diff --git a/w1/Jupyter Notebook Working.ipynb b/w1/Jupyter Notebook Working.ipynb
new file mode 100644
index 0000000..c3ad860
--- /dev/null
+++ b/w1/Jupyter Notebook Working.ipynb	
@@ -0,0 +1,95 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "944347d0",
+   "metadata": {},
+   "source": [
+    "<b>The Jupyter Notebook App</b> is a server-client application that allows editing and running notebook documents via a web browser. It can be executed on a local desktop requiring no internet access or can be installed on a remote server and accessed through the internet.\n",
+    "\n"
+   ]
+  },
+  {
+   "attachments": {
+    "image-2.png": {
+     "image/png": 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"
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+    }
+   },
+   "cell_type": "markdown",
+   "id": "459c4858",
+   "metadata": {},
+   "source": [
+    "<h3>Components of the User-Interface:</h3><br>\n",
+    "\n",
+    "<h4>Notebook Dashboard</h4><br>\n",
+    "It is the first window that launches in the browser upon opening the <b>Jupyter Notebook</b> through Anaconda Navigator.\n",
+    "Given below is the snapshot of the same.<br><br>\n",
+    "\n",
+    "![image.png](attachment:image.png)\n",
+    "\n",
+    "This is where the various notebooks are shown.<br><br><br>\n",
+    "\n",
+    "<h4>Notebook Editor</h4><br>\n",
+    "Once we click on a notebook, a notebook editor is opened.\n",
+    "\n",
+    "![image-2.png](attachment:image-2.png)\n",
+    "<br>\n",
+    "\n",
+    "The Notebook Editor is made up of various cells. When a cell is in <b>Edit mode</b>, the Cell Mode Indicator will change to reflect the cell’s state. This state is indicated by a small pencil icon on the top right of the interface. When the cell is in <b>Command mode</b>, there is no icon in that location.<br>\n",
+    "\n",
+    "The Notebook Editor also various provides controls in the ribbon, to add/delete cells, etc. One of them inlcude the option to choose the type of content to be added to the cell, vis-a-vis, <b>Markdown</b>, <b>Code</b>, <b>RawNBConvert</b>, <b>Heading</b>.<br>\n",
+    "<ul>\n",
+    "    <li><b>Markdown: </b>This is used to add text to the notebook.</li>\n",
+    "    <li><b>Code: </b>This is where the code is written in the language of the kernel</li>\n",
+    "</ul>\n",
+    "\n",
+    "\n",
+    "\n",
+    "<br><br><br>\n",
+    "\n",
+    "\n",
+    "<h4>Kernel</h4>\n",
+    "\n",
+    "A notebook kernel is a computational engine that executes the code contained in a Notebook document. The ipython kernel, referenced here, executes python code. Kernels for many other languages exist (official kernels).\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1f6d9791",
+   "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": "3.9.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/w1/Matplotlib Tutorial.ipynb b/w1/Matplotlib Tutorial.ipynb
new file mode 100644
index 0000000..b4150e0
--- /dev/null
+++ b/w1/Matplotlib Tutorial.ipynb	
@@ -0,0 +1,357 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "38faad38",
+   "metadata": {},
+   "source": [
+    "# Matplotlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ab57580",
+   "metadata": {},
+   "source": [
+    "It is the plotting library in python that serves as a visualization entity "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5701363",
+   "metadata": {},
+   "source": [
+    "The first step is to import <b> Matplotlib </b> library to the current jupyter notebook. Most of its utilities lie under the <b>pyplot</b> submodule."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "85363626",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt #Shorthand notation is given to facilitate easier further usage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "7f4e0cbe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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TxvclUoO+JACOWO5m9jowBuhgZnnAPcAYMxtK1S6XXOAmAOdchpm9CWQCFcDNOlJG5If2HiznwXdX8+/lm+neoQX/nnwio3po0JcEjjl3yF3ix1ViYqJbvny51zFEjosPM7Zx11vp7Nxfxo2n9uDWsb1pGqFBX3LszGyFcy7xULfpE6oix8n24lKmv5PBu6n59I9uxYvXjCAhprXXsSREqdxF6phzjrnfbeG++ZkcKK3kj+f04abTexLRUIO+pO6o3EXq0JY9B7lzbhqfZG/nhLiqQV+9OmrQl9Q9lbtIHfD5HK9+vZGZ72XhgOkXDOCq0Rr0JcePyl0kwNZv30dSchrf5O7i1N4deHBiArHtmnsdS8KMyl0kQCoqfTz/+QaeWLiGpo0a8OdLB3Pp8Bh9wlQ8oXIXCYCMrXuZmpxK+pYixg3sxP0XDaKjBn2Jh1TuIrVQUl7JXz9ey3Ofrqdt88Y8e+UJnJsQ7XUsEZW7SE2t2LiLKXNSWbd9P5NOiOHuCf1p07yx17FEAJW7yDHbX1rBnz/I5uWluXRp3YyXrxvJ6X002VTqF5W7yDH4bM12pqWksXXvQa4+sRu3j+9Hyyb6MZL6R/8qRY7C3gPl3P9uJnNW5NEjqgVv3jSaEfHtvI4lclgqd5EjeD89n7vfzmDX/jJ+PaYnvztLg76k/lO5ixxGYXEJ97ydwXvp2xjYpRUvXTuCQV016EuCg8pd5Hucc8xZkccD767mYHklU8b35cZTe2jQlwQVlbtINZt3HeCOuWl8vnYHI+LbMnPSYHpGtfQ6lsgxU7mLUDXo65WluTzyQTYG3HfRQH4+qhsNNOhLgpTKXcJeTuE+kpJTWb5xN6f1ieLBiYOIaatBXxLcVO4Stsorfcz6bD1PLlxLs8YNeeyyIVxyQlcN+pKQoHKXsJS+ZS9T5qSSmV/E+QnRTL9wIFGRTbyOJRIwKncJKyXllTy5aC2zPltPuxaNee7nwxk/qLPXsUQCTuUuYWNZ7i6mzkll/Y79/CQxhjvPG0Dr5hFexxKpEyp3CXn7Sit45P0sXlm6kZi2zfjX9aM4pXcHr2OJ1CmVu4S0xdmF3JmSRn5RCb84OZ4/ntOXFhr0JWFA/8olJO3eX8b98zNJ+W4LvTq2ZM4vT2J4t7ZexxI5blTuElKccyxI28Y989LZc6Cc353Zi5vP7EWTRhr0JeFF5S4ho7CohLveSufDzAISurbmletGMaBLK69jiXhC5S5BzznHf5bncf+7mZRV+Jh2bj+uP6U7jTToS8KYyl2C2qadVYO+luTsYGT3dsy8JIEeGvQlonKX4FTpc/zjy1we/SCbhg2MBy4exM9GxmnQl4ifyl2CztqCYqYkp/Ldpj2c0TeKGRMT6NKmmdexROqVI+6UNLPZZlZoZunVlrUzs4/MbK3/a9tqt00zsxwzyzazcXUVXMJPWYWPpxat5fynlpC7Yz9/+elQZl87QsUucghH847TP4Dx31uWBCxyzvUGFvmvY2YDgMuBgf7HPGNmOgZNai01bw8X/m0Jj3+0hnGDOvPRbadz8TBNcBQ5nCPulnHOfWZm8d9bfBEwxn/5ZeATYKp/+RvOuVJgg5nlACOBpQHKK2GmpLySJz5aw/OfrycqsgnPX53I2QM6eR1LpN6r6T73Ts65fADnXL6ZdfQv7wp8Ve1+ef5lP2Bmk4HJAHFxcTWMIaHsq/U7SUpOJXfnAa4YGUvSuf1p3UyDvkSORqDfUD3U78juUHd0zs0CZgEkJiYe8j4SnopLypn5Xhavfr2JuHbNee2GUZzUS4O+RI5FTcu9wMyi/Vvt0UChf3keEFvtfjHA1toElPDycVYBd85Np6CohBtO6c5t5/SheWMd1CVyrGr6Eb55wDX+y9cAb1dbfrmZNTGz7kBv4JvaRZRwsGt/Gbe+8R3X/WM5kU0bkfyrk7hrwgAVu0gNHfEnx8xep+rN0w5mlgfcA8wE3jSz64FNwGUAzrkMM3sTyAQqgJudc5V1lF1CgHOOd1LzmT4vg+KScm45qzc3n9GLxo00OkCkNo7maJkrDnPTWYe5/wxgRm1CSXjYtrdq0NfC1QUMiWnNw5eOol9nDfoSCQT9zivHnXOON5Zt5sF3V1Pu83Hnef257pTuNNToAJGAUbnLcbVx536SktNYun4nJ/Zox8xLBhPfoYXXsURCjspdjotKn+OlLzbw6IfZRDRowEOXJPDTxFgN+hKpIyp3qXPZ26oGfa3avIex/TvywMUJdG7d1OtYIiFN5S51pqzCxzOf5PD04hwim0bw1BXDuGBwtObBiBwHKnepEys372HqnFSyC4q5aGgX7rlgIO1aNPY6lkjYULlLQB0sq+SxD7OZ/cUGOkY25cVrEjmrvwZ9iRxvKncJmC/X7SApOY1Nuw7ws1FxJJ3bj1ZNNehLxAsqd6m1opJyHlqwmte/2Ux8++a8fuOJjO7Z3utYImFN5S61sjCzgDvfSmN7cSk3ndaDW8f2oVljnZ9FxGsqd6mRnftKmf5OJu+s2kq/zpE8f3Uig2PaeB1LRPxU7nJMnHO8vXIr976Twb7SCm47uw+/PL2nBn2J1DMqdzlqW/cc5K630vk4q5ChsW145NLB9OkU6XUsETkElbsckc/neO2bTcx8L4tKn+PuCQO49qR4DfoSqcdU7vKjNuzYT1JyKl9v2MXJvdrz0MTBxLVv7nUsETkClbscUkWljxeXbODxj9bQuFEDHpk0mMsSYzQ6QCRIqNzlBzK3FjE1OZW0LXs5e0AnHrh4EJ1aadCXSDBRucv/Ka2o5G8f5/DsJ+to0zyCp392AucldNbWukgQUrkLACs27mZqcio5hfu4ZFhX7p4wgLYa9CUStFTuYe5AWQV//iCbf3yZS3Srprz0ixGc0bej17FEpJZU7mFsydodJKWkkrf7IFeP7saU8f1o2UT/JERCgX6Sw9Deg+XMeDeTN5fn0b1DC968aTQju7fzOpaIBJDKPcx8kLGNu99KZ+f+Mn41pie3nNWbphEa9CUSalTuYWJ7cSnT52Xwblo+/aNb8eI1I0iIae11LBGpIyr3EOecI+XbLdw3P5ODZZXcPq4vk0/rQURDDfoSCWUq9xC2Zc9B7khJ49M12xnerS0PT0qgV0cN+hIJByr3EOTzOf719UYefi8LB0y/YABXj46ngQZ9iYQNlXuIWbd9H0nJqSzL3c2pvTvw4MQEYttp0JdIuFG5h4jySh/Pf76evyxcS9NGDfjzpYO5dLgGfYmEK5V7CEjfspepyalkbC1i/MDO3HfxQDpGatCXSDirVbmbWS5QDFQCFc65RDNrB/wbiAdygZ8453bXLqYcSkl5JX/9eC3Pfbqets0b8+yVJ3BuQrTXsUSkHgjElvsZzrkd1a4nAYucczPNLMl/fWoAXkeqWZ67iynJqazfvp9Lh8dw1/n9adNcg75EpEpd7Ja5CBjjv/wy8Akq94DZX1o16Ovlpbl0ad2MV64byWl9oryOJSL1TG3L3QEfmpkD/u6cmwV0cs7lAzjn8s3skCMGzWwyMBkgLi6uljHCw6drtnNHShpb9x7kmtHx3D6uLy006EtEDqG2zXCyc26rv8A/MrOso32g/z+CWQCJiYmuljlC2p4DZdw/fzXJ3+bRI6oF/7lpNInxGvQlIodXq3J3zm31fy00s7nASKDAzKL9W+3RQGEAcoat99LyufvtDHYfKOPmM3ry2zM16EtEjqzG5W5mLYAGzrli/+VzgPuAecA1wEz/17cDETTcFBaV8Ke3M3g/YxsDu7Ti5etGMLCLBn2JyNGpzZZ7J2Cu/0MyjYDXnHPvm9ky4E0zux7YBFxW+5jhwznHnBV53D8/k5IKH1PH9+PGU7vTSIO+ROQY1LjcnXPrgSGHWL4TOKs2ocLV5l0HuGNuGp+v3cGI+LbMnDSYnlEtvY4lIkFIh1rUA5U+xytLc/nzB9kYcP9FA7lyVDcN+hKRGlO5eyynsJipyWms2Lib0/tEMWPiIGLaatCXiNSOyt0j5ZU+/v7pOp5alEPzJg15/CdDmDisqwZ9iUhAqNw9kL5lL7fPSWV1fhHnD45m+gUDiYps4nUsEQkhKvfjqKS8kr8sXMvzn6+nXYvG/P2q4Ywb2NnrWCISglTux8k3G3aRlJzK+h37+WliLHec15/WzSO8jiUiIUrlXseKS8p55P1s/vnVRmLaNuNf14/ilN4dvI4lIiFO5V6HFmcXcmdKGvlFJVx3cnf+OK4PzRvrWy4idU9NUwd27y/j/vmZpHy3hV4dWzLnlycxvFtbr2OJSBhRuQeQc4530/K55+0M9h4s53dn9uLmM3vRpJEGfYnI8aVyD5CCohLufiudDzMLSOjamn/dMIr+0a28jiUiYUrlXkvOOd5cvpkH3l1NWYWPaef24/pTNOhLRLylcq+FTTsPkJSSypfrdjKyezsenjSY7h1aeB1LRETlXhOVPsc/vszl0Q+yadjAeODiQfxsZJwGfYlIvaFyP0ZrCoqZMieVlZv3cEbfKGZMTKBLm2ZexxIR+R8q96NUVuHjuU/X8deP19KySSOevHwoFw7pokFfIlIvqdyPwqrNe5ianErWtmIuGNKF6RcMoH1LDfoSkfpL5f4jDpZV8sTCNbzw+XqiIpvw/NWJnD2gk9exRESOSOV+GEvX7WRaSiq5Ow9wxchYpp3Xn1ZNNehLRIKDyv17ikrKmfleFq99vYm4ds157YZRnNRLg75EJLio3Kv5OKuAO1LSKSwu4cZTu3Pb2X1p1lijA0Qk+KjcgZ37SrlvfiZvr9xK306RPHfVcIbGtvE6lohIjYV1uTvnmLdqK/e+k0lxSTm3ju3Nr8f0onEjjQ4QkeAWtuWev/cgd81NZ1FWIUNi2/DIpMH07RzpdSwRkYAIu3L3+RxvLNvMQwtWU+7zcdf5/fnFyd1pqNEBIhJCwqrcc3fsJyklla/W72J0j/bMnJRAt/Ya9CUioScsyr3S55i9ZAOPfZRNRIMGPHRJApePiNXoABEJWSFf7lnbipg6J5VVeXsZ278jD1ycQOfWTb2OJSJSp0K23EsrKnl68TqeWZxD62YR/PWKYUwYHK2tdREJCyFZ7t9t2s3U5FTWFOzj4qFd+NMFA2nXorHXsUREjpuQKvcDZRU89uEaZn+xgc6tmjL72kTO7KdBXyISfuqs3M1sPPAk0BB4wTk3s65eC+DLnB0kpaSxadcBrhwVR9K5/YjUoC8RCVN1Uu5m1hB4GjgbyAOWmdk851xmoF9r78FyHlqwmjeWbSa+fXPemHwiJ/ZoH+iXEREJKnW15T4SyHHOrQcwszeAi4CAlntq3h5ufGU524tLuen0Hvx+bB+aRmjQl4hIXZV7V2Bztet5wKjqdzCzycBkgLi4uBq9SFy75vTpFMnzVycyOKZNzZKKiISguir3Qx1v6P7ninOzgFkAiYmJ7hD3P6I2zRvzz+tHHfmOIiJhpq7GH+YBsdWuxwBb6+i1RETke+qq3JcBvc2su5k1Bi4H5tXRa4mIyPfUyW4Z51yFmf0G+ICqQyFnO+cy6uK1RETkh+rsOHfn3AJgQV09v4iIHJ5OOSQiEoJU7iIiIUjlLiISglTuIiIhyJyr0eeHAhvCbDuwsRZP0QHYEaA4XgqV9QCtS30UKusBWpf/6uacizrUDfWi3GvLzJY75xK9zlFbobIeoHWpj0JlPUDrcjS0W0ZEJASp3EVEQlColPssrwMESKisB2hd6qNQWQ/QuhxRSOxzFxGR/xUqW+4iIlKNyl1EJAQFdbmb2XgzyzazHDNL8jpPTZnZbDMrNLN0r7PUlpnFmtliM1ttZhlmdovXmWrCzJqa2Tdmtsq/Hvd6nam2zKyhmX1nZvO9zlIbZpZrZmlmttLMlnudp6bMrI2ZzTGzLP/Py+iAPn+w7nP3n4R7DdVOwg1cURcn4a5rZnYasA94xTk3yOs8tWFm0UC0c+5bM4sEVgAXB9vfi5kZ0MI5t8/MIoAlwC3Oua88jlZjZnYbkAi0cs5N8DpPTZlZLpDonAvqDzGZ2cvA5865F/znvWjunNsTqOcP5i33/zsJt3OuDPjvSbiDjnPuM2CX1zkCwTmX75z71n+5GFhN1Tl1g4qrss9/NcL/Jzi3hAAziwHOB17wOouAmbUCTgNeBHDOlQWy2CG4y/1QJ+EOuhIJZWYWDwwDvvY4So34d2OsBAqBj5xzQbkefn8BpgA+j3MEggM+NLMVZjbZ6zA11APYDrzk31X2gpm1COQLBHO5H/Ek3OIdM2sJJAO3OueKvM5TE865SufcUKrOATzSzIJyl5mZTQAKnXMrvM4SICc7504AzgVu9u/WDDaNgBOAZ51zw4D9QEDfNwzmctdJuOsp/z7qZOBV51yK13lqy//r8ifAeG+T1NjJwIX+fdVvAGea2b+8jVRzzrmt/q+FwFyqdtEGmzwgr9pvg3OoKvuACeZy10m46yH/G5EvAqudc497naemzCzKzNr4LzcDxgJZnoaqIefcNOdcjHMunqqfk4+dcz/3OFaNmFkL/xv1+HdjnAME3VFmzrltwGYz6+tfdBYQ0IMO6uwcqnUtlE7CbWavA2OADmaWB9zjnHvR21Q1djJwFZDm318NcIf/nLrBJBp42X9UVgPgTedcUB9CGCI6AXOrtiFoBLzmnHvf20g19lvgVf/G6XrgF4F88qA9FFJERA4vmHfLiIjIYajcRURCkMpdRCQEqdxFREKQyl1EJASp3EVEQpDKXUQkBP0/jZzfOZvn01UAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "xpoints=np.array([0,6])\n",
+    "ypoints=np.array([0,250])\n",
+    "\n",
+    "plt.plot(xpoints,ypoints) #plots a line\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "50b74847",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#plotting multiple points\n",
+    "xpoints=np.array([1,5,7,8])\n",
+    "ypoints=np.array([2,4,5,6])\n",
+    "plt.plot(xpoints,ypoints)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "cfc51c0c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Markers\n",
+    "ypoints=np.array([1,2,5,4,7,6])\n",
+    "plt.plot(ypoints,marker=\"o\",ms=10,mec=\"magenta\",mfc=\"red\") #ms defines marker size, mec defines marker edge color, mfc defines marker face color\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "bd2afe0a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#linestyle\n",
+    "ypoints=np.array([1,2,5,4,7,6])\n",
+    "plt.plot(ypoints,marker=\"*\",ms=10,linestyle=\"dotted\",color=\"red\",lw=3) #color defines line color, lw defines linewidth\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "37a100d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.array([80, 85, 90, 95, 100, 105, 110, 115, 120, 125])\n",
+    "y = np.array([240, 250, 260, 270, 280, 290, 300, 310, 320, 330])\n",
+    "\n",
+    "plt.plot(x, y)\n",
+    "\n",
+    "plt.xlabel(\"Average Pulse\")    #create lables for the axes of the plot\n",
+    "plt.ylabel(\"Calorie Burnage\")\n",
+    "plt.title(\"Data\")  #give title to the plot\n",
+    "plt.grid()        #add grid to the plot\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2adf1fd3",
+   "metadata": {},
+   "source": [
+    "## Subplot function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ce5203b",
+   "metadata": {},
+   "source": [
