Simple funnel plots
Project description
Funnel plot
Simple funnel plots for visualising sub-group variance.
This package provides simple funnel plots in Python, using Matplotlib. This lets you quickly see whether sub-groups of a population are outliers compared to the full population.
Two methods are provided:
- parametric funnelplot which uses a standard distribution to estimate the intervals of the funnel (usually a normal distribution)
- bootstrap funnelplot which uses bootstrapped percentiles to estimate the intervals of the funnel
A utility function funnel()
to make it easy to plot data by grouping Pandas DataFrames in a Seaborn-like API is provided.
Example
Data of test performance for California schools from pydataset/Caschool
.
funnel(df=data("Caschool"), x="testscr", group="county")
Install
pip install funnelplot
Examples
Full caschool example
# load some example data
import pandas as pd
import matplotlib.pyplot as plt
from pydataset import data
from funnelplot.core import funnel
# create a suitable axis
fig,ax = plt.subplots(figsize=(4,6))
ax.set_frame_on(False)
# funnel plot, using 0.5% -> 99.5% interval
funnel(df=data("Caschool"), x="testscr", group="county", percentage=99.5, error_mode="data")
C:\Users\John\Dropbox\devel\funnelplot\funnelplot\core.py:14: RuntimeWarning: invalid value encountered in true_divide
return band / np.sqrt(group_size)
C:\Users\John\Dropbox\devel\funnelplot\funnelplot\core.py:14: RuntimeWarning: divide by zero encountered in true_divide
return band / np.sqrt(group_size)
# use bootstrap instead of normal fit
fig,ax = plt.subplots(figsize=(5,6))
ax.set_frame_on(False)
funnel(df=data("Caschool"), x='testscr', group="county", bootstrap_mode=True, error_mode="bootstrap")
Synthetic data example
## Synthetic data
import numpy as np
import random
random.seed(2020)
np.random.seed(2020)
groups = []
p_mean, p_std = 0, 1
# random groups, with different sizes, means and std. devs.
for i in range(25):
n_group = np.random.randint(1, 80)
g_std = np.random.uniform(0.1, 4.5)
g_mean = np.random.uniform(-1.9, 0.5)
groups.append(np.random.normal(p_mean + g_mean,
p_std + g_std,
n_group))
ax, fig = plt.subplots(figsize=(9, 4))
funnel_plot(
groups,
labels=[random.choice("abcdefg") * 4 for i in range(len(groups))],
percentage=95,
)
ax, fig = plt.subplots(figsize=(9, 4))
# bootstrap version, using medians instead of means
funnel_plot_bootstrap(
groups,
labels=[random.choice("abcdefg") * 4 for i in range(len(groups))],
percentage=95,
stat=np.median
)
API
-
funnel(df, x, group, bootstrap_mode=False)
show a DataFramedf
as a funnel plot, rendering columnx
and grouping the data bygroup
.Parameters: df: DataFrame The data to be shown. x: string, column name The column of the frame to render as datapoints. group: string, column name The column to group the frame by bootstrap_mode: boolean, optional (default False) If True, uses the funnel_plot_bootstrap() function; otherwise use the parameteric funnel_plot() function **kwargs: passed to funnel_plot() / funnel_plot_bootstrap()
-
funnel_plot(data_groups, ...)
plot a list of arrays as a funnel plot.Parameters: data_groups: list of 1D arrays a list of 1D arrays the individual groups to be analysed. ax: axis, optional an Matplotlib axis to draw onto dist: distribution function, like scipy.stats.norm(0,1) function to use to get the ppf and cdf of for plotting percentage: float, 0.0 -> 100.0 (default 95) percentage of interval enclosed (e.g. percentage=95 will enclose 2.5% to 97.5%) labels: list of strings, optional one label string per group, will be shown only for those groups that lie outside the funnel left_color: matplotlib color, optional (default C1) color to render points to the left of the funnel bounds (negative outliers) right_color: matplotlib color, optional (default C2) color to render points to the right of the funnel bounds (positive outliers) error_mode: string, optional (default "data") For each outlier group, can show: "data": original data values for that group as a dot plot "none": no error bars "bootstrap": 95% bootstrap intervals, as lines "ci": 95% CI intervals, as lines show_rug: boolean, optional (default False): If True, show a rug plot at the bottom of the graph, for the whole group population show_contours: boolean optional (default True) true if additional contours shown
-
funnel_plot_bootstrap(data_groups, ...)
plot a list of arrays as a funnel plot, using bootstrapped intervals instead of a parametric distribution.Parameters: data_groups: list of 1D arrays a list of 1D arrays the individual groups to be analysed. ax: axis, optional an Matplotlib axis to draw onto percentage: float, 0.0 -> 100.0 (default 95) percentage of interval enclosed (e.g. percentage=95 will enclose 2.5% to 97.5%) labels: list of strings, optional one label string per group, will be shown only for those groups that lie outside the funnel left_color: matplotlib color, optional (default C1) color to render points to the left of the funnel bounds (negative outliers) right_color: matplotlib color, optional (default C2) color to render points to the right of the funnel bounds (positive outliers) bootstrap_n: int, optional (default 1000) number of runs in the bootstrap error_mode: string, optional (default "data") For each outlier group, can show: "data": original data values for that group as a dot plot "none": no error bars "bootstrap": 95% bootstrap intervals, as lines "ci": 95% CI intervals, as lines show_rug: boolean, optional (default False): If True, show a rug plot at the bottom of the graph, for the whole group population show_contours: boolean optional (default True) true if additional contours shown stat: function like np.mean, optional statistic to use when plotting the funnel plot
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