HexnBit4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href='https://www.hexnbit.com/'> <img src='https://www.hexnbit.com/wp-content/uploads/2019/09/hexnbit_final_66px.png'/> </a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A4_Assignment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mentioned below are the tasks, the solution for each tasks has to be appended by inserting a new cell below the task,\n",
    "along with the following description:-\n",
    "1. Why the method was used (if applicable)\n",
    "2. Explanation of logic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task1\n",
    "- Create 2 Pandas Series from the given samples:\n",
    "    - **Sample 1:** [2, 4, 6, 8, 10]\n",
    "    - **Sample 2:** [1, 3, 5, 7, 9]\n",
    "    \n",
    "- Write a Pandas program to add, subtract, multiply and divide both Pandas Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Add two Series:\n",
      "0     3\n",
      "1     7\n",
      "2    11\n",
      "3    15\n",
      "4    19\n",
      "dtype: int64\n",
      "Subtract two Series:\n",
      "0    1\n",
      "1    1\n",
      "2    1\n",
      "3    1\n",
      "4    1\n",
      "dtype: int64\n",
      "Multiply two Series:\n",
      "0     2\n",
      "1    12\n",
      "2    30\n",
      "3    56\n",
      "4    90\n",
      "dtype: int64\n",
      "Divide Series1 by Series2:\n",
      "0    2.000000\n",
      "1    1.333333\n",
      "2    1.200000\n",
      "3    1.142857\n",
      "4    1.111111\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "ds1 = pd.Series([2, 4, 6, 8, 10])\n",
    "ds2 = pd.Series([1, 3, 5, 7, 9])\n",
    "ds = ds1 + ds2\n",
    "print(\"Add two Series:\")\n",
    "print(ds)\n",
    "print(\"Subtract two Series:\")\n",
    "ds = ds1 - ds2\n",
    "print(ds)\n",
    "print(\"Multiply two Series:\")\n",
    "ds = ds1 * ds2\n",
    "print(ds)\n",
    "print(\"Divide Series1 by Series2:\")\n",
    "ds = ds1 / ds2\n",
    "print(ds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task2\n",
    "\n",
    "Write a Pandas program to convert the given dictionary to a Pandas series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dictionary:\n",
      "{'a': 100, 'b': 200, 'c': 300, 'd': 400, 'e': 800}\n",
      "Converted series:\n",
      "a    100\n",
      "b    200\n",
      "c    300\n",
      "d    400\n",
      "e    800\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "d1 = {'a': 100, 'b': 200, 'c':300, 'd':400, 'e':800}\n",
    "print(\"Original dictionary:\")\n",
    "print(d1)\n",
    "new_series = pd.Series(d1)\n",
    "print(\"Converted series:\")\n",
    "print(new_series)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task3\n",
    "Create DataFrame from the following Dictionary & coresponding Index:-\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>name</th>\n",
       "      <th>score</th>\n",
       "      <th>attempts</th>\n",
       "      <th>qualify</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Anastasia</td>\n",
       "      <td>12.5</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dima</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Katherine</td>\n",
       "      <td>16.5</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>James</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Emily</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Michael</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Matthew</td>\n",
       "      <td>14.5</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Laura</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Kevin</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Jonas</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name  score  attempts qualify\n",
       "0  Anastasia   12.5         1     yes\n",
       "1       Dima    9.0         3      no\n",
       "2  Katherine   16.5         2     yes\n",
       "3      James    NaN         3      no\n",
       "4      Emily    9.0         2      no\n",
       "5    Michael   20.0         3     yes\n",
       "6    Matthew   14.5         1     yes\n",
       "7      Laura    NaN         1      no\n",
       "8      Kevin    8.0         2      no\n",
       "9      Jonas   19.0         1     yes"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dictionary\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "sample_dictionary={'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],\n",
    "'score': [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],\n",
    "'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
    "'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}\n",
    "new = pd.DataFrame.from_dict(sample_dictionary) \n",
    "  \n",
    "new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Index\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "sample_dictionary={'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],\n",
    "'score': [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],\n",
    "'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
    "'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}\n",
    "new = pd.DataFrame.from_dict(sample_dictionary) \n",
    "  \n",
    "new\n",
    "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']\n",
    "new['x']=labels\n",
    "new=new.set_index('x')\n",
    "del new.index.name \n",
    "new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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>name</th>\n",
       "      <th>score</th>\n",
       "      <th>attempts</th>\n",
       "      <th>qualify</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>Anastasia</td>\n",
       "      <td>12.5</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>Dima</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>Katherine</td>\n",
       "      <td>16.5</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>James</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>Emily</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>Michael</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>Matthew</td>\n",
       "      <td>14.5</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>Laura</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>Kevin</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>Jonas</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name  score  attempts qualify\n",
       "a  Anastasia   12.5         1     yes\n",
       "b       Dima    9.0         3      no\n",
       "c  Katherine   16.5         2     yes\n",
       "d      James    NaN         3      no\n",
       "e      Emily    9.0         2      no\n",
       "f    Michael   20.0         3     yes\n",
       "g    Matthew   14.5         1     yes\n",
