[数据分析与可视化] 基于matplotlib和plottable库绘制精美表格

彭彭加油鸭 08-24 681

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plottable是一个Python库，用于在matplotlib中绘制精美定制的图形表格。plottable的官方仓库地址为：plottable。本文主要参考其官方文档，plottable的官方文档地址为：plottable-doc。plottable安装命令如下：

pip install plottable

本文所有代码见：Python-Study-Notes

# jupyter notebook环境去除warningimport warningswarnings.filterwarnings("ignore")import plottable# 打印plottable版本print('plottable version:', plottable.__version__)# 打印matplotlib版本import matplotlib as pltprint('matplotlib version:', plt.__version__)

plottable version: 0.1.5matplotlib version: 3.5.3

1 使用说明1.1 基础使用

下面的代码展示了一个简单的图形表格绘制示例，plottable提供了Table类以创建和展示图形表格。

import matplotlib.pyplot as pltimport numpy as npimport pandas as pdfrom plottable import Table# 生成一个包含随机数据的表格d = pd.DataFrame(np.random.random((5, 5)), columns=["A", "B", "C", "D", "E"]).round(2)fig, ax = plt.subplots(figsize=(6, 5))# 基于pandas表格数据创建和展示图形表格tab = Table(d)# 保存图片plt.savefig("table.jpg", dpi=300,bbox_inches='tight')plt.show()

对于plottable的Table类，其构造参数介绍如下：

df: pd.DataFrame, 要显示为表格的DataFrame对象ax: mpl.axes.Axes, 绘制表格的坐标轴对象，默认为Noneindex_col: str, DataFrame中的索引列名。默认为Nonecolumns: List[str], 哪些列用于绘图。为None表示使用所有列column_definitions: List[ColumnDefinition], 需要设置样式列的style定义类，默认为Nonetextprops: Dict[str, Any], 文本属性的字典，默认为空字典cell_kw: Dict[str, Any], 单元格属性的字典，默认为空字典col_label_cell_kw: Dict[str, Any], 列标签单元格属性的字典，默认为空字典col_label_divider: bool, 是否在列标签下方绘制分隔线，默认为True。footer_divider: bool, 是否在表格下方绘制分隔线，默认为False。row_dividers: bool, 是否显示行分隔线，默认为Truerow_divider_kw: Dict[str, Any], 行分隔线属性的字典，默认为空字典col_label_divider_kw: Dict[str, Any], 列标签分隔线属性的字典，默认为空字典footer_divider_kw: Dict[str, Any], 页脚分隔线属性的字典，默认为空字典column_border_kw: Dict[str, Any], 列边框属性的字典，默认为空字典even_row_color: str | Tuple, 偶数行单元格的填充颜色，默认为Noneodd_row_color: str | Tuple, 奇数行单元格的填充颜色，默认为None

在这些参数之中，控制表格绘图效果的参数有以下几类：

column_definitions：列的样式自定义textprops：文本的样样式自定义cell_kw：表格单元格的样式自定义其他设置参数的样式

在这些参数中，最重要的参数是column_definitions，因为column_definitions可以控制几乎所有的绘图效果。接下来本文主要对column_definitions的使用进行具体介绍。

1.2 列的样式自定义

plottable提供了ColumnDefinition类（别名ColDef）来自定义图形表格的单个列的样式。ColumnDefinition类的构造参数如下：

name: str，要设置绘图效果的列名title: str = None，用于覆盖列名的绘图标题width: float = 1，列的宽度，默认情况下各列的宽度为轴的宽度/列的总数textprops: Dict[str, Any] = field(default_factory=dict)，提供给每个文本单元格的文本属性formatter: Callable = None，用于格式化文本外观的可调用函数cmap: Callable = None，根据单元格的值返回颜色的可调用函数text_cmap: Callable = None，根据单元格的值返回颜色的可调用函数group: str = None，设置每个组都会在列标签上方显示的分组列标签plot_fn: Callable = None，一个可调用函数，将单元格的值作为输入，并在每个单元格上创建一个子图并绘制在其上要向其传递其他参数plot_kw: Dict[str, Any] = field(default_factory=dict)，提供给plot_fn的附加关键字参数border: str | List = None，绘制垂直边界线，可以是"left" / "l"、"right" / "r"或"both"

