week 1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Welcome to Week 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You might me aware about using jupyter notebook.  \n",
    "It's simple to use and interactive enough to explain you everything.  \n",
    "This writen here is language called Markdown which helps us make it more interactive.  \n",
    "\n",
    "To run a code you need select it and click `Run`. \n",
    "\n",
    "If at any given moment your kernel stops responding restart and re-run the code using options from `Kernel`.\n",
    "You can also run a box by pressing `Shift`+`Enter`\n",
    "\n",
    "Now let's start our amazing journey, 1 step at a time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [Numpy](https://numpy.org/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy is a mathematical library which helps us in most of the mathematical functions.  \n",
    "\n",
    "Let's get clarified with arrays, lists and tuples\n",
    "\n",
    "### Array\n",
    "An array is a contiguous memory allocation for data storage of same datatype. The static array always has a predefined size and it is efficient for iterative works as the elements are stored side by side in the dedicated memory locations. It is not that effective when it comes to inserting a new element in between the already present elements.\n",
    "Similarly, it causes inefficient memory management when you delete an element in between the elements present. \n",
    "To avoid the drawbacks of static arrays to a certain extent, the dynamic array was introduced and the data structures like vectors, array list and likewise fall under the dynamic array. These dynamic arrays can be resized, and they can be placed in a scattered manner in the memory space as per availability.\n",
    "\n",
    "### List\n",
    "The concept of the dynamic array led to list or linked list as some like to call it. Unlike an array, a linked list is not continuous memory allocation. It has scatted memory allocation technique. Each node of a list consists of two parts. The first part contains the data while the second part is a pointer that had the memory reference of the next node wherever it is placed in the memory. This makes the memory management efficient in all scenarios and you can add or delete nodes in a list effortlessly with extremely high processing speed. Unlike an array, a list can have different data types.\n",
    "\n",
    "### Tuple\n",
    "A tuple is an object that can contain heterogeneous data. Out of all data structures, a tuple is the fastest and they consume the least amount of memory. Like a static array, a tuple is fixed in size and that is why tuples are replacing array completely as they are more efficient in all parameters. If you have a dataset which will be assigned only once, and its value should not change again, you need a tuple.\n",
    "\n",
    "More support material is available [here](https://github.com/kabirnagpal/100DaysofML/blob/master/Resources/General/Numpy.pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=[1,2,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b=np.array(a)\n",
    "type(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4]\n"
     ]
    }
   ],
   "source": [
    "a.append(4)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5]\n"
     ]
    }
   ],
   "source": [
    "a.insert(4,5)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7, 1, 2, 3, 4, 5]\n"
     ]
    }
   ],
   "source": [
    "a.insert(0,7)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "c=np.array([[1,2],[3,4]])\n",
    "d=np.array([[2,2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 4],\n",
       "       [6, 8]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c*d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  4],\n",
       "       [ 9, 16]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c**d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6],\n",
       "       [14]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(c,d.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding `.T` takes transpose of the matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.69314718],\n",
       "       [1.09861229, 1.38629436]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Do Check out [official docs](https://numpy.org/) or [blogs](https://www.geeksforgeeks.org/python-numpy/) to learn more array and mathematical functions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [Pandas](https://pandas.pydata.org/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This library is majorly used to deal with dataframes extracted from csv/tsv.  \n",
    "Machine learning is used on a well formated numerical datasets.  \n",
    "Hence you'll have to learn about some of the most used functions of this or similar libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset is already present in the data foulder. You can check it out [here](https://www.kaggle.com/geomack/spotifyclassification).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv('data/data_sound.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>Unnamed: 0</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>key</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>time_signature</th>\n",
       "      <th>valence</th>\n",
       "      <th>target</th>\n",
       "      <th>song_title</th>\n",
       "      <th>artist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0102</td>\n",
       "      <td>0.833</td>\n",
       "      <td>204600</td>\n",
       "      <td>0.434</td>\n",
       "      <td>0.021900</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1650</td>\n",
       "      <td>-8.795</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>150.062</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.286</td>\n",
       "      <td>1</td>\n",
       "      <td>Mask Off</td>\n",
       "      <td>Future</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1990</td>\n",
       "      <td>0.743</td>\n",
       "      <td>326933</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.006110</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1370</td>\n",
       "      <td>-10.401</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0794</td>\n",
       "      <td>160.083</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.588</td>\n",
       "      <td>1</td>\n",
       "      <td>Redbone</td>\n",
       "      <td>Childish Gambino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0344</td>\n",
       "      <td>0.838</td>\n",
       "      <td>185707</td>\n",
       "      <td>0.412</td>\n",
