BCA Standard Curve.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "studied-baltimore",
   "metadata": {},
   "source": [
    "## BCA Assay with Jupyter notebook\n",
    "\n",
    "Here's my notes on determine the trendline and R^2 value for BCA standard curve analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "noticed-bundle",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Before beginning, you need to have miniconda3 installed on your computer. You can find installation instructions here: https://docs.conda.io/en/latest/miniconda.html\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "interior-produce",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Next, you will need to access the terminal (command on a PC and terminal on a mac) and create a conda environment. You can create an environment with the name \"Bio133\" by typing the following code in your terminal:\n",
    "\n",
    "conda create -n bio133\n",
    "\n",
    "#Now that the environment is created, you can access it by typing the following command:\n",
    "\n",
    "conda activate bio133"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "loaded-guard",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now that you have activated your new environment, you will need to install some packages into it. For this tutorial, you will need to install matplotlib, pandas, seaborn, openpyxl, and jupyter notebook. Install each component by entering the following commands:\n",
    "\n",
    "conda install matplotlib #This will install matplotlib in your environment\n",
    "\n",
    "conda install pandas #This will install pandas in your environment\n",
    "\n",
    "conda install openpyxl #This will install openpyxl in your environment\n",
    "\n",
    "conda install jupyter notebook #This will install jupyter lab notebook in your environment\n",
    "\n",
    "conda install numpy #This will install numpy in your environment\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "hourly-footwear",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now you are ready to access jupyter notebook. You can do so by entering the following command into your environment:\n",
    "\n",
    "jupyter notebook\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "sapphire-georgia",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Once you are into Jupyter notebook, you will create a new file (Python 3 file). You can name this file whatever you want. In this file, we will create a graph by running the following code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "cubic-radiation",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's make a standard curve plotting with matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "  \n",
    "x = [3.5710, 2.3470, 1.7710, 1.4875, 1.2930, 1.1645, 1.1125, 1.0855]  # x axis values \n",
    "y = [1000,500,250,125,62.5,31.25,15.625,0] # corresponding y axis values \n",
    "   \n",
    "plt.plot(x, y) # plotting the points in line format \n",
    "\n",
