├── README.md ├── notebooks ├── README.md ├── EX-1.1-Something-You-Should-Not-Do.ipynb ├── iris.csv ├── iris_with_names.csv ├── EX-4-Pivot-Using-SQL-And-Pandas.ipynb ├── EX-1.0-Getting-to-Know-the-Clickhouse-driver-Client.ipynb ├── EX-3-Sql-Magic-Functions.ipynb ├── EX-5-Airline-OnTime-Data.ipynb └── EX-2-ClickHouse-SQL-Alchemy.ipynb ├── LICENSE └── .gitignore /README.md: -------------------------------------------------------------------------------- 1 | # ClickHouse Python Samples 2 | 3 | This project contains Python samples for ClickHouse. For now the samples 4 | consist of Jupyter Notebook files in the notebooks directory. 5 | -------------------------------------------------------------------------------- /notebooks/README.md: -------------------------------------------------------------------------------- 1 | # Juypter Notebooks for ClickHouse 2 | 3 | This directory contains sample notebooks showing how to connect Python 4 | to ClickHouse. 5 | 6 | Notebook code has been tested on Ubuntu using Anaconda and Python 3.7. 7 | They should work from Python 3.5 onwards. 8 | 9 | To get started you'll need to add the following packages using conda: 10 | ``` 11 | conda install -c conda-forge clickhouse-driver 12 | conda install -c conda-forge clickhouse-sqlalchemy 13 | ``` 14 | 15 | Examples use sample data from the iris dataset. 16 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 Altinity 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | .pytest_cache/ 49 | 50 | # Translations 51 | *.mo 52 | *.pot 53 | 54 | # Django stuff: 55 | *.log 56 | local_settings.py 57 | db.sqlite3 58 | 59 | # Flask stuff: 60 | instance/ 61 | .webassets-cache 62 | 63 | # Scrapy stuff: 64 | .scrapy 65 | 66 | # Sphinx documentation 67 | docs/_build/ 68 | 69 | # PyBuilder 70 | target/ 71 | 72 | # Jupyter Notebook 73 | .ipynb_checkpoints 74 | 75 | # pyenv 76 | .python-version 77 | 78 | # celery beat schedule file 79 | celerybeat-schedule 80 | 81 | # SageMath parsed files 82 | *.sage.py 83 | 84 | # Environments 85 | .env 86 | .venv 87 | env/ 88 | venv/ 89 | ENV/ 90 | env.bak/ 91 | venv.bak/ 92 | 93 | # Spyder project settings 94 | .spyderproject 95 | .spyproject 96 | 97 | # Rope project settings 98 | .ropeproject 99 | 100 | # mkdocs documentation 101 | /site 102 | 103 | # mypy 104 | .mypy_cache/ 105 | -------------------------------------------------------------------------------- /notebooks/EX-1.1-Something-You-Should-Not-Do.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Something You Should Not Do\n", 8 | "\n", 9 | "This notebook has samples that were included in the [Altinity blog article that introduces the clickhouse-driver client library](https://www.altinity.com/blog/clickhouse-and-python-getting-to-know-the-clickhouse-driver-client).\n", 10 | "\n", 11 | "_WARNING_: The final example hangs. It must be cancelled manually. " 12 | ] 13 | }, 14 | { 15 | "cell_type": "markdown", 16 | "metadata": {}, 17 | "source": [ 18 | "Load the clickhouse driver and connect to a local server. " 19 | ] 20 | }, 21 | { 22 | "cell_type": "code", 23 | "execution_count": null, 24 | "metadata": {}, 25 | "outputs": [], 26 | "source": [ 27 | "from clickhouse_driver import Client\n", 28 | "client = Client('localhost')" 29 | ] 30 | }, 31 | { 32 | "cell_type": "markdown", 33 | "metadata": {}, 34 | "source": [ 35 | "Create the iris table, dropping any previously existing table of the same name. " 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": null, 41 | "metadata": {}, 42 | "outputs": [], 43 | "source": [ 44 | "client.execute('DROP TABLE IF EXISTS iris')\n", 45 | "client.execute('CREATE TABLE iris ('\n", 46 | " 'sepal_length Float64, sepal_width Float64, '\n", 47 | " 'petal_length Float64, petal_width Float64, '\n", 48 | " 'species String) ENGINE = MergeTree '\n", 49 | " ' PARTITION BY species ORDER BY (species)')" 50 | ] 51 | }, 52 | { 53 | "cell_type": "markdown", 54 | "metadata": {}, 55 | "source": [ 56 | "If you try to insert values using a single string INSERT command that works with clickhouse-client the results will be disappointing. This command will hang. Once you see it hang, press the STOP button in your Jupyter environment. " 57 | ] 58 | }, 59 | { 60 | "cell_type": "code", 61 | "execution_count": null, 62 | "metadata": {}, 63 | "outputs": [], 64 | "source": [ 65 | "# Don’t do this.\n", 66 | "try:\n", 67 | " client.execute(\n", 68 | " \"INSERT INTO iris (sepal_length, sepal_width, petal_length, petal_width, species) \" \n", 69 | " \"VALUES (5.1, 3.7, 1.5, 0.4, 'Iris-setosa'), (4.6, 3.6, 1.0, 0.2, 'Iris-setosa')\"\n", 70 | " )\n", 71 | "except:\n", 72 | " print(\"The command failed as expected\")" 73 | ] 74 | }, 75 | { 76 | "cell_type": "markdown", 77 | "metadata": {}, 78 | "source": [ 79 | "You have to put insert data in a separate array. Python cannot translate this the way clickhouse-client does. (It creates the array transparently.)" 80 | ] 81 | }, 82 | { 83 | "cell_type": "code", 84 | "execution_count": null, 85 | "metadata": {}, 86 | "outputs": [], 87 | "source": [] 88 | } 89 | ], 90 | "metadata": { 91 | "kernelspec": { 92 | "display_name": "Python 3", 93 | "language": "python", 94 | "name": "python3" 95 | }, 96 | "language_info": { 97 | "codemirror_mode": { 98 | "name": "ipython", 99 | "version": 3 100 | }, 101 | "file_extension": ".py", 102 | "mimetype": "text/x-python", 103 | "name": "python", 104 | "nbconvert_exporter": "python", 105 | "pygments_lexer": "ipython3", 106 | "version": "3.7.1" 107 | } 108 | }, 109 | "nbformat": 4, 110 | "nbformat_minor": 2 111 | } 112 | -------------------------------------------------------------------------------- /notebooks/iris.csv: -------------------------------------------------------------------------------- 1 | 5.1,3.5,1.4,0.2,Iris-setosa 2 | 4.9,3.0,1.4,0.2,Iris-setosa 3 | 4.7,3.2,1.3,0.2,Iris-setosa 4 | 4.6,3.1,1.5,0.2,Iris-setosa 5 | 5.0,3.6,1.4,0.2,Iris-setosa 6 | 5.4,3.9,1.7,0.4,Iris-setosa 7 | 4.6,3.4,1.4,0.3,Iris-setosa 8 | 5.0,3.4,1.5,0.2,Iris-setosa 9 | 4.4,2.9,1.4,0.2,Iris-setosa 10 | 4.9,3.1,1.5,0.1,Iris-setosa 11 | 5.4,3.7,1.5,0.2,Iris-setosa 12 | 4.8,3.4,1.6,0.2,Iris-setosa 13 | 4.8,3.0,1.4,0.1,Iris-setosa 14 | 4.3,3.0,1.1,0.1,Iris-setosa 15 | 5.8,4.0,1.2,0.2,Iris-setosa 16 | 5.7,4.4,1.5,0.4,Iris-setosa 17 | 5.4,3.9,1.3,0.4,Iris-setosa 18 | 5.1,3.5,1.4,0.3,Iris-setosa 19 | 5.7,3.8,1.7,0.3,Iris-setosa 20 | 5.1,3.8,1.5,0.3,Iris-setosa 21 | 5.4,3.4,1.7,0.2,Iris-setosa 22 | 5.1,3.7,1.5,0.4,Iris-setosa 23 | 4.6,3.6,1.0,0.2,Iris-setosa 24 | 5.1,3.3,1.7,0.5,Iris-setosa 25 | 4.8,3.4,1.9,0.2,Iris-setosa 26 | 5.0,3.0,1.6,0.2,Iris-setosa 27 | 5.0,3.4,1.6,0.4,Iris-setosa 28 | 5.2,3.5,1.5,0.2,Iris-setosa 29 | 5.2,3.4,1.4,0.2,Iris-setosa 30 | 4.7,3.2,1.6,0.2,Iris-setosa 31 | 4.8,3.1,1.6,0.2,Iris-setosa 32 | 5.4,3.4,1.5,0.4,Iris-setosa 33 | 5.2,4.1,1.5,0.1,Iris-setosa 34 | 5.5,4.2,1.4,0.2,Iris-setosa 35 | 4.9,3.1,1.5,0.2,Iris-setosa 36 | 5.0,3.2,1.2,0.2,Iris-setosa 37 | 5.5,3.5,1.3,0.2,Iris-setosa 38 | 4.9,3.6,1.4,0.1,Iris-setosa 39 | 4.4,3.0,1.3,0.2,Iris-setosa 40 | 5.1,3.4,1.5,0.2,Iris-setosa 41 | 5.0,3.5,1.3,0.3,Iris-setosa 42 | 4.5,2.3,1.3,0.3,Iris-setosa 43 | 4.4,3.2,1.3,0.2,Iris-setosa 44 | 5.0,3.5,1.6,0.6,Iris-setosa 45 | 5.1,3.8,1.9,0.4,Iris-setosa 46 | 4.8,3.0,1.4,0.3,Iris-setosa 47 | 5.1,3.8,1.6,0.2,Iris-setosa 48 | 4.6,3.2,1.4,0.2,Iris-setosa 49 | 5.3,3.7,1.5,0.2,Iris-setosa 50 | 5.0,3.3,1.4,0.2,Iris-setosa 51 | 7.0,3.2,4.7,1.4,Iris-versicolor 52 | 6.4,3.2,4.5,1.5,Iris-versicolor 53 | 6.9,3.1,4.9,1.5,Iris-versicolor 54 | 5.5,2.3,4.0,1.3,Iris-versicolor 55 | 6.5,2.8,4.6,1.5,Iris-versicolor 56 | 5.7,2.8,4.5,1.3,Iris-versicolor 57 | 6.3,3.3,4.7,1.6,Iris-versicolor 58 | 4.9,2.4,3.3,1.0,Iris-versicolor 59 | 6.6,2.9,4.6,1.3,Iris-versicolor 60 | 5.2,2.7,3.9,1.4,Iris-versicolor 61 | 5.0,2.0,3.5,1.0,Iris-versicolor 62 | 5.9,3.0,4.2,1.5,Iris-versicolor 63 | 6.0,2.2,4.0,1.0,Iris-versicolor 64 | 6.1,2.9,4.7,1.4,Iris-versicolor 65 | 5.6,2.9,3.6,1.3,Iris-versicolor 66 | 6.7,3.1,4.4,1.4,Iris-versicolor 67 | 5.6,3.0,4.5,1.5,Iris-versicolor 68 | 5.8,2.7,4.1,1.0,Iris-versicolor 69 | 6.2,2.2,4.5,1.5,Iris-versicolor 70 | 5.6,2.5,3.9,1.1,Iris-versicolor 71 | 5.9,3.2,4.8,1.8,Iris-versicolor 72 | 6.1,2.8,4.0,1.3,Iris-versicolor 73 | 6.3,2.5,4.9,1.5,Iris-versicolor 74 | 6.1,2.8,4.7,1.2,Iris-versicolor 75 | 6.4,2.9,4.3,1.3,Iris-versicolor 76 | 6.6,3.0,4.4,1.4,Iris-versicolor 77 | 6.8,2.8,4.8,1.4,Iris-versicolor 78 | 6.7,3.0,5.0,1.7,Iris-versicolor 79 | 6.0,2.9,4.5,1.5,Iris-versicolor 80 | 5.7,2.6,3.5,1.0,Iris-versicolor 81 | 5.5,2.4,3.8,1.1,Iris-versicolor 82 | 5.5,2.4,3.7,1.0,Iris-versicolor 83 | 5.8,2.7,3.9,1.2,Iris-versicolor 84 | 6.0,2.7,5.1,1.6,Iris-versicolor 85 | 5.4,3.0,4.5,1.5,Iris-versicolor 86 | 6.0,3.4,4.5,1.6,Iris-versicolor 87 | 6.7,3.1,4.7,1.5,Iris-versicolor 88 | 6.3,2.3,4.4,1.3,Iris-versicolor 89 | 5.6,3.0,4.1,1.3,Iris-versicolor 90 | 5.5,2.5,4.0,1.3,Iris-versicolor 91 | 5.5,2.6,4.4,1.2,Iris-versicolor 92 | 6.1,3.0,4.6,1.4,Iris-versicolor 93 | 5.8,2.6,4.0,1.2,Iris-versicolor 94 | 5.0,2.3,3.3,1.0,Iris-versicolor 95 | 5.6,2.7,4.2,1.3,Iris-versicolor 96 | 5.7,3.0,4.2,1.2,Iris-versicolor 97 | 5.7,2.9,4.2,1.3,Iris-versicolor 98 | 6.2,2.9,4.3,1.3,Iris-versicolor 99 | 5.1,2.5,3.0,1.1,Iris-versicolor 100 | 5.7,2.8,4.1,1.3,Iris-versicolor 101 | 6.3,3.3,6.0,2.5,Iris-virginica 102 | 5.8,2.7,5.1,1.9,Iris-virginica 103 | 7.1,3.0,5.9,2.1,Iris-virginica 104 | 6.3,2.9,5.6,1.8,Iris-virginica 