udemy-ML/05-Seaborn/05-Seaborn-Grids.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "<a href='http://www.pieriandata.com'><img src='../Pierian_Data_Logo.png'/></a>\n",
    "___\n",
    "<center><em>Copyright by Pierian Data Inc.</em></center>\n",
    "<center><em>For more information, visit us at <a href='http://www.pieriandata.com'>www.pieriandata.com</a></em></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('StudentsPerformance.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "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>gender</th>\n",
       "      <th>race/ethnicity</th>\n",
       "      <th>parental level of education</th>\n",
       "      <th>lunch</th>\n",
       "      <th>test preparation course</th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>completed</td>\n",
       "      <td>69</td>\n",
       "      <td>90</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>master's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>90</td>\n",
       "      <td>95</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>group A</td>\n",
       "      <td>associate's degree</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>47</td>\n",
       "      <td>57</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>76</td>\n",
       "      <td>78</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender race/ethnicity parental level of education         lunch  \\\n",
       "0  female        group B           bachelor's degree      standard   \n",
       "1  female        group C                some college      standard   \n",
       "2  female        group B             master's degree      standard   \n",
       "3    male        group A          associate's degree  free/reduced   \n",
       "4    male        group C                some college      standard   \n",
       "\n",
       "  test preparation course  math score  reading score  writing score  \n",
       "0                    none          72             72             74  \n",
       "1               completed          69             90             88  \n",
       "2                    none          90             95             93  \n",
       "3                    none          47             57             44  \n",
       "4                    none          76             78             75  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## catplot()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1e24e235b08>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Kind Options are: “point”, “bar”, “strip”, “swarm”, “box”, “violin”, or “boxen”\n",