    "plt.xlabel('Absorbance') # naming the x axis \n",
    " \n",
    "plt.ylabel('Protein Concentration') # naming the y axis\n",
    "  \n",
    "plt.title('BSA Standard Curve') # give a title to your graph (note: it automatically puts the title on top) \n",
    "   \n",
    "plt.show() # function to show the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "finite-anime",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jSvYfDazdgfObmdkAFalZjI6IkwfrhBHxe0mrJE2IiOXAW4BH0msacHl6vyXtMg/4nqQrgFHAeGDhYMVjZmb5iiSLuyUdFRFLBvG8FwDXSdoFeAw4h6yWM1vSucATwJkAEbFU0myyZLIZOD8itgxiLGZmlkMRlZv2JT0CvApYCWwk60OIiPgf1Q9v+3V2dkZXV1e9wzAzG1IkLYqIzr7lRWoWp1QhHjMzG0JyO7gj4nGyDuY3p89/KrKfmZk1j9wvfUmXkv04bkYqGg5cW82gzMyssRSpIbwLOJ30uGxErAVeUc2gzMyssRRJFn+JrBc8ACTtUd2QzMys0RRJFrMl/TvQLunDwM+Ab1Q3LDMzayS5T0NFxBclvQ3YAEwAPhMRt1c9MjMzaxi5yULSOOA/exOEpDZJYyPid9UOzszMGkORZqgbgRdLlrekMjMzaxFFksXOac4J4KX5J3apXkhmZtZoiiSLbkmn9y6kSYqerl5IZmbWaIoM9/ERskH//j/ZuFCrgLOrGpVZE5u7eA0z5y9n7foeRrW3MX3SBKZMLDufl1nDKPI01H8Bx0vak2zgweerH5ZZc5q7eA0z5iyhZ1M2cPKa9T3MmJMN6OyEYY2syNNQuwLvJs2UJ2UT1+3ATHlmLWvm/OUvJYpePZu2MHP+cicLa2hFmqFuAZ4jmyt7Y3XDMWtua9f3DKjcrFHUfKY8s1Y2qr2NNWUSw6j2tjpEY1Zckaeh7pZ0VNUjMWsB0ydNoG34sK3K2oYPY/qkCXWKyKyYIjWLE4EPSRpSM+WZNaLefgk/DWVDjWfKM6uxKRM7nBxsyCk6U147cFp6tacyMzNrEUVmyrsQuA44IL2ulXRBtQMzM7PGUaQZ6lzgryPiBQBJ/xe4B7iymoGZmVnjKPI0lMhGmu21JZWZmVmLKFKz+DZwn6QfpOUpwDerFpGZmTWcImNDXSHpF2SP0Ao4JyIWVzswMzNrHP0mC0nHAvtHxE8i4gHggVR+uqSdImJRrYI0M7P6qtRnMRNYVqb8kbTOzMxaRKVksV+5ebYjYgWwX9UiMjOzhlMpWVQa2WyPwQ7EzMwaV6Vk8TNJn1fvBBaJpMuAO6oblpmZNZJKT0NdBPwHsELSg6nsaKAL+Lsqx2VmZg2k32SRfrH9fkmHAkek4qUR8dhgnFjSMLLEsyYiTpW0L/B9shn5fge8NyKeTdvOIPsl+Rbg4xExfzBiMDOzYooMJPhYRPwwvQYlUSQXsvXTVhcDCyJiPLAgLSPpcGAqWcI6GbgqJRozM6uRIsN9DDpJo4F3kjVz9ZoMzEqfZ5H9Ury3/IaI2BgRK4EVwHE1CtXMzKhTsgC+BPxv4MWSsgMjYh1Aej8glXcAq0q2W53KtiHpPEldkrq6u7sHPWgzs1ZVKFlIGiZplKSDe1/be0JJpwJPDeAX4OUGLYxyG0bE1RHRGRGdI0aM2N4Qzcysj9yxodLcFZcCT/JyTSCA7Z1W9QTgdEnvAHYD9pJ0LfCkpJERsU7SSOCptP1qYEzJ/qOBtdt5bjMz2w5FahYXAhMi4oiIOCq9tnv+7YiYERGjI2IsWcf1HRHxAWAeMC1tNg24JX2eB0yVtKukccB4YOH2nt/MzAauyBDlq4Dnqh0IcDkwW9K5wBPAmQARsVTSbLIxqTYD50fElv4PY2Zmg00RZZv/X95A+iYwAfgxsLG3PCKuqG5oO6azszO6urrqHYaZ2ZAiaVFEdPYtL1KzeCK9dkkvMzNrMUUmP7qsFoGYmVnjqjT50Zci4hOSfkiZR1Uj4vSqRmZmZg2jUs3iu+n9i7UIxMzMGlelgQQXpfc7JbUBB0fE8ppFZmZmDSP3dxaSTgMeBG5Ly6+RNK/KcZmZWQMp8qO8z5IN3LceICIeJBtG3MzMWkSRZLE5ImrxozwzM2tQRX5n8bCks4BhksYDHwfurm5YZmbWSIrULC4gm3hoI/A9sqE/LqxmUGZm1liK1CzeGRH/CPxjb4GkM4EbqxaVmZk1lCI1ixkFy8zMrElV+gX3KcA7gA5JXylZtRfZ6K9mZtYiKjVDrQW6gNOB0lntngc+Wc2gzMyssVT6BfdDwEOSvkc2temr06rlEbGpFsGZmVljKNLB/XrgO8DvyJLGGEnTIuKuagZmZmaNo0iyuAJ4e++4UJJeDVwPvLaagZmZWeMo8jTU8NIBBCPiN8Dw6oVkZmaNpkjNYlGaWrV3yPK/YesObzMza3JFksVHgPPJhvkQcBdwVTWDMjOzxlIxWUjaCVgUEUeS9V2YFTZ38Rpmzl/O2vU9jGpvY/qkCUyZ2FHvsMxsO1Tss4iIF8kenz24RvFYk5i7eA0z5ixhzfoeAlizvocZc5Ywd/GaeodmZtuhSDPUSGCppIXAC72FnoPbKpk5fzk9m7ZsVdazaQsz5y937cJsCCqSLC6rehTWdNau7xlQuZk1tkpjQ+1G1rn9KmAJ8M2I8JhQVsio9jbWlEkMo9rb6hCNme2oSn0Ws4BOskRxCvCvNYnImsL0SRNoGz5sq7K24cOYPmlCnSIysx1RqRnq8Ig4CiD9zmJhbUKyZtDbL+GnocyaQ6Vk8dJggRGxWVINwrFmMmVih5ODWZOolCyOlrQhfRbQlpYFRETsVfXozMysIVQaonxYf+vMzKy1FBlIcFBJGiPp55KWSVoq6cJUvq+k2yX9Nr3vU7LPDEkrJC2XNKnWMZuZtbqaJwuyKVkvioi/Ao4Hzpd0OHAxsCAixgML0jJp3VTgCOBk4CpJrvWYmdVQzZNFRKyLiAfS5+eBZUAHMJnscV3S+5T0eTJwQ0RsjIiVwArguJoGbWbW4upRs3iJpLHAROA+4MCIWAdZQgEOSJt1AKtKdludysod7zxJXZK6uru7qxa3mVmrqVuykLQncDPwiYjYUGnTMmVRbsOIuDoiOiOic8SIEYMRppmZUadkIWk4WaK4LiLmpOInJY1M60cCT6Xy1cCYkt1HA2trFauZmdXnaSgB3wSWRUTpHBnzgGnp8zTglpLyqZJ2lTQOGI9/TW5mVlNFRp0dbCcAHwSWSHowlV0CXA7MlnQu8ARwJkBELJU0G3iE7Emq8yNiyzZHNTOzqql5soiIX1K+HwLgLf3s83ng81ULyszMKqrr01BmZjY0OFmYmVkuJwszM8vlZGFmZrmcLMzMLJeThZmZ5arH7yysiuYuXuOpTM1s0DlZNJG5i9cwY84SejZlv1lcs76HGXOWADhhmNkOcTNUE5k5f/lLiaJXz6YtzJy/vE4RmVmzcLJoImvX9wyo3MysKCeLJjKqvW1A5WZmRTlZNJHpkybQNnzrGWfbhg9j+qQJdYrIzJqFO7ibSG8ntp+GMrPB5mTRZKZM7HByMLNB52YoMzPL5WRhZma5nCzMzCyXk4WZmeVysjAzs1x+GqrGPNCfmQ1FThY15IH+zGyocjNUDXmgPzMbqpwsasgD/ZnZUOVkUUMe6M/MhionixryQH9mNlS5g3s7bO8TTR7oz8yGKieLAdrRJ5o80J+ZDUVuhhogP9FkZq3INYt+9NfU5CeazKwVOVmUUampaVR7G2vKJAY/0WRmzczNUGVUamryE01m1oqGTLKQdLKk5ZJWSLq4GueYu3gNJ1x+R9maA2RNTVMmdvCFM46io70NAR3tbXzhjKPcaW1mTW1INENJGgZ8FXgbsBq4X9K8iHhksM7Rt+mpnN6mJj/RZGatZqjULI4DVkTEYxHxF+AGYPJgnqBc01MpNzWZWSsbKsmiA1hVsrw6lW1F0nmSuiR1dXd3D+gElZ5mclOTmbW6IdEMBahMWWxTEHE1cDVAZ2fnNusr6e8pp472Nn518ZsHcigzs6YzVGoWq4ExJcujgbWDeQI/5WRm1r+hUrO4HxgvaRywBpgKnDWYJ/C4TWZm/RsSySIiNkv6GDAfGAZ8KyKWDvZ5/JSTmVl5QyJZAETErcCt9Y7DzKwVDZU+CzMzqyMnCzMzy+VkYWZmuZwszMwslyIG9Nu1IUNSN/B4SdH+wNN1CqeeWvG6fc2toRWvGap/3YdExIi+hU2bLPqS1BURnfWOo9Za8bp9za2hFa8Z6nfdboYyM7NcThZmZparlZLF1fUOoE5a8bp9za2hFa8Z6nTdLdNnYWZm26+VahZmZradnCzMzCxX0yULSd+S9JSkh/tZL0lfkbRC0q8lHVPrGAdbgWs+SdJzkh5Mr8/UOsbBJmmMpJ9LWiZpqaQLy2zTVPe64DU31b2WtJukhZIeStd8WZltmu0+F7nm2t/niGiqF/BG4Bjg4X7WvwP4Cdnse8cD99U75hpc80nAj+od5yBf80jgmPT5FcBvgMOb+V4XvOamutfp3u2ZPg8H7gOOb/L7XOSaa36fm65mERF3AX+osMlk4DuRuRdolzSyNtFVR4FrbjoRsS4iHkifnweWse287E11rwtec1NJ9+6PaXF4evV9KqfZ7nORa665pksWBXQAq0qWV9Pk/+GS16Vq7U8kHVHvYAaTpLHARLK/wEo17b2ucM3QZPda0jBJDwJPAbdHRNPf5wLXDDW+z62YLFSmrO5Zu8oeIBvv5WjgSmBufcMZPJL2BG4GPhERG/quLrPLkL/XOdfcdPc6IrZExGuA0cBxko7ss0nT3ecC11zz+9yKyWI1MKZkeTSwtk6x1EREbOit1kY24+BwSfvXOawdJmk42ZfmdRExp8wmTXev8665We81QESsB34BnNxnVdPd5179XXM97nMrJot5wNnpCYrjgeciYl29g6omSQdJUvp8HNl9f6a+Ue2YdD3fBJZFxBX9bNZU97rINTfbvZY0QlJ7+twGvBV4tM9mzXafc6+5Hvd5yMzBXZSk68meFNhf0mrgUrIOIiLi62TzeL8DWAH8CTinPpEOngLX/B7go5I2Az3A1EiPVAxhJwAfBJaktl2AS4CDoWnvdZFrbrZ7PRKYJWkY2Rfi7Ij4kaSPQNPe5yLXXPP77OE+zMwsVys2Q5mZ2QA5WZiZWS4nCzMzy+VkYWZmuZwszMwsl5OFGSDpXZJC0mFp+SRJP6rCea6R9J7BPq5ZtTlZmGXeD/wSmFqtE0hqut81WetwsrCWl8ZaOgE4l62TxV6SfiDpEUlfl7RTGuDtGkkPS1oi6ZPpGK+RdG+aT+EHkvZJ5b+Q9C+S7gR65594q6T/lPQbSaem7camsgfS6/Wp/KR0jJskPSrpupJf7h4r6e40mNxCSa9I8c2UdH+K5e9r869ozc5/6ZjBFOC2iPiNpD/o5clzjgMOBx4HbgPOAFYCHRFxJEDvsAzAd4ALIuJOSZ8j+xX9J9K69oh4U9r+GmAs8CbglcDPJb2KbHTRt0XEnyWNB64HOtP+E4EjyMY7+hVwgqSFwPeB90XE/ZL2Ivsl77lkw10cK2lX4FeSfhoRKwftX8takmsWZlkT1A3p8w1pGWBhRDwWEVvIvrxPBB4DDpV0paSTgQ2S9iZLCHem/WaRTUjV6/t9zjc7Il6MiN+m4x1GNjzLNyQtAW4kS1K9FkbE6oh4EXiQLNlMANZFxP3w0sBym4G3k42T9CDZ8OX7AeO389/F7CWuWVhLk7Qf8GbgSEkBDCMb3vpWth3mOiLiWUlHA5OA84H3Ap/MOc0LfY9TZvmTwJPA0WR/xP25ZP3Gks9byP7fqsxxSOUXRMT8nJjMBsQ1C2t17yGbZe2QiBgbEWPImppOJJtHYJyknYD3Ab9UNgz0ThFxM/BpsmlOnwOelfSGdMwPAndue6qXnJn6P14JHAosB/Ymqym8mPYflhP3o8AoSccCpP6KnYH5ZAPMDU/lr5a0xwD/Tcy24ZqFtbr3A5f3KbsZ+ChwT1p3FHAX8IP0+dspgQDMSO/TgK9L2p2saanSyKfLyZLJgcBHUj/FVcDNks4Efs62tZGtRMRfJL0PuDINY91DNpT1f5A1Uz2QOsK7yfpkzHaIR501M7NcboYyM7NcThZmZpbLycLMzHI5WZiZWS4nCzMzy+VkYWZmuZwszMws138DT+ewyYaCiJ8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [3.5710, 