_method=None, suffixes=('_x', '_y'), how='outer') [source] Perform merge with optional filling/interpolation designed for ordered data like time series data. Optionally perform group-wise merge (see examples) Parameters: left : DataFrame right : DataFrame on : label or list Field names to join on. Must be found in both DataFrames. left_on : label or list, or array-like Field names to

pandas.merge_asof(left, right, on=None, left_on=None, right_on=None, left_index=False, right_index=False, by=None, left_by=None, right_by=None, suffixes=('_x', '_y'), tolerance=None, allow_exact_matches=True) [source] Perform an asof merge. This is similar to a left-join except that we match on nearest key rather than equal keys. For each row in the left DataFrame, we select the last row in the right DataFrame whose ?on? key is less than or equal to the left?s key. Both DataFrames must be s

pandas.merge(left, right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=True, indicator=False) [source] Merge DataFrame objects by performing a database-style join operation by columns or indexes. If joining columns on columns, the DataFrame indexes will be ignored. Otherwise if joining indexes on indexes or indexes on a column or columns, the index will be passed on. Parameters: left : DataFrame right : Data

pandas.melt(frame, id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None) [source] ?Unpivots? a DataFrame from wide format to long format, optionally leaving identifier variables set. This function is useful to massage a DataFrame into a format where one or more columns are identifier variables (id_vars), while all other columns, considered measured variables (value_vars), are ?unpivoted? to the row axis, leaving just two non-identifier columns, ?variable? and ?va

pandas.isnull(obj) [source] Detect missing values (NaN in numeric arrays, None/NaN in object arrays) Parameters: arr : ndarray or object value Object to check for null-ness Returns: isnulled : array-like of bool or bool Array or bool indicating whether an object is null or if an array is given which of the element is null. See also pandas.notnull boolean inverse of pandas.isnull

pandas.io.json.json_normalize(data, record_path=None, meta=None, meta_prefix=None, record_prefix=None) [source] ?Normalize? semi-structured JSON data into a flat table Parameters: data : dict or list of dicts Unserialized JSON objects record_path : string or list of strings, default None Path in each object to list of records. If not passed, data will be assumed to be an array of records meta : list of paths (string or list of strings), default None Fields to use as metadata for each