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PyvtTbl Overview

Description

PyvtTbl objects are containers for holding pivoted data. They are instantiated through DataFrame.pivot.

Public Methods

Additional methods inherented from np.ma.MaskedArray that are not explicitely listed here may also be available. Keep in mind methods not listed here may not been have extensively tested.


static PyvtTbl.__new__(data, val, conditions, rnames, cnames, aggregate, **kwds)

creates a new PyvtTbl from scratch

args:

data: np.ma.array object holding pivoted data

val: string label for the data in the table

conditions: Dictset representing the factors and levels in the table

rnames: list of row labels

cnames: list of column labels

aggregate: string describing the aggregate function applied to the data

kwds:

calc tots: bool specifying whether totals were calculated

row_tots: row totals in a MaskedArray

col_tots: column totals in a MaskedArray

grand_tot: float holding grand total

attach_rlabels: bool specifying whether row labels are part of the table

subclassing Numpy objects are a little different from subclassing other objects.

PyvtTbl.transpose()

returns a transposed PyvtTbl object


PyvtTbl.__getitem__(indx)

Return the item described by indx, as a PyvtTbl

args:
indx: index to array

can be int, tuple(int, int), tuple(slice, int), tuple(int, slice) or tuple(slice, slice)

x[int] <==> x[int,:]

returns:
PyvtTbl that is at least 2-dimensional (unless indx is tuple(int, int))
x.__getitem__(indx) <==> x[indx]

PyvtTbl.astype(dtype)

Convert the input to an array.

args:
a: array_like Input data, in any form that can be converted to an array. This
includes lists, lists of tuples, tuples, tuples of tuples, tuples of lists and ndarrays.
kwds:

dtype: data-type. By default, the data-type is inferred from the input data.

order: {‘C’, ‘F’} Whether to use row-major (‘C’) or column-major (‘F’ for FORTRAN)
memory representation. Defaults to ‘C’.
returns:
out: ndarray
Array interpretation of a. No copy is performed if the input is already an ndarray. If a is a subclass of ndarray, a base class ndarray is returned.

PyvtTbl.flatten()

returns a the PyvtTbl flattened as a MaskedArray


PyvtTbl.__iter__() <==> iter(x)

PyvtTbl.ndenumerate()

Multidimensional index iterator.

returns:
returns an iterator yielding pairs of array coordinates and values.

pyvttbl.PyvtTbl.flat

Flat iterator object to iterate over PyvtTbl.

A MaskedIterator iterator is returned by x.flat for any PyvtTbl
x. It allows iterating over the array as if it were a 1-D array,
either in a for-loop or by calling its next method.

Iteration is done in C-contiguous style, with the last index varying the
fastest. The iterator can also be indexed using basic slicing or
advanced indexing.

PyvtTbl.__repr__()

returns a machine friendly string representation of the object


PyvtTbl.__str__()

returns a human friendly string representation of the table


PyvtTbl.to_dataframe()

returns a DataFrame excluding row and column totals


PyvtTbl.__add__()

Add other to self, and return a new PyvtTbl.


PyvtTbl.__radd__()

Add other to self, and return a new PyvtTbl.


PyvtTbl.__sub__()

Subtract other to self, and return a new PyvtTbl.


PyvtTbl.__rsub__()

Subtract other to self, and return a new PyvtTbl.


PyvtTbl.__pow__()

Raise self to the power other, masking the potential NaNs/Infs


PyvtTbl.__mul__()

Multiply other by self, and return a new PyvtTbl.


PyvtTbl.__rmul__()

Multiply other by self, and return a new PyvtTbl.


PyvtTbl.__div__()

Divide other into self, and return a new PyvtTbl.


PyvtTbl.__rdiv__()

x.__rdiv__(y) <==> y/x


PyvtTbl.__truediv__()

Divide other into self, and return a new PyvtTbl.


PyvtTbl.__rtruediv__()

Divide other into self, and return a new PyvtTbl.


PyvtTbl.__floordiv__()

Divide other into self, and return a new PyvtTbl.


PyvtTbl.__rfloordiv__()

Divide other into self, and return a new PyvtTbl.


Private Methods

helper methods for PyvtTbl


PyvtTbl._get_rows()

returns a list of tuples containing row labels and conditions


PyvtTbl._get_cols()

returns a list of tuples containing column labels and conditions


This software is funded in part by NIH Grant P20 RR016454.
© Copyright 2012, Roger Lew. Created using Sphinx 1.1.3.