PlotDataframeRows.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## An example for plotting values of dataframe rows as function of headers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>2020</th>\n",
       "      <th>2030</th>\n",
       "      <th>2040</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  2020  2030  2040\n",
       "0   10     1     9     5\n",
       "1   20     2     8     6\n",
       "2   30     3     7     7\n",
       "3   40     4     6     8\n",
       "4   50     5     5     9"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {\n",
    "    \"Age\":  [10,20,30,40,50],\n",
    "    \"2020\": [1, 2, 3, 4, 5],\n",
    "    \"2030\": [9, 8, 7, 6, 5],\n",
    "    \"2040\": [5, 6, 7, 8, 9]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(d)\n",
    "df = df[['Age', '2020', '2030', '2040']] #this is just to make sure that my dataframe is in this order\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2020', '2030', '2040']\n"
     ]
    }
   ],
   "source": [
    "#get the labels from the headers\n",
    "labels = list(df)[1:]\n",
    "print(labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First a single row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "[3, 7, 7]\n"
     ]
    }
   ],
   "source": [
    "#Take a single row based on a condition for age\n",
    "ind = df.index[df.Age == 30][0] \n",
    "#The function index does not return a plain integer, therefore the last [0] is needed\n",
    "\n",
    "#Now the elements to be plotted (exclude the first column \"Age\")\n",
    "entries = list(df.iloc[ind])[1:]\n",
    "\n",
    "print(ind)\n",
    "print(entries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e983fc1400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "myx = range(len(labels)) #define x-axis as long as needed\n",
    "plt.bar(myx, entries) #bar chart\n",
    "plt.xticks(myx, labels) #use the values in \"labels\" as the tick marks for the x-axis\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jg80sL3jNNrPLyx1LY0y+prrjq9znuphZkZn9T7m26Iwvdw/9RVlZ4fRguwWw\nCOgNPA6MDdrHAr8OtgcCHYLtvsCacsf6EjgJMGAmcGHYfz69wn/VYIw1BRqU++zGcu81xvSq1fgq\n97lXgJeB/ynXFpXxFRMzd3df5+45wfZOYD5lz2C9FHg+6PY8cFnQJ9fd1wbtc4EmZnZ48FDulu4+\ny8v+ll44+BlJbjUYY7vd/UDQ3pjgofAaY1KZ6o4vADO7DFhOWQ472Ba18RUTyb08M+tK2cz8C6Ct\n/+eJTuuBtpV85DtAjrvvo+wvs6Dcvjp7ULfEr0jHmJkNMbO5wBxgVJDsNcbkG0UyvsysOfBj4KEK\nH4/a+Iqp5B78gV8F7nL3HeX3Bf+KeYX+fYBfA7fUW5AS16ozxtz9C3fvA5wI3G9mjes1WIk71Rhf\nPwd+7+5FdRVLJI/Zqxdm1pCyv5RMd38taN5gZu3dfV3w68rGcv07AdOBG9x9adC8hrKHcx9U6YO6\nJTlVd4wd5O7zzayI4PwOGmNSiWqOryHAFWb2OJAKlJrZ3uDzURlfMTFzD84G/xGY7+5PlNv1JvC9\nYPt7wBtB/1TgHcpOVHx+sHPw688OMzspOOYNBz8jya0GY6ybmTUIto8GegErNMakMtUdX+5+qrt3\ndfeuwJPAo+4+PprjKyZuYjKzU4BPKVvbLA2aH6BszWoa0IWyCpJXuvsWM/sJcD+wuNxhznP3jWaW\nAfwFaELZmebbPRb+kBKqGoyx6ym7umF/0P9hd389OJbGmHxNdcdXhc/+HChy998G76MyvmIiuYuI\nSHTFxLKMiIhEl5K7iEgCUnIXEUlASu4iIglIyV1EJAEpuYuIJCAldxGRBKTkLiKSgP4PoaAN+2vy\nCFYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e9843fe400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(myx, entries) #plot with a line\n",
    "plt.xticks(myx, labels) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For all rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[10, 20, 30, 40, 50]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get all ages\n",
    "ages = list(df['Age'])\n",
    "ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CCk7LAp68CQpT5Of9BsjllNAp0K5L42JpIJlFlby4LoGjmSUMDfTgrWnhdPa4/RaR3Ixq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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e9840456a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# loop over the ages\n",
    "for i in ages:\n",
    "    ind = df.index[df.Age == i][0] #this gets the right row\n",
    "    entries = list(df.iloc[ind])[1:] #this gets the values from that row\n",
    "    plt.plot(myx, entries, label=i)\n",
    "    \n",
    "plt.xticks(myx, labels) \n",
    "plt.legend()\n",
    "plt.show()\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}