├── .gitignore ├── Example_Notebooks ├── sparkora.html └── sparkora.ipynb ├── LICENSE ├── README.md ├── Sparkora ├── __init__.py ├── main.py ├── test_data.csv └── testsparkora.py ├── assets └── project_cover.gif ├── requirements.txt └── setup.py /.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 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | 131 | # added 132 | .idea/ 133 | -------------------------------------------------------------------------------- /Example_Notebooks/sparkora.ipynb: -------------------------------------------------------------------------------- 1 | {"cells":[{"cell_type":"markdown","source":["## Getting imports which are required"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"38c54c98-7f33-4a79-9a1c-69da4fa614a8"}}},{"cell_type":"code","source":["import pyspark.sql.functions as F\nimport pyspark.sql.types as T\nimport pandas as pd"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"4fddbe8e-231a-4ca8-a331-dc5a6e35006f"}},"outputs":[],"execution_count":0},{"cell_type":"markdown","source":["## Sparkora initialization"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"afabae15-20c5-4f6b-ab95-86b360d64913"}}},{"cell_type":"code","source":["from Sparkora import Sparkora"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"5bb9ef2b-c022-4b7b-b6ec-68426f94927e"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"Welcome to\n ____ __\n / __/__ ___ _____/ /__\n _\\ \\/ _ \\/ _ `/ __/ '_/\n /__ / .__/\\_,_/_/ /_/\\_\\ version 3.2.0\n /_/\n\nUsing Python version 3.8.10 (default, Sep 28 2021 16:10:42)\nSpark context Web UI available at http://10.172.203.167:48830\nSpark context available as 'sc' (master = local[8], app id = local-1636888245786).\nSparkSession available as 'spark'.\n","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"ansi","arguments":{}}},"output_type":"display_data","data":{"text/plain":["Welcome to\n ____ __\n / __/__ ___ _____/ /__\n _\\ \\/ _ \\/ _ `/ __/ '_/\n /__ / .__/\\_,_/_/ /_/\\_\\ version 3.2.0\n /_/\n\nUsing Python version 3.8.10 (default, Sep 28 2021 16:10:42)\nSpark context Web UI available at http://10.172.203.167:48830\nSpark context available as 'sc' (master = local[8], app id = local-1636888245786).\nSparkSession available as 'spark'.\n"]},"transient":null}],"execution_count":0},{"cell_type":"markdown","source":["## Creating the spark sample dataframe"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"934976d6-d755-43d1-883c-2fefe918ee20"}}},{"cell_type":"code","source":["df = pd.read_csv(\"https://raw.githubusercontent.com/Spratiher9/Sparkora/0.0.1/Sparkora/test_data.csv?token=AQWZISDC747WWFPLVRUCLXDBSDZIS\")\ndf = spark.createDataFrame(df)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"b700afd7-1346-4175-b238-a2a127612866"}},"outputs":[],"execution_count":0},{"cell_type":"code","source":["df.display()"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"79c0b732-480d-44fb-8de1-0be497b0559e"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"overflow":false,"datasetInfos":[],"data":[[1,2.0,0,"left",1],[4,null,1,"right",1],[7,8.0,2,"left",1]],"plotOptions":{"displayType":"table","customPlotOptions":{},"pivotColumns":null,"pivotAggregation":null,"xColumns":null,"yColumns":null},"columnCustomDisplayInfos":{},"aggType":"","isJsonSchema":true,"removedWidgets":[],"aggSchema":[],"schema":[{"name":"A","type":"\"long\"","metadata":"{}"},{"name":"B","type":"\"double\"","metadata":"{}"},{"name":"C","type":"\"long\"","metadata":"{}"},{"name":"D","type":"\"string\"","metadata":"{}"},{"name":"useless_feature","type":"\"long\"","metadata":"{}"}],"aggError":"","aggData":[],"addedWidgets":{},"metadata":{},"dbfsResultPath":null,"type":"table","aggOverflow":false,"aggSeriesLimitReached":false,"arguments":{}}},"output_type":"display_data","data":{"text/html":["
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_c0_c1_c2
12100
2null200
16null
"]},"transient":null}],"execution_count":0},{"cell_type":"markdown","source":["### Imputation"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"5b1e3134-aed9-43d5-9b55-8d85a4ecf22b"}}},{"cell_type":"code","source":["sparkora.impute_missing_values() # Imputation strategy can be passed here: mean, median, mode\nsparkora.data.display()"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"7ac5793a-bc21-4bee-acac-dc25d6362f09"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"overflow":false,"datasetInfos":[],"data":[[1,2,100],[2,4,200],[1,6,150]],"plotOptions":{"displayType":"table","customPlotOptions":{},"pivotColumns":null,"pivotAggregation":null,"xColumns":null,"yColumns":null},"columnCustomDisplayInfos":{},"aggType":"","isJsonSchema":true,"removedWidgets":[],"aggSchema":[],"schema":[{"name":"_c0","type":"\"integer\"","metadata":"{}"},{"name":"_c1","type":"\"integer\"","metadata":"{}"},{"name":"_c2","type":"\"integer\"","metadata":"{}"}],"aggError":"","aggData":[],"addedWidgets":{},"metadata":{},"dbfsResultPath":null,"type":"table","aggOverflow":false,"aggSeriesLimitReached":false,"arguments":{}}},"output_type":"display_data","data":{"text/html":["
