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Mizuno running, no notes. Artera Internal

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Norm Rasmussen
2023-06-05 17:09:23 -04:00
parent 092223eccc
commit 14f713c3af
67 changed files with 2652 additions and 65 deletions
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79
Scripts/Jupyter/app_usage.ipynb
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@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 482,
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
@ -16,7 +16,7 @@
},
{
"cell_type": "code",
"execution_count": 483,
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
@ -25,7 +25,7 @@
},
{
"cell_type": "code",
"execution_count": 484,
"execution_count": 16,
"metadata": {},
"outputs": [
{
@ -34,7 +34,7 @@
"(59433, 3)"
]
},
"execution_count": 484,
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
@ -45,7 +45,7 @@
},
{
"cell_type": "code",
"execution_count": 485,
"execution_count": 17,
"metadata": {},
"outputs": [
{
@ -153,7 +153,7 @@
"9 Vivvix University for Ad Intel /styling/general/edit 2"
]
},
"execution_count": 485,
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
@ -164,7 +164,7 @@
},
{
"cell_type": "code",
"execution_count": 486,
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
@ -174,7 +174,7 @@
},
{
"cell_type": "code",
"execution_count": 487,
"execution_count": 19,
"metadata": {},
"outputs": [
{
@ -265,7 +265,7 @@
"Ad Intel 2"
]
},
"execution_count": 487,
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
@ -283,7 +283,7 @@
},
{
"cell_type": "code",
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"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
@ -292,7 +292,7 @@
},
{
"cell_type": "code",
"execution_count": 489,
"execution_count": 21,
"metadata": {},
"outputs": [
{
@ -310,7 +310,7 @@
},
{
"cell_type": "code",
"execution_count": 490,
"execution_count": 22,
"metadata": {},
"outputs": [
{
@ -409,7 +409,7 @@
"[489 rows x 1 columns]"
]
},
"execution_count": 490,
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
@ -422,7 +422,7 @@
},
{
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"source": [
@ -432,7 +432,7 @@
},
{
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"execution_count": 24,
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"outputs": [
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@ -465,7 +465,7 @@
},
{
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"execution_count": 25,
"metadata": {},
"outputs": [
{
@ -483,7 +483,7 @@
},
{
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"execution_count": 456,
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
@ -495,7 +495,7 @@
},
{
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"metadata": {},
"outputs": [],
"source": [
@ -504,7 +504,7 @@
},
{
"cell_type": "code",
"execution_count": 458,
"execution_count": 28,
"metadata": {},
"outputs": [
{
@ -513,7 +513,7 @@
"(354, 1)"
]
},
"execution_count": 458,
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
@ -524,7 +524,7 @@
},
{
"cell_type": "code",
"execution_count": 459,
"execution_count": 29,
"metadata": {},
"outputs": [
{
@ -897,7 +897,7 @@
},
{
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"execution_count": 474,
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"metadata": {},
"outputs": [
{
@ -932,7 +932,7 @@
},
{
"cell_type": "code",
"execution_count": 480,
"execution_count": 31,
"metadata": {},
"outputs": [
{
@ -968,38 +968,7 @@
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"display:flex;flex-direction:row;justify-content:space-between;\">\n",
" <pre style=\"width: 100%; white-space: pre-wrap;\" id=\"0d623ee4-5d77-4ff3-ae4d-3e8594d5d6f2\">\n",
"# Create a list of all the unique words in the School Name column\n",
"words = pages['School Name'].str.split().explode().unique()\n",
"\n",
"# Create a new column in the dataframe with the words from the School Name column\n",
"pages['words'] = pages['School Name'].str.split().explode()\n",
"\n",
"# Create a dictionary mapping each word to its corresponding row index\n",
"word_index_map = {word: pages[pages['words'] == word].index for word in words}\n",
"\n",
"# Iterate through the dictionary and combine dataframes based on matching indexes\n",
"for word, index in word_index_map.items():\n",
" df1 = pages.loc[index]\n",
" df2 = other_df.loc[other_df['Index'].str.contains(word)]\n",
" combined_df = pd.concat([df1, df2], axis=1)\n",
"</pre>\n",
" <button style=\"height: fit-content;\" onclick=\"navigator.clipboard.writeText(JSON.parse(atob(`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`)))\">Copy</button>\n",
" </div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"outputs": [],
"source": []
},
{

23
Scripts/lookertest.md Normal file
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@ -0,0 +1,23 @@
if(
contains(${extract_course_completion.course_name}, "Employee"),
replace(
"Harassment Prevention & Gender Diversity - Employee",
${extract_course_completion.course_name},
"Harassment Prevention & Gender Diversity - Employee"),
${extract_course_completion.course_name})
case(when(yesno_arg, value_if_yes), when(yesno_arg, value_if_yes), ..., else_value)
case(when(contains(${extract_course_completion.course_name}, "Employee"), replace(
"Harassment Prevention & Gender Diversity - Employee",
${extract_course_completion.course_name},
"Harassment Prevention & Gender Diversity - Employee"),
when(contains(${extract_course_completion.course_name}, "Supervisor"), replace(
"Harassment Prevention & Gender Diversity - Supervisor",
${extract_course_completion.course_name},
"Harassment Prevention & Gender Diversity - Supervisor"),
${extract_course_completion.course_name}
))