fig, ax = plt.subplots(figsize=(2, 1))
stars = percentile_stars(ax, 85, background_color="#f0f0f0")
fig, ax = plt.subplots(figsize=(1, 1))
donut = progress_donut(ax, 73, textprops={"fontsize": 14})
plt.show()
对于待调用的函数，可以通过help函数查看这些函数的参数含义。
help(progress_donut)
Help on function progress_donut in module plottable.plots:
progress_donut(ax: matplotlib.axes._axes.Axes, val: float, radius: float = 0.45, color: str = None, background_color: str = None, width: float = 0.05, is_pct: bool = False, textprops: Dict[str, Any] = {}, formatter: Callable = None, **kwargs) -> List[matplotlib.patches.Wedge]
Plots a Progress Donut on the axes.
Args:
ax (matplotlib.axes.Axes): Axes
val (float): value
radius (float, optional):
radius of the progress donut. Defaults to 0.45.
color (str, optional):
color of the progress donut. Defaults to None.
background_color (str, optional):
background_color of the progress donut where the value is not reached. Defaults to None.
width (float, optional):
width of the donut wedge. Defaults to 0.05.
is_pct (bool, optional):
whether the value is given not as a decimal, but as a value between 0 and 100.
Defaults to False.
textprops (Dict[str, Any], optional):
textprops passed to ax.text. Defaults to {}.
formatter (Callable, optional):
a string formatter.
Can either be a string format, ie "{:2f}" for 2 decimal places.
Or a Callable that is applied to the value. Defaults to None.
Returns:
List[matplotlib.patches.Wedge]
通过plot_fn和plot_kw参数设置自定义绘图函数和函数输入参数，可以展示不同的绘图效果，如下所示：
from plottable.cmap import normed_cmap
import matplotlib.cm
d = pd.DataFrame(np.random.random((5, 5)), columns=["A", "B", "C", "D", "E"]).round(2)
fig, ax = plt.subplots(figsize=(6, 5))
tab = Table(d, textprops={"ha": "center"},
column_definitions=[ColumnDefinition(name="B", plot_fn=bar,plot_kw={'plot_bg_bar':True,'cmap':cmap,
'annotate':True, 'height':0.5}),
ColumnDefinition(name="D", plot_fn=progress_donut,plot_kw={'is_pct':True,})])
plt.show()
自定义文字格式
plottable提供了以下三个自定义函数来表示不同的文字格式：
- decimal_to_percent：将数值数据变为百分比
- tickcross：将数值格式化为✔或✖
- signed_integer：添加正负符号
我们可以通过ColumnDefinition的formatter来设置文字格式，如下所示：
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from plottable import ColumnDefinition, Table
from plottable.formatters import decimal_to_percent,tickcross,signed_integer
d =
pd.DataFrame(np.random.random((5, 5)), columns=["A", "B", "C", "D", "E"]).round(2)
fig, ax = plt.subplots(figsize=(6, 5))
tab = Table(d, column_definitions=[ColumnDefinition(name="A", formatter=decimal_to_percent),
ColumnDefinition(name="C", formatter=tickcross),
ColumnDefinition(name="D", formatter=signed_integer)])
plt.show()
此外，也可以自定义函数来设置文本格式，如下所示：
def setformat(x):
return "{:.2e}".format(x)
d = pd.DataFrame(np.random.random((5, 5)), columns=["A", "B", "C", "D", "E"]).round(2)
fig, ax = plt.subplots(figsize=(6, 5))
