今明兩天我們將嘗試複刻美國總統Donald Trump於2025年4月2日，在Truth Social上所公布的各國關稅表。

今天將先使用Polars進行資料處理後，再發揮創意使用偏向繪圖的Plotnine製表。

本日大綱如下：

0. 本日引入模組及準備工作
1. 建構dataframe
2. 製作表格
3. 執行程式

以下為本日作品預覽：

0. 本日引入模組及準備工作

import polars as pl
from matplotlib.figure import Figure
from plotnine import (
    aes,
    element_blank,
    element_rect,
    element_text,
    geom_segment,
    geom_text,
    ggplot,
    position_nudge,
    scale_color_identity,
    scale_size_identity,
    scale_y_discrete,
    theme,
    theme_void,
    watermark,
)

logo_filename = "logo_resized.png"

data = {
    "country": [
        "China",
        "European Union",
        "Vietnam",
        "Taiwan",
        "Japan",
        "India",
        "South Korea",
        "Thailand",
        "Switzerland",
        "Indonesia",
        "Malaysia",
        "Cambodia",
        "United Kingdom",
        "South Africa",
        "Brazil",
        "Bangladesh",
        "Singapore",
        "Israel",
        "Philippines",
        "Chile",
        "Australia",
        "Pakistan",
        "Turkey",
        "Sri Lanka",
        "Colombia",
    ],
    "tariffs_charged": [
        "67%",
        "39%",
        "90%",
        "64%",
        "46%",
        "52%",
        "50%",
        "72%",
        "61%",
        "64%",
        "47%",
        "97%",
        "10%",
        "60%",
        "10%",
        "74%",
        "10%",
        "33%",
        "34%",
        "10%",
        "10%",
        "58%",
        "10%",
        "88%",
        "10%",
    ],
    "reciprocal_tariffs": [
        "34%",
        "20%",
        "46%",
        "32%",
        "24%",
        "26%",
        "25%",
        "36%",
        "31%",
        "32%",
        "24%",
        "49%",
        "10%",
        "30%",
        "10%",
        "37%",
        "10%",
        "17%",
        "17%",
        "10%",
        "10%",
        "29%",
        "10%",
        "44%",
        "10%",
    ],
}

country, tariffs_charged, reciprocal_tariffs = data.keys()

dark_navy_blue = "#0B162A"  # background
light_blue = "#B5D3E7"  # row
white = "#FFFFFF"  # row
yellow = "#F6D588"  # "reciprocal_tariffs" column
gold = "#FFF8DE"  # logo

fontname_georgia = "Georgia"  # title
fontname_roboto = "Roboto"  # body

# column width
x_col1_start, x_col1_end = 5, 52.5
x_col2_start, x_col2_end = 60, 75
x_col3_start, x_col3_end = 82.5, 97.5

# x-position for body text
x_col1_text = 5
x_col2_text = x_col2_start + (x_col2_end - x_col2_start) / 3 + 1
x_col3_text = x_col3_start + (x_col3_end - x_col3_start) / 3 + 1

1. 建構兩個dataframe

1.1 df dataframe

將建構df dataframe的步驟封裝在tweak_df()中。

def tweak_df() -> pl.DataFrame:
    return (
        pl.DataFrame(data)
        .with_row_index()
        .with_columns(
            pl.col(country).cast(pl.Categorical),
            pl.when(pl.col("index").mod(2).eq(0))
            .then(pl.lit(light_blue))
            .otherwise(pl.lit(white))
            .alias("color_mod"),
        )
    )

分段說明如下：

• 以pl.DataFrame建構dataframe。
• 呼叫pl.DataFrame.with_row_index()生成索引列。
• 呼叫pl.with_columns()：
  • 將「"country"」列轉為pl.Categorical型別。
  • 新增「"color_mod"」列，依照其行索引值，分別指定為「"light_blue"」及「"white"」。

1.2 textdata_df dataframe

將建構textdata_df dataframe的步驟封裝在get_textdata_df()中，做為繪製標題及各列列名之用。

def get_textdata_df(
    x_ref: float = 0.0, y_ref: float = 0.0
) -> pl.DataFrame:
    title_fontsize = 16
    title_fontweight = "bold"
    heading_fontsize = 8
    heading_fontweight = "bold"
    subheading_fontsize = 6
    subheading_fontweight = "normal"

    textdata_df = pl.DataFrame(
        {
            "label": [
                "Reciprocal Tariffs",  # title
                "Country",  # col1
                "Tariffs Charged",  # col2
                "to the U.S.A.",
                "Including",
                "Currency Manipulation",
                "and Trade Barriers",
                "U.S.A. Discounted",  # col3
                "Reciprocal Tariffs",
            ],
            "x": [
                x_ref + 34.0,
                x_ref + 29.5,
                x_ref + 67.5,
                x_ref + 67.5,
                x_ref + 67.5,
                x_ref + 67.5,
                x_ref + 67.5,
                x_ref + 89.5,
                x_ref + 89.5,
            ],
            "y": [
                y_ref + 27,
                y_ref + 25.5,
                y_ref + 26.8,
                y_ref + 26.4,
                y_ref + 26.1,
                y_ref + 25.8,
                y_ref + 25.5,
                y_ref + 26.0,
                y_ref + 25.6,
            ],
            "color": [
                gold,
                white,
                white,
                white,
                white,
                white,
                white,
                white,
                white,
            ],
            "fontsize": [
                title_fontsize,
                heading_fontsize,
                heading_fontsize,
                heading_fontsize,
                subheading_fontsize,
                subheading_fontsize,
                subheading_fontsize,
                heading_fontsize,
                heading_fontsize,
            ],
            "fontweight": [
                title_fontweight,
                heading_fontweight,
                heading_fontweight,
                heading_fontweight,
                subheading_fontweight,
                subheading_fontweight,
                subheading_fontweight,
                heading_fontweight,
                heading_fontweight,
            ],
            "fontname": [
                fontname_georgia,
                fontname_georgia,
                fontname_georgia,
                fontname_georgia,
                fontname_georgia,
                fontname_georgia,
                fontname_georgia,
                fontname_georgia,
                fontname_georgia,
            ],
        }
    )
    return textdata_df

