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