    "sns.catplot(x='gender',y='math score',data=df,kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1e24e37b2c8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAALICAYAAABBxipSAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy86wFpkAAAACXBIWXMAAAsTAAALEwEAmpwYAAAuRUlEQVR4nO3de7jdZX3n/feHHZEA0kKMKY1FsGFkKJ7oxsdTHRSw0bbiVLH42BJbLmk7GtJ2ekBtiz6t1nk6jx2atraprYbWsaLSgpYJQhQd24puwkkEJ3s4xxRCtBwEwYTv/LF+kU2enWSR7LXuvfd6v65rX2v97vU7fNfO4sO977XWfaeqkCQN336tC5CkUWUAS1IjBrAkNWIAS1IjBrAkNWIAS1IjBrCGIskDc+Gcu7jOm5P84Aye74ok4/tw/IlJPj1T9agdA1jaszcDMxbAT1SSsVbX1mAZwBqqnXtvSf4kyZu7+7cmeXeSDUmuT3JM135wkg91bdcled2U49+T5NokX0qyZB9rG0vy4SRf7a71q0leD4wDH0lyTZKFSX43yVe6/dYkSXf8FUn+S5IvJ/lfSX6sa1+Y5O+62j8GLJxyzQ8kmUhyQ5J3T2m/tbvOF4HTkixPclO3/dP78jw1exjAmm3uqarjgQ8Av961/Q5wb1U9u6qeA3y2az8I+FJVPRf4AvCWnU+W5OVdcO7888/TXPt5wNKqOq6qng18qKo+AUwAb6qq51XVQ8CfVNUJVXUcvTD9ySnnWFBVLwB+BTi3a/tl4MGu9vcAPzpl/3dW1TjwHOA/JHnOlMe+U1UvBf4B+Evgp4AfA35gd79AzR0GsGabC7vbq4Aju/snA3+6Y4eq+lZ39xHg09Psz5R9P9cF584/L57m2jcDz0yyOsly4L5d1PjyJFcmuR54BfAje6j/ZcDfdvVcB1w3Zf83JNkAXN2d59gpj32suz0GuKWqNlZv7oC/3UVdmmMWtC5AI2cbj/8f/wE7Pf5wd7udx16fAaabtOS79dhkJlP3/54kLwf+aJpjH9w5hKvqW0meC/w48FbgDcAv7HS+A4A/A8ar6o4k79rpOUxXP9PVn+Qoer38E7prf3inc317d8dr7rMHrGG7DTg2yZOTfB9wUh/HfAZ4246NJIf2e7En0gNO8lRgv6r6JL1hj+O7h+4HntLd3xGQ9yQ5GHh9H2V8AXhTd43j6A03ABxCL2Tv7cavX7WL428Cjkryw932G/u4puYAe8Aaqq7XeAG9P8M30vvTe09+H/jTJF+l17N8N4/9qT+TlgIfSrKjY/L27vbDwJ8neQh4Eb3x2OuBW4Gv9HHeD3TnvQ64BvgyQFVdm+Rq4AZ6wx//NN3BVfWdJGcB/5jkHuCLwHFP9Mlp9onTUUpSGw5BSFIjBrAkNWIAS1IjBrAkNTKnPwWxfPnyWrduXesyJGlPMl3jnO4B33PPPa1LkKS9NqcDWJLmMgNYkhoxgCWpEQNYkhoxgCWpEQNYkhoxgCWpEQNYkhoxgCWpkYEFcJK/TnJ3N4n2jrbDklyWZGN3e+iUx96eZDLJ15P8+KDqkqTZYpA94A8Dy3dqOwdYX1VHA+u7bZIcC5xOb1HC5cCfJRkbYG2S1NzAAriqvgB8c6fmU4G13f21wGuntP9dVT1cVbcAk8ALBlWbJM0Gw54NbUlVbQaoqs1Jnta1LwW+NGW/O7u2eWX16tVMTk4O/bqbNm0CYOnS4f9Kly1bxsqVK4d+3VHha2pumy3TUU43Vdu0i9V1ixOeBXDEEUcMsqZ546GHHmpdguYZX1MzY6CLciY5Evh0VR3XbX8dOLHr/R4OXFFVz0rydoCq+oNuv0uBd1XVv+zu/OPj4zUxMTGw+ueLVatWAXDeeec1rkTzha+pJ2xWzAd8MbCiu78CuGhK++lJnpzkKOBouqW7JWm+GtgQRJKPAicCT01yJ3Au8D7ggiRnArcDpwFU1Q1JLgC+BmwD3lpV2wdVmyTNBgML4Kp64y4eOmkX+78HeM+g6pGk2cZvwklSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwtaFyDNB6tXr2ZycrJ1GUOz47muWrWqcSXDs2zZMlauXDmj5zSApRkwOTnJxhuu5oiDt7cuZSj2/27vj+eHb5toXMlw3P7A2EDOawBLM+SIg7fzjuPva12GBuC9Gw4ZyHmbjAEn+dUkNyT5apKPJjkgyWFJLkuysbs9tEVtkjQsQw/gJEuBs4HxqjoOGANOB84B1lfV0cD6bluS5q1Wn4JYACxMsgA4EPgGcCqwtnt8LfDaNqVJ0nAMPYCrahPwX4Hbgc3AvVX1GWBJVW3u9tkMPG2645OclWQiycSWLVuGVbYkzbgWQxCH0uvtHgX8IHBQkp/t9/iqWlNV41U1vnjx4kGVKUkD12II4mTglqraUlXfBS4EXgzcleRwgO727ga1SdLQtAjg24EXJjkwSYCTgBuBi4EV3T4rgIsa1CZJQzP0zwFX1ZVJPgFsALYBVwNrgIOBC5KcSS+kTxt2bZI0TE2+iFFV5wLn7tT8ML3esCSNBCfjkaRGDGBJamQk54Jw5qr5bxAzV0kzbSQDeHJykmu+eiPbDzysdSlDsd8jBcBVN9/VuJLhGHvwm61LkPoykgEMsP3Aw3jomFe3LkMDsPCmS1qXIPXFMWBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJamRk54KQZtKmTZv49v1jvHfDIa1L0QDcdv8YB23aNOPntQcsSY3YA5ZmwNKlS3l422becfx9rUvRALx3wyE8eenSGT+vPWBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJamQk54LYtGkTYw/ey8KbLmldigZg7MGtbNq0rXUZ0h7ZA5akRkayB7x06VL+9eEFPHTMq1uXogFYeNMlLF26pHUZ0h7ZA5akRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWpkJCfjkQbh9gfGeO+GQ1qXMRR3Pdjruy058NHGlQzH7Q+McfQAzmsASzNg2bJlrUsYqkcmJwF48jNG43kfzWD+jQ1gaQasXLmydQlDtWrVKgDOO++8xpXMbU3GgJN8f5JPJLkpyY1JXpTksCSXJdnY3R7aojZJGpZWb8KdB6yrqmOA5wI3AucA66vqaGB9ty1J89bQAzjJIcDLgL8CqKpHqurfgFOBtd1ua4HXDrs2SRqmFj3gZwJbgA8luTrJB5McBCypqs0A3e3Tpjs4yVlJJpJMbNmyZXhVS9IMaxHAC4DjgQ9U1fOBb/MEhhuqak1VjVfV+OLFiwdVoyQNXF+fgkjyDODoqro8yUJgQVXdv5fXvBO4s6qu7LY/QS+A70pyeFVtTnI4cPdenr8vYw9+c2SWpd/vO/cB8OgBo/EZ1bEHvwm4KKdmvz0GcJK3AGcBhwE/DDwd+HPgpL25YFX9a5I7kjyrqr7enedr3c8K4H3d7UV7c/5+jNpnNicne/+vXPbMUQmlJSP3b6y5qZ8e8FuBFwBXAlTVxiTTjs8+ASuBjyTZH7gZ+Hl6wyEXJDkTuB04bR+vseuL+5lNSbNAPwH8cFU9kgSAJAuA2peLVtU1wPg0D+1Vr1qS5qJ+3oT7fJJ3AAuTnAJ8HPjUYMuSpPmvnwD+LXofG7se+EXgEuC3B1mUJI2C3Q5BJNkPuK6qjgP+cjglSdJo2G0PuKoeBa5NcsSQ6pGkkdHPm3CHAzck+TK9L00AUFWvGVhVkjQC+gngdw+8CkkaQXsM4Kr6fJI
      "text/plain": [
       "<Figure size 360x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='gender',y='math score',data=df,kind='box',row='lunch')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1e24e856388>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='gender',y='math score',data=df,kind='box',row='lunch',col='test preparation course')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pairgrid\n",
    "\n",
    "Grid that pairplot is built on top of, allows for heavy customization of the pairplot seen earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.PairGrid(df)\n",
    "g = g.map_upper(sns.scatterplot)\n",
    "g = g.map_diag(sns.kdeplot, lw=2)\n",
    "g = g.map_lower(sns.kdeplot, colors=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\marcial\\anaconda3\\envs\\ml_master\\lib\\site-packages\\seaborn\\distributions.py:434: UserWarning: The following kwargs were not used by contour: 'marker'\n",
      "  cset = contour_func(xx, yy, z, n_levels, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 610.5x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.PairGrid(df, hue=\"gender\", palette=\"viridis\",hue_kws={\"marker\": [\"o\", \"+\"]})\n",
    "g = g.map_upper(sns.scatterplot, linewidths=1, edgecolor=\"w\", s=40)\n",
    "g = g.map_diag(sns.distplot)\n",
    "g = g.map_lower(sns.kdeplot)\n",
    "g = g.add_legend();\n",
    "\n",
    "# Safely ignore the warning, its telling you it didn't use the marker for kde plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FacetGrid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1e24be00748>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(data=df,col='gender',row='lunch')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1e24f4fcb08>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 440x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(data=df,col='gender',row='lunch')\n",
    "g = g.map(plt.scatter, \"math score\", \"reading score\", edgecolor=\"w\")\n",
    "g.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://stackoverflow.com/questions/43669229/increase-space-between-rows-on-facetgrid-plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 440x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(data=df,col='gender',row='lunch')\n",
    "g = g.map(plt.scatter, \"math score\", \"reading score\", edgecolor=\"w\")\n",
    "g.add_legend()\n",
    "\n",
    "plt.subplots_adjust(hspace=0.4, wspace=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "----"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}