mirror of
https://github.com/gsi-upm/sitc
synced 2024-11-16 19:42:28 +00:00
1236 lines
1.1 MiB
Plaintext
1236 lines
1.1 MiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "skip"
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}
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},
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"source": [
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"![](images/EscUpmPolit_p.gif \"UPM\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "skip"
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}
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},
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"source": [
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"# Course Notes for Learning Intelligent Systems"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "skip"
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}
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},
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"source": [
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"Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © Carlos A. Iglesias"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "skip"
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}
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},
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"source": [
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"## [Introduction to Visualization](00_Intro_Visualization.ipynb)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"# Distribution charts\n",
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"Charts for visualizing the distribution of a dataset."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"## Plotting Univariate variables\n",
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"**display** Draw a histogram and fit a kernel density estimate (KDE)."
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]
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},
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{
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"cell_type": "code",
|
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|
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"execution_count": 1,
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"metadata": {
|
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"outputs": [
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAeoAAAHpCAYAAABN+X+UAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAABHj0lEQVR4nO3deXhb5YE2/PtotWRL8iLve/bF2UhCQgIhUAikQAm0tJQCyQzlpW1IS7m6fAxtCTPvNFOYAp2ytPRtA21JoXSA0rKEAFkhgWxO4sTZHDtyvMuLZMnadb4/ZJuYrLYlPUfS/bsuXW1sR7rjGN15znkWSZZlGURERKRIKtEBiIiI6NxY1ERERArGoiYiIlIwFjUREZGCsaiJiIgUjEVNRESkYCxqIiIiBWNRExERKRiLmoiISMGSvqhlWYbT6QQ3YCMiokQktKjXrFmDuXPnwmQyIS8vD8uWLcORI0eGfM2KFSsgSdKQx/z58y/6NXp7e2GxWNDb2xvt+ERERDEntKg3b96MlStXYseOHdiwYQOCwSCWLFkCt9s95Ouuv/56tLS0DD7efvttQYmJiIjiSyPyxd99990hv167di3y8vKwe/duLFq0aPDjer0eBQUF8Y5HREQknKLuUTscDgBAdnb2kI9v2rQJeXl5mDBhAu699160t7ef8zl8Ph+cTueQBxERUaKSlHLMpSzLuPnmm9Hd3Y2tW7cOfvyVV15BRkYGysvLUV9fj5/+9KcIBoPYvXs39Hr9Gc+zevVqPProo2d83OFwwGw2x/TPQEREFG2KKeqVK1firbfewrZt21BSUnLOr2tpaUF5eTlefvll3HrrrWd83ufzwefzDf7a6XSitLSURU1ERAlJ6D3qAatWrcKbb76JLVu2nLekAaCwsBDl5eU4duzYWT+v1+vPOtImIiJKREKLWpZlrFq1Cq+//jo2bdqEysrKC/6ezs5ONDY2orCwMA4JiYiIxBI6mWzlypX485//jHXr1sFkMqG1tRWtra3weDwAAJfLhR/84AfYvn07GhoasGnTJtx0002wWq245ZZbREYnIiKKC6H3qCVJOuvH165dixUrVsDj8WDZsmXYu3cvenp6UFhYiKuuugr/8R//gdLS0ot6DafTCYvFwnvURESUkBQzmSxWWNRERJTIFLWOmoiIiIZiURMRESkYi5qIiEjBWNREREQKxqImIiJSMBY1ERGRgrGoiYiIFIxFTUREpGAsaiIiIgVTxOlZRBfLZrPBbreLjjFiVqsVZWVlomMQUQJhUVPCsNlsmDR5Mjx9faKjjJjBaMTh2lqWNRFdNBY1JQy73Q5PXx++8ePHkV82VnScYWuz1eGlX/wQdrudRU1EF41FTQknv2wsSsZPFR2DiCguOJmMiIhIwVjURERECsaiJiIiUjAWNRERkYKxqImIiBSMRU1ERKRgLGoiIiIFY1ETEREpGIuaiIhIwVjURERECsaiJiIiUjAWNRERkYKxqImIiBSMRU1ERKRgLGoiIiIFY1ETEREpGIuaiIhIwVjURERECsaiJiIiUjAWNRERkYKxqImIiBSMRU1ERKRgLGoiIiIFY1ETEREpGIuaiIhIwVjURERECsaiJiIiUjAWNRERkYKxqImIiBSMRU1ERKRgLGoiIiIFY1ETEREpGIuaiIhIwVjURERECsaiJiIiUjAWNRERkYKxqImIiBSMRU1ERKRgLGoiIiIFY1ETEREpGIuaiIhIwVjURERECsaiJiIiUjAWNRERkYKxqImIiBSMRU1ERKRgLGoiIiIFY1ETEREpGIuaiIhIwVjURERECsaiJiIiUjAWNRERkYKxqImIiBSMRU1ERKRgGtEBiJKFLMvocPlwqtuD7j4/XN4gZBlQqySY07SAWwVNdjFkWRYdlYgSCIuaaJT6/EHsP+XAwWYnXL7geb5Sg+J7f4uV73TgLudx3Da7BHnmtLjlJKLExKImGqFgKIydDd3YbetGKBwZJWtUEkqzjcjN0MNk0EAtSQiEwnB6g7C1dqLdHUKrC3h8/RH86v1j+Mb8Mnxn8TjkmvSC/zREpFQsaqIRaO7xYP3BVji9kRF0vlmPWaVZGJubDo367FM/TsltePKBr+O/X34f21ol7LH1YO1HDXh11yn8+PqJ+Ma8cqhUUjz/GESUAFjURMMgyzL22HrwUZ0dsgxk6DVYNMGKcbkZkKQLl6wc8GFxhRHfv2UWth2347F3j+BAkwM//ftBvLmvGU/dPgvFmYY4/EmIKFFw1jfRRQqHZXxwuB3bjkdKekJ+Bu6aX47xeaaLKunTSZKEK8bn4o2VC/Hol6YiXafGzoZufPFXW7HhUFuM/gRElIhY1EQXIRgO4+2aFhxsdkICsHhCLq6fWgCdZnT/CalVEpYvqMA731uEGSUWODwB3PvHXXhm43HODiciACxqogsKh2W8W9OKug431CoJN0wvxIzSzGGPos+nLMeIV7+1AMsvKwcQmWz2g1f3IxgKR+01iCgxsaiJzkOWZbx/uC1S0pKEL80owtjcjJi8lk6jwqM3V+H/LquCWiXhf/ecwqq/7IU/yLImSmUsaqLz2NnQjdqWXkgSsHRaAcqyjTF/zTvnl+M3d86GTq3COzWt+Nafd8MbCMX8dYlImYQW9Zo1azB37lyYTCbk5eVh2bJlOHLkyJCvkWUZq1evRlFREQwGAxYvXoyDBw8KSkyp5Hi7C9tPdAIArp6YF7OR9NlcOyUf/2/5HKRpVfjwcDvueXEn+vzn20yFiJKV0KLevHkzVq5ciR07dmDDhg0IBoNYsmQJ3G734Nc89thjeOKJJ/D0009j586dKCgowLXXXove3l6BySnZdbv9eO9QKwBgRokFVcWWuGdYNCEXL/zLpUjXqfHR8U786ws7ObImSkFCi/rdd9/FihUrMHXqVMyYMQNr166FzWbD7t27AURG00899RQefvhh3HrrraiqqsKLL76Ivr4+rFu3TmR0SmLBcBjv1LQiEJJRkmnAFeNzhWWZPyYHf/rmPGToNdhxogvfe3nv4C5oRJQaFHWP2uFwAACys7MBAPX19WhtbcWSJUsGv0av1+PKK6/Exx9/fNbn8Pl8cDqdQx5Ew/HRsU50uHwwaNW4rqoAasG7hV1SloXn747cs15/sA0Pv36AS7eIUohiilqWZTz44IO4/PLLUVVVBQBobY1ceszPzx/ytfn5+YOf+7w1a9bAYrEMPkpLS2MbnJKKrasP1ad6AETuE2folbF534KxVvzP12dCJQEv72zEf7935MK/iYiSgmKK+v7778f+/fvxl7/85YzPfX69qizL51zD+tBDD8HhcAw+GhsbY5KXko8/GMb7tZFdwaYXW1BpTRecaKjrqwrxn7dMAwA8s7EOL3xULzgREcWDIop61apVePPNN7Fx40aUlJQMfrygoAAAzhg9t7e3nzHKHqDX62E2m4c8iC7GR8ft6PUGYU7TYOE4q+g4Z/X1S8vww+smAgD+/Z+H8OFhbjdKlOyEFrUsy7j//vvx2muv4cMPP0RlZeWQz1dWVqKgoAAbNmwY/Jjf78fmzZuxYMGCeMelJNbi8GB/U2SOxDWT80e9NWgsfWfxWHxtTinCMrBq3V4cbuU8DKJkJvTdaOXKlfjzn/+MdevWwWQyobW1Fa2trfB4PAAil7wfeOAB/PznP8frr7+OmpoarFixAkajEXfccYfI6JREwmEZGw93AACmFJpRGodNTUZDkiT8x7IqXDYmB25/CPe8sAvtvV7RsYgoRoQW9XPPPQeHw4HFixejsLBw8PHKK68Mfs2PfvQjPPDAA/jOd76DOXPmoKmpCe+99x5MJpPA5JRM9jc50OHyQa9RYeG4HNFxLopOo8Jzd16CMdZ0NPV4cO8fuXsZUbISfun7bI8VK1YMfo0kSVi9ejVaWlrg9XqxefPmwVnhRKPV5w8O7j62cKwVRp0yZnlfjEyjDr9fMReZRi32Nfbgh3/bz2VbRElIuTfiiOLgkxNd8AfDyDXpMbU48SYeVlrT8Zs7Z0OjkvCPfc14dlOd6EhEFGUsakpZnS4fDjRHJpAtGm+FKorHVsbT/DE
|
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|
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"text/plain": [
|
||
|
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"<Figure size 500x500 with 1 Axes>"
|
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]
|
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},
|
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|
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"metadata": {},
|
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|
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"output_type": "display_data"
|
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}
|
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],
|
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"source": [
|
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|
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"import numpy as np\n",
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"import pandas as pd\n",
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"import seaborn as sns\n",
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"import matplotlib.pyplot as plt\n",
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"from scipy import stats\n",
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"\n",
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"x = np.random.normal(size=100)\n",
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"sns.displot(x, kde=True);"
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]
|
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|
|
},
|
||
|
