10-of-my-most-popular-text-to-image-series-prompts-78b0897e
generate a bunch of images, then you curate the results to handpick the best ones
Handle CSV files from construction software exports. Auto-detect delimiters, encodings, and clean messy data.
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CSV is the universal exchange format in construction - from scheduling exports to cost databases. This skill handles encoding issues, delimiter detection, and data cleaning.
import pandas as pd
import csv
from typing import Dict, Any, List, Optional, Tuple
from pathlib import Path
from dataclasses import dataclass
import chardet
@dataclass
class CSVProfile:
"""Profile of CSV file."""
encoding: str
delimiter: str
has_header: bool
row_count: int
column_count: int
columns: List[str]
class ConstructionCSVHandler:
"""Handle CSV files from construction software."""
COMMON_DELIMITERS = [',', ';', '\t', '|']
COMMON_ENCODINGS = ['utf-8', 'utf-8-sig', 'latin-1', 'cp1252', 'iso-8859-1']
def __init__(self):
self.last_profile: Optional[CSVProfile] = None
def detect_encoding(self, file_path: str) -> str:
"""Detect file encoding."""
with open(file_path, 'rb') as f:
raw = f.read(10000)
result = chardet.detect(raw)
return result.get('encoding', 'utf-8') or 'utf-8'
def detect_delimiter(self, file_path: str, encoding: str) -> str:
"""Detect CSV delimiter."""
with open(file_path, 'r', encoding=encoding, errors='replace') as f:
sample = f.read(5000)
# Count occurrences
counts = {d: sample.count(d) for d in self.COMMON_DELIMITERS}
# Return most common that appears consistently
if counts:
return max(counts, key=counts.get)
return ','
def profile_csv(self, file_path: str) -> CSVProfile:
"""Profile CSV file."""
encoding = self.detect_encoding(file_path)
delimiter = self.detect_delimiter(file_path, encoding)
# Read sample
df = pd.read_csv(file_path, encoding=encoding, delimiter=delimiter,...
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# CSV Handler for Construction Data
## Overview
CSV is the universal exchange format in construction - from scheduling exports to cost databases. This skill handles encoding issues, delimiter detection, and data cleaning.
## Python Implementation
```python
import pandas as pd
import csv
from typing import Dict, Any, List, Optional, Tuple
from pathlib import Path
from dataclasses import dataclass
import chardet
@dataclass
class CSVProfile:
"""Profile of CSV file."""
encoding: str
delimiter: str
has_header: bool
row_count: int
column_count: int
columns: List[str]
class ConstructionCSVHandler:
"""Handle CSV files from construction software."""
COMMON_DELIMITERS = [',', ';', '\t', '|']
COMMON_ENCODINGS = ['utf-8', 'utf-8-sig', 'latin-1', 'cp1252', 'iso-8859-1']
def __init__(self):
self.last_profile: Optional[CSVProfile] = None
def detect_encoding(self, file_path: str) -> str:
"""Detect file encoding."""
with open(file_path, 'rb') as f:
raw = f.read(10000)
result = chardet.detect(raw)
return result.get('encoding', 'utf-8') or 'utf-8'
def detect_delimiter(self, file_path: str, encoding: str) -> str:
"""Detect CSV delimiter."""
with open(file_path, 'r', encoding=encoding, errors='replace') as f:
sample = f.read(5000)
# Count occurrences
counts = {d: sample.count(d) for d in self.COMMON_DELIMITERS}
# Return most common that appears consistently
if counts:
return max(counts, key=counts.get)
return ','
def profile_csv(self, file_path: str) -> CSVProfile:
"""Profile CSV file."""
encoding = self.detect_encoding(file_path)
delimiter = self.detect_delimiter(file_path, encoding)
# Read sample
df = pd.read_csv(file_path, encoding=encoding, delimiter=delimiter,...generate a bunch of images, then you curate the results to handpick the best ones
generate a bunch of images, then you curate the results to handpick the best ones
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