通过ColumnDefinition类来设置Table类的column_definitions参数，可以实现不同表格列样式的效果。如果是同时多个列的绘图效果，则需要使用[ColumnDefinition,ColumnDefinition]列表的形式。一些使用示例如下

设置列标题和列宽

import matplotlib.pyplot as pltimport numpy as npimport pandas as pdfrom plottable import ColumnDefinition, ColDef, Tabled = pd.DataFrame(np.random.random((5, 5)), columns=["A", "B", "C", "D", "E"]).round(2)fig, ax = plt.subplots(figsize=(6, 5))# name表示设置哪个列的样式tab = Table(d, column_definitions=[ColumnDefinition(name="A", title="Title A"),                                   ColumnDefinition(name="D", width=2)])plt.show()

设置列的文字属性和文本格式

from plottable.formatters import decimal_to_percentd = pd.DataFrame(np.random.random((5, 5)), columns=["A", "B", "C", "D", "E"]).round(2)fig, ax = plt.subplots(figsize=(6, 5))# index列的文字居中，加粗# 列A的文本数值改为百分制tab = Table(d, column_definitions=[ColumnDefinition(name="index", textprops={"ha": "center", "weight": "bold"}),                                   ColumnDefinition(name="A", formatter=decimal_to_percent)])plt.show()

设置列单元格背景色和字体颜色

from plottable.cmap import normed_cmapimport matplotlib.cmd = pd.DataFrame(np.random.random((5, 5)), columns=["A", "B", "C", "D", "E"]).round(2)fig, ax = plt.subplots(figsize=(6, 5))# cmap设置单元格背景色tab = Table(d, column_definitions=[ColumnDefinition(name="A", cmap=matplotlib.cm.tab20, text_cmap=matplotlib.cm.Reds),                                   ColumnDefinition(name="B", cmap=matplotlib.cm.tab20b),                                   ColumnDefinition(name="C", text_cmap=matplotlib.cm.tab20c)])plt.show()

设置列的分组名

from plottable.cmap import normed_cmapimport matplotlib.cmd = pd.DataFrame(np.random.random((5, 5)), columns=["A", "B", "C", "D", "E"]).round(2)fig, ax = plt.subplots(figsize=(6, 5))# 将列B和列C视为同一组，该组命名为group_nametab = Table(d, column_definitions=[ColumnDefinition(name="B", group="group_name"),                                    ColumnDefinition(name="C", group="group_name")])plt.show()

设置列边框

from plottable.cmap import normed_cmapimport matplotlib.cmd = pd.DataFrame(np.random.random((5, 5)), columns=["A", "B", "C", "D", "E"]).round(2)fig, ax = plt.subplots(figsize=(6, 5))# 将列B和列C视为同一组，该组命名为group_nametab = Table(d, column_definitions=[ColumnDefinition(name="A", border="l"),                                    ColumnDefinition(name="C",  border="both")])plt.show()

调用函数的使用

ColumnDefinition类的plot_fn和plot_kw参数提供了自定义函数实现表格效果绘制的功能。其中plot_fn表示待调用的函数，plot_kw表示待调用函数的输入参数。此外在plotable.plots预置了一些效果函数，我们可以参考这些效果函数定义自己的绘图函数。预置效果函数如下：

from pathlib import Pathimport matplotlibimport matplotlib.pyplot as pltimport numpy as npimport pandas as pdfrom matplotlib.colors import LinearSegmentedColormapfrom plottable import ColumnDefinition, Table# 调用预置绘图函数from plottable.plots import image,monochrome_image,circled_image,bar,percentile_bars,percentile_stars,progress_donutcmap = matplotlib.cm.tab20

# 柱状图绘制fig, ax = plt.subplots(figsize=(1, 1))# 0.7表示数值,lw边框线宽b = bar(ax, 0.7, plot_bg_bar=True, cmap=cmap, annotate=True, lw=2, height=0.35)plt.show()

# 星星百分比图fig, ax = plt.subplots(figsize=(2, 1))stars = percentile_stars(ax, 85, background_color="#f0f0f0")

# 圆环图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_cmapimport matplotlib.cmd = pd.DataFrame(np.random.random((5, 5)), columns=["A", "B", "C", "D", "E"]).round(2)fig, ax = plt.subplots(figsize=(6, 5))# plot_fn和plot_kwtab = 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 matplotlibimport matplotlib.pyplot as pltimport numpy as npimport pandas as pdfrom plottable import ColumnDefinition, Tablefrom plottable.formatters import decimal_to_percent,tickcross,signed_integerd = 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):    # 使用format格式函数    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()

