Assignment_1(Apurva).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UOzd0zRXW5Uv"
   },
   "source": [
    "# Week-1 Assignment\n",
    "\n",
    "*Welcome to your first assignment for the SimuTech Winter Project 2022! I hope you are excited to implement and test everything you have learned up until now. There is an interesting set of questions for you to refine your acquired skills as you delve into hands-on coding and deepen your understanding of numpy, pandas, and data visualization libraries.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2XFUXyq9Y9TG"
   },
   "source": [
    "# Section 0 : Importing Libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D_bzcLusjSO0"
   },
   "source": [
    "*Let's begin by importing numpy, pandas and matplotlib.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "jdwX7bI7aGeY"
   },
   "outputs": [],
   "source": [
    "#your code here\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import os\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LM25mz5ta-Q_"
   },
   "source": [
    "# Section 1 : Playing with Python and Numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TcFQVqhAdQw_"
   },
   "source": [
    "### Q1. Matrix Multiplication"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6Dcai9pys0j8"
   },
   "source": [
    "##### (i) Check if matrix multiplication is valid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "0qC4txIss3gG"
   },
   "outputs": [],
   "source": [
    "def isValid(A,B):\n",
    "  #your code here\n",
    "  if np.shape(A)[1]==np.shape(B)[0]:\n",
    "        return 1\n",
    "  else:\n",
    "        return 0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OPTfRGlTdXxa"
   },
   "source": [
    "##### (ii) Using loops (without using numpy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "id": "V8F5ETsRct3S"
   },
   "outputs": [],
   "source": [
    "def matrix_multiply(A,B):\n",
    "  #your code here\n",
    "  row1=np.shape(A)[0]\n",
    "  column1=np.shape(A)[1]\n",
    "  row2=np.shape(B)[0]\n",
    "  column2=np.shape(B)[1]\n",
    "\n",
    "  #Creating a list with all values set to 0\n",
    "  result = [[0 for x in range(column2)] for y in range(row1)]\n",
    "\n",
    "  #Using loops\n",
    "  for i in range(row1):\n",
    "        for j in range(column2):\n",
    "            for k in range(column1):\n",
    "                result[i][j] = result[i][j]+ A[i][k] * B[k][j]\n",
    "\n",
    "  return result\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "umUgUTSWdos8"
   },
   "source": [
    "##### (iii) Using numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "-tdXlCHrduBP"
   },
   "outputs": [],
   "source": [
    "def matrix_multiply_2(A,B):\n",
    "  #your code here\n",
    "   return A@B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2nMFnl84edJG"
   },
   "source": [
    "##### (iv) Testing your code\n",
    "\n",
    "Run the following cell to check if your functions are working properly.\n",
    "\n",
    "*Expected output:*\n",
    "[ [102 108 114]\n",
    " [246 261 276]\n",
    " [390 414 438]\n",
    " [534 567 600] ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "09rX_Cm9ezmq"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Result using loops: [[102, 108, 114], [246, 261, 276], [390, 414, 438], [534, 567, 600]]\n",
      "Result using numpy: [[102 108 114]\n",
      " [246 261 276]\n",
      " [390 414 438]\n",
      " [534 567 600]]\n"
     ]
    }
   ],
   "source": [
    "A = np.array([\n",
    "    [1, 2, 3],\n",
    "    [4, 5, 6],\n",
    "    [7, 8, 9],\n",
    "    [10, 11, 12]\n",
    "])\n",
    "\n",
    "B = np.array([\n",
    "    [13, 14, 15],\n",
    "    [16, 17, 18],\n",
    "    [19, 20, 21]\n",
    "])\n",
    "\n",
    "if isValid(A,B):\n",
    "  print(f\"Result using loops: {matrix_multiply(A,B)}\")\n",
    "  print(f\"Result using numpy: {matrix_multiply_2(A,B)}\")\n",
    "else:\n",
    "  print(f\"Matrix multiplication is not valid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5hfP7Ao1fwqV"
   },
   "source": [
    "### Q2. Z-Score Normalisation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0e81Xx5Kw_CQ"
   },
   "source": [
    "Z-score normalization refers to the process of normalizing every value in a dataset such that the mean of all of the values is 0 and the standard deviation is 1.\n",
    "\n",
    "We use the following formula to perform a z-score normalization on every value in a dataset:\n",
    "\n",
    "New value = (x – μ) / σ\n",
    "\n",
    "where:\n",
    "\n",
    "x: Original value\n",
    "\n",
    "μ: Mean of data\n",
    "\n",
