Publication ready table with plottable

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 PIL import Image

from plottable import ColumnDefinition, Table
from plottable.cmap import normed_cmap
from plottable.plots import circled_image # image

df = pd.read_csv('cities.csv')

#Reindex the column to fit the two main ranked categories(Quality of life and Environment)
columns_titles = ["City", "Location", "Rank", "Index", "Stability","Healthcare", "Education", "Culture_Environment","Infrastructure"]
df=df.reindex(columns=columns_titles)

# Get a list of file paths to all the PNG images in the "country_flags" directory of your system
flag_paths = list(Path("country_flags").glob("*.png"))

# Create a dictionary mapping country names (extracted from file names without extensions) to their corresponding file paths
country_to_flagpath = {p.stem: p for p in flag_paths}

#Change Rank to Integer
df["Rank"] = df["Rank"].round().astype(int)

# Add a "Flag" column to df with country flag file paths 
df.insert(0, "Flag", df["Location"].apply(lambda x: country_to_flagpath.get(x)))

#set "City" as the index
df = df.set_index("City")

df.rename(columns={'Culture_Environment': 'Environment'}, inplace=True)

df.head()

# The 5 categories grouped into 2 maain ones
basic_services_cols = ["Stability", "Healthcare", "Education"]
infrastructure_env_cols = ["Environment", "Infrastructure"]

## Define column definitions for creating the table with specific properties.
col_defs = (
    [
        ColumnDefinition(
            name="Flag",
            title="",
            textprops={"ha": "center"},
            width=0.5,
            plot_fn=circled_image,
        ),
        ColumnDefinition(
            name="City",
            textprops={"ha": "left", "weight": "bold"},
            width=1.5,
        ),
        ColumnDefinition(
            name="Location",
            textprops={"ha": "center"},
            width=0.75,
        ),
         ColumnDefinition(
            name="Rank",
            textprops={"ha": "center"},
            width=0.75,
        ),
         ColumnDefinition(
            name="Index",
            textprops={"ha": "center"},
            width=0.75,
        ),
        ColumnDefinition(
            name="Stability",
            width=0.75,
            textprops={
                "ha": "center",
                "bbox": {"boxstyle": "circle", "pad": 0.35},
            },
            cmap=normed_cmap(df["Stability"], cmap=matplotlib.cm.PiYG, num_stds=4),
            group="Quality of Life",
        ),
        ColumnDefinition(
            name="Healthcare",
            width=0.75,
            textprops={
                "ha": "center",
                "bbox": {"boxstyle": "circle", "pad": 0.35},
            },
            cmap=normed_cmap(df["Healthcare"], cmap=matplotlib.cm.PiYG, num_stds=4),
            group="Quality of Life",
        ),
         ColumnDefinition(
            name="Education",
            width=0.75,
            textprops={
                "ha": "center",
                "bbox": {"boxstyle": "circle", "pad": 0.35},
            },
            cmap=normed_cmap(df["Education"], cmap=matplotlib.cm.PiYG, num_stds=4),
            group="Quality of Life",
        ),
          ColumnDefinition(
            name="Environment",
            width=0.75,
            textprops={
                "ha": "center",
                "bbox": {"boxstyle": "circle", "pad": 0.35},
            },
            cmap=normed_cmap(df["Environment"], cmap=matplotlib.cm.PiYG, num_stds=4),
            group="Environment",
        ),
             ColumnDefinition(
            name="Infrastructure",
            width=0.75,
            textprops={
                "ha": "center",
                "bbox": {"boxstyle": "circle", "pad": 0.35},
            },
            cmap=normed_cmap(df["Infrastructure"], cmap=matplotlib.cm.PiYG, num_stds=4),
            group="Environment",
        ),
    ])

# Setting font for text rendering to "DejaVu Sans" and adjust the figure's bounding box to be "tight."
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": "-"},
)

# Adding the bold header as a text annotation using \n to create a new line
header_text = "\n THE GLOBAL LIVEABILITY INDEX 2023"
header_props = {'fontsize': 18, 'fontweight': 'bold', 'va': 'center', 'ha': 'center'}
# Adjusting the y-coordinate to bring the header closer to the table
plt.text(0.5, 0.91, header_text, transform=fig.transFigure, **header_props)

# Adding the subtitle at the top in gray
subtitle_text = "\n The Table visualizes a list of the Top 10 and Bottom 10 cities to Live in 2023. \n It rates living conditions in 173 cities across these five categories: stability, health care, culture and environment, education and infrastructure."
subtitle_props = {'fontsize': 14, 'va': 'center', 'ha': 'center', 'color': 'gray'}
plt.text(0.5, 0.89, subtitle_text, transform=fig.transFigure, **subtitle_props)

# Adding the footer text
footer_text = "Source: The Economist Intelligence Unit • Visual and Analysis: Fortune Uwha"
footer_props = {'fontsize': 14, 'va': 'center', 'ha': 'center'}
# Adjusting the y-coordinate to position the footer closer to the bottom of the figure
plt.text(0.5, 0.09, footer_text, transform=fig.transFigure, **footer_props)

# Load and resize the logo image using Pillow
logo_path = "logo.png"  # Replace with the path to your logo image
logo = Image.open(logo_path)
# Resize the logo to the desired dimensions
new_logo_size = (180, 100)  # Adjust to your preferred size
logo = logo.resize(new_logo_size)
# Convert the PIL Image to a NumPy array
logo = np.array(logo)
# Position the logo on the right side of the page
xo = 0.5  # Adjust to position the logo where you want
yo = 0.09

plt.figimage(logo, xo=xo, yo=yo, origin='upper')

fig.savefig("eiu_table.png", facecolor=ax.get_facecolor(), dpi=200)