_c0_c1_c2
12100
24200
16150
"]},"transient":null}],"execution_count":0},{"cell_type":"markdown","source":["### Scaling the dataset"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"4a4bec8f-0718-4f58-8198-feac5ea2f28b"}}},{"cell_type":"code","source":["sparkora.scale_input_values()\nsparkora.data.display()"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"d59fc887-ad54-4c96-85ef-6ffcab52a1f6"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"overflow":false,"datasetInfos":[],"data":[[1,0.0,0.0],[2,0.5,1.0],[1,1.0,0.5]],"plotOptions":{"displayType":"table","customPlotOptions":{},"pivotColumns":null,"pivotAggregation":null,"xColumns":null,"yColumns":null},"columnCustomDisplayInfos":{},"aggType":"","isJsonSchema":true,"removedWidgets":[],"aggSchema":[],"schema":[{"name":"_c0","type":"\"integer\"","metadata":"{}"},{"name":"_c1","type":"\"double\"","metadata":"{}"},{"name":"_c2","type":"\"double\"","metadata":"{}"}],"aggError":"","aggData":[],"addedWidgets":{},"metadata":{},"dbfsResultPath":null,"type":"table","aggOverflow":false,"aggSeriesLimitReached":false,"arguments":{}}},"output_type":"display_data","data":{"text/html":["
_c0_c1_c2
10.00.0
20.51.0
11.00.5
"]},"transient":null}],"execution_count":0},{"cell_type":"markdown","source":["## Feature Selection & Extraction"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"756a85bb-c5ea-4243-88f2-ddae355bbbef"}}},{"cell_type":"code","source":["df = pd.read_csv(\"https://raw.githubusercontent.com/Spratiher9/Sparkora/0.0.1/Sparkora/test_data.csv?token=AQWZISDC747WWFPLVRUCLXDBSDZIS\")\ndf = spark.createDataFrame(df)\ndf.display()"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"f45f1c5f-faa8-44dc-833a-aa3c33662fb4"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"overflow":false,"datasetInfos":[],"data":[[1,2.0,0,"left",1],[4,null,1,"right",1],[7,8.0,2,"left",1]],"plotOptions":{"displayType":"table","customPlotOptions":{},"pivotColumns":null,"pivotAggregation":null,"xColumns":null,"yColumns":null},"columnCustomDisplayInfos":{},"aggType":"","isJsonSchema":true,"removedWidgets":[],"aggSchema":[],"schema":[{"name":"A","type":"\"long\"","metadata":"{}"},{"name":"B","type":"\"double\"","metadata":"{}"},{"name":"C","type":"\"long\"","metadata":"{}"},{"name":"D","type":"\"string\"","metadata":"{}"},{"name":"useless_feature","type":"\"long\"","metadata":"{}"}],"aggError":"","aggData":[],"addedWidgets":{},"metadata":{},"dbfsResultPath":null,"type":"table","aggOverflow":false,"aggSeriesLimitReached":false,"arguments":{}}},"output_type":"display_data","data":{"text/html":["
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ABCDuseless_feature
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ABCD
12.00left
4null1right
78.02left
"]},"transient":null}],"execution_count":0},{"cell_type":"markdown","source":["### Extracting an ordinal feature through one-hot encoding"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"28e1eaf3-68b9-4012-b925-6b6f1ed943a2"}}},{"cell_type":"code","source":["sparkora.extract_ordinal_feature('D')\nsparkora.data.display()"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"617d022a-946f-4c3d-a58a-7dd54bada07c"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"overflow":false,"datasetInfos":[],"data":[[1,2.0,0,1,0],[4,null,1,0,1],[7,8.0,2,1,0]],"plotOptions":{"displayType":"table","customPlotOptions":{},"pivotColumns":null,"pivotAggregation":null,"xColumns":null,"yColumns":null},"columnCustomDisplayInfos":{},"aggType":"","isJsonSchema":true,"removedWidgets":[],"aggSchema":[],"schema":[{"name":"A","type":"\"long\"","metadata":"{}"},{"name":"B","type":"\"double\"","metadata":"{}"},{"name":"C","type":"\"long\"","metadata":"{}"},{"name":"D=left","type":"\"integer\"","metadata":"{}"},{"name":"D=right","type":"\"integer\"","metadata":"{}"}],"aggError":"","aggData":[],"addedWidgets":{},"metadata":{},"dbfsResultPath":null,"type":"table","aggOverflow":false,"aggSeriesLimitReached":false,"arguments":{}}},"output_type":"display_data","data":{"text/html":["
ABCD=leftD=right
12.0010
4null101
78.0210
