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pyvttbl.stats API

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pyvttbl.plotting API

Description

pyvttbl.plotting contains a collection of functions for visualizing data in DataFrame objects.

Static Plotting Methods

Methods to get data into a DataFrame, manipulate and manage data, and write data.


static plotting.box_plot(df, val, factors=None, where=None, fname=None, output_dir='', quality='medium')

Makes a box plot

args:
df:
a pyvttbl.DataFrame object
val:
the label of the dependent variable
kwds:
factors:
a list of factors to include in boxplot
where:
a string, list of strings, or list of tuples applied to the DataFrame before plotting
fname:
output file name
quality:
{‘low’ | ‘medium’ | ‘high’} specifies image file dpi

static plotting.histogram_plot(df, val, where=None, bins=10, range=None, density=False, cumulative=False, fname=None, output_dir='', quality='medium')

Makes a histogram plot

args:
key: column label of dependent variable
kwds:

where: criterion to apply to table before running analysis

bins: number of bins (default = 10)

range: list of length 2 defining min and max bin edges


static plotting.interaction_plot(df, val, xaxis, seplines=None, sepxplots=None, sepyplots=None, xmin='AUTO', xmax='AUTO', ymin='AUTO', ymax='AUTO', where=None, fname=None, output_dir='', quality='low', yerr=None)

makes an interaction plot

args:
df:
a pyvttbl.DataFrame object
val:
the label of the dependent variable
xaxis:
the label of the variable to place on the xaxis of each subplot
kwds:
seplines:
label specifying seperate lines in each subplot
sepxplots:
label specifying seperate horizontal subplots
sepyplots:
label specifying separate vertical subplots
xmin:
(‘AUTO’ by default) minimum xaxis value across subplots
xmax:
(‘AUTO’ by default) maximum xaxis value across subplots
ymin:
(‘AUTO’ by default) minimum yaxis value across subplots
ymax:
(‘AUTO’ by default) maximum yaxis value across subplots
where:
a string, list of strings, or list of tuples applied to the DataFrame before plotting
fname:
output file name
quality:
{‘low’ | ‘medium’ | ‘high’} specifies image file dpi
yerr:
{float, ‘ci’, ‘stdev’, ‘sem’} designates errorbars across datapoints in all subplots

static plotting.scatter_matrix(df, variables, alpha=0.5, grid=False, diagonal=None, trend='linear', alternate_labels=True, fname=None, output_dir='', quality='medium', **kwds)

Plots a matrix of scatterplots

args:
variables:
column labels to include in scatter matrix
kwds:
alpha:
amount of transparency applied
grid:
setting this to True will show the grid
diagonal:

‘kde’: Kernel Density Estimation

‘hist’: 20 bin Histogram

None: just labels

trend :

None: no model fitting

‘linear’: f(x) = a + b*x (default)

‘exponential’: f(x) = a * x**b

‘logarithmic’: f(x) = a * log(x) + b

‘polynomial’: f(x) = a * x**2 + b*x + c

‘power’: f(x) = a * x**b

alternate_labels: Specifies whether the labels and ticks should
alternate. Default is True. When False tick labels will be on the left and botttom, and variable labels will be on the top and right.

static plotting.scatter_plot(df, aname, bname, where=None, trend=None, fname=None, output_dir='', quality='medium', alpha=0.6)

Creates a scatter plot with the specified parameters

args:

aname: variable on x-axis

bname: variable on y-axis

kwds:
alpha:
amount of transparency applied
trend :

None: no model fitting

‘linear’: f(x) = a + b*x

‘exponential’: f(x) = a * x**b

‘logarithmic’: f(x) = a * log(x) + b

‘polynomial’: f(x) = a * x**2 + b*x + c

‘power’: f(x) = a * x**b


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