DS_CHE_W1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tIol1e9EzB-Z"
   },
   "source": [
    "# DS : CHE\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lV7ZG3lKzB-d"
   },
   "source": [
    "# Week-1 Assignment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rLK_mGZlJ_Y_"
   },
   "source": [
    "**Welcome to your first assignment of SimuTech Winter Project 2022! I hope you are excited to implement and test everything you have learnt up until now. The dataset which you'll use 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 are temperatures.\n",
    "\n",
    "**There are some interesting set of questions for you to get a basic understanding of pandas and data visualization libraries. GOOD LUCK!**\n",
    "\n",
    "\n",
    "***Let's get started with importing numpy, pandas and matplotlib!***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3urmJxE7KKVj"
   },
   "source": [
    "### 1) Import all the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "2kLDKA3bKGMD"
   },
   "outputs": [],
   "source": [
    "#your code here\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0bpXri-3KVei"
   },
   "source": [
    "### 2) Read the csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "XwzSYh42Kdej"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Pressure Property    Liq_Sat      Vap_Sat           75          100  \\\n",
      "0         1.0        V     1.0000  129200.0000  160640.0000  172180.0000   \n",
      "1         1.0        U    29.3340    2385.2000    2480.8000    2516.4000   \n",
      "2         1.0        H    29.3350    2514.4000    2641.5000    2688.6000   \n",
      "3         1.0        S     0.1060       8.9767       9.3828       9.5136   \n",
      "4        10.0        V     1.0100   14670.0000   16030.0000   17190.0000   \n",
      "..        ...      ...        ...          ...          ...          ...   \n",
      "539   11200.0        S     3.4440       5.5476          NaN          NaN   \n",
      "540   11400.0        V     1.5040      15.2840          NaN          NaN   \n",
      "541   11400.0        U  1450.0000    2527.2000          NaN          NaN   \n",
      "542   11400.0        H  1467.2000    2701.5000          NaN          NaN   \n",
      "543   11400.0        S     3.4575       5.5357          NaN          NaN   \n",
      "\n",
      "             125          150          175          200  ...        425  \\\n",
      "0    183720.0000  195270.0000  206810.0000  218350.0000  ...        NaN   \n",
      "1      2552.3000    2588.5000    2624.9000    2661.7000  ...        NaN   \n",
      "2      2736.0000    2783.7000    2831.7000    2880.1000  ...        NaN   \n",
      "3         9.6365       9.7527       9.8629       9.9679  ...        NaN   \n",
      "4     18350.0000   19510.0000   20660.0000   21820.0000  ...        NaN   \n",
      "..           ...          ...          ...          ...  ...        ...   \n",
      "539          NaN          NaN          NaN          NaN  ...     6.2491   \n",
      "540          NaN          NaN          NaN          NaN  ...    24.1040   \n",
      "541          NaN          NaN          NaN          NaN  ...  2873.1000   \n",
      "542          NaN          NaN          NaN          NaN  ...  3147.9000   \n",
      "543          NaN          NaN          NaN          NaN  ...     6.2367   \n",
      "\n",
      "             450        475          500        525          550        575  \\\n",
      "0    333730.0000        NaN  356810.0000        NaN  379880.0000        NaN   \n",
      "1      3049.9000        NaN    3132.4000        NaN    3216.7000        NaN   \n",
      "2      3383.6000        NaN    3489.2000        NaN    3596.5000        NaN   \n",
      "3        10.8200        NaN      10.9612        NaN      11.0957        NaN   \n",
      "4     33370.0000        NaN   35670.0000        NaN   37980.0000        NaN   \n",
      "..           ...        ...          ...        ...          ...        ...   \n",
      "539       6.3515     6.4452       6.5324     6.6147       6.6929     6.7679   \n",
      "540      25.5990    27.0100      28.3590    29.6610      30.9250    32.1600   \n",
      "541    2929.4000  2982.6000    3033.9000  3083.9000    3133.1000  3181.9000   \n",
      "542    3221.2000  3290.5000    3357.2000  3422.1000    3485.7000  3548.5000   \n",
      "543       6.3399     6.4341       6.5218     6.6043       6.6828     6.7580   \n",
      "\n",
      "             600        625          650  \n",
      "0    402960.0000        NaN  426040.0000  \n",
      "1      3302.6000        NaN    3390.3000  \n",
      "2      3705.6000        NaN    3816.4000  \n",
      "3        11.2243        NaN      11.3476  \n",
