vamseeachanta
vamseeachanta/workspace-hub
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基于 SOC 职业分类
Write outbound email and external messages in Vamsee Achanta's voice — a subtle offer to help, never bold or rash claims. Load before drafting ANY email, LinkedIn/Collide reply, proposal note, or outreach sent under his name.
Save/publish analysis or computation results from ANY ecosystem repo to Hugging Face as a queryable, viewer-renderable dataset. Use when the user wants to "save results to hugging face", "publish dataset to HF", "hugging face data saving", "save analysis results", "hf dataset", "make results queryable", or "render via datasets-server API". Reshapes nested results into flat parquet tables, writes a dataset card with a viewer `configs:` block and provenance, applies license/public-vs-private routing, enforces a domain data-quality gate (faithful-to-source != correct), publishes to `aceengineer/<repo>-<projection>`, and verifies via the datasets-server API.
Clone, create, fork, configure, and manage GitHub repositories. Manage remotes, secrets, releases, and workflows. Works with gh CLI or falls back to git + GitHub REST API via curl.
正在显示 SKILL.md
| name | pandas-data-processing-4-multi-file-processing |
| description | Sub-skill of pandas-data-processing: 4. Multi-File Processing. |
| version | 1.0.0 |
| category | data |
| type | reference |
| scripts_exempt | true |
Batch CSV Loading:
def load_multiple_csv_files(
directory: Path,
pattern: str = '*.csv',
concat_axis: int = 0
) -> pd.DataFrame:
"""
Load and concatenate multiple CSV files.
Args:
directory: Directory containing CSV files
pattern: Glob pattern for file matching
concat_axis: Concatenation axis (0=rows, 1=columns)
Returns:
Concatenated DataFrame
"""
csv_files = sorted(directory.glob(pattern))
if not csv_files:
raise FileNotFoundError(f"No CSV files found matching {pattern} in {directory}")
# Load all files
dfs = []
for csv_file in csv_files:
df = pd.read_csv(csv_file)
df['source_file'] = csv_file.name # Track source
dfs.append(df)
# Concatenate
combined = pd.concat(dfs, axis=concat_axis, ignore_index=True)
print(f"Loaded {len(csv_files)} files, total {len(combined)} rows")
return combined
# Example: Load all mooring tension results
all_tensions = load_multiple_csv_files(
Path('data/processed/mooring_tensions/'),
pattern='tension_line*.csv'
)
print(f"Combined dataset: {all_tensions.shape}")
Multi-Format Data Loading:
def load_engineering_data(
file_path: Path,
file_type: str = None
) -> pd.DataFrame:
"""
Load data from multiple engineering file formats.
Args:
file_path: Path to data file
file_type: File type ('csv', 'excel', 'hdf5', 'parquet', 'json')
If None, inferred from extension
Returns:
Loaded DataFrame
"""
if file_type is None:
file_type = file_path.suffix.lstrip('.')
# Load based on type
if file_type == 'csv':
df = pd.read_csv(file_path)
elif file_type in ['xls', 'xlsx', 'excel']:
df = pd.read_excel(file_path)
elif file_type in ['h5', 'hdf5']:
df = pd.read_hdf(file_path)
elif file_type == 'parquet':
df = pd.read_parquet(file_path)
elif file_type == 'json':
df = pd.read_json(file_path)
else:
raise ValueError(f"Unsupported file type: {file_type}")
print(f"Loaded {file_type.upper()}: {df.shape[0]} rows, {df.shape[1]} columns")
return df
# Usage examples
csv_data = load_engineering_data(Path('data/processed/results.csv'))
excel_data = load_engineering_data(Path('data/processed/summary.xlsx'))
hdf5_data = load_engineering_data(Path('data/processed/large_dataset.h5'))