+    "It is used to draw multiple plots in one figure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "99388d02",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Gvc3cvLzyfRP9xxTlc8lZU1i/o8FSmUO07eAxOrtjNv5tcuZA9ATffMJ7actUrIEP0doNEUYV5HFDzawB719ZPdVSmcOQ3IG+xhKYJgeSacuYqufSlqlYAx+C1s5uHn+jnqsWz6B0kOlGK+eXA5bKHKpQpJnKycVMnWArEJvsS6Ytv3X1Qs+lLVOxBj4Ej246wImuXm69qGrQx0ybOJqFMyyVORSqSigctfFvkxNeT1umYg38NGIxZe2GCEtmlXBOReoPPVZWWypzKPY2Hefo8S4b/zZZ54e0ZSrWwE/jlXea2NN0nFsvrjztY5OpzOd32jBKKsnxb9uBx2Tbd5/e6fm0ZSrWwE9j9asRJo8t4spzpp/2sclU5nobB08pFGmmtLiQOWXjnC7F+NhLuxt54JW9rPJ42jIVa+Ap7G8+wbM7GrjxglmMKjj9hH9LZQ5NKBxlWWWp797OGvfom7b8qsfTlqlYA0/hwY37APjshacfPkmyVGZqTe0n2dN03Nb/NlnTN2354xu8n7ZMxRr4IDq7e3m4dh+XLyhnZsnQ92pMpjLX77DZKAPZFHHP+LeIjBaR10Vki4hsE5F/Sdw+SUSeEZHdie/OF2uGrG/a8nQTD7zOGvggnnrzENET3axKMXVwIKdSmduPWCpzAKFwM0UFeW6JMZ8EVqjqYmAJcIWILAfuAtar6lxgfeK68QA/pi1TsQY+iLUbwswpG8vFcyYP+2dXVk9lX/MJS2UOoDYcZXHFxCF9ppBtGpf8SypMfCnxvV5XJ25fDVyT++rMcPXGlDt9mLZMxRr4AOr2t7DlwDFWXVSV1gdtKxKLXVkq8706unrZWn/MVePfIpIvInXAEeCZxE5T5ap6CCDxfcApDCJym4iERCTU2NiYs5rNwH720h427m3mHp+lLVOxBj6ANRvCjC3K55PnzUzr56dPHGOpzAFsOdBCT0xdMf6dpKq9qroEqAAuEJFFw/jZ+1S1RlVrysrKslajOb1k2vIjC8u5zmdpy1SsgfdztP0kT205xCfPq2D86MK0n8dSme+X3IF+2Wz3nIEnqWoL8DxwBdAgItMBEt/trZSL9U1b/vsnzw3U9FRr4P08HNpPV2+MVRcNfergQCyV+X614SjzyscxsTj9/xgzSUTKRKQkcXkMcBmwA3gSuDXxsFuBdY4UaIbE72nLVKyB99EbUx58bR8XnTmZueXjR/Rclsp8r96YsjkSddX4NzAdeE5E3gRqiY+BPwXcC1wuIruByxPXjQu9vLvJ92nLVGwvqz7Wb2+gvqWDb3x85MmtvDxhxfypPPXmIbp6YhQVBPv/yl0NbbSd7HHb+PebwNIBbj8KrMx9RWY4gpK2TCXYXaWfNRsiTJ84msuqyzPyfCurLZWZlBz/tiVkTSYk05ZN7Sd9n7ZMxRp4wttH2nn57SZuunA2BfmZ+bVYKvNdteEo5RNGUVE69FSrMYNJpi3vuGyu79OWqVgDT/jlaxEK84Ubzp+dsee0VOa7NiXGv4M0Q8BkR9+05d9depbT5TjKGjjQfrKHRzcd4GPnTKds/KiMPveK+ZbKrG/poL6lg/Nt/0szQkFMW6ZiDRx4/I162k72cMsw1z0ZipXVlso8Nf7trhkoxoN+HsC0ZSqBb+CqytoNYRbNnMB5s0sy/vzJVOazAR4HD4WjjC3KZ/60kU3NNMH254OtfD+AactUAt/AX9vTzK6GdlYtT2/dk6FYWV3OpkiUaEBTmaFIlPMqSzP24bAJns7uXu54+I1Api1TCfy/qLWvhSkpLuTqJTOy9hrJVOZzAUxltnZ2s+Nwq00fNCOSTFt+N4Bpy1QC3cAPHevgD9sauL5mFqMLszePdNGMiZQFNJW5ORJFFduB3qStb9rywwFMW6YS6Ab+6437iKly8zC2TEtHXp6wcn4w98oMhaPk5wlLZpU4XYrxIEtbphbYBt7VE+NXr+/nw2dPZfbk7H+anUxl1oaDlcoMRZpZOGMCY0fZqg1meFSVuy1tmVJgG/jvtx6iqf3kiFcdHKpkKvNPAVojvKsnRt3+FpbZ/G+ThnV1B3nK0pYpBbaBr9kQoWpyMR+cm5uF+McU5XPxnMmBSmVuO3iMzu4Y59v8bzNMB6In+MYTW1kWkL0t0zWiBi4iJSLyXyKyQ0S2i8hFmSosm7bWH2NTJMrNyyvJy2GSa2V1eaBSmckd6GvsDNwMw3vSltcvsemnKYz0N/MT4GlVnQ8sBraPvKTsW7shwujCPK5bNiunrxu0VGZtuJnKycVMnTDa6VKMh/RNW+bi8ykvS7uBi8gE4IPA/QCq2pXYlsrVWk50sW5LPdcunZnznWGClMpUVULhqI1/m2GxtOXwjOQM/EygEfhPEXlDRH4uImP7P8htO3f/JnSAzu4YtyyvcuT1g5LK3Nt0nKPHu2z82wyZpS2HbyQNvAA4D/ipqi4FjgN39X+Qm3bujsWUta9FOL+qlAUzJjhSw8r5wUhlhhLj327agce42/f+YGnL4RpJAz8AHFDVjYnr/0W8obvWC7sa2dd8IiurDg7VOTODkcoMhZspLS5kTtk4p0sxHvDy7ibuf3kvtyy3tOVwpN3AVfUwsF9Ezk7ctBL4c0aqypI1G8KUjR/FFQunOVZDMpX5os9Tmcnxb3sbbE4nmbY8s2wsX7vS0pbDMdJZKP8APJjY1XsJ8O0RV5QlkaPHeX5XI5+5YLbjGwyvrC6nzcepzKPtJ9nTdNzW/zan1Tdt+RNLWw7biPLNqloH1GSmlOz65WsR8kW46cLMbZmWrkvOmkxRIpV5yVlTnC4n42z82wxVMm355b+cZ2nLNARihnxHVy8P1+7nIwunUe6COcnFRQVc4uNUZijcTFFBHotm2j9IM7j6lg6+sc7SliMRiAb+5JZ6Wjt7crbuyVD4OZVZG46yuGIiowrs7bAZWG9M+aeH64jFLG05Er7/rakqq1+NcHb5eC44wz1jsslU5vod/pqN0tHVy7aDxzwx/i0is0TkucQyENtE5PbE7d8SkXoRqUt8Xel0rX5zKm15laUtR8L3DXzzvih/PtTKqosrXTUjIpnKXO+z1Qm3HGihu1e9Mv7dA9ypqtXAcuALIrIgcd+PVHVJ4ut3zpXoP+9JW9ZY2nIkfN/AV78aYfyoAq5ZMtPpUt5n5fypvktlJnegP2+2+xu4qh5S1c2Jy23E1/Jx34HiI5a2zCxfN/AjbZ38fushPl1T4coNBVZWl/sulVkbjjKvfBwlxd5K0olIFbAUSAbTvigib4rIAyIy4P9GblsmwgssbZlZvm7gD72+n+5e5Zbl7vnwsq9TqUyfjIP3xpTN+6KeGP/uS0TGAY8Cd6hqK/BTYA7xbMMh4AcD/ZyblonwglfetrRlpvm2gXf3xvjVxn38xdwpnOnSOPepVOZOf6QydzW00dbZ45XxbwBEpJB4835QVR8DUNUGVe1V1RjwM+ACJ2v0g2MnurnzEUtbZppvG/gzf27gcGsnqxxc92QoVsyf6ptUZnL8u6bSG2fgEh+AvR/Yrqo/7HP79D4PuxbYmuva/ERV+foTbyX2tlxiacsMct/AcIas2RBmZskYVsx391u1D8yd4ptUZigSpXzCKCpKxzhdylBdAtwCvCUidYnbvgZ8RkSWAAqEgc87UZxf9E1bnltR4nQ5vuLLBr7zcBuv7Wnmn6+YT34Ot0xLR99U5jc/vsDTn8qHwvHxb6/8GVT1ZWCgYm3aYIZY2jK7fDmEsva1MEUFedxwfm63TEtXMpX5TqN3U5n1LR3Ut3TY/pfmlFhMufMRS1tmk+9+o62d3Ty2uZ6rzp3hmWlKftgrMzn+bTvwmKSfv7yH1/ZY2jKbfNfAH9t0gBNdvdx6sTunDg5k+sQxLJju7VTmpkiUsUX5zJ823ulSjAtsP9TK9/+wi79cYGnLbPJVA1dV1rwWYfGsEs99WHJZtbdTmbXhKOdVltrbZBNPWz5Ux8TiQu79lKUts8lX/9peefsoexqPc6uLVh0cKi+nMls7u9lxuNV2oDdAPG25s6HN0pY54KsGvnpDmElji7jynOmnf7DLeDmVuTkSRdXGv42lLXPNNw38QPQE67c3cOP5sxhd6L2gQF6esOJsb6YyN0Wi5OcJS2aVOF2KcZClLXPPNw38wY37ALjJpeueDMXKam+mMmvDzSycMcGVC4aZ3Ll73VZLW+aYLxp4Z3d8y7TLqsuZWeKZFOD79E1lekV3b4y6/S02/h1w6+rq+e2Wg9y+cq7nJhB4mS8a+H+/eYjm412uX/fkdLy4V+a2g610dsds/DvA6ls6uPuJeNry7y61tGUu+aKBr3ktwpllY7nkrMlOlzJiXktlvruAlZ2BB5GlLZ3l+d/2lv0tbNnfwqrl7toyLV3Jxbe8ksqsDTcze1IxUyeMdroU4wBLWzrL8w18zYYIY4vy+dQyf6S9ZpR4J5WpqokFrOzsO4gsbek8Tzfw5uNd/PbNg1x73kzGjy50upyM8UoqM3z0BEePd9n4dwAl05YTxhTy7588xxfvfr3I0w384dr9dPXEPP/hZX/JVObzu9w9jFJ7agErOwMPmu8n0pbfu+5cJo8b5XQ5geXZBt4bU375WoTlZ05iXrm/FlBKpjLdPg4eCjdTUlzImVPcuWWdyY5X3m7i55a2dAXPNvBndxyhvqWDW3129g3eSWWGwlFqKkvJc/mmGSZzLG3pLp5t4Gs2hJk2YTSXLyh3upSscHsq82j7SfY0HffcDvRmZCxt6S6ebODvNLbz0u4mbrpwtm/nnbo9lRmKRAEb/w4SS1u6jye739oNEQrzhRsvmO10KVnj9lRmKNxMUUEei2ZOdLoUkwPJtOV5s0ssbekinmvgx0/28OimA1x5znTKxvv70+8VLk5lhiJRFldMZFSBvY32u1hM+fIjW+JpyxssbekmnvubePyNetpO9rDKg5s2DNdKl6YyO7p62Vp/zMa/A+L+l/eyYc9R7rlqIZWTxzpdjunDUw1cVVm7IcLCGRM4b7b/x17dmsrccqCF7l619U8CYPuhVr73h52WtnQpTzXwjXub2dnQxqqL/LHuyVC4MZWZXMDKlpD1N0tbut+IG7iI5IvIGyLyVCYKSmXthggTxxRy9eKZ2X4p13BjKjMUiTKvfBwlxd7e71BEZonIcyKyXUS2icjtidsnicgzIrI78T2Q/1OdSlt+2tKWbpWJM/Dbge0ZeJ6UDh/r5Olth7m+piJQ80/dlsrsjSmbIlG/jH/3AHeqajWwHPiCiCwA7gLWq+pcYH3ieqC8mkhb3rx8Nh+eb2lLtxpRAxeRCuBjwM8zU87gfvX6PmKq3OzhLdPS4bZU5q6GNto6e3wx/q2qh1R1c+JyG/ETkZnAJ4DViYetBq5xpECHHDvRzZ2/iactv37lAqfLMSmM9Az8x8BXgEE7i4jcJiIhEQk1Njam9SJdPTF+tXEfl84rC+Sn4G5KZYZOLWDlizPwU0SkClgKbATKVfUQxJs8MOApaCaObTf6xrqtNLad5EfXW9rS7dJu4CLyceCIqm5K9ThVvU9Va1S1pqysLK3XenrbYZraT7Lq4qq0ft7rkqnM9S4YRglFopRPGEVFqXf3Hu1PRMYBjwJ3qGrrUH8uE8e226yrq+fJRNpy8awSp8sxpzGSM/BLgKtFJAw8BKwQkV9mpKp+1rwapnJyMR+a649/JMNVXFTAxXMms35Hg+OpzPgGDpN8MyNBRAqJN+8HVfWxxM0NIjI9cf90wPn/OXPA0pbek3YDV9WvqmqFqlYBNwLPqurNGassYdvBY4QiUW5ZXhnoVe9WVpcTOepsKvNgSwf1LR2+GP8GkPj/QvcD21X1h33uehK4NXH5VmBdrmvLNUtbepPr/5bWbogwujCP65bNcroUR7khlfnuAla+Gf++BLiF+LvHusTXlcC9wOUishu4PHHd15Jpy29etSCQnzN5VUEmnkRVnweez8Rz9XXsRDdP1NVzzZKZTCz2z5Zp6eibyvzbDznz9jYUbmZsUT7zp/ljAw1VfRkY7G3dylzW4qS+acvra4J9ouQ1rj4D/82m/XR2x7glAOueDIXTqczacJSls0vt7bWPdHb38qWHLW3pVa79lxiLKWtfi1BTWcrCGbZkKcRXJ3Qqldna2c2Ow622A73P/OCPO9lx2NKWXuXaBv7C7kYiR0/Y2Xcf5zqYynxjXwuqvhr/DrxX327iZy9Z2tLLXNvA126IMGXcKD66aLrTpbiGk6nMULiZ/Dxhic0N9oVTacsplrb0Mlc28H1HT/DcziN89oJZFBW4skTHJFOZoRynMmvDzSyYPoGxozLyubdx2Km0pe1t6Wmu7I6/3BghT4TPXmjDJ/29u1dm7oZRuntj1O1vsfFvn7C0pX+4roF3dPXycO1+PrKwnGkTRztdjus4kcrcdrCVzu6YjX/7wEFLW/qK6xr4b7cc5FhHN6suqnK6FNfKdSozOVzjlwRmUMViyp2WtvQVV/0NqiqrN4SZVz6OC8+ws73B5DqVWRtuZvakYqZOsHdEXmZpS/9xVQPfvK+FbQdbWXVRlQUKUkimMp/NQQNXTW7gYGffXmZpS39yVQNfsyHM+FEFXLs0OFumpWtl9VRCkeaspzLDR0/Q1N5l498eZmlL/3JNA29sO8nv3jrEp5ZV2FS1IcjVXpm1Nv7teZa29C/XNPCHXt9Hd69a8nKIcpXKDIWbKSkuZE7ZuKy+jsmOV9+xvS39zBUNvKc3xoMb9/EXc6dYoxiivqnM7t7spTJDkSg1laWBXovdq46d6ObOR7ZwxmRLW/qVKxr4M39u4HBrJ7cEbMPikVqR3Ctzb3ZSmUfbT7Kn8bhfdqAPHEtb+p8rGviaDRFmloxhZXW506V4yl9kOZWZ3MDBxr+9J5m2/EdLW/qa4w28uzfG1Amj+B+XVJFvb9OHJdupzFC4maKCPM6psOV8vSSZtlw6u4S/t7SlrznewAvz8/jJjUv5m7840+lSPCmbqcxQJMriiomMKrC3316RTFv2xpQfW9rS9+xv1+OSqcz1GR5G6ejqZWv9MRv/9pgHXomnLe+xtGUgWAP3uBklY6iePiHjDXzLgRa6e9XGvz1kx+FWvvu0pS2DxBq4D1yWhVTmpsQHmMusgXtCZ3cvdzxkacugsQbuA9lIZdaGm5lXPo6S4qKMPafJnmTa8rufPsfSlgFiDdwHzp05kSnjMpfK7I3FF7BaVmnj316QTFvedOFsVsy3qbhBYg3cB/LyhBXzyzKWytzV0EZbZw/n2wqErneso5svP7KFqslj+frHqp0ux+SYNXCfWFldnrFUZjLA4/cVCEXkARE5IiJb+9z2LRGpF5G6xNeVTtZ4Ot9ct5WGtpP8+IYlFBfZInBBYw3cJzKZygyFmymfMIqK0jEZqMzVfgFcMcDtP1LVJYmv3+W4piFbV1fPujrb2zLIrIH7RCZTmaFwlJrKSb6fyaCqLwLZWUgmyyxtacAauK+8m8o8nvZzHGzpoL6lI+g78HxRRN5MDLEM+osQkdtEJCQiocbGxpwVZ2lLk2R/8z6y4lQqsyHt5wjK+HcKPwXmAEuAQ8APBnugqt6nqjWqWlNWVpaj8t5NW37z45a2DDpr4D4yMwOpzFC4mbFF+cyfNj6DlXmHqjaoaq+qxoCfARc4XVNfybTlZdXl3HC+pS2Dzhq4z4w0lVkbjrJ0dmlg35aLyPQ+V68Ftg722Fw72fNu2vI7n7K0pbEG7jsjSWW2dnaz83BrYMa/ReTXwAbgbBE5ICKfA74rIm+JyJvAh4EvOVpkHz/44y5LW5r3sImjPpNMZa7ffoRrl1YM62ff2NdCTIMz/q2qnxng5vtzXsgQvPpOEz97aY+lLc172Bm4zyRTmS/sGn4qMxRuJj9PWGJzil3F0pZmMNbAfWhldTltncNPZdaGm1kwfQJjR9kbMzdJpi1/ZGlL00/aDVxEZonIcyKyXUS2icjtmSzMpC+dVGZ3b4y6/S2BGf/2iie3HGRd3UH+ccVce2dk3mckZ+A9wJ2qWg0sB74gIgsyU5YZiXRSmdsOttLZHQvM+LcXHGzp4O7H32Lp7BK+8GFLW5r3S7uBq+ohVd2cuNwGbAdmZqowMzIr508dViozFI4Pt9gOPO4Qiylf/s0WeixtaVLIyFEhIlXAUmDjAPc5EjcOuhXV8ZkKQ01lhsJRZk8qZuqE0dksywzRA6/s5dV3LG1pUhtxAxeRccCjwB2q2tr/fqfixkE3nFSmqhKKNNv4t0tY2tIM1YgauIgUEm/eD6rqY5kpyWTKUFOZ4aMnaGrvsvFvF3g3bVnAvZa2NKcxklkoQjz0sF1Vf5i5kkymrJg/lZjCC7tSD13V2vi3a7ybtjyXKZa2NKcxkjPwS4BbgBVe2b0kaBZXlCT2ykw9Dr4pHKWkuJA5ZeNyVJkZyIZ3jlra0gxL2qkAVX0ZsPd3LpZMZf5+62G6e2MUDjKToTbSTE1lKXl59tfplGMd3dz5SJ2lLc2w2NwknztdKvNo+0n2NB63HegdZmlLkw5r4D73gbNSpzLf3cDBxr+dYmlLky5r4D43dlQBF505eCpzUyRKUUEe51RMdKA6Y2lLMxLWwAPgsurBU5m14WYWV0xkVEG+A5UFW9+05Y+ut7SlGT47YgJgsFRmZ3cvW+uP2fi3Q/qmLaumWNrSDJ818AAYLJW5ZX8L3b1q498O2HG4le/+wdKWZmSsgQdEMpXZcuLdVGbyA8xlFuDJqVNpy9GWtjQjYw08IJKpzOd3vpvKrA03M698HCXFRQ5WFjyWtjSZYg08IPqnMmMxZVMkauPfOZZMW37W0pYmA6yBB0T/vTJ3HWmjrbPHxr9zqG/a8m5LW5oMsAYeIH1TmbXhZIDHzsBz5R5LW5oMs6MoQD5w1hSK8vNYv+MITe0nmTp+FBWlY5wuKxCe3HKQJ+oO8qXL5lna0mSMNfAAGTuqgIvmTGb99obE9MFJNgMiByxtabLFhlAC5rLqqYSPnqC+pSPwO/CIyAMickREtva5bZKIPCMiuxPfR/RLsrSlySY7mgImmcoEG/8GfgFc0e+2u4D1qjoXWJ+4nrZk2vIblrY0WWANPGCSqczionzmTxvvdDmOUtUXgf7r7H4CWJ24vBq4Jt3n33m47VTa8kZLW5ossDHwAPrnK87m0LFOezs/sHJVPQSgqodEZOpgDxSR24DbAGbPnv2++/MELjxjkqUtTdZYAw+gS88etCeZYVDV+4D7AGpqat63Vu/c8vGs/dyFOa/LBIedghnzXg0iMh0g8X3gnTCMcQFr4Ma815PArYnLtwLrHKzFmJSsgZvAEpFfAxuAs0XkgIh8DrgXuFxEdgOXJ64b40o2Bm4CS1U/M8hdK3NaiDFpsjNwY4zxKGvgxhjjUdbAjTHGo6yBG2OMR4nq+/IH2XsxkUYgMsjdU4CmnBWTOVZ3bqWqu1JVy3JZTFKKY9urv2fwbu1+rHvAYzunDTwVEQmpao3TdQyX1Z1bXqvba/X25dXag1S3DaEYY4xHWQM3xhiPclMDv8/pAtJkdeeW1+r2Wr19ebX2wNTtmjFwY4wxw+OmM3BjjDHD4HgDF5ErRGSniLwtIiPaviqXBtpP0QtEZJaIPCci20Vkm4jc7nRNQyEio0XkdRHZkqj7X5yu6XTs2M6doB7Xjg6hiEg+sIv4qm8HgFrgM6r6Z8eKGiIR+SDQDqxR1UVO1zNUiTWup6vqZhEZD2wCrnH771ziW9qMVdV2ESkEXgZuV9XXHC5tQHZs51ZQj2unz8AvAN5W1T2q2gU8RHxPQtcbZD9F11PVQ6q6OXG5DdgOzHS2qtPTuPbE1cLEl5s/wLFjO4eCelw73cBnAvv7XD+AB37pfiEiVcBSYKPDpQyJiOSLSB3xXXKeUVU3123HtkOCdFw73cAH2unVzWdVviEi44BHgTtUtdXpeoZCVXtVdQlQAVwgIm5+e2/HtgOCdlw73cAPALP6XK8ADjpUS2AkxtoeBR5U1cecrme4VLUFeB64wtlKUrJjO8eCeFw73cBrgbkicoaIFAE3Et+T0GRJ4kOT+4HtqvpDp+sZKhEpE5GSxOUxwGXADkeLSs2O7RwK6nHtaANX1R7gi8AfiH/o8IiqbnOypqEaZD9FL7gEuAVYISJ1ia8rnS5qCKYDz4nIm8Sb4zOq+pTDNQ3Kju2cC+RxbUlMY4zxKKeHUIwxxqTJGrgxxniUNXBjjPEoa+DGGONR1sCNMcajrIEbY4xHWQM3xhiPsgZujDEe9f8Bs5+B5E1V9bkAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#plot 1:\n",
+    "x = np.array([0, 1, 2, 3])\n",
+    "y = np.array([3, 8, 1, 10])\n",
+    "\n",
+    "plt.subplot(1, 2, 1)  \n",
+    "#plt.subplot takes three arguments, first: the number of rows, second: the number of columns, third: the position of the plot\n",
+    "plt.plot(x,y)\n",
+    "plt.title(\"plot1\")\n",
+    "\n",
+    "#plot 2:\n",
+    "x = np.array([0, 1, 2, 3])\n",
+    "y = np.array([10, 20, 30, 40])\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.plot(x,y)\n",
+    "plt.title(\"plot2\")\n",
+    " \n",
+    "plt.suptitle(\"Plots\") #add title for the whole figure\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4643c622",
+   "metadata": {},
+   "source": [
+    "## Working with CSV files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "5e7ba9da",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_csv(\"Data.csv\")\n",
+    "df.plot()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "7c4d7d1e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot(kind = 'scatter', x = 'Duration', y = 'Calories')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "21925362",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:ylabel='Frequency'>"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df[\"Duration\"].plot(kind=\"hist\") #plotting a histogram which gives us the frequency of each interval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e3510645",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "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": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/w1/Numpy Tutorial.ipynb b/w1/Numpy Tutorial.ipynb
new file mode 100644
index 0000000..fda6488
--- /dev/null
+++ b/w1/Numpy Tutorial.ipynb	
@@ -0,0 +1,480 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e986db8c",
+   "metadata": {},
+   "source": [
+    "<h1>Numpy</h1><br>\n",
+    "NumPy (Numerical Python) is an open source Python library. It contains multidimensional array and matrix data structures. It provides ndarray, a homogeneous n-dimensional array object, with methods to efficiently operate on it. NumPy can be used to perform a wide variety of mathematical operations on arrays"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81721f02",
