"""Automatic plot selection."""
from .barchart import barchart
from .boxplot import boxplot
from .common import col_cardinality
from .heatmap import heatmap
from .histogram import histogram
from .scatterplot import scatterplot
from .signatures import multivariate_recipe
from .stripplot import stripplot
from autosig import autosig
from numbers import Number
import pandas as pd
def bin_midpoints(xx, n):
"""Divide a vector in equal-width bins and return the midpoints if it is Numeric, otherwise the vector itself.
Parameters
----------
xx : Iterable
The vector to be binned or other iterable to return as-is.
n : integer
Number of bins.
Returns
-------
list or same as xx
The midpoints or xx itself.
"""
return (
list(map(lambda x: x.mid, list(pd.cut(xx, n))))
if issubclass(type(xx.iloc[0]), Number)
else xx
)
def overlap(data):
"""Find how dense a set of points is in relation to plotting them.
Parameters
----------
data : DataFrame
The data whose density needs to be evaluated.
Returns
-------
integer
The maximum number of data points having identical coordinates on categorical dimensions or falling within the same bin when Numeric, after an equal-width binning operation into 100 bins (100 is somewhat arbitrary, experience based).
"""
return data.apply(bin_midpoints, n=100).groupby(list(data.columns)).size().max()
def is_cat(xx):
"""Whether a list or vector is categorical (non Numeric).
Parameters
----------
xx : list or vector
A list or vector.
Returns
-------
bool
Whether `xx` is categorical.
"""
return not issubclass(type(xx.iloc[0]), Number)
[docs]@multivariate_recipe
def autoplot(data=None, columns=None, group_by=None, height=600, width=800):
"""Automatically choose and produce a statistical graphics based on up to three columns of data."""
assert group_by is None, "long data not supported yet"
vars_n = len(columns)
assert vars_n <= 3, "Only up to three vars supported at this time"
data = data[columns]
columns = sorted(columns, key=lambda x: -len(data[x].unique()))
y, x, z, *_ = columns + 2 * [None]
overlap_deg = overlap(data[columns[: min(vars_n, 2)]])
max_overlap = 10
high_overlap = overlap_deg >= max_overlap
no_overlap = overlap_deg == 1
low_overlap = 1 < overlap_deg < max_overlap
cat_vars_n = sum(map(lambda col: is_cat(data[col]), columns))
numeric_vars_n = vars_n - cat_vars_n
chart_type_selection = [
(scatterplot, numeric_vars_n >= 2 and not high_overlap),
(heatmap, numeric_vars_n >= 2 and high_overlap),
(stripplot, numeric_vars_n == 1 and not high_overlap),
(barchart, numeric_vars_n == 0),
(histogram, numeric_vars_n == 1 and cat_vars_n == 0 and high_overlap),
(boxplot, numeric_vars_n == 1 and cat_vars_n >= 1 and high_overlap),
]
chart_type = list(filter(lambda x: x[1], chart_type_selection))
assert len(chart_type) == 1
chart_type = chart_type[0][0]
use_facet = cat_vars_n >= 2 or (
cat_vars_n == 1 and numeric_vars_n == 2 and not no_overlap
)
use_color = vars_n == 3 and not (use_facet and chart_type is scatterplot)
use_opacity = (chart_type in (scatterplot, stripplot) and low_overlap) or (
chart_type is heatmap and high_overlap and numeric_vars_n == 3
) # heat
if chart_type is barchart:
args = dict(x=y, y="count()", height=height, width=width)
if use_facet:
args.update(x=x, color=x, width=width // col_cardinality(data, y))
facet_args = dict(column=y)
if z is not None:
args.update(height=height // col_cardinality(data, z))
facet_args.update(row=z)
chart = barchart(data, **args)
if use_facet:
chart = chart.facet(**facet_args)
if chart_type is boxplot:
chart = boxplot(
data,
columns=y,
group_by=x,
color=use_facet, # this is a redundant use of color, different from necessary uses. Encodes x again
height=height,
width=width // col_cardinality(data, z),
)
if use_facet:
chart = chart.facet(column=z)
if chart_type is heatmap:
args = dict(color="count()")
if use_opacity:
args.update(color=z, opacity="count()")
chart = heatmap(
data,
x=x,
y=y,
aggregate="average" if use_opacity else "count",
height=height // col_cardinality(data, z, use_facet),
width=width // col_cardinality(data, z, use_facet),
**args
)
if use_facet:
chart = chart.facet(row=z)
if chart_type is histogram:
chart = histogram(data, column=y, height=height, width=width)
if chart_type is stripplot:
chart = stripplot(
data,
columns=y,
group_by=x,
color=x if use_facet else None,
opacity=1 / overlap_deg if use_opacity else 1,
height=height,
width=width // col_cardinality(data, z, use_facet),
)
if use_facet:
chart = chart.facet(column=z)
if chart_type is scatterplot:
chart = scatterplot(
data,
x=x,
y=y,
color=z if use_color else None,
opacity=1 / overlap_deg if use_opacity else 1,
height=height // col_cardinality(data, z, use_facet),
width=width // col_cardinality(data, z, use_facet),
)
if use_facet:
chart = chart.facet(row=z)
return chart