1.3 行列自定义

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_cmapimport matplotlib.cmd = pd.DataFrame(np.random.random((5, 5)), columns=["A", "B", "C", "D", "E"]).round(2)fig, ax = plt.subplots(figsize=(6, 5))# 实例化Table对象tab = Table(d)

# 根据列名，提取整列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)

# 读取某列第1行的内容tab.columns['B'].cells[1].content

0.04

# 根据行索引，提取整行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)

# 提取表头列名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_cmapimport matplotlib.cmd = pd.DataFrame(np.random.random((5, 5)), columns=["A", "B", "C", "D", "E"]).round(2)fig, ax = plt.subplots(figsize=(6, 5))# 实例化Table对象tab = Table(d)# 设置行号为1的行的背景颜色tab.rows[1].set_facecolor("grey")# 设置B列的字体颜色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)

2 绘图实例2.1 多行样式设置

import matplotlib.pyplot as pltimport numpy as npimport pandas as pdfrom plottable import Tabled = pd.DataFrame(np.random.random((5, 5)), columns=["A", "B", "C", "D", "E"]).round(2)fig, ax = plt.subplots(figsize=(6, 3))# row_dividers显示行的分割线# odd_row_color奇数行颜色# even_row_color偶数行颜色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')

2.2 自定义单元格效果

import matplotlib.pyplot as pltimport numpy as npimport pandas as pdfrom matplotlib.colors import LinearSegmentedColormapfrom plottable import ColumnDefinition, Tablefrom plottable.formatters import decimal_to_percentfrom plottable.plots import bar, percentile_bars, percentile_stars, progress_donut# 自定义颜色cmap = LinearSegmentedColormap.from_list(    name="BuYl", colors=["#01a6ff", "#eafedb", "#fffdbb", "#ffc834"], N=256)fig, ax = plt.subplots(figsize=(6, 6))d = pd.DataFrame(np.random.random((5, 4)), columns=["A", "B", "C", "D"]).round(2)tab = Table(    d,    cell_kw={        "linewidth": 0,        "edgecolor": "k",    },    textprops={"ha": "center"},    column_definitions=[        ColumnDefinition("index", textprops={"ha": "left"}),        ColumnDefinition("A", plot_fn=percentile_bars, plot_kw={"is_pct": True}),        ColumnDefinition(            "B", width=1.5, plot_fn=percentile_stars, plot_kw={"is_pct": True}        ),        ColumnDefinition(            "C",            plot_fn=progress_donut,            plot_kw={                "is_pct": True,                "formatter": "{:.0%}"                },            ),        ColumnDefinition(            "D",            width=1.25,            plot_fn=bar,            plot_kw={                "cmap": cmap,                "plot_bg_bar": True,                "annotate": True,                "height": 0.5,                "lw": 0.5,                "formatter": decimal_to_percent,            },        ),    ],)plt.show()

2.3 热图

import matplotlib.pyplot as pltimport numpy as npimport pandas as pdfrom matplotlib.colors import LinearSegmentedColormap# ColDef是ColumnDefinition的别名from plottable import ColDef, Table# 自定义颜色cmap = LinearSegmentedColormap.from_list(    name="bugw", colors=["#ffffff", "#f2fbd2", "#c9ecb4", "#93d3ab", "#35b0ab"], N=256)

# 创建数据cities = [    "TORONTO",    "VANCOUVER",    "HALIFAX",    "CALGARY",    "OTTAWA",    "MONTREAL",    "WINNIPEG",    "EDMONTON",    "LONDON",    "ST. JONES",]months = [    "JAN",    "FEB",    "MAR",    "APR",    "MAY",    "JUN",    "JUL",    "AUG",    "SEP",    "OCT",    "NOV",    "DEC",]data = np.random.random((10, 12)) + np.abs(np.arange(12) - 5.5)data = (1 - data / (np.max(data)))data.shape

(10, 12)