+   "metadata": {},
+   "source": [
+    "The first step is to import the <b>Numpy</b> library to the current jupyter notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "bcd76cf5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np #Shorthand notation given to facilitate easier further usage."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f043d03",
+   "metadata": {},
+   "source": [
+    "<h2>Array</h2>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99c2f9a2",
+   "metadata": {},
+   "source": [
+    "A numpy array is a data structure which is similar to python lists, and it has a grid of elements that can be accesed in various ways.<br>\n",
+    "An array object in NumPy is called <b>ndarray</b>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "613fe783",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'numpy.ndarray'>\n",
+      "[1 2 3 4]\n",
+      "[[ 1  2  3  4]\n",
+      " [ 5  6  7  8]\n",
+      " [ 9 10 11 12]]\n",
+      "1\n",
+      "[0 1 2 3]\n",
+      "[ 0.  5. 10.]\n",
+      "[1 2 3 4]\n",
+      "2\n",
+      "8\n",
+      "[1 2 3]\n",
+      "[2]\n",
+      "[3 4]\n",
+      "[2 3]\n"
+     ]
+    }
+   ],
+   "source": [
+    "a=np.array([1,2,3,4])  #Initialising a 1D array.\n",
+    "print(type(a)) #returns type numpy.ndarray\n",
+    "b= np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12]]) #Initialising a multi-dimensioanl array(3 dimensional in this case.)\n",
+    "print(a) #To print the whole array.\n",
+    "print(b)\n",
+    "print(a[0]) #using indexing to print an element of the array.\n",
+    "\n",
+    "c=np.arange(4) #used to create an arrayof range of elements.\n",
+    "print(c)\n",
+    "\n",
+    "d=np.linspace(0,10,num=3) #creats an array with values that are spaced linearly in a specified interval.\n",
+    "print(d)\n",
+    "\n",
+    "#To print a single element of a multidimensional array, multidimensional indexing is required.\n",
+    "print(b[0]) #prints the 0th element of the 3-D array which in itself is a 1-D array.\n",
+    "print(b[0][1]) #prints the second element of the first 1-D array.\n",
+    "\n",
+    "#negative indexing using numpy\n",
+    "print(b[1][-1]) #prints the last element from ther second dimension.\n",
+    "\n",
+    "#Slicing of arrays created using numpy \n",
+    "print(a[:3]) #prints an array containing first 3 elements of the array.\n",
+    "print(a[1:2]) #prints the second element in the array.\n",
+    "print(a[2:]) #prints the element from the index 2 to the last element.\n",
+    "print(a[-3:-1]) #Slices the array from index 3 from the end to index 1 from the end.\n",
+    "#Similarly 2d arrays can be sliced.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "4c8c64c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2 3 4 5 6 7 8]\n",
+      "[1 2 3 4 5 4 8 7 3 2 6]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#sorting a numpy array\n",
+    "e = np.array([5,4,8,7,3,2,6]) #sorts the array in ascending order.\n",
+    "print(np.sort(e))\n",
+    "\n",
+    "#concatenate two arrays\n",
+    "f=np.concatenate((a,e))\n",
+    "print(f)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3cff1578",
+   "metadata": {},
+   "source": [
+    "### Shape and size of an array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "d91daa31",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3, 4)\n",
+      "2\n",
+      "12\n",
+      "[[ 1  2]\n",
+      " [ 3  4]\n",
+      " [ 5  6]\n",
+      " [ 7  8]\n",
+      " [ 9 10]\n",
+      " [11 12]]\n",
+      "[[ 1  2  3  4  5  6]\n",
+      " [ 7  8  9 10 11 12]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(np.shape(b)) #returns the shape of the aray\n",
+    "print(np.ndim(b)) #returns the dimension of the array\n",
+    "print(np.size(b)) #returns the number of elements in the array.\n",
+    "\n",
+    "#reshaping the array\n",
+    "\n",
+    "g = b.reshape(6,2) #an array can be reshaoped as long as the new array has the same number of elements.\n",
+    "print(g)\n",
+    "\n",
+    "#This can also be done by using np.reshape, some few more parameters can also be specified.\n",
+    "h= np.reshape(b, newshape=(2,6))\n",
+    "print(h)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd52f32a",
+   "metadata": {},
+   "source": [
+    "### Adding axis to an array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "32848044",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(4,)\n",
+      "[[1 2 3 4]]\n",
+      "(1, 4)\n",
+      "(4, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Converting 1D array to 2D array\n",
+    "print(a.shape)\n",
+    "\n",
+    "#np.newaxis\n",
+    "a2=a[np.newaxis, :] #1D array converted to a row vector. Similarly it can be turned into a column vector.\n",
+    "print(a2)\n",
+    "\n",
+    "#np.expand_dims\n",
+    "a3=np.expand_dims(a,axis=0)\n",
+    "print(a3.shape)\n",
+    "\n",
+    "a4=np.expand_dims(a,axis =1)\n",
+    "print(a4.shape)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1911376",
+   "metadata": {},
+   "source": [
+    "### Creating an array from existing data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "55cea3c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[5 4]\n",
+      " [6 2]\n",
+      " [1 2]\n",
+      " [3 4]]\n",
+      "[[5 4 1 2]\n",
+      " [6 2 3 4]]\n",
+      "[[ 5  2  3  4]\n",
+      " [ 5  6  7  8]\n",
+      " [ 9 10 11 12]]\n",
+      "[5 2 3 4]\n",
+      "[5 2 3 4]\n",
+      "[[ 5  2  3  4]\n",
+      " [ 5  6  7  8]\n",
+      " [ 9 10 11 12]]\n",
+      "[[ 5  2  3  4]\n",
+      " [ 5  6  7  8]\n",
+      " [ 9 10 11 12]]\n",
+      "[5 6 7 8]\n",
+      "[5 6 9 8]\n",
+      "[[ 5  2  3  4]\n",
+      " [ 5  6  7  8]\n",
+      " [ 9 10 11 12]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#stacking of arrays\n",
+    "b1=([5,4],[6,2])\n",
+    "b2=([1,2],[3,4])\n",
+    "print(np.vstack((b1,b2))) #vertical stacking of the two arrays\n",
+    "\n",
+    "print(np.hstack((b1,b2))) #horizontal stacking of the arrays\n",
+    "\n",
+    "\n",
+    "#view method - any changes made to the original array will affect the original array\n",
+    "print(b)\n",
+    "\n",
+    "b_new=b[0,:]\n",
+    "print(b_new)\n",
+    "b_new[0]=5\n",
+    "print(b_new)\n",
+    "print(b) #original array is modified\n",
+    "\n",
+    "\n",
+    "#copy method- any changes made in the copied array will not modify the original array\n",
+    "b_copy=b.copy()\n",
+    "print(b_copy)\n",
+    "b_copy_new=b_copy[1,:]\n",
+    "print(b_copy_new)\n",
+    "b_copy_new[2]=9\n",
+    "print(b_copy_new)\n",
+    "print(b) #remains the same.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99d65e77",
+   "metadata": {},
+   "source": [
+    "### Basic Array Operations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "01bf7df7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2 3]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Addition\n",
+    "arr=np.array([1,2])\n",
+    "arr_1=np.ones(2,dtype=int)\n",
+    "print(arr+arr_1)\n",
+    "\n",
+    "#Similarly Subtraction, Divison and Multiplication can be carried out\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ffad24d0",
+   "metadata": {},
+   "source": [
+    "### Broadcasting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "58c85c57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[4 8]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(arr*4) #operation carried out between an array and single number"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80e6a18e",
+   "metadata": {},
+   "source": [
+    "### Creating matrices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "c4c43139",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 5  2  3  4]\n",
+      " [ 5  6  7  8]\n",
+      " [ 9 10 11 12]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "6d385934",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n",
+      "[[ 5  6  7  8]\n",
+      " [ 9 10 11 12]]\n",
+      "[5 5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(b[0,1])\n",
+    "print(b[1:3])\n",
+    "print(b[0:2,0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "7cd964e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "12\n",
+      "2\n",
+      "82\n",
+      "[225 120 231 384]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(b.max())\n",
+    "print(b.min())\n",
+    "print(b.sum())\n",
+    "print(b.prod(axis=0))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "40f33e6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 5  2  3  4]\n",
+      " [ 5  6  7  8]\n",
+      " [ 9 10 11 12]]\n",
+      "[ 2  3  4  5  6  7  8  9 10 11 12]\n",
+      "(array([1, 2, 3, 4]), array([0, 1, 2, 3], dtype=int64))\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(b)\n",
+    "print(np.unique(b)) #prints unique elements in a numpy array\n",
+    "print(np.unique(a,return_index=True))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d6c01d67",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "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": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/w1/Pandas Tutorial.ipynb b/w1/Pandas Tutorial.ipynb
new file mode 100644
index 0000000..1cce34d
--- /dev/null
+++ b/w1/Pandas Tutorial.ipynb	
@@ -0,0 +1,1900 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2c93fd9c",
+   "metadata": {},
+   "source": [
+    "# Pandas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49f2d993",
+   "metadata": {},
+   "source": [
+    "Pandas is a library that is used for working with datasets. It allows us to analyse big data and make conclusions based on statistical theories."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd209017",
+   "metadata": {},
+   "source": [
+    "The first step is to import the <b>Pandas</b> library to the current jupyter notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ba1b7793",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd #Shorthand notation given to facilitate easier futher usage."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "edd9cb1f",
+   "metadata": {},
+   "source": [
+    "<b>Pandas Series</b> : It is a 1 dimensional array holding data of any type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a184c740",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    2\n",
+      "1    3\n",
+      "2    4\n",
+      "dtype: int64\n",
+      "2\n"
+     ]
+    }
+   ],
+   "source": [
+    "a=[2,3,4]\n",
+    "col=pd.Series(a)\n",
+    "print(col)\n",
+    "\n",
+    "print(col[0]) #returns the value at the index 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6e2c6b0c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "12PM    80\n",
+      "1PM     90\n",
+      "2PM     85\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "#From a dictionary\n",
+    "Heartbeat = {\"12PM\":80, \"1PM\":90, \"2PM\":85}\n",
+    "heartrate_series=pd.Series(Heartbeat)\n",
+    "print(heartrate_series)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fdb8a968",
+   "metadata": {},
+   "source": [
+    "<b>Pandas Dataframe </b>: It is a multidemsional data structure, like a mutlti dimensional array, or a table with rows and columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "2593f089",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   calories  heartrate\n",
+      "0       450         80\n",
+      "1       250         90\n",
+      "2       500         85\n"
+     ]
+    }
+   ],
+   "source": [
+    "data={\n",
+    "    \"calories\":[450,250,500],\n",
+    "    \"heartrate\":[80,90,85]\n",
+    "}\n",
+    "\n",
+    "df = pd.DataFrame(data) #laoding data into a DataFrame object.\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a9d1f47e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calories     450\n",
+      "heartrate     80\n",
+      "Name: 0, dtype: int64\n",
+      "   calories  heartrate\n",
+      "0       450         80\n",
+      "1       250         90\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Locating Rows using loc attribute\n",
+    "print(df.loc[0])\n",
+    "print(df.loc[0:1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "30a10288",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      calories  heartrate\n",
+      "Day1       450         80\n",
+      "Day2       250         90\n",