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{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 2,
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "subslide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
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"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAeoAAAHpCAYAAABN+X+UAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAgfElEQVR4nO3df2xVhf3/8deFwqWFttCV/kDaUhVBxOlUPji+DqnTDpYRK87MEQxkapwCjhHjgsxRXEYTN5FEFOcSq2Zj+o8/SDRiFSgaxACTIaQYmGVl0K5cftzblsst7T3fP0zvWnpb6OX2nvdtn4/kRu455577vucWnt4fvdfjOI4jAABg0hC3BwAAAD0j1AAAGEaoAQAwjFADAGAYoQYAwDBCDQCAYYQaAADDBnyoHcdRIBAQvy4OAEhGAz7UTU1NyszMVFNTk9ujAADQZwM+1AAAJDNCDQCAYYQaAADDCDUAAIYRagAADCPUAAAYRqgBADCMUAMAYBihBgDAMEINAIBhhBoAAMMINQAAhhFqAAAMI9QAABhGqAEAMIxQAwBgGKEGAMAwQg0AgGGEGgAAw1wNdUVFhaZNm6b09HTl5OSorKxMX3/9dZdtFi1aJI/H0+V06623ujQxAACJ5Wqoq6urtXjxYu3cuVNVVVVqa2tTaWmpWlpaumw3e/Zs1dfXR04ffPCBSxMDAJBYKW5e+YcfftjlfGVlpXJycrRnzx7NnDkzstzr9SovLy/R4wEA4DpTr1H7/X5JUlZWVpfl27ZtU05Ojq655ho9/PDDamxs7HEfoVBIgUCgywkAgGTlcRzHcXsISXIcR3fffbdOnz6tTz/9NLL8rbfe0qhRo1RUVKTa2lo9/fTTamtr0549e+T1ervtp7y8XKtXr+623O/3KyMjo19vAwAA8WYm1IsXL9b777+vzz77TOPHj+9xu/r6ehUVFenNN9/UvHnzuq0PhUIKhUKR84FAQAUFBYQaAJCUXH2NusPSpUu1adMmbd++vddIS1J+fr6Kiop06NChqOu9Xm/UR9oAACQjV0PtOI6WLl2qd955R9u2bVNxcfFFL3Py5EkdPXpU+fn5CZgQAAB3ufpmssWLF+uvf/2rNm7cqPT0dDU0NKihoUHBYFCS1NzcrCeeeEKff/65jhw5om3btmnu3LnKzs7WPffc4+boAAAkhKuvUXs8nqjLKysrtWjRIgWDQZWVlenLL7/UmTNnlJ+fr5KSEv3+979XQUHBJV1HIBBQZmYmr1EDAJKSmTeT9RdCDQBIZqZ+jxoAAHRFqAEAMIxQAwBgGKEGAMAwQg0AgGGEGgAAwwg1AACGEWoAAAwj1AAAGGbi27OAS1VXVyefz+f2GDHLzs5WYWGh22MASCKEGkmjrq5Okydfq2DwrNujxCw1NU0HD9YQawCXjFAjafh8PgWDZzX9F6uUkT/B7XH6LFB/RF+8ulo+n49QA7hkhBpJJyN/grIKJ7k9BgAkBG8mAwDAMEINAIBhhBoAAMMINQAAhhFqAAAMI9QAABhGqAEAMIxQAwBgGKEGAMAwQg0AgGGEGgAAwwg1AACGEWoAAAwj1AAAGEaoAQAwjFADAGAYoQYAwDBCDQCAYYQaAADDCDUAAIYRagAADCPUAAAYRqgBADCMUAMAYBihBgDAMEINAIBhhBoAAMMINQAAhhFqAAAMI9QAABhGqAEAMIxQAwBgGKEGAMAwQg0AgGGEGgAAwwg1AACGEWoAAAwj1AAAGEaoAQAwjFADAGAYoQYAwDBCDQCAYYQaAADDCDUAAIYRagAADCPUAAAYRqgBADCMUAMAYBihBgDAMEINAIBhhBoAAMMINQAAhhFqAAAMI9QAABhGqAEAMIxQAwBgGKEGAMAwQg0AgGGEGgAAwwg1AACGEWoAAAxzNdQVFRWaNm2a0tPTlZOTo7KyMn399dddtnEcR+Xl5Ro3bpxSU1M1a9YsHThwwKWJAQBILFdDXV1drcWLF2vnzp2qqqpSW1ubSktL1dLSEtnm2Wef1dq1a7V+/Xrt2rVLeXl5uuuuu9TU1OTi5AAAJEaKm1f+4YcfdjlfWVmpnJwc7dmzRzNnzpTjOFq3bp1WrlypefPmSZJef/115ebmauPGjXrkkUe67TMUCikUCkXOBwKB/r0RAAD0I1OvUfv9fklSVlaWJKm2tlYNDQ0qLS2NbOP1enX77bdrx44dUfdRUVGhzMzMyKmgoKD/BwcAoJ+YCbXjOFq+fLluu+02TZ06VZLU0NAgScrNze2ybW5ubmTdhVasWCG/3x85HT16tH8HBwCgH7n61HdnS5Ys0b59+/TZZ591W+fxeLqcdxyn27IOXq9XXq+3X2YEACDRTDyiXrp0qTZt2qStW7dq/PjxkeV5eXmS1O3Rc2NjY7dH2QAADESuhtpxHC1ZskRvv/22tmzZouLi4i7ri4uLlZeXp6qqqsiy1tZWVVdXa8aMGYkeFwCAhHP1qe/Fixdr48aNeu+995Senh555JyZmanU1FR5PB4tW7ZMa9as0cSJEzVx4kStWbNGaWlpmj9/vpujAwCQEK6GesOGDZKkWbNmdVleWVmpRYsWSZKefPJJBYNBPfbYYzp9+rSmT5+ujz76SOnp6QmeFgCAxHM11I7jXHQbj8ej8vJylZeX9/9AAAAYY+LNZAAAIDpCDQCAYYQaAADDCDUAAIYRagAADCPUAAAYRqgBADCMUAMAYJiZb88CBouamhq3R7gs2dnZKiwsdHsMYNAg1ECCBP0nJXm0YMECt0e5LKmpaTp4sIZYAwlCqIEEOX+2SZKjG+f/RmOLJ7s9TkwC9Uf0xaur5fP5CDWQIIQaSLBROYXKKpzk9hgAkgRvJgMAwDBCDQCAYYQaAADDCDUAAIYRagAADCPUAAAYRqgBADCMUAMAYBihBgDAMEINAIBhhBoAAMMINQAAhhFqAAAM49uzAPRZTU2N2yPELDs7m6/oRFIh1AAuWdB/UpJHCxYscHuUmKWmpungwRpijaRBqAFcsvNnmyQ5unH+bzS2eLLb4/RZoP6Ivnh1tXw+H6FG0iDUAPpsVE6hsgonuT0GMCjwZjIAAAwj1AAAGEaoAQAwjFADAGAYoQYAwDBCDQCAYYQaAADDCDUAAIYRagAADCPUAAAYRqgBADCMUAMAYBihBgDAMEINAIBhhBoAAMMINQAAhhFqAAAMI9QAABhGqAEAMIxQAwBgGKEGAMAwQg0AgGGEGgAAwwg1AACGEWoAAAwj1AAAGEaoAQAwjFADAGAYoQYAwDBCDQCAYYQaAADDCDUAAIYRagAADCPUAAAYRqgBADCMUAMAYBihBgDAMEINAIBhhBoAAMMINQAAhhFqAAAMI9QAABhGqAEAMIxQAwBgmKuh3r59u+bOnatx48bJ4/Ho3Xff7bJ+0aJF8ng8XU633nqrO8MCAOACV0Pd0tKiG264QevXr+9xm9mzZ6u+vj5y+uCDDxI4IQAA7kpx88rnzJmjOXPm9LqN1+tVXl5egiYCAMAW869Rb9u2TTk5Obrmmmv08MMPq7GxsdftQ6GQAoFAlxMAAMnKdKjnzJmjv/3tb9qyZYuee+457dq1S3fccYdCoVCPl6moqFBmZmbkVFBQkMCJAQCIL1ef+r6Yn/3sZ5E/T506VbfccouKior0/vvva968eVEvs2LFCi1fvjxyPhAIEGsAQNIyHeoL5efnq6ioSIcOHepxG6/XK6/Xm8CpAADoP6af+r7QyZMndfToUeXn57s9CgAACeHqI+rm5mYdPnw4cr62tlZ79+5VVlaWsrKyVF5ernvvvVf5+fk6cuSInnrqKWVnZ+uee+5xcWoAABLH1VDv3r1bJSUlkfMdry0vXLhQGzZs0FdffaU33nhDZ86cUX5+vkpKSvTWW28pPT3drZEBAEgoV0M9a9YsOY7T4/rNmzcncBoAAOxJqteoAQAYbAg1AACGEWoAAAwj1AAAGEaoAQAwjFADAGBYTKG+8sordfLkyW7Lz5w5oyuvvPKyhwIAAN+KKdRHjhxRe3t7t+WhUEjHjh277KEAAMC3+vSBJ5s2bYr8efPmzcrMzIycb29v1ye
