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added mongodb, duckDB and Kdb+ dbs benchmarks

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flashlan 3 years ago
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1 changed files with 553 additions and 122 deletions
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  1. 675
      compareDBs.ipynb

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compareDBs.ipynb
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@ -47,7 +47,7 @@
},
{
"cell_type": "code",
"execution_count": 74,
"execution_count": 18,
"id": "ab6c6c81-6ac1-4668-a79b-a9a0341fb35a",
"metadata": {
"tags": []
@ -59,7 +59,7 @@
"False"
]
},
"execution_count": 74,
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
@ -68,8 +68,11 @@
"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",
@ -77,41 +80,12 @@
"from pytz import timezone\n",
"from sqlalchemy import create_engine\n",
"\n",
"load_dotenv()\n",
"\n",
"\n",
"# import io\n",
"# import time\n",
"# import numpy as np\n",
"# import clickhouse_connect\n",
"# pip install python-dotenv\n",
"# import psycopg2\n",
"# import os\n",
"# import pyarrow as pa\n",
"# import pyarrow.parquet as pq\n",
"# import s3fs\n",
"# from friendly.jupyter import Friendly\n",
"# from minio.error import S3Error\n",
"# from pyarrow import Table\n",
"# import os\n",
"# from influxdb_client import InfluxDBClient, Point, WritePrecision\n",
"# from influxdb_client.client.write_api import SYNCHRONOUS\n",
"# Friendly.dark()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "01d88282-32a1-404f-92da-488a23302fd0",
"metadata": {},
"outputs": [],
"source": [
"# teset"
"load_dotenv()"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"id": "55c3cd57-0996-4723-beb5-8f3196c96009",
"metadata": {
"tags": []
@ -124,7 +98,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 5,
"id": "968403e3-2e5e-4834-b969-be4600e2963a",
"metadata": {
"tags": []
@ -196,6 +170,7 @@
"cell_type": "markdown",
"id": "274cc026-2f48-4e38-b80f-b1a9ff982060",
"metadata": {
"jp-MarkdownHeadingCollapsed": true,
"tags": []
},
"source": [
@ -220,6 +195,7 @@
"cell_type": "markdown",
"id": "4a8d5703-9bc9-4d38-83ff-457159304d58",
"metadata": {
"jp-MarkdownHeadingCollapsed": true,
"tags": []
},
"source": [
@ -297,14 +273,15 @@
},
"outputs": [],
"source": [
"%%time\n",
"df = pd.DataFrame(client.query_dataframe(\"SELECT * FROM default.{}\".format(dbname)))"
"# %%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": [
@ -468,6 +445,7 @@
"cell_type": "markdown",
"id": "f9e0393d-7d1d-406a-a068-9dbf4968e977",
"metadata": {
"jp-MarkdownHeadingCollapsed": true,
"tags": []
},
"source": [
@ -476,7 +454,7 @@
},
{
"cell_type": "code",
"execution_count": 72,
"execution_count": null,
"id": "60a990e2-4607-4654-84ec-17d4985adae2",
"metadata": {
"tags": []
@ -515,36 +493,257 @@
},
{
"cell_type": "code",
"execution_count": 73,
"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": [
"<pymongo.results.InsertManyResult at 0x7f8e39c991e0>"
]
},
"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": [
"Bucket 'data' already exists\n",
"CPU times: user 610 ms, sys: 133 ms, total: 743 ms\n",
"Wall time: 4.05 s\n"
"[(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": [
"<duckdb.DuckDBPyConnection at 0x7f8e3b2261b0>"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"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)"
"conn = duckdb.connect()\n",
"data = pd.read_csv(\"out.csv\")\n",
"conn.register(\"EURUSDtest\", data)"
]
},
{
"cell_type": "code",
"execution_count": 71,
"id": "a9e07143-8c11-4b68-a869-c3922cda9092",
"execution_count": 47,
"id": "c6abdaaa-3ac2-425b-9208-d6cb79afe966",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
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"</table>\n",
