13.11 Bar Plots with Seaborn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](img/cover4.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><em>Copyright! This material is protected, please do not copy or distribute.    by:Taher Assaf</em></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "<h1 align=\"center\">Udemy course : Python Bootcamp for Data Science 2021 Numpy Pandas & Seaborn</h1> \n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 13.11 Bar Plots with Seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we import pandas and seaborn libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we read a dataframe from a csv file using the function **pd.read_csv()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hide_input": false
   },
   "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>year</th>\n",
       "      <th>quarters</th>\n",
       "      <th>sale1</th>\n",
       "      <th>sale2</th>\n",
       "      <th>sale3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010</td>\n",
       "      <td>A</td>\n",
       "      <td>200</td>\n",
       "      <td>258</td>\n",
       "      <td>592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>B</td>\n",
       "      <td>328</td>\n",
       "      <td>664</td>\n",
       "      <td>606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010</td>\n",
       "      <td>C</td>\n",
       "      <td>870</td>\n",
       "      <td>249</td>\n",
       "      <td>918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010</td>\n",
       "      <td>D</td>\n",
       "      <td>209</td>\n",
       "      <td>971</td>\n",
       "      <td>629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011</td>\n",
       "      <td>A</td>\n",
       "      <td>348</td>\n",
       "      <td>863</td>\n",
       "      <td>929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2011</td>\n",
       "      <td>B</td>\n",
       "      <td>475</td>\n",
       "      <td>999</td>\n",
       "      <td>610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2011</td>\n",
       "      <td>C</td>\n",
       "      <td>944</td>\n",
       "      <td>356</td>\n",
       "      <td>540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2011</td>\n",
       "      <td>D</td>\n",
       "      <td>190</td>\n",
       "      <td>12</td>\n",
       "      <td>673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2012</td>\n",
       "      <td>A</td>\n",
       "      <td>129</td>\n",
       "      <td>936</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2012</td>\n",
       "      <td>B</td>\n",
       "      <td>31</td>\n",
       "      <td>877</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2012</td>\n",
       "      <td>C</td>\n",
       "      <td>646</td>\n",
       "      <td>768</td>\n",
       "      <td>862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2012</td>\n",
       "      <td>D</td>\n",
       "      <td>284</td>\n",
       "      <td>792</td>\n",
       "      <td>688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year quarters  sale1  sale2  sale3\n",
       "0   2010        A    200    258    592\n",
       "1   2010        B    328    664    606\n",
       "2   2010        C    870    249    918\n",
       "3   2010        D    209    971    629\n",
       "4   2011        A    348    863    929\n",
       "5   2011        B    475    999    610\n",
       "6   2011        C    944    356    540\n",
       "7   2011        D    190     12    673\n",
       "8   2012        A    129    936     14\n",
       "9   2012        B     31    877    181\n",
       "10  2012        C    646    768    862\n",