      "4     40290.0000        NaN   42600.0000  \n",
      "..           ...        ...          ...  \n",
      "539       6.8401     6.9099       6.9777  \n",
      "540      33.3700    34.5600      35.7330  \n",
      "541    3230.4000  3278.8000    3327.2000  \n",
      "542    3610.8000  3672.8000    3734.6000  \n",
      "543       6.8304     6.9004       6.9683  \n",
      "\n",
      "[544 rows x 37 columns]\n"
     ]
    }
   ],
   "source": [
    "#your code here\n",
    "import pandas as pd\n",
    "df =pd.read_csv('superheated_vapor_properties.csv')\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K72xx38QKeRU"
   },
   "source": [
    "### 3) Display shape of dataframe\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "YPqY3euIKoCk"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(544, 37)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qJm2x6TMKolc"
   },
   "source": [
    "### 4) Print all columns of dataframe\n",
    "Return an array containing names of all the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "ocVSOb3kKvQV"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Pressure', 'Property', 'Liq_Sat', 'Vap_Sat', '75', '100', '125', '150',\n",
       "       '175', '200', '220', '225', '240', '250', '260', '275', '280', '290',\n",
       "       '300', '320', '325', '340', '350', '360', '375', '380', '400', '425',\n",
       "       '450', '475', '500', '525', '550', '575', '600', '625', '650'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HE0-ugnfKvl9"
   },
   "source": [
    "### 5) Display the number of null values in each column of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "xU-1sxGCK7UF"
   },
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "df.isna().sum()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "04L2giwTLElt"
   },
   "source": [
    "###  6) Print a list of all the unique items in the Property column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "Svgs2agDLYxt"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['V', 'U', 'H', 'S'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "df['Property'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "68YuzJ5zXJAx"
   },
   "source": [
    "### 7) 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "igpYFRjLXLiZ"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26    3075.1\n",
      "Name: 300, dtype: float64\n",
      "59   NaN\n",
      "Name: 220, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#your code here\n",
    "#your code here\n",
    "df2 = df['Property'].astype(str)+\" \"+\"at\"+\" \"+df[\"Pressure\"].astype(str)\n",
    "df['prop at press'] = df2\n",
    "r1 = df[df['prop at press']=='H at 75.0']\n",
    "r2 =df[df['prop at press']=='S at 250.0']\n",
    "ans1 = r1['300']\n",
    "print(ans1)\n",
    "ans2 = r2['220']\n",
    "print(ans2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SR7p7NhNU2vy"
   },
   "source": [
    "### 8) Find out the column with the highest number of missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "Jj4cUag7WZYB"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'75'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "df.count().idxmin()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yL9loV4IWZwp"
   },
   "source": [
    "### 9) What is the average enthalpy of Sat. Liq. at all different pressures in the dataset ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "WjOwyU89WlwR"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "936.9707720588237"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "df.loc[df['Property']==\"H\",'ANS'] = df['Liq_Sat']\n",
    "value = df['ANS'].mean()\n",
    "value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PfUPj9e7Wmkx"
   },
   "source": [
    "### 10) 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": 12,
   "metadata": {
    "id": "Qoy0rPpUW2lp"
   },
   "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>Pressure</th>\n",
       "      <th>Property</th>\n",
       "      <th>Liq_Sat</th>\n",
       "      <th>Vap_Sat</th>\n",
       "      <th>75</th>\n",
       "      <th>100</th>\n",
       "      <th>125</th>\n",
       "      <th>150</th>\n",
       "      <th>175</th>\n",
       "      <th>200</th>\n",
       "      <th>...</th>\n",
       "      <th>475</th>\n",
       "      <th>500</th>\n",
       "      <th>525</th>\n",
       "      <th>550</th>\n",
       "      <th>575</th>\n",
       "      <th>600</th>\n",
       "      <th>625</th>\n",
       "      <th>650</th>\n",
       "      <th>prop at press</th>\n",
       "      <th>ANS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>V</td>\n",
       "      <td>1.000</td>\n",
       "      <td>129200.000</td>\n",
       "      <td>160640.0</td>\n",
       "      <td>172180.0</td>\n",