tab = Table(d, textprops={"ha": "center"},column_definitions=[ColumnDefinition(name="B", formatter=setformat),
ColumnDefinition(name="D", formatter=lambda x: round(x, 2))])
plt.show()
访问行列单元格
plottable提供了直接访问Table实例的某一行、某一列的方法，如下所示：
from plottable.cmap import normed_cmap
import matplotlib.cm
d = pd.DataFrame(np.random.random((5, 5)), columns=["A", "B", "C", "D", "E"]).round(2)
fig, ax = plt.subplots(figsize=(6, 5))
tab = Table(d)
tab.columns['A']
Column(cells=[TextCell(xy=(1, 0), content=0.0, row_idx=0, col_idx=1), TextCell(xy=(1, 1), content=0.09, row_idx=1, col_idx=1), TextCell(xy=(1, 2), content=0.95, row_idx=2, col_idx=1), TextCell(xy=(1, 3), content=0.08, row_idx=3, col_idx=1), TextCell(xy=(1, 4), content=0.92, row_idx=4, col_idx=1)], index=1)
tab.columns['B'].cells[1].content
tab.rows[1]
Row(cells=[TextCell(xy=(0, 1), content=1, row_idx=1, col_idx=0), TextCell(xy=(1, 1), content=0.09, row_idx=1, col_idx=1), TextCell(xy=(2, 1), content=0.04, row_idx=1, col_idx=2), TextCell(xy=(3, 1), content=0.42, row_idx=1, col_idx=3), TextCell(xy=(4, 1), content=0.64, row_idx=1, col_idx=4), TextCell(xy=(5, 1), content=0.26, row_idx=1, col_idx=5)], index=1)
tab.col_label_row
Row(cells=[TextCell(xy=(0, -1), content=index, row_idx=-1, col_idx=0), TextCell(xy=(1, -1), content=A, row_idx=-1, col_idx=1), TextCell(xy=(2, -1), content=B, row_idx=-1, col_idx=2), TextCell(xy=(3, -1), content=C, row_idx=-1, col_idx=3), TextCell(xy=(4, -1), content=D, row_idx=-1, col_idx=4), TextCell(xy=(5, -1), content=E, row_idx=-1, col_idx=5)], index=-1)
设置单元格样式
在上面的例子可以看到plottable直接访问表格行列对象，因此我们可以通过设置这些对象的绘图属性来直接更改其绘图效果或文字效果，所支持更改的属性如下：
- 单元格属性
- set_alpha：设置单元格的透明度。
- set_color：设置单元格的颜色。
- set_edgecolor：设置单元格边缘的颜色。
- set_facecolor：设置单元格内部的颜色。
- set_fill：设置单元格是否填充。
- set_hatch：设置单元格的填充图案。
- set_linestyle：设置单元格边缘线的样式。
- set_linewidth：设置单元格边缘线的宽度。
- 字体属性
- set_fontcolor：设置字体的颜色。
- set_fontfamily：设置字体的家族。
- set_fontsize：设置字体的大小。
- set_ha：设置文本的水平对齐方式。
- set_ma：设置文本的垂直对齐方式。
示例代码如下：
from plottable.cmap import normed_cmap
import matplotlib.cm
d = pd.DataFrame(np.random.random((5, 5)), columns=["A", "B", "C", "D", "E"]).round(2)
fig, ax = plt.subplots(figsize=(6, 5))
tab = Table(d)
tab.rows[1].set_facecolor("grey")
tab.columns['B'].set_fontcolor("red")
Column(cells=[TextCell(xy=(2, 0), content=0.38, row_idx=0, col_idx=2), TextCell(xy=(2, 1), content=0.69, row_idx=1, col_idx=2), TextCell(xy=(2, 2), content=0.15, row_idx=2, col_idx=2), TextCell(xy=(2, 3), content=0.74, row_idx=3, col_idx=2), TextCell(xy=(2, 4), content=0.41, row_idx=4, col_idx=2)], index=2)
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from plottable import Table
d = pd.DataFrame(np.random.random((5, 5)), columns=["A", "B", "C", "D", "E"]).round(2)
fig, ax = plt.subplots(figsize=(6, 3))
tab = Table(d, row_dividers=False, odd_row_color="#f0f0f0", even_row_color="#e0f6ff")
plt.show()
fig.savefig(
"table.jpg",dpi=300,bbox_inches='tight')