    "σ: Standard deviation of data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GP_MuyUbguSR"
   },
   "source": [
    "##### (i) Without using numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "id": "0vix8qaaghwP"
   },
   "outputs": [],
   "source": [
    "def mean(x):\n",
    "  #your code here\n",
    "    numerator=0\n",
    "    \n",
    "    for i in x:\n",
    "        numerator=numerator+i\n",
    "    denominator=len(x)\n",
    "\n",
    "    return (numerator/denominator)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "id": "j1qabel-g_f5"
   },
   "outputs": [],
   "source": [
    "def standard_deviation(x):\n",
    "  #your code here\n",
    "  numerator=0\n",
    "  \n",
    "  for i in x:\n",
    "    numerator=numerator+((i-mean(x))**2)\n",
    "\n",
    "  variance = numerator / len(x)\n",
    "  \n",
    "  SD=variance**0.5\n",
    "  \n",
    "  return SD\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "id": "797ewbNqhEpY"
   },
   "outputs": [],
   "source": [
    "def zscore_normalisation(x):\n",
    "  #your code here\n",
    "    normalized_data = [(i - mean(x)) / standard_deviation(x) for i in x]\n",
    "    \n",
    "    return normalized_data\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k8dq1wqRhbUB"
   },
   "source": [
    "##### (ii) Using numpy\n",
    "\n",
    "Numpy has in_built functions for calculating mean and standard deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "id": "WjxJTUDihsYg"
   },
   "outputs": [],
   "source": [
    "def zscore_normalisation_2(x):\n",
    "  #your code here\n",
    "    avg1=np.mean(x)\n",
    "    std1=np.std(x)\n",
    "\n",
    "    normalized_data2 = [(i - avg1) / std1 for i in x]\n",
    "    \n",
    "    return normalized_data2\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "glRqKU-_1pue"
   },
   "source": [
    "##### (iii) Testing your code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Kl7XBymOigCU"
   },
   "source": [
    "Run the following cell to check if your functions are working properly.\n",
    "\n",
    "*Expected Output:* [-1.06753267 -0.99745394 -0.99745394 -0.81057732 -0.41346451 -0.06307086\n",
    "  0.31068237  0.91803138  1.22170588  1.89913361]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "id": "78ptZxf6ipZp"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Result without using numpy: [-1.0675326683028088, -0.9974539373420117, -0.9974539373420117, -0.8105773214465528, -0.41346451266870277, -0.06307085786471743, 0.3106823739262003, 0.9180313755864415, 1.2217058764165623, 1.8991336090376005]\n",
      "Result using numpy: [-1.0675326683028088, -0.9974539373420117, -0.9974539373420117, -0.8105773214465528, -0.41346451266870277, -0.06307085786471743, 0.3106823739262003, 0.9180313755864415, 1.2217058764165623, 1.8991336090376005]\n"
     ]
    }
   ],
   "source": [
    "x = [4, 7, 7, 15, 32, 47, 63, 89, 102, 131]\n",
    "print(f\"Result without using numpy: {zscore_normalisation(x)}\")\n",
    "print(f\"Result using numpy: {zscore_normalisation_2(x)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0WVscJ0z14rp"
   },
   "source": [
    "### Q3. Sigmoid fn and its derivative"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Jfntb0Rg94Tu"
   },
   "source": [
    "The sigmoid function is a mathematical function that maps any input value to a value between 0 and 1.\n",
    "\n",
    "It is defined mathematically as s(x) = 1/(1+e^(-x))."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8SCAPkjO2m7u"
   },
   "source": [
    "##### (i) Write a fn to implement sigmoid fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "id": "jL_TRQFG2l5m"
   },
   "outputs": [],
   "source": [
    "def sigmoidfn(x):\n",
    "  #your code here\n",
    "  e=2.718281828459045\n",
    "  return 1/(1+(e**(-x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t7KBbl7N3AUM"
   },
   "source": [
    "##### (ii) Write a fn to implement derivative of sigmoid fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "id": "okjuOpba3KOJ"
   },
   "outputs": [],
   "source": [
    "def derivative(x):\n",
    "  #your code here\n",
    "  e=2.718281828459045\n",
    "  numerator=-(e**(-x))\n",
    "  denominator=(1+(e**(-x)))**2\n",
    "  return numerator/denominator\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NdGTSpsp3mwr"
   },
   "source": [
    "##### (iii) Test your code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lWY8RBex7gnd"
   },
   "source": [
    "Run the following cell to check if your functions are working properly.\n",
    "\n",
    "*Expected output:*\n",
    "\n",
    "x on applying sigmoid activation fn is: [ [0.99987661 0.88079708 0.99330715 0.5        0.5       ]\n",
