Federal Reserve list of largest commercial banks

bank_chart.py

import pandas as pd
import matplotlib.pyplot as plt
import requests
from io import StringIO

# Fetch the table from the Federal Reserve page with proper headers
url = "https://www.federalreserve.gov/releases/lbr/current/"
headers = {
    "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36"
}
response = requests.get(url, headers=headers)
response.raise_for_status()

tables = pd.read_html(StringIO(response.text))

# The main table should be the largest one
df = max(tables, key=len)

# Display columns to understand structure
print("Columns:", df.columns.tolist())
print("\nFirst few rows:")
print(df.head())

# Find the assets column (contains "Consol" and "Assets")
assets_col = [
    c for c in df.columns if "consol" in str(c).lower() and "asset" in str(c).lower()
][0]
name_col = [c for c in df.columns if "name" in str(c).lower()][0]

print(f"\nUsing columns: {name_col}, {assets_col}")

# Clean the data
df_clean = df[[name_col, assets_col]].copy()
df_clean.columns = ["Bank", "Assets"]

# Convert assets to numeric (remove commas if string)
df_clean["Assets"] = pd.to_numeric(
    df_clean["Assets"].astype(str).str.replace(",", ""), errors="coerce"
)

# Get top 10
top10 = df_clean.nlargest(10, "Assets")
print("\nTop 10 banks by Consolidated Assets:")
print(top10)

# Create the bar chart with nice styling
plt.style.use("seaborn-v0_8-whitegrid")
fig, ax = plt.subplots(figsize=(12, 8))

# Create horizontal bar chart (easier to read bank names)
colors = plt.cm.Blues([(i + 3) / 13 for i in range(10)])[::-1]
bars = ax.barh(
    range(len(top10)),
    top10["Assets"].values / 1e6,
    color=colors,
    edgecolor="navy",
    linewidth=0.5,
)

# Set labels
ax.set_yticks(range(len(top10)))
ax.set_yticklabels(top10["Bank"].values, fontsize=10)
ax.invert_yaxis()  # Largest at top

# Format x-axis as trillions
ax.set_xlabel("Consolidated Assets (Trillions $)", fontsize=12, fontweight="bold")
ax.set_title(
    "Top 10 Large Commercial Banks by Consolidated Assets\n(Federal Reserve Data, September 2025)",
    fontsize=14,
    fontweight="bold",
    pad=20,
)

# Add value labels on bars
for i, (bar, val) in enumerate(zip(bars, top10["Assets"].values)):
    ax.text(
        bar.get_width() + 0.02,
        bar.get_y() + bar.get_height() / 2,
        f"${val/1e6:.2f}T",
        va="center",
        fontsize=9,
        fontweight="bold",
    )

# Adjust x-axis limit to make room for labels
ax.set_xlim(0, top10["Assets"].max() / 1e6 * 1.15)

# Style tweaks
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.tick_params(axis="y", length=0)

plt.tight_layout()
plt.savefig("top10_banks.png", dpi=150, bbox_inches="tight", facecolor="white")
plt.savefig("top10_banks.pdf", bbox_inches="tight", facecolor="white")
print("\nSaved: top10_banks.png and top10_banks.pdf")