142 lines
4.4 KiB
Python
142 lines
4.4 KiB
Python
"""Standalone utility to download a .sas7bdat file from S3 and print a
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column-level summary of the first 10 rows.
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Configure the four constants below, then run::
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python3 file_viewer.py
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Python 3.14 compatible.
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"""
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from __future__ import annotations
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import os
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import sys
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import boto3
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import pandas as pd
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import pyreadstat
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# ---------------------------------------------------------------------------
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# Configuration — edit these before running
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# ---------------------------------------------------------------------------
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S3_BUCKET: str = "my-bucket"
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"""S3 bucket name."""
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S3_KEY: str = "path/to/file.sas7bdat"
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"""Object key (path) within the bucket to the .sas7bdat file."""
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LOCAL_FOLDER: str = "./downloads"
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"""Local directory to download the file into."""
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AWS_PROFILE: str = "default"
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"""AWS CLI profile name used for authentication."""
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# ---------------------------------------------------------------------------
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# Helpers
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# ---------------------------------------------------------------------------
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def _ensure_local_copy(bucket: str, key: str, local_path: str) -> None:
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"""Download *key* from *bucket* to *local_path*, skipping if already present.
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If *local_path* exists and its size matches the S3 object's size, the
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download is skipped and a message is printed.
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"""
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session = boto3.Session(profile_name=AWS_PROFILE)
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s3 = session.client("s3")
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remote_size = s3.head_object(Bucket=bucket, Key=key)["ContentLength"]
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if os.path.exists(local_path):
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local_size = os.path.getsize(local_path)
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if local_size == remote_size:
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print(
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f"Local file {local_path} already matches s3://{bucket}/{key} "
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f"({local_size} bytes); skipping download."
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)
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return
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print(
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f"Local file {local_path} size ({local_size} bytes) differs from "
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f"S3 ({remote_size} bytes); re-downloading."
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)
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print(f"Downloading s3://{bucket}/{key} -> {local_path}")
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s3.download_file(bucket, key, local_path)
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print("Download complete.")
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def _read_sas_head(path: str, row_count: int = 10) -> pd.DataFrame:
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"""Read the first *row_count* rows of a .sas7bdat file."""
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df, _ = pyreadstat.read_sas7bdat(path, row_offset=0, row_limit=row_count)
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return df
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def _sample_values(series: pd.Series, n: int = 3) -> str:
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"""Return up to *n* non-null sample values as a comma-separated string."""
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non_null = series.dropna()
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samples = non_null.head(n).tolist()
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if not samples:
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return "(all null)"
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return ", ".join(repr(v) for v in samples)
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def _print_summary(df: pd.DataFrame) -> None:
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"""Print a nicely formatted summary table to stdout."""
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# Pre-compute column data
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rows = []
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for col in df.columns:
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rows.append((col, str(df[col].dtype), _sample_values(df[col], 3)))
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# Determine column widths
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hdr_name = "Column Name"
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hdr_dtype = "Data Type"
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hdr_samples = "Sample Values (up to 3)"
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w_name = max(len(hdr_name), *(len(r[0]) for r in rows))
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w_dtype = max(len(hdr_dtype), *(len(r[1]) for r in rows))
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w_samples = max(len(hdr_samples), *(len(r[2]) for r in rows))
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fmt = f" {{:<{w_name}}} {{:<{w_dtype}}} {{:<{w_samples}}}"
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sep = f" {'-' * w_name} {'-' * w_dtype} {'-' * w_samples}"
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print()
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print(f" Summary of first {len(df)} row(s) ({len(df.columns)} columns)")
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print(sep)
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print(fmt.format(hdr_name, hdr_dtype, hdr_samples))
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print(sep)
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for name, dtype, samples in rows:
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print(fmt.format(name, dtype, samples))
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print(sep)
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print()
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# ---------------------------------------------------------------------------
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# Main
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# ---------------------------------------------------------------------------
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if __name__ == "__main__":
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# --- Download -----------------------------------------------------------
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os.makedirs(LOCAL_FOLDER, exist_ok=True)
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local_filename = os.path.basename(S3_KEY)
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local_path = os.path.join(LOCAL_FOLDER, local_filename)
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try:
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_ensure_local_copy(S3_BUCKET, S3_KEY, local_path)
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except Exception as exc:
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print(f"S3 download error: {exc}", file=sys.stderr)
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sys.exit(1)
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# --- Read & summarize ---------------------------------------------------
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try:
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df = _read_sas_head(local_path, row_count=10)
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except Exception as exc:
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print(f"File read error: {exc}", file=sys.stderr)
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sys.exit(2)
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_print_summary(df)
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