       "      <td>183720.0</td>\n",
       "      <td>195270.0</td>\n",
       "      <td>206810.0</td>\n",
       "      <td>218350.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>356810.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>379880.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>402960.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>426040.000</td>\n",
       "      <td>V at 1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.0</td>\n",
       "      <td>V</td>\n",
       "      <td>1.010</td>\n",
       "      <td>14670.000</td>\n",
       "      <td>16030.0</td>\n",
       "      <td>17190.0</td>\n",
       "      <td>18350.0</td>\n",
       "      <td>19510.0</td>\n",
       "      <td>20660.0</td>\n",
       "      <td>21820.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35670.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37980.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40290.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42600.000</td>\n",
       "      <td>V at 10.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20.0</td>\n",
       "      <td>V</td>\n",
       "      <td>1.017</td>\n",
       "      <td>7649.800</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>8584.7</td>\n",
       "      <td>9167.1</td>\n",
       "      <td>9748.0</td>\n",
       "      <td>10320.0</td>\n",
       "      <td>10900.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17830.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18990.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20140.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21300.000</td>\n",
       "      <td>V at 20.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>30.0</td>\n",
       "      <td>V</td>\n",
       "      <td>1.022</td>\n",
       "      <td>5229.300</td>\n",
       "      <td>5322.0</td>\n",
       "      <td>5714.4</td>\n",
       "      <td>6104.6</td>\n",
       "      <td>6493.2</td>\n",
       "      <td>6880.8</td>\n",
       "      <td>7267.5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11890.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12660.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13430.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14190.000</td>\n",
       "      <td>V at 30.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>40.0</td>\n",
       "      <td>V</td>\n",
       "      <td>1.027</td>\n",
       "      <td>3993.400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4279.2</td>\n",
       "      <td>4573.3</td>\n",
       "      <td>4865.8</td>\n",
       "      <td>5157.2</td>\n",
       "      <td>5447.8</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8917.600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9494.900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10070.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10640.000</td>\n",
       "      <td>V at 40.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>10600.0</td>\n",
       "      <td>V</td>\n",
       "      <td>1.474</td>\n",
       "      <td>16.778</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>29.313</td>\n",
       "      <td>30.732</td>\n",
       "      <td>32.106</td>\n",
       "      <td>33.444</td>\n",
       "      <td>34.753</td>\n",
       "      <td>36.039</td>\n",
       "      <td>37.304</td>\n",
       "      <td>38.552</td>\n",
       "      <td>V at 10600.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>10800.0</td>\n",
       "      <td>V</td>\n",
       "      <td>1.481</td>\n",
       "      <td>16.385</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>28.706</td>\n",
       "      <td>30.106</td>\n",
       "      <td>31.461</td>\n",
       "      <td>32.779</td>\n",
       "      <td>34.069</td>\n",
       "      <td>35.335</td>\n",
       "      <td>36.580</td>\n",
       "      <td>37.808</td>\n",
       "      <td>V at 10800.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>11000.0</td>\n",
       "      <td>V</td>\n",
       "      <td>1.489</td>\n",
       "      <td>16.006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>28.120</td>\n",
       "      <td>29.503</td>\n",
       "      <td>30.839</td>\n",
       "      <td>32.139</td>\n",
       "      <td>33.410</td>\n",
       "      <td>34.656</td>\n",
       "      <td>35.882</td>\n",
       "      <td>37.091</td>\n",
       "      <td>V at 11000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>11200.0</td>\n",
       "      <td>V</td>\n",
       "      <td>1.496</td>\n",
       "      <td>15.639</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>27.555</td>\n",
       "      <td>28.921</td>\n",
       "      <td>30.240</td>\n",
       "      <td>31.521</td>\n",