    " [0.99908895 0.99330715 0.5        0.5        0.5       ] ]\n",
    "\n",
    "x on applying derivative of sigmoid activation fn is: [ [-1.23379350e-04 -1.04993585e-01 -6.64805667e-03 -2.50000000e-01\n",
    "  -2.50000000e-01]\n",
    " [-9.10221180e-04 -6.64805667e-03 -2.50000000e-01 -2.50000000e-01\n",
    "  -2.50000000e-01] ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "id": "ssDGpmS33vdA"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x on applying sigmoid activation fn is: [[0.99987661 0.88079708 0.99330715 0.5        0.5       ]\n",
      " [0.99908895 0.99330715 0.5        0.5        0.5       ]]\n",
      "x on applying derivative of sigmoid activation fn is: [[-1.23379350e-04 -1.04993585e-01 -6.64805667e-03 -2.50000000e-01\n",
      "  -2.50000000e-01]\n",
      " [-9.10221180e-04 -6.64805667e-03 -2.50000000e-01 -2.50000000e-01\n",
      "  -2.50000000e-01]]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([\n",
    "    [9,2,5,0,0],\n",
    "    [7,5,0,0,0]\n",
    "])\n",
    "print(f\"x on applying sigmoid activation fn is: {sigmoidfn(x)}\")\n",
    "print(f\"x on applying derivative of sigmoid activation fn is: {derivative(x)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PMa0cseyZpa1"
   },
   "source": [
    "# Section 2: Exploring Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*You have been provided with a dataset which includes information about properties of superheated vapor.*\n",
    "\n",
    "*The dataset consists of the thermophysical properties: specific volume, specific internal energy, specific enthalpy, specific entropy of superheated vapor.*\n",
    "\n",
    "*Pressure is in kPa and Temperature in centigrade. In the dataframe 75, 100, 125, etc. are temperatures.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "i5Okb_jxZ_VW"
   },
   "source": [
    "### Read the csv file\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "id": "aQgvyavrcM1l"
   },
   "outputs": [
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       "      <th>0</th>\n",
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       "      <td>206810.0000</td>\n",
       "      <td>218350.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>333730.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>356810.0000</td>\n",
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       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>2480.8000</td>\n",
       "      <td>2516.4000</td>\n",
       "      <td>2552.3000</td>\n",
       "      <td>2588.5000</td>\n",
       "      <td>2624.9000</td>\n",
       "      <td>2661.7000</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>3596.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3705.6000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>S</td>\n",
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       "      <td>8.9767</td>\n",
       "      <td>9.3828</td>\n",
       "      <td>9.5136</td>\n",
       "      <td>9.6365</td>\n",
       "      <td>9.7527</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>42600.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pressure Property  Liq_Sat      Vap_Sat           75          100  \\\n",
       "0       1.0        V    1.000  129200.0000  160640.0000  172180.0000   \n",
       "1       1.0        U   29.334    2385.2000    2480.8000    2516.4000   \n",
       "2       1.0        H   29.335    2514.4000    2641.5000    2688.6000   \n",
       "3       1.0        S    0.106       8.9767       9.3828       9.5136   \n",
       "4      10.0        V    1.010   14670.0000   16030.0000   17190.0000   \n",
       "\n",
       "           125          150          175          200  ...  425        450  \\\n",
       "0  183720.0000  195270.0000  206810.0000  218350.0000  ...  NaN  333730.00   \n",
       "1    2552.3000    2588.5000    2624.9000    2661.7000  ...  NaN    3049.90   \n",
       "2    2736.0000    2783.7000    2831.7000    2880.1000  ...  NaN    3383.60   \n",
       "3       9.6365       9.7527       9.8629       9.9679  ...  NaN      10.82   \n",
       "4   18350.0000   19510.0000   20660.0000   21820.0000  ...  NaN   33370.00   \n",
       "\n",
       "   475          500  525          550  575          600  625          650  \n",
       "0  NaN  356810.0000  NaN  379880.0000  NaN  402960.0000  NaN  426040.0000  \n",
       "1  NaN    3132.4000  NaN    3216.7000  NaN    3302.6000  NaN    3390.3000  \n",
       "2  NaN    3489.2000  NaN    3596.5000  NaN    3705.6000  NaN    3816.4000  \n",
       "3  NaN      10.9612  NaN      11.0957  NaN      11.2243  NaN      11.3476  \n",