"]},"transient":null}],"execution_count":0},{"cell_type":"markdown","source":["### Feature creation from existing feature(s)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"9b6bb34d-b541-4d54-8093-d2892a2518bc"}}},{"cell_type":"markdown","source":["#### single feature input"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"0c1eb3d8-37f8-42cc-b1f3-2bbe4620fd39"}}},{"cell_type":"code","source":["f_udf = F.udf(lambda x: x * 2, T.IntegerType())\nsparkora.extract_feature('C', 'twoC', f_udf)\nsparkora.data.display()"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"31c28c1e-f3dd-4e3a-97cc-05f4713a06e2"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"overflow":false,"datasetInfos":[],"data":[[1,2.0,0,1,0,0],[4,null,1,0,1,2],[7,8.0,2,1,0,4]],"plotOptions":{"displayType":"table","customPlotOptions":{},"pivotColumns":null,"pivotAggregation":null,"xColumns":null,"yColumns":null},"columnCustomDisplayInfos":{},"aggType":"","isJsonSchema":true,"removedWidgets":[],"aggSchema":[],"schema":[{"name":"A","type":"\"long\"","metadata":"{}"},{"name":"B","type":"\"double\"","metadata":"{}"},{"name":"C","type":"\"long\"","metadata":"{}"},{"name":"D=left","type":"\"integer\"","metadata":"{}"},{"name":"D=right","type":"\"integer\"","metadata":"{}"},{"name":"twoC","type":"\"integer\"","metadata":"{}"}],"aggError":"","aggData":[],"addedWidgets":{},"metadata":{},"dbfsResultPath":null,"type":"table","aggOverflow":false,"aggSeriesLimitReached":false,"arguments":{}}},"output_type":"display_data","data":{"text/html":["
ABCD=leftD=righttwoC
12.00100
4null1012
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ABCD=leftD=righttwoCnewC
12.001000
4null10124
78.0210414
"]},"transient":null}],"execution_count":0},{"cell_type":"markdown","source":["## Visualizing feature against output feature"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"d6766c6a-6ae4-4819-b076-1572ac8b288a"}}},{"cell_type":"code","source":["sparkora.plot_feature('newC')"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"c6c575fb-3d1e-401e-87a2-cfcc204f2ba5"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"overflow":false,"datasetInfos":[],"data":[[0,1],[4,4],[14,7]],"plotOptions":{"displayType":"plotlyLine","customPlotOptions":{"plotlyBar":[{"key":"grouped","value":true},{"key":"stacked","value":false},{"key":"100_stacked","value":false}]},"pivotColumns":[],"pivotAggregation":"sum","xColumns":["newC"],"yColumns":["A"]},"columnCustomDisplayInfos":{},"aggType":"","isJsonSchema":true,"removedWidgets":[],"aggSchema":[],"schema":[{"name":"newC","type":"\"integer\"","metadata":"{}"},{"name":"A","type":"\"long\"","metadata":"{}"}],"aggError":"","aggData":[],"addedWidgets":{},"metadata":{},"dbfsResultPath":null,"type":"table","aggOverflow":false,"aggSeriesLimitReached":false,"arguments":{}}},"output_type":"display_data","data":{"text/html":["
newCA
01
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A
"]},"transient":null}],"execution_count":0},{"cell_type":"markdown","source":["## Versioning tranformations and states and logging the steps in each state"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"cb8d1de1-c6e0-4b9b-9892-d01a46b05bc9"}}},{"cell_type":"code","source":["df = pd.read_csv(\"https://raw.githubusercontent.com/Spratiher9/Sparkora/0.0.1/Sparkora/test_data.csv?token=AQWZISCH3QUFGHB653KPVBTBSD2XO\")\ndf = spark.createDataFrame(df)\ndf.display()"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"8e3856c0-71be-46f9-a49b-0ef838ecfd4f"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"overflow":false,"datasetInfos":[],"data":[[1,2.0,0,"left",1],[4,null,1,"right",1],[7,8.0,2,"left",1]],"plotOptions":{"displayType":"table","customPlotOptions":{},"pivotColumns":null,"pivotAggregation":null,"xColumns":null,"yColumns":null},"columnCustomDisplayInfos":{},"aggType":"","isJsonSchema":true,"removedWidgets":[],"aggSchema":[],"schema":[{"name":"A","type":"\"long\"","metadata":"{}"},{"name":"B","type":"\"double\"","metadata":"{}"},{"name":"C","type":"\"long\"","metadata":"{}"},{"name":"D","type":"\"string\"","metadata":"{}"},{"name":"useless_feature","type":"\"long\"","metadata":"{}"}],"aggError":"","aggData":[],"addedWidgets":{},"metadata":{},"dbfsResultPath":null,"type":"table","aggOverflow":false,"aggSeriesLimitReached":false,"arguments":{}}},"output_type":"display_data","data":{"text/html":["
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But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ![Sparkora project logo](./assets/project_cover.gif) 2 | # Sparkora [![PyPI version](https://badge.fury.io/py/Sparkora.svg)](https://badge.fury.io/py/Sparkora) [![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](https://GitHub.com/Spratiher9/Sparkora/graphs/commit-activity) [![Downloads](https://static.pepy.tech/personalized-badge/sparkora?period=total&units=international_system&left_color=black&right_color=orange&left_text=PYPI%20Downloads)](https://pepy.tech/project/sparkora) 3 | Exploratory data analysis toolkit for Pyspark. 4 | 5 | Dont forget to hit the star ⭐ button if you like this project and would love to see more such utilities. 