       "11  2012        D    284    792    688"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = pd.read_csv('data/ex5.csv' )\n",
    "frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use seaborn library to create a bar plot using the function **sns.barplot()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 3,
     "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": [
    "sns.barplot(data = frame)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also select which column to be plotted by passing column names in a list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 4,
     "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": [
    "sns.barplot(data = frame[['sale1','sale2','sale3']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To remove confidence interval (The black lines on top of bars), we use the argument **(ci = False)**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hide_input": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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f637FewPwR0keBHYBo1V1JsnvAy+YcliA41X16qvbraYaxvwl2cHk1eKmqbcDNFhD/u/vE8BngPcOuO1F4ZX7HHWfpn+3qj4G/Anwim7Xt5K8iOnvuZ4ERpK8ujvHDUmmXl3oKhj0/CXZArwLeFNVfXfob+A6N4T5W9cz7k3A40Nr/irzyn3uXgrcl+QZ4P+A3wDuZvLXxW8w+Xd1nqWqnu6+kvVnSb6fyX/vHwCOJ/k5Ju8HjgCfSXKsqjZfhfdxvRro/AEfApYx+Ss/wBer6teH/i6uX4Oev3uT3AY8A3wTaGbu/PMDktQgb8tIUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktSg/wei4EgkHzRCyQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data = frame[['sale1','sale2','sale3']], ci = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To plot a single column, the data must be passed as **x for x-axis** and **y for y axis**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year', ylabel='sale1'>"
      ]
     },
     "execution_count": 6,
     "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": [
    "sns.barplot(x= frame.year, y = frame.sale1, ci = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also pass x and y using only the names of the column. but in this case, we need to pass the dataframe as a reference: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year', ylabel='sale1'>"
      ]
     },
     "execution_count": 7,
     "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": [
    "sns.barplot(x= 'year', y = 'sale1', data= frame, ci = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also you pass x and y using **dataframe indexing**, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year', ylabel='sale1'>"
      ]
     },
     "execution_count": 8,
     "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": [
    "sns.barplot(x= frame['year'], y = frame['sale1'], ci = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can create a horizontal bar plot in seaborn simply by adding the argument **orient = 'h'**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sale1', ylabel='year'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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v1F7J6LIKBwF/ADwNeBVwCPAPwG/XHl94SnIacAmwArgHeHONrr47NTw/b+9B6nKGIe2fM4C7quoXq+oU4KvApVX1nOHxIcArp28wXP/qU8Drquo04DPAh+a5bulRMzCk/bMFeEmSjyT5lar6b+CFSW5KsgV4EfCMPbZ5GnAKo6uJbgbez2iGIi1oHpKS9kNVfXc4vHQm8OEkXwPOBdZW1Y+TXAQ8YY/NAmytqufNb7XS/nGGIe2HJD8H/KSqPg98nNHP5gLcM/xOx0xnRd0OHJvkecM+lifZcxYiLTjOMKT980zgY0keYXTl4Lcy+gnMLYx+X/lbe25QVQ8Op9d+MsnhjP4dfgLYmuQ1jD7fOBb4SpLNVfXyeXgf0j55tVpJUouHpCRJLQaGJKnFwJAktRgYkqQWA0OS1GJgSJJaDAxJUsv/AgfzxD+3QF4gAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot( x = frame['sale1'], y= frame['year'], orient = 'h', ci= False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use the **hue** option (which enables us to split