       "4  NaN   35670.0000  NaN   37980.0000  NaN   40290.0000  NaN   42600.0000  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "x=pd.read_csv(\"superheated_vapor_properties.csv\")\n",
    "df=pd.DataFrame(x)\n",
    "df.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(\"C:\\\\Users\\\\apurv\\\\OneDrive\\\\Documents\\\\230180_Apurva\\\\Machine-Learning-with-Python\\\\Assignments\\\\Assignment 1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zp8F3rk2cNxy"
   },
   "source": [
    "### Display the shape of data frame\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "id": "v3Q8kQkucgK0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of data frame is  (544, 37)\n"
     ]
    }
   ],
   "source": [
    "#your code here\n",
    "S=df.shape\n",
    "\n",
    "print(\"Shape of data frame is \",S)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "w49cp-8zcgd2"
   },
   "source": [
    "### Return an array containing names of all the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "id": "fz4HRb3JcsZp"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The array of column names is \n",
      " ['Pressure' 'Property' 'Liq_Sat' 'Vap_Sat' '75' '100' '125' '150' '175'\n",
      " '200' '220' '225' '240' '250' '260' '275' '280' '290' '300' '320' '325'\n",
      " '340' '350' '360' '375' '380' '400' '425' '450' '475' '500' '525' '550'\n",
      " '575' '600' '625' '650']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "column_name=df.columns\n",
    "array=column_name.to_numpy()\n",
    "\n",
    "print(\"The array of column names is \\n\",array)\n",
    "\n",
    "type(array)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YkTH9tRWcrH-"
   },
   "source": [
    "### Display the number of null values in each column of the dataframe\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "id": "GK9-PJPxc3Ot"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pressure      0\n",
       "Property      0\n",
       "Liq_Sat       0\n",
       "Vap_Sat       0\n",
       "75          528\n",
       "100         508\n",
       "125         488\n",
       "150         448\n",
       "175         384\n",
       "200         320\n",
       "220         408\n",
       "225         400\n",
       "240         408\n",
       "250         344\n",
       "260         384\n",
       "275         340\n",
       "280         380\n",
       "290         488\n",
       "300          60\n",
       "320         480\n",
       "325         136\n",
       "340         476\n",
       "350          68\n",
       "360         476\n",
       "375         204\n",
       "380         476\n",
       "400           0\n",
       "425         204\n",
       "450           0\n",
       "475         204\n",
       "500           0\n",
       "525         272\n",
       "550           0\n",
       "575         340\n",
       "600           0\n",
       "625         476\n",
       "650           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "df.isnull().sum(axis=0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Av-lcft2c3mx"
   },
   "source": [
    "### Create a column which contains the Pressure and Property columns, seperated with 'at' (For eg. V at 1, H at 101.325). Using this print the following:\n",
    "- Enthalpy at 75 kPa and 573 K\n",
    "- Entropy at 493 K and 250 kPa\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "id": "B2AihQj_c32C"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enthalpy at 75 kPa and 573 K is:  [3520.5]\n",
      "Entropy at 493K and 250 kPa is:  [nan]\n"
     ]
    }
   ],
   "source": [
    "#your code here\n",
    "#I am using string concatenation and so converting columns into 'str' type\n",
    "df['Combined'] = df['Property'].astype(str) + ' at ' + df['Pressure'].astype(str)\n",
    "\n",
    "#Considering the given temperatures are in degree celsius \n",
    "\n",
    "row_1=df.loc[(df[\"Combined\"]==\"V at 75.0\")].index #Using the Combined column\n",
    "column_1=df.columns.get_loc(\"300\")\n",
    "print(\"Enthalpy at 75 kPa and 573 K is: \",df.iloc[row_1,column_1].values)\n",
    "\n",
    "row_2=df.loc[(df[\"Combined\"]==\"S at 250.0\")].index #Using the Combined column\n",
    "column_2=df.columns.get_loc(\"220\")\n",
    "print(\"Entropy at 493K and 250 kPa is: \",df.iloc[row_2,column_2].values)\n",
    "\n",
    "#The column 220Deg celsius is not present therefore Nan value\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GpLtXYRLc4Ho"
   },
   "source": [
