An attempt to use plotnine to create a barplot with a line.

Plotnine bar and line plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from plotnine import *\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>letter</th>\n",
       "      <th>num_of_letters</th>\n",
       "      <th>pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alpha</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beta</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Delta</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gamma</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  letter  num_of_letters  pos\n",
       "0  Alpha               5    1\n",
       "1   Beta               4    2\n",
       "2  Delta               5    3\n",
       "3  Gamma               5    4"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'letter': ['Alpha', 'Beta', 'Delta', 'Gamma'],\n",
    "     'pos': [1, 2, 3, 4],\n",
    "     'num_of_letters': [5, 4, 5, 5]}\n",
    "\n",
    "df = pd.DataFrame(d)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Derek-T\\Anaconda3\\envs\\myers_briggs\\lib\\site-packages\\plotnine\\geoms\\geom_path.py:82: UserWarning: geom_path: Each group consist of only one observation. Do you need to adjust the group aesthetic?\n",
      "  warn(\"geom_path: Each group consist of only one \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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1a9dq+fLluvXWW1W+fPkC7TdgGztvOQLscuLECWv69OlWu3btLK/XawUFBVmlS5e26tev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\n+woVKujw4cOB6XLlyuUY63a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      "text/plain": [
       "<matplotlib.figure.Figure at 0x20b831c6940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (140529205624)>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ggplot(df) + \n",
    "   geom_bar(aes(x='letter',y='pos'), stat='identity') + \n",
    "   geom_line(aes(x='letter',y='num_of_letters'), stat='identity') + \n",
    "   ggtitle('Greek Letter Analysis')\n",
    ")"
   ]
  }
 ],
 "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": "3.6.2"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

Very subtle but it is all in the warning. The issue is you have 4 groups (Alpha, Beta, ...) and each of those groups has a single point. You may consider this an edge case where the automatic grouping that is done for you is not sufficient (or just not what you expect). Solution is to manually set the group for geom_line.

(ggplot(df) + 
   geom_bar(aes(x='letter',y='pos'), stat='identity') + 
   geom_line(aes(x='letter',y='num_of_letters'), stat='identity', group=1) + 
   ggtitle('Greek Letter Analysis')
)

To understand why the behaviour behind the warning is not wrong, check this out

d = {'letter': ['Alpha', 'Beta', 'Delta', 'Gamma'] * 2,
     'pos': [1, 2, 3, 4] * 2,
     'num_of_letters': [5, 4, 5, 5] * 2}

df = pd.DataFrame(d)

df.loc[4:, 'num_of_letters'] += 0.8
df

(ggplot(df) +
   geom_bar(aes(x='letter',y='pos'), stat='identity') +
   geom_line(aes(x='letter',y='num_of_letters')) +
   ggtitle('Greek Letter Analysis')
)

With the above there are 2 points per group so you can get lines. Depending on what you are trying to do you may want to improve on such a plot and add some color.

(ggplot(df)
   geom_bar(aes(x='letter',y='pos', fill='letter'), stat='identity') +
   geom_line(aes(x='letter', y='num_of_letters', color='letter'), size=1) +
   scale_color_hue(l=0.45)  + # some contrast to make the lines stick out
  ggtitle('Greek Letter Analysis') +
)

Hey, that is totally fair, I just couldn't figure that out on my own or via documentation. Its a great package, I do wish there was more community support.

Its very kind of you to respond with such an indepth explanation. I really appreciate that @has2k1

Great. I will add this example to the tutorials page of the documentation. That should have been my thought in the first place.

sorry for the noob question, but I am trying to change the colors of bars plotted using geom_col. I used fill to color them by a category from my dataframe, but I would like to be able to specify which color represents a specific group, I am failing to find a clear way to do this.

Plotnine is wonderful, its my go to now.

Thanks!