#Mosaic plot series

If a DataFrame is provided, it expects a list with the name of the columns. By using Mosaic Plot panels, we can construct something like this: First, for each region, we've added two heatmaps One that shows the number of requests per server over time (white to blue) and one that shows the average error rate per server over time (green to red) By using Compact Mode we achieve an incredibly high data resolution. It does not support named indexes for hierarchical Series. And it looks like more than half of the people surveyed were students. If not specified will default to the given order. So this mosaic plot, what it does above and beyond a segmented bar chart, is it gives us the width that shows us how many, for example, students versus staff versus parents were sampled or surveyed. index : list, optional Gives the preferred order for the category ordering. If a np.ndarray is provided, it will generate a simple numerical labels.

#Mosaic plot series

If a dict of a Series (or any other dict like object) is used, it will take the keys as labels. The order of the keys will be the same as the one of insertion. It expects that all the combination of keys to be represents if that is not true, will automatically consider the missing values as 0. unless the variable is called in x, it will be added to the formula in. ) alpha: add an alpha transparency to the selected variable. Each category should contain a non-negative number with a tuple as index. In geommosaic (), the following aesthetics can be specified: weight: select a weighting variable. The size of each rectangle is proportional to the percentage of observations from that. linspace ( 0.5, 1.0, L + 1 ) # fourth level, the hatch L = len ( categories_levels ) if Nlevels > 3 else 1 hatch = # convert in list and merge with the levels hue = lzip ( list ( hue ), categories_levels ) saturation = lzip ( list ( saturation ), categories_levels if Nlevels > 1 else ) value = lzip ( list ( value ), categories_levels if Nlevels > 2 else ) hatch = lzip ( list ( hatch ), categories_levels if Nlevels > 3 else ) # create the properties dictionary properties = The contingency table that contains the data. This macro generates a mosaic plot for 3 categorical variables. The best- known graph in this category is a pie chart. linspace ( 0.5, 1.0, L + 1 ) # third level, the value L = len ( categories_levels ) if Nlevels > 2 else 1 value = np. The mosaic plot resides in the category of visualizations that feature part-to-whole relationships. ''' # Author: Enrico Giampieri - from import lrange, lzip from itertools import product import numpy as np from numpy import array, cumsum, iterable, r_ from pandas import DataFrame from aphics import utils _all_ = def _normalize_split ( proportion ): ''' return a list of proportions of the available space given the division if only a number is given, it will assume a split in two pieces ''' if not iterable ( proportion ): if proportion = 0 : proportion = array () elif proportion >= 1 : proportion = array () elif proportion 1 else 1 saturation = np. see the docstring of the mosaic function for more informations. It allows to visualize multivariate categorical data in a rigorous and informative way. '''Create a mosaic plot from a contingency table.