    "### Find out the column with the highest number of missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "id": "q80Grgeyc4Xn"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The column at the top has the maximum null values  \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "75          528\n",
       "100         508\n",
       "290         488\n",
       "125         488\n",
       "320         480\n",
       "625         476\n",
       "340         476\n",
       "360         476\n",
       "380         476\n",
       "150         448\n",
       "220         408\n",
       "240         408\n",
       "225         400\n",
       "175         384\n",
       "260         384\n",
       "280         380\n",
       "250         344\n",
       "575         340\n",
       "275         340\n",
       "200         320\n",
       "525         272\n",
       "375         204\n",
       "425         204\n",
       "475         204\n",
       "325         136\n",
       "350          68\n",
       "300          60\n",
       "600           0\n",
       "650           0\n",
       "550           0\n",
       "Pressure      0\n",
       "500           0\n",
       "450           0\n",
       "400           0\n",
       "Property      0\n",
       "Vap_Sat       0\n",
       "Liq_Sat       0\n",
       "Combined      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "print(\"The column at the top has the maximum null values  \\n\")\n",
    "df.isnull().sum().sort_values(ascending=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "74NVHTTac4nv"
   },
   "source": [
    "### What is the average enthalpy of Sat. Liq. at all different pressures in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "id": "g3mByhBDdpVr"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The avg enthalpy of Sat. Liq. at different pressures is : \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "936.9707720588235"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "g = df.groupby(df.Property)\n",
    "h = g.get_group(\"H\")\n",
    "print(\"The avg enthalpy of Sat. Liq. at different pressures is : \")\n",
    "h[\"Liq_Sat\"].mean()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uS-SuqU8dpxg"
   },
   "source": [
    "### Separate out the V,U,H,S data from the dataset into V_data, U_data, H_data, S_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "id": "Sg3V9Toyd1Av"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V_data is: \n",
      "      Pressure Property  Liq_Sat     Vap_Sat        75       100       125  \\\n",
      "0         1.0        V    1.000  129200.000  160640.0  172180.0  183720.0   \n",
      "4        10.0        V    1.010   14670.000   16030.0   17190.0   18350.0   \n",
      "8        20.0        V    1.017    7649.800    8000.0    8584.7    9167.1   \n",
      "12       30.0        V    1.022    5229.300    5322.0    5714.4    6104.6   \n",
      "16       40.0        V    1.027    3993.400       NaN    4279.2    4573.3   \n",
      "..        ...      ...      ...         ...       ...       ...       ...   \n",
      "524   10600.0        V    1.474      16.778       NaN       NaN       NaN   \n",
      "528   10800.0        V    1.481      16.385       NaN       NaN       NaN   \n",
      "532   11000.0        V    1.489      16.006       NaN       NaN       NaN   \n",
      "536   11200.0        V    1.496      15.639       NaN       NaN       NaN   \n",
      "540   11400.0        V    1.504      15.284       NaN       NaN       NaN   \n",
      "\n",
      "          150       175       200  ...         450     475         500  \\\n",
      "0    195270.0  206810.0  218350.0  ...  333730.000     NaN  356810.000   \n",
      "4     19510.0   20660.0   21820.0  ...   33370.000     NaN   35670.000   \n",
      "8      9748.0   10320.0   10900.0  ...   16680.000     NaN   17830.000   \n",
      "12     6493.2    6880.8    7267.5  ...   11120.000     NaN   11890.000   \n",
      "16     4865.8    5157.2    5447.8  ...    8340.100     NaN    8917.600   \n",
      "..        ...       ...       ...  ...         ...     ...         ...   \n",
      "524       NaN       NaN       NaN  ...      27.834  29.313      30.732   \n",
      "528       NaN       NaN       NaN  ...      27.245  28.706      30.106   \n",
      "532       NaN       NaN       NaN  ...      26.676  28.120      29.503   \n",
      "536       NaN       NaN       NaN  ...      26.128  27.555      28.921   \n",
      "540       NaN       NaN       NaN  ...      25.599  27.010      28.359   \n",
      "\n",
      "        525         550     575         600     625         650      Combined  \n",
      "0       NaN  379880.000     NaN  402960.000     NaN  426040.000      V at 1.0  \n",