2.3470, 1.7710, 1.4875, 1.2930, 1.1645, 1.1125, 1.0855]  # x axis values \n",
    "y = [1000,500,250,125,62.5,31.25,15.625,0] # corresponding y axis values \n",
    "   \n",
    "plt.plot(x, y,'o') # plotting the points in scatter plot format \n",
    "\n",
    "plt.xlabel('Absorbance') # naming the x axis \n",
    " \n",
    "plt.ylabel('Protein Concentration') # naming the y axis\n",
    "  \n",
    "plt.title('BSA Standard Curve') # give a title to your graph (note: it automatically puts the title on top) \n",
    "   \n",
    "plt.show() # function to show the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "alleged-treaty",
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r^2 value is:  0.9981275231607426\n"
     ]
    }
   ],
   "source": [
    "#You can add a trendline to the data using the following code:\n",
    "import numpy as np\n",
    "\n",
    "x = [3.5710, 2.3470, 1.7710, 1.4875, 1.2930, 1.1645, 1.1125, 1.0855]  # x axis values \n",
    "y = [1000,500,250,125,62.5,31.25,15.625,0] # corresponding y axis values \n",
    "\n",
    "plt.plot(x, y,'o') # plotting the points in scatter plot format \n",
    "z = np.polyfit(x, y, 1) # Polynomial fit\n",
    "p = np.poly1d(z) # one-dimensional polynomial class\n",
    "plt.plot(x,p(x),\"r--\")# plot the trendline red and dashed\n",
    "plt.title(\"y = %.6fx + %.6f\"%(z[0],z[1])) #adds the trendline equation\n",
    "\n",
    "plt.xlabel('Absorbance') # naming the x axis  \n",
    "plt.ylabel('Protein Concentration') # naming the y axis\n",
    "\n",
    "plt.show() # function to show the plot\n",
    "\n",
    "#Now let's calculate the r squared value and display it on the bottom\n",
    "correlation_matrix = np.corrcoef(x, y)\n",
    "correlation_xy = correlation_matrix[0,1]\n",
    "r_squared = correlation_xy**2\n",
    "print(\"r^2 value is: \", r_squared)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "brazilian-cartridge",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Conc      OD\n",
      "0  1000.000  3.6710\n",
      "1   500.000  2.3470\n",
      "2   250.000  1.7710\n",
      "3   125.000  1.4875\n",
      "4    62.500  1.2930\n",
      "5    31.250  1.1645\n",
      "6    15.625  1.1125\n",
      "7     0.000  1.0855\n"
     ]
    }
   ],
   "source": [
    "# Now let's try importing an excel document using pandas (and openpyxl)\n",
    "import pandas as pd\n",
    "\n",
    "#Notice in the following command I also set the first column 0 to be the index column. This will be helpful when trying to make heat maps later... Be sure to specify the actual location of the file - place \"r\" before the path string to address special character, such as '\\'.\n",