"</div>"
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"text/plain": [
"Empty DataFrame\n",
"Columns: [name]\n",
"Index: []"
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(conn.execute(\"SHOW TABLES\").df())"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "2acce0f3-f0b2-47d0-8e0d-f9e9687efc18",
"metadata": {
"tags": []
},
@ -572,6 +771,7 @@
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>id</th>\n",
" <th>from</th>\n",
" <th>at</th>\n",
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" <th>open</th>\n",
@ -580,24 +780,13 @@
" <th>max</th>\n",
" <th>volume</th>\n",
" </tr>\n",
" <tr>\n",
" <th>from</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>2023-01-02 15:58:45</th>\n",
" <th>0</th>\n",
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" <td>7730801</td>\n",
" <td>2023-01-02 15:58:45</td>\n",
" <td>1672675140000000000</td>\n",
" <td>2023-01-02 15:59:00</td>\n",
" <td>1.065995</td>\n",
@ -607,9 +796,10 @@
" <td>57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-01-02 15:59:00</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>7730802</td>\n",
" <td>2023-01-02 15:59:00</td>\n",
" <td>1672675155000000000</td>\n",
" <td>2023-01-02 15:59:15</td>\n",
" <td>1.066055</td>\n",
@ -619,9 +809,10 @@
" <td>52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-01-02 15:59:15</th>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>7730803</td>\n",
" <td>2023-01-02 15:59:15</td>\n",
" <td>1672675170000000000</td>\n",
" <td>2023-01-02 15:59:30</td>\n",
" <td>1.066080</td>\n",
@ -631,9 +822,10 @@
" <td>57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-01-02 15:59:30</th>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>7730804</td>\n",
" <td>2023-01-02 15:59:30</td>\n",
" <td>1672675185000000000</td>\n",
" <td>2023-01-02 15:59:45</td>\n",
" <td>1.065980</td>\n",
@ -643,9 +835,10 @@
" <td>64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-01-02 15:59:45</th>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>7730805</td>\n",
" <td>2023-01-02 15:59:45</td>\n",
" <td>1672675200000000000</td>\n",
" <td>2023-01-02 16:00:00</td>\n",
" <td>1.065975</td>\n",
@ -654,130 +847,368 @@
" <td>1.066055</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999995</th>\n",
" <td>999995</td>\n",
" <td>7984748</td>\n",
" <td>2023-03-03 18:13:30</td>\n",
" <td>1677867225000000000</td>\n",
" <td>2023-03-03 18:13:45</td>\n",
" <td>1.062695</td>\n",
" <td>1.062635</td>\n",
" <td>1.062630</td>\n",
" <td>1.062700</td>\n",
" <td>64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999996</th>\n",
" <td>999996</td>\n",
" <td>7984749</td>\n",
" <td>2023-03-03 18:13:45</td>\n",
" <td>1677867240000000000</td>\n",
" <td>2023-03-03 18:14:00</td>\n",
" <td>1.062645</td>\n",
" <td>1.062650</td>\n",
" <td>1.062625</td>\n",
" <td>1.062650</td>\n",
" <td>43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999997</th>\n",
" <td>999997</td>\n",
" <td>7984750</td>\n",
" <td>2023-03-03 18:14:00</td>\n",
" <td>1677867255000000000</td>\n",
" <td>2023-03-03 18:14:15</td>\n",
" <td>1.062640</td>\n",
" <td>1.062625</td>\n",
" <td>1.062620</td>\n",
" <td>1.062665</td>\n",
" <td>47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999998</th>\n",
" <td>999998</td>\n",
" <td>7984751</td>\n",
" <td>2023-03-03 18:14:15</td>\n",
" <td>1677867270000000000</td>\n",
" <td>2023-03-03 18:14:30</td>\n",
" <td>1.062625</td>\n",
" <td>1.062535</td>\n",
" <td>1.062535</td>\n",
" <td>1.062645</td>\n",
" <td>43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999999</th>\n",
" <td>999999</td>\n",
" <td>7984752</td>\n",
" <td>2023-03-03 18:14:30</td>\n",
" <td>1677867285000000000</td>\n",
" <td>2023-03-03 18:14:45</td>\n",
" <td>1.062535</td>\n",
" <td>1.062520</td>\n",
" <td>1.062520</td>\n",