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||
|
|
"text/plain": [
|
||
|
|
"<Figure size 500x500 with 1 Axes>"
|
||
|
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]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
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"output_type": "display_data"
|
||
|
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}
|
||
|
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],
|
||
|
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"source": [
|
||
|
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"# Only the histogram and a rug plot (the observations)\n",
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|
|
"sns.displot(x, kde=False, rug=True);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 3,
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "subslide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 500x500 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Only the KDE and the rug plot\n",
|
||
|
|
"sns.displot(x, kde=True, rug=True);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "subslide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<Axes: ylabel='Density'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# We can use a shortcut: kdeplot if we want only kdeplot\n",
|
||
|
|
"sns.kdeplot(x, fill=True) #check fill=False\n",
|
||
|
|
"#sns.rugplot(x)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "slide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"## Plotting Bivariate distributions\n",
|
||
|
|
"The most common chart is the **scatterplot**, where each observation is shown with a point at the x and y values.\n",
|
||
|
|
"\n",
|
||
|
|
"This is analogous to a rug plot in two dimensions. You can draw a **scatterplot** with **lmplot())** and **jointplot()**.\n",
|
||
|
|
"\n",
|
||
|
|
"It helps us to discover **correlations** (positive if the values of both variables increase or decrease together; or negative if one increases when the other decreases)."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "slide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"<div>\n",
|
||
|
|
"<style scoped>\n",
|
||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
||
|
|
" vertical-align: middle;\n",
|
||
|
|
" }\n",
|
||
|
|
"\n",
|
||
|
|
" .dataframe tbody tr th {\n",
|
||
|
|
" vertical-align: top;\n",
|
||
|
|
" }\n",
|
||
|
|
"\n",
|
||
|
|
" .dataframe thead th {\n",
|
||
|
|
" text-align: right;\n",
|
||
|
|
" }\n",
|
||
|
|
"</style>\n",
|
||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||
|
|
" <thead>\n",
|
||
|
|
" <tr style=\"text-align: right;\">\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" <th>total_bill</th>\n",
|
||
|
|
" <th>tip</th>\n",
|
||
|
|
" <th>sex</th>\n",
|
||
|
|
" <th>smoker</th>\n",
|
||
|
|
" <th>day</th>\n",
|
||
|
|
" <th>time</th>\n",
|
||
|
|
" <th>size</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>0</th>\n",
|
||
|
|
" <td>16.99</td>\n",
|
||
|
|
" <td>1.01</td>\n",
|
||
|
|
" <td>Female</td>\n",
|
||
|
|
" <td>No</td>\n",
|
||
|
|
" <td>Sun</td>\n",
|
||
|
|
" <td>Dinner</td>\n",
|
||
|
|
" <td>2</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>1</th>\n",
|
||
|
|
" <td>10.34</td>\n",
|
||
|
|
" <td>1.66</td>\n",
|
||
|
|
" <td>Male</td>\n",
|
||
|
|
" <td>No</td>\n",
|
||
|
|
" <td>Sun</td>\n",
|
||
|
|
" <td>Dinner</td>\n",
|
||
|
|
" <td>3</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>2</th>\n",
|
||
|
|
" <td>21.01</td>\n",
|
||
|
|
" <td>3.50</td>\n",
|
||
|
|
" <td>Male</td>\n",
|
||
|
|
" <td>No</td>\n",
|
||
|
|
" <td>Sun</td>\n",
|
||
|
|
" <td>Dinner</td>\n",
|
||
|
|
" <td>3</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>3</th>\n",
|
||
|
|
" <td>23.68</td>\n",