105 | 6.5,3.0,5.8,2.2,Iris-virginica 106 | 7.6,3.0,6.6,2.1,Iris-virginica 107 | 4.9,2.5,4.5,1.7,Iris-virginica 108 | 7.3,2.9,6.3,1.8,Iris-virginica 109 | 6.7,2.5,5.8,1.8,Iris-virginica 110 | 7.2,3.6,6.1,2.5,Iris-virginica 111 | 6.5,3.2,5.1,2.0,Iris-virginica 112 | 6.4,2.7,5.3,1.9,Iris-virginica 113 | 6.8,3.0,5.5,2.1,Iris-virginica 114 | 5.7,2.5,5.0,2.0,Iris-virginica 115 | 5.8,2.8,5.1,2.4,Iris-virginica 116 | 6.4,3.2,5.3,2.3,Iris-virginica 117 | 6.5,3.0,5.5,1.8,Iris-virginica 118 | 7.7,3.8,6.7,2.2,Iris-virginica 119 | 7.7,2.6,6.9,2.3,Iris-virginica 120 | 6.0,2.2,5.0,1.5,Iris-virginica 121 | 6.9,3.2,5.7,2.3,Iris-virginica 122 | 5.6,2.8,4.9,2.0,Iris-virginica 123 | 7.7,2.8,6.7,2.0,Iris-virginica 124 | 6.3,2.7,4.9,1.8,Iris-virginica 125 | 6.7,3.3,5.7,2.1,Iris-virginica 126 | 7.2,3.2,6.0,1.8,Iris-virginica 127 | 6.2,2.8,4.8,1.8,Iris-virginica 128 | 6.1,3.0,4.9,1.8,Iris-virginica 129 | 6.4,2.8,5.6,2.1,Iris-virginica 130 | 7.2,3.0,5.8,1.6,Iris-virginica 131 | 7.4,2.8,6.1,1.9,Iris-virginica 132 | 7.9,3.8,6.4,2.0,Iris-virginica 133 | 6.4,2.8,5.6,2.2,Iris-virginica 134 | 6.3,2.8,5.1,1.5,Iris-virginica 135 | 6.1,2.6,5.6,1.4,Iris-virginica 136 | 7.7,3.0,6.1,2.3,Iris-virginica 137 | 6.3,3.4,5.6,2.4,Iris-virginica 138 | 6.4,3.1,5.5,1.8,Iris-virginica 139 | 6.0,3.0,4.8,1.8,Iris-virginica 140 | 6.9,3.1,5.4,2.1,Iris-virginica 141 | 6.7,3.1,5.6,2.4,Iris-virginica 142 | 6.9,3.1,5.1,2.3,Iris-virginica 143 | 5.8,2.7,5.1,1.9,Iris-virginica 144 | 6.8,3.2,5.9,2.3,Iris-virginica 145 | 6.7,3.3,5.7,2.5,Iris-virginica 146 | 6.7,3.0,5.2,2.3,Iris-virginica 147 | 6.3,2.5,5.0,1.9,Iris-virginica 148 | 6.5,3.0,5.2,2.0,Iris-virginica 149 | 6.2,3.4,5.4,2.3,Iris-virginica 150 | 5.9,3.0,5.1,1.8,Iris-virginica 151 | -------------------------------------------------------------------------------- /notebooks/iris_with_names.csv: -------------------------------------------------------------------------------- 1 | sepal_length,sepal_width,petal_length,petal_width,species 2 | 5.1,3.5,1.4,0.2,Iris-setosa 3 | 4.9,3.0,1.4,0.2,Iris-setosa 4 | 4.7,3.2,1.3,0.2,Iris-setosa 5 | 4.6,3.1,1.5,0.2,Iris-setosa 6 | 5.0,3.6,1.4,0.2,Iris-setosa 7 | 5.4,3.9,1.7,0.4,Iris-setosa 8 | 4.6,3.4,1.4,0.3,Iris-setosa 9 | 5.0,3.4,1.5,0.2,Iris-setosa 10 | 4.4,2.9,1.4,0.2,Iris-setosa 11 | 4.9,3.1,1.5,0.1,Iris-setosa 12 | 5.4,3.7,1.5,0.2,Iris-setosa 13 | 4.8,3.4,1.6,0.2,Iris-setosa 14 | 4.8,3.0,1.4,0.1,Iris-setosa 15 | 4.3,3.0,1.1,0.1,Iris-setosa 16 | 5.8,4.0,1.2,0.2,Iris-setosa 17 | 5.7,4.4,1.5,0.4,Iris-setosa 18 | 5.4,3.9,1.3,0.4,Iris-setosa 19 | 5.1,3.5,1.4,0.3,Iris-setosa 20 | 5.7,3.8,1.7,0.3,Iris-setosa 21 | 5.1,3.8,1.5,0.3,Iris-setosa 22 | 5.4,3.4,1.7,0.2,Iris-setosa 23 | 5.1,3.7,1.5,0.4,Iris-setosa 24 | 4.6,3.6,1.0,0.2,Iris-setosa 25 | 5.1,3.3,1.7,0.5,Iris-setosa 26 | 4.8,3.4,1.9,0.2,Iris-setosa 27 | 5.0,3.0,1.6,0.2,Iris-setosa 28 | 5.0,3.4,1.6,0.4,Iris-setosa 29 | 5.2,3.5,1.5,0.2,Iris-setosa 30 | 5.2,3.4,1.4,0.2,Iris-setosa 31 | 4.7,3.2,1.6,0.2,Iris-setosa 32 | 4.8,3.1,1.6,0.2,Iris-setosa 33 | 5.4,3.4,1.5,0.4,Iris-setosa 34 | 5.2,4.1,1.5,0.1,Iris-setosa 35 | 5.5,4.2,1.4,0.2,Iris-setosa 36 | 4.9,3.1,1.5,0.2,Iris-setosa 37 | 5.0,3.2,1.2,0.2,Iris-setosa 38 | 5.5,3.5,1.3,0.2,Iris-setosa 39 | 4.9,3.6,1.4,0.1,Iris-setosa 40 | 4.4,3.0,1.3,0.2,Iris-setosa 41 | 5.1,3.4,1.5,0.2,Iris-setosa 42 | 5.0,3.5,1.3,0.3,Iris-setosa 43 | 4.5,2.3,1.3,0.3,Iris-setosa 44 | 4.4,3.2,1.3,0.2,Iris-setosa 45 | 5.0,3.5,1.6,0.6,Iris-setosa 46 | 5.1,3.8,1.9,0.4,Iris-setosa 47 | 4.8,3.0,1.4,0.3,Iris-setosa 48 | 5.1,3.8,1.6,0.2,Iris-setosa 49 | 4.6,3.2,1.4,0.2,Iris-setosa 50 | 5.3,3.7,1.5,0.2,Iris-setosa 51 | 5.0,3.3,1.4,0.2,Iris-setosa 52 | 7.0,3.2,4.7,1.4,Iris-versicolor 53 | 6.4,3.2,4.5,1.5,Iris-versicolor 54 | 6.9,3.1,4.9,1.5,Iris-versicolor 55 | 5.5,2.3,4.0,1.3,Iris-versicolor 56 | 6.5,2.8,4.6,1.5,Iris-versicolor 57 | 5.7,2.8,4.5,1.3,Iris-versicolor 58 | 6.3,3.3,4.7,1.6,Iris-versicolor 59 | 4.9,2.4,3.3,1.0,Iris-versicolor 60 | 6.6,2.9,4.6,1.3,Iris-versicolor 61 | 5.2,2.7,3.9,1.4,Iris-versicolor 62 | 5.0,2.0,3.5,1.0,Iris-versicolor 63 | 5.9,3.0,4.2,1.5,Iris-versicolor 64 | 6.0,2.2,4.0,1.0,Iris-versicolor 65 | 6.1,2.9,4.7,1.4,Iris-versicolor 66 | 5.6,2.9,3.6,1.3,Iris-versicolor 67 | 6.7,3.1,4.4,1.4,Iris-versicolor 68 | 5.6,3.0,4.5,1.5,Iris-versicolor 69 | 5.8,2.7,4.1,1.0,Iris-versicolor 70 | 6.2,2.2,4.5,1.5,Iris-versicolor 71 | 5.6,2.5,3.9,1.1,Iris-versicolor 72 | 5.9,3.2,4.8,1.8,Iris-versicolor 73 | 6.1,2.8,4.0,1.3,Iris-versicolor 74 | 6.3,2.5,4.9,1.5,Iris-versicolor 75 | 6.1,2.8,4.7,1.2,Iris-versicolor 76 | 6.4,2.9,4.3,1.3,Iris-versicolor 77 | 6.6,3.0,4.4,1.4,Iris-versicolor 78 | 6.8,2.8,4.8,1.4,Iris-versicolor 79 | 6.7,3.0,5.0,1.7,Iris-versicolor 80 | 6.0,2.9,4.5,1.5,Iris-versicolor 81 | 5.7,2.6,3.5,1.0,Iris-versicolor 82 | 5.5,2.4,3.8,1.1,Iris-versicolor 83 | 5.5,2.4,3.7,1.0,Iris-versicolor 84 | 5.8,2.7,3.9,1.2,Iris-versicolor 85 | 6.0,2.7,5.1,1.6,Iris-versicolor 86 | 5.4,3.0,4.5,1.5,Iris-versicolor 87 | 6.0,3.4,4.5,1.6,Iris-versicolor 88 | 6.7,3.1,4.7,1.5,Iris-versicolor 89 | 6.3,2.3,4.4,1.3,Iris-versicolor 90 | 5.6,3.0,4.1,1.3,Iris-versicolor 91 | 5.5,2.5,4.0,1.3,Iris-versicolor 92 | 5.5,2.6,4.4,1.2,Iris-versicolor 93 | 6.1,3.0,4.6,1.4,Iris-versicolor 94 | 5.8,2.6,4.0,1.2,Iris-versicolor 95 | 5.0,2.3,3.3,1.0,Iris-versicolor 96 | 5.6,2.7,4.2,1.3,Iris-versicolor 97 | 5.7,3.0,4.2,1.2,Iris-versicolor 98 | 5.7,2.9,4.2,1.3,Iris-versicolor 99 | 6.2,2.9,4.3,1.3,Iris-versicolor 100 | 5.1,2.5,3.0,1.1,Iris-versicolor 101 | 5.7,2.8,4.1,1.3,Iris-versicolor 102 | 6.3,3.3,6.0,2.5,Iris-virginica 103 | 5.8,2.7,5.1,1.9,Iris-virginica 104 | 7.1,3.0,5.9,2.1,Iris-virginica 105 | 6.3,2.9,5.6,1.8,Iris-virginica 106 | 6.5,3.0,5.8,2.2,Iris-virginica 107 | 7.6,3.0,6.6,2.1,Iris-virginica 108 | 4.9,2.5,4.5,1.7,Iris-virginica 109 | 7.3,2.9,6.3,1.8,Iris-virginica 110 | 6.7,2.5,5.8,1.8,Iris-virginica 111 | 7.2,3.6,6.1,2.5,Iris-virginica 112 | 6.5,3.2,5.1,2.0,Iris-virginica 113 | 6.4,2.7,5.3,1.9,Iris-virginica 114 | 6.8,3.0,5.5,2.1,Iris-virginica 115 | 5.7,2.5,5.0,2.0,Iris-virginica 116 | 5.8,2.8,5.1,2.4,Iris-virginica 117 | 6.4,3.2,5.3,2.3,Iris-virginica 118 | 6.5,3.0,5.5,1.8,Iris-virginica 119 | 7.7,3.8,6.7,2.2,Iris-virginica 120 | 7.7,2.6,6.9,2.3,Iris-virginica 121 | 6.0,2.2,5.0,1.5,Iris-virginica 122 | 6.9,3.2,5.7,2.3,Iris-virginica 123 | 5.6,2.8,4.9,2.0,Iris-virginica 124 | 7.7,2.8,6.7,2.0,Iris-virginica 125 | 6.3,2.7,4.9,1.8,Iris-virginica 126 | 6.7,3.3,5.7,2.1,Iris-virginica 127 | 7.2,3.2,6.0,1.8,Iris-virginica 128 | 6.2,2.8,4.8,1.8,Iris-virginica 129 | 6.1,3.0,4.9,1.8,Iris-virginica 130 | 6.4,2.8,5.6,2.1,Iris-virginica 131 | 7.2,3.0,5.8,1.6,Iris-virginica 132 | 7.4,2.8,6.1,1.9,Iris-virginica 133 | 7.9,3.8,6.4,2.0,Iris-virginica 134 | 6.4,2.8,5.6,2.2,Iris-virginica 135 | 6.3,2.8,5.1,1.5,Iris-virginica 136 | 6.1,2.6,5.6,1.4,Iris-virginica 137 | 7.7,3.0,6.1,2.3,Iris-virginica 138 | 6.3,3.4,5.6,2.4,Iris-virginica 139 | 6.4,3.1,5.5,1.8,Iris-virginica 140 | 6.0,3.0,4.8,1.8,Iris-virginica 141 | 6.9,3.1,5.4,2.1,Iris-virginica 142 | 6.7,3.1,5.6,2.4,Iris-virginica 143 | 6.9,3.1,5.1,2.3,Iris-virginica 144 | 5.8,2.7,5.1,1.9,Iris-virginica 145 | 6.8,3.2,5.9,2.3,Iris-virginica 146 | 6.7,3.3,5.7,2.5,Iris-virginica 147 | 6.7,3.0,5.2,2.3,Iris-virginica 148 | 6.3,2.5,5.0,1.9,Iris-virginica 149 | 6.5,3.0,5.2,2.0,Iris-virginica 150 | 6.2,3.4,5.4,2.3,Iris-virginica 151 | 5.9,3.0,5.1,1.8,Iris-virginica 152 | -------------------------------------------------------------------------------- /notebooks/EX-4-Pivot-Using-SQL-And-Pandas.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Pivoting SQL results in Panda\n", 8 | "\n", 9 | "This notebook shows how to pivot array data using SQL ARRAY JOIN and pandas DataFrame.pivot(). This problem appeared as a [question on Stack Overflow](https://stackoverflow.com/questions/54811905/return-clickhouse-array-as-column). " 10 | ] 11 | }, 12 | { 13 | "cell_type": "markdown", 14 | "metadata": {}, 15 | "source": [ 16 | "First create some test data. We'll use clickhouse-driver for this so we can see the SQL. " 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": 1, 22 | "metadata": {}, 23 | "outputs": [], 24 | "source": [ 25 | "from clickhouse_driver import Client\n", 26 | "client = Client('localhost')\n", 27 | "client.execute('CREATE TABLE IF NOT EXISTS f '\n", 28 | " '(f1 String, f2 Array(Int32), f3 Array(String)) '\n", 29 | " 'ENGINE = Memory')\n", 30 | "client.execute('TRUNCATE TABLE f')\n", 31 | "client.execute(\n", 32 | " 'INSERT INTO f (f1, f2, f3) VALUES', [\n", 33 | " ('a', [1,2,3], ['x', 'y', 'z']),\n", 34 | " ('b', [4,5,6], ['x', 'y', 'z']),\n", 35 | " ]\n", 36 | ")" 37 | ] 38 | }, 39 | { 40 | "cell_type": "markdown", 41 | "metadata": {}, 42 | "source": [ 43 | "Now load SQLAlchemy. " 44 | ] 45 | }, 46 | { 47 | "cell_type": "code", 48 | "execution_count": 2, 49 | "metadata": { 50 | "scrolled": true 51 | }, 52 | "outputs": [], 53 | "source": [ 54 | "from sqlalchemy import create_engine\n", 55 | "%load_ext sql" 56 | ] 57 | }, 58 | { 59 | "cell_type": "markdown", 60 | "metadata": {}, 61 | "source": [ 62 | "Connect to ClickHouse, which is assumed to be on localhost with default user. " 63 | ] 64 | }, 65 | { 66 | "cell_type": "code", 67 | "execution_count": 3, 68 | "metadata": {}, 69 | "outputs": [ 70 | { 71 | "data": { 72 | "text/plain": [ 73 | "'Connected: default@default'" 74 | ] 75 | }, 76 | "execution_count": 3, 77 | "metadata": {}, 78 | "output_type": "execute_result" 79 | } 80 | ], 81 | "source": [ 82 | "%sql clickhouse://default:@localhost/default" 83 | ] 84 | }, 85 | { 86 | "cell_type": "markdown", 87 | "metadata": {}, 88 | "source": [ 89 | "Use SQL query with ARRAY JOIN to flip matching array indexes in f2, f3 to row values with f1. " 90 | ] 91 | }, 92 | { 93 | "cell_type": "code", 94 | "execution_count": 13, 95 | "metadata": {}, 96 | "outputs": [ 97 | { 98 | "name": "stdout", 99 | "output_type": "stream", 100 | "text": [ 101 | " * clickhouse://default:***@localhost/default\n", 102 | "Done.\n" 103 | ] 104 | }, 105 | { 106 | "data": { 107 | "text/html": [ 108 | "