       "      <td>0.000234</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>-7.148</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2890</td>\n",
       "      <td>75.044</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.173</td>\n",
       "      <td>1</td>\n",
       "      <td>Xanny Family</td>\n",
       "      <td>Future</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.6040</td>\n",
       "      <td>0.494</td>\n",
       "      <td>199413</td>\n",
       "      <td>0.338</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0922</td>\n",
       "      <td>-15.236</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0261</td>\n",
       "      <td>86.468</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.230</td>\n",
       "      <td>1</td>\n",
       "      <td>Master Of None</td>\n",
       "      <td>Beach House</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.1800</td>\n",
       "      <td>0.678</td>\n",
       "      <td>392893</td>\n",
       "      <td>0.561</td>\n",
       "      <td>0.512000</td>\n",
       "      <td>5</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>-11.648</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0694</td>\n",
       "      <td>174.004</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.904</td>\n",
       "      <td>1</td>\n",
       "      <td>Parallel Lines</td>\n",
       "      <td>Junior Boys</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  acousticness  danceability  duration_ms  energy  \\\n",
       "0           0        0.0102         0.833       204600   0.434   \n",
       "1           1        0.1990         0.743       326933   0.359   \n",
       "2           2        0.0344         0.838       185707   0.412   \n",
       "3           3        0.6040         0.494       199413   0.338   \n",
       "4           4        0.1800         0.678       392893   0.561   \n",
       "\n",
       "   instrumentalness  key  liveness  loudness  mode  speechiness    tempo  \\\n",
       "0          0.021900    2    0.1650    -8.795     1       0.4310  150.062   \n",
       "1          0.006110    1    0.1370   -10.401     1       0.0794  160.083   \n",
       "2          0.000234    2    0.1590    -7.148     1       0.2890   75.044   \n",
       "3          0.510000    5    0.0922   -15.236     1       0.0261   86.468   \n",
       "4          0.512000    5    0.4390   -11.648     0       0.0694  174.004   \n",
       "\n",
       "   time_signature  valence  target      song_title            artist  \n",
       "0             4.0    0.286       1        Mask Off            Future  \n",
       "1             4.0    0.588       1         Redbone  Childish Gambino  \n",
       "2             4.0    0.173       1    Xanny Family            Future  \n",
       "3             4.0    0.230       1  Master Of None       Beach House  \n",
       "4             4.0    0.904       1  Parallel Lines       Junior Boys  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print first 5 rows\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>key</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>time_signature</th>\n",
       "      <th>valence</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2.017000e+03</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1008.000000</td>\n",
       "      <td>0.187590</td>\n",
       "      <td>0.618422</td>\n",
       "      <td>2.463062e+05</td>\n",
       "      <td>0.681577</td>\n",
       "      <td>0.133286</td>\n",
       "      <td>5.342588</td>\n",
       "      <td>0.190844</td>\n",
       "      <td>-7.085624</td>\n",
       "      <td>0.612295</td>\n",
       "      <td>0.092664</td>\n",
       "      <td>121.603272</td>\n",
       "      <td>3.968270</td>\n",
       "      <td>0.496815</td>\n",
       "      <td>0.505702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>582.402066</td>\n",
       "      <td>0.259989</td>\n",
       "      <td>0.161029</td>\n",
       "      <td>8.198181e+04</td>\n",
       "      <td>0.210273</td>\n",
       "      <td>0.273162</td>\n",
       "      <td>3.648240</td>\n",
       "      <td>0.155453</td>\n",
       "      <td>3.761684</td>\n",
       "      <td>0.487347</td>\n",
       "      <td>0.089931</td>\n",
       "      <td>26.685604</td>\n",
       "      <td>0.255853</td>\n",
       "      <td>0.247195</td>\n",
       "      <td>0.500091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.122000</td>\n",
       "      <td>1.604200e+04</td>\n",
       "      <td>0.014800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018800</td>\n",
       "      <td>-33.097000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.023100</td>\n",
       "      <td>47.859000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.034800</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>504.000000</td>\n",
       "      <td>0.009630</td>\n",
       "      <td>0.514000</td>\n",
       "      <td>2.000150e+05</td>\n",
       "      <td>0.563000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.092300</td>\n",
       "      <td>-8.394000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.037500</td>\n",
       "      <td>100.189000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.295000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1008.000000</td>\n",
       "      <td>0.063300</td>\n",
       "      <td>0.631000</td>\n",
       "      <td>2.292610e+05</td>\n",
       "      <td>0.715000</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.127000</td>\n",
       "      <td>-6.248000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.054900</td>\n",
       "      <td>121.427000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.492000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1512.000000</td>\n",
       "      <td>0.265000</td>\n",
       "      <td>0.738000</td>\n",
       "      <td>2.703330e+05</td>\n",
       "      <td>0.846000</td>\n",
       "      <td>0.054000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.247000</td>\n",
       "      <td>-4.746000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.108000</td>\n",
       "      <td>137.849000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.691000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>0.995000</td>\n",
       "      <td>0.984000</td>\n",
       "      <td>1.004627e+06</td>\n",
       "      <td>0.998000</td>\n",
       "      <td>0.976000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.969000</td>\n",
       "      <td>-0.307000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.816000</td>\n",
