Example showing common plotting problems with bar charts in pandas data frames

df_plot_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2f873489",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from matplotlib import cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "50df16fa",
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0\n",
       "0  1\n",
       "1  2\n",
       "2  3\n",
       "3  4\n",
       "4  5"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame( [ 1, 2, 3, 4, 5 ] )\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e9e07beb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind=\"bar\", color=cm.tab10.colors[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6c3e7e10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[:,0].plot(kind=\"bar\", color=cm.tab10.colors[0:5]) # slice into a pandas series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4c83e81c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series( [ 1, 2, 3, 4, 5 ] ).plot(kind=\"bar\", color=cm.tab10.colors[0:5]) # already a pandas series"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}