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f1f2f3
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" 219 | ], 220 | "text/plain": [ 221 | " f1 f2 f3\n", 222 | "0 a 1 x\n", 223 | "1 a 2 y\n", 224 | "2 a 3 z\n", 225 | "3 b 4 x\n", 226 | "4 b 5 y\n", 227 | "5 b 6 z\n", 228 | "6 c 7 y\n", 229 | "7 c 8 z\n", 230 | "8 c 9 aa\n", 231 | "9 c 10 bb\n", 232 | "10 c 7 y\n", 233 | "11 c 8 z\n", 234 | "12 c 9 aa\n", 235 | "13 c 10 bb" 236 | ] 237 | }, 238 | "execution_count": 13, 239 | "metadata": {}, 240 | "output_type": "execute_result" 241 | } 242 | ], 243 | "source": [ 244 | "result = %sql SELECT * FROM f ARRAY JOIN f2, f3\n", 245 | "df = result.DataFrame()\n", 246 | "df" 247 | ] 248 | }, 249 | { 250 | "cell_type": "markdown", 251 | "metadata": {}, 252 | "source": [ 253 | "Now we can pivot f2 and f3 into a new data frame that has the f3 array entries as columns. " 254 | ] 255 | }, 256 | { 257 | "cell_type": "code", 258 | "execution_count": 15, 259 | "metadata": {}, 260 | "outputs": [ 261 | { 262 | "name": "stdout", 263 | "output_type": "stream", 264 | "text": [ 265 | "f3 aa bb x y z\n", 266 | "f1 \n", 267 | "a NaN NaN 1.0 2.0 3.0\n", 268 | "b NaN NaN 4.0 5.0 6.0\n", 269 | "c 9.0 10.0 NaN 7.0 8.0\n" 270 | ] 271 | } 272 | ], 273 | "source": [ 274 | "dfp = df.pivot_table(columns='f3', values='f2', index='f1')\n", 275 | "print(dfp)" 276 | ] 277 | }, 278 | { 279 | "cell_type": "markdown", 280 | "metadata": {}, 281 | "source": [ 282 | "This approach works if we add additional data with new property names in the f3 array. Try adding a row to the table and then rerun the cells that select and pivot data. " 283 | ] 284 | }, 285 | { 286 | "cell_type": "code", 287 | "execution_count": 12, 288 | "metadata": { 289 | "scrolled": false 290 | }, 291 | "outputs": [], 292 | "source": [ 293 | "client.execute(\n", 294 | " 'INSERT INTO f (f1, f2, f3) VALUES', [\n", 295 | " ('c', [7,8,9,10], ['y', 'z', 'aa', 'bb']),\n", 296 | " ]\n", 297 | ")" 298 | ] 299 | }, 300 | { 301 | "cell_type": "markdown", 302 | "metadata": {}, 303 | "source": [ 304 | "If you try this again the duplicate rows will be ignored. " 305 | ] 306 | } 307 | ], 308 | "metadata": { 309 | "kernelspec": { 310 | "display_name": "Python 3", 311 | "language": "python", 312 | "name": "python3" 313 | }, 314 | "language_info": { 315 | "codemirror_mode": { 316 | "name": "ipython", 317 | "version": 3 318 | }, 319 | "file_extension": ".py", 320 | "mimetype": "text/x-python", 321 | "name": "python", 322 | "nbconvert_exporter": "python", 323 | "pygments_lexer": "ipython3", 324 | "version": "3.7.1" 325 | }, 326 | "widgets": { 327 | "application/vnd.jupyter.widget-state+json": { 328 | "state": {}, 329 | "version_major": 1, 330 | "version_minor": 0 331 | } 332 | } 333 | }, 334 | "nbformat": 4, 335 | "nbformat_minor": 2 336 | } 337 | -------------------------------------------------------------------------------- /notebooks/EX-1.0-Getting-to-Know-the-Clickhouse-driver-Client.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Getting to know the ClickHouse-driver Client\n", 8 | "\n", 9 | "This notebook has samples that were included in the [Altinity blog article that introduces the clickhouse-driver client library](https://www.altinity.com/blog/clickhouse-and-python-getting-to-know-the-clickhouse-driver-client).\n", 10 | "\n", 11 | "_WARNING_: If you run the whole notebook it will hang. You must run the samples one by one as one of them is designed to hang and must be cancelled manually. " 12 | ] 13 | }, 14 | { 15 | "cell_type": "markdown", 16 | "metadata": {}, 17 | "source": [ 18 | "It's easy to load the clickhouse driver. The `Client` class is the main client interface. " 19 | ] 20 | }, 21 | { 22 | "cell_type": "code", 23 | "execution_count": null, 24 | "metadata": {}, 25 | "outputs": [], 26 | "source": [ 27 | "from clickhouse_driver import Client" 28 | ] 29 | }, 30 | { 31 | "cell_type": "markdown", 32 | "metadata": {}, 33 | "source": [ 34 | "If you are running against an unencrypted local server setting up a connection is as simple as the following. Instantiating a client does not actually connect to ClickHouse. It just sets up the data structure used to connect later on when your code does something. " 35 | ] 36 | }, 37 | { 38 | "cell_type": "code", 39 | "execution_count": null, 40 | "metadata": {}, 41 | "outputs": [], 42 | "source": [ 43 | "client = Client('localhost')" 44 | ] 45 | }, 46 | { 47 | "cell_type": "markdown", 48 | "metadata": {}, 49 | "source": [ 50 | "Servers with sensitive data should be encrypted with a user/password and encrypted communications. The following command shows how to connect to a server with self-signed certificate using an explicit database name. " 51 | ] 52 | }, 53 | { 54 | "cell_type": "code", 55 | "execution_count": null, 56 | "metadata": {}, 57 | "outputs": [], 58 | "source": [ 59 | "client = Client('localhost', \n", 60 | " user='python', \n", 61 | " password='secret', \n", 62 | " secure=True, \n", 63 | " verify=False, \n", 64 | " database='default',\n", 65 | " compression=True)" 66 | ] 67 | }, 68 | { 69 | "cell_type": "markdown", 70 | "metadata": {}, 71 | "source": [ 72 | "The Python driver uses the Client.execute() method to issue select commands. Results are returned as a list of tuples. Let's send a very simple query and take apart the results to see values and types. \n", 73 | "\n", 74 | "*NOTE*: If you get an error about an unknown timezone, ensure your server has the timezone set properly. " 75 | ] 76 | }, 77 | { 78 | "cell_type": "code", 79 | "execution_count": null, 80 | "metadata": { 81 | "scrolled": false 82 | }, 83 | "outputs": [], 84 | "source": [ 85 | "result = client.execute('SELECT now(), version()')\n", 86 | "print(\"RESULT: {0}: {1}\".format(type(result), result))\n", 87 | "for t in result:\n", 88 | " print(\" ROW: {0}: {1}\".format(type(t), t))\n", 89 | " for v in t:\n", 90 | " print(\" COLUMN: {0}: {1}\".format(type(v), v))" 91 | ] 92 | }, 93 | { 94 | "cell_type": "markdown", 95 | "metadata": {}, 96 | "source": [ 97 | "Create the iris table, dropping any previously existing table of the same name. The print statements show that result sets from DDL are empty. " 98 | ] 99 | }, 100 | { 101 | "cell_type": "code", 102 | "execution_count": null, 103 | "metadata": {}, 104 | "outputs": [], 105 | "source": [ 106 | "r1 = client.execute('DROP TABLE IF EXISTS iris')\n", 107 | "print(r1)\n", 108 | "r2 = client.execute('CREATE TABLE iris ('\n", 109 | " 'sepal_length Float64, sepal_width Float64, '\n", 110 | " 'petal_length Float64, petal_width Float64, '\n", 111 | " 'species String) ENGINE = MergeTree '\n", 112 | " ' PARTITION BY species ORDER BY (species)')\n", 113 | "print(r2)" 114 | ] 115 | }, 116 | { 117 | "cell_type": "markdown", 118 | "metadata": {}, 119 | "source": [ 120 | "Add some data to the table. Note that the values are given in a separate array of tuples. " 121 | ] 122 | }, 123 | { 124 | "cell_type": "code", 125 | "execution_count": null, 126 | "metadata": {}, 127 | "outputs": [], 128 | "source": [ 129 | "client.execute(\n", 130 | " 'INSERT INTO iris (sepal_length, sepal_width, petal_length, petal_width, species) VALUES',\n", 131 | " [(5.1, 3.7, 1.5, 0.4, 'Iris-setosa'), (4.6, 3.6, 1.0, 0.2, 'Iris-setosa')]\n", 132 | ")\n", 133 | "print(client.execute(\"SELECT * FROM iris\"))" 134 | ] 135 | }, 136 | { 137 | "cell_type": "markdown", 138 | "metadata": {}, 139 | "source": [ 140 | "Here is an example of how to insert CSV. We read the values line by line using csv.DictReader() running inside the generator function row_reader(). This results in a tuple for each line. Note that *you must* assign types or your values will not convert. The csv module converts everything to a string. " 141 | ] 142 | }, 143 | { 144 | "cell_type": "code", 145 | "execution_count": null, 146 | "metadata": { 147 | "scrolled": true 148 | }, 149 | "outputs": [], 150 | "source": [ 151 | "client.execute(\"TRUNCATE TABLE iris\")\n", 152 | "\n", 153 | "import datetime\n", 154 | "import csv\n", 155 | "\n", 156 | "# Create a generator to fetch parsed rows. CSV must have variable names in header row.\n", 157 | "def row_reader():\n", 158 | " with open('iris_with_names.csv') as iris_csv:\n", 159 | " # Use DictReader to get values as a dictionary with column names.