+      "Day3       500         85\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Naming indexes\n",
+    "df2=pd.DataFrame(data,index=[\"Day1\",\"Day2\",\"Day3\"])\n",
+    "print(df2)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "66704750",
+   "metadata": {},
+   "source": [
+    "## Working with CSV files using Pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f4e5e8a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Duration  Pulse  Maxpulse  Calories\n",
+      "0          60    110       130     409.1\n",
+      "1          60    117       145     479.0\n",
+      "2          60    103       135     340.0\n",
+      "3          45    109       175     282.4\n",
+      "4          45    117       148     406.0\n",
+      "5          60    102       127     300.0\n",
+      "6          60    110       136     374.0\n",
+      "7          45    104       134     253.3\n",
+      "8          30    109       133     195.1\n",
+      "9          60     98       124     269.0\n",
+      "10         60    103       147     329.3\n",
+      "11         60    100       120     250.7\n",
+      "12         60    106       128     345.3\n",
+      "13         60    104       132     379.3\n",
+      "14         60     98       123     275.0\n",
+      "15         60     98       120     215.2\n",
+      "16         60    100       120     300.0\n",
+      "17         45     90       112       NaN\n",
+      "18         60    103       123     323.0\n",
+      "19         45     97       125     243.0\n",
+      "20         60    108       131     364.2\n",
+      "21         45    100       119     282.0\n",
+      "22         60    130       101     300.0\n",
+      "23         45    105       132     246.0\n",
+      "24         60    102       126     334.5\n",
+      "25         60    100       120     250.0\n",
+      "26         60     92       118     241.0\n",
+      "27         60    103       132       NaN\n",
+      "28         60    100       132     280.0\n",
+      "29         60    102       129     380.3\n",
+      "30         60     92       115     243.0\n",
+      "31         45     90       112     180.1\n",
+      "32         60    101       124     299.0\n",
+      "33         60     93       113     223.0\n",
+      "34         60    107       136     361.0\n",
+      "35         60    114       140     415.0\n",
+      "36         60    102       127     300.0\n",
+      "37         60    100       120     300.0\n",
+      "38         60    100       120     300.0\n",
+      "39         45    104       129     266.0\n",
+      "40         45     90       112     180.1\n",
+      "41         60     98       126     286.0\n",
+      "42         60    100       122     329.4\n",
+      "43         60    111       138     400.0\n",
+      "44         60    111       131     397.0\n",
+      "45         60     99       119     273.0\n",
+      "46         60    109       153     387.6\n",
+      "47         45    111       136     300.0\n",
+      "48         45    108       129     298.0\n",
+      "49         60    111       139     397.6\n",
+      "50         60    107       136     380.2\n",
+      "51         80    123       146     643.1\n",
+      "52         60    106       130     263.0\n",
+      "53         60    118       151     486.0\n",
+      "54         30    136       175     238.0\n",
+      "55         60    121       146     450.7\n",
+      "56         60    118       121     413.0\n",
+      "57         45    115       144     305.0\n",
+      "58         20    153       172     226.4\n",
+      "59         45    123       152     321.0\n",
+      "60        210    108       160    1376.0\n",
+      "61        160    110       137    1034.4\n",
+      "62        160    109       135     853.0\n",
+      "63         45    118       141     341.0\n",
+      "64         20    110       130     131.4\n",
+      "65        180     90       130     800.4\n",
+      "66        150    105       135     873.4\n",
+      "67        150    107       130     816.0\n",
+      "68         20    106       136     110.4\n",
+      "69        300    108       143    1500.2\n",
+      "70        150     97       129    1115.0\n",
+      "71         60    109       153     387.6\n",
+      "72         90    100       127     700.0\n",
+      "73        150     97       127     953.2\n",
+      "74         45    114       146     304.0\n",
+      "75         90     98       125     563.2\n",
+      "76         45    105       134     251.0\n",
+      "77         45    110       141     300.0\n",
+      "78        120    100       130     500.4\n",
+      "79        270    100       131    1729.0\n",
+      "80         30    159       182     319.2\n",
+      "81         45    149       169     344.0\n",
+      "82         30    103       139     151.1\n",
+      "83        120    100       130     500.0\n",
+      "84         45    100       120     225.3\n",
+      "85         30    151       170     300.0\n",
+      "86         45    102       136     234.0\n",
+      "87        120    100       157    1000.1\n",
+      "88         45    129       103     242.0\n",
+      "89         20     83       107      50.3\n",
+      "90        180    101       127     600.1\n",
+      "91         45    107       137       NaN\n",
+      "92         30     90       107     105.3\n",
+      "93         15     80       100      50.5\n",
+      "94         20    150       171     127.4\n",
+      "95         20    151       168     229.4\n",
+      "96         30     95       128     128.2\n",
+      "97         25    152       168     244.2\n",
+      "98         30    109       131     188.2\n",
+      "99         90     93       124     604.1\n",
+      "100        20     95       112      77.7\n",
+      "101        90     90       110     500.0\n",
+      "102        90     90       100     500.0\n",
+      "103        90     90       100     500.4\n",
+      "104        30     92       108      92.7\n",
+      "105        30     93       128     124.0\n",
+      "106       180     90       120     800.3\n",
+      "107        30     90       120      86.2\n",
+      "108        90     90       120     500.3\n",
+      "109       210    137       184    1860.4\n",
+      "110        60    102       124     325.2\n",
+      "111        45    107       124     275.0\n",
+      "112        15    124       139     124.2\n",
+      "113        45    100       120     225.3\n",
+      "114        60    108       131     367.6\n",
+      "115        60    108       151     351.7\n",
+      "116        60    116       141     443.0\n",
+      "117        60     97       122     277.4\n",
+      "118        60    105       125       NaN\n",
+      "119        60    103       124     332.7\n",
+      "120        30    112       137     193.9\n",
+      "121        45    100       120     100.7\n",
+      "122        60    119       169     336.7\n",
+      "123        60    107       127     344.9\n",
+      "124        60    111       151     368.5\n",
+      "125        60     98       122     271.0\n",
+      "126        60     97       124     275.3\n",
+      "127        60    109       127     382.0\n",
+      "128        90     99       125     466.4\n",
+      "129        60    114       151     384.0\n",
+      "130        60    104       134     342.5\n",
+      "131        60    107       138     357.5\n",
+      "132        60    103       133     335.0\n",
+      "133        60    106       132     327.5\n",
+      "134        60    103       136     339.0\n",
+      "135        20    136       156     189.0\n",
+      "136        45    117       143     317.7\n",
+      "137        45    115       137     318.0\n",
+      "138        45    113       138     308.0\n",
+      "139        20    141       162     222.4\n",
+      "140        60    108       135     390.0\n",
+      "141        60     97       127       NaN\n",
+      "142        45    100       120     250.4\n",
+      "143        45    122       149     335.4\n",
+      "144        60    136       170     470.2\n",
+      "145        45    106       126     270.8\n",
+      "146        60    107       136     400.0\n",
+      "147        60    112       146     361.9\n",
+      "148        30    103       127     185.0\n",
+      "149        60    110       150     409.4\n",
+      "150        60    106       134     343.0\n",
+      "151        60    109       129     353.2\n",
+      "152        60    109       138     374.0\n",
+      "153        30    150       167     275.8\n",
+      "154        60    105       128     328.0\n",
+      "155        60    111       151     368.5\n",
+      "156        60     97       131     270.4\n",
+      "157        60    100       120     270.4\n",
+      "158        60    114       150     382.8\n",
+      "159        30     80       120     240.9\n",
+      "160        30     85       120     250.4\n",
+      "161        45     90       130     260.4\n",
+      "162        45     95       130     270.0\n",
+      "163        45    100       140     280.9\n",
+      "164        60    105       140     290.8\n",
+      "165        60    110       145     300.0\n",
+      "166        60    115       145     310.2\n",
+      "167        75    120       150     320.4\n",
+      "168        75    125       150     330.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "df=pd.read_csv('data.csv') #reading the csv file\n",
+    "print(df.to_string()) #used to print the whole DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "419e2c3d",
+   "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>Duration</th>\n",
+       "      <th>Pulse</th>\n",
+       "      <th>Maxpulse</th>\n",
+       "      <th>Calories</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>60</td>\n",
+       "      <td>110</td>\n",
+       "      <td>130</td>\n",
+       "      <td>409.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>60</td>\n",
+       "      <td>117</td>\n",
+       "      <td>145</td>\n",
+       "      <td>479.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>60</td>\n",
+       "      <td>103</td>\n",
+       "      <td>135</td>\n",
+       "      <td>340.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>45</td>\n",
+       "      <td>109</td>\n",
+       "      <td>175</td>\n",
+       "      <td>282.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>45</td>\n",
+       "      <td>117</td>\n",
+       "      <td>148</td>\n",
+       "      <td>406.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Duration  Pulse  Maxpulse  Calories\n",
+       "0        60    110       130     409.1\n",
+       "1        60    117       145     479.0\n",
+       "2        60    103       135     340.0\n",
+       "3        45    109       175     282.4\n",
+       "4        45    117       148     406.0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head() #prints the first 5 rows of a dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "0352a257",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 169 entries, 0 to 168\n",
+      "Data columns (total 4 columns):\n",
+      " #   Column    Non-Null Count  Dtype  \n",
+      "---  ------    --------------  -----  \n",
+      " 0   Duration  169 non-null    int64  \n",
+      " 1   Pulse     169 non-null    int64  \n",
+      " 2   Maxpulse  169 non-null    int64  \n",
+      " 3   Calories  164 non-null    float64\n",
+      "dtypes: float64(1), int64(3)\n",
+      "memory usage: 5.4 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info() #gives the information about the data simplifying it's analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "83047224",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    Duration          Date  Pulse  Maxpulse  Calories\n",
+      "0         60  '2020/12/01'    110       130     409.1\n",
+      "1         60  '2020/12/02'    117       145     479.0\n",
+      "2         60  '2020/12/03'    103       135     340.0\n",
+      "3         45  '2020/12/04'    109       175     282.4\n",
+      "4         45  '2020/12/05'    117       148     406.0\n",
+      "5         60  '2020/12/06'    102       127     300.0\n",
+      "6         60  '2020/12/07'    110       136     374.0\n",
+      "7        450  '2020/12/08'    104       134     253.3\n",
+      "8         30  '2020/12/09'    109       133     195.1\n",
+      "9         60  '2020/12/10'     98       124     269.0\n",