      "4       NaN   37980.000     NaN   40290.000     NaN   42600.000     V at 10.0  \n",
      "8       NaN   18990.000     NaN   20140.000     NaN   21300.000     V at 20.0  \n",
      "12      NaN   12660.000     NaN   13430.000     NaN   14190.000     V at 30.0  \n",
      "16      NaN    9494.900     NaN   10070.000     NaN   10640.000     V at 40.0  \n",
      "..      ...         ...     ...         ...     ...         ...           ...  \n",
      "524  32.106      33.444  34.753      36.039  37.304      38.552  V at 10600.0  \n",
      "528  31.461      32.779  34.069      35.335  36.580      37.808  V at 10800.0  \n",
      "532  30.839      32.139  33.410      34.656  35.882      37.091  V at 11000.0  \n",
      "536  30.240      31.521  32.774      34.002  35.210      36.400  V at 11200.0  \n",
      "540  29.661      30.925  32.160      33.370  34.560      35.733  V at 11400.0  \n",
      "\n",
      "[136 rows x 38 columns]\n",
      "U_data is: \n",
      "      Pressure Property   Liq_Sat  Vap_Sat      75     100     125     150  \\\n",
      "1         1.0        U    29.334   2385.2  2480.8  2516.4  2552.3  2588.5   \n",
      "5        10.0        U   191.822   2438.0  2479.7  2515.6  2551.6  2588.0   \n",
      "9        20.0        U   251.432   2456.9  2478.4  2514.6  2550.9  2587.4   \n",
      "13       30.0        U   289.271   2468.6  2477.1  2513.6  2550.2  2586.8   \n",
      "17       40.0        U   317.609   2477.1     NaN  2512.6  2549.4  2586.2   \n",
      "..        ...      ...       ...      ...     ...     ...     ...     ...   \n",
      "525   10600.0        U  1418.100   2539.0     NaN     NaN     NaN     NaN   \n",
      "529   10800.0        U  1426.200   2536.2     NaN     NaN     NaN     NaN   \n",
      "533   11000.0        U  1434.200   2533.2     NaN     NaN     NaN     NaN   \n",
      "537   11200.0        U  1442.100   2530.3     NaN     NaN     NaN     NaN   \n",
      "541   11400.0        U  1450.000   2527.2     NaN     NaN     NaN     NaN   \n",
      "\n",
      "        175     200  ...     450     475     500     525     550     575  \\\n",
      "1    2624.9  2661.7  ...  3049.9     NaN  3132.4     NaN  3216.7     NaN   \n",
      "5    2624.5  2661.4  ...  3049.8     NaN  3132.3     NaN  3216.6     NaN   \n",
      "9    2624.1  2661.0  ...  3049.7     NaN  3132.3     NaN  3216.5     NaN   \n",
      "13   2623.6  2660.7  ...  3049.6     NaN  3132.2     NaN  3216.5     NaN   \n",
      "17   2623.2  2660.3  ...  3049.5     NaN  3132.1     NaN  3216.4     NaN   \n",
      "..      ...     ...  ...     ...     ...     ...     ...     ...     ...   \n",
      "525     NaN     NaN  ...  2939.1  2991.1  3041.4  3090.7  3139.3  3187.5   \n",
      "529     NaN     NaN  ...  2936.7  2989.0  3039.6  3089.0  3137.8  3186.1   \n",
      "533     NaN     NaN  ...  2934.3  2986.9  3037.7  3087.3  3136.2  3184.7   \n",
      "537     NaN     NaN  ...  2931.8  2984.8  3035.8  3085.6  3134.7  3183.3   \n",
      "541     NaN     NaN  ...  2929.4  2982.6  3033.9  3083.9  3133.1  3181.9   \n",
      "\n",
      "        600     625     650      Combined  \n",
      "1    3302.6     NaN  3390.3      U at 1.0  \n",
      "5    3302.6     NaN  3390.3     U at 10.0  \n",
      "9    3302.5     NaN  3390.2     U at 20.0  \n",
      "13   3302.5     NaN  3390.2     U at 30.0  \n",
      "17   3302.4     NaN  3390.1     U at 40.0  \n",
      "..      ...     ...     ...           ...  \n",
      "525  3235.6  3283.6  3331.7  U at 10600.0  \n",
      "529  3234.3  3282.4  3330.6  U at 10800.0  \n",
      "533  3233.0  3281.2  3329.5  U at 11000.0  \n",
      "537  3231.7  3280.0  3328.4  U at 11200.0  \n",
      "541  3230.4  3278.8  3327.2  U at 11400.0  \n",
      "\n",
      "[136 rows x 38 columns]\n",
      "H_data is: \n",
      "      Pressure Property   Liq_Sat  Vap_Sat      75     100     125     150  \\\n",
      "2         1.0        H    29.335   2514.4  2641.5  2688.6  2736.0  2783.7   \n",
      "6        10.0        H   191.832   2584.8  2640.0  2687.5  2735.2  2783.1   \n",
      "10       20.0        H   251.453   2609.9  2638.4  2686.3  2734.2  2782.3   \n",
      "14       30.0        H   289.302   2625.4  2636.8  2685.1  2733.3  2781.6   \n",
      "18       40.0        H   317.650   2636.9     NaN  2683.8  2732.3  2780.9   \n",
      "..        ...      ...       ...      ...     ...     ...     ...     ...   \n",
      "526   10600.0        H  1433.700   2716.9     NaN     NaN     NaN     NaN   \n",
      "530   10800.0        H  1442.200   2713.1     NaN     NaN     NaN     NaN   \n",