\n", 160 | " for line in csv.DictReader(iris_csv):\n", 161 | " yield {\n", 162 | " 'sepal_length': float(line['sepal_length']), \n", 163 | " 'sepal_width': float(line['sepal_width']), \n", 164 | " 'petal_length': float(line['petal_length']), \n", 165 | " 'petal_width': float(line['petal_width']), \n", 166 | " 'species': str(line['species']), \n", 167 | " }\n", 168 | "\n", 169 | "# Use a generator expression to load values as a list of dictionaries. \n", 170 | "client.execute(\"INSERT INTO iris VALUES\", (line for line in row_reader()))\n", 171 | "client.execute(\"SELECT count(*) FROM iris\")" 172 | ] 173 | }, 174 | { 175 | "cell_type": "markdown", 176 | "metadata": {}, 177 | "source": [ 178 | "That was painful. We dislike pain. A better approach to non-toy CSV files is to use Pandas, which has a very good method for reading CSV that automatically coerces types. This is much simpler! " 179 | ] 180 | }, 181 | { 182 | "cell_type": "code", 183 | "execution_count": null, 184 | "metadata": {}, 185 | "outputs": [], 186 | "source": [ 187 | "client.execute(\"TRUNCATE TABLE iris\")\n", 188 | "\n", 189 | "import pandas as pd\n", 190 | "df = pd.read_csv('iris_with_names.csv')\n", 191 | "\n", 192 | "client.execute(\"INSERT INTO iris VALUES\", [tuple(x) for x in df.values])\n", 193 | "client.execute(\"SELECT count(*) FROM iris\")" 194 | ] 195 | }, 196 | { 197 | "cell_type": "markdown", 198 | "metadata": {}, 199 | "source": [ 200 | "The next few queries show examples of select statements. " 201 | ] 202 | }, 203 | { 204 | "cell_type": "code", 205 | "execution_count": null, 206 | "metadata": {}, 207 | "outputs": [], 208 | "source": [ 209 | "result = client.execute('SELECT COUNT(*), species FROM iris '\n", 210 | " 'WHERE petal_length > 3.4 '\n", 211 | " 'GROUP BY species ORDER BY species')\n", 212 | "print(result)" 213 | ] 214 | }, 215 | { 216 | "cell_type": "code", 217 | "execution_count": null, 218 | "metadata": {}, 219 | "outputs": [], 220 | "source": [ 221 | "result = client.execute('SELECT COUNT(*), species FROM iris '\n", 222 | " 'WHERE petal_length > %(max_len)s '\n", 223 | " 'GROUP BY species ORDER BY species', \n", 224 | " {'max_len': 3.4})\n", 225 | "print(result)" 226 | ] 227 | }, 228 | { 229 | "cell_type": "markdown", 230 | "metadata": {}, 231 | "source": [ 232 | "Show how to get the column names for results. Note that we also get the column types, which is convenient for conversions. " 233 | ] 234 | }, 235 | { 236 | "cell_type": "code", 237 | "execution_count": null, 238 | "metadata": {}, 239 | "outputs": [], 240 | "source": [ 241 | "result, columns = client.execute('SELECT COUNT(*), species FROM iris '\n", 242 | " 'WHERE petal_length > %(max_len)s '\n", 243 | " 'GROUP BY species ORDER BY species', \n", 244 | " {'max_len': 3.4},\n", 245 | " with_column_types=True)\n", 246 | "print(result)\n", 247 | "print(columns)" 248 | ] 249 | }, 250 | { 251 | "cell_type": "markdown", 252 | "metadata": {}, 253 | "source": [ 254 | "This final example shows how to put a result set into a pandas data frame. We'll use the column names so that the DataFrame has correct labels." 255 | ] 256 | }, 257 | { 258 | "cell_type": "code", 259 | "execution_count": null, 260 | "metadata": { 261 | "scrolled": true 262 | }, 263 | "outputs": [], 264 | "source": [ 265 | "import pandas\n", 266 | "result, columns = client.execute('SELECT * FROM iris WHERE species = %(species)s LIMIT 5', \n", 267 | " {'species': \"Iris-setosa\"}, with_column_types=True)\n", 268 | "df = pandas.DataFrame(result, columns=[tuple[0] for tuple in columns])\n", 269 | "df.tail()" 270 | ] 271 | }, 272 | { 273 | "cell_type": "markdown", 274 | "metadata": {}, 275 | "source": [ 276 | "Since we're using pandas and may like to put this data into graphs, etc., we need to ensure the data types are correct. Let's describe the data set to ensure that the columns with numbers really appear as numbers. The following should show metrics for length and width values but nothing for species, which is a string. " 277 | ] 278 | }, 279 | { 280 | "cell_type": "code", 281 | "execution_count": null, 282 | "metadata": {}, 283 | "outputs": [], 284 | "source": [ 285 | "df.describe()" 286 | ] 287 | }, 288 | { 289 | "cell_type": "code", 290 | "execution_count": null, 291 | "metadata": {}, 292 | "outputs": [], 293 | "source": [] 294 | } 295 | ], 296 | "metadata": { 297 | "kernelspec": { 298 | "display_name": "Python 3", 299 | "language": "python", 300 | "name": "python3" 301 | }, 302 | "language_info": { 303 | "codemirror_mode": { 304 | "name": "ipython", 305 | "version": 3 306 | }, 307 | "file_extension": ".py", 308 | "mimetype": "text/x-python", 309 | "name": "python", 310 | "nbconvert_exporter": "python", 311 | "pygments_lexer": "ipython3", 312 | "version": "3.7.1" 313 | } 314 | }, 315 | "nbformat": 4, 316 | "nbformat_minor": 2 317 | } 318 | -------------------------------------------------------------------------------- /notebooks/EX-3-Sql-Magic-Functions.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Using Sql Magic Function with ClickHouse\n", 8 | "\n", 9 | "This notebook goes from nothing to a nice graph in three steps. We'll extract data using the %sql magic function. You'll need to have created the iris table for this to work. Run the EX-1-Getting-to-Know-the-Clickhouse-driver-Client.ipynb notebook to load data into the iris table if you have not already done so. " 10 | ] 11 | }, 12 | { 13 | "cell_type": "markdown", 14 | "metadata": {}, 15 | "source": [ 16 | "First import SQLAlchemy and activate the %sql function. This just needs to be done once. " 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": 1, 22 | "metadata": { 23 | "scrolled": true 24 | }, 25 | "outputs": [], 26 | "source": [ 27 | "from sqlalchemy import create_engine\n", 28 | "%load_ext sql" 29 | ] 30 | }, 31 | { 32 | "cell_type": "markdown", 33 | "metadata": {}, 34 | "source": [ 35 | "Connect to a local server using native protocol, select data from the iris table, and pop it into a data frame. You should see 150 rows in the data frame. " 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": 2, 41 | "metadata": { 42 | "scrolled": true 43 | }, 44 | "outputs": [ 45 | { 46 | "name": "stdout", 47 | "output_type": "stream", 48 | "text": [ 49 | " * clickhouse://default:***@localhost/default\n", 50 | "Done.\n" 51 | ] 52 | }, 53 | { 54 | "data": { 55 | "text/html": [ 56 | "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
1455.73.04.21.2Iris-versicolor
1465.72.94.21.3Iris-versicolor
1476.22.94.31.3Iris-versicolor
1485.12.53.01.1Iris-versicolor
1495.72.84.11.3Iris-versicolor
\n", 124 | "
" 125 | ], 126 | "text/plain": [ 127 | " sepal_length sepal_width petal_length petal_width species\n", 128 | "145 5.7 3.0 4.2 1.2 Iris-versicolor\n", 129 | "146 5.7 2.9 4.2 1.3 Iris-versicolor\n", 130 | "147 6.2 2.9 4.3 1.3 Iris-versicolor\n", 131 | "148 5.1 2.5 3.0 1.1 Iris-versicolor\n", 132 | "149 5.7 2.8 4.1 1.3 Iris-versicolor" 133 | ] 134 | }, 135 | "execution_count": 2, 136 | "metadata": {}, 137 | "output_type": "execute_result" 138 | } 139 | ], 140 | "source": [ 141 | "%sql clickhouse://default:@localhost/default\n", 142 | "result = %sql SELECT * FROM iris\n", 143 | "df = result.DataFrame()\n", 144 | "df.tail()" 145 | ] 146 | }, 147 | { 148 | "cell_type": "markdown", 149 | "metadata": {}, 150 | "source": [ 151 | "Everybody likes a nice picture. Let's make a scatter graph that shows data with different markers for each species of iris. We'll use standard pandas tricks to find the species names and query for each one of the. \n", 152 | "\n", 153 | "Pro tip: For large datasets you could get unique names using SQL. We'll just cheat and use the pandas unique() method on the data frame." 154 | ] 155 | }, 156 | { 157 | "cell_type": "code", 158 | "execution_count": 5, 159 | "metadata": {}, 160 | "outputs": [ 161 | { 162 | "data": { 163 | "image/png": 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NGsSuXbvqrsJPOOEEWrdu7eoYCxYsYOTIkbRt2xag7nXvvPMO5557LuDMjf/WW29FvK6yspLt27czcOBAAC688EIWL15c9/zZZ5+d4lky4byo985ZsZn+kxdwyIRX6D95wW5deSfaR660Mg7vXsJZPUsoCg6YLBLhrJ75VzrJVRlN+Kq6Mliu6aqqw1X1m0weD5yr+1qtBaBWaz2/yk9neuSSkhLatGnD6tWrmTFjBmPGjAGcqY9nzZpVN0Xyxo0b6+aoCT9erGOEU1XExchiN9u4ec/GveHdS1gy4Xg+m3wKSyYcn3KyT7fckmwffpRS3BxjzorNzCrfTCA4x1dAlVnlm62jySONaqRt6Oq+urYagOra6oxc5ceyYcMG2rdvz7hx47j44otZvnw5I0aMqEvivXo5cxuNGTOG++67j8rKyrrvAYYNG8Zf//rXujnvV6xY4foY4YYMGcLMmTPZunUrANu2bQOcTqJnn30WgKeffprjjjsu4nX77rsv++23X913Ak8++WTd1b7JPj9Gn+ZKK6N1NGVWo2q5CL+6Dwld5f/+Z7/P6LHdTI8MMHLkSK6++mpuvrm+Wenmm2/mmmuuoWvXrqgqnTp14uWXX075GMcccww33XQTAwcOpKioiO7duzN9+nT+8pe/cNFFF3H//ffTrl07pk2b1mDfjz/+OJdddhlVVVUceuihMbcx2eHH6FO3k5Klw80x8qWjKV81qumRhzw3hK+qvmqwvn2L9swfNd+TGE09mx7ZH/0nL4g5qrOkVXOWTDjet31A5ls3vYqzkBTs9MiW1E1j5MX9Tzu1iT0VQKc27mv0fswQma17vRaKRlXDN6Yx8qLL5931sfsl4q2PxY/6er6OYM0XeXGF77b7xPgnl0qBmeZFGeP3c9bwzNIvCKhSJMI5fQ/iruFdXD+f7qjOQJx/r3jrY/Grvp6PI1jzRc5f4Tdr1oytW7cWVILJdarK1q1badasWbZDyTgvWiJ/P2cNT727MaLV8Kl3N/L7OWtcPe+FeNdLqVxH2SjY/JfzV/gdOnRg06ZNVFS4nzzJZF6zZs3o0KFDtsPIuERlDLdXoc8s/SLu+ruGd0n6vBeaN21CVXVtzPVuWX09/+V8wi8uLo45stUYP3hRxkhWTnFTbkm3rLQzRrKPXp/sGH60bprMyvmEb0w2eXGjiyKRmEk9NH2AALFSfqja4kV3TLL34fYYVl/Pbzlfwzcmm7wYgXpO34MSrm+xR1HM50PrveiOSfY+bIRrYbArfGMS8KKMEarDx+vCqfoxEPN1ofVelJWSvQ8b4VoYLOEbk0SyMoab+vpdw7vE/QI2WbnFq/unJnofbo9h9+bNb1bSMSYNXrRtJiu35MrEZnZv3vxnCd+YNHhR+042ujRX7tFqdf78ZyUdY9LgVe27bMM2vqzchQJfVu6ibMM237tjkh3D6vz5z67wjUmDF6NP/Rhp6wUbaZv/LOEbkwYv6uuJRtrmErvfbP6zko4xafCibdOPkbZe7MNG2uY/S/jGpCnd+nqykbhejLT1ai57G2mb36ykY0yWJRuJ68c9bU1hsCt8k9fOe+Qdlny6rW65/2GteXrcsXXLbsoYFVUVXPDaBTx58pO0bd42I3EmiiPZSFw/7mnrlg28ym9xE76IvOTi9dtUdax34RjjXnSyB1jy6TbOe+Qdnh53rOsyxpTVU9j8/eaM3ezeTRyJRuK2alHMN1XVMde75cVoXT9ucWgyK1FJ56fAAwkefwR6ZzpAY+KJTvbR692UMSqqKnjxkxdRlDmfzOHrnV97Hme65ZR49/5J5Z5AXnTYWFko/yUq6dykqm8kerGI3J7k+c+B74AAUOP2zurGeMFNGWPK6inUqjMnfK3WZuQqP91ySuXOhlf3idbH4kWHjQ28yn9xr/BVdWayF7vZBhisqqWW7E2q5qzYTP/JCzhkwiv0n7wg5Tlbkg