       "      <td>219.331000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.992000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0  acousticness  danceability   duration_ms       energy  \\\n",
       "count  2017.000000   2017.000000   2017.000000  2.017000e+03  2017.000000   \n",
       "mean   1008.000000      0.187590      0.618422  2.463062e+05     0.681577   \n",
       "std     582.402066      0.259989      0.161029  8.198181e+04     0.210273   \n",
       "min       0.000000      0.000003      0.122000  1.604200e+04     0.014800   \n",
       "25%     504.000000      0.009630      0.514000  2.000150e+05     0.563000   \n",
       "50%    1008.000000      0.063300      0.631000  2.292610e+05     0.715000   \n",
       "75%    1512.000000      0.265000      0.738000  2.703330e+05     0.846000   \n",
       "max    2016.000000      0.995000      0.984000  1.004627e+06     0.998000   \n",
       "\n",
       "       instrumentalness          key     liveness     loudness         mode  \\\n",
       "count       2017.000000  2017.000000  2017.000000  2017.000000  2017.000000   \n",
       "mean           0.133286     5.342588     0.190844    -7.085624     0.612295   \n",
       "std            0.273162     3.648240     0.155453     3.761684     0.487347   \n",
       "min            0.000000     0.000000     0.018800   -33.097000     0.000000   \n",
       "25%            0.000000     2.000000     0.092300    -8.394000     0.000000   \n",
       "50%            0.000076     6.000000     0.127000    -6.248000     1.000000   \n",
       "75%            0.054000     9.000000     0.247000    -4.746000     1.000000   \n",
       "max            0.976000    11.000000     0.969000    -0.307000     1.000000   \n",
       "\n",
       "       speechiness        tempo  time_signature      valence       target  \n",
       "count  2017.000000  2017.000000     2017.000000  2017.000000  2017.000000  \n",
       "mean      0.092664   121.603272        3.968270     0.496815     0.505702  \n",
       "std       0.089931    26.685604        0.255853     0.247195     0.500091  \n",
       "min       0.023100    47.859000        1.000000     0.034800     0.000000  \n",
       "25%       0.037500   100.189000        4.000000     0.295000     0.000000  \n",
       "50%       0.054900   121.427000        4.000000     0.492000     1.000000  \n",
       "75%       0.108000   137.849000        4.000000     0.691000     1.000000  \n",
       "max       0.816000   219.331000        5.000000     0.992000     1.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gives the details of value range in each column\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.0102\n",
       "1    0.1990\n",
       "2    0.0344\n",
       "3    0.6040\n",
       "4    0.1800\n",
       "Name: acousticness, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transfering acousticness column to different dataset\n",
    "data1=data[\"acousticness\"]\n",
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18759003442241004"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>key</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>time_signature</th>\n",
       "      <th>valence</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>963.629156</td>\n",
       "      <td>0.165369</td>\n",
       "      <td>0.628106</td>\n",
       "      <td>254011.260870</td>\n",
       "      <td>0.700139</td>\n",
       "      <td>0.143615</td>\n",
       "      <td>6.236573</td>\n",
       "      <td>0.194474</td>\n",
       "      <td>-6.877605</td>\n",
       "      <td>0.098246</td>\n",
       "      <td>123.614758</td>\n",
       "      <td>3.980818</td>\n",
       "      <td>0.507026</td>\n",
       "      <td>0.551151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1036.095547</td>\n",
       "      <td>0.201660</td>\n",
       "      <td>0.612290</td>\n",
       "      <td>241427.363563</td>\n",
       "      <td>0.669824</td>\n",
       "      <td>0.126745</td>\n",
       "      <td>4.776518</td>\n",
       "      <td>0.188545</td>\n",
       "      <td>-7.217342</td>\n",
       "      <td>0.089130</td>\n",
       "      <td>120.329602</td>\n",
       "      <td>3.960324</td>\n",
       "      <td>0.490349</td>\n",
       "      <td>0.476923</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  acousticness  danceability    duration_ms    energy  \\\n",
       "mode                                                                     \n",
       "0      963.629156      0.165369      0.628106  254011.260870  0.700139   \n",
       "1     1036.095547      0.201660      0.612290  241427.363563  0.669824   \n",
       "\n",
       "      instrumentalness       key  liveness  loudness  speechiness       tempo  \\\n",
       "mode                                                                            \n",
       "0             0.143615  6.236573  0.194474 -6.877605     0.098246  123.614758   \n",
       "1             0.126745  4.776518  0.188545 -7.217342     0.089130  120.329602   \n",
       "\n",
       "      time_signature   valence    target  \n",
       "mode                                      \n",
       "0           3.980818  0.507026  0.551151  \n",
       "1           3.960324  0.490349  0.476923  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(['mode']).mean()\n",
    "# over here we've grouped the data on the basis of Modes and taken the mean the of other columns\n",
    "# similarly we can apply similar functions such as sum, aggregate etc as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the major SQL commands can also be performed on the DataFrame. \n",
    "This helps us to extract insights from the data.  \n",
    "There is a method called magic box sql that allows us to change dataset using sql commands. \n",
    "You can learn more from [this](https://www.geeksforgeeks.org/python-pandas-dataframe/) link. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering on Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Drop columns which seem irrelevant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.drop([\"song_title\",\"artist\",\"Unnamed: 0\",\"target\",],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "acousticness        0\n",
       "danceability        0\n",
       "duration_ms         0\n",
       "energy              0\n",
       "instrumentalness    0\n",
       "key                 0\n",
       "liveness            0\n",
       "loudness            0\n",
       "mode                0\n",
       "speechiness         0\n",
       "tempo               0\n",
