{ "cells": [ { "cell_type": "markdown", "id": "7f1f5a4a-ae34-4a27-9efa-399edc0e384a", "metadata": { "tags": [] }, "source": [ "## Benchmark: ClickHouse Vs. InfluxDB Vs. Postgresql Vs. Parquet \n", "\n", "-----\n", "\n", "#### How to use:\n", "* Rename the file \"properties-model.ini\" to \"properties.ini\"\n", "* Fill with your own credentials\n", "----\n", "\n", "The proposal of this work is to compare the speed in read/writing a midle level of data ( a dataset with 9 columns and 50.000 lines) to four diferent databases:\n", "* ClickHouse\n", "* InfluxDB\n", "* Postgresql\n", "* Parquet (in a S3 Minio Storage)
\n", "ToDo:
\n", "* DuckDB with Polars\n", "* MongoDB\n", "* Kdb+\n", "\n", " \n", "Deve-se relevar:\n", "é uma \"cold-storage\" ou \"frezze-storage\"?
\n", "influxdb: alta leitura e possui a vantagem da indexaçõa para vizualização de dados em gráficos.\n", "\n", "notas: \n", "* comparar tamanho do csv com parquet" ] }, { "cell_type": "markdown", "id": "6bb26ce7-1e84-4665-accd-916bb977f95d", "metadata": { "tags": [] }, "source": [ "### Imports " ] }, { "cell_type": "code", "execution_count": 18, "id": "ab6c6c81-6ac1-4668-a79b-a9a0341fb35a", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import configparser\n", "from datetime import datetime\n", "\n", "import duckdb\n", "import influxdb_client\n", "import pandas as pd\n", "\n", "# import pymongo\n", "from clickhouse_driver import Client\n", "from dotenv import load_dotenv\n", "from minio import Minio\n", "from pymongo import MongoClient\n", "from pytz import timezone\n", "from sqlalchemy import create_engine\n", "\n", "load_dotenv()" ] }, { "cell_type": "code", "execution_count": 1, "id": "55c3cd57-0996-4723-beb5-8f3196c96009", "metadata": { "tags": [] }, "outputs": [], "source": [ "# Variables\n", "dbname = \"EURUSDtest\"" ] }, { "cell_type": "code", "execution_count": 5, "id": "968403e3-2e5e-4834-b969-be4600e2963a", "metadata": { "tags": [] }, "outputs": [], "source": [ "arq = configparser.RawConfigParser()\n", "arq.read(\"properties.ini\")\n", "ClickHouseUser = arq.get(\"CLICKHOUSE\", \"user\")\n", "ClickHouseKey = arq.get(\"CLICKHOUSE\", \"key\")\n", "ClickHouseUrl = arq.get(\"CLICKHOUSE\", \"url\")\n", "\n", "InfluxDBUser = arq.get(\"INFLUXDB\", \"user\")\n", "InfluxDBKey = arq.get(\"INFLUXDB\", \"key\")\n", "InfluxDBUrl = arq.get(\"INFLUXDB\", \"url\")\n", "InfluxDBBucket = arq.get(\"INFLUXDB\", \"bucket\")\n", "\n", "PostgresqlUser = arq.get(\"POSTGRESQL\", \"user\")\n", "PostgresqlKey = arq.get(\"POSTGRESQL\", \"key\")\n", "PostgresqlUrl = arq.get(\"POSTGRESQL\", \"url\")\n", "PostgresqlDB = arq.get(\"POSTGRESQL\", \"database\")\n", "\n", "S3MinioUser = arq.get(\"S3MINIO\", \"user\")\n", "S3MinioKey = arq.get(\"S3MINIO\", \"key\")\n", "S3MinioUrl = arq.get(\"S3MINIO\", \"url\")\n", "S3MinioRegion = arq.get(\"S3MINIO\", \"region\")\n", "\n", "MongoUser = arq.get(\"MONGODB\", \"user\")\n", "MongoKey = arq.get(\"MONGODB\", \"key\")\n", "MongoUrl = arq.get(\"MONGODB\", \"url\")" ] }, { "cell_type": "code", "execution_count": null, "id": "3634a4ec-04c2-4f1e-8659-5d22eb17a254", "metadata": {}, "outputs": [], "source": [ "%%time\n", "# Load Dataset\n", "df = pd.read_csv(\"out.csv\", index_col=0)" ] }, { "cell_type": "code", "execution_count": null, "id": "7e7c46b6-90ee-4ca3-8b5a-553b09ece913", "metadata": {}, "outputs": [], "source": [ "# df.head()" ] }, { "cell_type": "code", "execution_count": null, "id": "76199f91-31d6-416b-9f15-5d435b3792c9", "metadata": {}, "outputs": [], "source": [ "df[\"from\"] = pd.to_datetime(df[\"from\"], unit=\"s\")\n", "df[\"to\"] = pd.to_datetime(df[\"to\"], unit=\"s\")\n", "# Optional use when not transoformed yet\n", "# Transform Datetime" ] }, { "cell_type": "markdown", "id": "274cc026-2f48-4e38-b80f-b1a9ff982060", "metadata": { "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ "#### Funçoes\n", "\n", "-> Class" ] }, { "cell_type": "code", "execution_count": null, "id": "27de1ec8-4de1-440a-b555-b4a46c5ef7ce", "metadata": {}, "outputs": [], "source": [ "def timestamp2dataHora(x, timezone_=\"America/Sao_Paulo\"):\n", " d = datetime.fromtimestamp(x, tz=timezone(timezone_))\n", " return d" ] }, { "cell_type": "markdown", "id": "4a8d5703-9bc9-4d38-83ff-457159304d58", "metadata": { "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ "### ClickHouse" ] }, { "cell_type": "code", "execution_count": null, "id": "9cf86669-7722-4a2c-895c-51f0a5eebefc", "metadata": { "tags": [] }, "outputs": [], "source": [ "# !! O client oficial usa um driver http, nesse exemplo vamos usar a biblioteca\n", "# de terceirtos clickhouse_driver recomendada, por sua vez que usa tcp.