0Uqqiq4IWP51Bd6yTO6tpqZn082/Or/HQHLHk14Gl49xLmXNWFo3r/jRev7ppyGcYGXuW/pF06ItJLRGaLyHIRWS0ia0RktR/BmcLlZqKu/oe1jvna0PpkZYwb5j9AdSCyRFEdCHDD/Ac8fCcw+Kh2Ka2P5uWAp/DvK1JlA6/yn5u2zKeBacBZwGnAqcGfbigwT0TKRWT87oVoCpGbevHT445tkPTDu3SSTQhWVvEW0iTyGNIkwHtfvenpe1n4Yez7McdbH82rydPS/b7Cj0ncTGa5acusUFU3HTux9FfVLSLSHviniHyoqovDNwj+IRgPcPDBB+/mYUw+StTi57ZeHN6CGUuigULffTwx4a0F3cTpRihmafotLTpOoerzy9FAy5RvYJJuYvXi+4rh3Uvof+QeGW9jNZnh5gr/VhF5VETOEZEzQw83O1fVLcGfXwGzgT4xtpmqqr1UtVe7du4+4pr8l6xk40e92M0xvJgDPtQ+uUeb+UjxN+zRdn7Eej+Eru7Dv6/Y3a6kdMpCJrvcJPxfAaXAiTilnFBZJyER2UtEWoZ+B4YC7+9+qKYxSVayyZWbfnjRiqjqXN0XtypHRCluVYYUfZdSW2W6wq/uQ0JX+anwo43VZI6bhN8teAV+oar+Kvi4yMXr9gfeEpFVwDLgFVWdm1a0ptFIVrLxsl5cUVXBSbNOapCchncv4ayeJXVz1hSJcFbPyNKJm9JSsm6iyp3V7NFmPtQVkJQ92s5Pqa0yXYu+WFR3dR9SXVvNwi8WRqyLd65CYpWFTP5wU8N/V0SOVtV1qexYVdcD3XYvLNPYuRn56dVEXfFG0s5ZsZlZ5Zsj5qGfVb6ZXh1b1x03WZxuRp8e0PpHvmtVXvcFsTQJUNyqjNbVp6T93tyaP2q+q+0SjTqOVxa6rNtlVsvPE26u8I8DVorIR9aWabziV4tfohKEm3JNsjjd7OOnP10KDb4eVo4+emka78x7yco1XpWFTPa4SfgnAofj1OBTbcs0Jia/WvwSlSDclGuSxelmHxt2vRez/fPznct26z1lSrJyjduykMldbko6PwHWqup3AMEvYo8GNmQyMNP4ZXpu9WQlCLcTig3vXsJRB3/HmFfGMO3UmRzRuiRi22T7CJVT/JiVM5l4Mbgp17gtC+XC+zSxubnCfwj4Pmx5R3CdMTktWQkilbLShDcnENAA1y++PmJ9KvvIhXbGeDF4Wa7JhfdpYnOT8EW1voFMVWuxefRNHkhWgnBbVvpw64d8WvkpAJ9WfsrH2z6ue87tPnKhnTFRDF6Va3LhfZr4RJM0A4vIC8Ai6q/qr8CZEG2418H06tVLy8rKvN6tMWkZPmd4XcIHOGzfw5gzfE5K+7jz3TuZ/e/ZVNdWU9ykmDMPPzPlUa5uSiWJtvEihmT8OIaJJCLlbiendHOFfxnQD9gMbAL6EpwKwZjGLvzqPiT6Kj8Zr0a5uimVxNvGy5G28fhxDJOepAlfVb9S1TGq2l5V91fVc4NTJRjT6E14c0LM9dG1/ES8qI+7KZUk2saPlkpr28x9cRO+m9ktbQZM76U7B7yfko3K9GIfXhwjmQ+3fkjpE6Uxr9o/+/azmK+JXp8oTi9GuboZ4ZpoGz9aKq1tM/fFreGLyHrgd4leC9yhqsd4FUyh1/CjR22C0/GRq1PQ3vnunTz30XOMPnL0btdpk+3Di2MkE6rRx6rNV1RVcNILJ/FD4Ie6dXsW7cncs+ZG1MgzeS7cxOA2TtP4eFXDf4P6ydJiPU4F/pleqCZcPt0z1ItujGT78KPjI1EHDrgrU2T6XLiJwcopxo1Etzj8lYvHNX4G29jl0z1DvZhEK9k+/JioK7pGH12bd1OmyPS5cBODlVOMG0nbMv1U6CWd/pMXxBy1WdKqOUsmHJ+FiGJLpXyQaGRnon34UaL4cOuHjHp5VIP1s06bxRGtj3C1Dz/ORSqStW7aKNjGx+u2TOOTfLlnaCrlg90d2elHicLvDpxcGOVqo2ALmyX8HJIv9wxNpetkd0d2+lGicNuBk4gf58KtXPhOxOQ2NyNt98S5gXknwqZUUNU7vA6m0Es6jU0hjbpMVipxcy7SLbckO0Yh/XsUEq9LOi8CZwA1OBOnhR7GxFVooy4TlUrcnot0yi3JjlFo/x4mNjcJv4Oqnq2q96nqA6FHxiMzea2Q2gS9uHFIuuWWXPhOxOQ+Nwn/bRHpkvFITKPi1T1UE42CdSvTo3W9uHFIuq2dufCdiMl9iUbarsG5L1tTnDterQd+wBlhq6ra1etgrIZfeJKNUE00CtarY6TDi5ZKGyVr0uFVDT90K8OTgP+D3eLQeCxZGSPZKFgvjpEuL0olVm4xfkk00naDqm4A7gr9Hr7OvxBNY5WsjJFsFKwXx0iXF6USK7cYv7hpy1yuqj3ClouANap6tNfBWEmncCQrY6QyCtaPEazG5CpPSjoiMlFEvgO6isi3wcd3wFc4rZrG7LZkZYxURsH6MYLVmMYgUUnnHlVtCdyvqvsEHy1VtY2qTnR7ABEpEpEVIvKyJxGbRiFZGSOVeegzPYLVmMbCTVvmcyLSI+pxmIi4vZH51cAHacRo8li8tsr5o+Yz+sjRFDcpBqC4STFnH3k280fNB2DVBatiPr/qglUR+5myegqBWmdK6UBtIOLqff6o+ay5cE2DR+gYIW7aNv24EYsxmeYm4f8/4F1gKvBI8PdngY9FZGiiF4pIB+AU4NE04zR5asKbEwhooEEpxouRoaFtarQGgBqt8f1escbkEzcJ/3Ogu6r2UtWeQCnwPvAL4L4kr30QuB6oTbKdaYQStVV6MTI0/Oo+JPoqP5l07xVrTD5xk/CPUtW1oQVVXYfzB2B9oheJyKnAV6panmS78SJSJiJlFRUVroI23sh0mSJRW6UXI0MXfbGo7uo+pEZrUqrRp3uvWGPyiZu2zBnANpwyDsDZQFvgfOAtVe0d53X3BLepAZoB+wAvqOov4x3L2jL9lckRqG7bKtOJId22S7tXrGkMvJ4tcyzwCXANcC3OFAtjgWpgcLwXqepEVe2gqp2AMcCCRMne+CvTZQo3bZVeTBgW0KiSjrov6di9Yk2hSZrwVXVncIbMEao6XFX/oKpVqlqrqt/7EaTxXqbLFG7aKr2YMKymNqqkU+u+pGP3ijWFxk1Jpz9wG9CRyBugHOp1MFbS8YefZYpMjoK1cosx3pd0HgP+CBwH9A57mDzlZ5kik6NgrdxiTGrcJPxKVX1NVb9S1a2hR8YjMxnjV5ki06NgrdxiTGrclHQmA0XACzjz4QOgqsu9DsZKOo3Lne/eyQsfv0CN1tBUmnLWEWfZPVSN8VgqJR030yP0Df4M36ECx6camCkc8UbBXtbtMquvG5MlSRO+qsZtvTQmnkSjYO0q35jsSFrDF5H9ReQxEXktuHy0iFyc+dBMPnM7CtYmJTPGP26+tJ0OvA4cGFz+GGcQljFxPXvKs+xZtGfEuj2L9mTGqTMi1tmkZMb4x03Cb6uqMwlOgKaqNUAg8UtMoXPTMmmTkhnjLzcJf4eItMH5ohYR+RlQmdGoTN5z0zJpk5IZ4y83XTq/AV4CDhORJUA7YGRGozJJxRvBmiuibzISLd5899FdPLn+Po3JJ27m0lkODAT6AZcCx6jq6kwHZhLL99q321Gy+f4+jckliW5ifmboAZwOHAkcAZwWXGeypDHUvt2UfBrD+zQmlyQq6ZyW4DnFGXlrsiBW7TvfetuTlXygcbxPY3JJ0qkV/GRTKyRXKDNEFsr7NCZdXs+WaXJIocwQWSjv0xg/WcLPQYlGnxbKDJGF8j6N8ZObtkzjs/DOlOiadaj2ncn70eYCNzV+Y0xqXHXpxHr4GWQhcdOZYt0rxpjdkaikc1qCx6mZD60wuRl9aiNUjTG7w7p0coibzhTrXjHGhPO8S0dEThGR60XkltAjvRBNLG46U6x7xRizu9zMhz8FOBv4NSDAKKBjhuMqSG46U6x7xRizu9zc03a1qnYN+7k38IKqDvU6mEIv6RhjTKq8LunsDP6sEpEDgWrgkN0NzhhjTHa46cN/WURaAfcDy3Hm0Xk0o1EZY4zxnJsr/PtUdbuqzsKp3R8F3JXsRSLSTESWicgqEVkrIrenG6xxz+4Va4yJ5ibhvxP6RVV/UNXK8HUJ/AAcr6rOZtyrAAATZ0lEQVTdgFLgxODdsowPbB55Y0y0RCNtDxCRnkBzEekuIj2Cj0FAi2Q7Vsf3wcXi4CN3mv4bMRuJa4yJJdEV/jDgD0AH4I/AA8HHtcCNbnYuIkUishL4Cvinqi6Nsc14ESkTkbKKiopU4zcx2EhcY0wsbtoyzwrW73f/IM6XvrOBX6vq+/G2s7bM9NlIXGMKi9dtmUtE5DEReS2486NF5OJUAlLV7cAi4MRUXmdSZyNxjTHxuEn404DXgQODyx8D1yR7kYi0C17ZIyLNgV8AH+5mnMYlG4lrjInHTR9+W1WdKSITAVS1RkQCLl73E+BxESnC+cMyU1VfTiNW44LNI2+MicdNwt8hIm0IdtgEWysrk71IVVcD3dMLzxhjjFfcJPzfAC8Bh4nIEqAdMDKjURljjPFc0oSvqstFZCBwJM5smR+panWSlxljjMkxSRO+iDQDrgCOwynrvCkiU1R1V6aDM8YY4x03JZ0ngO+AvwaXzwGexJkX3xhjTJ5wk/CPDM6HE7JQRFZlKiBjjDGZ4aYPf0X4pGci0hdYkrmQjDHGZIKbK/y+wAUisjG4fDDwgYiswZkjrWvGojPGGOMZNwnfpkMwxphGwE1b5gY/AjHGGJNZbmr4xhhjGgFL+MYYUyAs4RtjTIGwhG+MMQXCEr4xxhQIS/jGGFMgLOEbY0yBsIRvjDEFwhK+McYUCEv4xhhTICzhG2NMgbCEb4wxBcISvjHGFAhL+MYYUyAs4RtjTIHIWMIXkYNEZKGIfCAia0Xk6kwdyxhjTHJu7ni1u2qA36rqchFpCZSLyD9VdV0Gj2mMMSaOjF3hq+p/VHV58PfvgA+AkkwdzxhjTGK+1PBFpBPQHVga47nxIlImImUVFRV+hJNdqomX8+UYxpi8k/GELyJ7A7OAa1T12+jnVXWqqvZS1V7t2rXLdDjZtfAemDuxPgGrOssL78mvYxhj8lJGE76IFOMk+6dV9YVMHivnqcKuSlj6UH1CnjvRWd5V6c1VuB/HMMbkrYx9aSsiAjwGfKCqf8zUcfKGCJwYvMpe+pDzAOh7ubNeJD+OYYzJW5m8wu8PnA8cLyIrg4+TM3i83BeekEO8TsR+HMMYk5cy2aXzlqqKqnZV1dLg49VMHS8vhEos4cLr7flyDGNMXrKRtn4Jr6f3vRxu3e78DK+358MxQmprEy97wbqNjPFUJgdemXAi0GzfyHp6qPTSbF/vavhfroYDusCwu53lYXfDhrec9V6VdaadDD98C+MXQ5MmTrKfOgD23Ad+5dGHuIX3OF80h85V6I9Zs31h8MTkrzfGNGBX+H4aPDGynh5K+l4lMFU4oCt8uQZev9FZfv1GZ/mArt5cIdfWOsn+yzVOkg8l+y/XOOu9uNK3biNjMkI0h/7n6dWrl5aVlWU7jPwWnhxDvO7SCU/yIQd0qb/i94If78OYRkBEylW1l5tt7Qrfa8nqzl7UvgOB+MuhMk64UHnHK02aOMk9nJfJHqzbyJgMsITvpWSjXKedXF8Ggfor5WkpdKv+qTM8cHh9kg8EnOU/dQ7GcLezz3BTBzjrvRKKO/oYXn5xa91GxnjOEr5XktWdA4H0a9+BAFRXQdXW+qT/wOHOcnUVVFfDR68Ga/Zd4JZvnJ9frnHWe5GQw+OOPoZXSd/PbiNjCoh16aRCNbKkEL7sZpTr+MX1yfKO/Zzno2vftbWRpZHw5aIi+O2/65P8na2d9S3aOOuLiuCIk524wo+xf2dnfbz9xlqO916bNHG6cdp3ro97/GKY8nNnvRdlnbqOpsuiOprUu44mYwqQXeG75WZSsmR152S1bzcln8X3wdFnRe7j6LOc9QCr/gHfbol8/tstzvqQZMdx9V6jzk8mcnD0hbxd2BuTFkv4brhtE0xWd05U+3bT7qgKO7ZB2dTIfZRNddbX1MCPO2Dntsjnd25z1gcCyY8TCLgrTf33/cjX//d979syl02JjGHZFGvLNCYNhdWWmagk4+a1r94A7z1cv673pXDyvZEDg0J15xPviVweOgkeGVhf+w4v74SWAR7+uZM8Q/bvDJe+6XwKCK/ZR2vRBn7zMcy7EZY93PD5PpfCScFYa2vh4QHw37C2yv27wKXBTxvJWiLdtmWme76tLdOYpKwtM5Z054mffgp88Xbkui/edtZD/JG0fS931hcVwTcboGkzuGSRkxgvWeQsf7PBWX781NjHDq0vKoIfvgMpinxeguubNnW+nG3eOvL55q2d9aFE+ca90LFf5DYd+znrQ+8lndIUpH++rS3TGM8VRsJPd+RmeBkkXHSHTaKRtLW1sF9HqNkFjw5ylh8d5Czv19Epx+yqjLy6B2d5V2V92aftkaBRffgacNbX1MAeLWOXdPZoWV8W2vlNw08Byx521qumV5oCb0bKWlumMZ4rjC6ddOeJF4GD+jVM+OCsD3999L6ir4rjdem4fR8d+0WWYkJCV+w7/hv7tTv+W58sN7wde5sNbztJe95NsUtTkLg0NXVA/ZV+Ouc7UXkM7ErfmN1UGFf44CSIoZMi1w2d1LDGHC60LAIt9nNq9uF6X+qsd7MPSFwKadLEKf3s3zny+f07O+ubNHGO03w/6D0+Ko7xzvqmTaG4hVPPD9eijbO+qCh4nH2cmn3Ecbo464uKkpem9gy+Prwtc/8ukW2Z6Yz4TVYes2RvzG4pnIQ/7WTnC89wDxzuvhVx4A2xa/gDb6hfTraPZKWQC1+OHXv4eq2FtbMin187y1kPcPVq2Gv/yOf32t9ZHzL2ldg1/LGvOL8nm+St08+d7cOf79jPWV93LtIc8ZvpieaMKUCFkfADAaj40OluadEGbt7m/Kza6qwP1c8TtSImG13qZqRton3U1NS3N4Y/H97+GAhA2WOx30fZY85I26kDoGJd5D4q1kXGOXeiU7MPH8W67OHIP1bxSlN1LZMPR7VMPlxfn6+t9WbEb7wYjDG7pTBq+EVF0PNiKH+s4QjVnhc7pZBkNec994lsPQzVrsPLGOnso2lTd8doexR8/WHD99H2KCgudrePdObld/N9iIgzshcafl8RPeI3Xem0fhpTYAqrDz8QqE+S4FwhF4W1OKrC7a3ql2/dHpk8kk1H4MU+ki0vvAeqtsF7YYOveo+HFq3ryx1u40wnUSZ7n6HjhpI9OFf6XiZ7u0mKMdaHH1NtrdNdEu6RgZGthMnaAKOTVawkmu4+Ei2rws7tkckenOWd2+uPk2yfkF65xM37DN18JVzopixesJukGJOyxpPwE3XHJJvhMRBIf3ZGv2Z4jJeX/apiuHmffpyL8M6dpQ85nzbC2zitrGNMA42jhp/so31ohsd4te14rYjgvg3Qr3vWNmvlzCK5dEr9+r6XOev9SHJu32emz0UolhPviZx+wZK9MXHlfw0/2Rw24Qlgd6cETjWeTH6JqAqvTXAmEgvpcxmcNNnfROfmffpxLmy+HVPgUqnh5/8VfiqjaJPVtr1oA8xkK2H4rJHRf9xC58GvROfmffpxLmw0rjGuZSzhi8jfgVOBr1S1c7Lt0zyYPx/ts90C6EfZKF/YuTAmZRkr6YjIAOB74Am3CX+32zL9+GifSy2A2f7Dk0vsXJgClxNtmaq6GNiWdMP0D5T5jpBcawG0Eaj17FwY41rjqOH70R2TzuyPxhiTAzLapSMinYCXE5V0RGQ8MB7g4IMP7rlhw4bdO5gfH+3djC41xhgf5URJxy1VnaqqvVS1V7t27XZ/R5n+aG835DDG5LmsJ/y84NcoWmOMyaBMtmU+AwwC2orIJuBWVX0sU8fLKGsBNMY0Avk/0tZP1gJojMkxeVXDzyvWAmiMyWOW8I0xpkBYwjfGmAJhCd8YYwqEJXxjjCkQlvCNMaZA5FRbpohUALs5t4Kn2gJfZzsIFyxO7+RDjGBxeq0xxNlRVV1NU5BTCT9XiEiZ277WbLI4vZMPMYLF6bVCi9NKOsYYUyAs4RtjTIGwhB/b1GwH4JLF6Z18iBEsTq8VVJxWwzfGmAJhV/jGGFMgCj7hi0iRiKwQkZdjPDdWRCpEZGXwcUmWYvxcRNYEY2gwnag4/iIin4jIahHpkYMxDhKRyrBzeYvfMQbjaCUiz4vIhyLygYgcG/V81s+lyzizfj5F5Miw468UkW9F5JqobbJ+Pl3GmfXzGYzjWhFZKyLvi8gzItIs6vk9RWRG8HwuDd5V0LX8v6dt+q4GPgD2ifP8DFW90sd44hmsqvH6cE8CDg8++gIPBX/6LVGMAG+q6qm+RRPbn4G5qjpSRPYAWkQ9nyvnMlmckOXzqaofAaXgXDgBm4HZUZtl/Xy6jBOyfD5FpAS4CjhaVXeKyExgDDA9bLOLgW9U9f+IyBjgXuBst8co6Ct8EekAnAI8mu1Y0nQG8IQ63gVaichPsh1UrhGRfYABwGMAqvqjqm6P2izr59JlnLlmCPCpqkYPnMz6+YwSL85c0RRoLiJNcf7Ib4l6/gzg8eDvzwNDRNzP017QCR94ELgeqE2wzVnBj6LPi8hBPsUVTYF5IlIevOl7tBLgi7DlTcF1fkoWI8CxIrJKRF4TkWP8DC7oUKACmBYs4z0qIntFbZML59JNnJD98xluDPBMjPW5cD7DxYsTsnw+VXUz8AdgI/AfoFJV50VtVnc+VbUGqATauD1GwSZ8ETkV+EpVyxNs9r9AJ1XtCvyL+r+sfuuvqj1wPh7/XxEZEPV8rL/wfrdfJYtxOc4Q8G7AX4E5PscHztVTD+AhVe0O7AAmRG2TC+fSTZy5cD4BCJacTgeei/V0jHVZaQ1MEmfWz6eI7IdzBX8IcCCwl4j8MnqzGC91fT4LNuED/YHTReRz4FngeBF5KnwDVd2qqj8EFx8BevobYl0cW4I/v8KpPfaJ2mQTEP7powMNPwpmVLIYVfVbVf0++PurQLGItPUzRpzztElVlwaXn8dJrNHbZPVc4iLOHDmfIScBy1X1vzGey4XzGRI3zhw5n78APlPVClWtBl4A+kVtU3c+g2WffYFtbg9QsAlfVSeqagdV7YTzMW+Bqkb8NY2qNZ6O8+Wur0RkLxFpGfodGAq8H7XZS8AFwY6In+F8FPxPLsUoIgeEao0i0gfnv72tfsUIoKpfAl+IyJHBVUOAdVGbZfVcuo0zF85nmHOIXybJ+vkMEzfOHDmfG4GfiUiLYCxDaJhzXgIuDP4+Eidvub7Cty6dKCJyB1Cmqi8BV4nI6UANzl/RsVkIaX9gdvC/xabAP1R1rohcBqCqU4BXgZOBT4Aq4Fc5GONI4HIRqQF2AmNS+Q/VQ78Gng5+vF8P/CrHzqXbOHPifIpIC+AE4NKwdTl3Pl3EmfXzqapLReR5nPJSDbACmBqVkx4DnhSRT3By0phUjmEjbY0xpkAUbEnHGGMKjSV8Y4wpEJbwjTGmQFjCN8aYAmEJ3xhjCoQlfNMoBWc/jDUDasz1HhxvuIgcHba8SEQS3oNU6mdofNWD4zcXZ5bHH7M4AMvkOEv4xnhjOHB00q0aelNVT0734Kq6U1VLyd4oVpMHLOGbrAiOzn0lOFnV+yJydnB9TxF5IzgJ2+uh0c7BK+YHReTt4PZ9guv7BNetCP48MtFxY8TwdxF5L/j6M4Lrx4rICyIyV0T+LSL3hb3mYhH5OBjPIyLyNxHphzMS+/7gVfZhwc1Hiciy4PY/dxnT9eLcV2CViEwOe+9/EpHF4syN3zsY379F5C6379cYG2lrsuVEYIuqngIgIvuKSDHOxFVnqGpF8I/AJOCi4Gv2UtV+4kzM9negM/AhMEBVa0TkF8DdwFkuY7gJZ2j6RSLSClgmIv8KPlcKdAd+AD4Skb8CAeBmnHltvgMWAKtU9W0ReQl4WVWfD74fgKaq2kdETgZuxZkrJS4ROQnnk0JfVa0SkdZhT/+oqgNE5GrgRZx5nbYBn4rIn1Q1W9MqmDxiCd9kyxrgDyJyL06ifFNEOuMk8X8GE2YRzjSxIc8AqOpiEdknmKRbAo+LyOE4swYWpxDDUJwJ9H4XXG4GHBz8fb6qVgKIyDqgI9AWeENVtwXXPwcckWD/LwR/lgOdXMTzC2CaqlYBhI4T9FLw5xpgbWg+GhFZjzOZliV8k5QlfJMVqvqxiPTEmWflHhGZhzPL5lpVPTbey2Is3wksVNUR4tzubVEKYQhwVvCOSPUrRfriXNmHBHD+X3F9o4mg0D5Cr3cTT7y5TkL7qo2Krdblvo2xGr7JDhE5EKhS1adwbvrQA/gIaCfB+7eKSLFE3ogiVOc/DmfWxUqc6WE3B58fm2IYrwO/Ds5MiIh0T7L9MmCgiOwnztS04aWj73A+baRjHnBRcKIvoko6xqTNEr7Jli44NfOVOLX0u1T1R5xZC+8VkVXASiLnA/9GRN4GpuDc2xPgPpxPCEtwSkCpuBOnBLRaRN4PLscVvCPR3cBSnBvirMO54xA491S4Lvjl72FxdpGQqs7FKd2UBc/L75K8xJiU2GyZJi+IyCLgd6paluU49lbV74NX+LOBv6tqrBtiu9nXIJz35NmNs8W5oU8vTXwzeVOg7ArfmNTcFrz6fh/4jPRuhfcj0NnLgVc4n1gS3aPZFDC7wjfGmAJhV/jGGFMgLOEbY0yBsIRvjDEFwhK+McYUCEv4xhhTICzhG2NMgfj/x/4u12cipAEAAAAASUVORK5CYII=\n", 164 | "text/plain": [ 165 | "
" 166 | ] 167 | }, 168 | "metadata": { 169 | "needs_background": "light" 170 | }, 171 | "output_type": "display_data" 172 | } 173 | ], 174 | "source": [ 175 | "import matplotlib.pyplot as plt\n", 176 | "%matplotlib inline\n", 177 | "\n", 178 | "# Break up the data frame and graph each species separately. \n", 179 | "species = df.species.unique()\n", 180 | "markers = ['o', 'x', '^']\n", 181 | "for i in range(3):\n", 182 | " df_segment = df[df['species'] == species[i]]\n", 183 | " plt.scatter('sepal_length', \n", 184 | " 'petal_length', \n", 185 | " data=df_segment, \n", 186 | " marker=markers[i],\n", 187 | " label=species[i])\n", 188 | "# Add proper labels and show the result. \n", 189 | "plt.xlabel('sepal length [cm]')\n", 190 | "plt.ylabel('petal length [cm]')\n", 191 | "plt.legend(loc='upper left')\n", 192 | "plt.show()" 193 | ] 194 | }, 195 | { 196 | "cell_type": "markdown", 197 | "metadata": {}, 198 | "source": [ 199 | "From this we can see that iris-setosa is linearly separable from the other two species. This is significant for running machine learning. \n", 200 | "\n", 201 | "One final note is that when you don't know what's coming back it's good to describe the data set to ensure the values you think are numeric actually are. For example ClickHouse Decimal values don't automatically coerce to floats which means data science stack operations won't work as expected. " 202 | ] 203 | }, 204 | { 205 | "cell_type": "code", 206 | "execution_count": 4, 207 | "metadata": {}, 208 | "outputs": [ 209 | { 210 | "data": { 211 | "text/html": [ 212 | "
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sepal_lengthsepal_widthpetal_lengthpetal_width
count150.000000150.000000150.000000150.000000
mean5.8433333.0573333.7580001.199333
std0.8280660.4358661.7652980.762238
min4.3000002.0000001.0000000.100000
25%5.1000002.8000001.6000000.300000
50%5.8000003.0000004.3500001.300000
75%6.4000003.3000005.1000001.800000
max7.9000004.4000006.9000002.500000
\n", 295 | "
" 296 | ], 297 | "text/plain": [ 298 | " sepal_length sepal_width petal_length petal_width\n", 299 | "count 150.000000 150.000000 150.000000 150.000000\n", 300 | "mean 5.843333 3.057333 3.758000 1.199333\n", 301 | "std 0.828066 0.435866 1.765298 0.762238\n", 302 | "min 4.300000 2.000000 1.000000 0.100000\n", 303 | "25% 5.100000 2.800000 1.600000 0.300000\n", 304 | "50% 5.800000 3.000000 4.350000 1.300000\n", 305 | "75% 6.400000 3.300000 5.100000 1.800000\n", 306 | "max 7.900000 4.400000 6.900000 2.500000" 307 | ] 308 | }, 309 | "execution_count": 4, 310 | "metadata": {}, 311 | "output_type": "execute_result" 312 | } 313 | ], 314 | "source": [ 315 | "import pandas\n", 316 | "df.describe()" 317 | ] 318 | }, 319 | { 320 | "cell_type": "code", 321 | "execution_count": null, 322 | "metadata": {}, 323 | "outputs": [], 324 | "source": [] 325 | } 326 | ], 327 | "metadata": { 328 | "kernelspec": { 329 | "display_name": "Python 3", 330 | "language": "python", 331 | "name": "python3" 332 | }, 333 | "language_info": { 334 | "codemirror_mode": { 335 | "name": "ipython", 336 | "version": 3 337 | }, 338 | "file_extension": ".py", 339 | "mimetype": "text/x-python", 340 | "name": "python", 341 | "nbconvert_exporter": "python", 342 | "pygments_lexer": "ipython3", 343 | "version": "3.7.1" 344 | } 345 | }, 346 | "nbformat": 4, 347 | "nbformat_minor": 2 348 | } 349 | -------------------------------------------------------------------------------- /notebooks/EX-5-Airline-OnTime-Data.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Graphing Airline Ontime Data\n", 8 | "\n", 9 | "This notebook shows how to create a time-series graph from airline ontime data, which can be downloaded from the [US Bureau of Transportation Statistics](https://www.transtats.bts.gov/tables.asp?DB_ID=120). See https://github.com/Altinity/altinity-datasets for tools to help with loading. " 10 | ] 11 | }, 12 | { 13 | "cell_type": "markdown", 14 | "metadata": {}, 15 | "source": [ 16 | "First import SQLAlchemy and activate the %sql function. This just needs to be done once. " 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": 1, 22 | "metadata": { 23 | "scrolled": true 24 | }, 25 | "outputs": [], 26 | "source": [ 27 | "from sqlalchemy import create_engine\n", 28 | "%load_ext sql" 29 | ] 30 | }, 31 | { 32 | "cell_type": "markdown", 33 | "metadata": {}, 34 | "source": [ 35 | "Run a query using %%sql. This needs to go in a separate cell. " 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": 2, 41 | "metadata": {}, 42 | "outputs": [ 43 | { 44 | "name": "stdout", 45 | "output_type": "stream", 46 | "text": [ 47 | "Done.\n" 48 | ] 49 | }, 50 | { 51 | "data": { 52 | "text/html": [ 53 | "\n", 54 | " \n", 55 | " \n", 56 | " \n", 57 | " \n", 58 | " \n", 59 | " \n", 60 | " \n", 61 | " \n", 62 | " \n", 63 | " \n", 64 | " \n", 65 | " \n", 66 | " \n", 67 | " \n", 68 | " \n", 69 | " \n", 70 | " \n", 71 | " \n", 72 | " \n", 73 | " \n", 74 | " \n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | " \n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | " \n", 162 | " \n", 163 | " \n", 164 | " \n", 165 | " \n", 166 | " \n", 167 | " \n", 168 | " \n", 169 | " \n", 170 | " \n", 171 | " \n", 172 | " \n", 173 | " \n", 174 | " \n", 175 | " \n", 176 | " \n", 177 | " \n", 178 | " \n", 179 | " \n", 180 | " \n", 181 | " \n", 182 | " \n", 183 | " \n", 184 | " \n", 185 | " \n", 186 | " \n", 187 | " \n", 188 | " \n", 189 | " \n", 190 | " \n", 191 | " \n", 192 | " \n", 193 | " \n", 194 | " \n", 195 | " \n", 196 | " \n", 197 | " \n", 198 | " \n", 199 | " \n", 200 | " \n", 201 | " \n", 202 | " \n", 203 | " \n", 204 | " \n", 205 | " \n", 206 | " \n", 207 | " \n", 208 | " \n", 209 | " \n", 210 | " \n", 211 | " \n", 212 | " \n", 213 | " \n", 214 | " \n", 215 | " \n", 216 | " \n", 217 | " \n", 218 | " \n", 219 | "
tcancelleddelayed
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tcancelleddelayed
2720140.0218190.205397
2820150.0154460.181739
2920160.0117240.169740
3020170.0139990.194787
3120180.0106690.177533
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" 341 | ], 342 | "text/plain": [ 343 | " t cancelled delayed\n", 344 | "27 2014 0.021819 0.205397\n", 345 | "28 2015 0.015446 0.181739\n", 346 | "29 2016 0.011724 0.169740\n", 347 | "30 2017 0.013999 0.194787\n", 348 | "31 2018 0.010669 0.177533" 349 | ] 350 | }, 351 | "execution_count": 3, 352 | "metadata": {}, 353 | "output_type": "execute_result" 354 | } 355 | ], 356 | "source": [ 357 | "result = _\n", 358 | "df = result.DataFrame()\n", 359 | "df.tail()" 360 | ] 361 | }, 362 | { 363 | "cell_type": "markdown", 364 | "metadata": {}, 365 | "source": [ 366 | "Time to make a quick graph using matplotlib. I'm not the greatest at this but once you have a data frame everything is possible. " 367 | ] 368 | }, 369 | { 370 | "cell_type": "code", 371 | "execution_count": 4, 372 | "metadata": {}, 373 | "outputs": [ 374 | { 375 | "data": { 376 | "image/png": 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377 | "text/plain": [ 378 | "
" 379 | ] 380 | }, 381 | "metadata": { 382 | "needs_background": "light" 383 | }, 384 | "output_type": "display_data" 385 | } 386 | ], 387 | "source": [ 388 | "import matplotlib.pyplot as plt\n", 389 | "%matplotlib inline\n", 390 | "\n", 391 | "plt.plot('t', 'cancelled', \n", 392 | " data=df, linestyle='--', \n", 393 | " marker='o', label='Cancelled')\n", 394 | "plt.plot('t', 'delayed', \n", 395 | " data=df, linestyle='--', \n", 396 | " marker='o', label='Delayed')\n", 397 | "plt.xlabel('Year')\n", 398 | "plt.ylabel('Percentage')\n", 399 | "plt.legend(loc='upper left')\n", 400 | "plt.title('Fetch data the easy way')\n", 401 | "plt.show()" 402 | ] 403 | }, 404 | { 405 | "cell_type": "markdown", 406 | "metadata": {}, 407 | "source": [ 408 | "The %sql magic function is great but we can also do the same thing using the clickhouse-driver client library and direct API calls. " 409 | ] 410 | }, 411 | { 412 | "cell_type": "code", 413 | "execution_count": 5, 414 | "metadata": {}, 415 | "outputs": [ 416 | { 417 | "data": { 418 | "text/html": [ 419 | "