# 绘图d = pd.DataFrame(data, columns=months, index=cities).round(2)fig, ax = plt.subplots(figsize=(14, 5))# 自定义各列的绘图效果column_definitions = [    ColDef(name, cmap=cmap, formatter=lambda x: "") for name in months] + [ColDef("index", title="", width=1.5, textprops={"ha": "right"})]tab = Table(    d,    column_definitions=column_definitions,    row_dividers=False,    col_label_divider=False,    textprops={"ha": "center", "fontname": "Roboto"},    # 设置各个单元格的效果    cell_kw={        "edgecolor": "black",        "linewidth": 0,    },)# 设置列标题文字和背景颜色tab.col_label_row.set_facecolor("white")tab.col_label_row.set_fontcolor("black")# 设置行标题文字和背景颜色tab.columns["index"].set_facecolor("black")tab.columns["index"].set_fontcolor("white")tab.columns["index"].set_linewidth(0)plt.show()

2.4 女子世界杯预测数据展示

step1 准备数据

下载示例数据，所有示例数据在plottable-example_notebooks。

# 下载数据集# !wget

from pathlib import Pathimport matplotlibimport matplotlib.pyplot as pltimport numpy as npimport pandas as pdfrom matplotlib.colors import LinearSegmentedColormapfrom plottable import ColumnDefinition, Tablefrom plottable.cmap import normed_cmapfrom plottable.formatters import decimal_to_percentfrom plottable.plots import circled_image # image

cols = [    "team",    "points",    "group",    "spi",    "global_o",    "global_d",    "group_1",    "group_2",    "group_3",    "make_round_of_16",    "make_quarters",    "make_semis",    "make_final",    "win_league",]# 读取数据df = pd.read_csv(    "data/wwc_forecasts.csv",    usecols=cols,)# 展示数据df.head()

team

group

spi

global_o

global_d

group_1

group_2

group_3

make_round_of_16

make_quarters

make_semis

make_final

win_league

points

USA

98.32748

5.52561

0.58179

0.82956

0.17044

0.00000

1.0

0.78079

0.47307

0.35076

0.23618

France

96.29671

4.31375

0.52137

0.99483

0.00515

0.00002

1.0

0.78367

0.42052

0.30038

0.19428

Germany

93.76549

3.96791

0.67818

0.98483

0.01517

0.00000

1.0

0.89280

0.48039

0.27710

0.12256

Canada

93.51599

3.67537

0.56980

0.38830

0.61170

0.00000

1.0

0.59192

0.36140

0.20157

0.09031

England

91.92311

3.51560

0.63717

0.70570

0.29430

0.00000

1.0

0.68510

0.43053

0.16465

0.08003

此外，我们需要准备每个国家对应的国旗图片，该数据也在plottable-example_notebooks下。

# 读取图片路径flag_paths = list(Path("data/country_flags").glob("*.png"))country_to_flagpath = {p.stem: p for p in flag_paths}

step2 数据处理

该步需要合并数据，将其转换为plottable可用的数据结构。

# 重置列名colnames = [    "Team",    "Points",    "Group",    "SPI",    "OFF",    "DEF",    "1st Place",    "2nd Place",    "3rd Place",    "Make Rd Of 16",    "Make Quarters",    "Make Semis",    "Make Finals",    "Win World Cup",]col_to_name = dict(zip(cols, colnames))col_to_name

{'team': 'Team', 'points': 'Points', 'group': 'Group', 'spi': 'SPI', 'global_o': 'OFF', 'global_d': 'DEF', 'group_1': '1st Place', 'group_2': '2nd Place', 'group_3': '3rd Place', 'make_round_of_16': 'Make Rd Of 16', 'make_quarters': 'Make Quarters', 'make_semis': 'Make Semis', 'make_final': 'Make Finals', 'win_league': 'Win World Cup'}

df[["spi", "global_o", "global_d"]] = df[["spi", "global_o", "global_d"]].round(1)df = df.rename(col_to_name, axis=1)# 删除Points列df = df.drop("Points", axis=1)# 插入列df.insert(0, "Flag", df["Team"].apply(lambda x: country_to_flagpath.get(x)))df = df.set_index("Team")df.head()

