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Examples/cdata/plotting_multiple_curves_in_seaborn.ipynb

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"cells": [
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{
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"cell_type": "markdown",
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"id": "floating-button",
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"id": "rapid-beaver",
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"metadata": {},
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"source": [
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"A common challenge in data analytics is: easilly plotting multiple curves\n",
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "russian-perspective",
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"id": "younger-break",
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"metadata": {},
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"outputs": [],
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"source": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "ongoing-chart",
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"id": "asian-perception",
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"metadata": {},
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"outputs": [
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{
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},
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{
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"cell_type": "markdown",
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"id": "developmental-exhibition",
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"id": "consistent-spine",
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"metadata": {},
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"source": [
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"The above is a data frame of an A/B test plan as a function of the test size `n`. \n",
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "express-requirement",
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"id": "accepting-hungarian",
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"metadata": {},
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"outputs": [
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},
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"cell_type": "markdown",
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"id": "grateful-bachelor",
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"id": "interracial-reducing",
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"metadata": {},
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"source": [
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"For plotting we want this record to look like this:"
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "fourth-champion",
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"id": "operating-divorce",
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"metadata": {},
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"outputs": [
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{
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},
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{
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"cell_type": "markdown",
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"id": "floral-serbia",
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"id": "celtic-wings",
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"metadata": {},
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"source": [
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"That is, we want a record that spans multiple rows that picks up a set of the columns of the original record.\n",
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "freelance-rebel",
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"id": "technical-clearing",
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"metadata": {},
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"outputs": [],
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},
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"cell_type": "markdown",
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"id": "intermediate-complexity",
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"id": "multiple-zimbabwe",
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"metadata": {},
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"source": [
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"Now that we have our pivot object we can use it to realize the transform."
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "gorgeous-customer",
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"id": "allied-nursery",
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"metadata": {},
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"outputs": [
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{
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},
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"cell_type": "markdown",
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"id": "parental-caution",
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"id": "fleet-level",
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"metadata": {},
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"source": [
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"Notice the transform exactly realizes our desired record format. The point is: this transform is being applied to all\n",
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "laden-daily",
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"id": "worth-tumor",
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"metadata": {},
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"outputs": [
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},
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{
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"cell_type": "markdown",
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"id": "foster-feelings",
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"id": "imperial-glasgow",
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"metadata": {},
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"And we are done."
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]
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},
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{
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"cell_type": "markdown",
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"id": "following-board",
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"id": "split-today",
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"metadata": {},
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"A neat side-feature of the transform is: it is pretty much invertable. For fun let's look at that."
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{
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"cell_type": "code",
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"id": "organizational-investigation",
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"id": "racial-queue",
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "suitable-archive",
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"id": "parental-musician",
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"metadata": {},
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},
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"cell_type": "markdown",
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"id": "olive-strike",
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"id": "european-connection",
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"metadata": {},
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"source": [
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"Notice the `blocks_in` and `blocks_out` reversed roles when we took the inverse. Let's see what the inverse does."
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{
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"execution_count": 10,
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"id": "transsexual-right",
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"id": "phantom-working",
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"metadata": {},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "muslim-ceramic",
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"id": "configured-deviation",
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"metadata": {},
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"outputs": [
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"cell_type": "markdown",
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"id": "opened-parker",
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"id": "careful-exhibition",
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"source": [
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"The inverse has re-assembled the columns that were present into essentially the original format.\n",
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"Some follow-up links:\n",
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"\n",
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" * The `Python` `data_algebra` package that supplies the `cdata` implementation: [https://github.com/WinVector/data_algebra](https://github.com/WinVector/data_algebra).\n",
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" * Some examples: [https://github.com/WinVector/data_algebra/tree/main/Examples/cdata](https://github.com/WinVector/data_algebra/tree/main/Examples/cdata).\n",
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" * Some examples: [https://github.com/WinVector/data_algebra/tree/main/Examples/cdata](https://github.com/WinVector/data_algebra/tree/main/Examples/cdata) (including this example [here](https://github.com/WinVector/data_algebra/blob/main/Examples/cdata/plotting_multiple_curves_in_seaborn.ipynb)).\n",
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" * The `R` version of the package: [https://github.com/WinVector/cdata](https://github.com/WinVector/cdata) (where\n",
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" we did a lot of the original research).\n",
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" * A tutorial video on the \"coordinatized data\" methodology (in `R`, but the concepts carry over): [https://youtu.be/4cYbP3kbc0k](https://youtu.be/4cYbP3kbc0k).\n"
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "color-stuart",
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"id": "necessary-buddy",
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"metadata": {},
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"outputs": [],
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"source": []

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