confidence-interval-tools 0.1.6

A small package for calculating drawing confidence intervals.

Confidence Interval Tools

A small python library for calculating and drawing confidence intervals.

1. Requirements
2. Status
3. Documentation
   • Installation
   • Usage
   • Classes and methods
4. Roadmap
5. Contribution

Requirements

Python^3.12   ## might also work with lower versions, but untested  
pandas^2.2  
matplotlib^3.9  
seaborn^0.13  
scipy^1.14  
numpy^1.26

Status

[!WARNING]
The project is in a very early development phase. Expect important changes between updates.

Latest version: 0.1.6

Updated: August 2024

Changes since previous version:

• implemented of the ste_ci method for calculating the standard error.
• added documentation

Documentation

[!NOTE]
Last documentation update: August 2024

Installation

This project is published in PyPi under the name confidence_interval_tools.

With pip

pip install confidence_interval_tools

For updating to the latest available version:

pip install -U confidence_interval_tools

To force a specific version (for example 0.2.0):

pip install --force-reinstall -v "confidence_interval_tools==0.2.0"

With poetry

poetry add confidence_interval_tools@latest

For updating:

poetry update

In a Jupyter notebook (notice the exclamation mark)

!pip install -U confidence_interval_tools

Usage

Methods and classes can be imported directly, for example:

from confidence_interval_tools import CI_Drawer

## [...]

a = CI_Drawer(data=data, x="x", y="y", kind=["bars", "area"], ci_type="std")

However, for the sake of readability and traceability, it might be better to import (and alias) the whole package at once:

import confidence_interval_tools as cit

## [...]

a = cit.CI_Drawer(data=data, x="x", y="y", kind=["bars", "area"], ci_type="std")

As this package aims to be a complement to Seaborn and Matplotlib, we recommend reading the respective documentation of these two packages:

• Seaborn
• Matplotlib

And additionally:

• Pandas
• Numpy
• Scipy

Classes and methods

Main module

CI_Drawer (>=0.1.5)

"A class for drawing a confidence interval in whatever way you prefer."

Arguments:

