import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.table import Table

# Simulating the cache as a DataFrame
data = {
    'key': ['BTC/USD_2h_binance', 'ETH/USD_1h_coinbase'],
    'data': ['{"open": 50000, "close": 50500}', '{"open": 1800, "close": 1825}']
}
cache_df = pd.DataFrame(data)


# Visualization function
def visualize_cache(df):
    fig, ax = plt.subplots(figsize=(6, 3))
    ax.set_axis_off()
    tb = Table(ax, bbox=[0, 0, 1, 1])

    # Adding column headers
    for i, column in enumerate(df.columns):
        tb.add_cell(0, i, width=0.4, height=0.3, text=column, loc='center', facecolor='lightgrey')

    # Adding rows and cells
    for i in range(len(df)):
        for j, value in enumerate(df.iloc[i]):
            tb.add_cell(i + 1, j, width=0.4, height=0.3, text=value, loc='center', facecolor='white')

    ax.add_table(tb)
    plt.title("Visualizing Cache Data")
    plt.show()


visualize_cache(cache_df)