      "534   11000.0        H  1450.600   2709.3     NaN     NaN     NaN     NaN   \n",
      "538   11200.0        H  1458.900   2705.4     NaN     NaN     NaN     NaN   \n",
      "542   11400.0        H  1467.200   2701.5     NaN     NaN     NaN     NaN   \n",
      "\n",
      "        175     200  ...     450     475     500     525     550     575  \\\n",
      "2    2831.7  2880.1  ...  3383.6     NaN  3489.2     NaN  3596.5     NaN   \n",
      "6    2831.2  2879.6  ...  3383.5     NaN  3489.1     NaN  3596.5     NaN   \n",
      "10   2830.6  2879.2  ...  3383.4     NaN  3489.0     NaN  3596.4     NaN   \n",
      "14   2830.0  2878.7  ...  3383.3     NaN  3488.9     NaN  3596.3     NaN   \n",
      "18   2829.5  2878.2  ...  3383.1     NaN  3488.8     NaN  3596.2     NaN   \n",
      "..      ...     ...  ...     ...     ...     ...     ...     ...     ...   \n",
      "526     NaN     NaN  ...  3234.1  3301.8  3367.2  3431.0  3493.8  3555.9   \n",
      "530     NaN     NaN  ...  3230.9  3299.0  3364.7  3428.8  3491.8  3554.1   \n",
      "534     NaN     NaN  ...  3227.7  3296.2  3362.2  3426.5  3489.7  3552.2   \n",
      "538     NaN     NaN  ...  3224.5  3293.4  3359.7  3424.3  3487.7  3550.4   \n",
      "542     NaN     NaN  ...  3221.2  3290.5  3357.2  3422.1  3485.7  3548.5   \n",
      "\n",
      "        600     625     650      Combined  \n",
      "2    3705.6     NaN  3816.4      H at 1.0  \n",
      "6    3705.5     NaN  3816.3     H at 10.0  \n",
      "10   3705.4     NaN  3816.2     H at 20.0  \n",
      "14   3705.4     NaN  3816.2     H at 30.0  \n",
      "18   3705.3     NaN  3816.1     H at 40.0  \n",
      "..      ...     ...     ...           ...  \n",
      "526  3617.6  3679.1  3740.4  H at 10600.0  \n",
      "530  3615.9  3677.5  3738.9  H at 10800.0  \n",
      "534  3614.2  3675.9  3737.5  H at 11000.0  \n",
      "538  3612.5  3674.4  3736.0  H at 11200.0  \n",
      "542  3610.8  3672.8  3734.6  H at 11400.0  \n",
      "\n",
      "[136 rows x 38 columns]\n",
      "S_data is: \n",
      "      Pressure Property  Liq_Sat  Vap_Sat      75     100     125     150  \\\n",
      "3         1.0        S   0.1060   8.9767  9.3828  9.5136  9.6365  9.7527   \n",
      "7        10.0        S   0.6493   8.1511  8.3168  8.4486  8.5722  8.6888   \n",
      "11       20.0        S   0.8321   7.9094  7.9933  8.1261  8.2504  8.3676   \n",
      "15       30.0        S   0.9441   7.7695  7.8024  7.9363  8.0614  8.1791   \n",
      "19       40.0        S   1.0261   7.6709     NaN  7.8009  7.9268  8.0450   \n",
      "..        ...      ...      ...      ...     ...     ...     ...     ...   \n",
      "527   10600.0        S   3.4029   5.5835     NaN     NaN     NaN     NaN   \n",
      "531   10800.0        S   3.4167   5.5715     NaN     NaN     NaN     NaN   \n",
      "535   11000.0        S   3.4304   5.5595     NaN     NaN     NaN     NaN   \n",
      "539   11200.0        S   3.4440   5.5476     NaN     NaN     NaN     NaN   \n",
      "543   11400.0        S   3.4575   5.5357     NaN     NaN     NaN     NaN   \n",
      "\n",
      "        175     200  ...      450     475      500     525      550     575  \\\n",
      "3    9.8629  9.9679  ...  10.8200     NaN  10.9612     NaN  11.0957     NaN   \n",
      "7    8.7994  8.9045  ...   9.7572     NaN   9.8984     NaN  10.0329     NaN   \n",
      "11   8.4785  8.5839  ...   9.4372     NaN   9.5784     NaN   9.7130     NaN   \n",
      "15   8.2903  8.3960  ...   9.2499     NaN   9.3912     NaN   9.5257     NaN   \n",
      "19   8.1566  8.2624  ...   9.1170     NaN   9.2583     NaN   9.3929     NaN   \n",
      "..      ...     ...  ...      ...     ...      ...     ...      ...     ...   \n",
      "527     NaN     NaN  ...   6.3872  6.4793   6.5652  6.6465   6.7239  6.7983   \n",
      "531     NaN     NaN  ...   6.3752  6.4678   6.5542  6.6357   6.7134  6.7880   \n",
      "535     NaN     NaN  ...   6.3633  6.4564   6.5432  6.6251   6.7031  6.7779   \n",
      "539     NaN     NaN  ...   6.3515  6.4452   6.5324  6.6147   6.6929  6.7679   \n",
      "543     NaN     NaN  ...   6.3399  6.4341   6.5218  6.6043   6.6828  6.7580   \n",
      "\n",
      "         600     625      650      Combined  \n",
      "3    11.2243     NaN  11.3476      S at 1.0  \n",
      "7    10.1616     NaN  10.2849     S at 10.0  \n",
      "11    9.8416     NaN   9.9650     S at 20.0  \n",
      "15    9.6544     NaN   9.7778     S at 30.0  \n",
      "19    9.5216     NaN   9.6450     S at 40.0  \n",
      "..       ...     ...      ...           ...  \n",
      "527   6.8700  6.9394   7.0067  S at 10600.0  \n",
