mwaskom/histplot_api_examples.ipynb

## histplot_api_examples.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot a univariate distribution along the x axis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11844ed00>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns; sns.set(style=\"white\")\n",
    "penguins = sns.load_dataset(\"penguins\")\n",
    "sns.histplot(data=penguins, x=\"flipper_length_mm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Flip the plot by assigning the data variable to the y axis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a59c4f0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=penguins, y=\"flipper_length_mm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check how well the histogram represents the data by specifying a different bin width:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a6ab100>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=penguins, x=\"flipper_length_mm\", binwidth=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also define the total number of bins to use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a792940>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=penguins, x=\"flipper_length_mm\", bins=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add a kernel density estimate to smooth the histogram, providing complementary information about the shape of the distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a8db9a0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=penguins, x=\"flipper_length_mm\", kde=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If neither `x` nor `y` is assigned, the dataset is treated as wide-form, and a histogram is drawn for each numeric column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a978250>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris = sns.load_dataset(\"iris\")\n",
    "sns.histplot(data=iris)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also draw multiple histograms from a long-form dataset with hue mapping:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11eb3d6a0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=penguins, x=\"flipper_length_mm\", hue=\"species\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default approach to plotting multiple distributions is to \"layer\" them, but you can also \"stack\" them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11ec52fd0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=penguins, x=\"flipper_length_mm\", hue=\"species\", multiple=\"stack\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overlapping bars can be hard to visually resolve. A different approach would be to draw a step function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11ed7c1c0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(penguins, x=\"flipper_length_mm\", hue=\"species\", element=\"step\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can move even farther away from bars by drawing a polygon with vertices in the center of each bin. This may make it easier to see the shape of the distribution, but use with caution: it will be less obvious to your audience that they are looking at a histogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a5e28b0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(penguins, x=\"flipper_length_mm\", hue=\"species\", element=\"poly\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To compare the distribution of subsets that differ substantially in size, use indepdendent density normalization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11ef1a1f0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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A7Nmzp1n7ERGROEQLCjs7O4SHhyMoKAjTpk2Dr68vnJ2dERISgpMnT+rcNyoqCjt37sTkyZNx4sQJvP3222KVSURETRD1PQq5XA65XK61rqFheVevXq217ODgoBlXhYgebW9kLkVp5Y02/9wnLLsiRb6yye0KCwvh4+ODvn37AgCqq6sxfPhwvPPOO+jevXnvYnR0fDObiNq10sobiPIMb/PPjTmS2OxtZTKZ5i1sQRCwdu1ahIWFYceOHW1eV3vEoCBqhT8+DIK6ukJv/RmZW+GZd7bqrT9qnEQiwVtvvYUxY8Zg69at+Prrr6FWq/Hss89i+fLliI2Nxfnz51FXV4eQkBD4+vqivLwcS5cuhUKhQHFxMdzd3bFixQr8+OOP2LhxI6RSKQoLCzF+/HhYWlrim2++AQBs2rSpXZy1MCiIWkFdXSHKrGuNEWMIC2o9U1NT9OrVC927d8cff/yBI0eOoEuXLkhISICTkxPef/99lJeXIzAwEEOHDsWvv/6KgQMHIikpCTU1NZgyZQp+++03AMCvv/6Kffv2wcbGBqNHj8aiRYuQnp6OJUuWYN++fQgODjbw0TIoiIhaRSKRwNzcHL1790aXLl0A3B2Rorq6WjPuU2VlJc6fPw9fX1/k5+djy5YtuHTpEm7evInKykoAQP/+/dGjRw8AQNeuXeHu7g4AePLJJ3H79m0DHFl9DAoDCozcj4oqld76szTT79dtaWYC+TsZeunLykKKL+In66UvopqaGvz+++8oLS2Fubm5Zr1arcaaNWvg5OQEACgpKcHjjz+Obdu24cCBA3jxxRcxevRonDt3TjMvhVQq1fpsY2Nj/R1IMzEoDKiiSoWVb4wxdBmiiXxFf0MkL005pre+6NGmVquRnJyMoUOHomfPnlptbm5u+PzzzxEfH4/i4mJMmzYNX3zxBY4dO4a///3vkMvlOHnyJM6cOQO1Wg0jo44xJRCDgojatScsu7boCaWWfG5zFRcXw8/PD8DdoBg4cCDWrl2LM2fOaG0XGhqK6Oho+Pr6amas69mzJ4KDgxEdHY1NmzbB2toaw4YNQ2FhYb2gaa8YFETUrjXnXQcxPfXUUzh16lSDba6urlqTCzU214S7uzsOHDjQ6Gfcc//8Fm+99VZrS25zHeO8h4iIDIZBQUREOjEoiKjdufdEELW91vxuGRRE1K4YGxtDpdLfY+OPmqqqqnqP5DaFQUFE7YqNjQ0UCgXUarWhS+lUBEFAZWUlioqKWjzHD596IqJ2pXv37igsLMTZs2cNXUqnI5VKYWdnh8cee6xF+zEoiKhdMTIy6jDvFzwqeOmJiIh04hkFEWmZm/4PVKiq9NafldQCm/3X6q0/ajkGBRFpqVBViTJRUGPEGJ6D2hYvPRERkU4MCiIi0olBQUREOjEoiIhIJwYFERHpxKAgIiKdRA2KzMxMTJ48GV5eXti+fXu99oKCAvj7+8Pb2xvLli1DbW0tAKCwsBCzZ8+Gn58f5syZg6KiIjHLJCIiHUQLCoVCgcTEROzYsQN79uxBWloaLly4oLVNREQEli9fjgMHDkAQBOzcuRMAsG7dOkyZMgUZGRnw8vJCYiKfsyYiMhTRgiInJwdubm6wsbGBpaUlvL29kZWVpWkvKipCdXU1XFxcAAD+/v6adrVajfLycgB3h8Q1NzcXq0wiImqCaG9mFxcXw9bWVrMsk8mQn5/faLutrS0UCgUAYMGCBQgMDMS2bdugUqmQlpYmVplERNQE0c4o1Go1JBKJZlkQBK1lXe2LFi1CbGwsjh49ipiYGISGhnLGKyIiAxHtjMLe3h4nTpzQLCuVSq3JMuzt7aFUKjXLJSUlkMlkKCsrw6VLlzBx4kQAgLe3N6KionDjxg1069ZNrHKJ2jWJqQUurXhBL31FGUkAT710RR2EaEExevRoJCcno6ysDBYWFjh48CDi4uI07Q4ODjAzM0NeXh5GjBiBjIwMeHh4oGvXrjAzM8OJEycwcuRI5OXlwcrKiiFBjzT7Fxfrra/rn0VBf2PHUkcgWlDY2dkhPDwcQUFBUKlUCAgIgLOzM0JCQhAWFoYhQ4YgISEBkZGRKC8vh5OTE4KCgiCRSPDxxx8jLi4O1dXVsLKyQnJyslhlEhFRE0QdZlwul0Mul2utS01N1fzs6OiI3bt319vP2dkZu3btErM0IiJqpmbdzH7rrbeQk5Mjdi1ERNQONSso/va3v2HDhg3w9vbGp59+ips3b4pdFxERtRPNCoqpU6fis88+w4YNG1BaWoqAgABERERovRdBRESdU7Pfo1Cr1bh8+TL++OMP1NXV4YknnkB0dDSSkpLErI+IiAysWTezExMTkZ6ejqeffhqzZs3CunXrIJVKUVlZCU9PT4SFhYldJxERGUizgqKsrAypqalwdHTUWm9paYkPP/xQlMKIiKh9aNalp7q6unohce8sYuzYsW1fFRERtRs6zyiioqKgUCiQl5eHsrIyzfra2lpcvXpV9OKIiMjwdAZFQEAAzp8/j7Nnz8Lb21uz3tjYWDM8OBERdW46g2LIkCEYMmQIxowZAzs7O33VRERE7YjOoFiwYAHWrVuHefPmNdiemZkpSlFERNR+6AyKkJAQAMB7772nl2KIiKj90fnU0+DBgwEAo0aNQo8ePTBq1ChUVlbi+PHjGDhwoF4KJCIiw2rW47HLly9HamoqLl68iMjISBQWFmLp0qVi10ZERO1As4Li1KlTiI6OxqFDhzB9+nSsWrUKRUVFYtdGRETtQLOCQhAEGBkZ4dixY3BzcwMAVFdXi1oYERG1D80Kip49eyIkJASFhYUYNWoU3nnnHQwYMEDs2oiIqB1o1lhPq1atwqFDhzBixAhIpVKMHDkS06ZNE7s2IiJqB5p1RmFpaYmRI0fi9u3b+O233+Ds7IxLly6JXRsREbUDzTqjWLduHf7nf/4HTzzxhGadRCJBdna2aIUREVH70KygyMjIwMGDBzmMBxHRI6hZl5569OjBkCAiekQ164zC3d0dH3zwASZMmABzc3PNeicnJ9EKIyKi9qFZQZGeng4AyMrK0qzjPQoiokdDs4Li8OHDrfrwzMxMpKSkoLa2FsHBwZg9e7ZWe0FBAZYtW4aKigqMHDkSMTExMDExQXFxMSIjI1FcXAxzc3MkJCTgqaeealUNRET0cJoVFBUVFfjwww9x8eJFrFu3DmvXrsWiRYtgZWXV6D4KhQKJiYlIT0+HqakpAgMD4erqin79+mm2iYiIQHx8PFxcXLB06VLs3LkTs2bNwrvvvgtvb2/MnDkTn3/+ORISEvDRRx89/NEStaGYw4l668vcxAyLPN7UW39E92tWUMTHx0Mmk6G0tBRmZmYoLy/H8uXL8eGHHza6T05ODtzc3GBjYwMA8Pb2RlZWFkJDQwEARUVFqK6u1syU5+/vj6SkJPj4+ODMmTPYvHkzAOCFF16Au7v7Qx0kkRiCh83QW1//+nmX3voielCznnoqKChAeHg4TExMYGFhgYSEBBQUFOjcp7i4GLa2tpplmUwGhULRaLutrS0UCgWuXr2KJ598EqtXr8YLL7yAsLAwSKXSlh4XERG1kWYFhZGR9mZ1dXX11j1IrVZDIpFolgVB0FpurL22thanT5+Gm5sbvvzyS0yYMAGLFy9u1sEQEVHba1ZQPPfcc1izZg2qq6tx9OhRhIaGwtXVVec+9vb2UCqVmmWlUgmZTNZoe0lJCWQyGWxtbWFlZQVPT08AgK+vL/Lz81t0UERE1HaadY9i4cKF2LRpE7p06YKPPvoIY8eOxZtv6r6xNnr0aCQnJ6OsrAwWFhY4ePAg4uLiNO0ODg4wMzNDXl4eRowYgYyMDHh4eKBnz56wt7fHd999h3HjxuHIkSN8X6ODWn10Papra/TU2yg99UP06GkyKA4dOoRPP/0UZ8+ehbm5OQYMGIDhw4fDzMxM5352dnYIDw9HUFAQVCoVAgIC4OzsjJCQEISFhWHIkCFISEhAZGQkysvL4eTkhKCgIABAcnIyoqKisGbNGlhbW2P16tVtc7SkV9W1NQgeFqCXvlLPXNFLP0SPIp1B8b//+79ITExEWFgYHB0dIZFIcPLkSaxYsQJ37tyBl5eXzg+Xy+WQy+Va61JTUzU/Ozo6Yvfu3fX269OnD7Zt29aS4yAiIpHoDIqtW7diy5YtePLJJzXr+vbti6FDh2Lp0qVNBgUREXV8Om9mV1RUaIXEPb1798adO3dEK4qIiNoPnWcUxsbGjbYJgtDmxRCR4VUbSWCRukRv/UUZSZreiAyqWU89Ueeg36eQADNjU731RW1nq/3jen3r3GJfatMbkUHpDIqzZ89i+PDh9dYLgoCaGv39waG2oc+nkIio89AZFIcOHdJXHURE1E7pDAoHBwd91UFERO1Us4bwICKiRxeDgoiIdGJQEBGRTgwKIiLSiUFBREQ6MSiIiEgnvpltYNFHEvXWF9+UJqLWYFAYGN+UbjuXVrygt76qjSTgCEX0qGBQUKfR46UYvfUVczgRwXrrjciwGBTUKZijBi8mXdRbf0ZGPsAwvXVHZFAMCuoU5nU5gif+Nldv/cWkX9NbX0SGxqAg0Zhm/QsSPQ1rXiPhHQMisTAoSDSS2hqo3Hz10tfB89/yngGRSPgeBRER6cSgICIinRgURESkE4OCiIh0EjUoMjMzMXnyZHh5eWH79u312gsKCuDv7w9vb28sW7YMtbW1Wu2nT5/G4MGDxSyRiIiaIFpQKBQKJCYmYseOHdizZw/S0tJw4cIFrW0iIiKwfPlyHDhwAIIgYOfOnZq2qqoqxMXFQaVSiVUiERE1g2hBkZOTAzc3N9jY2MDS0hLe3t7IysrStBcVFaG6uhouLi4AAH9/f6321atXIziYDzwSERmaaEFRXFwMW1tbzbJMJoNCoWi03dbWVtOenZ2N6upq+Pj4iFUeERE1k2gv3KnVakjue1tWEASt5cbalUolUlJSsGXLFrFKIyKiFhDtjMLe3h5KpVKzrFQqIZPJGm0vKSmBTCbDt99+i5s3b2L27Nnw8/MDAPj5+aG8vFysUomISAfRgmL06NHIzc1FWVkZqqqqcPDgQXh4eGjaHRwcYGZmhry8PABARkYGPDw8MGPGDHzzzTfIyMhARkaGps3a2lqsUomISAfRgsLOzg7h4eEICgrCtGnT4OvrC2dnZ4SEhODkyZMAgISEBKxatQo+Pj6orKxEUFCQWOUQEVEriToooFwuh1wu11qXmpqq+dnR0RG7d+/W+Rlnz54VpTYiImoevplNREQ6cZjxR4g+54cAAMFEqre+iEg8DIpHiD7nhyCizoOXnoiISCcGBRER6cSgICIinRgURESkE4OCiIh0YlAQEZFODAoiItKJQUFERDoxKIiISCe+mU2dgomxFP/6aZceexyjx74AU2NTxBxO1FtfRPdjUFCnMKGPfv9wf/KHSq/9zXT202t/RPfjpSciItKJQUFERDoxKIiISCcGBRER6cSgICIinRgURESkE4OCiIh04nsUDwiM3I+KKj09I2+s32fxiYhag0HxgIoqFVa+oZ+Xt6KPJALoq5e+iIhai5eeiIhIJ1GDIjMzE5MnT4aXlxe2b99er72goAD+/v7w9vbGsmXLUFtbCwDIy8tDQEAA/Pz8EBwcjKKiIjHLJCIiHUQLCoVCgcTEROzYsQN79uxBWloaLly4oLVNREQEli9fjgMHDkAQBOzcuVOzPj4+HhkZGZDL5YiPjxerTCIiaoJoQZGTkwM3NzfY2NjA0tIS3t7eyMrK0rQXFRWhuroaLi4uAAB/f39kZWWhpqYGCxYsgKOjIwBgwIABuH79ulhlEhFRE0QLiuLiYtja2mqWZTIZFApFo+22trZQKBQwNTWFn9/dkTLVajU+/vhjTJw4UawyiYioCaIFhVqthkQi0SwLgqC13FR7TU0NFi5ciNraWrz++utilUlERE0Q7fFYe3t7nDhxQrOsVCohk8m02pVKpWa5pKRE015RUYE33ngDNjY2SElJgVQqFatMgwq+dhNmRal6608w6Zy/RyISl2hBMXr0aCQnJ6OsrAwWFhY4ePAg4uLiNO0ODg4wMzNDXl4eRowYgYyMDHh4eAC4ezO7V69eiImJgf+9+qAAAA60SURBVJFR532C10wQoHLzNXQZREQ6iRYUdnZ2CA8PR1BQEFQqFQICAuDs7IyQkBCEhYVhyJAhSEhIQGRkJMrLy+Hk5ISgoCCcPn0a2dnZ6NevH6ZPnw7g7v2N1FT9/c+biIj+n6hvZsvlcsjlcq119//Bd3R0xO7du7XaBw0ahLNnz4pZFhERtUDnva5DRERtgmM9EbWCmRR4d9sdvfVnYQrE/N1Mb/3p24tpb+itLyupBTb7r9Vbf50Bg4KoFV5+Xr9PkH1yqHOPNBzlGa63vmKOJOqtr86Cl56IiEgnBgUREenEoCAiIp0YFEREpBNvZhORQQlSM1ikLtFbf1FGkqY3Ii0MCiIyqBqvIL32Z7GPozy0FC89ERGRTgwKIiLSiZeeGhCtpxdyXtNLL0RED4dB0YDgYQH66UiPc1EQEbUWLz0REZFODAoiItKJQUFERDoxKIiISCcGBRER6cSgICIinfh4LBE9UqqNJLi04gW99GVkboVn3tmql77ExKAgokfKVvvHETVePzPqXf8sSi/9iI2XnoiISCeeURDRI8XU2BQxh/U3TM+LaW/opS8AsJJaYLP/2jb/XAZFA8z26mdoDcFEqpd+iOj/zXT201tfgnIrVl8o1lt/VSLNtSFqUGRmZiIlJQW1tbUIDg7G7NmztdoLCgqwbNkyVFRUYOTIkYiJiYGJiQmuXbuGiIgIlJaWonfv3khISICVlZWYpWpRufnqrS8i6rw6y1wbot2jUCgUSExMxI4dO7Bnzx6kpaXhwoULWttERERg+fLlOHDgAARBwM6dOwEAMTExmDVrFrKysjB48GBs2LBBrDKJiKgJop1R5OTkwM3NDTY2NgAAb29vZGVlITQ0FABQVFSE6upquLi4AAD8/f2RlJSEGTNm4Pjx41i/fr1m/UsvvYSIiIgm+6yrqwMA/Pnnn62uW1VZhrJSXhKi9kVVqYKi2NTQZVA7Z1ZeA9PCwhbvd+9v5r2/oQ8SLSiKi4tha2urWZbJZMjPz2+03dbWFgqFAjdu3IC1tTVMTEy01jeHUqkEgHqXuFpqxeGH2p1IFK/z3yU1R/qEVu+qVCrRq1eveutFCwq1Wg2J5P9vrAiCoLXcWPuD2wGot9yYwYMHY/v27bC1tYWxsfFDHgER0aOhrq4OSqUSgwcPbrBdtKCwt7fHiRMnNMtKpRIymUyr/d4ZAACUlJRAJpOhW7du+Ouvv1BXVwdjY+N6++libm6OkSNHtt1BEBE9Iho6k7hHtJvZo0ePRm5uLsrKylBVVYWDBw/Cw8ND0+7g4AAzMzPk5eUBADIyMuDh4QGpVIqRI0di//79AIA9e/Zo7UdERPolEQRBEOvDMzMz8cknn0ClUiEgIAAhISEICQlBWFgYhgwZgjNnziAyMhLl5eVwcnLCqlWrYGpqiqKiIixevBilpaXo0aMH1q5di8cff1ysMomISAdRg4KIiDo+jvVEREQ6MSiIiEgnBgUREenEoCAiIp04eqwBrVu3DgcOHIBEIkFAQADmzp2LJUuWIC8vDxYWFgCA0NBQ/O1vfzNwpa33/vvv48aNG1i9enWjg0B2VPcf28cff4wvv/wSjz32GADgxRdffOgRAgxlzpw5KCsr03w3sbGxuHLlis4BPjuKho4tISGh3rqhQ4cassxWO3z4MD7++GNUVVVhzJgxiIyMRE5ODlatWoU7d+5g0qRJCA9vxaRNAhnEDz/8IAQGBgoqlUqoqqoSPD09hYsXLwq+vr6CQqEwdHltIicnR3B1dRUWLVokCIIgTJkyRfj5558FQRCEJUuWCNu3bzdkeQ/lwWN7/fXXhZ9++snAVT08tVotjB07VlCpVJp1f/75p+Dp6SncuHFDqKioEORyuXD+/HkDVtk6DR1bQ+s6qitXrghjx44Vrl+/LtTU1AgzZ84Uvv32W2HcuHHClStXBJVKJbzyyivCt99+2+LP5qUnAxk1ahS2bt0KExMTlJaWoq6uDubm5rh27RqWLl0KuVyOpKQkqNVqQ5faKjdv3kRiYiLmz58PoOFBILOysgxZYqs9eGwAcOrUKXzyySeQy+WIjY3FnTt3DFhh6126dAkA8Morr2Dq1Kn47LPPtAb4tLS01Azw2dE0dGwNreuoDh06hMmTJ8Pe3h5SqRSJiYmwsLBAr1698PTTT8PExARyubxV3x2DwoCkUimSkpIwZcoUuLu7o7a2Fm5ubli5ciV27tyJEydOYPfu3YYus1WWL1+O8PBwzaWYxgaB7IgePLaKigoMHDgQERER+Oqrr3D79u0OOzT+7du34e7ujvXr12PLli344osvcO3atXoDfHbE766hY8vKyqq37tixY4YutVUuX76Muro6zJ8/H35+ftixY0eDg7O25rtjUBhYWFgYcnNzcf36deTm5mL9+vWQyWSwsLDAnDlz8N133xm6xBbbtWsXevToAXd3d826pgaJ7CgaOjYrKyukpqaib9++MDExwSuvvNIhvzcAGDZsGD744AN06dIF3bp1Q0BAAJKSkjrFd9fQsd26daveuo763dXV1SE3NxcrV65EWloa8vPzcfXq1Tb57jruncQO7uLFi6ipqcHAgQNhYWEBLy8v7N+/HzY2NvD29gZw90vtiDd79+/fD6VSCT8/P9y6dQuVlZWQSCQNDgLZ0TR0bEuWLMGIESMQEBAAoON+bwBw4sQJqFQqTRAKggAHBwet764lA3W2Jw0d25kzZ5Cbm6u1rqN+d927d4e7uzu6desGAJg4cSKysrK0RtJu7XfHMwoDKSwsRGRkJGpqalBTU4Ps7Gw899xzWLlyJW7dugWVSoW0tLQO+cTT5s2bsXfvXmRkZCAsLAzjx4/HqlWrGhwEsqNp6NgiIiKwZs0aXL16FYIgYPv27R3yewOAv/76Cx988AHu3LmD8vJyfPXVV1izZo3OAT47ioaOzdXVtd66jvrdeXp64j//+Q9u376Nuro6HD16FD4+Pvj99981l6X27t3bqu+uY0ZnJzBu3Djk5+dj2rRpMDY2hpeXF0JDQ9G1a1fMnDkTtbW18PLygq9v55m/OyEhQWsQyKAg/c4nLJZu3bohNjYWb7zxBlQqFYYPH465c+cauqxW8fT0xK+//opp06ZBrVZj1qxZGDFiBMLDwxEUFKQZ4NPZ2dnQpbZYQ8cWHBwMlUqltW7YsGGGLrVVhg4dinnz5mHWrFlQqVQYM2YMZs6ciT59+uCtt97CnTt3MG7cOPj4+LT4szkoIBER6cRLT0REpBODgoiIdGJQEBGRTgwKIiLSiUFBREQ6MSioU0lOTkZsbKxBaxg/fjxOnjwpyme/8sorKCsrE70fovsxKIg6kI46DhF1bHzhjtq93bt3Y/PmzTAyMkLXrl3h7++Pf/7zn9i7dy8A4IcffkBcXJxm+Z7x48fD19cX33//PW7duoV58+bhp59+wm+//QYTExOkpKTAzs4OCoUCsbGxuH79OlQqFaZMmYL58+ejsLAQL7/8MsaNG4dff/0Vt2/fRkRERIve3D18+DBSUlKgUqlgbm6ORYsWYdiwYUhOTkZRURGUSiWKiopgZ2eHNWvWQCaTIT8/H9HR0VCpVOjZsyeuXbuGxYsXY8+ePQCA4OBgbNq0CQCQlpaGqKgolJWVwc/Pr8m5BhYvXgxzc3OcO3cOpaWlGD9+PGxsbHDkyBEolUrEx8fD3d292dvRI6KtxkInEkNBQYHg6uoqXLt2TRAEQdi8ebPg7e0tTJkyRbPN999/r1lOSkoSYmJiBEEQBE9PT2HlypWCIAjCvn37BEdHR6GgoEAQBEF48803hZSUFEEQBGHOnDlCdna2IAiCUF1dLcyZM0fYt2+fcPXqVaF///7C4cOHBUEQhKysLOH5559vsmZPT08hPz9f+P333wVfX1+hrKxMEARBOHfunDBmzBihoqJCSEpKEiZMmCD89ddfgiDcnc9i3bp1gkqlEjw8PDRzBuTm5goDBgwQvv/+e0EQBKF///5CaWmppp/Y2FhBEAShuLhYGDx4sOb31JhFixYJM2bMEGpqaoTi4mKhf//+wtatWwVBEIQtW7YIc+fObdF29GjgGQW1a7m5uRg7dix69OgBAHj55ZcxcOBAxMXFNWt/Ly8vAMDTTz+N7t27w9HREQDQs2dPzaB+x48fx61bt7Bu3ToAQGVlJc6cOQNnZ2dIpVKMGzcOADBo0CDcvHmz2bUfO3YMxcXFePnllzXrJBIJrly5AuDunCTW1taaz7516xbOnTsHAJo+3dzc8Oyzzzbax70hXmxtbdG9e3eUlpZqfleN8fT0hFQqha2tLSwtLfFf//Vfmt/J/cfX3O2o82NQULtmbGysNSxydXU1JBIJhPtGnlGpVI3ub2pqqvlZKpXWa1er1RAEAV988YVm+tmysjKYmZnhxo0bkEqlMDK6eyuvpcMzq9VquLu746OPPtKsu379OmQyGQ4dOgRzc3PN+nvHZGxsrHVsALRG/3zQ/SOdPvh7acz9v5MHP6M121Hnx5vZ1K65uroiNzcXxcXFAIAvvvgCGzduxLVr11BaWgpBELBv375Wf761tTVcXFywefNmAHcnt5k5cyays7MfunZ3d3ccO3YMFy9eBAB89913mDp1Kqqrqxvdp2/fvjA1NcW///1vAEB+fj7OnTunCSljY2PU1tY+dG1ELcH/IlC7NmDAAERERGDevHkA7l5iWbVqFbZs2YIXXngBtra2eP755x/qMdGEhATExcVBLpejpqYGvr6+mDp1KgoLCx+q9n79+iE2Nhb/+Mc/NPMcpKSkwMrKqtF9TExMkJycjKioKKxduxbPPPMMunfvrjn78PHxwZw5c5CcnPxQtRG1BEePJWpn3n//fbz66qvo3r07rl+/Dj8/P3zzzTeaqVeJ9I1nFEQt9PXXX+PTTz9tsE0ul2vOflrLwcEBL7/8MkxMTCAIAuLj45sdEpcuXWr0EdnevXtr3S8hai6eURARkU68mU1ERDoxKIiISCcGBRER6cSgICIinRgURESkE4OCiIh0+j+pL048ddunwAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(\n",
    "    penguins, x=\"culmen_length_mm\", hue=\"island\", element=\"step\",\n",
