Proceso para extraer los hemogramas de umls_old_dx

Hemograms/JKES/utils_sql.py

+# -*- coding: utf-8 -*-
+"""
+Created on Tue Feb  2 11:07:37 2021
+
+@author: ctb
+"""
+
+import mysql.connector
+import pandas as pd
+
+def generate_db_connection(ip, port, user, password, db_name):
+    db_conn = mysql.connector.connect(
+         host=ip,
+         port=port,
+         user=user,
+         password=password,
+         database=db_name,
+         auth_plugin='mysql_native_password'
+    )
+    db_cursor = db_conn.cursor()
+    
+    return {"cnx": db_conn, "cursor": db_cursor}
+
+def get_columns_from_table(db_mngr, table_name):
+    db_mngr["db_crs"].execute("show columns from "+table_name)
+    semantic_annotation_table_cols = db_mngr["db_crs"].fetchall()
+    col_list = []
+    for x in semantic_annotation_table_cols:
+        col_list.append(x[0])
+        
+    return col_list
+
+# Genera un dataframe con el contenido de la tabla indicada en 'table_name'.
+# Si se pasan numeros enteros en los campos opcionales 'batch_size' y 'offset', se devolvera el dataframe
+# correspondiente a un numero de 'batch_size' filas de la tabla, desde la fila indicada por 'offset'.
+def compose_dataframe_from_query(db_mngr, table_name, batch_size = None, offset = None, where_clause = None, join_clause = None):
+    if(join_clause is not None):
+        query = "Select u.document_id, u.ehr,  u.concept, d.subcategory, u.begin, u.end from " + table_name + " u inner join document d on d.ID = u.document_id";
+    else:
+        query = "Select document_id, ehr, sentence, sentence_id, concept, begin, end from " + table_name
+
+    if where_clause is not None:
+        query = query + " where " + where_clause
+
+    #if group_by is not None:
+    #    query = query + " group by " + group_by
+
+    if batch_size is not None and offset is not None:
+        query = query + " limit " + str(batch_size) + " offset " + str(offset)
+    
+    db_mngr["db_crs"].execute(query)
+    result = db_mngr["db_crs"].fetchall()
+
+
+    if(join_clause is not None):
+        table_cols = ["document_id", "ehr", "concept", "subcategory", "begin", "end"]
+    else:
+        table_cols = ["document_id", "ehr", "sentence", "sentence_id", "concept", "begin", "end"]
+        
+    if len(result) > 0:
+        result = pd.DataFrame(result)
+        result.columns = table_cols
+    else:
+        result = pd.DataFrame(columns=table_cols)        
+        
+    return result
+