      "531   6.8599  6.9294   6.9969  S at 10800.0  \n",
      "535   6.8499  6.9196   6.9872  S at 11000.0  \n",
      "539   6.8401  6.9099   6.9777  S at 11200.0  \n",
      "543   6.8304  6.9004   6.9683  S at 11400.0  \n",
      "\n",
      "[136 rows x 38 columns]\n"
     ]
    }
   ],
   "source": [
    "#your code here\n",
    "g = df.groupby(df.Property)\n",
    "V_data = g.get_group(\"V\")\n",
    "print (\"V_data is: \\n\",V_data)\n",
    "\n",
    "g = df.groupby(df.Property)\n",
    "U_data = g.get_group(\"U\")\n",
    "print (\"U_data is: \\n\",U_data)\n",
    "\n",
    "g = df.groupby(df.Property)\n",
    "H_data = g.get_group(\"H\")\n",
    "print (\"H_data is: \\n\",H_data)\n",
    "\n",
    "g = df.groupby(df.Property)\n",
    "S_data = g.get_group(\"S\")\n",
    "print (\"S_data is: \\n\",S_data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ghkj0-0-d1W7"
   },
   "source": [
    "# Section 3: Plotting with Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ka0qrDcZeAL6"
   },
   "source": [
    "### Plot the properties (specific volume, specific internal energy, specific enthalpy, specific entropy) vs Pressure for saturated liquid.\n",
    "\n",
    "Note:\n",
    "- Try using the subplot feature of matplotlib(Explore it!!)\n",
    "- Provide appropriate title, labels, markersize and other parameters to the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "id": "XbKdm-cUePKA"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'S v/s P')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#your code here\n",
    "graph, axes = plt.subplots(2, 2)\n",
    "graph.suptitle(\"V,U,H,S v/s P graph for Saturated Liquid \",color='c')\n",
    "graph.supxlabel('Pressure',color='c')\n",
    "graph.supylabel('Properties',color='c')\n",
    "axes[0, 0].plot(V_data[\"Pressure\"],V_data[\"Liq_Sat\"],color='b',marker='o') \n",
    "axes[0, 0].set_title(\"V v/s P\")\n",
    "\n",
    "axes[0, 1].plot(U_data[\"Pressure\"],U_data[\"Liq_Sat\"],color='m', marker='*' ) \n",
    "axes[0, 1].set_title(\"U v/s P\")\n",
    "\n",
    "axes[1, 0].plot(H_data[\"Pressure\"],H_data[\"Liq_Sat\"],color='g',marker='>')\n",
    "axes[1, 0].set_title(\"H v/s P\")\n",
    "\n",
    "axes[1, 1].plot(S_data[\"Pressure\"],S_data[\"Liq_Sat\"], color='y',marker='*')\n",
    "axes[1, 1].set_title(\"S v/s P\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the same for saturated vapor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'S v/s P')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#your code here\n",
    "graph, axes = plt.subplots(2, 2)\n",
    "\n",
    "graph.suptitle(\"V,U,H,S v/s P graph for Sat_Vap \",color='c')\n",
    "graph.supxlabel('Pressure',color='c')\n",
    "graph.supylabel('Properties',color='c')\n",
    "\n",
    "axes[0, 0].plot(V_data[\"Pressure\"],V_data[\"Vap_Sat\"],color='b',marker='>') \n",
    "axes[0, 0].set_title(\"V v/s P\")\n",
    "\n",
    "axes[0, 1].plot(U_data[\"Pressure\"],U_data[\"Vap_Sat\"],color='r', marker='*' ) \n",
    "axes[0, 1].set_title(\"U v/s P\")\n",
    "\n",
    "axes[1, 0].plot(H_data[\"Pressure\"],H_data[\"Vap_Sat\"],color='g',marker='o')\n",
    "axes[1, 0].set_title(\"H v/s P\")\n",
    "\n",
    "axes[1, 1].plot(S_data[\"Pressure\"],S_data[\"Vap_Sat\"], color='y',marker='s')\n",
    "axes[1, 1].set_title(\"S v/s P\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SCLRji6TeQgh"
   },
   "source": [
    "### Plot the specific volume of saturated liquid between 300 kPa and 1500 kPa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "id": "u6DyyI7MeYgE"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Specific Volume v/s Pressure graph')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#your code here\n",
    "vplot=V_data.loc[(V_data['Pressure'] > 300) & (V_data['Pressure'] < 1500)]\n",
    "plt.plot(vplot[\"Pressure\"],vplot[\"Liq_Sat\"],color='r',marker='3')\n",
    "plt.xlabel(\"Pressure (in kPa)\",color='c')\n",
    "plt.ylabel(\"Specific Volume\",color='c')\n",
    "plt.title(\"Specific Volume v/s Pressure graph\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section 4 : Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Congratulations on reaching this point! I hope you had fun solving your first assignment and have also built confidence in applying these libraries. If you are wondering, we will cover more about z-score normalization in Week 2, and the sigmoid function will be used in Week 3. After completing this assignment, you are now prepared to learn about machine learning techniques and implement your own machine learning models.*"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}