Source code for vaex.viz


[docs]class DataFrameAccessorViz(object):
[docs] def __init__(self, df): self.df = df
# this will add methods to the above class from . import mpl
[docs]class ExpressionAccessorViz:
[docs] def __init__(self, expression): self.expression = expression self.df = self.expression.df
[docs] def histogram(self, what="count(*)", grid=None, shape=64, facet=None, limits=None, figsize=None, f="identity", n=None, normalize_axis=None, xlabel=None, ylabel=None, label=None, selection=None, show=False, tight_layout=True, hardcopy=None, progress=None, **kwargs): """ Plot a histogram of the expression. This is a convenience method for `df.histogram(...)` Example: >>> df.x.histogram() >>> df.x.histogram(limits=[0, 100], shape=100) >>> df.x.histogram(what='mean(y)', limits=[0, 100], shape=100) If you want to do a computation yourself, pass the grid argument, but you are responsible for passing the same limits arguments: >>> counts = df.mean(df.y, binby=df.x, limits=[0, 100], shape=100)/100. >>> df.plot1d(df.x, limits=[0, 100], shape=100, grid=means, label='mean(y)/100') :param x: Expression to bin in the x direction :param what: What to plot, count(*) will show a N-d histogram, mean('x'), the mean of the x column, sum('x') the sum :param grid: If the binning is done before by yourself, you can pass it :param facet: Expression to produce facetted plots ( facet='x:0,1,12' will produce 12 plots with x in a range between 0 and 1) :param limits: list of [xmin, xmax], or a description such as 'minmax', '99%' :param figsize: (x, y) tuple passed to pylab.figure for setting the figure size :param f: transform values by: 'identity' does nothing 'log' or 'log10' will show the log of the value :param n: normalization function, currently only 'normalize' is supported, or None for no normalization :param normalize_axis: which axes to normalize on, None means normalize by the global maximum. :param normalize_axis: :param xlabel: String for label on x axis (may contain latex) :param ylabel: Same for y axis :param: tight_layout: call pylab.tight_layout or not :param kwargs: extra argument passed to pylab.plot :return: """ return self.df.viz.histogram(self.expression, what=what, grid=grid, shape=shape, facet=facet, limits=limits, figsize=figsize, f=f, n=n, normalize_axis=normalize_axis, xlabel=xlabel, ylabel=ylabel, label=label, selection=selection, show=show, tight_layout=tight_layout, hardcopy=hardcopy, progress=progress)