\n", "client = Client(\n", " host=ClickHouseUrl,\n", " user=ClickHouseUser,\n", " password=ClickHouseKey,\n", " settings={\"use_numpy\": True},\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "a0a1f67b-2e63-462e-be66-d322d99837ea", "metadata": {}, "outputs": [], "source": [ "# Create Tables in ClickHouse\n", "# !! ALTERAR TIPOS !!\n", "# ENGINE: 'Memory' desaparece quando server é reiniciado\n", "client.execute(\n", " \"CREATE TABLE IF NOT EXISTS {} (id UInt32,\"\n", " \"from DateTime, at UInt64, to DateTime, open Float64,\"\n", " \"close Float64, min Float64, max Float64, volume UInt32)\"\n", " \"ENGINE MergeTree ORDER BY to\".format(dbname)\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "3a029a09-46f4-43c3-b3df-cfbed33fb0dc", "metadata": {}, "outputs": [], "source": [ "%%time\n", "# Write dataframe to db\n", "client.insert_dataframe(\"INSERT INTO {} VALUES\".format(dbname), df)" ] }, { "cell_type": "code", "execution_count": null, "id": "17251288-2442-43ee-98f2-ca680c3c4f13", "metadata": { "tags": [] }, "outputs": [], "source": [ "%%time\n", "client.query_dataframe(\"SELECT * FROM default.{}\".format(dbname)) # LIMIT 10000" ] }, { "cell_type": "code", "execution_count": null, "id": "51497522-bd6c-44a8-aaea-ec5dda30b95b", "metadata": { "tags": [] }, "outputs": [], "source": [ "# %%time\n", "# df = pd.DataFrame(client.query_dataframe(\"SELECT * FROM default.{}\".format(dbname)))" ] }, { "cell_type": "markdown", "id": "1d389546-911f-43f7-aad1-49f7bcc83503", "metadata": { "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ "### InfluxDB\n" ] }, { "cell_type": "code", "execution_count": null, "id": "c3e7ebfd-76f1-4ac4-9833-312eb1a531af", "metadata": {}, "outputs": [], "source": [ "client = influxdb_client.InfluxDBClient(\n", " url=InfluxDBUrl, token=InfluxDBKey, org=InfluxDBUser\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "cbf61f12-830b-4c57-804a-2257d8b3599a", "metadata": { "tags": [] }, "outputs": [], "source": [ "# Read data from CSV without index and parse 'TimeStamp' as date.\n", "df = pd.read_csv(\"out.csv\", sep=\",\", index_col=False, parse_dates=[\"from\"])\n", "# Set 'TimeStamp' field as index of dataframe # test another indexs\n", "df.set_index(\"from\", inplace=True)" ] }, { "cell_type": "code", "execution_count": null, "id": "54342a28-ba2b-4ade-a692-00566b53a639", "metadata": { "tags": [] }, "outputs": [], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": null, "id": "f861fab2-f1b1-49dd-b758-12d10aef3462", "metadata": {}, "outputs": [], "source": [ "%%time\n", "# gravando... demorou... mas deu certo\n", "with client.write_api() as writer:\n", " writer.write(\n", " bucket=InfluxDBBucket,\n", " record=df,\n", " data_frame_measurement_name=\"id\",\n", " data_frame_tag_columns=[\"volume\"],\n", " )" ] }, { "cell_type": "code", "execution_count": null, "id": "0bb2563d-68e2-4ff4-8842-70ac730dc6b1", "metadata": {}, "outputs": [], "source": [ "# data\n", "# |> pivot(\n", "# rowKey:[\"_time\"],\n", "# columnKey: [\"_field\"],\n", "# valueColumn: \"_value\"\n", "# )" ] }, { "cell_type": "code", "execution_count": null, "id": "bb1596f9-4cee-4642-803a-ee61c9dddf64", "metadata": {}, "outputs": [], "source": [ "# Read" ] }, { "cell_type": "markdown", "id": "b9ddfdc6-c899-4f6c-9b4e-8ec6ab6d7e05", "metadata": { "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ "### Postgresql" ] }, { "cell_type": "code", "execution_count": null, "id": "16cd8eb7-333d-43fd-88e0-ee983645d3fd", "metadata": {}, "outputs": [], "source": [ "# Connect / Create Tables\n", "engine = create_engine(\n", " \"postgresql+psycopg2://{}:{}@{}:5432/{}\".format(\n", " PostgresqlUser, PostgresqlKey, PostgresqlUrl, PostgresqlDB\n", " )\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "be31f3a0-b7ed-48e6-9b65-dc16319fb8d1", "metadata": {}, "outputs": [], "source": [ "# Drop old table and create new empty table\n", "df.head(0).to_sql(\"comparedbs\", engine, if_exists=\"replace\", index=False)" ] }, { "cell_type": "code", "execution_count": null, "id": "a7883c4d-4609-4380-8a45-246b7ca2f9c5", "metadata": { "tags": [] }, "outputs": [], "source": [ "%%time\n", "# Write\n", "conn = engine.raw_connection()\n", "cur = conn.cursor()\n", "output = io.StringIO()\n", "df.to_csv(output, sep=\"\\t\", header=False, index=False)\n", "output.seek(0)\n", "contents = output.getvalue()\n", "\n", "cur.copy_from(output, \"comparedbs\") # , null=\"\") # null values become ''\n", "conn.commit()\n", "cur.close()\n", "conn.close()" ] }, { "cell_type": "code", "execution_count": null, "id": "e37a93e1-fc0e-4d27-9e16-dca6c8aea324", "metadata": {}, "outputs": [], "source": [ "# Read" ] }, { "cell_type": "markdown", "id": "f9e0393d-7d1d-406a-a068-9dbf4968e977", "metadata": { "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ "### S3 Parquet" ] }, { "cell_type": "code", "execution_count": null, "id": "60a990e2-4607-4654-84ec-17d4985adae2", "metadata": { "tags": [] }, "outputs": [], "source": [ "# fazer sem funçao para ver se melhora\n", "# verifique se esta no ssd os arquivos da pasta git\n", "def main():\n", " client = Minio(\n", " S3MinioUrl,\n", " secure=False,\n", " region=S3MinioRegion,\n", " access_key=\"MatMPA7NyHltz7DQ\",\n", " secret_key=\"SO1IG5iBPSjNPZanYUaHCLcoSbjphLCP\",\n", " )\n", "\n", " # Make bucket if not exist.\n", " found = client.bucket_exists(\"data\")\n", " if not found:\n", " client.make_bucket(\"data\")\n", " else:\n", " print(\"Bucket 'data' already exists\")\n", "\n", " # Upload\n", " client.fput_object(\n", " \"data\",\n", " \"data.parquet\",\n", " \"data/data.parquet\",\n", " )\n", " # print(\n", " # \"'data/data.parquet' is successfully uploaded as \"\n", " # \"object 'data.parquet' to bucket 'data'.\"\n", " # )" ] }, { "cell_type": "code", "execution_count": null, "id": "390918c8-c88f-404a-96c4-685d578fdad0", "metadata": { "tags": [] }, "outputs": [], "source": [ "%%time\n", "df.to_parquet(\"data/data.parquet\")\n", "if __name__ == \"__main__\":\n", " try:\n", " main()\n", " except S3Error as exc:\n", " print(\"error occurred.\", exc)" ] }, { "cell_type": "code", "execution_count": null, "id": "a9e07143-8c11-4b68-a869-c3922cda9092", "metadata": { "tags": [] }, "outputs": [], "source": [ "pq = pd.read_parquet(\"data/data.parquet\", engine=\"pyarrow\")\n", "pq.head()" ] }, { "cell_type": "markdown", "id": "50d1fc58-89a7-4507-aff0-6e943656cfe0", "metadata": { "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ "### MongoDB" ] }, { "cell_type": "code", "execution_count": 10, "id": "d104d9af-fa34-4261-8478-329a28ee4f2e", "metadata": { "tags": [] }, "outputs": [], "source": [ "# Load csv dataset\n", "data = pd.read_csv(\"out.csv\")" ] }, { "cell_type": "code", "execution_count": 36, "id": "0af8f72c-5b58-4dfc-af36-c5b4bc79f127", "metadata": { "tags": [] }, "outputs": [], "source": [ "# Connect to MongoDB\n", "client = MongoClient(\n", " # \"mongodb://192.168.1.133:27017\"\n", " \"mongodb://{}:{}@{}/EURUSDtest?retryWrites=true&w=majority\".format(\n", " MongoUser, MongoKey, MongoUrl\n", " ),\n", " authSource=\"admin\",\n", ")" ] }, { "cell_type": "code", "execution_count": 37, "id": "f1b20d15-f5af-463c-813f-ffae61119de1", "metadata": { "tags": [] }, "outputs": [], "source": [ "db = client[\"EUROUSDtest\"]\n", "collection = db[\"finance\"]\n", "# data.reset_index(inplace=True)\n", "data_dict = data.to_dict(\"records\")" ] }, { "cell_type": "code", "execution_count": 38, "id": "70674d23-f375-4659-87ec-c745dec96d54", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 19.2 s, sys: 269 ms, total: 19.5 s\n", "Wall time: 50.1 s\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "# Insert collection\n", "collection.insert_many(data_dict)" ] }, { "cell_type": "code", "execution_count": null, "id": "81a4a33d-5914-45d8-af4e-2b0aabd2ac38", "metadata": { "tags": [] }, "outputs": [], "source": [ "# read" ] }, { "cell_type": "markdown", "id": "97405e42-61dc-42c7-8220-237a312c0ec7", "metadata": { "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ "### DuckDB" ] }, { "cell_type": "code", "execution_count": 39, "id": "bbcdb883-d6dc-46db-88db-4c90b84522ba", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(42,)]\n" ] } ], "source": [ "cursor = duckdb.connect()\n", "print(cursor.execute(\"SELECT 42\").fetchall())" ] }, { "cell_type": "code", "execution_count": 45, "id": "35025a6e-9dc7-46cf-a792-76b3d84f1ac0", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.53 s, sys: 63.6 ms, total: 1.59 s\n", "Wall time: 1.59 s\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "conn = duckdb.connect()\n", "data = pd.read_csv(\"out.csv\")\n", "conn.register(\"EURUSDtest\", data)" ] }, { "cell_type": "code", "execution_count": 47, "id": "c6abdaaa-3ac2-425b-9208-d6cb79afe966", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Unnamed: 0 id from at \n", "0 0 7730801 2023-01-02 15:58:45 1672675140000000000 \\\n", "1 1 7730802 2023-01-02 15:59:00 1672675155000000000 \n", "2 2 7730803 2023-01-02 15:59:15 1672675170000000000 \n", "3 3 7730804 2023-01-02 15:59:30 1672675185000000000 \n", "4 4 7730805 2023-01-02 15:59:45 1672675200000000000 \n", "... ... ... ... ... \n", "999995 999995 7984748 2023-03-03 18:13:30 1677867225000000000 \n", "999996 999996 7984749 2023-03-03 18:13:45 1677867240000000000 \n", "999997 999997 7984750 2023-03-03 18:14:00 1677867255000000000 \n", "999998 999998 7984751 2023-03-03 18:14:15 1677867270000000000 \n", "999999 999999 7984752 2023-03-03 18:14:30 1677867285000000000 \n", "\n", " to open close min max volume \n", "0 2023-01-02 15:59:00 1.065995 1.066035 1.065930 1.066070 57 \n", "1 2023-01-02 15:59:15 1.066055 1.066085 1.066005 1.066115 52 \n", "2 2023-01-02 15:59:30 1.066080 1.066025 1.066025 1.066110 57 \n", "3 2023-01-02 15:59:45 1.065980 1.065985 1.065885 1.066045 64 \n", "4 2023-01-02 16:00:00 1.065975 1.066055 1.065830 1.066055 50 \n", "... ... ... ... ... ... ... \n", "999995 2023-03-03 18:13:45 1.062695 1.062635 1.062630 1.062700 64 \n", "999996 2023-03-03 18:14:00 1.062645 1.062650 1.062625 1.062650 43 \n", "999997 2023-03-03 18:14:15 1.062640 1.062625 1.062620 1.062665 47 \n", "999998 2023-03-03 18:14:30 1.062625 1.062535 1.062535 1.062645 43 \n", "999999 2023-03-03 18:14:45 1.062535 1.062520 1.062520 1.062580 59 \n", "\n", "[1000000 rows x 10 columns]" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "df = conn.execute(\"SELECT * FROM EURUSDtest\").df()\n", "df" ] }, { "cell_type": "markdown", "id": "4409cc89-ed14-4313-ac89-65b826038533", "metadata": {}, "source": [ "### Kdb+" ] }, { "cell_type": "code", "execution_count": 66, "id": "14f63810-1943-4e28-9bce-2148be6be02d", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "np.bool = np.bool_\n", "from qpython import qconnection" ] }, { "cell_type": "code", "execution_count": 49, "id": "8ff6c090-7e02-435a-a179-f2aab81da972", "metadata": {}, "outputs": [], "source": [ "# read csv\n", "data = pd.read_csv(\"out.csv\")" ] }, { "cell_type": "code", "execution_count": 50, "id": "b4eb8ab9-81e8-4732-8cf7-51f0981d3d57", "metadata": { "tags": [] }, "outputs": [], "source": [ "# open connection\n", "q = qconnection.QConnection(host=\"localhost\", port=5001)\n", "q.open()" ] }, { "cell_type": "code", "execution_count": 51, "id": "97cb6b5b-65a5-46a0-a4ee-e5c535a716ab", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 837 ms, sys: 40 ms, total: 877 ms\n", "Wall time: 1.16 s\n" ] } ], "source": [ "# send df to kd+ in memory bank\n", "%%time\n", "q.sendSync(\"{t::x}\", data)" ] }, { "cell_type": "code", "execution_count": 52, "id": "c2ed2d51-bc8e-4207-892a-35fc55d43570", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "b':/home/sandman/q/tab1'" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# write to on disk table\n", "q.sendSync(\"`:/home/sandman/q/tab1 set t\")" ] }, { "cell_type": "code", "execution_count": 53, "id": "9c055a95-f73f-43a3-8fbd-61e42235117e", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.78 ms, sys: 2 µs, total: 1.79 ms\n", "Wall time: 329 ms\n" ] } ], "source": [ "%%time\n", "# read from on disk table\n", "df2 = q.sendSync(\"tab2: get `:/home/sandman/q/tab1\")" ] }, { "cell_type": "code", "execution_count": 54, "id": "9760de38-9f04-4322-bfff-c7ee12d5dee5", "metadata": { "tags": [] }, "outputs": [], "source": [ "# print(df2)" ] }, { "cell_type": "code", "execution_count": 55, "id": "c06c9222-c69d-4872-9d21-052281a013e2", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 957 ms, sys: 87.9 ms, total: 1.05 s\n", "Wall time: 1.13 s\n" ] } ], "source": [ "%%time\n", "# load to variable df2\n", "df2 = q.sendSync(\"tab2\")" ] }, { "cell_type": "code", "execution_count": 58, "id": "8815f01c-fd0a-4f94-ab7f-f8ede84ba4e7", "metadata": { "tags": [] }, "outputs": [ { "ename": "TypeError", "evalue": "'QTable' object is not callable", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[58], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdf2\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[43m)\u001b[49m\n", "\u001b[0;31mTypeError\u001b[0m: 'QTable' object is not callable" ] } ], "source": [ "# df2(type)" ] }, { "cell_type": "code", "execution_count": 67, "id": "e6ed3927-4395-45cd-9a28-88c5db01f2e5", "metadata": { "tags": [] }, "outputs": [ { "ename": "AttributeError", "evalue": "'bool' object has no attribute 'to_numpy'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "File \u001b[0;32m:2\u001b[0m\n", "File \u001b[0;32m~/dev/pipenv/lib/python3.10/site-packages/qpython/qconnection.py:385\u001b[0m, in \u001b[0;36mQConnection.