Women’s World Cup Example

%load_ext autoreload
%autoreload 2

from pathlib import Path

import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib.colors import LinearSegmentedColormap

from plottable import ColumnDefinition, Table
from plottable.cmap import normed_cmap
from plottable.formatters import decimal_to_percent
from 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",
]

df = pd.read_csv(
    "data/wwc_forecasts.csv",
    usecols=cols,
)

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))

flag_paths = list(Path("country_flags").glob("*.png"))
country_to_flagpath = {p.stem: p for p in flag_paths}

df[["spi", "global_o", "global_d"]] = df[["spi", "global_o", "global_d"]].round(1)

df = df.rename(col_to_name, axis=1)
df = df.drop("Points", axis=1)
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	country_flags/USA.png	F	98.3	5.5	0.6	0.82956	0.17044	0.00000	1.0	0.78079	0.47307	0.35076	0.23618
France	country_flags/France.png	A	96.3	4.3	0.5	0.99483	0.00515	0.00002	1.0	0.78367	0.42052	0.30038	0.19428
Germany	country_flags/Germany.png	B	93.8	4.0	0.7	0.98483	0.01517	0.00000	1.0	0.89280	0.48039	0.27710	0.12256
Canada	country_flags/Canada.png	E	93.5	3.7	0.6	0.38830	0.61170	0.00000	1.0	0.59192	0.36140	0.20157	0.09031
England	country_flags/England.png	D	91.9	3.5	0.6	0.70570	0.29430	0.00000	1.0	0.68510	0.43053	0.16465	0.08003

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:])


col_defs = (
    [
        ColumnDefinition(
            name="Flag",
            title="",
            textprops={"ha": "center"},
            width=0.5,
            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",
                "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",
        ),
    ]
    + [
        ColumnDefinition(
            name=group_stage_cols[0],
            title=group_stage_cols[0].replace(" ", "\n", 1),
            formatter=decimal_to_percent,
            group="Group Stage Chances",
            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:]
    ]
    + [
        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:]
    ]
)

plt.rcParams["font.family"] = ["DejaVu Sans"]
plt.rcParams["savefig.bbox"] = "tight"

fig, ax = plt.subplots(figsize=(20, 22))

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"])

fig.savefig("images/wwc_table.png", facecolor=ax.get_facecolor(), dpi=200)