|
||
|
|
" <td>3.31</td>\n",
|
||
|
|
" <td>Male</td>\n",
|
||
|
|
" <td>No</td>\n",
|
||
|
|
" <td>Sun</td>\n",
|
||
|
|
" <td>Dinner</td>\n",
|
||
|
|
" <td>2</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>4</th>\n",
|
||
|
|
" <td>24.59</td>\n",
|
||
|
|
" <td>3.61</td>\n",
|
||
|
|
" <td>Female</td>\n",
|
||
|
|
" <td>No</td>\n",
|
||
|
|
" <td>Sun</td>\n",
|
||
|
|
" <td>Dinner</td>\n",
|
||
|
|
" <td>4</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>5</th>\n",
|
||
|
|
" <td>25.29</td>\n",
|
||
|
|
" <td>4.71</td>\n",
|
||
|
|
" <td>Male</td>\n",
|
||
|
|
" <td>No</td>\n",
|
||
|
|
" <td>Sun</td>\n",
|
||
|
|
" <td>Dinner</td>\n",
|
||
|
|
" <td>4</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>6</th>\n",
|
||
|
|
" <td>8.77</td>\n",
|
||
|
|
" <td>2.00</td>\n",
|
||
|
|
" <td>Male</td>\n",
|
||
|
|
" <td>No</td>\n",
|
||
|
|
" <td>Sun</td>\n",
|
||
|
|
" <td>Dinner</td>\n",
|
||
|
|
" <td>2</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>7</th>\n",
|
||
|
|
" <td>26.88</td>\n",
|
||
|
|
" <td>3.12</td>\n",
|
||
|
|
" <td>Male</td>\n",
|
||
|
|
" <td>No</td>\n",
|
||
|
|
" <td>Sun</td>\n",
|
||
|
|
" <td>Dinner</td>\n",
|
||
|
|
" <td>4</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>8</th>\n",
|
||
|
|
" <td>15.04</td>\n",
|
||
|
|
" <td>1.96</td>\n",
|
||
|
|
" <td>Male</td>\n",
|
||
|
|
" <td>No</td>\n",
|
||
|
|
" <td>Sun</td>\n",
|
||
|
|
" <td>Dinner</td>\n",
|
||
|
|
" <td>2</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>9</th>\n",
|
||
|
|
" <td>14.78</td>\n",
|
||
|
|
" <td>3.23</td>\n",
|
||
|
|
" <td>Male</td>\n",
|
||
|
|
" <td>No</td>\n",
|
||
|
|
" <td>Sun</td>\n",
|
||
|
|
" <td>Dinner</td>\n",
|
||
|
|
" <td>2</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" total_bill tip sex smoker day time size\n",
|
||
|
|
"0 16.99 1.01 Female No Sun Dinner 2\n",
|
||
|
|
"1 10.34 1.66 Male No Sun Dinner 3\n",
|
||
|
|
"2 21.01 3.50 Male No Sun Dinner 3\n",
|
||
|
|
"3 23.68 3.31 Male No Sun Dinner 2\n",
|
||
|
|
"4 24.59 3.61 Female No Sun Dinner 4\n",
|
||
|
|
"5 25.29 4.71 Male No Sun Dinner 4\n",
|
||
|
|
"6 8.77 2.00 Male No Sun Dinner 2\n",
|
||
|
|
"7 26.88 3.12 Male No Sun Dinner 4\n",
|
||
|
|
"8 15.04 1.96 Male No Sun Dinner 2\n",
|
||
|
|
"9 14.78 3.23 Male No Sun Dinner 2"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df = sns.load_dataset('tips')\n",
|
||
|
|
"df.head(10)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "slide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"**lmplot** shows us a scatterplot of variables x and y, fit a regression model (y ~ x), and plots the resulting regression line and a 95% confidence interval for that regression. \n",
|
||
|
|
"\n",
|
||
|
|
"An alternative is **regplot** (only for one relationship and enables detailed configuration of the axes)."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "subslide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<seaborn.axisgrid.FacetGrid at 0x7f9179351660>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 500x500 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# 2 variables\n",
|
||
|
|
"sns.lmplot(x=\"total_bill\", y=\"tip\", data=df)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "fragment"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"We can observe a positive correlation and a few outliers.\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "subslide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<seaborn.axisgrid.FacetGrid at 0x7f91793f26e0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 600.25x500 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"#Even 3 variables\n",
|
||
|
|
"sns.lmplot(x=\"total_bill\", y=\"tip\", hue=\"sex\", data=df)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 11,