__call__\u001b[0;34m(self, *parameters, **options)\u001b[0m\n\u001b[1;32m 384\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39mparameters, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions):\n\u001b[0;32m--> 385\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msendSync\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mparameters\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/dev/pipenv/lib/python3.10/site-packages/qpython/qconnection.py:303\u001b[0m, in \u001b[0;36mQConnection.sendSync\u001b[0;34m(self, query, *parameters, **options)\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m'''Performs a synchronous query against a q service and returns parsed \u001b[39;00m\n\u001b[1;32m 250\u001b[0m \u001b[38;5;124;03mdata.\u001b[39;00m\n\u001b[1;32m 251\u001b[0m \u001b[38;5;124;03m\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 300\u001b[0m \u001b[38;5;124;03m :class:`.QReaderException`\u001b[39;00m\n\u001b[1;32m 301\u001b[0m \u001b[38;5;124;03m'''\u001b[39;00m\n\u001b[1;32m 302\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mquery(MessageType\u001b[38;5;241m.\u001b[39mSYNC, query, \u001b[38;5;241m*\u001b[39mparameters, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions)\n\u001b[0;32m--> 303\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreceive\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_only\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 305\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m response\u001b[38;5;241m.\u001b[39mtype \u001b[38;5;241m==\u001b[39m MessageType\u001b[38;5;241m.\u001b[39mRESPONSE:\n\u001b[1;32m 306\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m response\u001b[38;5;241m.\u001b[39mdata\n", "File \u001b[0;32m~/dev/pipenv/lib/python3.10/site-packages/qpython/qconnection.py:380\u001b[0m, in \u001b[0;36mQConnection.receive\u001b[0;34m(self, data_only, **options)\u001b[0m\n\u001b[1;32m 341\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mreceive\u001b[39m(\u001b[38;5;28mself\u001b[39m, data_only \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions):\n\u001b[1;32m 342\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m'''Reads and (optionally) parses the response from a q service.\u001b[39;00m\n\u001b[1;32m 343\u001b[0m \u001b[38;5;124;03m \u001b[39;00m\n\u001b[1;32m 344\u001b[0m \u001b[38;5;124;03m Retrieves query result along with meta-information:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 378\u001b[0m \u001b[38;5;124;03m :raises: :class:`.QReaderException`\u001b[39;00m\n\u001b[1;32m 379\u001b[0m \u001b[38;5;124;03m '''\u001b[39;00m\n\u001b[0;32m--> 380\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reader\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_options\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munion_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 381\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\u001b[38;5;241m.\u001b[39mdata \u001b[38;5;28;01mif\u001b[39;00m data_only \u001b[38;5;28;01melse\u001b[39;00m result\n", "File \u001b[0;32m~/dev/pipenv/lib/python3.10/site-packages/qpython/qreader.py:139\u001b[0m, in \u001b[0;36mQReader.read\u001b[0;34m(self, source, **options)\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m'''\u001b[39;00m\n\u001b[1;32m 121\u001b[0m \u001b[38;5;124;03mReads and optionally parses a single message.\u001b[39;00m\n\u001b[1;32m 122\u001b[0m \u001b[38;5;124;03m\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 136\u001b[0m \u001b[38;5;124;03m with meta information\u001b[39;00m\n\u001b[1;32m 137\u001b[0m \u001b[38;5;124;03m'''\u001b[39;00m\n\u001b[1;32m 138\u001b[0m message \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mread_header(source)\n\u001b[0;32m--> 139\u001b[0m message\u001b[38;5;241m.\u001b[39mdata \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmessage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmessage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mis_compressed\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m message\n", "File \u001b[0;32m~/dev/pipenv/lib/python3.10/site-packages/qpython/qreader.py:216\u001b[0m, in \u001b[0;36mQReader.read_data\u001b[0;34m(self, message_size, is_compressed, **options)\u001b[0m\n\u001b[1;32m 213\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stream \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_options\u001b[38;5;241m.\u001b[39mraw:\n\u001b[1;32m 214\u001b[0m raw_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_buffer\u001b[38;5;241m.\u001b[39mraw(message_size \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m8\u001b[39m)\n\u001b[0;32m--> 216\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m raw_data \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_options\u001b[38;5;241m.