" <td>1.062580</td>\n",
" <td>59</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1000000 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 id at \n",
"from \n",
"2023-01-02 15:58:45 0 7730801 1672675140000000000 \\\n",
"2023-01-02 15:59:00 1 7730802 1672675155000000000 \n",
"2023-01-02 15:59:15 2 7730803 1672675170000000000 \n",
"2023-01-02 15:59:30 3 7730804 1672675185000000000 \n",
"2023-01-02 15:59:45 4 7730805 1672675200000000000 \n",
" 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 \n",
"from \n",
"2023-01-02 15:58:45 2023-01-02 15:59:00 1.065995 1.066035 1.065930 \\\n",
"2023-01-02 15:59:00 2023-01-02 15:59:15 1.066055 1.066085 1.066005 \n",
"2023-01-02 15:59:15 2023-01-02 15:59:30 1.066080 1.066025 1.066025 \n",
"2023-01-02 15:59:30 2023-01-02 15:59:45 1.065980 1.065985 1.065885 \n",
"2023-01-02 15:59:45 2023-01-02 16:00:00 1.065975 1.066055 1.065830 \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",
" max volume \n",
"from \n",
"2023-01-02 15:58:45 1.066070 57 \n",
"2023-01-02 15:59:00 1.066115 52 \n",
"2023-01-02 15:59:15 1.066110 57 \n",
"2023-01-02 15:59:30 1.066045 64 \n",
"2023-01-02 15:59:45 1.066055 50 "
"[1000000 rows x 10 columns]"
]
},
"execution_count": 71,
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pq = pd.read_parquet(\"data/data.parquet\", engine=\"pyarrow\")\n",
"pq.head()"
"%%time\n",
"df = conn.execute(\"SELECT * FROM EURUSDtest\").df()\n",
"df"
]
},
{
"cell_type": "markdown",
"id": "50d1fc58-89a7-4507-aff0-6e943656cfe0",
"id": "4409cc89-ed14-4313-ac89-65b826038533",
"metadata": {},
"source": [
"### MongoDB"
"### Kdb+"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "81a4a33d-5914-45d8-af4e-2b0aabd2ac38",
"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": [
"client = MongoClient(MongoUrl);"
"# open connection\n",
"q = qconnection.QConnection(host=\"localhost\", port=5001)\n",
"q.open()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3e634d34-ad62-432e-aa0c-07cd4b7556e2",
"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": [],
"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": [
"DB = client[\"collection_name\"]"
"%%time\n",
"# read from on disk table\n",
"df2 = q.sendSync(\"tab2: get `:/home/sandman/q/tab1\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dd871028-41c3-4f0d-aefc-f2ea4ee866e7",
"execution_count": 54,
"id": "9760de38-9f04-4322-bfff-c7ee12d5dee5",
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"db = client[\"test\"]"
"# print(df2)"
]
},
{
"cell_type": "markdown",
"id": "97405e42-61dc-42c7-8220-237a312c0ec7",
"metadata": {},
"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": [
"### DuckDB"
"%%time\n",
"# load to variable df2\n",
"df2 = q.sendSync(\"tab2\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bbcdb883-d6dc-46db-88db-4c90b84522ba",
"metadata": {},
"outputs": [],
"source": []
"execution_count": 58,
"id": "8815f01c-fd0a-4f94-ab7f-f8ede84ba4e7",
"metadata": {
"tags": []
},
"outputs": [
{
"cell_type": "markdown",
"id": "4409cc89-ed14-4313-ac89-65b826038533",
"metadata": {},
"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": [
"### Kdb+"
"# df2(type)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "14f63810-1943-4e28-9bce-2148be6be02d",
"metadata": {},
"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<timed exec>: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<listcomp>\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": []
"source": [
"%%time\n",
"# select\n",
"df3 = q.sendSync(\"select from t\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3bf5e29b-fd38-4ec6-b583-f53e504073ab",
"id": "c88646ca-3d25-4a85-80b5-f9e559f568dd",
"metadata": {},
"outputs": [],
"source": []
"source": [
"q.close()"
]
}
],
"metadata": {

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