merge_statistics

merge_statistics.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nThis scripts merges the statistics of 2 sensors into 1\\nIt does this for multiple sensors because i had added almost the same sensors via 2 different integrations\\n'"
      ]
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     "execution_count": 1,
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   "source": [
    "\"\"\"\n",
    "This scripts merges the statistics of 2 sensors into 1\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mapping of the sensors, statistics of the left ones will be merged into the right ones\n",
    "mapping={\n",
    "         'sensor.old1': 'sensor.new1',\n",
    "         'sensor.old2': 'sensor.new2',\n",
    "        }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Connect to DB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "import sqlalchemy\n",
    "import pandas\n",
    "pandas.options.plotting.backend = \"plotly\"\n",
    "\n",
    "# Read sqlite query results into a pandas DataFrame\n",
    "engine = sqlalchemy.create_engine(\"mysql://USERNAME:PASSWORD@core-mariadb/homeassistant\")\n",
    "con = engine.connect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def merge(source_sensor, target_sensor, table='statistics'):\n",
    "    print(f\"source: {source_sensor}, target: {target_sensor}\")\n",
    "    # read data from target sensor\n",
    "    target_sensor_id=pandas.read_sql_query(f\"\"\"select id FROM statistics_meta \n",
    "                                                where statistic_id like '{target_sensor}';\"\"\", con).loc[0,'id']\n",
    "    target_df=pandas.read_sql_query(\n",
    "            f\"select * FROM {table} where metadata_id = '{target_sensor_id}';\", con\n",
    "        )\n",
    "    print(f\"length of existing statistics for target sensor: {len(target_df)}\")\n",
    "\n",
    "    # read data from source sensor\n",
    "    source_sensor_id=pandas.read_sql_query(f\"\"\"select id FROM statistics_meta \n",
    "                                                where statistic_id like '{source_sensor}';\"\"\", con).loc[0,'id']\n",
    "    source_df=pandas.read_sql_query(\n",
    "            f\"select * FROM {table} where metadata_id = '{source_sensor_id}';\", con\n",
    "        )\n",
    "    print(f\"length of existing statistics for source sensor: {len(source_df)}\")\n",
    "    \n",
    "    print(source_sensor_id)\n",
    "    print(target_sensor_id)\n",
    "    \n",
    "    stmnt = sqlalchemy.text(\n",
    "        f\"\"\"UPDATE {table}\n",
    "            SET metadata_id = {target_sensor_id} \n",
    "            WHERE metadata_id = {source_sensor_id};\"\"\"\n",
    "    )\n",
    "    con.execute(stmnt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "statistics\n",
      "source: sensor.gas_verbruik, target: sensor.meterkast_gasverbruik\n",
      "length of existing statistics for target sensor: 4\n",
      "length of existing statistics for source sensor: 13705\n",
      "652\n",
      "1113\n",
      "statistics_short_term\n",
      "source: sensor.gas_verbruik, target: sensor.meterkast_gasverbruik\n",
      "length of existing statistics for target sensor: 40\n",
      "length of existing statistics for source sensor: 3042\n",
      "652\n",
      "1113\n"
     ]
    }
   ],
   "source": [
    "for source_sensor, target_sensor in mapping.items():\n",
    "    for table in (\"statistics\", \"statistics_short_term\"):    \n",
    "        print(table)\n",
    "        merge(source_sensor, target_sensor, table)"
   ]
  }
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