update reader csv
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dmsanhrProject
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62bd02d660
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@ -1,3 +1,89 @@
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# import pandas as pd
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# import re
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# import csv
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# import os
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# def detect_header_line(path, max_rows=10):
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# with open(path, 'r', encoding='utf-8', errors='ignore') as f:
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# lines = [next(f) for _ in range(max_rows)]
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# header_line_idx = 0
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# best_score = -1
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# for i, line in enumerate(lines):
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# cells = re.split(r'[;,|\t]', line.strip())
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# alpha_ratio = sum(bool(re.search(r'[A-Za-z]', c)) for c in cells) / max(len(cells), 1)
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# digit_ratio = sum(bool(re.search(r'\d', c)) for c in cells) / max(len(cells), 1)
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# score = alpha_ratio - digit_ratio
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# if score > best_score:
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# best_score = score
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# header_line_idx = i
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# return header_line_idx
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# def detect_delimiter(path, sample_size=2048):
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# with open(path, 'r', encoding='utf-8', errors='ignore') as f:
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# sample = f.read(sample_size)
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# sniffer = csv.Sniffer()
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# try:
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# dialect = sniffer.sniff(sample)
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# return dialect.delimiter
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# except Exception:
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# for delim in [',', ';', '\t', '|']:
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# if delim in sample:
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# return delim
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# return ','
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# def read_csv(path: str):
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# ext = os.path.splitext(path)[1].lower() # ambil ekstensi file
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# try:
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# if ext in ['.csv', '.txt']:
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# # === Baca file CSV ===
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# header_line = detect_header_line(path)
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# delimiter = detect_delimiter(path)
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# print(f"[INFO] Detected header line: {header_line + 1}, delimiter: '{delimiter}'")
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# df = pd.read_csv(path, header=header_line, sep=delimiter, encoding='utf-8', low_memory=False, thousands=',')
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# elif ext in ['.xlsx', '.xls']:
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# # === Baca file Excel ===
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# print(f"[INFO] Membaca file Excel: {os.path.basename(path)}")
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# pre_df = pd.read_excel(path, header=0, dtype=str) # baca semua sebagai string
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# df = pre_df.copy()
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# for col in df.columns:
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# if df[col].str.replace(',', '', regex=False).str.match(r'^-?\d+(\.\d+)?$').any():
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# df[col] = df[col].str.replace(',', '', regex=False)
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# df[col] = pd.to_numeric(df[col], errors='ignore')
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# else:
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# raise ValueError("Format file tidak dikenali (hanya .csv, .txt, .xlsx, .xls)")
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# except Exception as e:
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# print(f"[WARN] Gagal membaca file ({e}), fallback ke default")
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# df = pd.read_csv(path, encoding='utf-8', low_memory=False, thousands=',')
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# # Bersihkan kolom dan baris kosong
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# df = df.loc[:, ~df.columns.astype(str).str.contains('^Unnamed')]
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# df.columns = [str(c).strip() for c in df.columns]
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# df = df.dropna(how='all')
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# return df
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import pandas as pd
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import re
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import csv
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@ -6,23 +92,18 @@ import os
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def detect_header_line(path, max_rows=10):
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with open(path, 'r', encoding='utf-8', errors='ignore') as f:
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lines = [next(f) for _ in range(max_rows)]
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header_line_idx = 0
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best_score = -1
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for i, line in enumerate(lines):
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cells = re.split(r'[;,|\t]', line.strip())
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alpha_ratio = sum(bool(re.search(r'[A-Za-z]', c)) for c in cells) / max(len(cells), 1)
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digit_ratio = sum(bool(re.search(r'\d', c)) for c in cells) / max(len(cells), 1)
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score = alpha_ratio - digit_ratio
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score = alpha_ratio - digit_ratio
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if score > best_score:
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best_score = score
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header_line_idx = i
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return header_line_idx
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def detect_delimiter(path, sample_size=2048):
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with open(path, 'r', encoding='utf-8', errors='ignore') as f:
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sample = f.read(sample_size)
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@ -36,9 +117,8 @@ def detect_delimiter(path, sample_size=2048):
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return delim
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return ','
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def read_csv(path: str):
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ext = os.path.splitext(path)[1].lower() # ambil ekstensi file
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ext = os.path.splitext(path)[1].lower()
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try:
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if ext in ['.csv', '.txt']:
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@ -52,18 +132,54 @@ def read_csv(path: str):
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elif ext in ['.xlsx', '.xls']:
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# === Baca file Excel ===
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print(f"[INFO] Membaca file Excel: {os.path.basename(path)}")
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pre_df = pd.read_excel(path, header=0, dtype=str) # baca semua sebagai string
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df = pre_df.copy()
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xls = pd.ExcelFile(path)
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print(f"[INFO] Ditemukan {len(xls.sheet_names)} sheet: {xls.sheet_names}")
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# Evaluasi tiap sheet untuk mencari yang paling relevan
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best_sheet = None
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best_score = -1
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best_df = None
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for sheet_name in xls.sheet_names:
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try:
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df = pd.read_excel(xls, sheet_name=sheet_name, header=0, dtype=str)
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df = df.dropna(how='all').dropna(axis=1, how='all')
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if len(df) == 0 or len(df.columns) < 2:
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continue
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# hitung "skor relevansi"
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text_ratio = df.applymap(lambda x: isinstance(x, str)).sum().sum() / (df.size or 1)
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row_score = len(df)
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score = (row_score * 0.7) + (text_ratio * 100)
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if score > best_score:
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best_score = score
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best_sheet = sheet_name
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best_df = df
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except Exception as e:
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print(f"[WARN] Gagal membaca sheet {sheet_name}: {e}")
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continue
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if best_df is not None:
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print(f"[INFO] Sheet terpilih: '{best_sheet}' dengan skor {best_score:.2f}")
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df = best_df
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else:
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raise ValueError("Tidak ada sheet valid yang dapat dibaca.")
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# Konversi tipe numerik jika ada
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for col in df.columns:
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if df[col].str.replace(',', '', regex=False).str.match(r'^-?\d+(\.\d+)?$').any():
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df[col] = df[col].str.replace(',', '', regex=False)
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if df[col].astype(str).str.replace(',', '', regex=False).str.match(r'^-?\d+(\.\d+)?$').any():
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df[col] = df[col].astype(str).str.replace(',', '', regex=False)
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df[col] = pd.to_numeric(df[col], errors='ignore')
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else:
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raise ValueError("Format file tidak dikenali (hanya .csv, .txt, .xlsx, .xls)")
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except Exception as e:
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print(f"[WARN] Gagal membaca file ({e}), fallback ke default")
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print(f"[WARN] Gagal membaca file ({e}), fallback ke default reader.")
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df = pd.read_csv(path, encoding='utf-8', low_memory=False, thousands=',')
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# Bersihkan kolom dan baris kosong
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@ -71,4 +187,4 @@ def read_csv(path: str):
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df.columns = [str(c).strip() for c in df.columns]
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df = df.dropna(how='all')
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return df
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return df
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