dataoutsider 1.1.0

Data visualization toolbox.

Welcome to the dataoutsider visualization toolbox!

Featuring:

• Multi-Chord Diagram
  • 
• Pie-Tree Chart (or Pie-Map Chart)
  • 

Multi-Chord Diagram

Have too many sets for a Venn Diagram?

1. Prepare groups a. Select 2 fields that identify 1. a group (ex: sports event) and 2. an entity (ex: athlete) to identify the many-to-many relationships (ex: many athletes to many events) or b. Pre-group data and provide each unique group combination (ex: athletes that competed in only the [100 meter] vs those that competed in only the [100 meter & 200 meter], etc.) and associated entity count (ex: # of athletes per group)
2. Configure the parent-group order
3. The multi-chord diagram will create bonds between many-to-many relationships, sized by the entity count per each unique group combination (ex: 10 athletes competed in only the [50 meter, 100 meter, & meter], 23 athletes competed in only the [100 meter], etc.)

Assumes 1 row = 1 unit for 1. a.

Example 1 (1. a.):

from dataoutsider import multi_chord as mc

df = mc.load_olympics_df()
df = df.loc[(df['Year'] >= 2014) & (df['Sex'] == 'F') & (df['Sport'] == 'Speed Skating')]
outer = 'Event'
inner = 'ID'
lookup = mc.multi_chord_get_alias(df, outer, inner)
print(lookup)
df_mc = mc.multi_chord_alias(df, outer, inner, percent = 75.)

df_mc_venn = mc.multi_chord_venn(df_mc)

mc.multi_chord_plot(df_mc, level = 4, transparency = 0.5)

Example 2 (1. b.):

from dataoutsider import multi_chord as mc
import pandas as pd

data = [['a', 25.5], ['a,b', 15], ['a,c', 14.4], ['a,c,d', 5], ['c,d', 13], ['d', 14], ['c,b', 10], ['b', 7]]
df = pd.DataFrame(data, columns = ['group', 'value'])
df_mc = mc.multi_chord_on_groups_alias(df, percent=33.)
df_mc_venn = mc.multi_chord_venn(df_mc)
mc.multi_chord_plot(df_mc, level = 3, transparency = 0.5)

Example 3 (1. a.):

from dataoutsider import multi_chord as mc
import pandas as pd

data = [['5', 'a'], ['5', 'b'], ['5', 'd'], ['2', 'b'], ['2', 'c'], ['3', 'b'], ['3', 'd'], ['4', 'c']]
df = pd.DataFrame(data, columns = ['inner', 'outer'])
lookup = mc.multi_chord_get_alias(df, 'outer', 'inner')
print(lookup) # use outer_ID (alias) to set manual order
df_mc = mc.multi_chord_alias(df, outer='outer', inner='inner', order='1,3,2,4', percent=33.) #, order = 'b,c,d,a'
mc.multi_chord_plot(df_mc, level = 3, transparency = 0.5)

Example 4 (1. b. version of Example 3):

from dataoutsider import multi_chord as mc
import pandas as pd

data = [['a,b,d', 1], ['b,c', 1], ['b,d', 1], ['c', 1]]
df = pd.DataFrame(data, columns = ['group', 'value'])
df_mc = mc.multi_chord_on_groups_alias(df, order = 'b,c,d,a', percent=33.)
mc.multi_chord_plot(df_mc, level = 3, transparency = 0.5)

Functions:

multi_chord_alias(df, outer, inner, percent=100., order=None, buffer = 1., elements_offset = 0.04, elements_height = 0.04, group_offset = 0., group_height = 0.04)

multi_chord_get_alias(df, outer, inner)

multi_chord_on_groups_alias(df_chord, percent=100., order=None, buffer = 1., elements_offset = 0.04, elements_height = 0.04, group_offset = 0., group_height = 0.04)

multi_chord_venn(df_mc)

multi_chord_plot(df_mc, level = None, transparency = 0.5)

• multi_chord_alias: Multi-Chord Diagram generator that encodes the supplied groups with an alias
• multi_chord_get_alias: retreive the alias for the supplied groups (can use to create a manual ordering)
• multi_chord_on_groups_alias: Multi-Chord Diagram generator based on pre-defined groupings
• multi_chord_venn: output for creating an Upset plot
• multi_chord_plot: generate a plot using Matplotlib

Parameters:

Parameter	Values	Description
df	(DataFrame)	Pandas DataFrame
outer	(string)	Column name of group
inner	(string)	Column name of group entity
percent	(float: 0-100)	Percent of full circle to draw within
order	(csv string)	Group order
buffer	(float)	Radial distance between groups
elements_offset	(float)	Distance offset of element legend for entities
elements_height	(float)	Height of element legend for entities
group_offset	(float)	Distance offset of group legend
group_height	(float)	Height of group legend
df_mc	(DataFrame)	Pandas DataFrame of multi-chord results
level	(int)	Bold chords by count of groups in chord (optional)
transparency	(float: 0-1)	Add color transparency (optional)

Pie-Tree Chart

Need to display hierarchical density as pie-shaped areas?

1. Select [1 to n] categorical columns from a dataframe to group by
2. Configure the chart ordering and hierarchy orientation
3. The pie-tree chart will create a hierarchical set of areas sized by the number of rows in each group at each level

Assumes 1 row = 1 unit

Example:

from dataoutsider import pie_tree as pt

df = pt.load_aircraft_df()
levels = ['Registrant', 'Aircraft', 'Engine', 'seats_bin']

inner_radius = 0.5
outer_radius = 2.0
starting_angle = 0.0
ending_angle = 360.0
point_resolution = 200
pie_tree_df = pt.pie_tree_calc(
    df, levels, 
    inner_radius, 
    outer_radius, 
    starting_angle, 
    ending_angle, 
    point_resolution)

pt.pie_tree_plot(pie_tree_df, 4)

Functions:

def pie_tree_calc(df, groupers, r1, r2, start_angle, end_angle, points, default_sort = False, default_sort_override = True, default_sort_override_reversed = False, all_vertical = False)

pie_tree_plot(pie_tree_df, level = 1, transparency = 0.5, line_level = 0)

• def pie_tree_calc: Pie-Tree (Pie-Map) Chart generator
• multi_chord_plot: generate a plot using Matplotlib

Parameters:

Parameter	Values	Description
df	(DataFrame)	Pandas DataFrame
groupers	list of (string)	Columns names in df
r1	(float)	Innder radius
r2	(float)	Outer radius (> inner radius)
start_angle	(float)	Angle to start drawing
end_angle	(float)	Angle to end drawing
points	(int)	Resolution for curve drawing
default_sort	True/False	Default: False, True: pandas sort, False: data sort
default_sort_override	True/False	Default: True, True: overrides default_sort
default_sort_override_reversed	True/False	Default: False, sort areas True: desc, False: asc
all_vertical	True/False	Default: False, True: break levels vertically, False: alternate
level	(int)	Hierarchy level to plot (optional)
transparency	(float: 0-1)	Add color transparency (optional)
line_level	(int)	Hierarchy level to bold (optional)

Variations:

Tableau users

Output:

• Polygon chart: Columns: [x], Rows: [y], Path: [path], Detail: varies by algorithm, use grouping fields as needed (see examples below)

Examples:

• Multi-Chord Diagram: Sink or Swim
• Pie Tree Chart: Takeoff

Check back soon for updates!