    "    stat=\"density\", common_norm=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's also possible to normalize so that each bar's height shows a probability, which make more sense for discrete variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11ee703a0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEJCAYAAACUk1DVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAZU0lEQVR4nO3de1SUdeLH8Q8CilQbaQzTdlHTPa4pqOUpRMW8QYIjirRbesRL4pK6JO3qMdM0zeu6cdTWUo5rq0EHNQFxdxGN1ZOCWa5HNC9pHU1XhVHWvIAIzPz++I3z+5E1jMjDiL1ff/F9vs8zfJ5zio/PPDcvu91uFwDgZ6+JpwMAAO4OFAIAQBKFAABwoBAAAJIoBACAg4+nA9TF9evXdejQIQUGBsrb29vTcQCgUaiurpbValWnTp3k5+d3y3yjLIRDhw5pxIgRno4BAI1SWlqaunXrdsvyRlkIgYGBkv53p8xms4fTAEDjcP78eY0YMcL5N/SHGmUh3PyayGw267HHHvNwGgBoXH7qq3ZOKgMAJFEIAAAHCgEAIIlCAAA4UAgAAEkUAgDAgUIAAEiiEBq9yiqbpyPUm3tpX4DGqFHemIb/4+vTRNNX7PZ0jHoxf0IPT0cAftY4QgAASKIQAAAOFAIAQBKFAABwoBAAAJIoBACAA4UAAJBkcCHk5OQoKipKERERSktLu2X+22+/1ciRIzV48GC98sor+v77742MAwBwwbBCKC4uVkpKitLT05WVlaWMjAydOHHCOW+32/Xqq68qISFBmzdvVocOHbRq1Sqj4gAAamFYIRQUFCg0NFQBAQHy9/dXZGSkcnNznfNfffWV/P39FR4eLklKTEzUiBEjjIoDAKiFYYVQUlJS40XOJpNJxcXFzvF3332nhx9+WNOnT9fQoUM1a9Ys+fv7GxUHAFALwwrBZrPJy8vLObbb7TXGVVVV2rt3r15++WVlZmbq8ccf18KFC42KAwCohWGFYDabZbVanWOr1SqTyeQcBwYGqlWrVgoODpYkDRo0SEVFRUbFAQDUwrBCCAsLU2FhoUpLS1VeXq68vDzn+QJJ6tq1q0pLS3X06FFJUn5+vjp27GhUHABALQx7/HVQUJCSk5MVHx+vyspKxcXFKSQkRAkJCUpKSlJwcLD+8pe/aMaMGSovL5fZbNbixYuNigMAqIWh70OwWCyyWCw1lqWmpjp/7ty5szZu3GhkBACAm7hTGQAgiUIAADhQCAAASRQCAMCBQgAASKIQAAAOFAIAQBKFAABwoBAAAJIoBACAA4UAAJBEIQAAHCgEAIAkCgEA4EAhAAAkUQgAAAcKAQAgiUIAADhQCAAASRQCAMCBQgAASKIQAAAOFAIAQBKFAABwMLQQcnJyFBUVpYiICKWlpd0y/95776lPnz6KiYlRTEzMj64DAGgYPkZ9cHFxsVJSUrRp0yY1bdpUL730kp577jm1a9fOuc6hQ4f07rvvqmvXrkbFAAC4ybAjhIKCAoWGhiogIED+/v6KjIxUbm5ujXUOHTqklStXymKxaM6cOaqoqDAqDgCgFoYVQklJiQIDA51jk8mk4uJi5/jatWvq0KGDpkyZoszMTF2+fFkrVqwwKg4AoBaGFYLNZpOXl5dzbLfba4zvu+8+paamqm3btvLx8dHYsWO1c+dOo+IAAGphWCGYzWZZrVbn2Gq1ymQyOcdnz57Vxo0bnWO73S4fH8NOaQAAamFYIYSFhamwsFClpaUqLy9XXl6ewsPDnfN+fn7605/+pNOnT8tutystLU0DBgwwKg4AoBaGFUJQUJCSk5MVHx+vIUOGaNCgQQoJCVFCQoIOHjyoFi1aaM6cOXr11Vf1wgsvyG63a8yYMUbFAQDUwtDvaCwWiywWS41lqampzp8jIyMVGRlpZAQAgJu4UxkAIIlCAAA4UAgAAEkUAgDAgUIAAEiiEAAADhQCAEAShQAAcKAQAACSKAQAgAOFAACQRCEAABwoBACAJAoBAOBAIQAAJFEIAAAHCgEAIIlCAAA4UAgAAEkUAgDAgUIAAEiiEAAADm4VQn5+vux2u9FZAAAe5FYhrFu3Tv369dOKFStktVrd/vCcnBxFRUUpIiJCaWlpP7nejh071LdvX7c/FwBQ/9wqhDVr1ujDDz9UWVmZfvOb3+i1115TYWGhy22Ki4uVkpKi9PR0ZWVlKSMjQydOnLhlvQsXLmjRokV1Sw8AqDdun0N44oknlJycrGnTpunQoUN6/fXXZbFYVFRU9KPrFxQUKDQ0VAEBAfL391dkZKRyc3NvWW/GjBmaNGlS3fcAAFAvfNxZ6dSpU1q/fr2ys7PVvn17TZ8+XX369NGBAwc0efJk5efn37JNSUmJAgMDnWOTyXRLeaxdu1ZPPfWUOnfufIe7AQC4U24VwosvvqihQ4fqo48+UuvWrZ3Lu3btqmefffZHt7HZbPLy8nKO7XZ7jfHXX3+tvLw8ffjhhzp//nwd4wMA6otbXxnNnDlTb7zxRo0yyMrKkiQtXLjwR7cxm801TkBbrVaZTCbnODc3V1arVcOGDdP48eNVUlKi4cOH12UfAAD1wOURQn5+vqqqqrR06VL5+fk5Lz2tqqrS8uXLNWTIkJ/cNiwsTMuXL1dpaamaN2+uvLw8zZ071zmflJSkpKQkSdKZM2cUHx+v9PT0+tgnAEAduCyEI0eOaM+ePbp48aLWrl37fxv5+Gj06NEuPzgoKEjJycmKj49XZWWl4uLiFBISooSEBCUlJSk4OLhedgAAUD9cFsLEiRM1ceJEpaWlacSIEbf94RaLRRaLpcay1NTUW9Z77LHHfvTENACg4bgshOzsbMXExKiiokJr1qy5ZX7MmDGGBQMANCyXhXDq1ClJ0vHjxxskDADAc1wWws2TvgsWLGiQMAAAz3FZCD/8/v+HcnJy6jUMAMBzXBbCzJkzGyoHAMDDXBZCy5Yt1bZtW3311VcNlQcA4CEuC2Hx4sVauXKlfv/7398y5+XlpU8//dSwYACAhuWyEFauXClJ3CMAAD8Dbj3crqysTO+//752794tX19fhYeHKyEhQU2bNjU6HwCggbj1cLu3335b58+f15QpU/Taa6/p+PHjeuedd4zOBgBoQG4dIRw+fLjGJabPPfecYmJiDAsFAGh4bh0hPPjgg7p06ZJzXFZWpgceeMCwUACAhufyCOHm10I+Pj6KjY1VRESEmjRpovz8fLVr165BAgIAGobLQggICJAkdevWTd26dXMuHzRokLGpAAANzmUhTJo06SfnysrK6j0MAMBz3DqpvH37di1btkxlZWWy2+2y2Wy6dOmS9u/fb3Q+AEADcasQFi9erMmTJ+vjjz9WQkKCtm/frvvuu8/obACABuTWVUbNmzdXVFSUunTpombNmmn27NnasWOHwdEAAA3JrUJo1qyZbty4oSeeeEJHjhxRkyZN5OXlZXQ2AEADcusro759+2r8+PFatGiRfvvb32rfvn166KGHjM4GAGhAbhVCYmKiBg8erKCgIK1YsUJffPEFl54CwD3GrUKQpG+++Ubr1q2Tj4+PevXqpZYtWxqZCwDQwNw6h/DBBx9owYIF8vPzU5MmTTRz5kylpaUZnQ0A0IDcOkLYsmWL1q9fr/vvv1+SNHbsWA0fPlwjRoxwuV1OTo7ef/99VVVVadSoUbesv23bNi1btkw2m03BwcGaM2cOj9QGAA9x+yqj/3/fwYMPPqhmzZq53Ka4uFgpKSlKT09XVlaWMjIydOLECed8WVmZ5syZozVr1ujvf/+7KioqlJmZWcfdAADcKZdHCHl5eZKkNm3aaMKECXrxxRfl7e2trKwsderUyeUHFxQUKDQ01Pk8pMjISOXm5jofh+Hv76/8/Hz5+vqqvLxcFy9e1C9+8Yv62CcAQB24LIR169bVGK9Zs8b588WLF11+cElJiQIDA51jk8mkoqKiGuv4+vpq586dmjp1qkwmk3r27Ol2cABA/bqtQqiqqpLdbpevr2+tH2yz2WrcvGa323/0ZrbevXvr888/17vvvqvZs2frz3/+s7vZAQD1yK1zCBcvXtS4cePUpUsXhYSEKD4+XsXFxS63MZvNslqtzrHVapXJZHKOL126pF27djnHFotFx44du938AIB64lYhzJkzR126dFFBQYEKCgrUrVs3zZ492+U2YWFhKiwsVGlpqcrLy5WXl6fw8HDnvN1u15QpU3T27FlJUm5urp5++um67wkA4I64ddnpyZMntXTpUuc4KSlJ0dHRLrcJCgpScnKy4uPjVVlZqbi4OIWEhCghIUFJSUkKDg7W3Llz9bvf/U5eXl5q166d3n777TvbGwBAnblVCFVVVaqoqHBealpeXu7Ww+0sFossFkuNZampqc6f+/fvr/79+99OXgCAQdwqhKioKI0ePVqxsbHy8vLSJ598osjISKOzAQAakFuFMHHiRJnNZn322Wey2WyKjY1VXFyc0dkAAA3IrUIYNWqU/va3v2nYsGFG5wEAeIhbVxlduXJFZWVlRmcBAHiQW0cIzZs3V58+fdS+fXv5+/s7l3/wwQeGBQMANKxaC+Hrr79Wv3791LNnT5nN5obIBADwAJeF8Mknn2jRokVq1aqVvvvuOy1ZskS9evVqqGwAgAZU67OMcnJyFBQUpP379yslJYVCAIB7VK0nlYOCgiRJXbt21X//+1/DAwEAPMNlIfzwbmRvb29DwwAAPMety05vcudxFQCAxsnlOYRjx47VeALp9evX9fTTTzvfbfDvf//b8IAAgIbhshC2bdvWUDkAAB7mshAeffTRhsoBAPCw2zqHAAC4d1EIAABJFAIAwIFCAABIohAAAA4UAgBAEoUAAHCgEAAAkigEAIADhQAAkGRwIeTk5CgqKkoRERFKS0u7ZX779u2KiYnR4MGDNWHCBH3//fdGxgEAuGBYIRQXFyslJUXp6enKyspSRkaGTpw44Zy/evWqZs+erVWrVmnz5s1q3769li9fblQcAEAtDCuEgoIChYaGKiAgQP7+/oqMjFRubq5zvrKyUrNmzXK+ka19+/Y6d+6cUXEAALUwrBBKSkoUGBjoHJtMJhUXFzvHDz30kAYMGCDpf9+zsGrVKvXv39+oOACAWhhWCDabrcYb1m6+VOeHrly5ovHjx+vXv/61hg4dalQcAEAtDCsEs9ksq9XqHFutVplMphrrlJSUaPjw4Wrfvr3mzZtnVBQAgBsMK4SwsDAVFhaqtLRU5eXlysvLU3h4uHO+urpaiYmJGjhwoN58803e1wwAHubyjWl3IigoSMnJyYqPj1dlZaXi4uIUEhKihIQEJSUl6fz58zp8+LCqq6u1detWSVKnTp04UgAADzGsECTJYrHIYrHUWJaamipJCg4O1tGjR4389QCA28CdygAASRQCAMCBQgAASKIQAAAOFAIAQBKFAABwoBAAAJIoBACAA4UAAJBEIeAuUlll83SEenMv7Qt+Pgx9dAVwO3x9mmj6it2ejlEv3h7f3dMR6kVllU2+Pvy78eeCQgAMcK+U2/wJPTwdAQ2I6gcASKIQAAAOFAIAQBKFAABwoBAAAJIoBACAA4UAAJBEIQAAHCgEAIAkCgEA4EAhAAAkGVwIOTk5ioqKUkREhNLS0n5yvalTp2rTpk1GRgEA1MKwQiguLlZKSorS09OVlZWljIwMnThx4pZ1EhMTtXXrVqNiAADcZFghFBQUKDQ0VAEBAfL391dkZKRyc3NrrJOTk6N+/fpp4MCBRsUAALjJsMdfl5SUKDAw0Dk2mUwqKiqqsc64ceMkSfv27TMqBgDATYYdIdhsNnl5eTnHdru9xhgAcHcxrBDMZrOsVqtzbLVaZTKZjPp1AIA7ZFghhIWFqbCwUKWlpSovL1deXp7Cw8ON+nUAgDtkWCEEBQUpOTlZ8fHxGjJkiAYNGqSQkBAlJCTo4MGDRv1aAEAdGfpOZYvFIovFUmNZamrqLestXLjQyBgAADdwpzIAQBKFAABwoBAAAJIoBACAA4UAAJBEIQAAHCgEAIAkCgEA4EAhAAAkUQgAAAcKAQAgiUIAADhQCAAASRQCAMCBQgAASKIQAAAOFAIAQBKFAABwoBAAAJIoBACAA4UAAJBEIQBwobLK5ukI9eZe2hej+Hg6gCdUVtnk60MXArXx9Wmi6St2ezpGvXh7fHdPR6g3Rv0N+1kWwr30H/n8CT08HQFoFPj/vnaG/jM5JydHUVFRioiIUFpa2i3zR44cUWxsrCIjI/Xmm2+qqqrKyDgAABcMK4Ti4mKlpKQoPT1dWVlZysjI0IkTJ2qsM2XKFL311lvaunWr7Ha71q9fb1QcAEAtDPvKqKCgQKGhoQoICJAkRUZGKjc3V5MmTZIk/ec//9H169fVpUsXSVJsbKyWLVum4cOH1/rZ1dXVkqTz58/XOd+1yxfqvO3d5MyZM+zLXehe2Zd7ZT+ke29f6uLm38ybf0N/yLBCKCkpUWBgoHNsMplUVFT0k/OBgYEqLi5267OtVqskacSIEfWUtvHKXePpBPWHfbn73Cv7IbEv/5/ValWrVq1uWW5YIdhsNnl5eTnHdru9xri2eVc6deqktLQ0BQYGytvbu/5CA8A9rLq6WlarVZ06dfrRecMKwWw268svv3SOrVarTCZTjfmb/9KXpAsXLtSYd8XPz0/dunWrv7AA8DPxY0cGNxl2UjksLEyFhYUqLS1VeXm58vLyFB4e7px/9NFH1axZM+3bt0+SlJ2dXWMeANCwvOx2u92oD8/JydHKlStVWVmpuLg4JSQkKCEhQUlJSQoODtbRo0c1Y8YMXb16VR07dtSCBQvUtGlTo+IAAFwwtBAAAI0Hz28AAEiiEAAADhQCAEAShQAAcKAQDHL16lUNGjSozreY3y3ee+89RUdHKzo6WosXL/Z0nDuydOlSRUVFKTo6WmvWNP7bVhctWqRp06Z5OsYdGTlypKKjoxUTE6OYmBgdOHDA05HqLD8/X7GxsRo4cKDeeecdT8epk5/l46+NduDAAc2YMUMnT570dJQ7UlBQoF27dikzM1NeXl4aN26ctm3bpgEDBng62m3bu3ev9uzZo82bN6uqqkpRUVHq3bu3nnzySU9Hq5PCwkJlZmbq+eef93SUOrPb7Tp58qT+9a9/ycencf8pOn36tGbNmqUNGzaoZcuWGjVqlHbu3KnevXt7Otpt4QjBAOvXr9esWbPcvvP6bhUYGKhp06apadOm8vX1Vdu2bXX27FlPx6qTZ599VmvXrpWPj48uXryo6upq+fv7ezpWnVy6dEkpKSlKTEz0dJQ78u2330qSxo4dq8GDB+ujjz7ycKK627Ztm6KiomQ2m+Xr66uUlBR17tzZ07FuW+Ou5bvUvHnzPB2hXvzqV79y/nzy5En985//1Mcff+zBRHfG19dXy5Yt01//+le98MILCgoK8nSkOnnrrbeUnJysc+fOeTrKHbl8+bK6d++umTNnqrKyUvHx8WrTpo169Gh8L306deqUfH19lZiYqHPnzun555/X5MmTPR3rtnGEgFodP35cY8eO1dSpU9W6dWtPx7kjSUlJKiws1Llz5xrl+zc2bNigRx55RN27N/7XQXbt2lWLFy/WAw88oBYtWiguLk47d+70dKw6qa6uVmFhoebPn6+MjAwVFRUpMzPT07FuG4UAl/bt26fRo0frD3/4g4YOHerpOHX2zTff6MiRI5Kk5s2bKyIiQseOHfNwqtv3j3/8Q7t371ZMTIyWLVum/Px8zZ8/39Ox6uTLL79UYWGhc2y32xvtuYSHH35Y3bt3V4sWLeTn56f+/fvXeNx/Y0Eh4CedO3dOEydO1JIlSxQdHe3pOHfkzJkzmjFjhm7cuKEbN27o008/1TPPPOPpWLdtzZo12rJli7Kzs5WUlKS+fftq+vTpno5VJ1euXNHixYtVUVGhq1evKjMzs1FesCBJffr00a5du3T58mVVV1frs88+U8eOHT0d67Y1zjpGg1i9erUqKiq0cOFC57KXXnpJL7/8sgdT1U3v3r1VVFSkIUOGyNvbWxEREY2+5Bq7Pn366MCBAxoyZIhsNpuGDx+url27ejpWnXTu3Fnjxo3T8OHDVVlZqR49emjYsGGejnXbeLgdAEASXxkBABwoBACAJAoBAOBAIQAAJFEIAAAHCgG4AwcPHlRSUpKnYwD1gstOAQCSuDENcNu1a9f0xhtv6NSpU2rSpIk6duyo6OhozZs3T1u2bNErr7yiCxcuSJLKysp0+vRp5ebm6pe//KWWLFmiL774QtXV1Xrqqac0Y8YM3X///R7eI6AmvjIC3LRt2zZdu3ZN2dnZ2rhxoyTVeAHS6tWrlZ2drQ0bNigoKEivv/66WrdurVWrVsnb21ubNm3S5s2bZTKZtGTJEk/tBvCTOEIA3PTMM88oJSVFI0eOVFhYmEaNGqXS0tIa69hsNv3xj3/Uk08+qfHjx0uSduzYoStXrqigoECSVFlZqZYtWzZ4fqA2FALgpscff1zbtm3T559/rj179mjMmDGaM2dOjXXmzZun8vJypaSkOJfZbDZNnz7d+fasa9euqaKiokGzA+6gEAA3paena9++fVqyZIl69eqlixcv6vDhw875VatWaf/+/Vq3bp28vb2dy3v27Km0tDR1795dPj4+mjlzpvz9/Rvte3dx7+IqI8BNZWVlmj59uo4dO6bmzZvrkUce0ZAhQ7R06VKtXr1avXv3Vps2beTn5yebzSbpf1/I06NHDy1atEh79+5VdXW1OnTooLlz53JSGXcdCgEAIImrjAAADhQCAEAShQAAcKAQAACSKAQAgAOFAACQRCEAABwoBACAJOl/AKFzWBbHSWIwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tips = sns.load_dataset(\"tips\")\n",
    "sns.histplot(data=tips, x=\"size\", stat=\"probability\", discrete=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can even draw a histogram over categorical variables (although this is an experimental feature):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11ee42f10>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=tips, x=\"day\", shrink=.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When using a ``hue`` semantic with discrete data, it can make sense to \"dodge\" the levels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11f0de850>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=tips, x=\"day\", hue=\"sex\", multiple=\"dodge\", discrete=True, shrink=.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For heavily skewed distributions, it's better to define the bins in log space. Compare:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11f257610>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "planets = sns.load_dataset(\"planets\")\n",