    "bca = pd.read_excel(r'/Users/jonathanokerblom/BCA_StandardCurve.xlsx') \n",
    "print (bca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "million-collapse",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Conc</th>\n",
       "      <th>OD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000.000</td>\n",
       "      <td>3.6710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>500.000</td>\n",
       "      <td>2.3470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>250.000</td>\n",
       "      <td>1.7710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>125.000</td>\n",
       "      <td>1.4875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62.500</td>\n",
       "      <td>1.2930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31.250</td>\n",
       "      <td>1.1645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15.625</td>\n",
       "      <td>1.1125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Conc      OD\n",
       "0  1000.000  3.6710\n",
       "1   500.000  2.3470\n",
       "2   250.000  1.7710\n",
       "3   125.000  1.4875\n",
       "4    62.500  1.2930\n",
       "5    31.250  1.1645\n",
       "6    15.625  1.1125\n",
       "7     0.000  1.0855"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#It's easy to view this data in table form. Just type:\n",
    "\n",
    "bca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "operating-south",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Conc', 'OD'], dtype='object')"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bca.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "clinical-victim",
   "metadata": {
    "scrolled": true
   },
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r^2 value is:  0.9984254602245002\n"
     ]
    }
   ],
   "source": [
    "#Now let's make a scatter plot of the data using numpy and matplotlib.pyplot                    \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = bca['OD'] #Assign OD column to x\n",
    "y = bca['Conc'] # Assign Conc column to y\n",
    "\n",
    "plt.plot(x, y,'o') # plotting the points in scatter plot format \n",
    "z = np.polyfit(x, y, 1) # Polynomial fit\n",
    "p = np.poly1d(z) # one-dimensional polynomial class\n",
    "plt.plot(x,p(x),\"r--\")# plot the trendline red and dashed\n",
    "plt.title(\"y = %.6fx + %.6f\"%(z[0],z[1])) #adds the trendline equation\n",
    "\n",
    "plt.xlabel('Absorbance') # naming the x axis  \n",
    "plt.ylabel('Protein Concentration') # naming the y axis\n",
    "\n",
    "plt.show() # function to show the plot\n",
    "\n",
    "#Now let's calculate the r squared value and display it on the bottom\n",
    "correlation_matrix = np.corrcoef(x, y)\n",
    "correlation_xy = correlation_matrix[0,1]\n",
    "r_squared = correlation_xy**2\n",
    "print(\"r^2 value is: \", r_squared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "widespread-airport",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}