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Sub-skill of pandas-data-processing: 4. Multi-File Processing.
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| 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'))