    "sns.histplot(data=planets, x=\"distance\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To the log-scale version:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11f5339a0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=planets, x=\"distance\", log_scale=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are also a number of options for how the histogram appears. You can show unfilled bars:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11f5db340>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=planets, x=\"distance\", log_scale=True, fill=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or an unfilled step function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11f526b50>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nWrFihRITE3XkyBH95Cc/0fbt2xUbG+vNsgAA3+IXz4uuWrVK6enpqqmpUb9+/TRlyhRflwT4jKtPERWXVbk0uAw4w2fhYLPZZLPZJEnR0dHasmWLr0oB/EbckG4un9MzspNb5wF34xc9BwA3uPoUEeApTJ8BADAhHAAAJoQDAMCEcAAAmBAOAAATwgEAYEI4AABMCAcAgAnhAAAwIRwAACZMnwHAKa5OCCixtGggIxwA3JM7E/sVl1VJEuEQoAgHAPfkzoSALC0a2BhzAACYEA4AABPCAQBgQjgAAEwIBwCACeEAADAhHAAAJoQDAMCEcAAAmPgkHNauXatx48Zp3LhxysrKkiQVFBQoKSlJo0ePVnZ2ti/KAgDc5PVwKCgo0IEDB7R161Zt27ZNX3zxhXbt2qXU1FStW7dOu3fvVlFRkfLz871dGgDgJq/PrWS1WrVgwQK1b99ektSrVy+VlJQoKipK3bt3lyQlJSUpNzdXcXFx3i4PgA/lFpYo/+g5l85h5lfP8HrPoU+fPho8eLAkqaSkRB999JEsFousVqvjmPDwcJWXl3u7NAA+ln/0nGM2V2cUl1W5HCZwjs9mZT1z5oxeeeUVzZ8/X0FBQSopKXG8ZhiGLBaLr0oD0EJcXQOiuKxKPSM7acWrI5w6nplfPccn4XDkyBHNnj1bqampGjdunD777DPZ7XbH63a7XeHh4b4orU1xtQt/6xcXcIY7a0D0jOzk1nloeV4Ph/Pnz2vmzJnKzs5WTEyMJGnQoEEqLi7W2bNn1a1bN+3atUvjx4/3dmltzq0uvLMf+PziwhXurAEB/+H1cFi/fr3q6+uVmZnp2JecnKzMzEzNmjVL9fX1iouLU3x8vLdLa5Nc6cIDaDu8Hg7p6elKT0+/42s7duzwcjUAgDvhG9IAABPWkG4l3Hk+nAFmAN+FnkMr4erz4RIDzAC+Gz2HVoTBZQAthZ4DAMCEcAAAmBAOAAATwgEAYMKANICA5urkfu5qa1ODEw4AApa3HsW+9Zg44QAAAcBbk/u1xanBGXMAAJgQDgAAE8IBAGBCOAAATBiQ9kPMsArA1+g5+CFmWAXga/Qc/BQzrALwJXoOAAATeg4A4ARXp+lwdboNd8YaJWnUT3+oJ4f+0OXz7oWeAwDcQ9yQbi498FFcVuXyB707Y42eRM8BAO7B1Wk63J1uw5/GGuk5AABM/KrnsHPnTv3ud79TQ0ODpk6dqsmTJ3v0evsOf6m8z750+TxP30vkOwtA4HN1jMLffu/9JhzKy8uVnZ2tDz/8UO3bt1dycrIeffRR9e7d29el3cadqXtv3Ut09j+e7ywAgc2d319/+733m3AoKCjQY489ps6dO0uSxowZo9zcXP3617++63mNjY2SpAsXLrh8zb4R7dT35z1cOifr3cM69X/Fmp1Z6fQ5pRe+VveI+zXTxWudO+f6kwsAfK9/92D1797DrXO99Xt/6zPz1mfot/lNOFRUVMhqtTq2w8PD9fnnn9/zPLvdLkkevwX1bSfcOH7PBk9UAgDus9vtioqKMu33m3BoamqSxWJxbBuGcdv2d+nfv782bdokq9WqoKAgT5YIAK1GY2Oj7Ha7+vfvf8fX/SYcIiIidPjwYce23W5XeHj4Pc/r0KGDhg4d6snSAKBVulOP4Ra/eZR12LBhKiws1MWLF3X16lX94x//UGxsrK/LAoA2yW96Dl27dtXcuXM1ZcoUXb9+XRMmTNDAgQN9XRYAtEkWwzAMXxcBAPAvfnNbCQDgPwgHAIAJ4QAAMCEcAAAmhAMAwKTNhMPFixc1b948LVq0SHv37vV1OQHpxIkTmjZtmq/LCEj//ve/NX/+fKWkpGjz5s2+LifgnDlzRrNnz9aCBQt08OBBX5cTkBobG/X888/r+PHjTh3fZsJh48aNmjp1qpYsWaKcnBxflxNwSktL9cknnzBFiZuqq6v1xhtvaOXKlfrnP//p63ICTm1trVJTUzVv3jzt2rXL1+UEpHfeecepWSduaTPhUFlZqYiICF+XEbC6d++uV199VcHBfvO9yYDy1FNPKSQkRKtWrdKUKVN8XU7AGTRokOrq6jRr1iw9/vjjvi4n4OzZs0d9+vRRjx49nD6nzYRDRESEYwZXwNuqq6uVnp6uhIQEDRs2zNflBJyioiI9+OCDev/997VlyxZflxNw9uzZowMHDujjjz/WX/7yF6fOaTN/Bj777LPKyspSSEiIkpOTfV0O2pilS5fqwoUL+vOf/6yHHnpI8+bN83VJAaW+vl5paWkKCwtTXFycr8sJOG+99ZYkac2aNRo5cqRzJxkB7uuvvzbGjRtnlJaWOvbt2LHDSEhIMEaNGmW8++67PqzO/9F+zUP7NQ/t1zyebL+ADof//Oc/RmJiotGvXz9H41y4cMF44oknjEuXLhlXrlwxkpKSjDNnzvi4Uv9E+zUP7dc8tF/zeLr9AnrMIScnRxkZGbeNwH9zudGOHTs6lhuFGe3XPLRf89B+zePp9gvoMYdly5aZ9rm73GhbRPs1D+3XPLRf83i6/QK653An7i43ihtov+ah/ZqH9muelmy/VhcO335k1dnlRnED7dc8tF/z0H7N05Lt1+rCgeVGm4f2ax7ar3lov+ZpyfYL6DGHO2G50eah/ZqH9mse2q95WrL9WCYUAGDS6m4rAQCaj3AAAJgQDgAAE8IBAGBCOAAATAgHAIAJ4QB8h9zcXD3//PN6++23tW3btrseu3btWtYmR6vS6r4EB7S0OXPm3POYQ4cOqXfv3l6oBvAOwgH4hrfffls7d+5U586dFRUVJUlasGCB+vTpo5deekmrV69WXl6eQkJC9L3vfU8rVqxQXl6eioqKlJWVpaCgIPXu3VtvvPGGrly5IrvdrujoaP32t79VaGioBgwYoOnTp+vgwYOqqKjQyy+/rEmTJkmSfv/732vr1q0KDg5WVFSUMjMzdf/992vz5s1677331NTUpM6dO2vRokXq1auXL5sJbUGLrDoBtAJ5eXnG2LFjja+//tq4fv26MX36dOO5554zXnvtNeOPf/yj8dVXXxkPP/ywUV9fbxiGYaxfv97Iy8szDMMwnnvuOeOjjz4yDMMwMjMzjW3bthmGYRjXrl0zEhMTjdzcXMMwDKNv377Gxo0bDcMwjOPHjxv9+/c36urqjL179xqjR482Ll++bBiGYSxfvtxYt26dcejQIWPSpElGbW2tYRiGsX//fiM+Pt57jYI2i54DcFNhYaFGjRqlsLAwSdL48eO1ceNGx+tdu3ZVdHS0nnnmGcXGxio2NlYxMTGm90lJSdHBgwf1hz/8QSUlJaqoqFBtba3j9aeeekqS1K9fP127dk21tbUqLCxUfHy8OnXqJElauHChJCkrK0tnz569bd3z6upqXb58WZ07d275RgBuIhyAbzC+MdVYUFDQba+1a9dO7777ro4fP67CwkItX75cjz/+uObPn3/bcb/5zW/U2NiohIQEjRw5UufPn7/tfUNDQyXJMc++YRgKCgq6bd796upqVVdXq6mpSU8//bRSUlIk3Zivv6KiwhEigKfwtBJwU2xsrHJzcx0fytu3b7/t9VOnTikxMVG9evXSK6+8omnTpun48eOSbgRJQ0ODJOnAgQOaOXOmxo4dK0k6duyYGhsb73rtYcOGKS8vTzU1NZKkNWvW6E9/+pNGjBihv//976qoqJAkvffee5o6dWqL/ruBO6HnANwUFxen06dPa/z48XrggQcUHR2tS5cuOV6Pjo5WQkKCxo8fr44dO6pDhw5KT0+XJD355JN66623dP36dc2dO1czZ85Ux44dFRYWpkceeURffvnlPa/93//+VxMnTpQk9e7dW0uWLFFYWJh++ctf6sUXX5TFYlFYWJjWrl3L6mjwOKbsBgCYcFsJAGBCOAAATAgHAIAJ4QAAMCEcAAAmhAMAwIRwAACYEA4AAJP/B/LosswDQtsdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=planets, x=\"distance\", log_scale=True, element=\"step\", fill=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step functions, esepcially when unfilled, make it easy to compare cumulative histograms:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11fc92ca0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zzz+na9eut13+yJEjzJ49m9WrV9OuXTuOHDlCUlJSoWXc3NxuGtNANPqZSHFMfQ9h8+bN9OjRg65duxIbG3vT/MOHD9O/f3969erFs88+y6VLl8wsp1Lq0aMHx44dIzIykqioqGK7sb7//vtp0aKFbfjMxo0bEx4eXmiZRx55hBEjRmiA+98pGP2sJDT6mVQrhknOnTtnPProo8aFCxeMzMxMIzIy0jhx4kShZQYNGmR8/vnnhmEYxty5c42FCxfate3k5GTjT3/6k5GcnHzTvCNHjpS9eHEYM/9/E9/40pj4xpembV8M4+yqqcbZVVMdXYbcRlHHTsMwDNPOEPbs2UPbtm2pVasWPj4+REREEBcXV2iZvLw8MjMzAcjKysLb29usckREpBimBUJqaip+fn62aX9/fywWS6FlJk6cSExMDB06dGDPnj0MHDjQrHJERKQYpgVCXl5eoQHYjfzB4wtkZ2czZcoU3nnnHXbt2sXgwYN56aWXzCpHRESKYVogBAYGYrVabdNWqxV/f3/b9A8//ICXlxdhYWEA/OUvf2Hfvn3lsm/D+UYFFfR/E3E00wKhffv2JCYmkp6eTlZWFvHx8YWehmnYsCHnzp3j5MmTAGzfvp3Q0NAy79fb25u0tDQdXJyMYRikpaXpPpKIA5n2HkJAQABjx44lKiqKnJwcBgwYQFhYGNHR0YwZM4bQ0FDmzp3L//zP/2AYBnXq1OGVV14p834bNGhASkpKobMTcQ7e3t63HPhbRCqGqS+mRUZGEhkZWeizFStW2P7u2LEjHTt2LNd9enh4EBSkjrJEREpKA+SIiAigQBARkXwKBBERAdS5nYjchga7qX50hiAit1Qw2E1JaLAb56YzBBG5LQ12U73oDEFERAAFgoiI5FMgiIgIoEAQEZF8CgQREQEUCCIikk+BICIigAJBRETyKRBERARQIIiISD4FgoiIAAoEERHJp0AQERFAgSAiIvkUCCIiAigQREQknwJBREQABYKIiORTIIiICKBAEBGRfO6OLkCktOIST7Hz2xS7l086e5Gg+jXNK0jEyekMQZzWzm9TSDp70e7lg+rXpGPLBiZWJOLcdIYgTi2ofk3mjuzg6DKcwqVv4sk4vMvu5a9ZkvAMCDKxIqlsdIYgUk1kHN7FNUuS3ct7BgThG6KwrU50hiBSjXgGBFFvyCxHlyGVlM4QREQEMDkQNm/eTI8ePejatSuxsbE3zT958iRDhgyhV69eDB8+nIsX7b9BKCIi5cu0QLBYLCxatIi1a9eyceNG3n//fX788UfbfMMw+Pvf/050dDQff/wxTZs2Zfny5WaVIyIixbArEP7xj3+wZ8+eEm14z549tG3bllq1auHj40NERARxcXG2+YcPH8bHx4fw8HAAnnvuOZ566qkS7UNERMqPXYHw2GOP8eabbxIREcHKlSv59ddfi10nNTUVPz8/27S/vz8Wi8U2febMGe666y4mT55M3759mT59Oj4+PqX4CiIiUh7sCoRevXqxZs0a3nzzTdLS0hgwYADjx4/n+++/v+06eXl5uLi42KYNwyg0ff36dfbt28egQYP46KOPuPvuu5k3b14ZvoqIiJSF3fcQ8vLyOH36NKdOnSI3N5c6deowY8YMli5desvlAwMDsVqttmmr1Yq/v79t2s/Pj4YNGxIaGgpAz549iwwYERExl12BsGjRIjp27Mjbb79Njx49iI+PZ+LEiaxZs+aWTw8BtG/fnsTERNLT08nKyiI+Pt52vwCgZcuWpKenc+zYMQB27NhBSEhIOXwlEREpDbteTEtPT2fFihUEBwcX+tzHx4fXXnvtlusEBAQwduxYoqKiyMnJYcCAAYSFhREdHc2YMWMIDQ3ljTfeICYmhqysLAIDA1mwYEHZv5GIiJSKXYGQm5t7UxiMGTOGpUuX0qHD7V9tj4yMJDIystBnK1assP3dvHlzPvjgg5LUKyIiJikyEKZPn47FYuHAgQOkp6fbPr9+/TrJycmmFyciIhWnyEAYMGAAJ06c4Pjx40RERNg+d3Nzo0WLFqYXJyIiFafIQAgNDSU0NJSHHnqIgICAiqpJREQcoMhAeP7551myZAnPPPPMLedv3rzZlKJERKTiFRkI0dHRAEydOrVCihEREccp8j2EZs2aAdCmTRvq1q1LmzZtuHLlCvv376dp06YVUqCIiFQMu15MmzZtGitWrOCnn34iJiaGlJQUJk+ebHZtIiJSgewKhEOHDjFjxgwSEhLo27cvc+fO5ezZs2bXJiIiFciuQDAMA1dXV3bv3k3btm0ByM7ONrUwERGpWHYFwj333EN0dDQpKSm0adOGcePG0aRJE7NrExGRCmRX1xVz584lISGBP//5z3h4eNCqVSv69Oljdm0iIlKB7DpD8PHxoVWrVly6dInDhw8TFhbGyZMnza5NREQqkF1nCEuWLOF///d/qVOnju0zFxcXtm/fblphIiJSsewKhE2bNhEfH6/uK0REqjC7LhnVrVtXYSAiUsXZdYbQrl07FixYQOfOnfH29rZ9rhHORESqDrsC4cMPPwQgLi7O9pnuIYiIVC12BcKOHTvMrkNERBzMrnsImZmZzJo1i6FDh/Lrr78ybdo0MjMzza5NREQqkF2BMGfOHGrUqEFaWhpeXl5kZGQwbdo0s2sTEZEKZFcgHD16lLFjx+Lu7s4f/vAHXn31VY4ePWp2bSIiUoHsCgRX18KL5ebm3vSZiIg4N7tuKrdu3Zp//vOfZGdn8+WXX7JmzRoefPBBs2sTkdu49E08GYd3lWida5YkPAOCTKpIqgK7fua/+OKL+Pj4UKNGDRYvXkxwcDATJkwwuzYRuY2Mw7u4Zkkq0TqeAUH4hnQwqSKpCoo9Q0hISGDlypUcP34cb29vmjRpwgMPPICXl1dF1Ccit+EZEES9IbMcXYZUIUUGwieffMKiRYsYM2YMwcHBuLi4cPDgQV5++WWuXr1K165dK6pOERExWZGBsGrVKt555x3q1atn+6xRo0Y0b96cyZMnKxBERKqQIu8hZGZmFgqDAkFBQVy9etW0okREpOIVGQhubm63nWcYRrkXIyIijqOXCUREBCjmHsLx48d54IEHbvrcMAyuXbtmWlEiIlLxigyEhISEiqpDREQcrMhAqF+/fkXVIdVcXOIpdn6bUqJ1ks5eJKh+TXMKEqmGTL2HsHnzZnr06EHXrl2JjY297XKff/45nTp1MrMUqeR2fptC0tmLJVonqH5NOrZsYFJFItWPXX0ZlYbFYmHRokV8+OGHeHp6MnDgQB588EEaN25caLnz588zf/58s8oQJxJUvyZzR6prBRFHMe0MYc+ePbRt25ZatWrh4+NDREREoSE4C8TExDB69GizyhARETuZFgipqan4+fnZpv39/bFYLIWWWbVqFffffz/Nmzc3qwwREbGTaYGQl5eHi4uLbdowjELTP/zwA/Hx8YwcOdKsEkREpARMC4TAwECsVqtt2mq14u/vb5uOi4vDarXSv39/RowYQWpqKoMHDzarHBERKYZpgdC+fXsSExNJT08nKyuL+Ph4wsPDbfPHjBnDtm3b2LRpE8uXL8ff35+1a9eaVY6IiBTDtEAICAhg7NixREVF0adPH3r27ElYWBjR0dEcPHjQrN2KiEgpmfbYKUBkZCSRkZGFPluxYsVNyzVo0IAdO3aYWYqIiBRDnduJiAigQBARkXwKBBERARQIIiKST4EgIiKAAkFERPIpEEREBFAgiIhIPgWCiIgACgQREcmnQBAREUCBICIi+RQIIiICKBBERCSfAkFERAAFgoiI5DN1gBwRKd6lb+LJOLyrROtcsyThGRBkUkVSXekMQcTBMg7v4polqUTreAYE4RvSwaSKpLrSGYJIJeAZEES9IbMcXYZUczpDEBERQIEgIiL5FAgiIgIoEEREJJ8CQUREAD1lJCaISzzFzm9TSrRO0tmLBNWvaU5BImIXnSFIudv5bQpJZy+WaJ2g+jXp2LKBSRWJiD10hiCmCKpfk7kj9eKUiDPRGYKIiAAKBBERyadAEBERQIEgIiL5FAgiIgKYHAibN2+mR48edO3aldjY2Jvmf/rpp/Tu3ZtevXoxcuRILl4s2aOKIiJSfkwLBIvFwqJFi1i7di0bN27k/fff58cff7TNz8jIYMaMGSxfvpyPP/6YJk2asGzZMrPKERGRYpgWCHv27KFt27bUqlULHx8fIiIiiIuLs83Pyclh+vTpBAQEANCkSRN++eUXs8oREZFimBYIqamp+Pn52ab9/f2xWCy26dq1a/PYY48BkJ2dzfLly+nSpYtZ5YiISDFMC4S8vDxcXFxs04ZhFJoucPnyZUaMGEFwcDB9+/Y1qxwRESmGaYEQGBiI1Wq1TVutVvz9/Qstk5qayuDBg2nSpAkvv/yyWaWIiIgdTAuE9u3bk5iYSHp6OllZWcTHxxMeHm6bn5uby3PPPUf37t2ZMmXKLc8eRESk4pjWuV1AQABjx44lKiqKnJwcBgwYQFhYGNHR0YwZM4Zz585x5MgRcnNz2bZtGwDNmjXTmYKIiIOY2ttpZGQkkZGRhT5bsWIFAKGhoRw7dszM3Ys4xKVv4sk4vMvu5a9ZkvAMCDKxIhH7qPtrKVZJB7yp7oPdZBzeVaKDvGdAEL4h6ipcHE+BIMUqGPDG3oO8Bru5cZCvN2SWo8sQKREFgthFA96IVH3q3E5ERAAFgoiI5FMgiIgIoEAQEZF8CgQREQEUCCIikk+BICIigAJBRETyKRBERARQIIiISD4FgoiIAAoEERHJp0AQERFAgSAiIvkUCCIiAmg8BJEilXQ4TNCQmOK8dIYgUoSC4TBLQkNiirPSGUI1U9LxkUFjJGs4TKkudIZQzRSMj1wSGiNZpHrQGUI1pPGRReRWdIYgIiKAAkFERPLpkpFUG3qEVKRoCgQnpieGSqbgEdKSHOD1CKlUJwoEJ1bwxFBJDvDV/YkhPUIqcnsKBCenJ4ZEpLzoprKIiAAKBBERyadLRuK0SvrUkJ4YEimaAqESKelTQ9X5iSEo+VNDemJIpGimBsLmzZt56623uH79OkOHDuWpp54qNP/o0aNMmTKFzMxMWrVqxcyZM3F3r74ZVdKnhqr7E0Ogp4ZEypNpR1+LxcKiRYv48MMP8fT0ZODAgTz44IM0btzYtsz48eOZM2cOLVq0YPLkyaxfv57BgwebVVKFKss7AnpqSEQcwbRA2LNnD23btqVWrVoAREREEBcXx+jRowE4e/Ys2dnZtGjRAoB+/fqxdOlSuwIhNzcXgHPnzpW4rs+2fcmeI2klXq+kkq7cAUCQT6bd69QG7rH+yjfLPjKpqqrl2vlkPO+6m7yUkgWvSHVVcMwsOIb+nmmBkJqaip+fn23a39+f77///rbz/fz8sFgsdm3barUC3HQJqjIq2dAqsMOUKqqy7+D1LY4uQsSpWK1WGjZseNPnpgVCXl4eLi4utmnDMApNFze/KM2aNSM2NhY/Pz/c3NzKr2gRkSosNzcXq9VKs2bNbjnftEAIDAzk66+/tk1brVb8/f0LzS/4pQ9w/vz5QvOL4u3tTatWrcqvWBGRauJWZwYFTHsxrX379iQmJpKenk5WVhbx8fGEh4fb5tevXx8vLy8OHDgAwKZNmwrNFxGRiuViGIZh1sY3b97Mv//9b3JychgwYADR0dFER0czZswYQkNDOXbsGDExMWRkZBASEsLcuXPx9PQ0qxwRESmCqYEgIiLOQ30ZiYgIoEAQEZF8CgQREQEUCCIikk+BICIiQBUPhPT0dMaNG8fUqVP59NNPHV2O0zpy5AjDhg1zdBlOZ//+/UyYMIHx48ezYcMGR5fjdE6cOMGYMWOYOHEiu3fvdnQ5Tik3N5chQ4Zw8OBBu5av0oGwevVqhg4dyuzZs1m/fr2jy3FKycnJfP755+oipBQuXbrErFmzmD9/Ptu3b3d0OU7nypUrTJ48mXHjxrFli/qrKo