+      "10        60  '2020/12/11'    103       147     329.3\n",
+      "11        60  '2020/12/12'    100       120     250.7\n",
+      "12        60  '2020/12/12'    100       120     250.7\n",
+      "13        60  '2020/12/13'    106       128     345.3\n",
+      "14        60  '2020/12/14'    104       132     379.3\n",
+      "15        60  '2020/12/15'     98       123     275.0\n",
+      "16        60  '2020/12/16'     98       120     215.2\n",
+      "17        60  '2020/12/17'    100       120     300.0\n",
+      "18        45  '2020/12/18'     90       112       NaN\n",
+      "19        60  '2020/12/19'    103       123     323.0\n",
+      "20        45  '2020/12/20'     97       125     243.0\n",
+      "21        60  '2020/12/21'    108       131     364.2\n",
+      "22        45           NaN    100       119     282.0\n",
+      "23        60  '2020/12/23'    130       101     300.0\n",
+      "24        45  '2020/12/24'    105       132     246.0\n",
+      "25        60  '2020/12/25'    102       126     334.5\n",
+      "26        60      20201226    100       120     250.0\n",
+      "27        60  '2020/12/27'     92       118     241.0\n",
+      "28        60  '2020/12/28'    103       132       NaN\n",
+      "29        60  '2020/12/29'    100       132     280.0\n",
+      "30        60  '2020/12/30'    102       129     380.3\n",
+      "31        60  '2020/12/31'     92       115     243.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df=pd.read_csv(\"dirtydata.csv\")\n",
+    "print(df.to_string())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "584bd89e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    Duration          Date  Pulse  Maxpulse  Calories\n",
+      "0         60  '2020/12/01'    110       130     409.1\n",
+      "1         60  '2020/12/02'    117       145     479.0\n",
+      "2         60  '2020/12/03'    103       135     340.0\n",
+      "3         45  '2020/12/04'    109       175     282.4\n",
+      "4         45  '2020/12/05'    117       148     406.0\n",
+      "5         60  '2020/12/06'    102       127     300.0\n",
+      "6         60  '2020/12/07'    110       136     374.0\n",
+      "7        450  '2020/12/08'    104       134     253.3\n",
+      "8         30  '2020/12/09'    109       133     195.1\n",
+      "9         60  '2020/12/10'     98       124     269.0\n",
+      "10        60  '2020/12/11'    103       147     329.3\n",
+      "11        60  '2020/12/12'    100       120     250.7\n",
+      "12        60  '2020/12/12'    100       120     250.7\n",
+      "13        60  '2020/12/13'    106       128     345.3\n",
+      "14        60  '2020/12/14'    104       132     379.3\n",
+      "15        60  '2020/12/15'     98       123     275.0\n",
+      "16        60  '2020/12/16'     98       120     215.2\n",
+      "17        60  '2020/12/17'    100       120     300.0\n",
+      "19        60  '2020/12/19'    103       123     323.0\n",
+      "20        45  '2020/12/20'     97       125     243.0\n",
+      "21        60  '2020/12/21'    108       131     364.2\n",
+      "23        60  '2020/12/23'    130       101     300.0\n",
+      "24        45  '2020/12/24'    105       132     246.0\n",
+      "25        60  '2020/12/25'    102       126     334.5\n",
+      "26        60      20201226    100       120     250.0\n",
+      "27        60  '2020/12/27'     92       118     241.0\n",
+      "29        60  '2020/12/29'    100       132     280.0\n",
+      "30        60  '2020/12/30'    102       129     380.3\n",
+      "31        60  '2020/12/31'     92       115     243.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "new_df=df.dropna() #drops the rows with null values and creates a new DataFrame\n",
+    "print(new_df.to_string())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "f825b16a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    Duration          Date  Pulse  Maxpulse  Calories\n",
+      "0         60  '2020/12/01'    110       130     409.1\n",
+      "1         60  '2020/12/02'    117       145     479.0\n",
+      "2         60  '2020/12/03'    103       135     340.0\n",
+      "3         45  '2020/12/04'    109       175     282.4\n",
+      "4         45  '2020/12/05'    117       148     406.0\n",
+      "5         60  '2020/12/06'    102       127     300.0\n",
+      "6         60  '2020/12/07'    110       136     374.0\n",
+      "7        450  '2020/12/08'    104       134     253.3\n",
+      "8         30  '2020/12/09'    109       133     195.1\n",
+      "9         60  '2020/12/10'     98       124     269.0\n",
+      "10        60  '2020/12/11'    103       147     329.3\n",
+      "11        60  '2020/12/12'    100       120     250.7\n",
+      "12        60  '2020/12/12'    100       120     250.7\n",
+      "13        60  '2020/12/13'    106       128     345.3\n",
+      "14        60  '2020/12/14'    104       132     379.3\n",
+      "15        60  '2020/12/15'     98       123     275.0\n",
+      "16        60  '2020/12/16'     98       120     215.2\n",
+      "17        60  '2020/12/17'    100       120     300.0\n",
+      "19        60  '2020/12/19'    103       123     323.0\n",
+      "20        45  '2020/12/20'     97       125     243.0\n",
+      "21        60  '2020/12/21'    108       131     364.2\n",
+      "23        60  '2020/12/23'    130       101     300.0\n",
+      "24        45  '2020/12/24'    105       132     246.0\n",
+      "25        60  '2020/12/25'    102       126     334.5\n",
+      "26        60      20201226    100       120     250.0\n",
+      "27        60  '2020/12/27'     92       118     241.0\n",
+      "29        60  '2020/12/29'    100       132     280.0\n",
+      "30        60  '2020/12/30'    102       129     380.3\n",
+      "31        60  '2020/12/31'     92       115     243.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.dropna(inplace=True) #makes changes to the original DataFrame\n",
+    "print(df.to_string())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "add5c7a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Duration  Pulse  Maxpulse  Calories\n",
+      "0          60    110       130     409.1\n",
+      "1          60    117       145     479.0\n",
+      "2          60    103       135     340.0\n",
+      "3          45    109       175     282.4\n",
+      "4          45    117       148     406.0\n",
+      "5          60    102       127     300.0\n",
+      "6          60    110       136     374.0\n",
+      "7          45    104       134     253.3\n",
+      "8          30    109       133     195.1\n",
+      "9          60     98       124     269.0\n",
+      "10         60    103       147     329.3\n",
+      "11         60    100       120     250.7\n",
+      "12         60    106       128     345.3\n",
+      "13         60    104       132     379.3\n",
+      "14         60     98       123     275.0\n",
+      "15         60     98       120     215.2\n",
+      "16         60    100       120     300.0\n",
+      "17         45     90       112       NaN\n",
+      "18         60    103       123     323.0\n",
+      "19         45     97       125     243.0\n",
+      "20         60    108       131     364.2\n",
+      "21         45    100       119     282.0\n",
+      "22         60    130       101     300.0\n",
+      "23         45    105       132     246.0\n",
+      "24         60    102       126     334.5\n",
+      "25         60    100       120     250.0\n",
+      "26         60     92       118     241.0\n",
+      "27         60    103       132       NaN\n",
+      "28         60    100       132     280.0\n",
+      "29         60    102       129     380.3\n",
+      "30         60     92       115     243.0\n",
+      "31         45     90       112     180.1\n",
+      "32         60    101       124     299.0\n",
+      "33         60     93       113     223.0\n",
+      "34         60    107       136     361.0\n",
+      "35         60    114       140     415.0\n",
+      "36         60    102       127     300.0\n",
+      "37         60    100       120     300.0\n",
+      "38         60    100       120     300.0\n",
+      "39         45    104       129     266.0\n",
+      "40         45     90       112     180.1\n",
+      "41         60     98       126     286.0\n",
+      "42         60    100       122     329.4\n",
+      "43         60    111       138     400.0\n",
+      "44         60    111       131     397.0\n",
+      "45         60     99       119     273.0\n",
+      "46         60    109       153     387.6\n",
+      "47         45    111       136     300.0\n",
+      "48         45    108       129     298.0\n",
+      "49         60    111       139     397.6\n",
+      "50         60    107       136     380.2\n",
+      "51         80    123       146     643.1\n",
+      "52         60    106       130     263.0\n",
+      "53         60    118       151     486.0\n",
+      "54         30    136       175     238.0\n",
+      "55         60    121       146     450.7\n",
+      "56         60    118       121     413.0\n",
+      "57         45    115       144     305.0\n",
+      "58         20    153       172     226.4\n",
+      "59         45    123       152     321.0\n",
+      "60        210    108       160    1376.0\n",
+      "61        160    110       137    1034.4\n",
+      "62        160    109       135     853.0\n",
+      "63         45    118       141     341.0\n",
+      "64         20    110       130     131.4\n",
+      "65        180     90       130     800.4\n",
+      "66        150    105       135     873.4\n",
+      "67        150    107       130     816.0\n",
+      "68         20    106       136     110.4\n",
+      "69        300    108       143    1500.2\n",
+      "70        150     97       129    1115.0\n",
+      "71         60    109       153     387.6\n",
+      "72         90    100       127     700.0\n",
+      "73        150     97       127     953.2\n",
+      "74         45    114       146     304.0\n",
+      "75         90     98       125     563.2\n",
+      "76         45    105       134     251.0\n",
+      "77         45    110       141     300.0\n",
+      "78        120    100       130     500.4\n",
+      "79        270    100       131    1729.0\n",
+      "80         30    159       182     319.2\n",
+      "81         45    149       169     344.0\n",
+      "82         30    103       139     151.1\n",
+      "83        120    100       130     500.0\n",
+      "84         45    100       120     225.3\n",
+      "85         30    151       170     300.0\n",
+      "86         45    102       136     234.0\n",
+      "87        120    100       157    1000.1\n",
+      "88         45    129       103     242.0\n",
+      "89         20     83       107      50.3\n",
+      "90        180    101       127     600.1\n",
+      "91         45    107       137       NaN\n",
+      "92         30     90       107     105.3\n",
+      "93         15     80       100      50.5\n",
+      "94         20    150       171     127.4\n",
+      "95         20    151       168     229.4\n",
+      "96         30     95       128     128.2\n",
+      "97         25    152       168     244.2\n",
+      "98         30    109       131     188.2\n",
+      "99         90     93       124     604.1\n",
+      "100        20     95       112      77.7\n",
+      "101        90     90       110     500.0\n",
+      "102        90     90       100     500.0\n",
+      "103        90     90       100     500.4\n",
+      "104        30     92       108      92.7\n",
+      "105        30     93       128     124.0\n",
+      "106       180     90       120     800.3\n",
+      "107        30     90       120      86.2\n",
+      "108        90     90       120     500.3\n",
+      "109       210    137       184    1860.4\n",
+      "110        60    102       124     325.2\n",
+      "111        45    107       124     275.0\n",
+      "112        15    124       139     124.2\n",
+      "113        45    100       120     225.3\n",
+      "114        60    108       131     367.6\n",
+      "115        60    108       151     351.7\n",
+      "116        60    116       141     443.0\n",
+      "117        60     97       122     277.4\n",
+      "118        60    105       125       NaN\n",