• data (pandas.DataFrame, optional): a pandas dataframe containing the necessary information to draw confidence intervals. If data is provided, x, y, lower, upper, and std can be given as column names.
• x (str | data type, optional): column name or list / array / series with information about the horizontal coordinate of the data. If not provided, it will be assumed to be [1, 2, 3, 4, ...].
• y (str | data type, optional): column name or list / array / series with information about the vertical coordinate of the data. Usually required unless lower and upper are provided directly.
• lower (str | data type, optional): bypass the internal calculation by directly providing values for the lower bound of each confidence interval.
• upper (str | data type, optional): bypass the internal calculation by directly providing values for the upper bound of each confidence interval.
• kind ("lines" | "bars" | "area" | "scatterplot" | "none", optional): a selection of what kind of confidence interval is to be drawn. The default is "none" (does nothing). Several kinds can be seleted at once and passed as a list or tuple, e.g., ["area", "bars"].
• ci_type ("std" | "ste", optional): the type of calculation used for the confidence intervals. Currently available types are: standard deviation (std), standard error (ste). The default is set to "std".
• std (str | data type, optional): bypass the internal calculation for the standard deviation by providing pre-calculated values.
• std_multiplier (numerical type, optional): constant to be used as a multiplier of the standard deviation or standard error when a normal approximation is done. Currently used for "std" and "ste" CI types. Default is 1.96 (i.e., alpha risk level of 5%, two-sided).
• orientation ("horizontal" | "vertical", optional): orientation of the confidence interval, i.e., whether a confidence interval should be calculated for each value of x ("vertical"), or each value of y ("horizontal").
• CI lines options:
  • draw_lines (bool, optional): manual toggle for the drawing of CI lines. Same as using kind="lines".
  • draw_lower_line (bool, optional): manual toggle for the drawing of a line for the lower bound of the confidence interval.
  • draw_upper_line (bool, optional): manual toggle for the drawing of a line for the upper bound of the confidence interval.
  • lines_style (matplotlib linestyles type, optional): style for the CI lines. Follows the same syntax as Matplotlib linestyles. Default: "solid".
  • lower_line_style (matplotlib linestyles type, optional): specify a different linestyle for the lower bound. Cf: lines_style.
  • upper_line_style (matplotlib linestyles type, optional): specify a different linestyle for the upper bound. Cf: lines_style.
  • lines_color (matplotlib colors type, optional): colo(u)r of the CI lines. See the lst of available Matplotlib named colo(u)rs. Default: "black".
  • lower_line_color (matplotlib colors type, optional): specify a different colo(u)r for the lower bound. Cf: lines_color.
  • upper_line_color (matplotlib colors type, optional): specify a different colo(u)r for the upper bound. Cf: lines_color.
  • lines_linewidth (numerical type, optional): linewidth for the CI lines. Default: 1 (pt).
  • lower_line_linewidth (numerical type, optional): specify the linewidth for the lower bound. Cf: lines_linewidth.
  • upper_line_linewidth (numerical type, optional): specify the linewidth for the upper bound. Cf: lines_linewidth.
  • lines_alpha (numerical type, optional): opacity / transparency value (a.k.a. "alpha channel") for the CI lines. Must be a decimal value between 0 and 1. Default: 0.8.
  • lower_line_alpha (numerical type, optional): specify the opacity for the lower bound. Cf: lines_alpha.
  • upper_line_alpha (numerical type, optional): specify the opacity for the upper bound. Cf: lines_alpha.
• CI bars options:
  • draw_bars (bool, optional): manual toggle for the drawing of CI bars. Same as using kind="bars".
  • draw_bar_ends (bool, optional): whether to draw the perpendicular ends of the CI bars. Default: True when draw_bars is activated. Can be "abused" to draw the ends without drawing the actual body of the CI bars.
  • draw_lower_bar_end (bool, optional): specify whether to draw the perpendicular ends of the CI bars for the lower bound.
  • draw_upper_bar_end (bool, optional): specify whether to draw the perpendicular ends of the CI bars for the upper bound.
  • bars_style (matplotlib linestyles type, optional): linestyle for the CI bars. See Matplotlib linestyles. Default: "solid".
  • bars_color (matplotlib colors type, optional): colo(u)r of the CI bars. See Matplotlib named colo(u)rs. Default: "black".
  • bars_linewidth (numerical type, optional): linewidth for the CI bars. Default: 1 (pt).
  • bars_alpha (numerical type, optional): opacity of the CI bars. Default: 1.
  • bar_ends_style (matplotlib linestyles type, optional): specify the linestyle used for the perpendicular ends of the CI bars. The default is "solid" and is independent from the linestyle of the main body of the bars.
  • bar_ends_color (matplotlib colors type, optional): specify the colo(u)r of both ends of the CI bars. CF: bars_color.
  • lower_bar_end_color (matplotlib colors type, optional): specify a colo(u)r for the lower bound.
  • upper_bar_end_color (matplotlib colors type, optional): specify a colo(u)r for the upper bound.
  • bar_ends_width (numerical type, optional): specify a fixed width for the perpendicular ends of the CI bars. Currently relative to the scale of the data, might change in the future (see roadmap). Takes priority over the bar_ends_ratio if specified.
  • bar_ends_ratio (numerical type, optional): width of the perpendicular ends of the CI bars, expressed as a proportion of the average distance between two adjacent x (or y) coordinates. Values greater than 1 should result in overlaps between adjacent CI bars, which is usually not a desired behaviour. Default: 0.3.
  • hide_bars_center_portion (bool, optional): when set to True, the middle part of the CI bars will not be drawn, so as to avoid obscuring the plot (for example if a central tendency was already plotted). Default: False.
  • bars_center_portion_length (numerical type, optional): length of the central portion (i.e., the "middle part) of the CI bars. Currently relative to the scale of the data. Takes priority over bars_center_portion_ratio when specified. Used with hide_bars_center_portion.
  • bars_center_portion_ratio (numerical type, optional): length of the central portion of the CI bars, expressed as a proportion of the bars' length. Used with hide_bars_center_portion. Default: 0.5.
• CI area options:
  • fill_area (bool, optional): manual toggle for the drawing of the confidence interval as a shaded area. Same as using kind="area".
  • fill_color (matplotlib colors type, optional): colo(u)r used for the shading of the CI area. See Matplotlib named colo(u)rs. Default: "lavender".
  • fill_alpha (numerical type, optional): opacity of the shaded area. Default: 0.4.
• options for the scatterplot of the lowers and upper bounds:
  • plot_limits (bool, optional): manual toggle for plotting the lower and upper bounds of the confidence intervals as separate datapoints. Same as using kind="scatterplot".
  • plot_lower_limit (bool, optional): whether to plot the lower bound.
  • plot_upper_limit (bool, optional): whether to plot the upper bound.
  • plot_marker (matplotlib markers type, optional): marker to be used when plotting the lower and upper bounds. See the list of Matplotlib markers. Default: see lower_plot_marker and upper_plot_marker.
  • lower_plot_marker (matplotlib markers type, optional): marker to be used when plotting the lower bound. Cf: plot_marker.
  • upper_plot_marker (matplotlib markers type, optional): marker to be used when plotting the upper bound. Cf: plot_marker.
  • plot_color (matplotlib colors type, optional): colo(u)r of the markers. See Matplotlib named colo(u)rs. Default: "black".
  • lower_plot_color (matplotlib colors type, optional): specify a colo(u)r for the lower bound. Cf: plot_color.
  • upper_plot_color (matplotlib colors type, optional): specify a colo(u)r for the upper bound. Cf: plot_color.
  • plot_alpha (numerical type, optional): opacity of the markers used when plotting the lower and upper bounds. Default: 0.8.
  • lower_plot_alpha (numerical type, optional): specify the opacity for the lower bound.
  • upper_plot_alpha (numerical type, optional): specify the opacity for the upper bound.
  • plot_size (numerical type, optional): size of the markers (in pt square) when plotting the lower and upper bounds. Default: None (let Seaborn / Matplotlib decide).
  • lower_plot_size (numerical type, optional): specify a size for the markers of the lower bound. Cf: plot_size.
  • upper_plot_size (numerical type, optional): specify a size for the markers of the upper bound. Cf: plot_size.
• ax (matplotlib.axes.Axes, optional): a matplotlib Axes object to be used for drawing the confidence intervals. Defaut: last used object, identified with matplotlib.pyplot.gca().