\n", 420 | "\n", 433 | "\n", 434 | " \n", 435 | " \n", 436 | " \n", 437 | " \n", 438 | " \n", 439 | " \n", 440 | " \n", 441 | " \n", 442 | " \n", 443 | " \n", 444 | " \n", 445 | " \n", 446 | " \n", 447 | " \n", 448 | " \n", 449 | " \n", 450 | " \n", 451 | " \n", 452 | " \n", 453 | " \n", 454 | " \n", 455 | " \n", 456 | " \n", 457 | " \n", 458 | " \n", 459 | " \n", 460 | " \n", 461 | " \n", 462 | " \n", 463 | " \n", 464 | " \n", 465 | " \n", 466 | " \n", 467 | " \n", 468 | " \n", 469 | " \n", 470 | " \n", 471 | " \n", 472 | " \n", 473 | " \n", 474 | "
tcancelleddelayed
2720140.0218190.205397
2820150.0154460.181739
2920160.0117240.169740
3020170.0139990.194787
3120180.0106690.177533
\n", 475 | "
" 476 | ], 477 | "text/plain": [ 478 | " t cancelled delayed\n", 479 | "27 2014 0.021819 0.205397\n", 480 | "28 2015 0.015446 0.181739\n", 481 | "29 2016 0.011724 0.169740\n", 482 | "30 2017 0.013999 0.194787\n", 483 | "31 2018 0.010669 0.177533" 484 | ] 485 | }, 486 | "execution_count": 5, 487 | "metadata": {}, 488 | "output_type": "execute_result" 489 | } 490 | ], 491 | "source": [ 492 | "import pandas\n", 493 | "from clickhouse_driver import Client\n", 494 | "\n", 495 | "client = Client('localhost', database='airline')\n", 496 | "result, columns = client.execute(\n", 497 | " 'SELECT toYear(FlightDate) t,'\n", 498 | " 'sum(Cancelled)/count(*) cancelled,'\n", 499 | " 'sum(DepDel15)/count(*) delayed '\n", 500 | " 'FROM airline.ontime GROUP BY t ORDER BY t', \n", 501 | " with_column_types=True)\n", 502 | "\n", 503 | "df2 = pandas.DataFrame(result, columns=[tuple[0] for tuple in columns])\n", 504 | "df2.tail()" 505 | ] 506 | }, 507 | { 508 | "cell_type": "markdown", 509 | "metadata": {}, 510 | "source": [ 511 | "The graph looks just the same, so we change the title to tell them apart. " 512 | ] 513 | }, 514 | { 515 | "cell_type": "code", 516 | "execution_count": 6, 517 | "metadata": {}, 518 | "outputs": [ 519 | { 520 | "data": { 521 | "image/png": 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\n", 522 | "text/plain": [ 523 | "
" 524 | ] 525 | }, 526 | "metadata": { 527 | "needs_background": "light" 528 | }, 529 | "output_type": "display_data" 530 | } 531 | ], 532 | "source": [ 533 | "import matplotlib.pyplot as plt\n", 534 | "%matplotlib inline\n", 535 | "\n", 536 | "plt.plot('t', 'cancelled', data=df2, \n", 537 | " linestyle='--', marker='o', label='Cancelled')\n", 538 | "plt.plot('t', 'delayed', data=df2, \n", 539 | " linestyle='--', marker='o', label='Delayed')\n", 540 | "plt.xlabel('Year')\n", 541 | "plt.ylabel('Percentage')\n", 542 | "plt.legend(loc='upper left')\n", 543 | "plt.title('Fetch data the hard way')\n", 544 | "plt.show()" 545 | ] 546 | }, 547 | { 548 | "cell_type": "markdown", 549 | "metadata": {}, 550 | "source": [ 551 | "That's all folks!" 552 | ] 553 | }, 554 | { 555 | "cell_type": "code", 556 | "execution_count": null, 557 | "metadata": {}, 558 | "outputs": [], 559 | "source": [] 560 | } 561 | ], 562 | "metadata": { 563 | "kernelspec": { 564 | "display_name": "Python 3", 565 | "language": "python", 566 | "name": "python3" 567 | }, 568 | "language_info": { 569 | "codemirror_mode": { 570 | "name": "ipython", 571 | "version": 3 572 | }, 573 | "file_extension": ".py", 574 | "mimetype": "text/x-python", 575 | "name": "python", 576 | "nbconvert_exporter": "python", 577 | "pygments_lexer": "ipython3", 578 | "version": "3.7.1" 579 | } 580 | }, 581 | "nbformat": 4, 582 | "nbformat_minor": 2 583 | } 584 | -------------------------------------------------------------------------------- /notebooks/EX-2-ClickHouse-SQL-Alchemy.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# ClickHouse SQLAlchemy\n", 8 | "\n", 9 | "This notebook provides simple examples from the clickhouse-sqlalchemy driver including a demonstration of integration with pandas and matplotlib. " 10 | ] 11 | }, 12 | { 13 | "cell_type": "markdown", 14 | "metadata": {}, 15 | "source": [ 16 | "Import SQLAlchemy + clickhouse-sqlalchemy entities." 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": 1, 22 | "metadata": { 23 | "scrolled": true 24 | }, 25 | "outputs": [], 26 | "source": [ 27 | "from sqlalchemy import create_engine, Column, MetaData, literal\n", 28 | "from clickhouse_sqlalchemy import Table, make_session, get_declarative_base, types, engines" 29 | ] 30 | }, 31 | { 32 | "cell_type": "markdown", 33 | "metadata": {}, 34 | "source": [ 35 | "Initialize SQLAlchemy to use local server with native connectivity. If you leave off '+native' the driver will use http[s]. " 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": 2, 41 | "metadata": {}, 42 | "outputs": [], 43 | "source": [ 44 | "uri = 'clickhouse+native://default:@localhost/default'\n", 45 | "\n", 46 | "engine = create_engine(uri)\n", 47 | "session = make_session(engine)\n", 48 | "metadata = MetaData(bind=engine)\n", 49 | "\n", 50 | "Base = get_declarative_base(metadata=metadata)" 51 | ] 52 | }, 53 | { 54 | "cell_type": "markdown", 55 | "metadata": {}, 56 | "source": [ 57 | "Define a class to represent sensor data from devices. " 58 | ] 59 | }, 60 | { 61 | "cell_type": "code", 62 | "execution_count": 3, 63 | "metadata": {}, 64 | "outputs": [], 65 | "source": [ 66 | "class SensorData(Base):\n", 67 | " dev_id = Column(types.Int32, primary_key=True)\n", 68 | " type = Column(types.String)\n", 69 | " mdate = Column(types.Date)\n", 70 | " mdatetime = Column(types.DateTime, primary_key=True)\n", 71 | " value = Column(types.Float64)\n", 72 | "\n", 73 | " __table_args__ = (\n", 74 | " engines.MergeTree('mdate', ('dev_id', 'mdate')),\n", 75 | " )" 76 | ] 77 | }, 78 | { 79 | "cell_type": "markdown", 80 | "metadata": {}, 81 | "source": [ 82 | "Drop and then recreate the SQL table. Ignore errors if the table does not exist previously. " 83 | ] 84 | }, 85 | { 86 | "cell_type": "code", 87 | "execution_count": 4, 88 | "metadata": {}, 89 | "outputs": [], 90 | "source": [ 91 | "table = SensorData.__table__\n", 92 | "try:\n", 93 | " table.drop()\n", 94 | "except:\n", 95 | " # Exceptions are ignored\n", 96 | " pass\n", 97 | "table.create()" 98 | ] 99 | }, 100 | { 101 | "cell_type": "markdown", 102 | "metadata": {}, 103 | "source": [ 104 | "Create sensor data for 5 mythical devices. Readings increase linearly from a base that is randomly selected for each device. " 105 | ] 106 | }, 107 | { 108 | "cell_type": "code", 109 | "execution_count": 5, 110 | "metadata": {}, 111 | "outputs": [ 112 | { 113 | "data": { 114 | "text/plain": [ 115 | "" 116 | ] 117 | }, 118 | "execution_count": 5, 119 | "metadata": {}, 120 | "output_type": "execute_result" 121 | } 122 | ], 123 | "source": [ 124 | "from datetime import date, datetime, timedelta\n", 125 | "from random import random\n", 126 | "today = date.today()\n", 127 | "this_instant = datetime.today()\n", 128 | "data = []\n", 129 | "for i in range(5):\n", 130 | " base = random()\n", 131 | " for j in range(10): \n", 132 | " data.append({'dev_id': i, \n", 133 | " 'type': 'widget-a', \n", 134 | " 'mdate': today, \n", 135 | " 'mdatetime': this_instant + timedelta(minutes=j), \n", 136 | " 'value': base + j * 0.1})\n", 137 | "session.execute(table.insert(), data)" 138 | ] 139 | }, 140 | { 141 | "cell_type": "markdown", 142 | "metadata": {}, 143 | "source": [ 144 | "Enable %sql magic function. " 145 | ] 146 | }, 147 | { 148 | "cell_type": "code", 149 | "execution_count": 6, 150 | "metadata": {}, 151 | "outputs": [ 152 | { 153 | "data": { 154 | "text/plain": [ 155 | "'Connected: default@default'" 156 | ] 157 | }, 158 | "execution_count": 6, 159 | "metadata": {}, 160 | "output_type": "execute_result" 161 | } 162 | ], 163 | "source": [ 164 | "%load_ext sql\n", 165 | "%sql clickhouse://default:@localhost/default" 166 | ] 167 | }, 168 | { 169 | "cell_type": "markdown", 170 | "metadata": {}, 171 | "source": [ 172 | "Prove that the magic function works by showing tables. %sql can handle any query. " 173 | ] 174 | }, 175 | { 176 | "cell_type": "code", 177 | "execution_count": 7, 178 | "metadata": {}, 179 | "outputs": [ 180 | { 181 | "name": "stdout", 182 | "output_type": "stream", 183 | "text": [ 184 | " * clickhouse://default:***@localhost/default\n", 185 | "Done.\n" 186 | ] 187 | }, 188 | { 189 | "data": { 190 | "text/html": [ 191 | "\n", 192 | " \n", 193 | " \n", 194 | " \n", 195 | " \n", 196 | " \n", 197 | " \n", 198 | " \n", 199 | " \n", 200 | " \n", 201 | " \n", 202 | " \n", 203 | " \n", 204 | " \n", 205 | " \n", 206 | " \n", 207 | " \n", 208 | " \n", 209 | " \n", 210 | " \n", 211 | " \n", 212 | " \n", 213 | " \n", 214 | " \n", 215 | " \n", 216 | " \n", 217 | " \n", 218 | " \n", 219 | " \n", 220 | " \n", 221 | " \n", 222 | " \n", 223 | " \n", 224 | " \n", 225 | " \n", 226 | " \n", 227 | " \n", 228 | " \n", 229 | " \n", 230 | " \n", 231 | "
name
airports
f
f_1
f_long
iris
iris_from_csv
iris_remote
iris_url_local
iris_web
ontime
sdata
sensor_data