+      "119        60    103       124     332.7\n",
+      "120        30    112       137     193.9\n",
+      "121        45    100       120     100.7\n",
+      "122        60    119       169     336.7\n",
+      "123        60    107       127     344.9\n",
+      "124        60    111       151     368.5\n",
+      "125        60     98       122     271.0\n",
+      "126        60     97       124     275.3\n",
+      "127        60    109       127     382.0\n",
+      "128        90     99       125     466.4\n",
+      "129        60    114       151     384.0\n",
+      "130        60    104       134     342.5\n",
+      "131        60    107       138     357.5\n",
+      "132        60    103       133     335.0\n",
+      "133        60    106       132     327.5\n",
+      "134        60    103       136     339.0\n",
+      "135        20    136       156     189.0\n",
+      "136        45    117       143     317.7\n",
+      "137        45    115       137     318.0\n",
+      "138        45    113       138     308.0\n",
+      "139        20    141       162     222.4\n",
+      "140        60    108       135     390.0\n",
+      "141        60     97       127       NaN\n",
+      "142        45    100       120     250.4\n",
+      "143        45    122       149     335.4\n",
+      "144        60    136       170     470.2\n",
+      "145        45    106       126     270.8\n",
+      "146        60    107       136     400.0\n",
+      "147        60    112       146     361.9\n",
+      "148        30    103       127     185.0\n",
+      "149        60    110       150     409.4\n",
+      "150        60    106       134     343.0\n",
+      "151        60    109       129     353.2\n",
+      "152        60    109       138     374.0\n",
+      "153        30    150       167     275.8\n",
+      "154        60    105       128     328.0\n",
+      "155        60    111       151     368.5\n",
+      "156        60     97       131     270.4\n",
+      "157        60    100       120     270.4\n",
+      "158        60    114       150     382.8\n",
+      "159        30     80       120     240.9\n",
+      "160        30     85       120     250.4\n",
+      "161        45     90       130     260.4\n",
+      "162        45     95       130     270.0\n",
+      "163        45    100       140     280.9\n",
+      "164        60    105       140     290.8\n",
+      "165        60    110       145     300.0\n",
+      "166        60    115       145     310.2\n",
+      "167        75    120       150     320.4\n",
+      "168        75    125       150     330.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "df=pd.read_csv(\"data.csv\")\n",
+    "print(df.to_string())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "a6ed4942",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Duration  Pulse  Maxpulse  Calories\n",
+      "0          60    110       130     409.1\n",
+      "1          60    117       145     479.0\n",
+      "2          60    103       135     340.0\n",
+      "3          45    109       175     282.4\n",
+      "4          45    117       148     406.0\n",
+      "5          60    102       127     300.0\n",
+      "6          60    110       136     374.0\n",
+      "7          45    104       134     253.3\n",
+      "8          30    109       133     195.1\n",
+      "9          60     98       124     269.0\n",
+      "10         60    103       147     329.3\n",
+      "11         60    100       120     250.7\n",
+      "12         60    106       128     345.3\n",
+      "13         60    104       132     379.3\n",
+      "14         60     98       123     275.0\n",
+      "15         60     98       120     215.2\n",
+      "16         60    100       120     300.0\n",
+      "17         45     90       112     120.0\n",
+      "18         60    103       123     323.0\n",
+      "19         45     97       125     243.0\n",
+      "20         60    108       131     364.2\n",
+      "21         45    100       119     282.0\n",
+      "22         60    130       101     300.0\n",
+      "23         45    105       132     246.0\n",
+      "24         60    102       126     334.5\n",
+      "25         60    100       120     250.0\n",
+      "26         60     92       118     241.0\n",
+      "27         60    103       132     120.0\n",
+      "28         60    100       132     280.0\n",
+      "29         60    102       129     380.3\n",
+      "30         60     92       115     243.0\n",
+      "31         45     90       112     180.1\n",
+      "32         60    101       124     299.0\n",
+      "33         60     93       113     223.0\n",
+      "34         60    107       136     361.0\n",
+      "35         60    114       140     415.0\n",
+      "36         60    102       127     300.0\n",
+      "37         60    100       120     300.0\n",
+      "38         60    100       120     300.0\n",
+      "39         45    104       129     266.0\n",
+      "40         45     90       112     180.1\n",
+      "41         60     98       126     286.0\n",
+      "42         60    100       122     329.4\n",
+      "43         60    111       138     400.0\n",
+      "44         60    111       131     397.0\n",
+      "45         60     99       119     273.0\n",
+      "46         60    109       153     387.6\n",
+      "47         45    111       136     300.0\n",
+      "48         45    108       129     298.0\n",
+      "49         60    111       139     397.6\n",
+      "50         60    107       136     380.2\n",
+      "51         80    123       146     643.1\n",
+      "52         60    106       130     263.0\n",
+      "53         60    118       151     486.0\n",
+      "54         30    136       175     238.0\n",
+      "55         60    121       146     450.7\n",
+      "56         60    118       121     413.0\n",
+      "57         45    115       144     305.0\n",
+      "58         20    153       172     226.4\n",
+      "59         45    123       152     321.0\n",
+      "60        210    108       160    1376.0\n",
+      "61        160    110       137    1034.4\n",
+      "62        160    109       135     853.0\n",
+      "63         45    118       141     341.0\n",
+      "64         20    110       130     131.4\n",
+      "65        180     90       130     800.4\n",
+      "66        150    105       135     873.4\n",
+      "67        150    107       130     816.0\n",
+      "68         20    106       136     110.4\n",
+      "69        300    108       143    1500.2\n",
+      "70        150     97       129    1115.0\n",
+      "71         60    109       153     387.6\n",
+      "72         90    100       127     700.0\n",
+      "73        150     97       127     953.2\n",
+      "74         45    114       146     304.0\n",
+      "75         90     98       125     563.2\n",
+      "76         45    105       134     251.0\n",
+      "77         45    110       141     300.0\n",
+      "78        120    100       130     500.4\n",
+      "79        270    100       131    1729.0\n",
+      "80         30    159       182     319.2\n",
+      "81         45    149       169     344.0\n",
+      "82         30    103       139     151.1\n",
+      "83        120    100       130     500.0\n",
+      "84         45    100       120     225.3\n",
+      "85         30    151       170     300.0\n",
+      "86         45    102       136     234.0\n",
+      "87        120    100       157    1000.1\n",
+      "88         45    129       103     242.0\n",
+      "89         20     83       107      50.3\n",
+      "90        180    101       127     600.1\n",
+      "91         45    107       137     120.0\n",
+      "92         30     90       107     105.3\n",
+      "93         15     80       100      50.5\n",
+      "94         20    150       171     127.4\n",
+      "95         20    151       168     229.4\n",
+      "96         30     95       128     128.2\n",
+      "97         25    152       168     244.2\n",
+      "98         30    109       131     188.2\n",
+      "99         90     93       124     604.1\n",
+      "100        20     95       112      77.7\n",
+      "101        90     90       110     500.0\n",
+      "102        90     90       100     500.0\n",
+      "103        90     90       100     500.4\n",
+      "104        30     92       108      92.7\n",
+      "105        30     93       128     124.0\n",
+      "106       180     90       120     800.3\n",
+      "107        30     90       120      86.2\n",
+      "108        90     90       120     500.3\n",
+      "109       210    137       184    1860.4\n",
+      "110        60    102       124     325.2\n",
+      "111        45    107       124     275.0\n",
+      "112        15    124       139     124.2\n",
+      "113        45    100       120     225.3\n",
+      "114        60    108       131     367.6\n",
+      "115        60    108       151     351.7\n",
+      "116        60    116       141     443.0\n",
+      "117        60     97       122     277.4\n",
+      "118        60    105       125     120.0\n",
+      "119        60    103       124     332.7\n",
+      "120        30    112       137     193.9\n",
+      "121        45    100       120     100.7\n",
+      "122        60    119       169     336.7\n",
+      "123        60    107       127     344.9\n",
+      "124        60    111       151     368.5\n",
+      "125        60     98       122     271.0\n",
+      "126        60     97       124     275.3\n",
+      "127        60    109       127     382.0\n",
+      "128        90     99       125     466.4\n",
+      "129        60    114       151     384.0\n",
+      "130        60    104       134     342.5\n",
+      "131        60    107       138     357.5\n",
+      "132        60    103       133     335.0\n",
+      "133        60    106       132     327.5\n",
+      "134        60    103       136     339.0\n",
+      "135        20    136       156     189.0\n",
+      "136        45    117       143     317.7\n",
+      "137        45    115       137     318.0\n",
+      "138        45    113       138     308.0\n",
+      "139        20    141       162     222.4\n",
+      "140        60    108       135     390.0\n",
+      "141        60     97       127     120.0\n",
+      "142        45    100       120     250.4\n",
+      "143        45    122       149     335.4\n",
+      "144        60    136       170     470.2\n",
+      "145        45    106       126     270.8\n",
+      "146        60    107       136     400.0\n",
+      "147        60    112       146     361.9\n",
+      "148        30    103       127     185.0\n",
+      "149        60    110       150     409.4\n",
+      "150        60    106       134     343.0\n",
+      "151        60    109       129     353.2\n",
+      "152        60    109       138     374.0\n",
+      "153        30    150       167     275.8\n",
+      "154        60    105       128     328.0\n",
+      "155        60    111       151     368.5\n",
+      "156        60     97       131     270.4\n",
+      "157        60    100       120     270.4\n",
+      "158        60    114       150     382.8\n",
+      "159        30     80       120     240.9\n",
+      "160        30     85       120     250.4\n",
+      "161        45     90       130     260.4\n",
+      "162        45     95       130     270.0\n",
+      "163        45    100       140     280.9\n",
+      "164        60    105       140     290.8\n",
+      "165        60    110       145     300.0\n",
+      "166        60    115       145     310.2\n",
+      "167        75    120       150     320.4\n",
+      "168        75    125       150     330.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.fillna(120,inplace=True) #replaces the null value instead of deleting the entire row\n",
+    "print(df.to_string())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "fb3646bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Duration  Pulse  Maxpulse     Calories\n",
+      "0          60    110       130   409.100000\n",
+      "1          60    117       145   479.000000\n",
+      "2          60    103       135   340.000000\n",
+      "3          45    109       175   282.400000\n",
+      "4          45    117       148   406.000000\n",