       "      <td>32.774</td>\n",
       "      <td>34.002</td>\n",
       "      <td>35.210</td>\n",
       "      <td>36.400</td>\n",
       "      <td>V at 11200.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>11400.0</td>\n",
       "      <td>V</td>\n",
       "      <td>1.504</td>\n",
       "      <td>15.284</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>27.010</td>\n",
       "      <td>28.359</td>\n",
       "      <td>29.661</td>\n",
       "      <td>30.925</td>\n",
       "      <td>32.160</td>\n",
       "      <td>33.370</td>\n",
       "      <td>34.560</td>\n",
       "      <td>35.733</td>\n",
       "      <td>V at 11400.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>136 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     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  ...     475         500     525  \\\n",
       "0    195270.0  206810.0  218350.0  ...     NaN  356810.000     NaN   \n",
       "4     19510.0   20660.0   21820.0  ...     NaN   35670.000     NaN   \n",
       "8      9748.0   10320.0   10900.0  ...     NaN   17830.000     NaN   \n",
       "12     6493.2    6880.8    7267.5  ...     NaN   11890.000     NaN   \n",
       "16     4865.8    5157.2    5447.8  ...     NaN    8917.600     NaN   \n",
       "..        ...       ...       ...  ...     ...         ...     ...   \n",
       "524       NaN       NaN       NaN  ...  29.313      30.732  32.106   \n",
       "528       NaN       NaN       NaN  ...  28.706      30.106  31.461   \n",
       "532       NaN       NaN       NaN  ...  28.120      29.503  30.839   \n",
       "536       NaN       NaN       NaN  ...  27.555      28.921  30.240   \n",
       "540       NaN       NaN       NaN  ...  27.010      28.359  29.661   \n",
       "\n",
       "            550     575         600     625         650  prop at press  ANS  \n",
       "0    379880.000     NaN  402960.000     NaN  426040.000       V at 1.0  NaN  \n",
       "4     37980.000     NaN   40290.000     NaN   42600.000      V at 10.0  NaN  \n",
       "8     18990.000     NaN   20140.000     NaN   21300.000      V at 20.0  NaN  \n",
       "12    12660.000     NaN   13430.000     NaN   14190.000      V at 30.0  NaN  \n",
       "16     9494.900     NaN   10070.000     NaN   10640.000      V at 40.0  NaN  \n",
       "..          ...     ...         ...     ...         ...            ...  ...  \n",
       "524      33.444  34.753      36.039  37.304      38.552   V at 10600.0  NaN  \n",
       "528      32.779  34.069      35.335  36.580      37.808   V at 10800.0  NaN  \n",
       "532      32.139  33.410      34.656  35.882      37.091   V at 11000.0  NaN  \n",
       "536      31.521  32.774      34.002  35.210      36.400   V at 11200.0  NaN  \n",
       "540      30.925  32.160      33.370  34.560      35.733   V at 11400.0  NaN  \n",
       "\n",
       "[136 rows x 39 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "#your code here\n",
    "#df.drop('ANS',axis=1,inplace=True)\n",
    "V_data =pd.DataFrame()\n",
    "U_data =pd.DataFrame()\n",
    "H_data =pd.DataFrame()\n",
    "S_data =pd.DataFrame()\n",
    "H_data =df[df.Property =='H']\n",
    "V_data =df[df.Property =='V']\n",
    "U_data =df[df.Property =='U']\n",
    "S_data =df[df.Property =='S']\n",
    "V_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0IqKY_vvW216"
   },
   "source": [
    "### 11) 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": 13,
   "metadata": {
    "id": "ZsUW7kZzXeKR"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#your code here\n",
    "spec_vol = V_data['Liq_Sat']\n",
    "spec_U = U_data['Liq_Sat']\n",
    "spec_H = H_data['Liq_Sat']\n",
    "spec_S = S_data['Liq_Sat']\n",
    "plt.subplot(4,1,1)\n",
    "plt.plot(V_data['Pressure'],spec_vol,'go')\n",
    "plt.title(\"1st subplot\")\n",
    "\n",
    "plt.subplot(4,1,2)\n",
    "plt.plot(U_data['Pressure'],spec_U,'r^')\n",
    "plt.title(\"2nd subplot\")\n",
    "\n",
    "plt.subplot(4,1,3)\n",
    "plt.plot(H_data['Pressure'],spec_H,'bo')\n",
    "plt.title(\"3rd subplot\")\n",
    "\n",
    "plt.subplot(4,1,4)\n",
    "plt.plot( S_data['Pressure'],spec_S,'y^')\n",
    "plt.title(\"4th subplot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ds33LC6dXema"
   },
   "source": [
    "### 12) Plot the same for saturated vapor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "TRy2Tb_GXqcp"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#your code here\n",
    "#your code here\n",
    "spec_vol2 = V_data['Vap_Sat']\n",
    "spec_U2 = U_data['Vap_Sat']\n",
    "spec_H2 = H_data['Vap_Sat']\n",
    "spec_S2 = S_data['Vap_Sat']\n",
    "plt.subplot(4,1,1)\n",
    "plt.plot(V_data['Pressure'],spec_vol2,'yo')\n",
