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{ | ||
"cells": [ | ||
{ | ||
"cell_type": "markdown", | ||
"id": "60af5256", | ||
"metadata": {}, | ||
"source": [ | ||
"# Merge List of Profiles\n", | ||
"\n", | ||
"This is an example of a new utils in the dataprofiler for distributed merging of profile objects. This assumes the user is providing a list of profile objects to the utils function for merging all the profiles together." | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"id": "7eee37ff", | ||
"metadata": {}, | ||
"source": [ | ||
"## Imports\n", | ||
"\n", | ||
"Let's start by importing the necessary packages..." | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"id": "f0d27009", | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"import os\n", | ||
"import sys\n", | ||
"import json\n", | ||
"\n", | ||
"import pandas as pd\n", | ||
"import tensorflow as tf\n", | ||
"\n", | ||
"try:\n", | ||
" sys.path.insert(0, '..')\n", | ||
" import dataprofiler as dp\n", | ||
" from dataprofiler.profilers.utils import merge_profile_list\n", | ||
"except ImportError:\n", | ||
" import dataprofiler as dp\n", | ||
" from dataprofiler.profilers.utils import merge_profile_list\n", | ||
"\n", | ||
"# remove extra tf loggin\n", | ||
"tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"id": "b4369e64", | ||
"metadata": {}, | ||
"source": [ | ||
"## Setup the Data and Profiler" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"id": "410c3c4d", | ||
"metadata": {}, | ||
"source": [ | ||
"This section shows the basic example of the Data Profiler. \n", | ||
"\n", | ||
"1. Instantiate a Pandas dataframe with dummy data\n", | ||
"2. Pass the dataframe to the `Profiler` and instantiate two separate profilers in a list" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"id": "d3567c82", | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"d = {'col1': [1, 2], 'col2': [3, 4]}\n", | ||
"df = pd.DataFrame(data=d)\n", | ||
"\n", | ||
"list_of_profiles = [dp.Profiler(df), dp.Profiler(df)]" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"id": "350502eb", | ||
"metadata": {}, | ||
"source": [ | ||
"Take a look at the list of profiles... " | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"id": "b649db32", | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"list_of_profiles" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"id": "4ed4fc12", | ||
"metadata": {}, | ||
"source": [ | ||
"## Run Merge on List of Profiles\n", | ||
"\n", | ||
"Now let's merge the list of profiles into a `single_profile`" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"id": "4a636047", | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"single_profile = merge_profile_list(list_of_profiles=list_of_profiles)" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"id": "0aa88720", | ||
"metadata": {}, | ||
"source": [ | ||
"And check out the `.report` on the single profile:" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"id": "34059c21", | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"single_profile.report()" | ||
] | ||
} | ||
], | ||
"metadata": { | ||
"kernelspec": { | ||
"display_name": "dataprofiler", | ||
"language": "python", | ||
"name": "dataprofiler" | ||
}, | ||
"language_info": { | ||
"codemirror_mode": { | ||
"name": "ipython", | ||
"version": 3 | ||
}, | ||
"file_extension": ".py", | ||
"mimetype": "text/x-python", | ||
"name": "python", | ||
"nbconvert_exporter": "python", | ||
"pygments_lexer": "ipython3", | ||
"version": "3.7.12" | ||
} | ||
}, | ||
"nbformat": 4, | ||
"nbformat_minor": 5 | ||
} |
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