       "time_signature      0\n",
       "valence             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of missing values in each column\n",
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What to do with incomplete dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Option 1\n",
    "Remove those rows ( recommended only if number of missing values are less )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.dropna(how='any',axis=0) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Option 2\n",
    "Replace the with mean, median or mode of the column/row.  \n",
    "For this we need to extract the values from the dataframe and store it in `x`.  \n",
    "I'm considering all values except the last two columns because they're string type.  \n",
    "For more detail refer the [docs](https://scikit-learn.org/stable/modules/generated/sklearn.impute.SimpleImputer.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=data.iloc[:,:-2].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer as Imputer\n",
    "x=Imputer(missing_values=np.nan, strategy = 'mean').fit_transform(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Option 3\n",
    "Go and complaint the person who collected :P"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What about variation in dataset\n",
    "At times on observing the dataset we find that some values are too large and others too small.  \n",
    "In our case, scale of duration is too large while that of acousticness is too small.    \n",
    "To give equal weightage to all the variables we need scale them down to some specific range.  \n",
    "Refer [here](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.preprocessing) for more methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.682467</td>\n",
       "      <td>1.332874</td>\n",
       "      <td>-0.508851</td>\n",
       "      <td>-1.177700</td>\n",
       "      <td>-0.407864</td>\n",
       "      <td>-0.916446</td>\n",
       "      <td>-0.166291</td>\n",
       "      <td>-0.454530</td>\n",
       "      <td>0.795738</td>\n",
       "      <td>3.763084</td>\n",
       "      <td>1.066709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.043897</td>\n",
       "      <td>0.773830</td>\n",
       "      <td>0.983716</td>\n",
       "      <td>-1.534468</td>\n",
       "      <td>-0.465683</td>\n",
       "      <td>-1.190619</td>\n",
       "      <td>-0.346454</td>\n",
       "      <td>-0.881573</td>\n",
       "      <td>0.795738</td>\n",
       "      <td>-0.147529</td>\n",
       "      <td>1.442323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.589363</td>\n",
       "      <td>1.363932</td>\n",
       "      <td>-0.739362</td>\n",
       "      <td>-1.282352</td>\n",
       "      <td>-0.487200</td>\n",
       "      <td>-0.916446</td>\n",
       "      <td>-0.204897</td>\n",
       "      <td>-0.016586</td>\n",
       "      <td>0.795738</td>\n",
       "      <td>2.183712</td>\n",
       "      <td>-1.745166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.602040</td>\n",
       "      <td>-0.772860</td>\n",
       "      <td>-0.572137</td>\n",
       "      <td>-1.634363</td>\n",
       "      <td>1.379429</td>\n",
       "      <td>-0.093928</td>\n",
       "      <td>-0.634715</td>\n",
       "      <td>-2.167220</td>\n",
       "      <td>0.795738</td>\n",
       "      <td>-0.740350</td>\n",
       "      <td>-1.316964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.029201</td>\n",
       "      <td>0.370075</td>\n",
       "      <td>1.788484</td>\n",
       "      <td>-0.573574</td>\n",
       "      <td>1.386753</td>\n",
       "      <td>-0.093928</td>\n",
       "      <td>1.596735</td>\n",
       "      <td>-1.213155</td>\n",
       "      <td>-1.256696</td>\n",
       "      <td>-0.258753</td>\n",
       "      <td>1.964120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6  \\\n",
       "0 -0.682467  1.332874 -0.508851 -1.177700 -0.407864 -0.916446 -0.166291   \n",
       "1  0.043897  0.773830  0.983716 -1.534468 -0.465683 -1.190619 -0.346454   \n",
       "2 -0.589363  1.363932 -0.739362 -1.282352 -0.487200 -0.916446 -0.204897   \n",
       "3  1.602040 -0.772860 -0.572137 -1.634363  1.379429 -0.093928 -0.634715   \n",
       "4 -0.029201  0.370075  1.788484 -0.573574  1.386753 -0.093928  1.596735   \n",
       "\n",
       "          7         8         9        10  \n",
       "0 -0.454530  0.795738  3.763084  1.066709  \n",
       "1 -0.881573  0.795738 -0.147529  1.442323  \n",
       "2 -0.016586  0.795738  2.183712 -1.745166  \n",
       "3 -2.167220  0.795738 -0.740350 -1.316964  \n",
       "4 -1.213155 -1.256696 -0.258753  1.964120  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "x=StandardScaler().fit_transform(x)\n",
    "x=pd.DataFrame(x)\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see all the values have now been scaled down.    \n",
    "You can also scale down using these methods.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>key</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>time_signature</th>\n",
       "      <th>valence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.682298</td>\n",
       "      <td>1.332543</td>\n",
       "      <td>-0.508725</td>\n",
       "      <td>-1.177408</td>\n",
       "      <td>-0.407763</td>\n",
       "      <td>-0.916219</td>\n",
       "      <td>-0.166250</td>\n",
       "      <td>-0.454418</td>\n",
       "      <td>0.795540</td>\n",
       "      <td>3.762151</td>\n",
       "      <td>1.066445</td>\n",
       "      <td>0.124017</td>\n",
       "      <td>-0.852827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.043886</td>\n",
       "      <td>0.773638</td>\n",
       "      <td>0.983472</td>\n",
       "      <td>-1.534087</td>\n",
       "      <td>-0.465568</td>\n",
       "      <td>-1.190324</td>\n",
       "      <td>-0.346368</td>\n",
       "      <td>-0.881354</td>\n",
       "      <td>0.795540</td>\n",
       "      <td>-0.147493</td>\n",
       "      <td>1.441966</td>\n",
       "      <td>0.124017</td>\n",
       "      <td>0.368878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.589217</td>\n",
       "      <td>1.363594</td>\n",
       "      <td>-0.739179</td>\n",
       "      <td>-1.282034</td>\n",
       "      <td>-0.487079</td>\n",
       "      <td>-0.916219</td>\n",
       "      <td>-0.204846</td>\n",
       "      <td>-0.016582</td>\n",
       "      <td>0.795540</td>\n",
       "      <td>2.183171</td>\n",
       "      <td>-1.744734</td>\n",
       "      <td>0.124017</td>\n",
