boxplot

Boxplots in fivecentplots are modeled after the “Variability Chart” in JMP which provides convenient, multi-level group labels automatically along the x-axis. Data can be broken into multiple subsets for easy visualization by simply listing the DataFrame column names of interest in the groups keyword. At a minimum, the boxplot function requires the following keywords:

• df: a pandas DataFrame

• y: the name of the DataFrame column containing the y-axis data

Setup

Imports

%load_ext autoreload
%autoreload 2
%matplotlib inline
import fivecentplots as fcp
import pandas as pd
import numpy as np
import os, sys, pdb
osjoin = os.path.join
st = pdb.set_trace

Sample data

Read some fake boxplot data

df = pd.read_csv(osjoin(os.path.dirname(fcp.__file__), 'tests', 'fake_data_box.csv'))
df.head()

	Batch	Sample	Region	Value	ID
0	101	1	Alpha123	3.5	ID701223A
1	101	1	Alpha123	2.1	ID7700-1222B
2	101	1	Alpha123	3.3	ID701223A
3	101	1	Alpha123	3.2	ID7700-1222B
4	101	1	Alpha123	4.0	ID701223A

Set theme

Optionally set the design theme

#fcp.set_theme('gray')
#fcp.set_theme('white')

Other

SHOW = False

Groups

Consider the following boxplot of made-up data:

fcp.boxplot(df=df, y='Value', show=SHOW, tick_labels_minor=True, grid_minor=True)

Single group

Rather than lumping the data into a single box, we can separate them into categories to get more information. First, set a single group column of “Batch”:

fcp.boxplot(df=df, y='Value', groups='Batch', show=SHOW)

Multiple groups

We can dive deeper by specifying more than one value for groups:

fcp.boxplot(df=df, y='Value', groups=['Batch', 'Sample'], show=SHOW)

Groups + legend

Boxplots also support legending for another level of visualization:

fcp.boxplot(df=df, y='Value', groups=['Batch', 'Sample'], legend='Region', show=SHOW)

Grid plots

Like the plot function, boxplots can be broken into subplots based on “row” and/or “col” values or “wrap” values.

Column plots

fcp.boxplot(df=df, y='Value', groups=['Batch', 'Sample'], col='Region', show=SHOW, ax_size=[300, 300])

Row plots

fcp.boxplot(df=df, y='Value', groups=['Batch', 'Sample'], row='Region', show=SHOW, ax_size=[300, 300])

Wrap plots

fcp.boxplot(df=df, y='Value', groups=['Sample', 'Region'], wrap='Batch', show=SHOW, ax_size=[300, 300])

Alternatively, we can wrap multiple y column values and create a unique subplot for each column:

# Make a new y column
df['Value*2'] = 2*df['Value']

# Plot
fcp.boxplot(df=df, y=['Value', 'Value*2'], groups=['Batch', 'Sample', 'Region'], wrap='y', show=SHOW,
            ax_size=[300, 300])

Or if we disable y-axis range sharing:

fcp.boxplot(df=df, y=['Value', 'Value*2'], groups=['Batch', 'Sample', 'Region'], wrap='y', show=SHOW,
            ax_size=[300, 300], share_y=False)

Other options

Violins

We can also plot distributions with violin plots that show kernal density estimates of the data. By default, these violin plots also contain a small boxes with whiskers to indicate Q1, Q3, 1.5 * IQR and the median of the distribution. Discrete data points are disabled by default but can be turned on with the keyword violin_markers=True (box style shamelessly appropriated from seaborn).

fcp.boxplot(df=df, y='Value', groups=['Batch', 'Sample'], show=SHOW, violin=True)

We can change the style of the violin density profiles and the associated boxplot using keywords starting with violin. Note that the standard box styling attributes are ignored when adding the violin plot. The reason for this is to make it possilbe to maintain different default settings for regular box plots and violin plots in the same theme file.

fcp.boxplot(df=df, y='Value', groups=['Batch', 'Sample'], show=SHOW, violin=True,
            violin_fill_color='#eaef1a', violin_fill_alpha=1, violin_edge_color='#555555', violin_edge_width=2,
            violin_box_color='#ffffff', violin_whisker_color='#ff0000',
            violin_median_marker='+', violin_median_color='#00ffff', violin_median_size=10)

We can also disable the box overlay on the violin plot as follows:

fcp.boxplot(df=df, y='Value', groups=['Batch', 'Sample'], show=SHOW, violin=True, violin_box_on=False, violin_markers=True, jitter=False)

Stat line

In addition to displaying boxes with a median line and interquartile ranges, a connecting line can be drawn between boxes at some statistical value. By default, the line connects the mean value of each distribution but other DataFrame stat values can be selected. The stat line accepts the typical styling keywords of any line object with the prefix box_stat_line_ (i.e., box_stat_line_color or box_stat_line_width)

Mean

fcp.boxplot(df=df, y='Value', groups=['Batch', 'Sample'], show=SHOW, box_stat_line='mean', ax_size=[300, 300])

Median

fcp.boxplot(df=df, y='Value', groups=['Batch', 'Sample'], show=SHOW, box_stat_line='median', ax_size=[300, 300])

Std dev

fcp.boxplot(df=df, y='Value', groups=['Batch', 'Sample'], show=SHOW, box_stat_line='std', ax_size=[300, 300])

Dividers

Using the keyword box_divider, lines can be drawn on the boxplot to visually segrate main groups of boxes. These lines are enabled by default but can be turned off easily:

fcp.boxplot(df=df, y='Value', groups=['Batch', 'Sample'], show=SHOW, box_divider=False, ax_size=[300, 300])

Range lines

Because outlier points by definition fall outside of the span of the box, we can draw lines that span the entire range of the data. This is particularly useful to indicate when there are data points that fall outside of the limits of the y-axis. These lines are enabled by default but can be disabled or styled through keywords with the prefix box_range_lines_:

fcp.boxplot(df=df, y='Value', groups=['Batch', 'Sample'], show=SHOW, box_range_lines=False, ax_size=[300, 300])