Bar

A bar chart uses vertical bars to show comparisons between categories of data. Vertical bar charts illustrate sizes of data using different bar heights.

The Method To Use

The method is s.plt.bar()

It must contain the following input variables:

order: int
x: str
data: Union[str, DataFrame, List[Dict]]

And accepts the following input variables as optional:

y: Optional[List[str]]
x_axis_name: Optional[str]
y_axis_name: Optional[str]
title: Optional[str]
rows_size: Optional[int]
cols_size: Optional[int]
padding: Optional[List[int]]
show_values: Optional[List[str]]
option_modifications: Optional[Dict]

Examples

1. Default Configuration

By default the chart will have rows_size=2 and cols_size=12, also it will have no padding applied. An example on how to use the default configuration is:

data = [
    {'date': dt.date(2021, 1, 1), 'x': 1, 'y': 10},
    {'date': dt.date(2021, 1, 2), 'x': 2, 'y': 8},
    {'date': dt.date(2021, 1, 3), 'x': 3, 'y': 10},
    {'date': dt.date(2021, 1, 4), 'x': 4, 'y': 2},
    {'date': dt.date(2021, 1, 5), 'x': 5, 'y': 14},
]

s.plt.bar(data=data, order=0, x='date')

Bar chart in the default configuration

2. Customization And Context

It is possible to personalize the title of the chart, name of each axis, the legend, changing size and add padding. One example could be obtained using:

data = [
    {'date': dt.date(2021, 1, 1), 'store 1': 5, 'store 2': 10},
    {'date': dt.date(2021, 2, 1), 'store 1': 6, 'store 2': 8},
    {'date': dt.date(2021, 3, 1), 'store 1': 4, 'store 2': 10},
    {'date': dt.date(2021, 4, 1), 'store 1': 7, 'store 2': 2},
    {'date': dt.date(2021, 5, 1), 'store 1': 3, 'store 2': 14},
    {'date': dt.date(2021, 6, 1), 'store 1': 7, 'store 2': 12},
]

s.plt.bar(
    data=data, order=0,
    x='date', rows_size=2, cols_size=10,
    padding='0,1,0,1',
    x_axis_name='date',
    y_axis_name='revenue / store',
    title='Revenue per store, first half - 2021',
)

Bar chart with padding title and axis names.

And if you want to show just one variable, for example showing the average height of a thousand trees in the year 2021, you can provide the following data:

data = [
    {'date': 'Jan', 'Height': 3},
    {'date': 'Feb', 'Height': 3.5},
    {'date': 'Mar', 'Height': 4.2},
    {'date': 'Apr', 'Height': 5},
    {'date': 'May', 'Height': 5.8},
    {'date': 'Jun', 'Height': 6.3},
]

s.plt.bar(
    data=data, order=0, x='date', rows_size=2, cols_size=10,
    padding='0,1,0,1', x_axis_name='month', y_axis_name='height (m)',
    title='Evolution: Average Height of 1000 Trees - 2021',
)

Bar chart for one variable, fully customized.

Interesting Usages

• Visualizing survey results: Bar charts are a great way to display the results of a survey. For example, you could use a bar chart to show the percentage of people who answered "yes," "no," or "maybe" to a particular question.

  

  Survey results
• Comparing data: Bar charts are also useful for comparing data. For example, you could use a bar chart to compare the sales of different products over a given time period.

  

  Comparative sales of products

Variants

By setting the parameter variant to the following values the appearance of the chart can be changed:

Clean

variant="clean"

Minimal

variant="minimal"

Thin

variant="thin"

Clean Thin

variant="clean thin"

Minimal Thin

variant="minimal thin"

Shadow

variant="shadow"

Clean Shadow

variant="clean shadow"

Minimal Shadow

variant="minimal shadow"

Featured Content

Changing the Menu Path The menu_path can be modified.

Using the Grid

It is possible to use any number of rows.