\u001b[39mraw \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_read_object\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/dev/pipenv/lib/python3.10/site-packages/qpython/qreader.py:225\u001b[0m, in \u001b[0;36mQReader._read_object\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 222\u001b[0m reader \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_reader(qtype)\n\u001b[1;32m 224\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m reader:\n\u001b[0;32m--> 225\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mreader\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mqtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 226\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m qtype \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m QBOOL_LIST \u001b[38;5;129;01mand\u001b[39;00m qtype \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m QTIME_LIST:\n\u001b[1;32m 227\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_read_list(qtype)\n", "File \u001b[0;32m~/dev/pipenv/lib/python3.10/site-packages/qpython/_pandas.py:76\u001b[0m, in \u001b[0;36mPandasQReader._read_table\u001b[0;34m(self, qtype)\u001b[0m\n\u001b[1;32m 74\u001b[0m columns \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_read_object()\n\u001b[1;32m 75\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_buffer\u001b[38;5;241m.\u001b[39mskip() \u001b[38;5;66;03m# ignore generic list type indicator\u001b[39;00m\n\u001b[0;32m---> 76\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mQReader\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_read_general_list\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mqtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 78\u001b[0m odict \u001b[38;5;241m=\u001b[39m OrderedDict()\n\u001b[1;32m 79\u001b[0m meta \u001b[38;5;241m=\u001b[39m MetaData(qtype \u001b[38;5;241m=\u001b[39m QTABLE)\n", "File \u001b[0;32m~/dev/pipenv/lib/python3.10/site-packages/qpython/qreader.py:338\u001b[0m, in \u001b[0;36mQReader._read_general_list\u001b[0;34m(self, qtype)\u001b[0m\n\u001b[1;32m 335\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_buffer\u001b[38;5;241m.\u001b[39mskip() \u001b[38;5;66;03m# ignore attributes\u001b[39;00m\n\u001b[1;32m 336\u001b[0m length \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_buffer\u001b[38;5;241m.\u001b[39mget_int()\n\u001b[0;32m--> 338\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_read_object() \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(length)]\n", "File \u001b[0;32m~/dev/pipenv/lib/python3.10/site-packages/qpython/qreader.py:338\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 335\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_buffer\u001b[38;5;241m.\u001b[39mskip() \u001b[38;5;66;03m# ignore attributes\u001b[39;00m\n\u001b[1;32m 336\u001b[0m length \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_buffer\u001b[38;5;241m.\u001b[39mget_int()\n\u001b[0;32m--> 338\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_read_object\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(length)]\n", "File \u001b[0;32m~/dev/pipenv/lib/python3.10/site-packages/qpython/qreader.py:227\u001b[0m, in \u001b[0;36mQReader._read_object\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 225\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m reader(\u001b[38;5;28mself\u001b[39m, qtype)\n\u001b[1;32m 226\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m qtype \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m QBOOL_LIST \u001b[38;5;129;01mand\u001b[39;00m qtype \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m QTIME_LIST:\n\u001b[0;32m--> 227\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_read_list\u001b[49m\u001b[43m(\u001b[49m\u001b[43mqtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 228\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m qtype \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m QBOOL \u001b[38;5;129;01mand\u001b[39;00m qtype \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m QTIME:\n\u001b[1;32m 229\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_read_atom(qtype)\n", "File \u001b[0;32m~/dev/pipenv/lib/python3.10/site-packages/qpython/_pandas.py:116\u001b[0m, in \u001b[0;36mPandasQReader._read_list\u001b[0;34m(self, qtype)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;241m-\u001b[39m\u001b[38;5;28mabs\u001b[39m(qtype) \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m [QMONTH, QDATE, QDATETIME, QMINUTE, QSECOND, QTIME, QTIMESTAMP, QTIMESPAN, QSYMBOL]:\n\u001b[1;32m 115\u001b[0m null \u001b[38;5;241m=\u001b[39m QNULLMAP[\u001b[38;5;241m-\u001b[39m\u001b[38;5;28mabs\u001b[39m(qtype)][\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m--> 116\u001b[0m ps \u001b[38;5;241m=\u001b[39m \u001b[43mpandas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mSeries\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mqlist\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreplace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnull\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumpy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mNaN\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 117\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 118\u001b[0m ps \u001b[38;5;241m=\u001b[39m pandas\u001b[38;5;241m.\u001b[39mSeries(data \u001b[38;5;241m=\u001b[39m qlist)\n", "File \u001b[0;32m~/dev/pipenv/lib/python3.10/site-packages/pandas/core/series.py:5219\u001b[0m, in \u001b[0;36mSeries.replace\u001b[0;34m(self, to_replace, value, inplace, limit, regex, method)\u001b[0m\n\u001b[1;32m 5203\u001b[0m \u001b[38;5;129m@doc\u001b[39m(\n\u001b[1;32m 5204\u001b[0m NDFrame\u001b[38;5;241m.\u001b[39mreplace,\n\u001b[1;32m 5205\u001b[0m klass\u001b[38;5;241m=\u001b[39m_shared_doc_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mklass\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 5217\u001b[0m method: Literal[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpad\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mffill\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbfill\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m|\u001b[39m lib\u001b[38;5;241m.\u001b[39mNoDefault \u001b[38;5;241m=\u001b[39m lib\u001b[38;5;241m.\u001b[39mno_default,\n\u001b[1;32m 5218\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Series \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 5219\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreplace\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 5220\u001b[0m \u001b[43m \u001b[49m\u001b[43mto_replace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mto_replace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5221\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5222\u001b[0m \u001b[43m \u001b[49m\u001b[43minplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minplace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5223\u001b[0m \u001b[43m \u001b[49m\u001b[43mlimit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlimit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5224\u001b[0m \u001b[43m \u001b[49m\u001b[43mregex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mregex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5225\u001b[0m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5226\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/dev/pipenv/lib/python3.10/site-packages/pandas/core/generic.py:7389\u001b[0m, in \u001b[0;36mNDFrame.replace\u001b[0;34m(self, to_replace, value, inplace, limit, regex, method)\u001b[0m\n\u001b[1;32m 7383\u001b[0m new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mgr\u001b[38;5;241m.\u001b[39mreplace_regex(\n\u001b[1;32m 7384\u001b[0m to_replace\u001b[38;5;241m=\u001b[39mto_replace,\n\u001b[1;32m 7385\u001b[0m value\u001b[38;5;241m=\u001b[39mvalue,\n\u001b[1;32m 7386\u001b[0m inplace\u001b[38;5;241m=\u001b[39minplace,\n\u001b[1;32m 7387\u001b[0m )\n\u001b[1;32m 7388\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 7389\u001b[0m new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreplace\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 7390\u001b[0m \u001b[43m \u001b[49m\u001b[43mto_replace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mto_replace\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minplace\u001b[49m\n\u001b[1;32m 7391\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 7392\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 7393\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m 7394\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mInvalid \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mto_replace\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m type: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mrepr\u001b[39m(\u001b[38;5;28mtype\u001b[39m(to_replace)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 7395\u001b[0m )\n", "File \u001b[0;32m~/dev/pipenv/lib/python3.10/site-packages/pandas/core/internals/managers.py:475\u001b[0m, in \u001b[0;36mBaseBlockManager.replace\u001b[0;34m(self, to_replace, value, inplace)\u001b[0m\n\u001b[1;32m 