1//etfdvcAAVU8EM6fP09gYKCjy3Bqd999NyNHjqzW41SUVufOnfHw8ODVV18lKirK0eU4nebNm5Odnc0//vEPHn74YUeX43S2bdvGfffdx7333mv3OlU6EH7fX5JIRbp06RIxMTF0796d9urD66EAAAVZSURBVO3bO7ocp3Po0CHuuusu1q1bxwcffODocpzOtm3b2LVrF5999hmrVq2ya50q/bPviSeeYMGCBXh4eDBw4EBHlyPVzJw5czh37hzvvvsudevWZdy4cY4uyalcvXqVKVOm4OvrS8eOHR1djtNZuHAhAMuWLeORRx6xbyXDCV2+fNl4/PHHjeTkZNtnH3/8sdG9e3fjscceM9asWePA6pyD2rD01HZlo/YrGzPbz+kC4b///a/Rs2dPIyQkxNYg586dMx599FHjwoULRmZmphEZGWmcOHHCwZVWXmrD0lPblY3ar2zMbj+nu4ewfv16pk+fXujO+W+H6/Tx8bEN1ym3pjYsPbVd2aj9ysbs9nO6ewgvv/zyTZ8VN1ynFKY2LD21Xdmo/crG7PZzujOEWynLcJxyg9qw9NR2ZaP2K5vybL8qEQi/f7z098N1SvHUhqWntisbtV/ZlGf7VYlAKG64Time2rD01HZlo/Yrm/JsP6e7h3ArAQEBjB07lqioKNtwnWFhYY4uy6moDUtPbVc2ar+yKc/20xCaIiICVJFLRiIiUnYKBBERARQIIiKST4EgIiKAAkFERPIpEEREBFAgiBQSFxfHkCFDWLJkCRs3bixy2ddff11jdUuVUiVeTBMpb88//3yxy+zdu5fGjRtXQDUiFUOBINXekiVL2Lx5M7Vq1aJhw4YATJw4kfvuu4/hw4ezdOlSEhIS8PDwoHbt2sydO5eEhAQOHTrEggULcHNzo3HjxsyaNYvMzEysVivBwcEsXrwYLy8vQkNDGTFiBLt37yY1NZVnnnmGwYMHA/Dvf/+bjz76CHd3dxo2bMi8efOoUaMGGzZs4L333iMvL49atWoxdepUGjVq5MhmkuqgXEZtEHFSCQkJRo8ePYzLly8bOTk5xogRI4ynn37aeOmll4y3337b+Pnnn40HHnjAuHr1qmEYhrFy5UojISHBMAzDePrpp41PPvnEMAzDmDdvnrFx40bDMAzj2rVrRs+ePY24uDjDMAzjT3/6k7F69WrDMAzj4MGDRrNmzYzs7Gzj008/Nbp27Wr8+uuvhmEYxiuvvGK8+eabxt69e43BgwcbV65cMQzDML788kujW7duFdcoUm3pDEGqtcTERB577DF8fX0B6N+/P6tXr7bNDwgIIDg4mL59+xIeHk54eDjt2rW7aTvjx49n9+7drFixglOnTpGamsqVK1ds8zt37gxASEgI165d48qVKyQmJtKtWzdq1qwJwKRJkwBYsGABp0+fLjQO+KVLl/j111+pVatW+TeCSD4FglR7xm+683Jzcys0z9XVlTVr1nDw4EESExN55ZVXePjhh5kwYUKh5V544QVyc3Pp3r07jzzyCL/88kuh7Xp5eQHY+qk3DAM3N7dC/dZfunSJS5cukZeXR+/evRk/fjxwo7/71NRUW3CImEVPGUm1Fh4eTlxcnO1AvGnTpkLzjx07Rs+ePWnUqBHPPvssw4YN4+DBg8CN8Lh+/ToAu3btYtSoUfTo0QOA7777jtzc3CL33b59exISEsjIyABg2bJlvPPOO3To0IGtW7eSmpoKwHvvvcfQoUPL9XuL3IrOEKRa69ixI8ePH6d///7ceeedBAcHc+HCBdv84OBgunfvTv/+/fHx8cHb25uYmBgAOnXqxMKFC8nJyWHs2LGMGjUKHx8ffH19ad26NWfOnCl23z/++CODBg0CoHHjxsyePRtfX1+io6P529/+houLC76+vrz++usaRUxMp+6vRUQE0CUjERHJp0AQERFAgSAiIvkUCCIiAigQREQknwJBREQABYKIiORTIIiICAD/Dzyv9EHGJLDMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(\n",
    "    data=planets, x=\"distance\", hue=\"method\",\n",
    "    hue_order=[\"Radial Velocity\", \"Transit\"],\n",
    "    log_scale=True, element=\"step\", fill=False,\n",
    "    cumulative=True, stat=\"density\", common_norm=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When both ``x`` and ``y`` are assigned, a bivariate histogram is computed and shown as a heatmap:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11fbe5d30>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(penguins, x=\"culmen_depth_mm\", y=\"body_mass_g\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's possible to assign a ``hue`` variable too, although this will not work well if data from the different levels have substantial overlap:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11ff27160>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(penguins, x=\"culmen_depth_mm\", y=\"body_mass_g\", hue=\"species\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Multiple color maps can make sense when one of the variables is discrete:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1200ed040>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(\n",
    "    penguins, x=\"culmen_depth_mm\", y=\"species\", hue=\"species\", legend=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The bivariate histogram accepts all of the same options for computation as its univariate counterpart, using tuples to parametrize ``x`` and ``y`` independently:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11eef8a30>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(\n",
    "    planets, x=\"year\", y=\"distance\",\n",
    "    bins=30, discrete=(True, False), log_scale=(False, True),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default behavior makes cells with no observations transparent, although this can be disabled: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11ee29d60>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(\n",
    "    planets, x=\"year\", y=\"distance\",\n",
    "    bins=30, discrete=(True, False), log_scale=(False, True),\n",
    "    thresh=None,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's also possible to set the threshold and colormap saturation point in terms of the proportion of cumulative counts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12031ebb0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(\n",
    "    planets, x=\"year\", y=\"distance\",\n",
    "    bins=30, discrete=(True, False), log_scale=(False, True),\n",
    "    pthresh=.05, pmax=.9,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To annotate the colormap, add a colorbar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12043f280>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(\n",
    "    planets, x=\"year\", y=\"distance\",\n",
    "    bins=30, discrete=(True, False), log_scale=(False, True),\n",
    "    cbar=True, cbar_kws=dict(shrink=.75),\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "seaborn-refactor (py38)",
   "language": "python",
   "name": "seaborn-refactor"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}