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "subslide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<seaborn.axisgrid.FacetGrid at 0x7f9179278df0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 11,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 572.125x500 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.lmplot(x=\"total_bill\", y=\"tip\", hue=\"smoker\", data=df)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "slide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"A more straightforward function for relating variables with scatter plots is **relplot**.\n",
|
||
|
|
"\n",
|
||
|
|
"**relplot** can also be used for continuous variables."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "fragment"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<seaborn.axisgrid.FacetGrid at 0x7f9179139270>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 572.125x500 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.relplot(x=\"total_bill\", y=\"tip\", hue=\"smoker\", data=df)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "subslide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<seaborn.axisgrid.FacetGrid at 0x7f91791a3970>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 601x500 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# We can even show four variables\n",
|
||
|
|
"sns.relplot(x=\"total_bill\", y=\"tip\", hue=\"smoker\", style=\"time\", data=df)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 14,
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "subslide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 500x500 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Continuous variables\n",
|
||
|
|
"fmri = sns.load_dataset(\"fmri\")\n",
|
||
|
|
"sns.relplot(x=\"timepoint\", y=\"signal\", kind=\"line\", data=fmri);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "slide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"**joinplot** draws a scatterplot showing each observation with a point at the x and y values. It is like a rug plot for two variables.\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 15,
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "subslide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<seaborn.axisgrid.JointGrid at 0x7f9179208640>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 15,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 600x600 with 3 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.jointplot(x=\"total_bill\", y=\"tip\", data=df)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "slide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"The default value for kind is \"scatter\". If we modify this parameter, we have different charts.\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 16,
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "fragment"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<seaborn.axisgrid.JointGrid at 0x7f91790d6560>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 16,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 600x600 with 3 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Add regression and kernel density fits\n",
|
||
|
|
"sns.jointplot(x=\"total_bill\", y=\"tip\", data=df, kind=\"reg\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "slide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"We can group individual samples into hexagons with kind=\"hex\" for large datasets. \n",
|
||
|
|
"\n",
|
||
|
|
"This chart is called **hexagonal bin plot** or simply **hexbin plot**.\n",
|
||
|
|
"\n",
|
||
|
|
"It uses a lighter or darker color to represent the density of data."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 17,
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "subslide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<seaborn.axisgrid.JointGrid at 0x7f91792569b0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 17,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 600x600 with 3 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.jointplot(x=\"total_bill\", y=\"tip\", data=df, kind=\"hex\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "slide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"We can also show a **contour plot**. KDE plot replaces the scatterplots and histograms with density estimates.\n",
