{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.0.5'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.10.1'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({k: np.random.random(100) for k in \"abc\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x117d9c208>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data=df, corner=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'PairGrid' object has no attribute '_despine'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-57b99bef2bc1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpairplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcorner\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrid_kws\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdespine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/opt/miniconda3/envs/curv/lib/python3.7/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mpairplot\u001b[0;34m(data, hue, hue_order, palette, vars, x_vars, y_vars, kind, diag_kind, markers, height, aspect, corner, dropna, plot_kws, diag_kws, grid_kws, size)\u001b[0m\n\u001b[1;32m   2107\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msquare_grid\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2108\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdiag_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"hist\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2109\u001b[0;31m             \u001b[0mgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_diag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mdiag_kws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2110\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mdiag_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"kde\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2111\u001b[0m             \u001b[0mdiag_kws\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"shade\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/curv/lib/python3.7/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mmap_diag\u001b[0;34m(self, func, **kwargs)\u001b[0m\n\u001b[1;32m   1440\u001b[0m                         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_corner\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m                             \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_visible\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1442\u001b[0;31m                             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_despine\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1443\u001b[0m                                 \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdespine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1444\u001b[0m                             \u001b[0;31m# TODO add optional density ticks (on the right)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'PairGrid' object has no attribute '_despine'"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 540x540 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data=df, corner=True, grid_kws=dict(despine=False))"
   ]
  }
 ],
 "metadata": {
  "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}