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 dataimportpandasaspdimportmatplotlib.pyplotaspltfrompydatasetimportdatafromfunnelplot.coreimportfunnel# create a suitable axisfig,ax=plt.subplots(figsize=(4,6))
ax.set_frame_on(False)
# funnel plot, using 0.5% -> 99.5% intervalfunnel(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 fitfig,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 dataimportnumpyasnpimportrandomrandom.seed(2020)
np.random.seed(2020)
groups= []
p_mean, p_std=0, 1# random groups, with different sizes, means and std. devs.foriinrange(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") *4foriinrange(len(groups))],
percentage=95,
)

ax, fig=plt.subplots(figsize=(9, 4))
# bootstrap version, using medians instead of meansfunnel_plot_bootstrap(
groups,
labels=[random.choice("abcdefg") *4foriinrange(len(groups))],
percentage=95,
stat=np.median
)

API

• funnel(df, x, group, bootstrap_mode=False) show a DataFrame df as a funnel plot, rendering column x and grouping the data by group.

   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