       "      <td>-1.309955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.601643</td>\n",
       "      <td>-0.772668</td>\n",
       "      <td>-0.571995</td>\n",
       "      <td>-1.633957</td>\n",
       "      <td>1.379087</td>\n",
       "      <td>-0.093905</td>\n",
       "      <td>-0.634558</td>\n",
       "      <td>-2.166683</td>\n",
       "      <td>0.795540</td>\n",
       "      <td>-0.740166</td>\n",
       "      <td>-1.316638</td>\n",
       "      <td>0.124017</td>\n",
       "      <td>-1.079369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.029194</td>\n",
       "      <td>0.369984</td>\n",
       "      <td>1.788040</td>\n",
       "      <td>-0.573431</td>\n",
       "      <td>1.386409</td>\n",
       "      <td>-0.093905</td>\n",
       "      <td>1.596339</td>\n",
       "      <td>-1.212855</td>\n",
       "      <td>-1.256384</td>\n",
       "      <td>-0.258689</td>\n",
       "      <td>1.963633</td>\n",
       "      <td>0.124017</td>\n",
       "      <td>1.647219</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   acousticness  danceability  duration_ms    energy  instrumentalness  \\\n",
       "0     -0.682298      1.332543    -0.508725 -1.177408         -0.407763   \n",
       "1      0.043886      0.773638     0.983472 -1.534087         -0.465568   \n",
       "2     -0.589217      1.363594    -0.739179 -1.282034         -0.487079   \n",
       "3      1.601643     -0.772668    -0.571995 -1.633957          1.379087   \n",
       "4     -0.029194      0.369984     1.788040 -0.573431          1.386409   \n",
       "\n",
       "        key  liveness  loudness      mode  speechiness     tempo  \\\n",
       "0 -0.916219 -0.166250 -0.454418  0.795540     3.762151  1.066445   \n",
       "1 -1.190324 -0.346368 -0.881354  0.795540    -0.147493  1.441966   \n",
       "2 -0.916219 -0.204846 -0.016582  0.795540     2.183171 -1.744734   \n",
       "3 -0.093905 -0.634558 -2.166683  0.795540    -0.740166 -1.316638   \n",
       "4 -0.093905  1.596339 -1.212855 -1.256384    -0.258689  1.963633   \n",
       "\n",
       "   time_signature   valence  \n",
       "0        0.124017 -0.852827  \n",
       "1        0.124017  0.368878  \n",
       "2        0.124017 -1.309955  \n",
       "3        0.124017 -1.079369  \n",
       "4        0.124017  1.647219  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=data\n",
    "normalized_df=(df-df.mean())/df.std()\n",
    "normalized_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>key</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>time_signature</th>\n",
       "      <th>valence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.010248</td>\n",
       "      <td>0.824826</td>\n",
       "      <td>0.190735</td>\n",
       "      <td>0.426363</td>\n",
       "      <td>0.022439</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.153862</td>\n",
       "      <td>0.741141</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.514441</td>\n",
       "      <td>0.596033</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.262432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.199998</td>\n",
       "      <td>0.720418</td>\n",
       "      <td>0.314481</td>\n",
       "      <td>0.350081</td>\n",
       "      <td>0.006260</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.124395</td>\n",
       "      <td>0.692162</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.071005</td>\n",
       "      <td>0.654474</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.577936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.034570</td>\n",
       "      <td>0.830626</td>\n",
       "      <td>0.171624</td>\n",
       "      <td>0.403987</td>\n",
       "      <td>0.000240</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.147548</td>\n",
       "      <td>0.791369</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.335351</td>\n",
       "      <td>0.158539</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.144379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.607034</td>\n",
       "      <td>0.431555</td>\n",
       "      <td>0.185488</td>\n",
       "      <td>0.328723</td>\n",
       "      <td>0.522541</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.077247</td>\n",
       "      <td>0.544709</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003784</td>\n",
       "      <td>0.225162</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.203928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.180902</td>\n",
       "      <td>0.645012</td>\n",
       "      <td>0.381202</td>\n",
       "      <td>0.555533</td>\n",
       "      <td>0.524590</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.442223</td>\n",
       "      <td>0.654132</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.058393</td>\n",
       "      <td>0.735659</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.908065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   acousticness  danceability  duration_ms    energy  instrumentalness  \\\n",
       "0      0.010248      0.824826     0.190735  0.426363          0.022439   \n",
       "1      0.199998      0.720418     0.314481  0.350081          0.006260   \n",
       "2      0.034570      0.830626     0.171624  0.403987          0.000240   \n",
       "3      0.607034      0.431555     0.185488  0.328723          0.522541   \n",
       "4      0.180902      0.645012     0.381202  0.555533          0.524590   \n",
       "\n",
       "        key  liveness  loudness  mode  speechiness     tempo  time_signature  \\\n",
       "0  0.181818  0.153862  0.741141   1.0     0.514441  0.596033            0.75   \n",
       "1  0.090909  0.124395  0.692162   1.0     0.071005  0.654474            0.75   \n",
       "2  0.181818  0.147548  0.791369   1.0     0.335351  0.158539            0.75   \n",
       "3  0.454545  0.077247  0.544709   1.0     0.003784  0.225162            0.75   \n",
       "4  0.454545  0.442223  0.654132   0.0     0.058393  0.735659            0.75   \n",
       "\n",
       "    valence  \n",
       "0  0.262432  \n",
       "1  0.577936  \n",
       "2  0.144379  \n",
       "3  0.203928  \n",
       "4  0.908065  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_df=(df-df.min())/(df.max()-df.min())\n",
    "normalized_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An important part of Machine Learning includes finding relation in a dataset and also make it easily undestandable for people and your team.  \n",
    "It also helps us undestand the data and apply appropiate methodolgy to solve the problem.  \n",