the bars by an additional categorical variable). Here we splitted the bars by the categorical variable 'quarters':"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year', ylabel='sale1'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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69Z0ZEU9HxLKIOKPq2iRJW2vFTOGzwNJu72cA92TmEcA9xXuKm+Km0liJ9Uzgxohoa0F9kqRCpaEQEaNp3NtwU7fms4E5xes5wAe7tc/LzNcy87fA08DxVdYnSdpa1TOFrwH/Ddjcre2g4uqlLVcxjSzaDwFWduvXWbRtJSIuiohHI+LRtWvXVlK0JA1WlYVCRPwXYE1mdvR2kx7acpuGzNmZOTkzJ48YMWKPapQkba3XT17bDVOAsyLiP9N4WtsBEXEr8EJEjMrM1RExClhT9O9k62c0jAZ+X2F9kqQ3qGymkJkzM3N0Zo6lcQL53sz8KHAnMK3oNg24o3h9JzA1IvaOiHHAEcDDVdUnSdpWlTOF7bkWmB8RnwKeo/FoTzLziYiYDzxJ4yE+n8nMru0PI0lqtpaEQmbeD9xfvF4PnL6dfrOAWa2oSZK0Le9oliSVDAVJUslQkCSVDAVJUslQkCSV6rgkVdplk678biXjLti/kmGlfsuZgiSpZChIkkqGgiSpZChIkkqeaG6C564eX83Aww6oZlxJ2g5nCpKkkqEgSSoZCpKkkqEgSSoZCpKkkqEgSSoZCpKkkqEgSSp585oktdADJ7+nknHf8+ADTRnHmYIkqWQoSJJKhoIkqWQoSJJKhoIkqWQoSJJKhoIkqWQoSJJKhoIkqWQoSJJKhoIkqWQoSJJKhoIkqWQoSJJKhoIkqVRZKETEoRFxX0QsjYgnIuKzRfuBEXF3RCwvfg7rts3MiHg6IpZFxBlV1SZJ6lmVD9l5HfjbzFwUEfsDHRFxN/Bx4J7MvDYiZgAzgM9HxNHAVOAY4GDgJxFxZGZ2VVjjoFTVQz6geQ/6kFSPymYKmbk6MxcVr18BlgKHAGcDc4puc4APFq/PBuZl5muZ+VvgaeD4quqTJG2rJY/jjIixwHHAr4CDMnM1NIIjIkYW3Q4Bftlts86i7Y1jXQRcBDBmzJhe1zDpyu/uTum9smD/yoaWpJaq/ERzROwH/BCYnpkv76hrD225TUPm7MycnJmTR4wY0awyJUlUPFOIiKE0AuF7mfmjovmFiBhVzBJGAWuK9k7g0G6bjwZ+X2V9Un/U1x/8rv6tyquPAvg2sDQz/6HbR3cC04rX04A7urVPjYi9I2IccATwcFX1SZK2VeVMYQrwMeDXEbG4aPs74FpgfkR8CngO+DBAZj4REfOBJ2lcufQZrzySpNaqLBQy82f0fJ4A4PTtbDMLmFVVTZKkHfOOZklSyVCQJJUMBUlSyVCQJJUMBUlSyVCQJJUMBUlSyVCQJJUMBUlSyVCQJJVa8jwFSYNbVc8z6bjuwkrGHcycKUiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKk0pO4CJGl3PXf1+GoGHnZANeP2A84UJEklQ0GSVDIUJEmlPhcKEXFmRCyLiKcjYkbd9UjSYNKnQiEi2oBvAO8HjgbOj4ij661KkgaPPhUKwPHA05n5bGb+GZgHnF1zTZI0aPS1S1IPAVZ2e98J/GX3DhFxEXBR8XZDRCxrUW3bdVh1Q78VWNfsQU9p9oDdRVQ5etNVuO+gv+2/frbvoP/9vwd9Zv9t9z9dXwuFnr5VbvUmczYwuzXl1CsiHs3MyXXXod3j/uu/BvO+62uHjzqBQ7u9Hw38vqZaJGnQ6Wuh8AhwRESMi4g3AVOBO2uuSZIGjT51+CgzX4+IS4G7gDbg5sx8ouay6jQoDpMNYO6//mvQ7rvIzJ33kiQNCn3t8JEkqUaGgiSpZCi0UEQcGhH3RcTSiHgiIj5btB8YEXdHxPLi57CifXjRf0NE3PCGsSZFxK+L5UD+MaIfXmTezzR5/82KiJURsaGO7zLYNGvfRcS+EfF/IuKpYpxr6/pOVTEUWut14G8z8yjgBOAzxTIeM4B7MvMI4J7iPcBG4O+BK3oY65s0buI7ovh1ZsW1q7n778c07uBXazRz3301M98OHAdMiYj3V159CxkKLZSZqzNzUfH6FWApjbu4zwbmFN3mAB8s+vwxM39G4w9oKSJGAQdk5i+ycaXAd7dso+o0a/8Vn/0yM1e3om41b99l5p8y877i9Z+BRTTupxowDIWaRMRYGv/S+BVw0Ja/IIqfI3ey+SE0bvTborNoU4vs4f5TjZq17yLiLcBf0ZhhDBiGQg0iYj/gh8D0zHx5d4booc1ri1ukCftPNWnWvouIIcBc4B8z89lm1dcXGAotFhFDafyh/F5m/qhofqE4JLTl0NCanQzTydZTVpcDaZEm7T/VoMn7bjawPDO/1vRCa2YotFBxhdC3gaWZ+Q/dProTmFa8ngbcsaNximnuKxFxQjHmhTvbRnuuWftPrdfMfRcR/wP4C2B6k8vsE7yjuYUi4t3AT4FfA5uL5r+jcWxzPjAGeA74cGb+v2KbFcABwJuAF4H3ZeaTETEZ+BdgH+BfgcvSnVmpJu+/rwAXAAfTmOXdlJlXteq7DDbN2nfAyzSW938KeK0Y54bMvKkV36MVDAVJUsnDR5KkkqEgSSoZCpKkkqEgSSoZCpKkkqEgSSoZClLNIqKt7hqkLQwFaRdExJe2rMVfvJ8VEZdHxJUR8UhE/HtEfLHb57dHREex9v5F3do3RMTVEfEr4MQWfw1puwwFadd8m2JZhIjYC5gKvEDjmRbHAxOASRFxctH/k5k5CZgMXB4Rw4v2NwNLMvMviyWapT5hSN0FSP1JZq6IiPURcRxwEPAY8E4aSyA8VnTbj0ZIPEgjCM4p2g8t2tcDXTQWZ5P6FENB2nU3AR8H/gNwM3A6cE1m/nP3ThFxCvBe4MTM/FNE3A+0Fx9vzMyuFtUr9ZqHj6Rdt4DG40/fCdxV/PpksVY/EXFIRIyksZLmH4pAeDuNx0BKfZozBWkXZeafI+I+4MXiX/sLI+Io4BeNFZrZAHwU+Dfg4oj4d2AZ8Mu6apZ6y1VSpV1UnGBeRGOZ5eV11yM1k4ePpF0QEUcDTwP3GAgaiJwpSJJKzhQkSSVDQZJUMhQkSSVDQZJUMhQkSaX/D6Qu7oqzfKUBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot( x= frame['year'], y = frame['sale1'], hue = frame['quarters'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "<h1 align=\"center\">Thank You</h1> \n",
    "\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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}