CuteCharts: A Lightweight Python Visualization Library – Installation and Basic Examples

The article presents cutecharts , a lightweight Python visualization library created by the author of pyecharts . It highlights the library’s support for five basic chart types—Bar, Line, Pie, Radar, and Scatter—as well as composite Page charts.

Installation

Install the package via pip:

<code>pip install cutecharts</code>

Line Chart – Basic Example

<code>def set_options(self, labels: Iterable, x_label: str = "", y_label: str = "", y_tick_count: int = 3, legend_pos: str = "upLeft", colors: Optional[Iterable] = None, font_family: Optional[str] = None):
    """
    :param labels: X 坐标轴标签数据
    :param x_label: X 坐标轴名称
    :param y_label: Y 坐标轴名称
    :param y_tick_count: Y 轴刻度分割段数
    :param legend_pos: 图例位置，有 "upLeft", "upRight", "downLeft", "downRight" 可选
    :param colors: label 颜色数组
    :param font_family: CSS font-family
    """

def add_series(self, name: str, data: Iterable):
    """
    :param name: series 名称
    :param data: series 数据列表
    """
</code>

<code># 虚假数据
x_data = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu', 'OnePlus']
y_data_1 = [57, 134, 137, 129, 145, 60, 49]
y_data_2 = [114, 55, 27, 101, 125, 27, 105]

chart = Line("Mobile phone sales")
chart.set_options(labels=x_data, x_label="Brand", y_label="Sales")
chart.add_series("series-A", y_data_1)
chart.add_series("series-B", y_data_2)
chart.render_notebook()
</code>

Modifying Legend Position

<code># 虚假数据
x_data = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu', 'OnePlus']
y_data_1 = [57, 134, 137, 129, 145, 60, 49]
y_data_2 = [114, 55, 27, 101, 125, 27, 105]

chart = Line("Mobile phone sales")
chart.set_options(labels=x_data, x_label="Brand", y_label="Sales", legend_pos="upRight")
chart.add_series("series-A", y_data_1)
chart.add_series("series-B", y_data_2)
chart.render_notebook()
</code>

Bar Chart – Basic Example

<code># 虚假数据
x_data = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu', 'OnePlus']
y_data = [57, 134, 137, 129, 145, 60, 49]

chart = Bar("Mobile phone sales")
chart.set_options(labels=x_data, x_label="Brand", y_label="Sales", colors=Faker.colors)
chart.add_series("series-A", y_data)
chart.render_notebook()
</code>

Pie Chart – Basic Example

<code># 虚假数据
x_data = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu', 'OnePlus']
y_data = [57, 134, 137, 129, 145, 60, 49]

chart = Pie("Mobile phone sales")
chart.set_options(labels=x_data, colors=Faker.colors)
chart.add_series(y_data)
chart.render_notebook()
</code>

Modifying Inner Radius

<code># 虚假数据
x_data = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu', 'OnePlus']
y_data = [57, 134, 137, 129, 145, 60, 49]

chart = Pie("Mobile phone sales")
chart.set_options(labels=x_data, inner_radius=0, colors=Faker.colors)
chart.add_series(y_data)
chart.render_notebook()
</code>

Radar Chart – Basic Example

<code># 虚假数据
x_data = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu', 'OnePlus']
y_data_1 = [57, 134, 137, 129, 145, 60, 49]
y_data_2 = [114, 55, 27, 101, 125, 27, 105]

chart = Radar("Mobile phone sales")
chart.set_options(labels=x_data, is_show_legend=True, colors=Faker.colors)
chart.add_series("series-A", y_data_1)
chart.add_series("series-B", y_data_2)
chart.render_notebook()
</code>

Scatter Chart – Basic Example

<code># 随机生成数据
data_1 = [(random.randint(0, 100), random.randint(0, 100)) for _ in range(100)]
data_2 = [(random.randint(0, 100), random.randint(0, 100)) for _ in range(100)]

chart = Scatter("random dot")
chart.set_options(x_label = "I'm x-label", y_label = "I'm x-yabel", x_tick_count = 3, y_tick_count = 3, is_show_line = False, dot_size = 1, legend_pos = "upLeft", colors=Faker.colors)
chart.add_series("series-A", data_1)
chart.add_series("series-A", data_2)
chart.render_notebook()
</code>

Scatter Chart – Points Connected by Lines

<code># 随机生成数据
data_1 = [(random.randint(0, 100), random.randint(0, 100)) for _ in range(10)]
data_2 = [(random.randint(0, 100), random.randint(0, 100)) for _ in range(10)]

chart = Scatter("random dot")
chart.set_options(x_label = "I'm x-label", y_label = "I'm x-yabel", x_tick_count = 3, y_tick_count = 3, is_show_line = True, dot_size = 1, legend_pos = "upLeft", colors=Faker.colors)
chart.add_series("series-A", data_1)
chart.add_series("series-A", data_2)
chart.render_notebook()
</code>

Composite Page Chart

<code># 虚假数据
x_data = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu', 'OnePlus']
y_data = [57, 134, 137, 129, 145, 60, 49]

chart_1 = Pie("Mobile phone sales")
chart_1.set_options(labels=x_data, inner_radius=0.6, colors=Faker.colors)
chart_1.add_series(y_data)

chart_2 = Bar("Mobile phone sales")
chart_2.set_options(labels=x_data, x_label="Brand", y_label="Sales", colors=Faker.colors)
chart_2.add_series("series-A", y_data)

page = Page()
page.add(chart_1, chart_2)
page.render_notebook()
</code>

The article concludes with visual outputs for each chart type, demonstrating the ease of generating and customizing charts using cutecharts in a Jupyter notebook environment.