    "Major libraries to make graphs are\n",
    "* [Seaborn](https://seaborn.pydata.org/)\n",
    "* [Matplotlib](https://matplotlib.org/)  \n",
    "\n",
    "Let's plot some basic graphs. The graphs are also available in the official documentation.  \n",
    "Throughout the series we'll also learn some new graphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "t = [0.0, 2.0, 5.0 ] \n",
    "s = [ 0.0 , 10.0 , 25.0 ]\n",
    "\n",
    "plt.plot(t,s)\n",
    "plt.xlabel('X = t')\n",
    "plt.ylabel('Y = s')\n",
    "plt.title('Plot 1')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEWCAYAAABMoxE0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAS/klEQVR4nO3dfcxfZX3H8fdnIDJloaAN0ZbaOjsdkjhJgzg352TjQZ0lmzoWph3B9R82mZtzoFvI3Fg0W2AaJwmxKBojsupG4zNDjNsymVSMWhij47EdT1pAJ/hQ/e6P31X5rbt/9L573ed+fL+SO79zXec653edntIP13m47lQVkiQdrJ+Y7w5IkhY3g0SS1MUgkSR1MUgkSV0MEklSF4NEktTFIJFmUZLPJ3n9fPdDmksGiTRDSe5I8miS/0lyX5L3JzlihvtYm6SSHPo4bY5P8pkk30jiC19asAwS6eD8WlUdAZwAbAD+dIDv+AFwFXDOAPuWZo1BInWoqt3Ap4Dj91+X5CeS/GmSO5Pcn+QDSY5sq7/QPh9qI5sXTrHvW6pqC7BjsAOQZoFBInVIcizwMuDGKVb/Tvv5ZeCZwBHAu9u6F7fPFVV1RFX927A9lYYz8fqspMf1j0n2Ag8DnwD+aoo2ZwEXV9VtAEkuAL6e5Oy566Y0PINEOjhnVNU/HaDN04E7x8p3Mvpv7pjBeiXNAy9tScP5b+AZY+U1wF7gPsCnsLRkGCTScD4MvDHJuvZ48F8BH6mqvcADwI8Y3TuZUkYOBw5r5cOTPHEO+i3NiEEiDedy4IOMntC6Hfgu8PsAVfUIcBHwr0keSnLSFNs/A3iUx57aehS4ZehOSzMVf7GVJKmHIxJJUheDRJLUxSCRJHUxSCRJXZbkC4lPfepTa+3atfPdDUlaVLZv3/6Nqlo50+2WZJCsXbuWG264Yb67IUmLSpI7D9zq//PSliSpi0EiSepikEiSuhgkkqQuBokkqYtBIknqYpBIkroYJJKkLgaJJKnLknyzXZKWurXnf2LK+jve/vI57okjEklSJ4NEktTFIJEkdTFIJEldDBJJUheDRJLUxSCRJHUxSCRJXQwSSVIXg0SS1MUpUiRpHiykKU56OSKRJHUxSCRJXQwSSVIXg0SS1MUgkSR1MUgkSV0MEklSF4NEktTFIJEkdTFIJEldnCJFkg7CpClOYHFOc9LDEYkkqYtBIknqYpBIkroYJJKkLgaJJKmLQSJJ6jJokCR5Y5IdSb6e5MNJDk+yLsn1SXYm+UiSw1rbJ7byzrZ+7dh+Lmj1tyQ5dcg+S5JmZrAgSbIKeAOwoaqOBw4BzgTeAVxSVc8CHgTOaZucAzzY6i9p7UhyXNvuucBpwHuSHDJUvyVJMzP0pa1DgZ9McijwJOAe4KXA1rb+CuCMtryxlWnrT06SVn9lVX2vqm4HdgInDtxvSdI0DfZme1XtTvI3wF3Ao8Bnge3AQ1W1tzXbBaxqy6uAu9u2e5M8DDyl1X9xbNfj2/xYks3AZoA1a9bM+vFIWnomvZ2+3N5M7zXkpa2jGI0m1gFPB57M6NLUIKrqsqraUFUbVq5cOdTXSJL2M+SlrV8Bbq+qB6rqB8DHgBcBK9qlLoDVwO62vBs4FqCtPxL45nj9FNtIkubZkEFyF3BSkie1ex0nAzcB1wGvam02AVe35W2tTFv/uaqqVn9me6prHbAe+PcB+y1JmoEh75Fcn2Qr8GVgL3AjcBnwCeDKJH/Z6ra0TbYAH0yyE9jD6EktqmpHkqsYhdBe4Nyq+uFQ/ZYkzcyg08hX1YXAhftV38YUT11V1XeBV0/Yz0XARbPeQUlSN99slyR1MUgkSV0MEklSF4NEktTFIJEkdRn0qS1JGpJTnCwMjkgkSV0MEklSF4NEktTFIJEkdTFIJEldDBJJUheDRJLUxSCRJHUxSCRJXQwSSVIXp0iRNG+c4mRpcEQiSepikEiSuhgkkqQuBokkqYtBIknqYpBIkroYJJKkLgaJJKmLQSJJ6mKQSJK6OEWKpIM2aYoTcJqT5cQRiSSpi0EiSepikEiSuhgkkqQuBokkqYtBIknqYpBIkroMGiRJViTZmuQ/ktyc5IVJjk5yTZJb2+dRrW2SvCvJziRfTXLC2H42tfa3Jtk0ZJ8lSTMz9IjkncCnq+o5wPOAm4HzgWuraj1wbSsDnA6sbz+bgUsBkhwNXAi8ADgRuHBf+EiS5t9gQZLkSODFwBaAqvp+VT0EbASuaM2uAM5oyxuBD9TIF4EVSZ4GnApcU1V7qupB4BrgtKH6LUmamSGnSFkHPAC8L8nzgO3AecAxVXVPa3MvcExbXgXcPbb9rlY3qf7/SLKZ0UiGNWvWzN5RSEvcpGlOnOJE0zXkpa1DgROAS6vq+cB3eOwyFgBVVUDNxpdV1WVVtaGqNqxcuXI2dilJmoYhg2QXsKuqrm/lrYyC5b52yYr2eX9bvxs4dmz71a1uUr0kaQEYLEiq6l7g7iTPblUnAzcB24B9T15tAq5uy9uA17Wnt04CHm6XwD4DnJLkqHaT/ZRWJ0laAIaeRv73gQ8lOQy4DTibUXhdleQc4E7gNa3tJ4GXATuBR1pbqmpPkr8AvtTava2q9gzcb0nSNA0aJFX1FWDDFKtOnqJtAedO2M/lwOWz2ztJ0mzwzXZJUheDRJLUxSCRJHUxSCRJXQwSSVKXAz61leTJwKNV9aMkPwM8B/hUVf1g8N5JOiCnONF8m86I5AvA4UlWAZ8FXgu8f8hOSZIWj+kESarqEeDXgfdU1auB5w7bLUnSYjGtIEnyQuAsYN8Y+pDhuiRJWkymEyTnARcA/1BVO5I8E7hu2G5JkhaLA95sr6ovMLpPsq98G/CGITslSVo8fPxXktTFIJEkdTFIJEldphUkSX57/FOSpH2mOyL5w/0+JUkCZv6LrTJIL6RlbNIUJ+A0J1ocvEciSepikEiSuhgkkqQu0w2S/2yftwzVEUnS4jStIKmqM8c/JUnax0tbkqQuBokkqcvEIEnyySRr564rkqTF6PFGJO8DPpvkrUmeMFcdkiQtLhPfbK+qv0/yKeDPgBuSfBD40dj6i+egf5KkBe5AU6R8H/gO8ETgpxgLEkmPmTTNiVOcaDmYGCRJTgMuBrYBJ1TVI3PWK0nSovF4I5K3Aq+uqh1z1RlJ0uLzePdIfnEuOyJJWpx8j0SS1MUgkSR1MUgkSV0GD5IkhyS5McnHW3ldkuuT7EzykSSHtfontvLOtn7t2D4uaPW3JDl16D5LkqZvLkYk5wE3j5XfAVxSVc8CHgTOafXnAA+2+ktaO5IcB5wJPBc4DXhPkkPmoN+SpGkYNEiSrAZeDry3lQO8FNjamlwBnNGWN7Yybf3Jrf1G4Mqq+l5V3Q7sBE4cst+SpOkbekTyt8CbeeyN+KcAD1XV3lbeBaxqy6uAuwHa+odb+x/XT7GNJGmeHWiKlIOW5BXA/VW1PclLhvqese/bDGwGWLNmzdBfpyXGKU6kgzfkiORFwCuT3AFcyeiS1juBFUn2BdhqYHdb3g0cC9DWHwl8c7x+im1+rKouq6oNVbVh5cqVs380kqQpDRYkVXVBVa2uqrWMbpZ/rqrOAq4DXtWabQKubsvbWpm2/nNVVa3+zPZU1zpgPfDvQ/VbkjQzg13aehx/AlyZ5C+BG4EtrX4L8MEkO4E9jMKHqtqR5CrgJmAvcG5V/XDuuy1JmsqcBElVfR74fFu+jSmeuqqq7wKvnrD9RcBFw/VQknSwfLNdktTFIJEkdTFIJEldDBJJUheDRJLUxSCRJHWZj/dIpFk3aYoTcJoTaWiOSCRJXQwSSVIXg0SS1MUgkSR1MUgkSV0MEklSF4NEktTFIJEkdTFIJEldfLNdC8akt9N9M11a2ByRSJK6GCSSpC4GiSSpi0EiSepikEiSuhgkkqQuBokkqYtBIknqYpBIkroYJJKkLk6RolnjFCfS8uSIRJLUxSCRJHUxSCRJXQwSSVIXg0SS1MUgkSR1MUgkSV0GC5Ikxya5LslNSXYkOa/VH53kmiS3ts+jWn2SvCvJziRfTXLC2L42tfa3Jtk0VJ8lSTM35IhkL/BHVXUccBJwbpLjgPOBa6tqPXBtKwOcDqxvP5uBS2EUPMCFwAuAE4EL94WPJGn+DRYkVXVPVX25LX8buBlYBWwErmjNrgDOaMsbgQ/UyBeBFUmeBpwKXFNVe6rqQeAa4LSh+i1Jmpk5mSIlyVrg+cD1wDFVdU9bdS9wTFteBdw9ttmuVjepfv/v2MxoJMOaNWtmr/PLyKQpTsBpTiRNNvjN9iRHAB8F/qCqvjW+rqoKqNn4nqq6rKo2VNWGlStXzsYuJUnTMGiQJHkCoxD5UFV9rFXf1y5Z0T7vb/W7gWPHNl/d6ibVS5IWgCGf2gqwBbi5qi4eW7UN2Pfk1Sbg6rH617Wnt04CHm6XwD4DnJLkqHaT/ZRWJ0laAIa8R/Ii4LXA15J8pdW9BXg7cFWSc4A7gde0dZ8EXgbsBB4Bzgaoqj1J/gL4Umv3tqraM2C/JUkzMFiQVNW/AJmw+uQp2hdw7oR9XQ5cPnu9kyTNFt9slyR1MUgkSV0MEklSF4NEktTFIJEkdZmTKVI0dyZNc+IUJ5KG4ohEktTFIJEkdTFIJEldDBJJUheDRJLUxSCRJHUxSCRJXQwSSVIXg0SS1MUgkSR1cYqUBcYpTiQtNo5IJEldDBJJUheDRJLUxSCRJHUxSCRJXQwSSVIXg0SS1MUgkSR1MUgkSV0MEklSF6dImWWTpjgBpzmRtDQ5IpEkdTFIJEldDBJJUheDRJLUxSCRJHUxSCRJXQwSSVKXRRMkSU5LckuSnUnOn+/+SJJGFkWQJDkE+DvgdOA44LeSHDe/vZIkwSIJEuBEYGdV3VZV3weuBDbOc58kSUCqar77cEBJXgWcVlWvb+XXAi+oqt8ba7MZ2NyKzwZumaWvfyrwjVna12KyXI8bPHaPfXkZP+5nVNXKme5gycy1VVWXAZfN9n6T3FBVG2Z7vwvdcj1u8Ng99uVlNo57sVza2g0cO1Ze3eokSfNssQTJl4D1SdYlOQw4E9g2z32SJLFILm1V1d4kvwd8BjgEuLyqdszR18/65bJFYrkeN3jsy9VyPfbu414UN9slSQvXYrm0JUlaoAwSSVIXg2SC5TQlS5Jjk1yX5KYkO5Kc1+qPTnJNklvb51Hz3dchJDkkyY1JPt7K65Jc3879R9oDHktOkhVJtib5jyQ3J3nhMjrnb2x/17+e5MNJDl+q5z3J5UnuT/L1sbopz3NG3tX+DL6a5ITpfIdBMoVlOCXLXuCPquo44CTg3Ha85wPXVtV64NpWXorOA24eK78DuKSqngU8CJwzL70a3juBT1fVc4DnMfozWPLnPMkq4A3Ahqo6ntEDPGeydM/7+4HT9qubdJ5PB9a3n83ApdP5AoNkastqSpaquqeqvtyWv83oH5RVjI75itbsCuCM+enhcJKsBl4OvLeVA7wU2NqaLNXjPhJ4MbAFoKq+X1UPsQzOeXMo8JNJDgWeBNzDEj3vVfUFYM9+1ZPO80bgAzXyRWBFkqcd6DsMkqmtAu4eK+9qdUtekrXA84HrgWOq6p626l7gmHnq1pD+Fngz8KNWfgrwUFXtbeWleu7XAQ8A72uX9d6b5Mksg3NeVbuBvwHuYhQgDwPbWR7nfZ9J5/mg/u0zSPRjSY4APgr8QVV9a3xdjZ4TX1LPiid5BXB/VW2f777Mg0OBE4BLq+r5wHfY7zLWUjznAO1+wEZGYfp04Mn8/0s/y8ZsnGeDZGrLbkqWJE9gFCIfqqqPter79g1r2+f989W/gbwIeGWSOxhdvnwpo/sGK9olD1i6534XsKuqrm/lrYyCZamfc4BfAW6vqgeq6gfAxxj9XVgO532fSef5oP7tM0imtqymZGn3BbYAN1fVxWOrtgGb2vIm4Oq57tuQquqCqlpdVWsZnePPVdVZwHXAq1qzJXfcAFV1L3B3kme3qpOBm1ji57y5CzgpyZPa3/19x77kz/uYSed5G/C69vTWScDDY5fAJvLN9gmSvIzR9fN9U7JcNM9dGkySXwD+Gfgaj90reAuj+yRXAWuAO4HXVNX+N+2WhCQvAd5UVa9I8kxGI5SjgRuB366q781n/4aQ5OcYPWRwGHAbcDaj/7lc8uc8yZ8Dv8noicUbgdczuhew5M57kg8DL2E0Xfx9wIXAPzLFeW7B+m5Gl/oeAc6uqhsO+B0GiSSph5e2JEldDBJJUheDRJLUxSCRJHUxSCRJXQwSqUObOfn2JEe38lGtvLZzv2cs8YlCtYQYJFKHqrqb0Qypb29Vbwcuq6o7Ond9BqOZp6UFz/dIpE5tepntwOXA7wI/16beONj9/TzwcUaTCT4M/EZV/dds9FUagkEizYIkpwKfBk6pqmsmtPln4KemWPWmqvqn/dq+H/h4VW2dor20oBx64CaSpuF0RlOSHw9MGSRV9Ytz2iNpjhgkUqc2Z9WvMvrtkv+S5MqpJrqbyYhEWkwMEqlDm+TuUka/w+WuJH/N6JcmnbV/2xmOSL7N1KEjLTg+tSX1+V3grrH7Iu8BfjbJL3Xu90rgj9tvL/zpzn1Jg/JmuySpiyMSSVIXg0SS1MUgkSR1MUgkSV0MEklSF4NEktTFIJEkdflfXU4+ewcKDVwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = [ i for i in range(0,100,5) ] \n",