Flag

Group

SPI

OFF

DEF

1st Place

2nd Place

3rd Place

Make Rd Of 16

Make Quarters

Make Semis

Make Finals

Win World Cup

Team

USA

data/country_flags/USA.png

98.3

5.5

0.6

0.82956

0.17044

0.00000

1.0

0.78079

0.47307

0.35076

0.23618

France

data/country_flags/France.png

96.3

4.3

0.5

0.99483

0.00515

0.00002

1.0

0.78367

0.42052

0.30038

0.19428

Germany

data/country_flags/Germany.png

93.8

4.0

0.7

0.98483

0.01517

0.00000

1.0

0.89280

0.48039

0.27710

0.12256

Canada

data/country_flags/Canada.png

93.5

3.7

0.6

0.38830

0.61170

0.00000

1.0

0.59192

0.36140

0.20157

0.09031

England

data/country_flags/England.png

91.9

3.5

0.6

0.70570

0.29430

0.00000

1.0

0.68510

0.43053

0.16465

0.08003

step3 绘图

# 设置颜色cmap = LinearSegmentedColormap.from_list(    name="bugw", colors=["#ffffff", "#f2fbd2", "#c9ecb4", "#93d3ab", "#35b0ab"], N=256)

team_rating_cols = ["SPI", "OFF", "DEF"]group_stage_cols = ["1st Place", "2nd Place", "3rd Place"]knockout_stage_cols = list(df.columns[-5:])# 单独设置每一列的绘制参数col_defs = (    # 绘制第一部分效果    [        ColumnDefinition(            name="Flag",            title="",            textprops={"ha": "center"},            width=0.5,            # 设置自定义效果展示函数            plot_fn=circled_image,        ),        ColumnDefinition(            name="Team",            textprops={"ha": "left", "weight": "bold"},            width=1.5,        ),        ColumnDefinition(            name="Group",            textprops={"ha": "center"},            width=0.75,        ),        ColumnDefinition(            name="SPI",            group="Team Rating",            textprops={"ha": "center"},            width=0.75,        ),        ColumnDefinition(            name="OFF",            width=0.75,            textprops={                "ha": "center",                # 设置填充方式                "bbox": {"boxstyle": "circle", "pad": 0.35},            },            cmap=normed_cmap(df["OFF"], cmap=matplotlib.cm.PiYG, num_stds=2.5),            group="Team Rating",        ),        ColumnDefinition(            name="DEF",            width=0.75,            textprops={                "ha": "center",                "bbox": {"boxstyle": "circle", "pad": 0.35},            },            cmap=normed_cmap(df["DEF"], cmap=matplotlib.cm.PiYG_r, num_stds=2.5),            group="Team Rating",        ),    ]    # 绘制第二部分效果    + [        ColumnDefinition(            name=group_stage_cols[0],            title=group_stage_cols[0].replace(" ", "\n", 1),            formatter=decimal_to_percent,            group="Group Stage Chances",            # 设置边框            border="left",        )    ]    + [        ColumnDefinition(            name=col,            title=col.replace(" ", "\n", 1),            formatter=decimal_to_percent,            group="Group Stage Chances",        )        for col in group_stage_cols[1:]    ]    # 绘制第三部分效果    + [        ColumnDefinition(            name=knockout_stage_cols[0],            title=knockout_stage_cols[0].replace(" ", "\n", 1),            formatter=decimal_to_percent,            cmap=cmap,            group="Knockout Stage Chances",            border="left",        )    ]    + [        ColumnDefinition(            name=col,            title=col.replace(" ", "\n", 1),            formatter=decimal_to_percent,            cmap=cmap,            group="Knockout Stage Chances",        )        for col in knockout_stage_cols[1:]    ])

# 绘图fig, ax = plt.subplots(figsize=(18, 18))table = Table(    df,    column_definitions=col_defs,    row_dividers=True,    footer_divider=True,    ax=ax,    textprops={"fontsize": 14},    row_divider_kw={"linewidth": 1, "linestyle": (0, (1, 5))},    col_label_divider_kw={"linewidth": 1, "linestyle": "-"},    column_border_kw={"linewidth": 1, "linestyle": "-"},).autoset_fontcolors(colnames=["OFF", "DEF"])plt.show()# 保存图片fig.savefig("wwc_table.jpg", facecolor=ax.get_facecolor(), dpi=300,bbox_inches='tight')

