13.10 Bar Plots with Dataframes.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": [
    "## 13.10 Bar Plots with Dataframes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we import pandas library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we create a series using the fucntion **pd.Series()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    34\n",
       "B    76\n",
       "C    12\n",
       "D    89\n",
       "E    45\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series([34,76,12,89,45], index= list('ABCDE'))\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can create a **bar plot** for this series usignt the function **plot.bar()**: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can **rotate** the labels of the x-axis using the argument **rot**\n",
    "\n",
    "We can change the **color** of the bars using the argument **color**\n",
    "\n",
    "We can aslo change the **transperancy** of the bars using the argument **alpha**: "
   ]
  },
  {
   "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": [
    "data.plot.bar(rot = 0, color = 'r', alpha = 0.7 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create a horizontal bar plot, we use the function **plot.barh()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 5,
     "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": [
    "data.plot.barh(color = 'g', alpha = 0.7 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For dataframes, lets first read this dataframe from a csv file using the function **pd.read_csv()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hide_input": false,
    "scrolled": true
   },
   "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>sale1</th>\n",
       "      <th>sale2</th>\n",
       "      <th>sale3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>branch number</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>branch1</th>\n",
       "      <td>200</td>\n",
       "      <td>258</td>\n",
       "      <td>592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>branch2</th>\n",
       "      <td>328</td>\n",
       "      <td>664</td>\n",
       "      <td>606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>branch3</th>\n",
       "      <td>870</td>\n",
       "      <td>249</td>\n",
       "      <td>918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>branch4</th>\n",
       "      <td>209</td>\n",
       "      <td>971</td>\n",
       "      <td>629</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               sale1  sale2  sale3\n",
       "branch number                     \n",
       "branch1          200    258    592\n",
       "branch2          328    664    606\n",
       "branch3          870    249    918\n",
       "branch4          209    971    629"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame1 = pd.read_csv('data/ex6.csv', index_col = 'branch number')\n",
    "frame1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create a bar plot for the dataframe we use the same function which is **plot.bar()**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='branch number'>"
      ]
     },
     "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": [
    "frame1.plot.bar(rot = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create a horizontal bar plot we use the function **plot.barh()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='branch number'>"
      ]
     },
     "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": [
    "frame1.plot.barh()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create stacked bar plot, we add the argument **stacked = True**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='branch number'>"
      ]
     },
     "execution_count": 9,
     "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": [
    "frame1.plot.bar(stacked = True, rot =0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also create a horizontal stacked bar plot, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='branch number'>"
      ]
     },
     "execution_count": 10,
     "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": [
    "frame1.plot.barh(stacked = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another option to create a bar plot is to use the **plot()** function and the argument **kind = 'bar'**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='branch number'>"
      ]
     },
     "execution_count": 11,
     "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": [
    "frame1.plot(kind = 'bar', rot =0)"
   ]
  },
  {
   "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
}