" 232 | ], 233 | "text/plain": [ 234 | "[('airports',),\n", 235 | " ('f',),\n", 236 | " ('f_1',),\n", 237 | " ('f_long',),\n", 238 | " ('iris',),\n", 239 | " ('iris_from_csv',),\n", 240 | " ('iris_remote',),\n", 241 | " ('iris_url_local',),\n", 242 | " ('iris_web',),\n", 243 | " ('ontime',),\n", 244 | " ('sdata',),\n", 245 | " ('sensor_data',)]" 246 | ] 247 | }, 248 | "execution_count": 7, 249 | "metadata": {}, 250 | "output_type": "execute_result" 251 | } 252 | ], 253 | "source": [ 254 | "%sql show tables" 255 | ] 256 | }, 257 | { 258 | "cell_type": "markdown", 259 | "metadata": {}, 260 | "source": [ 261 | "Select all rows back and convert to a data frame. " 262 | ] 263 | }, 264 | { 265 | "cell_type": "code", 266 | "execution_count": 8, 267 | "metadata": {}, 268 | "outputs": [ 269 | { 270 | "name": "stdout", 271 | "output_type": "stream", 272 | "text": [ 273 | " * clickhouse://default:***@localhost/default\n", 274 | "Done.\n" 275 | ] 276 | }, 277 | { 278 | "data": { 279 | "text/html": [ 280 | "
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" 709 | ], 710 | "text/plain": [ 711 | " dev_id type mdate mdatetime value\n", 712 | "0 0 widget-a 2019-02-24 2019-02-24 00:03:59 0.470658\n", 713 | "1 0 widget-a 2019-02-24 2019-02-24 00:04:59 0.570658\n", 714 | "2 0 widget-a 2019-02-24 2019-02-24 00:05:59 0.670658\n", 715 | "3 0 widget-a 2019-02-24 2019-02-24 00:06:59 0.770658\n", 716 | "4 0 widget-a 2019-02-24 2019-02-24 00:07:59 0.870658\n", 717 | "5 0 widget-a 2019-02-24 2019-02-24 00:08:59 0.970658\n", 718 | "6 0 widget-a 2019-02-24 2019-02-24 00:09:59 1.070658\n", 719 | "7 0 widget-a 2019-02-24 2019-02-24 00:10:59 1.170658\n", 720 | "8 0 widget-a 2019-02-24 2019-02-24 00:11:59 1.270658\n", 721 | "9 0 widget-a 2019-02-24 2019-02-24 00:12:59 1.370658\n", 722 | "10 1 widget-a 2019-02-24 2019-02-24 00:03:59 0.308329\n", 723 | "11 1 widget-a 2019-02-24 2019-02-24 00:04:59 0.408329\n", 724 | "12 1 widget-a 2019-02-24 2019-02-24 00:05:59 0.508329\n", 725 | "13 1 widget-a 2019-02-24 2019-02-24 00:06:59 0.608329\n", 726 | "14 1 widget-a 2019-02-24 2019-02-24 00:07:59 0.708329\n", 727 | "15 1 widget-a 2019-02-24 2019-02-24 00:08:59 0.808329\n", 728 | "16 1 widget-a 2019-02-24 2019-02-24 00:09:59 0.908329\n", 729 | "17 1 widget-a 2019-02-24 2019-02-24 00:10:59 1.008329\n", 730 | "18 1 widget-a 2019-02-24 2019-02-24 00:11:59 1.108329\n", 731 | "19 1 widget-a 2019-02-24 2019-02-24 00:12:59 1.208329\n", 732 | "20 2 widget-a 2019-02-24 2019-02-24 00:03:59 0.766993\n", 733 | "21 2 widget-a 2019-02-24 2019-02-24 00:04:59 0.866993\n", 734 | "22 2 widget-a 2019-02-24 2019-02-24 00:05:59 0.966993\n", 735 | "23 2 widget-a 2019-02-24 2019-02-24 00:06:59 1.066993\n", 736 | "24 2 widget-a 2019-02-24 2019-02-24 00:07:59 1.166993\n", 737 | "25 2 widget-a 2019-02-24 2019-02-24 00:08:59 1.266993\n", 738 | "26 2 widget-a 2019-02-24 2019-02-24 00:09:59 1.366993\n", 739 | "27 2 widget-a 2019-02-24 2019-02-24 00:10:59 1.466993\n", 740 | "28 2 widget-a 2019-02-24 2019-02-24 00:11:59 1.566993\n", 741 | "29 2 widget-a 2019-02-24 2019-02-24 00:12:59 1.666993\n", 742 | "30 3 widget-a 2019-02-24 2019-02-24 00:03:59 0.880996\n", 743 | "31 3 widget-a 2019-02-24 2019-02-24 00:04:59 0.980996\n", 744 | "32 3 widget-a 2019-02-24 2019-02-24 00:05:59 1.080996\n", 745 | "33 3 widget-a 2019-02-24 2019-02-24 00:06:59 1.180996\n", 746 | "34 3 widget-a 2019-02-24 2019-02-24 00:07:59 1.280996\n", 747 | "35 3 widget-a 2019-02-24 2019-02-24 00:08:59 1.380996\n", 748 | "36 3 widget-a 2019-02-24 2019-02-24 00:09:59 1.480996\n", 749 | "37 3 widget-a 2019-02-24 2019-02-24 00:10:59 1.580996\n", 750 | "38 3 widget-a 2019-02-24 2019-02-24 00:11:59 1.680996\n", 751 | "39 3 widget-a 2019-02-24 2019-02-24 00:12:59 1.780996\n", 752 | "40 4 widget-a 2019-02-24 2019-02-24 00:03:59 0.673713\n", 753 | "41 4 widget-a 2019-02-24 2019-02-24 00:04:59 0.773713\n", 754 | "42 4 widget-a 2019-02-24 2019-02-24 00:05:59 0.873713\n", 755 | "43 4 widget-a 2019-02-24 2019-02-24 00:06:59 0.973713\n", 756 | "44 4 widget-a 2019-02-24 2019-02-24 00:07:59 1.073713\n", 757 | "45 4 widget-a 2019-02-24 2019-02-24 00:08:59 1.173713\n", 758 | "46 4 widget-a 2019-02-24 2019-02-24 00:09:59 1.273713\n", 759 | "47 4 widget-a 2019-02-24 2019-02-24 00:10:59 1.373713\n", 760 | "48 4 widget-a 2019-02-24 2019-02-24 00:11:59 1.473713\n", 761 | "49 4 widget-a 2019-02-24 2019-02-24 00:12:59 1.573713" 762 | ] 763 | }, 764 | "execution_count": 8, 765 | "metadata": {}, 766 | "output_type": "execute_result" 767 | } 768 | ], 769 | "source": [ 770 | "result = %sql select * from sensor_data\n", 771 | "df = result.DataFrame()\n", 772 | "df" 773 | ] 774 | }, 775 | { 776 | "cell_type": "code", 777 | "execution_count": 12, 778 | "metadata": {}, 779 | "outputs": [ 780 | { 781 | "data": { 782 | "text/html": [ 783 | "
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dev_idvalue
count50.00000050.000000
mean2.0000001.070138
std1.4285710.357015
min0.0000000.308329
25%1.0000000.822995
50%2.0000001.072185
75%3.0000001.345493
max4.0000001.780996
\n", 848 | "
" 849 | ], 850 | "text/plain": [ 851 | " dev_id value\n", 852 | "count 50.000000 50.000000\n", 853 | "mean 2.000000 1.070138\n", 854 | "std 1.428571 0.357015\n", 855 | "min 0.000000 0.308329\n", 856 | "25% 1.000000 0.822995\n", 857 | "50% 2.000000 1.072185\n", 858 | "75% 3.000000 1.345493\n", 859 | "max 4.000000 1.780996" 860 | ] 861 | }, 862 | "execution_count": 12, 863 | "metadata": {}, 864 | "output_type": "execute_result" 865 | } 866 | ], 867 | "source": [ 868 | "df.describe()" 869 | ] 870 | }, 871 | { 872 | "cell_type": "markdown", 873 | "metadata": {}, 874 | "source": [ 875 | "Data frames integrate nicely with graphics. Use selection on the data frame to pull out rows for each device in sucession and plot them as separate lines. " 876 | ] 877 | }, 878 | { 879 | "cell_type": "code", 880 | "execution_count": 9, 881 | "metadata": {}, 882 | "outputs": [ 883 | { 884 | "data": { 885 | "image/png": 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886 | "text/plain": [ 887 | "
" 888 | ] 889 | }, 890 | "metadata": { 891 | "needs_background": "light" 892 | }, 893 | "output_type": "display_data" 894 | } 895 | ], 896 | "source": [ 897 | "import matplotlib.pyplot as plt\n", 898 | "%matplotlib inline\n", 899 | "\n", 900 | "# Break up the data frame and graph each device separately. \n", 901 | "markers = ['o', 'x', '^', '+', '*']\n", 902 | "for i in range(5):\n", 903 | " df_segment = df[df['dev_id'] == i]\n", 904 | " plt.plot('mdatetime', 'value', data=df_segment, linestyle='--', marker=markers[i])\n", 905 | " \n", 906 | "plt.xticks(rotation=90)\n", 907 | "plt.show()" 908 | ] 909 | }, 910 | { 911 | "cell_type": "markdown", 912 | "metadata": {}, 913 | "source": [ 914 | "It's more common to use ClickHouse to compute aggregates. Find the min, average, and max values for each device and likewise convert them to a data frame." 915 | ] 916 | }, 917 | { 918 | "cell_type": "code", 919 | "execution_count": 10, 920 | "metadata": {}, 921 | "outputs": [ 922 | { 923 | "name": "stdout", 924 | "output_type": "stream", 925 | "text": [ 926 | " * clickhouse://default:***@localhost/default\n", 927 | "Done.\n" 928 | ] 929 | }, 930 | { 931 | "data": { 932 | "text/html": [ 933 | "
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dev_idmin(value)avg(value)max(value)
000.4706580.9206581.370658
110.3083290.7583291.208329
220.7669931.2169931.666993
330.8809961.3309961.780996
440.6737131.1237131.573713
\n", 995 | "
" 996 | ], 997 | "text/plain": [ 998 | " dev_id min(value) avg(value) max(value)\n", 999 | "0 0 0.470658 0.920658 1.370658\n", 1000 | "1 1 0.308329 0.758329 1.208329\n", 1001 | "2 2 0.766993 1.216993 1.666993\n", 1002 | "3 3 0.880996 1.330996 1.780996\n", 1003 | "4 4 0.673713 1.123713 1.573713" 1004 | ] 1005 | }, 1006 | "execution_count": 10, 1007 | "metadata": {}, 1008 | "output_type": "execute_result" 1009 | } 1010 | ], 1011 | "source": [ 1012 | "result = %sql select dev_id, min(value), avg(value), max(value) from sensor_data group by dev_id order by dev_id\n", 1013 | "df2 = result.DataFrame()\n", 1014 | "df2" 1015 | ] 1016 | }, 1017 | { 1018 | "cell_type": "markdown", 1019 | "metadata": {}, 1020 | "source": [ 1021 | "Let's put the average values per device into a nice bar chart. It's easy to add additional sets of bars or create subplots but this will do for today. " 1022 | ] 1023 | }, 1024 | { 1025 | "cell_type": "code", 1026 | "execution_count": 11, 1027 | "metadata": {}, 1028 | "outputs": [ 1029 | { 1030 | "data": { 1031 | "image/png": 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\n", 1032 | "text/plain": [ 1033 | "
" 1034 | ] 1035 | }, 1036 | "metadata": { 1037 | "needs_background": "light" 1038 | }, 1039 | "output_type": "display_data" 1040 | } 1041 | ], 1042 | "source": [ 1043 | "plt.bar('dev_id', 'avg(value)', data=df2, align='center', alpha=0.5)\n", 1044 | "plt.title('Average device measurements')\n", 1045 | "plt.xlabel('Device ID')\n", 1046 | "plt.ylabel('Value')\n", 1047 | "plt.show()" 1048 | ] 1049 | }, 1050 | { 1051 | "cell_type": "markdown", 1052 | "metadata": {}, 1053 | "source": [] 1054 | } 1055 | ], 1056 | "metadata": { 1057 | "kernelspec": { 1058 | "display_name": "Python 3", 1059 | "language": "python", 1060 | "name": "python3" 1061 | }, 1062 | "language_info": { 1063 | "codemirror_mode": { 1064 | "name": "ipython", 1065 | "version": 3 1066 | }, 1067 | "file_extension": ".py", 1068 | "mimetype": "text/x-python", 1069 | "name": "python", 1070 | "nbconvert_exporter": "python", 1071 | "pygments_lexer": "ipython3", 1072 | "version": "3.7.1" 1073 | } 1074 | }, 1075 | "nbformat": 4, 1076 | "nbformat_minor": 2 1077 | } 1078 | --------------------------------------------------------------------------------