2. 製作表格

將製表步驟封裝為plot_g()、themify()及add_ax_text()三個函數：

• plot_g()進行主要繪圖工作。
• themify()設定主題及微調圖表參數。

2.1 plot_g()

def plot_g() -> ggplot:
    geom_segment_props = {"size": 8, "lineend": "round"}

    geom_text_props = {
        "ha": "left",
        "va": "center",
        "position": position_nudge(y=-0.08),
        "size": 10,
        "fontweight": "bold",
    }

    return (
        ggplot(data=df, mapping=aes(y=country, yend=country))
        # col1 segment
        + geom_segment(
            mapping=aes(
                x=x_col1_start, xend=x_col1_end, color="color_mod"
            ),
            **geom_segment_props,
        )
        # col2 segment
        + geom_segment(
            mapping=aes(
                x=x_col2_start, xend=x_col2_end, color="color_mod"
            ),
            **geom_segment_props,
        )
        # col3 segment
        + geom_segment(
            mapping=aes(x=x_col3_start, xend=x_col3_end),
            color=yellow,
            **geom_segment_props,
        )
        # col1 text
        + geom_text(aes(x=x_col1_text, label=country), **geom_text_props)
        # col2 text
        + geom_text(
            aes(x=x_col2_text, label=tariffs_charged), **geom_text_props
        )
        # col3 text
        + geom_text(
            aes(x=x_col3_text, label=reciprocal_tariffs), **geom_text_props
        )
        # using "color_mod" column directly
        + scale_color_identity()
        # expand extra space
        + scale_y_discrete(
            limits=df.select(country).reverse().to_series().to_list(),
            expand=(0.02, 0, 0, 1.5),
        )
        # title and headers
        + geom_text(
            data=get_textdata_df(),
            mapping=aes(
                x="x",
                y="y",
                label="label",
                color="color",
                size="fontsize",
                fontweight="fontweight",
                fontname="fontname",
            ),
            va="bottom",
            ha="center",
        )
        # using "size" column directly
        + scale_size_identity()
        # logo
        + watermark(logo_filename, 100, 2235)
    )

分段說明如下：

• 呼叫ggplot()生成ggplot物件。指定data=為df，並使用aes將「"country"」列映射給y=及yend=後傳給mapping=。
• 呼叫三次geom_segment()建立帶有圓角的橫條。值得注意的是第三列中的color=，因為固定為黃色，所以不能置於aes中。如果將color=置於aes中，Plotnine會認為是希望針對yellow，也就是「"#F6D588"」列進行mapping。但df中並沒有「"#F6D588"」列存在，故會報錯。
• 呼叫三次geom_text()來填入各列資料。此處巧妙地將各列列名置入aes中做為label=。
• 呼叫scale_color_identity()來告知Plotnine直接使用aes中的color=，也就是「"color_mod"」列中的顏色，不必再次mapping。
• 呼叫scale_y_discrete()：
  • 由於y=也就是「"country"」列為pl.Categorical型別，所以可以透過指定limits=來控制其顯示順序。
  • 透過expand=來控制scaling的大小。
• 呼叫geom_text()，指定data=為get_textdata_df()，並利用aes設定映射關係後傳給mapping=。
• 呼叫scale_size_identity()來告知Plotnine直接aes中的size=，也就是「"fontsize"」列中的顏色，不必再次mapping。
• 呼叫watermark()來嵌入logo。

2.2 themify()

呼叫theme_void()做為基本主題後，再呼叫theme()進行細部微調：

def themify(p: ggplot) -> Figure:
    return (
        p
        + theme_void()
        + theme(
            legend_position="none",  # turns off the legend
            axis_text_x=element_blank(),
            axis_text_y=element_blank(),
            axis_title_x=element_blank(),
            axis_title_y=element_blank(),
            panel_background=element_rect(fill=dark_navy_blue),
            plot_background=element_rect(fill=dark_navy_blue),
            text=element_text(family=fontname_roboto),
            dpi=300,
            figure_size=(6, 8),
        )
    ).draw(show=False)

3. 執行程式

實際執行本日程式：

• 呼叫tweak_df()生成df dataframe。
• 呼叫plot_g()進行繪圖。
• 呼叫themify()設定主題。

df = tweak_df()
p = plot_g()
fig = themify(p)
fig

參考資料

個人部落格文章：Clone the Reciprocal Tariffs Table Using Plotnine。

Code

本日程式碼傳送門。