Seaborn regplot partial Series error

sns_partial_series_error.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seaborn regplot partial Series error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "sns.set_style('ticks')\n",
    "sns.set_context('notebook')\n",
    "sns.set_palette(\"colorblind\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = sns.load_dataset('iris')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Series' object has no attribute 'reshape'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-2b4213989996>\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[0mregplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'sepal_length'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'sepal_width'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_partial\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'petal_length'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0miris\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~/anaconda3/envs/playpen/lib/python3.7/site-packages/seaborn/regression.py\u001b[0m in \u001b[0;36mregplot\u001b[0;34m(x, y, data, x_estimator, x_bins, x_ci, scatter, fit_reg, ci, n_boot, units, order, logistic, lowess, robust, logx, x_partial, y_partial, truncate, dropna, x_jitter, y_jitter, label, color, marker, scatter_kws, line_kws, ax)\u001b[0m\n\u001b[1;32m    779\u001b[0m                                  \u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogistic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlowess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrobust\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    780\u001b[0m                                  \u001b[0mx_partial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_partial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtruncate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdropna\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 781\u001b[0;31m                                  x_jitter, y_jitter, color, label)\n\u001b[0m\u001b[1;32m    782\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    783\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0max\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/playpen/lib/python3.7/site-packages/seaborn/regression.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, x, y, data, x_estimator, x_bins, x_ci, scatter, fit_reg, ci, n_boot, units, order, logistic, lowess, robust, logx, x_partial, y_partial, truncate, dropna, x_jitter, y_jitter, color, label)\u001b[0m\n\u001b[1;32m    111\u001b[0m         \u001b[0;31m# Regress nuisance variables out of the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    112\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx_partial\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 113\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregress_out\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx_partial\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    114\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my_partial\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregress_out\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my_partial\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/playpen/lib/python3.7/site-packages/seaborn/regression.py\u001b[0m in \u001b[0;36mregress_out\u001b[0;34m(self, a, b)\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    314\u001b[0m         \u001b[0ma_prime\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpinv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\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[0;32m--> 315\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0ma_prime\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0ma_mean\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\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    316\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    317\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscatter_kws\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mline_kws\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[0;32m~/anaconda3/envs/playpen/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5065\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\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[1;32m   5066\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5067\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\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   5068\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5069\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\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[0;31mAttributeError\u001b[0m: 'Series' object has no attribute 'reshape'"
     ]
    }
   ],
   "source": [
    "sns.regplot(x='sepal_length', y='sepal_width', x_partial='petal_length', data=iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3eaca86f60>"
      ]
     },
     "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": {
      "image/png": {
       "height": 253,
       "width": 376
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=iris['sepal_length'].values, y=iris['sepal_width'].values, x_partial=iris['petal_length'].values, data=iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.9.0'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.1.0'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpl.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (playpen)",
   "language": "python",
   "name": "playpen"
  },
  "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": 2
}