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')

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',
)

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',
)

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