    "s = [ i for i in range(0,10000,500) ]\n",
    "\n",
    "plt.bar(t,s,width=2)\n",
    "plt.xlabel('X = t')\n",
    "plt.ylabel('Y = s')\n",
    "plt.title('Plot 1')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = [ i for i in range(0,100,10) ] \n",
    "s = [ i for i in range(0,10000,1000) ]\n",
    "\n",
    "# defining size of image\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.bar(t,s,width=2)\n",
    "\n",
    "# putting up labels against bars\n",
    "for index, value in zip(t,s):\n",
    "    plt.text(index,value, str(value))\n",
    "    \n",
    "plt.xlabel('X = t')\n",
    "plt.ylabel('Y = s')\n",
    "plt.title('Plot 1')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "t = [ i for i in range(0,200,5) ] \n",
    "s = [ ]\n",
    "\n",
    "# making random values\n",
    "for i in range(len(t)):\n",
    "    s.append(np.random.randint(50))\n",
    "\n",
    "plt.scatter(t,s)\n",
    "plt.xlabel('X = t')\n",
    "plt.ylabel('Y = s')\n",
    "plt.title('Plot 1')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'\n",
    "sizes = [15, 30, 45, 10]\n",
    "\n",
    "plt.pie(sizes, labels=labels)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = [ i for i in range(0,100,10) ] \n",
    "s1 = [ i for i in range(0,10000,1000) ]\n",
    "s2 = [ i for i in range(0,5000,500) ]\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "plt.bar(t,s1,width=2)\n",
    "plt.bar(t,s2,width=2)\n",
    "    \n",
    "plt.xlabel('X = t')\n",
    "plt.ylabel('Y = s')\n",
    "plt.title('Plot 1')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t  = [ i for i in range(0,100,10) ] \n",
    "s1 = [ i for i in range(0,10000,1000) ]\n",
    "s2 = [ i for i in range(0,5000,500) ]\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "# negative width shifts the bar left \n",
    "# positive shifts it towards right\n",
    "plt.bar( t, s1, width = -2, align = 'edge', color = 'b')\n",
    "plt.bar( t, s2, width = 2, align = 'edge', color = 'g')\n",
    "\n",
    "# provides anotation to graph\n",
    "plt.legend(['S1', 'S2'])\n",
    "plt.xlabel('X = t')\n",
    "plt.ylabel('Y = s')\n",
    "plt.title('Plot 1')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets start by ploting the most import of all  \n",
    "[**HEATMAP**](https://seaborn.pydata.org/generated/seaborn.heatmap.html)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7eff6889ad30>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(normalized_df.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You might me wondering, what's this?    \n",
    "It shows relation of each column with respect to other.    \n",
    "For example, energy vs accousticness it says -0.56.  \n",
    "Which means it has a negative (indirect) relation, i.e. when one increases other decrease  \n",
    "The closer the value is to 0, the more independent the variable is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# The graph depicts the higher the loudness, greater is the energy\n",
    "plt.bar(x=data[\"energy\"],height=data[\"loudness\"])\n",
    "plt.xlabel('Energy', fontsize=20)\n",
    "plt.ylabel('Loudness', fontsize=20)\n",
    "plt.title('Graph 1')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# over here we see that most of our songs have high energy and loudness and a direct relation is observed\n",
    "plt.scatter(data[\"energy\"],data[\"loudness\"])\n",
    "plt.xlabel('Energy', fontsize=20)\n",
    "plt.ylabel('Loudness', fontsize=20)\n",
    "plt.title('Graph 1')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7eff683b36a0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7eff65a97370>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7eff65a2f7f0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7eff65a57610>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7eff659f7dc0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7eff65ba46a0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7eff65ba48b0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7eff6877b880>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7eff6889ac70>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7eff68969df0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7eff689314f0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7eff689d3b80>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7eff6898c160>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7eff6b153640>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7eff689a14c0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7eff6888dd00>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets see our data distribution\n",
    "df.hist(figsize=(20,20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Such graphs help us understand weather our data is imbalanced or not.  \n",
    "There are tonnes of visualization methods and these were the most used ones  \n",
    "For visualising data you need to change the dataset accordingly  \n",
    "for more you may visit [here](https://matplotlib.org/tutorials/introductory/sample_plots.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time for an assignmnet\n",
    "Load the csv in new python notebook  \n",
    "Data_Science_Topics_Survey.csv  \n",
    "Do feature engineering  \n",
    "Plot the graph  \n",
    "We know it'll take some time, but its worth devoting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>Timestamp</th>\n",
       "      <th>What's your level of interest for the following areas of Data Science? [Data Visualization]</th>\n",
       "      <th>What's your level of interest for the following areas of Data Science? [Machine Learning]</th>\n",
       "      <th>What's your level of interest for the following areas of Data Science? [Data Analysis / Statistics]</th>\n",
       "      <th>What's your level of interest for the following areas of Data Science? [Big Data (Spark / Hadoop)]</th>\n",
       "      <th>What's your level of interest for the following areas of Data Science? [Data Journalism]</th>\n",