6 | 7 | ## Contents 8 | - [Summary](#summary) 9 | - [Setup](#setup) 10 | - [Examples](#examples) 11 | - [Usage](#use) 12 | - [Reading Data & Configuration](#config) 13 | - [Cleaning](#clean) 14 | - [Feature Selection & Extraction](#feature) 15 | - [Visualization](#visual) 16 | - [Model Validation](#model) 17 | - [Data Versioning](#version) 18 | - [Testing](#test) 19 | - [Contribute](#contribute) 20 | - [License](#license) 21 | 22 | 23 | ## Summary 24 | 25 | Sparkora is a Python library designed to automate the painful parts of exploratory data analysis in Apache Spark. 26 | 27 | The library contains convenient functions for data cleaning, feature selection & extraction, visualization 28 | (databricks native), partitioning data for model validation, and versioning transformations of data. 29 | 30 | The library uses the following modules: 31 | * pyspark 32 | * ipython 33 | * os 34 | * copy 35 | 36 | It is intended to be a helpful addition to common Pyspark data analysis tools. 37 | 38 | 39 | ## Setup 40 | 41 | ``` 42 | $ pip install Sparkora 43 | $ python 44 | >>> from Sparkora import Sparkora 45 | ``` 46 | 47 | 48 | ## Examples 49 | Go to this jupyter [notebook](Example_Notebooks/sparkora.ipynb) or import [this](Example_Notebooks/sparkora.html) in databricks workspace 50 | 51 | 52 | ## Usage 53 | 54 | 55 | #### Reading Data & Configuration 56 | 57 | ```python 58 | # without initial config 59 | >>> sparkora = Sparkora() 60 | >>> sparkora.configure(output = 'A', data = 'path/to/data.csv') 61 | 62 | # is the same as 63 | >>> from pyspark.shell import spark 64 | >>> dataframe = spark.read.csv('path/to/data.csv', header=True) 65 | >>> sparkora = Sparkora(output = 'A', data = dataframe) 66 | 67 | >>> sparkora.data 68 | A B C D useless_feature 69 | 0 1 2 0 left 1 70 | 1 4 NaN 1 right 1 71 | 2 7 8 2 left 1 72 | ``` 73 | 74 | 75 | #### Cleaning 76 | 77 | ```python 78 | # read data with missing and poorly scaled values 79 | >>> from pyspark.shell import spark 80 | >>> d = [ 81 | ... (1, 2, 100), 82 | ... (2, None, 200), 83 | ... (1, 6, None) 84 | ... ] 85 | >>> df = spark.createDataFrame(d, "_c0 int, _c1 int, _c2 int") 86 | >>> sparkora = Sparkora(output = '_c0', data = df) 87 | >>> sparkora.data 88 | _c0 _c1 _c2 89 | 0 1 2 100 90 | 1 2 NaN 200 91 | 2 1 6 NaN 92 | 93 | # impute the missing values (by default, using 'mean' other options are 'median', 'mode') 94 | # sparkora.impute_missing_values(strategy = 'mean') 95 | >>> sparkora.impute_missing_values() 96 | >>> sparkora.data 97 | _c0 _c1 _c2 98 | 0 1 2 100 99 | 1 2 4 200 100 | 2 1 6 150 101 | 102 | # scale the values of the input variables (center to mean and scale to unit variance) 103 | >>> sparkora.scale_input_values() 104 | >>> sparkora.data 105 | _c0 _c1 _c2 106 | 0 1 -1.224745 -1.224745 107 | 1 2 0.000000 1.224745 108 | 2 1 1.224745 0.000000 109 | ``` 110 | 111 | 112 | #### Feature Selection & Extraction 113 | 114 | ```python 115 | # feature selection / removing a feature 116 | >>> sparkora.data 117 | A B C D useless_feature 118 | 0 1 2 0 left 1 119 | 1 4 NaN 1 right 1 120 | 2 7 8 2 left 1 121 | 122 | >>> sparkora.remove_feature('useless_feature') 123 | >>> sparkora.data 124 | A B C D 125 | 0 1 2 0 left 126 | 1 4 NaN 1 right 127 | 2 7 8 2 left 128 | 129 | # extract an ordinal feature through one-hot encoding 130 | >>> sparkora.extract_ordinal_feature('D') 131 | >>> sparkora.data 132 | A B C D=left D=right 133 | 0 1 2 0 1 0 134 | 1 4 NaN 1 0 1 135 | 2 7 8 2 1 0 136 | 137 | # extract a transformation of another feature 138 | >>> f_udf = F.udf(lambda x: x * 2, T.IntegerType()) 139 | >>> sparkora.extract_feature('C', 'twoC', f_udf) 140 | >>> sparkora.data 141 | A B C D=left D=right twoC 142 | 0 1 2 0 1 0 0 143 | 1 4 NaN 1 0 1 2 144 | 2 7 8 2 1 0 4 145 | 146 | # extract a transformation of multiple other features 147 | >>> f_udf = F.udf(lambda x,y: x * y, T.IntegerType()) 148 | >>> sparkora.extract_feature(['C','A'], 'newC', f_udf) 149 | A B C D=left D=right newC 150 | 0 1 2 0 1 0 0 151 | 1 4 NaN 1 0 1 4 152 | 2 7 8 2 1 0 14 153 | ``` 154 | 155 | 156 | #### Visualization 157 | 158 | ```python 159 | # Visualization features are only available if the platform is Databricks 160 | 161 | # plot a single feature against the output variable 162 | sparkora.plot_feature('column-name') 163 | 164 | # render plots of each feature against the output variable 165 | sparkora.explore() 166 | ``` 167 | 168 | 169 | #### Model Validation 170 | 171 | ```python 172 | # create random partition of training / validation data (~ 80/20 split) 173 | sparkora.set_training_and_validation(0.8) 174 | # pass train size only, the validation size is calculated automatically 175 | 176 | # train a model on the data 177 | X = sparkora.training_data.select(sparkora.input_columns()) 178 | y = sparkora.training_data.select(sparkora.output) 179 | 180 | some_model.fit(X, y) 181 | 182 | # validate the model 183 | X = sparkora.validating_data.select(sparkora.input_columns()) 184 | y = sparkora.validating_data.select(sparkora.output) 185 | 186 | some_model.score(X, y) 187 | ``` 188 | 189 | 190 | #### Data Versioning 191 | 192 | ```python 193 | # save a version of your data 194 | >>> sparkora.data 195 | A B C D useless_feature 196 | 0 1 2 0 left 1 197 | 1 4 NaN 1 right 1 198 | 2 7 8 2 left 1 199 | >>> sparkora.snapshot('initial_data') 200 | 201 | # keep track of changes to data 202 | >>> sparkora.remove_feature('useless_feature') 203 | >>> sparkora.extract_ordinal_feature('D') 204 | >>> sparkora.impute_missing_values() 205 | >>> sparkora.scale_input_values() 206 | >>> sparkora.data 207 | A B C D=left D=right 208 | 0 1 -1.224745 -1.224745 0.707107 -0.707107 209 | 1 4 0.000000 0.000000 -1.414214 1.414214 210 | 2 7 1.224745 1.224745 0.707107 -0.707107 211 | 212 | >>> sparkora.logs 213 | ["self.remove_feature('useless_feature')", "self.extract_ordinal_feature('D')", 'self.impute_missing_values()', 'self.scale_input_values()'] 214 | 215 | # use a previous version of the data 216 | >>> sparkora.snapshot('transform1') 217 | >>> sparkora.use_snapshot('initial_data') 218 | >>> sparkora.data 219 | A B C D useless_feature 220 | 0 1 2 0 left 1 221 | 1 4 NaN 1 right 1 222 | 2 7 8 2 left 1 223 | >>> sparkora.logs 224 | ["sparkora.snapshot('initial_data')"] 225 | 226 | # switch back to your transformation 227 | >>> sparkora.use_snapshot('transform1') 228 | >>> sparkora.data 229 | A B C D=left D=right 230 | 0 1 -1.224745 -1.224745 0.707107 -0.707107 231 | 1 4 0.000000 0.000000 -1.414214 1.414214 232 | 2 7 1.224745 1.224745 0.707107 -0.707107 233 | >>> sparkora.logs 234 | ["self.remove_feature('useless_feature')", "self.extract_ordinal_feature('D')", 'self.impute_missing_values()', 'self.scale_input_values()'] 235 | ``` 236 | 237 | 238 | ## Testing 239 | 240 | To run the test suite, simply run `python testsparkora.py` from the `Sparkora` directory. 241 | 242 | 243 | ## Contribute 244 | 245 | Pull requests welcome! Feature requests / bugs will be addressed through issues on this repository. While not every feature request will necessarily be handled by me, maintaining a record for interested contributors is useful. 246 | 247 | Additionally, feel free to submit pull requests which add features or address bugs yourself. 248 | 249 | 250 | 251 | ## License 252 | 253 | GNU GENERAL PUBLIC LICENSE 254 | Version 3, 29 June 2007 255 | 256 | Copyright (C) 2007 Free Software Foundation, Inc. 257 | Everyone is permitted to copy and distribute verbatim copies 258 | of this license document, but changing it is not allowed. 259 | 260 | Preamble 261 | 262 | The GNU General Public License is a free, copyleft license for 263 | software and other kinds of works. 264 | 265 | The licenses for most software and other practical works are designed 266 | to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. 865 | 866 | If the disclaimer of warranty and limitation of liability provided 867 | above cannot be given local legal effect according to their terms, 868 | reviewing courts shall apply local law that most closely approximates 869 | an absolute waiver of all civil liability in connection with the 870 | Program, unless a warranty or assumption of liability accompanies a 871 | copy of the Program in return for a fee. 872 | 873 | END OF TERMS AND CONDITIONS 874 | 875 | How to Apply These Terms to Your New Programs 876 | 877 | If you develop a new program, and you want it to be of the greatest 878 | possible use to the public, the best way to achieve this is to make it 879 | free software which everyone can redistribute and change under these terms. 880 | 881 | To do so, attach the following notices to the program. It is safest 882 | to attach them to the start of each source file to most effectively 883 | state the exclusion of warranty; and each file should have at least 884 | the "copyright" line and a pointer to where the full notice is found. 885 | 886 | 887 | Copyright (C) 2021 Souvik Pratiher 888 | 889 | This program is free software: you can redistribute it and/or modify 890 | it under the terms of the GNU General Public License as published by 891 | the Free Software Foundation, either version 3 of the License, or 892 | (at your option) any later version. 893 | 894 | This program is distributed in the hope that it will be useful, 895 | but WITHOUT ANY WARRANTY; without even the implied warranty of 896 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 897 | GNU General Public License for more details. 898 | 899 | You should have received a copy of the GNU General Public License 900 | along with this program. If not, see . 