473\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_list_like(to_replace)\n\u001b[1;32m 474\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_list_like(value)\n\u001b[0;32m--> 475\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 476\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mreplace\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 477\u001b[0m \u001b[43m \u001b[49m\u001b[43mto_replace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mto_replace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 478\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 479\u001b[0m \u001b[43m \u001b[49m\u001b[43minplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minplace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 480\u001b[0m \u001b[43m \u001b[49m\u001b[43musing_cow\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43musing_copy_on_write\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 481\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/dev/pipenv/lib/python3.10/site-packages/pandas/core/internals/managers.py:352\u001b[0m, in \u001b[0;36mBaseBlockManager.apply\u001b[0;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[1;32m 350\u001b[0m applied \u001b[38;5;241m=\u001b[39m b\u001b[38;5;241m.\u001b[39mapply(f, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 351\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 352\u001b[0m applied \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 353\u001b[0m result_blocks \u001b[38;5;241m=\u001b[39m extend_blocks(applied, result_blocks)\n\u001b[1;32m 355\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39mfrom_blocks(result_blocks, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes)\n", "File \u001b[0;32m~/dev/pipenv/lib/python3.10/site-packages/pandas/core/internals/blocks.py:593\u001b[0m, in \u001b[0;36mBlock.replace\u001b[0;34m(self, to_replace, value, inplace, mask, using_cow)\u001b[0m\n\u001b[1;32m 590\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [\u001b[38;5;28mself\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m inplace \u001b[38;5;28;01melse\u001b[39;00m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcopy()]\n\u001b[1;32m 592\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 593\u001b[0m mask \u001b[38;5;241m=\u001b[39m \u001b[43mmissing\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmask_missing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mto_replace\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 594\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m mask\u001b[38;5;241m.\u001b[39many():\n\u001b[1;32m 595\u001b[0m \u001b[38;5;66;03m# Note: we get here with test_replace_extension_other incorrectly\u001b[39;00m\n\u001b[1;32m 596\u001b[0m \u001b[38;5;66;03m# bc _can_hold_element is incorrect.\u001b[39;00m\n\u001b[1;32m 597\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m using_cow:\n", "File \u001b[0;32m~/dev/pipenv/lib/python3.10/site-packages/pandas/core/missing.py:112\u001b[0m, in \u001b[0;36mmask_missing\u001b[0;34m(arr, values_to_mask)\u001b[0m\n\u001b[1;32m 108\u001b[0m new_mask \u001b[38;5;241m=\u001b[39m arr \u001b[38;5;241m==\u001b[39m x\n\u001b[1;32m 110\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(new_mask, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[1;32m 111\u001b[0m \u001b[38;5;66;03m# usually BooleanArray\u001b[39;00m\n\u001b[0;32m--> 112\u001b[0m new_mask \u001b[38;5;241m=\u001b[39m \u001b[43mnew_mask\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_numpy\u001b[49m(dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mbool\u001b[39m, na_value\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 113\u001b[0m mask \u001b[38;5;241m|\u001b[39m\u001b[38;5;241m=\u001b[39m new_mask\n\u001b[1;32m 115\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m na_mask\u001b[38;5;241m.\u001b[39many():\n", "\u001b[0;31mAttributeError\u001b[0m: 'bool' object has no attribute 'to_numpy'" ] } ], "source": [ "%%time\n", "# converto to dataframe\n", "df = pd.DataFrame(q(\"t\", pandas=True))\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 19, "id": "0fc7f16b-6c39-4ebe-88d2-ff857e30ab62", "metadata": { "tags": [] }, "outputs": [], "source": [ "%%time\n", "# select\n", "df3 = q.sendSync(\"select from t\")" ] }, { "cell_type": "code", "execution_count": null, "id": "c88646ca-3d25-4a85-80b5-f9e559f568dd", "metadata": {}, "outputs": [], "source": [ "q.close()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.11" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": {}, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 5 }