|
||
|
|
"\n",
|
||
|
|
"Contour plots or 2d density plots show the density of points, much as a topographic map shows elevation. "
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 18,
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "subslide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<seaborn.axisgrid.JointGrid at 0x7f917962f0a0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 18,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 600x600 with 3 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.jointplot(x=\"total_bill\", y=\"tip\", data=df, kind=\"kde\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "slide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"## Pairwise relationships of the dataset\n",
|
||
|
|
"We can plot multiple pairwise bivariate distributions in a dataset with **pairplot**."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 19,
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "subslide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 750x750 with 12 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.pairplot(df);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 52,
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "subslide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 612.125x540 with 12 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.pairplot(df, hue='sex');"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 20,
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "subslide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<seaborn.axisgrid.PairGrid at 0x7f9172a3f880>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 20,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 850.25x750 with 12 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.pairplot(df, hue='sex', diag_kind=\"hist\") ## Previously it has used diag_kind='kde'"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "slide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"## Categorical distributions\n",
|
||
|
|
"We can represent categorical scatter plots with **catplot**. The main challenge is that all the values of the categorical variables correspond with a point.\n",
|
||
|
|
"\n",
|
||
|
|
"There are two implementations:\n",
|
||
|
|
"* **strip** (the default), which adjusts the positions of points on the categorical axis with a small amount of random “jitter”:\n",
|
||
|
|
"* **swarm**, which prevents points from overlapping.\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 21,
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "subslide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 511.111x500 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.catplot(x=\"day\", y=\"total_bill\", data=df, kind='strip');"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 22,
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "subslide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 511.111x500 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.catplot(x=\"day\", y=\"total_bill\", data=df, kind='swarm');"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 23,
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "subslide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 600.25x500 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"#Adding a a third variable\n",
|
||
|
|
"sns.catplot(x=\"day\", y=\"total_bill\", hue='sex', data=df, kind='swarm');"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "slide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"## Distribution of observations within categories\n",
|
||
|
|
"Boxplots and violin plots provide an excellent way to understand the distribution of categorical variables when we have a lot of data."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "subslide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"### Boxplot\n",
|
||
|
|
"Boxplot shows the three quartile values of the distribution along with extreme values.\n",
|
||
|
|
"\n",
|
||
|
|
