z230

80 CtiKapitaceZTabulkyFAK.py

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+import re
+import pandas as pd
+import firebirdsql as fb
+from datetime import datetime, timedelta
+from pathlib import Path
+from striprtf.striprtf import rtf_to_text
+from openpyxl.styles import Font, Alignment, PatternFill, Border, Side
+from openpyxl.utils import get_column_letter
+import textwrap
+
+# ================== CONFIGURATION ==================
+FDB_PATH = r"z:\Medicus 3\data\MEDICUS.FDB"
+EXPORT_DIR = Path(r"u:\Dropbox\!!!Days\Downloads Z230")
+
+# ================== FIREBIRD CONNECTION ==================
+conn = fb.connect(
+    host="192.168.1.4",
+    port=3050,
+    database=FDB_PATH,
+    user="SYSDBA",
+    password="masterkey",
+    charset="WIN1250",
+)
+
+def query_df(sql, params=None):
+    cur = conn.cursor()
+    cur.execute(sql, params or ())
+    rows = cur.fetchall()
+    cols = [d[0].strip().lower() for d in cur.description]  # 👈 normalize
+    return pd.DataFrame(rows, columns=cols)
+# ================== QUERY ==================
+sql = """
+SELECT id, cisfak, poj, datkapod, datkapdo, kapdetail
+FROM fak
+WHERE EXTRACT(YEAR FROM datkapod) = 2025
+  AND EXTRACT(MONTH FROM datkapod) = 9
+  AND poj = '211'
+"""
+df = query_df(sql)
+
+# print(df.columns.tolist())
+# print(df.head())
+
+# Display the first KAPDETAIL text, wrapped nicely
+kap_text = df.loc[0, 'kapdetail']
+# print("\n".join(textwrap.wrap(kap_text, width=100)))
+
+# ========== SOURCE STRING ==========
+text = kap_text
+
+# Extract triplets: patientid;age|price;
+pattern = r"(\d{4});(\d{1,3})\|([\d.]+);"
+matches = re.findall(pattern, text)
+
+# Create dataframe
+dfdavka = pd.DataFrame(matches, columns=["patientid", "age", "price"])
+
+# ✅ Fix small typo: use dfdavka (not df) for conversions
+dfdavka["patientid"] = dfdavka["patientid"].astype(int)
+dfdavka["age"] = dfdavka["age"].astype(int)
+dfdavka["price"] = dfdavka["price"].astype(float)
+
+# ========== LOAD KAR ==========
+sql = "SELECT idpac, rodcis FROM kar"
+dfkar = query_df(sql)
+
+# ========== MERGE ==========
+dfmerged = pd.merge(dfdavka, dfkar, left_on="patientid", right_on="idpac", how="left")
+
+# Optional: reorder columns
+dfmerged = dfmerged[["patientid", "rodcis", "age", "price"]]
+
+print(dfmerged.head())
+print(dfmerged.info())
+
+# ========== OPTIONAL EXPORT ==========
+# outfile = EXPORT_DIR / "kapdetail_merged.xlsx"
+# dfmerged.to_excel(outfile, index=False)
+# print(f"✅ Exported to {outfile}")
+
+
+
+# ========== 1️⃣ Load Excel and prepare dfpoj ==========
+xlsx_path = Path(r"u:\Dropbox\!!!Days\Downloads Z230\prehled 09_2025 zpmvcr.xlsx")
+df = pd.read_excel(xlsx_path)
+
+print("Columns in Excel:", df.columns.tolist())
+
+# Select 2nd column (index 1)
+dfpoj = df.iloc[:, [1]].copy()
+dfpoj.columns = ["rodcis"]  # give proper name
+dfpoj["rodcis"] = dfpoj["rodcis"].astype(str).str.strip()
+
+print(dfpoj.head())
+
+# ========== 2️⃣ Compare dfmerged vs dfpoj ==========
+dfmerged["rodcis"] = dfmerged["rodcis"].astype(str).str.strip()
+
+# Find those in dfmerged but not in dfpoj
+df_missing = dfmerged[~dfmerged["rodcis"].isin(dfpoj["rodcis"])].copy()
+
+print(f"❌ Počet pacientů v dfmerged, kteří NEJSOU v dfpoj: {len(df_missing)}")
+print(df_missing.head())
+
+# ========== 3️⃣ (Optional) Export differences ==========
+EXPORT_DIR = Path(r"u:\Dropbox\!!!Days\Downloads Z230")
+timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+xlsx_out = EXPORT_DIR / f"chybějící_pacienti_{timestamp}.xlsx"
+
+df_missing.to_excel(xlsx_out, index=False)
+print(f"✅ Výsledek uložen do {xlsx_out}")