901 | 902 | Also add information on how to contact you by electronic and paper mail. 903 | 904 | If the program does terminal interaction, make it output a short 905 | notice like this when it starts in an interactive mode: 906 | 907 | Copyright (C) 2021 Souvik Pratiher 908 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 909 | This is free software, and you are welcome to redistribute it 910 | under certain conditions; type `show c' for details. 911 | 912 | The hypothetical commands `show w' and `show c' should show the appropriate 913 | parts of the General Public License. Of course, your program's commands 914 | might be different; for a GUI interface, you would use an "about box". 915 | 916 | You should also get your employer (if you work as a programmer) or school, 917 | if any, to sign a "copyright disclaimer" for the program, if necessary. 918 | For more information on this, and how to apply and follow the GNU GPL, see 919 | . 920 | 921 | The GNU General Public License does not permit incorporating your program 922 | into proprietary programs. If your program is a subroutine library, you 923 | may consider it more useful to permit linking proprietary applications with 924 | the library. If this is what you want to do, use the GNU Lesser General 925 | Public License instead of this License. But first, please read 926 | . 927 | -------------------------------------------------------------------------------- /Sparkora/__init__.py: -------------------------------------------------------------------------------- 1 | from .main import Sparkora -------------------------------------------------------------------------------- /Sparkora/main.py: -------------------------------------------------------------------------------- 1 | from pyspark.shell import spark 2 | from pyspark.sql.dataframe import DataFrame 3 | from pyspark.sql import functions as F 4 | from pyspark.sql import types as T 5 | import os 6 | from copy import deepcopy 7 | from pyspark.ml.feature import Imputer 8 | from IPython import get_ipython 9 | 10 | 11 | class Sparkora: 12 | def __init__(self, data=None, output=None): 13 | """ 14 | Sparkora core class. Entry point to the Sparkora functionality. 15 | 16 | :param data: can be dataframe or path to csv. 17 | :param output: output data column(s). 18 | """ 19 | self.snapshots = {} 20 | self.logs = [] 21 | self.data = None 22 | self.output = None 23 | self.training_data = None 24 | self.validating_data = None 25 | self.initial_data = None 26 | self.configure(data=data, output=output) 27 | 28 | def configure(self, data=None, output=None): 29 | """ 30 | Method for configuring the Sparkora object. 31 | 32 | :param data: dataset on which Sparkora object will be wrapped. 33 | :param output: output column name. 34 | :return: None 35 | """ 36 | if type(output) is str: 37 | self.output = output 38 | if type(data) is str: 39 | self.initial_data = spark.read.csv(data, header=True) 40 | self.data = self.initial_data 41 | self.logs = [] 42 | if type(data) is DataFrame: 43 | self.initial_data = data 44 | self.data = data 45 | self.logs = [] 46 | 47 | def remove_feature(self, feature_name): 48 | """ 49 | Method for removing a feature. 50 | 51 | :param feature_name: name of the feature to be dropped. 52 | :return: None 53 | """ 54 | self.data = self.data.drop(feature_name) 55 | self._log("self.remove_feature('{0}')".format(feature_name)) 56 | 57 | def extract_feature(self, old_feat, new_feat, mapper): 58 | """ 59 | Method for extracting new feature from existing feature(s). 60 | 61 | :param old_feat: feature(s) which will be used to build the new feature. 62 | :param new_feat: new feature's name. 63 | :param mapper: the logic to be applied for the creation of the new feature. 64 | :return: None 65 | """ 66 | if type(old_feat) is str: 67 | self.data = self.data.withColumn(new_feat, mapper(self.data[old_feat])) 68 | else: 69 | self.data = self.data.withColumn(new_feat, mapper(*old_feat)) 70 | self._log("self.extract_feature({0}, {1}, {2})".format(old_feat, new_feat, mapper)) 71 | 72 | def impute_missing_values(self, strategy="mean"): 73 | """ 74 | Method for imputing missing values with a chosen strategy of imputation. 75 | There are 3 strategies for imputation: 76 | 77 | 1. mean 78 | 2. median 79 | 3. mode 80 | 81 | :param strategy: type of strategy to be used for imputation, by default mean is used. 82 | :return: None 83 | """ 84 | column_names = self.input_columns() 85 | dtype_ls = self.data.select(column_names).dtypes 86 | column_names = [i[0] for i in dtype_ls if i[1] in ('int','float','double','long','short','decimal')] 87 | imp = Imputer(inputCols=column_names, outputCols=column_names).setStrategy(strategy) 88 | self.data = imp.fit(self.data).transform(self.data) 89 | self._log("self.impute_missing_values({0})".format(strategy)) 90 | 91 | def scale_input_values(self): 92 | """ 93 | Method for normalizing the data with Max-Min Scaler. 