"A rectangle is drawn to represent the second and third quartiles, usually with a vertical line inside it, to indicate the median value. The lower and upper quartiles are shown as horizontal lines on either side of this rectangle.\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 24,
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "subslide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<seaborn.axisgrid.FacetGrid at 0x7f9171ed3df0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 24,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 511.111x500 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.catplot(x=\"day\", y=\"total_bill\", kind=\"box\", data=df)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 25,
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "subslide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<seaborn.axisgrid.FacetGrid at 0x7f9171d2bf10>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 25,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 572.125x500 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.catplot(x=\"day\", y=\"total_bill\", hue=\"smoker\", kind=\"box\", data=df)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "slide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"### Violinplot\n",
|
||
|
|
"Violinplots combine a boxplot with the kernel density estimation.\n",
|
||
|
|
"\n",
|
||
|
|
"* the thin line at the center is the interquartile range\n",
|
||
|
|
"* the thin line is the 95% confidence interval\n",
|
||
|
|
"* the white dot is the median\n",
|
||
|
|
"* the spread of the violin shape is a kernel density estimation\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 59,
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "subslide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<seaborn.axisgrid.FacetGrid at 0x7f81c0fc4880>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 59,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 360x360 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.catplot(x=\"total_bill\", y=\"day\", kind=\"violin\", data=df)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 26,
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "subslide"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<seaborn.axisgrid.FacetGrid at 0x7f9171dc4ac0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 26,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 596.375x500 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.catplot(y=\"total_bill\", x=\"day\", hue=\"time\", kind=\"violin\", data=df)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "skip"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"# References\n",
|
||
|
|
"* [Seaborn](http://seaborn.pydata.org) documentation"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "skip"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"## Licence\n",
|
||
|
|
"The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n",
|
||
|
|
"\n",
|
||
|
|
"© Carlos A. Iglesias, Universidad Politécnica de Madrid."
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"datacleaner": {
|
||
|
|
"position": {
|
||
|
|
"top": "50px"
|
||
|
|
},
|
||
|
|
"python": {
|
||
|
|
"varRefreshCmd": "try:\n print(_datacleaner.dataframe_metadata())\nexcept:\n print([])"
|
||
|
|
},
|
||
|
|
"window_display": false
|
||
|
|
},
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3 (ipykernel)",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.10.13"
|
||
|
|
},
|
||
|
|
"latex_envs": {
|
||
|
|
"LaTeX_envs_menu_present": true,
|
||
|
|
"autocomplete": true,
|
||
|
|
"bibliofile": "biblio.bib",
|
||
|
|
"cite_by": "apalike",
|
||
|
|
"current_citInitial": 1,
|
||
|
|
"eqLabelWithNumbers": true,
|
||
|
|
"eqNumInitial": 1,
|
||
|
|
"hotkeys": {
|
||
|
|
"equation": "Ctrl-E",
|
||
|
|
"itemize": "Ctrl-I"
|
||
|
|
},
|
||
|
|
"labels_anchors": false,
|
||
|
|
"latex_user_defs": false,
|
||
|
|
"report_style_numbering": false,
|
||
|
|
"user_envs_cfg": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 4
|
||
|
|
}
|