+      "5          60    102       127   300.000000\n",
+      "6          60    110       136   374.000000\n",
+      "7          45    104       134   253.300000\n",
+      "8          30    109       133   195.100000\n",
+      "9          60     98       124   269.000000\n",
+      "10         60    103       147   329.300000\n",
+      "11         60    100       120   250.700000\n",
+      "12         60    106       128   345.300000\n",
+      "13         60    104       132   379.300000\n",
+      "14         60     98       123   275.000000\n",
+      "15         60     98       120   215.200000\n",
+      "16         60    100       120   300.000000\n",
+      "17         45     90       112   375.790244\n",
+      "18         60    103       123   323.000000\n",
+      "19         45     97       125   243.000000\n",
+      "20         60    108       131   364.200000\n",
+      "21         45    100       119   282.000000\n",
+      "22         60    130       101   300.000000\n",
+      "23         45    105       132   246.000000\n",
+      "24         60    102       126   334.500000\n",
+      "25         60    100       120   250.000000\n",
+      "26         60     92       118   241.000000\n",
+      "27         60    103       132   375.790244\n",
+      "28         60    100       132   280.000000\n",
+      "29         60    102       129   380.300000\n",
+      "30         60     92       115   243.000000\n",
+      "31         45     90       112   180.100000\n",
+      "32         60    101       124   299.000000\n",
+      "33         60     93       113   223.000000\n",
+      "34         60    107       136   361.000000\n",
+      "35         60    114       140   415.000000\n",
+      "36         60    102       127   300.000000\n",
+      "37         60    100       120   300.000000\n",
+      "38         60    100       120   300.000000\n",
+      "39         45    104       129   266.000000\n",
+      "40         45     90       112   180.100000\n",
+      "41         60     98       126   286.000000\n",
+      "42         60    100       122   329.400000\n",
+      "43         60    111       138   400.000000\n",
+      "44         60    111       131   397.000000\n",
+      "45         60     99       119   273.000000\n",
+      "46         60    109       153   387.600000\n",
+      "47         45    111       136   300.000000\n",
+      "48         45    108       129   298.000000\n",
+      "49         60    111       139   397.600000\n",
+      "50         60    107       136   380.200000\n",
+      "51         80    123       146   643.100000\n",
+      "52         60    106       130   263.000000\n",
+      "53         60    118       151   486.000000\n",
+      "54         30    136       175   238.000000\n",
+      "55         60    121       146   450.700000\n",
+      "56         60    118       121   413.000000\n",
+      "57         45    115       144   305.000000\n",
+      "58         20    153       172   226.400000\n",
+      "59         45    123       152   321.000000\n",
+      "60        210    108       160  1376.000000\n",
+      "61        160    110       137  1034.400000\n",
+      "62        160    109       135   853.000000\n",
+      "63         45    118       141   341.000000\n",
+      "64         20    110       130   131.400000\n",
+      "65        180     90       130   800.400000\n",
+      "66        150    105       135   873.400000\n",
+      "67        150    107       130   816.000000\n",
+      "68         20    106       136   110.400000\n",
+      "69        300    108       143  1500.200000\n",
+      "70        150     97       129  1115.000000\n",
+      "71         60    109       153   387.600000\n",
+      "72         90    100       127   700.000000\n",
+      "73        150     97       127   953.200000\n",
+      "74         45    114       146   304.000000\n",
+      "75         90     98       125   563.200000\n",
+      "76         45    105       134   251.000000\n",
+      "77         45    110       141   300.000000\n",
+      "78        120    100       130   500.400000\n",
+      "79        270    100       131  1729.000000\n",
+      "80         30    159       182   319.200000\n",
+      "81         45    149       169   344.000000\n",
+      "82         30    103       139   151.100000\n",
+      "83        120    100       130   500.000000\n",
+      "84         45    100       120   225.300000\n",
+      "85         30    151       170   300.000000\n",
+      "86         45    102       136   234.000000\n",
+      "87        120    100       157  1000.100000\n",
+      "88         45    129       103   242.000000\n",
+      "89         20     83       107    50.300000\n",
+      "90        180    101       127   600.100000\n",
+      "91         45    107       137   375.790244\n",
+      "92         30     90       107   105.300000\n",
+      "93         15     80       100    50.500000\n",
+      "94         20    150       171   127.400000\n",
+      "95         20    151       168   229.400000\n",
+      "96         30     95       128   128.200000\n",
+      "97         25    152       168   244.200000\n",
+      "98         30    109       131   188.200000\n",
+      "99         90     93       124   604.100000\n",
+      "100        20     95       112    77.700000\n",
+      "101        90     90       110   500.000000\n",
+      "102        90     90       100   500.000000\n",
+      "103        90     90       100   500.400000\n",
+      "104        30     92       108    92.700000\n",
+      "105        30     93       128   124.000000\n",
+      "106       180     90       120   800.300000\n",
+      "107        30     90       120    86.200000\n",
+      "108        90     90       120   500.300000\n",
+      "109       210    137       184  1860.400000\n",
+      "110        60    102       124   325.200000\n",
+      "111        45    107       124   275.000000\n",
+      "112        15    124       139   124.200000\n",
+      "113        45    100       120   225.300000\n",
+      "114        60    108       131   367.600000\n",
+      "115        60    108       151   351.700000\n",
+      "116        60    116       141   443.000000\n",
+      "117        60     97       122   277.400000\n",
+      "118        60    105       125   375.790244\n",
+      "119        60    103       124   332.700000\n",
+      "120        30    112       137   193.900000\n",
+      "121        45    100       120   100.700000\n",
+      "122        60    119       169   336.700000\n",
+      "123        60    107       127   344.900000\n",
+      "124        60    111       151   368.500000\n",
+      "125        60     98       122   271.000000\n",
+      "126        60     97       124   275.300000\n",
+      "127        60    109       127   382.000000\n",
+      "128        90     99       125   466.400000\n",
+      "129        60    114       151   384.000000\n",
+      "130        60    104       134   342.500000\n",
+      "131        60    107       138   357.500000\n",
+      "132        60    103       133   335.000000\n",
+      "133        60    106       132   327.500000\n",
+      "134        60    103       136   339.000000\n",
+      "135        20    136       156   189.000000\n",
+      "136        45    117       143   317.700000\n",
+      "137        45    115       137   318.000000\n",
+      "138        45    113       138   308.000000\n",
+      "139        20    141       162   222.400000\n",
+      "140        60    108       135   390.000000\n",
+      "141        60     97       127   375.790244\n",
+      "142        45    100       120   250.400000\n",
+      "143        45    122       149   335.400000\n",
+      "144        60    136       170   470.200000\n",
+      "145        45    106       126   270.800000\n",
+      "146        60    107       136   400.000000\n",
+      "147        60    112       146   361.900000\n",
+      "148        30    103       127   185.000000\n",
+      "149        60    110       150   409.400000\n",
+      "150        60    106       134   343.000000\n",
+      "151        60    109       129   353.200000\n",
+      "152        60    109       138   374.000000\n",
+      "153        30    150       167   275.800000\n",
+      "154        60    105       128   328.000000\n",
+      "155        60    111       151   368.500000\n",
+      "156        60     97       131   270.400000\n",
+      "157        60    100       120   270.400000\n",
+      "158        60    114       150   382.800000\n",
+      "159        30     80       120   240.900000\n",
+      "160        30     85       120   250.400000\n",
+      "161        45     90       130   260.400000\n",
+      "162        45     95       130   270.000000\n",
+      "163        45    100       140   280.900000\n",
+      "164        60    105       140   290.800000\n",
+      "165        60    110       145   300.000000\n",
+      "166        60    115       145   310.200000\n",
+      "167        75    120       150   320.400000\n",
+      "168        75    125       150   330.400000\n"
+     ]
+    }
+   ],
+   "source": [
+    "df=pd.read_csv(\"data.csv\")\n",
+    "x=df[\"Calories\"].mean()  \n",
+    "df[\"Calories\"].fillna(x,inplace=True) #replaces the null values with the mean of the rest of the column values\n",
+    "print(df.to_string())\n",
+    "#same can be done with median and mode"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "b714b361",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    Duration          Date  Pulse  Maxpulse  Calories\n",
+      "0         60  '2020/12/01'    110       130     409.1\n",
+      "1         60  '2020/12/02'    117       145     479.0\n",
+      "2         60  '2020/12/03'    103       135     340.0\n",
+      "3         45  '2020/12/04'    109       175     282.4\n",
+      "4         45  '2020/12/05'    117       148     406.0\n",
+      "5         60  '2020/12/06'    102       127     300.0\n",
+      "6         60  '2020/12/07'    110       136     374.0\n",
+      "7        450  '2020/12/08'    104       134     253.3\n",
+      "8         30  '2020/12/09'    109       133     195.1\n",
+      "9         60  '2020/12/10'     98       124     269.0\n",
+      "10        60  '2020/12/11'    103       147     329.3\n",
+      "11        60  '2020/12/12'    100       120     250.7\n",
+      "12        60  '2020/12/12'    100       120     250.7\n",
+      "13        60  '2020/12/13'    106       128     345.3\n",
+      "14        60  '2020/12/14'    104       132     379.3\n",
+      "15        60  '2020/12/15'     98       123     275.0\n",
+      "16        60  '2020/12/16'     98       120     215.2\n",
+      "17        60  '2020/12/17'    100       120     300.0\n",
+      "18        45  '2020/12/18'     90       112       NaN\n",
+      "19        60  '2020/12/19'    103       123     323.0\n",
+      "20        45  '2020/12/20'     97       125     243.0\n",
+      "21        60  '2020/12/21'    108       131     364.2\n",
+      "22        45           NaN    100       119     282.0\n",
+      "23        60  '2020/12/23'    130       101     300.0\n",
+      "24        45  '2020/12/24'    105       132     246.0\n",
+      "25        60  '2020/12/25'    102       126     334.5\n",
+      "26        60      20201226    100       120     250.0\n",
+      "27        60  '2020/12/27'     92       118     241.0\n",
+      "28        60  '2020/12/28'    103       132       NaN\n",
+      "29        60  '2020/12/29'    100       132     280.0\n",
+      "30        60  '2020/12/30'    102       129     380.3\n",
+      "31        60  '2020/12/31'     92       115     243.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df=pd.read_csv(\"dirtydata.csv\")\n",
+    "print(df.to_string())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "8a67787a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    Duration       Date  Pulse  Maxpulse  Calories\n",
+      "0         60 2020-12-01    110       130     409.1\n",
+      "1         60 2020-12-02    117       145     479.0\n",
+      "2         60 2020-12-03    103       135     340.0\n",
+      "3         45 2020-12-04    109       175     282.4\n",
+      "4         45 2020-12-05    117       148     406.0\n",
+      "5         60 2020-12-06    102       127     300.0\n",
+      "6         60 2020-12-07    110       136     374.0\n",
