examples/roux_viz_ds.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "da10a7c9-2d1c-4cba-9b7e-69aa47e2517b",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<!-- <a href=\"https://github.com/rraadd88/roux/blob/master/examples/roux_viz_df.ipynb\"><img align=\"right\" style=\"float:right;\" src=\"https://img.shields.io/badge/-source-cccccc?style=flat-square\"></a>\n",
    " -->\n",
    " \n",
    "## 📈 Wrapper around Series plotting functions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3109b89e-01b1-4a75-96f6-926567014e64",
   "metadata": {},
   "source": [
    "**Demo data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9b079b16-7d07-409a-94a5-551f9288a1c4",
   "metadata": {},
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species  id\n",
       "0           5.1          3.5           1.4          0.2  setosa   0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "df1=sns.load_dataset('iris')\n",
    "# df1=df1.loc[df1['species'].isin(['setosa','virginica']),:]\n",
    "df1['id']=range(len(df1))\n",
    "df1.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "12b3c71d-9e76-452d-87e5-f1091302b865",
   "metadata": {},
   "outputs": [],
   "source": [
    "## loading roux-series attributes\n",
    "import roux.viz.ds as rs #noqa"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7600e38b-1b38-4c15-be63-f005f9c4496f",
   "metadata": {},
   "source": [
    "## `.hist`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "96ca4212-60a3-4def-b3ad-abf7998a4b4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_=df1['sepal_length'].rs.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e275000-d142-4143-8d6b-22d97502fe9d",
   "metadata": {},
   "source": [
    "## Documentation\n",
    "[`roux.viz.ds`](https://github.com/rraadd88/roux#module-rouxvizds)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "parcan",
   "language": "python",
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