bar

This section describes various options available for bar charts in fivecentplots

See the full API

Setup

Import packages:

import fivecentplots as fcp
import pandas as pd
from pathlib import Path

Read some fake bar chart data with 3 replicates of pH data for various liquids measured at multiple temperatures:

df = pd.read_csv(Path(fcp.__file__).parent / 'test_data/fake_data_bar.csv')
df.groupby(['Liquid', 'Measurement', 'T [C]']).count()

			pH
Liquid	Measurement	T [C]
Battery acid	A	25	1
		100	1
	B	25	1
		100	1
	C	25	1
		100	1
Bottled water	A	25	1
		100	1
	B	25	1
		100	1
	C	25	1
		100	1
Coke	A	25	1
		100	1
	B	25	1
		100	1
	C	25	1
		100	1
Lemon juice	A	25	1
		100	1
	B	25	1
		100	1
	C	25	1
		100	1
Orange juice	A	25	1
		100	1
	B	25	1
		100	1
	C	25	1
		100	1

Optionally set the design theme (skipping here and using default):

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

Bar plot types

Vertical bars

We can plot a basic vertical bar chart as follows:

fcp.bar(df, x='Liquid', y='pH', filter='Measurement=="A" & T [C]==25', tick_labels_major_x_rotation=90)

Note

filter applied to show only measurement “A” at 25C

Note

tick labels are rotated manually 90° for readability (unlike fcp.heatmap, fcp.bar does not apply auto-rotation for long tick labels

Horizontal bars

We can plot the same data with horizontal bars by adding the keyword horizontal=True.

Note

For convenience, fivecentplots often supports two versions of plot-specific keywords for the same feature (in this case, horizontal or bar_horizontal). For inline plotting the shorter version may be preferred but for cases where kwargs are listed in theme files or when precision is required to avoid inadvertently applying a particular kwarg to other Elements in the plot, the long version is recommended.

fcp.bar(df, x='Liquid', y='pH', filter='Measurement=="A" & T [C]==25', horizontal=True)

Error bars

Next we group all measured data by liquid, regardless of replicate or temperature, into a single bars (this means each bar represents the mean value of a given liquid across all data sets). We add error bars to indicate the standard deviation across measurements with the error_bars keyword:

fcp.bar(df, x='Liquid', y='pH', tick_labels_major_x_rotation=90, error_bars=True)

Grouping

Like all other plots in fivecentplots, bar plots can grouped in various ways to provide further insight into the data set. This includes:

• legend

• stacking

• row

• col

• wrap

keyword options.

Legend

When a legend is applied, each measurement set is displayed in its own bar:

fcp.bar(df, x='Liquid', y='pH', tick_labels_major_x_rotation=90, legend='Measurement')

Stacked

Alternatively, we can stack the measurements to give a cumulative bar using stacked=True (which makes absolutely no sense for this example but whatever):

fcp.bar(df, x='Liquid', y='pH', tick_labels_major_x_rotation=90, stacked=True, legend='Measurement')

Row/column plot

We can separate different conditions using row/col plots as shown below. (Negative pH values?? These data are truly fake!)

fcp.bar(df, x='Liquid', y='pH', tick_labels_major_x_rotation=90, col='Measurement', row='T [C]', ax_hlines=0, ax_size=[300, 300])

Wrap plot

We can use a wrap plot to visualize:

fcp.bar(df, x='Liquid', y='pH', tick_labels_major_x_rotation=90, wrap='Measurement', ax_size=[300, 300])

Rolling mean

fivecentplots can automatically apply a rolling mean to bar chart data using rolling_mean, to which we pass a positive integer for the window size of the mean calculation. For this example, we read in some different data.

df2 = pd.read_csv(Path(fcp.__file__).parent / 'test_data/real_data_bar.csv')

Default line styling

By default, the rolling mean curve will have markers disabled and will use the second color in the default color list (red) and a line width of 2:

fcp.bar(df2, x='date', y='cases', ax_size=[800, 500], tick_labels_major_x_rotation=90, rolling_mean=14)

Custom line style

The rolling mean curve style can be easily modified using standard kwargs for line plots with a rolling_mean_ prefix. Markers can also be enabled using the markers element object:

fcp.bar(df2, x='date', y='cases', ax_size=[800, 500], tick_labels_major_x_rotation=90, rolling_mean=14,
        bar_fill_color='#aaaaaa', rolling_mean_line_color='#000000', rolling_mean_line_width = 1, markers=True, marker_size=4)

Styles

Bar plots have many style options. A few are demonstrated below.

Color by bar

color_by_bar gives each bar in the chart a different color. These colors can be set manually or the fcp.DEFAULT_COLORS scheme is used by default.

fcp.bar(df, x='Liquid', y='pH', filter='Measurement=="A" & T [C]==25', tick_labels_major_x_rotation=90, color_by_bar=True)

fcp.bar(df, x='Liquid', y='pH', filter='Measurement=="A" & T [C]==25', tick_labels_major_x_rotation=90,
        color_by_bar=True, colors=['#FF0000', '#FF00FF', '#FFFF00', '#00FF00', '#0000FF'])

Other color kwargs

fcp.bar(df, x='Liquid', y='pH', tick_labels_major_x_rotation=90, error_bars=True,
        bar_error_color='#FF0000', bar_fill_alpha=1, bar_edge_color='#000000', bar_edge_width=3)