       "      <th>What's your level of interest for the following areas of Data Science? [Deep Learning]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017/10/17 1:11:52 PM MDT</td>\n",
       "      <td>Very interested</td>\n",
       "      <td>Very interested</td>\n",
       "      <td>Somewhat interested</td>\n",
       "      <td>Not interested</td>\n",
       "      <td>Somewhat interested</td>\n",
       "      <td>Somewhat interested</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017/10/17 2:26:07 PM MDT</td>\n",
       "      <td>Very interested</td>\n",
       "      <td>Very interested</td>\n",
       "      <td>Very interested</td>\n",
       "      <td>Somewhat interested</td>\n",
       "      <td>Not interested</td>\n",
       "      <td>Very interested</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017/10/17 2:37:08 PM MDT</td>\n",
       "      <td>Not interested</td>\n",
       "      <td>Very interested</td>\n",
       "      <td>Somewhat interested</td>\n",
       "      <td>Very interested</td>\n",
       "      <td>Not interested</td>\n",
       "      <td>Very interested</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017/10/17 2:39:26 PM MDT</td>\n",
       "      <td>Very interested</td>\n",
       "      <td>Very interested</td>\n",
       "      <td>Very interested</td>\n",
       "      <td>Very interested</td>\n",
       "      <td>Somewhat interested</td>\n",
       "      <td>Very interested</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017/10/17 2:43:59 PM MDT</td>\n",
       "      <td>Somewhat interested</td>\n",
       "      <td>Very interested</td>\n",
       "      <td>Very interested</td>\n",
       "      <td>Very interested</td>\n",
       "      <td>Not interested</td>\n",
       "      <td>Somewhat interested</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Timestamp  \\\n",
       "0  2017/10/17 1:11:52 PM MDT   \n",
       "1  2017/10/17 2:26:07 PM MDT   \n",
       "2  2017/10/17 2:37:08 PM MDT   \n",
       "3  2017/10/17 2:39:26 PM MDT   \n",
       "4  2017/10/17 2:43:59 PM MDT   \n",
       "\n",
       "  What's your level of interest for the following areas of Data Science? [Data Visualization]  \\\n",
       "0                                    Very interested                                            \n",
       "1                                    Very interested                                            \n",
       "2                                     Not interested                                            \n",
       "3                                    Very interested                                            \n",
       "4                                Somewhat interested                                            \n",
       "\n",
       "  What's your level of interest for the following areas of Data Science? [Machine Learning]  \\\n",
       "0                                    Very interested                                          \n",
       "1                                    Very interested                                          \n",
       "2                                    Very interested                                          \n",
       "3                                    Very interested                                          \n",
       "4                                    Very interested                                          \n",
       "\n",
       "  What's your level of interest for the following areas of Data Science? [Data Analysis / Statistics]  \\\n",
       "0                                Somewhat interested                                                    \n",
       "1                                    Very interested                                                    \n",
       "2                                Somewhat interested                                                    \n",
       "3                                    Very interested                                                    \n",
       "4                                    Very interested                                                    \n",
       "\n",
       "  What's your level of interest for the following areas of Data Science? [Big Data (Spark / Hadoop)]  \\\n",
       "0                                     Not interested                                                   \n",
       "1                                Somewhat interested                                                   \n",
       "2                                    Very interested                                                   \n",
       "3                                    Very interested                                                   \n",
       "4                                    Very interested                                                   \n",
       "\n",
       "  What's your level of interest for the following areas of Data Science? [Data Journalism]  \\\n",
       "0                                Somewhat interested                                         \n",
       "1                                     Not interested                                         \n",
       "2                                     Not interested                                         \n",
       "3                                Somewhat interested                                         \n",
       "4                                     Not interested                                         \n",
       "\n",
       "  What's your level of interest for the following areas of Data Science? [Deep Learning]  \n",
       "0                                Somewhat interested                                      \n",
       "1                                    Very interested                                      \n",
       "2                                    Very interested                                      \n",
       "3                                    Very interested                                      \n",
       "4                                Somewhat interested                                      "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datagen=pd.read_csv(\"data/Data_Science_Topics_Survey.csv\")\n",
    "datagen.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try to make a stacked bar graph, depicting number of students of each category for each question."
   ]
  }
 ],
 "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",
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 },
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