       "h      Laura    NaN         1      no\n",
       "i      Kevin    8.0         2      no\n",
       "j      Jonas   19.0         1     yes"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Hint: Sample Output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task4\n",
    "- Print the first 3 rows from the DataFrame created in Task 3\n",
    "- Print the 'name' and 'score' columns from the DataFrame created in Task 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        name  score  attempts qualify  x\n",
      "0  Anastasia   12.5         1     yes  a\n",
      "1       Dima    9.0         3      no  b\n",
      "2  Katherine   16.5         2     yes  c\n",
      "0    Anastasia\n",
      "1         Dima\n",
      "2    Katherine\n",
      "3        James\n",
      "4        Emily\n",
      "5      Michael\n",
      "6      Matthew\n",
      "7        Laura\n",
      "8        Kevin\n",
      "9        Jonas\n",
      "Name: name, dtype: object\n",
      "0    12.5\n",
      "1     9.0\n",
      "2    16.5\n",
      "3     NaN\n",
      "4     9.0\n",
      "5    20.0\n",
      "6    14.5\n",
      "7     NaN\n",
      "8     8.0\n",
      "9    19.0\n",
      "Name: score, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(new.iloc[:3])\n",
    "print(new['name'])\n",
    "print(new['score'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task5\n",
    "Write a Python program to display a bar chart of the popularity of programming languages.<br>\n",
    "Data in below cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "languages=['Java', 'Python', 'PHP', 'JavaScript', 'C#', 'C++']\n",
    "popularity=[22.2, 17.6, 8.8, 8, 7.7, 6.7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 6 artists>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NwCVJ/6/NCFyS9KMMcElqygCXpKYMcElqygCXpKb+D8gm0GJG0LvwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.bar(languages, popularity, color ='maroon',  \n",
    "        width = 0.4) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task6\n",
    "Write a Python programming to create a pie chart of Gold Medal achievements of five most successful countries in 2016 Summer Olympics.<br>\n",
    "Read the data from a csv file \"medal.csv\"\n",
    "\n",
    "**Hints:**<br>\n",
    "- Import the required module & read the csv file.<br>\n",
    "- Read the \"gold_medal\" and \"country\" columns<br>\n",
    "- Give different colors to different segment & Plot the pie chart.<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    46\n",
      "1    27\n",
      "2    26\n",
      "3    19\n",
      "4     1\n",
      "Name: gold_medal, dtype: int64\n",
      "0    united States\n",
      "1    Great Britain\n",
      "2            China\n",
      "3           Russia\n",
      "4          Germany\n",
      "Name: country, dtype: object\n",
      "               gold_medal\n",
      "country                  \n",
      "united States          46\n",
      "Great Britain          27\n",
      "China                  26\n",
      "Russia                 19\n",
      "Germany                 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_frame=pd.read_csv('medal.csv')\n",
    "print(data_frame['gold_medal'])\n",
    "print(data_frame['country'])\n",
    "data_frame=data_frame.set_index('country')\n",
    "print(data_frame)\n",
    "plot = data_frame.plot.pie(y='gold_medal', figsize=(5, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task7\n",
    "- Write a Python GUI program to add a \"Quit\" button in your application using tkinter module.<br>\n",
    "- When the button is pressed, the GUI must close\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tkinter as tk \n",
    "parent = tk.Tk() \n",
    "parent.title('Title - button') \n",
    "my_button = tk.Button(parent, text='Quit', height=1, width=35, command=parent.destroy) \n",
    "my_button.pack() \n",
    "parent.mainloop()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task8\n",
    "Write a Python program to create a GUI with three radio buttons widgets using tkinter module.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tkinter as tk\n",
    "parent = tk.Tk()\n",
    "parent.title(\"Radiobutton \")\n",
    "parent.geometry('350x200')\n",
    "radio1 = tk.Radiobutton(parent, text='First', value=1)\n",
    "radio2 = tk.Radiobutton(parent, text='Second', value=2)\n",
    "radio3 = tk.Radiobutton(parent, text='Third', value=3)\n",
    "radio1.grid(column=0, row=0)\n",
    "radio2.grid(column=1, row=0)\n",
    "radio3.grid(column=3, row=0)\n",
    "\n",
    "parent.mainloop()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task9\n",
    "Write a Python program to create a GUI with Listbox bar widgets using tkinter module\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tkinter as tk\n",
    "parent = tk.Tk()\n",
    "parent.geometry(\"250x200\")\n",
    "label1 = tk.Label(parent,text = \"A list of favourite languages...\")\n",
    "listbox = tk.Listbox(parent)\n",
    "listbox.insert(1,\"PHP\")\n",
    "listbox.insert(2, \"Python\")\n",
    "listbox.insert(3, \"Java\")\n",
    "listbox.insert(4, \"C#\")\n",
    "label1.pack()\n",
    "listbox.pack()\n",
    "parent.mainloop()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task10\n",
    "- Write a Python program to create a GUI with two buttons, \"Exit\" and \"Hello\" using tkinter module.<br>\n",
    "- When the \"Hello\" button is pressed, print the text “Tkinter is easy to create GUI” on the terminal.<br>\n",
    "- When the \"Exit\" button is pressed, the GUI must close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tkinter is easy to create GUI!\n",
      "Tkinter is easy to create GUI!\n",
      "Tkinter is easy to create GUI!\n",
      "Tkinter is easy to create GUI!\n",
      "Tkinter is easy to create GUI!\n",
      "Tkinter is easy to create GUI!\n",
      "Tkinter is easy to create GUI!\n"
     ]
    }
   ],
   "source": [
    "import tkinter as tk   \n",
    "\n",
    "def write_text():\n",
    "    print(\"Tkinter is easy to create GUI!\")\n",
    "\n",
    "parent = tk.Tk()\n",
    "frame = tk.Frame(parent)\n",
    "frame.pack()\n",
    "\n",
    "text_disp= tk.Button(frame, \n",
    "                   text=\"Hello\", \n",
    "                   command=write_text\n",
    "                   )\n",
    "\n",
    "text_disp.pack(side=tk.LEFT)\n",
    "\n",
    "exit_button = tk.Button(frame,\n",
    "                   text=\"Exit\",\n",
    "                   fg=\"green\",\n",
    "                   command=quit)\n",
    "exit_button.pack(side=tk.RIGHT)\n",
    "\n",
    "parent.mainloop()"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}