marco-ostaska/matplotib-barchart-showvalues.ipynb

## matplotib-barchart-showvalues.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from past.builtins import map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load titatic dataset from seaborn just to test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic = sns.load_dataset('titanic')\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function to print values in the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "def barplot_values(ax):\n",
    "    for i in ax.patches:\n",
    "        x = i.get_x()+0.07 #adjust the numbers (higher numbers = to the right, lower = to the left)\n",
    "        height = i.get_height()+0.1 #adjust the numbers (higher numbers = up, lower = down)\n",
    "        value = float(\"{0:.2f}\".format(i.get_height()))\n",
    "\n",
    "        ax.text(x, height, value, fontsize=10,color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c972828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = sns.barplot(x=\"sex\", y=\"survived\", hue=\"class\", data=titanic);\n",
    "barplot_values(plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function to print values in the graph in %"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "def barplot_values_percent(ax):\n",
    "    heightlst = []\n",
    "    for i in ax.patches:\n",
    "        heightlst.append(i.get_height())\n",
    "    total = sum(heightlst)\n",
    "    \n",
    "    for i in ax.patches:\n",
    "        x = i.get_x()+0.05 #adjust the numbers (higher numbers = to the right, lower = to the left)\n",
    "        height = i.get_height()+0.1 #adjust the numbers (higher numbers = up, lower = down)\n",
    "        value = (\"{0:.2f}\".format((i.get_height()/total)*100)+'%')\n",
    "        \n",
    "        ax.text(x, height, value, fontsize=10,color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10baaccf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = sns.barplot(x=\"sex\", y=\"survived\", hue=\"class\", data=titanic);\n",
    "barplot_values_percent(plot)"
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}