94 | 95 | :return: None 96 | """ 97 | column_names = self.input_columns() 98 | dtype_ls = self.data.select(column_names).dtypes 99 | column_names = [i[0] for i in dtype_ls if i[1] in ('int', 'float', 'double', 'long', 'short', 'decimal')] 100 | for col in column_names: 101 | values = self.data.agg(F.max(col).alias("max_value"), F.min(col).alias("min_value")).collect()[0] 102 | max_value = values["max_value"] 103 | min_value = values["min_value"] 104 | denom = max_value - min_value 105 | scaler_udf = F.udf(lambda x: (x - min_value) / denom, T.DoubleType()) 106 | self.data = self.data.withColumn(col, scaler_udf(self.data[col])) 107 | self._log("self.scale_input_values()") 108 | 109 | def extract_ordinal_feature(self, feature_name): 110 | """ 111 | Method for one hot encoding and extracting the encoded features for a given feature. 112 | 113 | :param feature_name: feature to be parsed and extracted from. 114 | :return: None 115 | """ 116 | categories = self.data.select(feature_name).distinct().rdd.flatMap(lambda x: x).collect() 117 | for category in categories: 118 | self.data = self.data.withColumn("{}={}".format(feature_name, category), 119 | F.when(F.col(feature_name) == category, F.lit(1)).otherwise(0)) 120 | self.data = self.data.drop(feature_name) 121 | self._log("self.extract_ordinal_feature('{0}')".format(feature_name)) 122 | 123 | def set_training_and_validation(self, train_size=0.8): 124 | """ 125 | Method for splitting the data into training and validation sets. 126 | 127 | :param train_size: floating point value indicating the amount of data to be kept in training set (remaining 128 | in validation set). 129 | :return: None 130 | """ 131 | validation_size = 1 - train_size 132 | self.training_data, self.validating_data = self.data.randomSplit(weights=[train_size, validation_size], 133 | seed=200) 134 | self._log("self.set_training_and_validation({})".format(train_size)) 135 | 136 | def plot_feature(self, feature_name): 137 | """ 138 | Method for plotting feature against the output column. (Available only for Databricks) 139 | 140 | :param feature_name: feature to be plotted against output. 141 | :return: None 142 | """ 143 | if "DATABRICKS_RUNTIME_VERSION" in os.environ.keys(): 144 | display = get_ipython().user_ns['display'] 145 | display(self.data.select(feature_name, self.output)) 146 | self._log("self.plot_feature('{}')".format(feature_name)) 147 | else: 148 | raise Exception("plotting feature's only available in Databricks 💀") 149 | 150 | def input_columns(self): 151 | """ 152 | Method for setting the input columns in the dataset. 153 | 154 | :return: list of input column names 155 | """ 156 | column_names = self.data.columns 157 | column_names.remove(self.output) 158 | return column_names 159 | 160 | def explore(self): 161 | """ 162 | Method for plotting the features of the dataset against the output(s) of the dataset. 163 | Best viewed in databricks runtime 10.0.0 and above. 164 | 165 | :return: None 166 | """ 167 | if "DATABRICKS_RUNTIME_VERSION" in os.environ.keys(): 168 | features = self.input_columns() 169 | self._log("self.explore() begins") 170 | for feature in features: 171 | self.plot_feature(feature) 172 | self._log("self.explore() ends") 173 | else: 174 | raise Exception("explore only available in Databricks 💀") 175 | 176 | def snapshot(self, name): 177 | """ 178 | Method for taking a snapshot of the current state of the data in the Sparkora object. 179 | 180 | :param name: name of the snapshot by which we can refer later if required. 181 | :return: None 182 | """ 183 | self._log("self.snapshot('{}')".format(name)) 184 | snapshot = { 185 | "data": self.data, 186 | "logs": deepcopy(self.logs) 187 | } 188 | self.snapshots[name] = snapshot 189 | self.logs = [] 190 | 191 | def use_snapshot(self, name): 192 | """ 193 | Method for restoring back to a particular state. 194 | 195 | :param name: name of the snapshot to which the state has to be restored to 196 | :return: None 197 | """ 198 | self.data = self.snapshots[name]["data"] 199 | self.logs = self.snapshots[name]["logs"] 200 | 201 | def _log(self, string): 202 | """ 203 | Method for storing operation statements as part of logging. 