2.5 德甲积分排名榜展示

step1 准备数据

from pathlib import Pathimport matplotlib.pyplot as pltimport numpy as npimport pandas as pdfrom plottable import ColDef, Tablefrom plottable.plots import image

# 下载联赛数据# !wget  !wget

# 数据地址FIVETHIRTYEIGHT_URLS = {    "SPI_MATCHES": ";,    "SPI_MATCHES_LATEST": ";,}# 读取数据# df = pd.read_csv(FIVETHIRTYEIGHT_URLS["SPI_MATCHES_LATEST"])df = pd.read_csv("data/spi_matches_latest.csv")df.head()

season

date

league_id

league

team1

team2

spi1

spi2

prob1

prob2

...

importance1

importance2

score1

score2

xg1

xg2

nsxg1

nsxg2

adj_score1

adj_score2

2019

2019-03-01

1979

Chinese Super League

Shandong Luneng

Guizhou Renhe

48.22

37.83

0.5755

0.1740

...

45.9

22.1

1.0

0.0

1.39

0.26

2.05

0.54

1.05

0.00

2019

2019-03-01

1979

Chinese Super League

Shanghai Greenland

Shanghai SIPG

39.81

60.08

0.2387

0.5203

...

25.6

63.4

0.0

4.0

0.57

2.76

0.80

1.50

0.00

3.26

2019

2019-03-01

1979

Chinese Super League

Guangzhou Evergrande

Tianjin Quanujian

65.59

39.99

0.7832

0.0673

...

77.1

28.8

3.0

0.0

0.49

0.45

1.05

0.75

3.15

0.00

2019

2019-03-01

1979

Chinese Super League

Wuhan Zall

Beijing Guoan

32.25

54.82

0.2276

0.5226

...

35.8

58.9

0.0

1.0

1.12

0.97

1.51

0.94

0.00

1.05

2019

2019-03-01

1979

Chinese Super League

Chongqing Lifan

Guangzhou RF

38.24

40.45

0.4403

0.2932

...

26.2

21.3

2.0

2.77

3.17

1.05

2.08

2.10

5 rows × 23 columns

# 筛选德甲联赛数据,并删除为空数据bl = df.loc[df.league == "German Bundesliga"].dropna()bl.head()

season

date

league_id

league

team1

team2

spi1

spi2

prob1

prob2

...

importance1

importance2

score1

score2

xg1

xg2

nsxg1

nsxg2

adj_score1

adj_score2

497

2022

2022-08-05

1845

German Bundesliga

Eintracht Frankfurt

Bayern Munich

68.47

91.75

0.1350

0.6796

...

32.6

71.9

1.0

6.0

0.83

4.50

0.65

2.72

1.05

5.96

514

2022

2022-08-06

1845

German Bundesliga

VfL Bochum

Mainz

60.73

68.88

0.3568

0.3629

...

33.5

34.5

1.0

2.0

1.00

1.62

0.96

0.86

1.05

2.10

515

2022

2022-08-06

1845

German Bundesliga

Borussia Monchengladbach

TSG Hoffenheim

69.38

66.77

0.4872

0.2742

...

40.2

33.3

3.0

1.0

1.86

0.10

2.51

0.31

2.36

1.05

516

2022

2022-08-06

1845

German Bundesliga

VfL Wolfsburg

Werder Bremen

68.18

59.82

0.5319

0.2014

...

30.2

33.3

2.0

0.81

0.97

1.07

1.25

2.10

517

2022

2022-08-06

1845

German Bundesliga

1. FC Union Berlin

Hertha Berlin

69.98

59.70

0.5479

0.1860

...

34.9

33.0

3.0

1.0

1.25

0.40

1.66

0.36

3.15

1.05

5 rows × 23 columns

step2 数据处理

# 统计得分def add_points(df: pd.DataFrame) -> pd.DataFrame:    # 三元表达式    # df["score1"] > df["score2"]，则返回3    # np.where(df["score1"] == df["score2"]，则返回1    # 否则为0    df["pts_home"] = np.where(        df["score1"] > df["score2"], 3, np.where(df["score1"] == df["score2"], 1, 0)    )    df["pts_away"] = np.where(        df["score1"] < df["score2"], 3, np.where(df["score1"] == df["score2"], 1, 0)    )        return df# 统计得分数据bl = add_points(bl)

