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Generate Docs for 0.7.10 (#593)
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micdavis authored Aug 9, 2022
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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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