    "plt.title(\"1st subplot\")\n",
    "\n",
    "plt.subplot(4,1,2)\n",
    "plt.plot(U_data['Pressure'],spec_U2,'r^')\n",
    "plt.title(\"2nd subplot\")\n",
    "\n",
    "plt.subplot(4,1,3)\n",
    "plt.plot(H_data['Pressure'],spec_H2,'bo')\n",
    "plt.title(\"3rd subplot\")\n",
    "\n",
    "plt.subplot(4,1,4)\n",
    "plt.plot(S_data['Pressure'],spec_S2, 'g^')\n",
    "plt.title(\"4th subplot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ate8hMAkYCph"
   },
   "source": [
    "### 13) Plot the specific volume of saturated liquid between 300 kPa and 1500 kPa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "pbBwBWxGZhAz"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#your code here\n",
    "#your code here\n",
    "press = V_data[(V_data['Pressure']>=300) & (V_data['Pressure']<=1500)]\n",
    "press\n",
    "plt.plot(press['Pressure'],press['Liq_Sat'])\n",
    "plt.title(\"Plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KeUT5_QFWySs"
   },
   "source": [
    "## Playing with Numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xTFgCWtqdZSi"
   },
   "source": [
    "### 14) Implement the derivative of the sigmoid function using numpy. \n",
    "\n",
    "Note: \n",
    "- Read about sigmoid function over the internet.\n",
    "- Input of the function can be a scalar or a numpy array.\n",
    "\n",
    "**Test Case:**\n",
    "\n",
    "**Input:**\n",
    "\n",
    " [[9, 2, 5, 0, 0]\n",
    "\n",
    "   [7, 5, 0, 0, 0]]\n",
    "\n",
    "Output:\n",
    "\n",
    "[[0.99987661, 0.88079708, 0.99330715, 0.5, 0.5 ]\n",
    "\n",
    "[0.99908895, 0.99330715, 0.5,  0.5,  0.5 ]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "BMLOKdEQdg_J"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[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]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "my_list =[[9,2,5,0,0],[7,5,0,0,0]]\n",
    "arr =np.array(my_list)\n",
    "brr =np.exp(arr)/(np.exp(arr)+1)**2\n",
    "brr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.99987661, 0.88079708, 0.99330715, 0.5       , 0.5       ],\n",
       "       [0.99908895, 0.99330715, 0.5       , 0.5       , 0.5       ]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "def sigmoid(x):\n",
    "    arr =np.array(x)\n",
    "    brr = np.exp(arr)/(np.exp(arr)+1)\n",
    "    return brr\n",
    "x= [[9,2,5,0,0],[7,5,0,0,0]]\n",
    "brr = sigmoid(x)\n",
    "brr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0nJ76FFIeTKq"
   },
   "source": [
    "### 15) Define a function which takes in a a numpy array of shape (length, height, depth) and returns a vector of shape (length\\*height*depth, 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "e6XecJLkeUmK"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 1])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#your code here\n",
    "x =np.array([1,2,3])\n",
    "a =x[0]\n",
    "b=x[1]\n",
    "c=x[2]\n",
    "y =np.array([a*b*c,1])\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TyyKAFLGdHtx"
   },
   "source": [
    "### 16) Define a softmax function that calculates the softmax for each row of the input x.\n",
    "\n",
    "Note:\n",
    "- The code should work for a row vector and also for matrices of shape (n, m)\n",
    "\n",
    "**Test Case:**\n",
    "\n",
    "**Input:**\n",
    "\n",
    " [[9 2 5 0 0]\n",
    "\n",
    "   [7 5 0 0 0]]\n",
    "\n",
    "Output:\n",
    "\n",
    "[[9.80897665e-01 8.94462891e-04 1.79657674e-02 1.21052389e-04\n",
    "  1.21052389e-04]\n",
    "\n",
    " [8.78679856e-01 1.18916387e-01 8.01252314e-04 8.01252314e-04\n",
    "  8.01252314e-04]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "wvcifD48dYsC"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([90,  1])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "import numpy as np\n",
    "def volume(x):\n",
    "    y =np.array(x)\n",
    "    z= np.array([y[0]*y[1]*y[2],1])\n",
    "    return z\n",
    "x=[9,2,5]\n",
    "y =volume(x)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9.80897665e-01, 8.94462891e-04, 1.79657674e-02, 1.21052389e-04,\n",
       "        1.21052389e-04],\n",
       "       [8.78679856e-01, 1.18916387e-01, 8.01252314e-04, 8.01252314e-04,\n",
       "        8.01252314e-04]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "import numpy as np\n",
    "def softmax(x,a):\n",
    " xx =np.array(x)\n",
    " y=np.sum(np.exp(x))\n",
    " z=np.exp(x)/y\n",
    " aa= np.array(a)\n",
    " b=np.sum(np.exp(a))\n",
    " c=np.exp(a)/b\n",
    " d=np.vstack((z,c))\n",
    " return d\n",
    "x = [9,2,5,0,0]\n",
    "a=[7,5,0,0,0]\n",
    "ans =softmax(x,a)\n",
    "ans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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