# 总得分、总进球数、总助攻数和总黄牌数# 以下代码先分别统计team1和team2的得分数据，然后将两组数据相加perform = (    bl.groupby("team1")[[        "pts_home",        "score1",        "score2",        "xg1",        "xg2",    ]]    .sum()    .set_axis(        [            "pts",            "gf",            "ga",            "xgf",            "xga",        ],        axis=1,    )    .add(        bl.groupby("team2")[[            "pts_away",            "score2",            "score1",            "xg2",            "xg1",        ]]        .sum()        .set_axis(            [                "pts",                "gf",                "ga",                "xgf",                "xga",            ],            axis=1,        )    ))# 由于python和pandas版本问题，如果上面的代码出问题，则使用下面代码# t1= bl.groupby("team1")[["pts_home","score1","score2","xg1","xg2", ]]# t1 = t1.sum()# t1.set_axis( ["pts","gf","ga","xgf","xga", ], axis=1,)# t2 = bl.groupby("team1")[["pts_home","score1","score2","xg1","xg2", ]]# t2 = t2.sum()# t2.set_axis( ["pts","gf","ga","xgf","xga", ], axis=1,)# perform = (t1.add(t2))perform.shape

(18, 5)

# 汇总得分表现数据perform.index.name = "team"perform["gd"] = perform["gf"] - perform["ga"]perform = perform[    [        "pts",        "gd",        "gf",        "ga",        "xgf",        "xga",    ]]perform["games"] = bl.groupby("team1").size().add(bl.groupby("team2").size())perform.head()

pts

xgf

xga

games

team

1. FC Union Berlin

13.0

51.0

38.0

35.93

43.06

Bayer Leverkusen

8.0

57.0

49.0

53.62

48.20

Bayern Munich

54.0

92.0

38.0

84.93

40.12

Borussia Dortmund

39.0

83.0

44.0

75.96

42.69

Borussia Monchengladbach

-3.0

52.0

55.0

53.05

58.88

# 统计各队的胜负数据def get_wins_draws_losses(games: pd.DataFrame) -> pd.DataFrame:    return (        games.rename({"pts_home": "pts", "team1": "team"}, axis=1)        .groupby("team")["pts"]        .value_counts()        .add(            games.rename({"pts_away": "pts", "team2": "team"}, axis=1)            .groupby("team")["pts"]            .value_counts(),            fill_value=0,        )        .astype(int)        .rename("count")        .reset_index(level=1)        .pivot(columns="pts", values="count")        .rename({0: "L", 1: "D", 3: "W"}, axis=1)[["W", "D", "L"]]    )wins_draws_losses = get_wins_draws_losses(bl)wins_draws_losses.head()

pts

team

1. FC Union Berlin

Bayer Leverkusen

Bayern Munich

Borussia Dortmund

Borussia Monchengladbach

# 合并得分和胜负数据perform = pd.concat([perform, wins_draws_losses], axis=1)perform.head()

pts

xgf

xga

games

team

1. FC Union Berlin

13.0

51.0

38.0

35.93

43.06

Bayer Leverkusen

8.0

57.0

49.0

53.62

48.20

Bayern Munich

54.0

92.0

38.0

84.93

40.12

Borussia Dortmund

39.0

83.0

44.0

75.96

42.69

Borussia Monchengladbach

-3.0

52.0

55.0

53.05

58.88

step3 映射队标图片

队标图片地址为：plottable-example_notebooks

# 创建队名和队标的索引数据club_logo_path = Path("data/bundesliga_crests_22_23")club_logo_files = list(club_logo_path.glob("*.png"))club_logos_paths = {f.stem: f for f in club_logo_files}

perform = perform.reset_index()# 添加新列perform.insert(0, "crest", perform["team"])perform["crest"] = perform["crest"].replace(club_logos_paths)# 数据排序perform = perform.sort_values(by=["pts", "gd", "gf"], ascending=False)for colname in ["gd", "gf", "ga"]:    perform[colname] = perform[colname].astype("int32")perform["goal_difference"] = perform["gf"].astype(str) + ":" + perform["ga"].astype(str)# 添加排名perform["rank"] = list(range(1, 19))perform.head()

crest

team

pts

xgf

xga

games

goal_difference

rank

data/bundesliga_crests_22_23/Bayern Munich.png

Bayern Munich

84.93

40.12