204 | 205 | :param string: the statement to be logged 206 | :return: 207 | """ 208 | self.logs.append(string) 209 | -------------------------------------------------------------------------------- /Sparkora/test_data.csv: -------------------------------------------------------------------------------- 1 | "A","B","C","D","useless_feature" 2 | 1,2,0,"left",1 3 | 4,,1,"right",1 4 | 7,8,2,"left",1 -------------------------------------------------------------------------------- /Sparkora/testsparkora.py: -------------------------------------------------------------------------------- 1 | import unittest 2 | from main import Sparkora 3 | from pyspark.shell import spark 4 | import pandas as pd 5 | 6 | class TestSparkora(unittest.TestCase): 7 | def setUp(self): 8 | self.sparkora = Sparkora() 9 | self.sparkora.configure(output = 'A', data = './test_data.csv') 10 | 11 | def test_configure(self): 12 | data = spark.read.csv('./test_data.csv',header=True) 13 | self.assertEqual(self.sparkora.output, 'A') 14 | self.assertEqual(self.sparkora.data,data) 15 | 16 | def test_remove_feature(self): 17 | self.sparkora.remove_feature('useless_feature') 18 | self.assertFalse('useless_feature' in self.sparkora.data.columns) 19 | 20 | def test_extract_feature(self): 21 | self.sparkora.extract_feature( 22 | 'useless_feature', 23 | 'another_useless_feature', 24 | lambda x: x * 2 25 | ) 26 | actual_column = self.sparkora.data.select("another_useless_feature").rdd.flatMap(lambda x: x).collect() 27 | expected_column = [2, 2, 2] 28 | self.assertEqual(actual_column, expected_column) 29 | 30 | def test_impute_missing_values(self): 31 | self.sparkora.impute_missing_values() 32 | actual_column = self.sparkora.data.select("B").rdd.flatMap(lambda x: x).collect() 33 | expected_column = [2.0, 5.0, 8.0] 34 | self.assertEqual(actual_column, expected_column) 35 | 36 | def test_scale_input_values(self): 37 | self.sparkora.scale_input_values() 38 | actual_column = self.sparkora.data.select("C").rdd.flatMap(lambda x: x).collect() 39 | expected_column = [-1.224745, 0.0, 1.224745] 40 | pairwise_diffs = map( 41 | lambda actual, expected: abs(actual - expected), 42 | actual_column, 43 | expected_column 44 | ) 45 | total_diff = sum(pairwise_diffs) 46 | self.assertAlmostEqual(total_diff, 0, places = 6) 47 | 48 | def test_extract_ordinal_feature(self): 49 | self.sparkora.extract_ordinal_feature('D') 50 | features = self.sparkora.data.columns 51 | self.assertTrue('D=left' in features and 'D=right' in features) 52 | 53 | def test_input_columns(self): 54 | actual_input_columns = self.sparkora.input_columns() 55 | expected_input_columns = self.sparkora.data.columns 56 | expected_input_columns.remove(self.sparkora.output) 57 | self.assertEqual(actual_input_columns, expected_input_columns) 58 | 59 | def test_logs(self): 60 | self.sparkora.extract_ordinal_feature('D') 61 | self.sparkora.impute_missing_values() 62 | self.sparkora.scale_input_values() 63 | 64 | actual_logs = self.sparkora.logs 65 | expected_logs = [ 66 | "self.extract_ordinal_feature('D')", 67 | 'self.impute_missing_values()', 68 | 'self.scale_input_values()' 69 | ] 70 | self.assertEqual(actual_logs, expected_logs) 71 | 72 | def test_snapshots(self): 73 | self.sparkora.snapshot('start') 74 | self.sparkora.extract_ordinal_feature('D') 75 | self.sparkora.use_snapshot('start') 76 | 77 | self.assertEqual(self.sparkora.logs, []) 78 | self.assertTrue(self.sparkora.data.equals(self.sparkora.initial_data)) 79 | 80 | if __name__ == '__main__': 81 | unittest.main() -------------------------------------------------------------------------------- /assets/project_cover.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Spratiher9/Sparkora/27dd981a9ae0272732ae4f16e4bd435f68e6ad04/assets/project_cover.gif -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Spratiher9/Sparkora/27dd981a9ae0272732ae4f16e4bd435f68e6ad04/requirements.txt -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | # Author: Souvik Pratiher 2 | # Project: Sparkora v0.0.1 3 | 4 | 5 | import os 6 | from setuptools import setup 7 | 8 | if os.path.isfile('README.md'): 9 | with open('README.md', encoding='utf-8') as readme: 10 | bdescription = readme.read() 11 | else: 12 | bdescription = "EDA Toolkit for Apache Spark based workflows" 13 | 14 | setup( 15 | name="Sparkora", 16 | version="0.0.1", 17 | author="Souvik Pratiher", 18 | author_email="spratiher9@gmail.com", 19 | description="Exploratory data analysis toolkit for Pyspark", 20 | license="GPL-3.0 License", 21 | download_url='https://github.com/Spratiher9/Sparkora/archive/refs/tags/0.0.1.tar.gz', 22 | keywords=["exploratory data analysis", "EDA", "pyspark", "preprocessing"], 23 | long_description=bdescription, 24 | long_description_content_type='text/markdown', 25 | install_requires=[ 26 | "pyspark>=3.1.0", 27 | "IPython", 28 | ], 29 | classifiers=[ 30 | 'Development Status :: 5 - Production/Stable', 31 | 'Intended Audience :: Developers', 32 | 'License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)', 33 | 'Topic :: Software Development :: Build Tools', 34 | 'Programming Language :: Python :: 3.7', 35 | 'Programming Language :: Python :: 3.8', 36 | 'Programming Language :: Python :: 3.9', 37 | 'Programming Language :: Python :: 3.10', 38 | ], 39 | packages=['Sparkora'] 40 | ) 41 | --------------------------------------------------------------------------------