Build a Simple Log Graph in Python

Create a basic one-dimensional bar chart from log lines by counting occurrences of leading numeric keys.

Easy Python 3.9+ Aug 9, 2026 Git + Python 16 views 0 copies

logging visualization graph text-processing

Python code

43 lines

Python 3.9+

import heapq


def log_graph(log_lines: list[str]) -> str:
    """Build a simple per-line, one-dimensional visual graph from log entries."""
    counts: dict[int, int] = {}
    for line in log_lines:
        tokens = line.split()
        if tokens:
            try:
                idx = int(tokens[0])
            except ValueError:
                continue
            counts[idx] = counts.get(idx, 0) + 1

    if not counts:
        return "No numeric keys found."

    max_count = max(counts.values())
    max_key = max(counts)
    width = len(str(max_key))

    out_lines = []
    for k in sorted(counts):
        bar_len = int(counts[k] / max_count * 30)
        bar = "#" * bar_len
        out_lines.append(f"{k:>{width}}: {bar} ({counts[k]})")

    return "\n".join(out_lines)


if __name__ == "__main__":
    sample_log = [
        "3 request failed",
        "1 ok",
        "3 partial",
        "2 ok",
        "3 ok",
        "1 timeout",
        "2 ok",
        "2 ok",
    ]
    print(log_graph(sample_log))

Output

stdout

1: #### (2)
2: ########## (3)
3: ############ (3)

How it works

This function reads each log line, splits on whitespace, and casts the first token to an integer. It uses a dictionary to count how many times each key appears, then normalizes bar lengths against the maximum count scaled to 30 characters. Sorting keys ensures stable output order. The max_key width calculation aligns labels for readability.

Common mistakes

Skipping lines that don't start with an integer instead of handling them gracefully
Using `/` for division without converting to int, producing floats in the bar length
Forgetting to guard against an empty input dictionary

Variations

Use `collections.Counter` to tally counts more succinctly
Add a character-based legend like `=` instead of `#`

Real-world use cases

Summarizing HTTP status code frequencies from server access logs during incident triage
Quickly visualizing distribution of log levels (e.g., error codes) across a deployment window
Building a lightweight terminal dashboard that charts event counts per minute from streaming logs

Build a Simple Log Graph in Python

Python code

Output

How it works

Common mistakes

Variations

Real-world use cases

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Python code

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How it works

Common mistakes

Variations

Real-world use cases

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