Returns: a new instance of the CI_Drawer class.

Instance attributes and methods:

• .data (pandas.DataFrame): a copy of the dataframe passed as argument.
• .x, .y (pandas.Series): a copy of the x and y data passed as arguments.
• .lower, .upper (pandas.Series): series containing the (calculated or specified) lower bounds and upper bounds.
• .unique_x, .unique_y (pandas.Series): series containing the unique values filtered from x and y respectively.
• .std (pandas.Series): series containing the (calculated or specified) standard deviation for each unique value of x (vertical CI) or y (horizontal CI).
• .mean (pandas.Series): series containing the calculated mean for each unique value of x or y.
• .median (pandas.Series): series containing the calculated median for each unique value of or y.
• .q1, .q3 (pandas.Series): series containing the calculated first and third quartiles for each unique value of x or y.
• .as_datafrae() (pandas.DataFrame): returns a dataframe containing most of the information listed above.
• .params (dict): dictionary containing most of the parameters used for deciding what to draw and how to draw.
• .draw() (None): method for drawing (or, redrawing) the confidence intervals with the given parameters.
• .help() (None): method to return a help message in an interpreter or a jupyter notebook. Not yet implemented.

std_ci (>=0.1.5)

"Upper and lower bounds of a CI based on standard deviation (normal approximation around mean)"

Arguments:

• v (data type): a one-dimensional data vector (for example, all y values for a unique value of x)
• std_multiplier (numerical type): a number by which the standard deviation is multiplied to yield the confidence interval.

Returns: a tuple, of the form (<lower bound>, <upper bound>).

ste_ci (>=0.1.6)

"Upper and lower bounds of a CI based on standard error (normal approximation around mean)"

Arguments:

• v (data type): a one-dimensional data vector (for example, all y values for a unique value of x)
• ste_multiplier (numerical type): a number by which the standard error is multiplied to yield the confidence interval.

Returns: a tuple, of the form (<lower bound>, <upper bound>).

vectorized_to_df (>=0.1.5)

"General utility function, to return a dataframe calculated with several vectors, from a function accepting a single vector"

Arguments:

• func (callable): a callable function (such as std_ci or ste_ci), accepting a vector (pandas Series) as argument and returning a tuple.
• *args, **kwargs: any other positional or keyword argument to be passed to the function.

Returns: a pandas.DataFrame built from the output of func for each individual vector (stacked vertically).

Roadmap

Features to be added, changes to be implemented in future versions:

• Add more methods for the calculation of confidence intervals
• Ensure the support of Matplotlib's parametrized linestyles.
• Create and expose a submodule for drawing methods (e.g., lines, bars, etc.)
• Add the capability to draw and configure boxes (as in boxplots)? ... See solutions by Seaborn and Matplotlib for now.
• Write a more detailed, wiki-like documentation, either on Gitlab or a separate website like readthedocs.com
• Support passing a configuration dictionary to avoid re-typing all arguments every time.
• Add support for providing a nominal alpha risk level, in complement of the std_multiplier argument.
• Add a clipping option, for bounded scales.
• Add a rounding option, for categorical scales.
• Express the width of bar_ends_width in pt or similarly convenient unit of measurement, independent of the scale of the data.
• Add support for individual values of bars_center_portion_length and bars_center_portion_ratio.
• Improve loading time if possible.
• Maybe more to come...

Contribution

Feel free to contribute, report bugs, suggest features, etc., on GitLab.