+      "7        450 2020-12-08    104       134     253.3\n",
+      "8         30 2020-12-09    109       133     195.1\n",
+      "9         60 2020-12-10     98       124     269.0\n",
+      "10        60 2020-12-11    103       147     329.3\n",
+      "11        60 2020-12-12    100       120     250.7\n",
+      "12        60 2020-12-12    100       120     250.7\n",
+      "13        60 2020-12-13    106       128     345.3\n",
+      "14        60 2020-12-14    104       132     379.3\n",
+      "15        60 2020-12-15     98       123     275.0\n",
+      "16        60 2020-12-16     98       120     215.2\n",
+      "17        60 2020-12-17    100       120     300.0\n",
+      "18        45 2020-12-18     90       112       NaN\n",
+      "19        60 2020-12-19    103       123     323.0\n",
+      "20        45 2020-12-20     97       125     243.0\n",
+      "21        60 2020-12-21    108       131     364.2\n",
+      "22        45        NaT    100       119     282.0\n",
+      "23        60 2020-12-23    130       101     300.0\n",
+      "24        45 2020-12-24    105       132     246.0\n",
+      "25        60 2020-12-25    102       126     334.5\n",
+      "26        60 2020-12-26    100       120     250.0\n",
+      "27        60 2020-12-27     92       118     241.0\n",
+      "28        60 2020-12-28    103       132       NaN\n",
+      "29        60 2020-12-29    100       132     280.0\n",
+      "30        60 2020-12-30    102       129     380.3\n",
+      "31        60 2020-12-31     92       115     243.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "#the dates in the rows 22 and 26 are in the wrong format\n",
+    "df[\"Date\"] = pd.to_datetime(df[\"Date\"])\n",
+    "print(df.to_string())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "06761e1b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    Duration       Date  Pulse  Maxpulse  Calories\n",
+      "0         60 2020-12-01    110       130     409.1\n",
+      "1         60 2020-12-02    117       145     479.0\n",
+      "2         60 2020-12-03    103       135     340.0\n",
+      "3         45 2020-12-04    109       175     282.4\n",
+      "4         45 2020-12-05    117       148     406.0\n",
+      "5         60 2020-12-06    102       127     300.0\n",
+      "6         60 2020-12-07    110       136     374.0\n",
+      "7        450 2020-12-08    104       134     253.3\n",
+      "8         30 2020-12-09    109       133     195.1\n",
+      "9         60 2020-12-10     98       124     269.0\n",
+      "10        60 2020-12-11    103       147     329.3\n",
+      "11        60 2020-12-12    100       120     250.7\n",
+      "12        60 2020-12-12    100       120     250.7\n",
+      "13        60 2020-12-13    106       128     345.3\n",
+      "14        60 2020-12-14    104       132     379.3\n",
+      "15        60 2020-12-15     98       123     275.0\n",
+      "16        60 2020-12-16     98       120     215.2\n",
+      "17        60 2020-12-17    100       120     300.0\n",
+      "18        45 2020-12-18     90       112       NaN\n",
+      "19        60 2020-12-19    103       123     323.0\n",
+      "20        45 2020-12-20     97       125     243.0\n",
+      "21        60 2020-12-21    108       131     364.2\n",
+      "23        60 2020-12-23    130       101     300.0\n",
+      "24        45 2020-12-24    105       132     246.0\n",
+      "25        60 2020-12-25    102       126     334.5\n",
+      "26        60 2020-12-26    100       120     250.0\n",
+      "27        60 2020-12-27     92       118     241.0\n",
+      "28        60 2020-12-28    103       132       NaN\n",
+      "29        60 2020-12-29    100       132     280.0\n",
+      "30        60 2020-12-30    102       129     380.3\n",
+      "31        60 2020-12-31     92       115     243.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "#the format gets changed but there is NaT generated, to delele the row\n",
+    "df.dropna(subset=[\"Date\"],inplace=True)\n",
+    "print(df.to_string())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "f4a08313",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    Duration       Date  Pulse  Maxpulse  Calories\n",
+      "0         60 2020-12-01    110       130     409.1\n",
+      "1         60 2020-12-02    117       145     479.0\n",
+      "2         60 2020-12-03    103       135     340.0\n",
+      "3         45 2020-12-04    109       175     282.4\n",
+      "4         45 2020-12-05    117       148     406.0\n",
+      "5         60 2020-12-06    102       127     300.0\n",
+      "6         60 2020-12-07    110       136     374.0\n",
+      "7         45 2020-12-08    104       134     253.3\n",
+      "8         30 2020-12-09    109       133     195.1\n",
+      "9         60 2020-12-10     98       124     269.0\n",
+      "10        60 2020-12-11    103       147     329.3\n",
+      "11        60 2020-12-12    100       120     250.7\n",
+      "12        60 2020-12-12    100       120     250.7\n",
+      "13        60 2020-12-13    106       128     345.3\n",
+      "14        60 2020-12-14    104       132     379.3\n",
+      "15        60 2020-12-15     98       123     275.0\n",
+      "16        60 2020-12-16     98       120     215.2\n",
+      "17        60 2020-12-17    100       120     300.0\n",
+      "18        45 2020-12-18     90       112       NaN\n",
+      "19        60 2020-12-19    103       123     323.0\n",
+      "20        45 2020-12-20     97       125     243.0\n",
+      "21        60 2020-12-21    108       131     364.2\n",
+      "23        60 2020-12-23    130       101     300.0\n",
+      "24        45 2020-12-24    105       132     246.0\n",
+      "25        60 2020-12-25    102       126     334.5\n",
+      "26        60 2020-12-26    100       120     250.0\n",
+      "27        60 2020-12-27     92       118     241.0\n",
+      "28        60 2020-12-28    103       132       NaN\n",
+      "29        60 2020-12-29    100       132     280.0\n",
+      "30        60 2020-12-30    102       129     380.3\n",
+      "31        60 2020-12-31     92       115     243.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "#replacing wrong values\n",
+    "df.loc[7,\"Duration\"]=45\n",
+    "print(df.to_string())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "39d62c79",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Duration  Pulse  Maxpulse  Calories\n",
+      "0          60    110       130     409.1\n",
+      "1          60    117       145     479.0\n",
+      "2          60    103       135     340.0\n",
+      "3          45    109       175     282.4\n",
+      "4          45    117       148     406.0\n",
+      "5          60    102       127     300.0\n",
+      "6          60    110       136     374.0\n",
+      "7          45    104       134     253.3\n",
+      "8          30    109       133     195.1\n",
+      "9          60     98       124     269.0\n",
+      "10         60    103       147     329.3\n",
+      "11         60    100       120     250.7\n",
+      "12         60    106       128     345.3\n",
+      "13         60    104       132     379.3\n",
+      "14         60     98       123     275.0\n",
+      "15         60     98       120     215.2\n",
+      "16         60    100       120     300.0\n",
+      "17         45     90       112       NaN\n",
+      "18         60    103       123     323.0\n",
+      "19         45     97       125     243.0\n",
+      "20         60    108       131     364.2\n",
+      "21         45    100       119     282.0\n",
+      "22         60    130       101     300.0\n",
+      "23         45    105       132     246.0\n",
+      "24         60    102       126     334.5\n",
+      "25         60    100       120     250.0\n",
+      "26         60     92       118     241.0\n",
+      "27         60    103       132       NaN\n",
+      "28         60    100       132     280.0\n",
+      "29         60    102       129     380.3\n",
+      "30         60     92       115     243.0\n",
+      "31         45     90       112     180.1\n",
+      "32         60    101       124     299.0\n",
+      "33         60     93       113     223.0\n",
+      "34         60    107       136     361.0\n",
+      "35         60    114       140     415.0\n",
+      "36         60    102       127     300.0\n",
+      "37         60    100       120     300.0\n",
+      "38         60    100       120     300.0\n",
+      "39         45    104       129     266.0\n",
+      "40         45     90       112     180.1\n",
+      "41         60     98       126     286.0\n",
+      "42         60    100       122     329.4\n",
+      "43         60    111       138     400.0\n",
+      "44         60    111       131     397.0\n",
+      "45         60     99       119     273.0\n",
+      "46         60    109       153     387.6\n",
+      "47         45    111       136     300.0\n",
+      "48         45    108       129     298.0\n",
+      "49         60    111       139     397.6\n",
+      "50         60    107       136     380.2\n",
+      "51         80    123       146     643.1\n",
+      "52         60    106       130     263.0\n",
+      "53         60    118       151     486.0\n",
+      "54         30    136       175     238.0\n",
+      "55         60    121       146     450.7\n",
+      "56         60    118       121     413.0\n",
+      "57         45    115       144     305.0\n",
+      "58         20    153       172     226.4\n",
+      "59         45    123       152     321.0\n",
+      "60        120    108       160    1376.0\n",
+      "61        120    110       137    1034.4\n",
+      "62        120    109       135     853.0\n",
+      "63         45    118       141     341.0\n",
+      "64         20    110       130     131.4\n",
+      "65        120     90       130     800.4\n",
+      "66        120    105       135     873.4\n",
+      "67        120    107       130     816.0\n",
+      "68         20    106       136     110.4\n",
+      "69        120    108       143    1500.2\n",
+      "70        120     97       129    1115.0\n",
+      "71         60    109       153     387.6\n",
+      "72         90    100       127     700.0\n",
+      "73        120     97       127     953.2\n",
+      "74         45    114       146     304.0\n",
+      "75         90     98       125     563.2\n",
+      "76         45    105       134     251.0\n",
+      "77         45    110       141     300.0\n",
+      "78        120    100       130     500.4\n",
+      "79        120    100       131    1729.0\n",
+      "80         30    159       182     319.2\n",
+      "81         45    149       169     344.0\n",
+      "82         30    103       139     151.1\n",
+      "83        120    100       130     500.0\n",
+      "84         45    100       120     225.3\n",
+      "85         30    151       170     300.0\n",
+      "86         45    102       136     234.0\n",
+      "87        120    100       157    1000.1\n",
+      "88         45    129       103     242.0\n",
+      "89         20     83       107      50.3\n",
+      "90        120    101       127     600.1\n",
+      "91         45    107       137       NaN\n",
+      "92         30     90       107     105.3\n",
+      "93         15     80       100      50.5\n",
+      "94         20    150       171     127.4\n",
+      "95         20    151       168     229.4\n",
+      "96         30     95       128     128.2\n",
+      "97         25    152       168     244.2\n",
+      "98         30    109       131     188.2\n",
+      "99         90     93       124     604.1\n",
+      "100        20     95       112      77.7\n",
+      "101        90     90       110     500.0\n",
+      "102        90     90       100     500.0\n",
+      "103        90     90       100     500.4\n",
+      "104        30     92       108      92.7\n",
+      "105        30     93       128     124.0\n",
+      "106       120     90       120     800.3\n",
+      "107        30     90       120      86.2\n",
+      "108        90     90       120     500.3\n",
+      "109       120    137       184    1860.4\n",
+      "110        60    102       124     325.2\n",
+      "111        45    107       124     275.0\n",
+      "112        15    124       139     124.2\n",
+      "113        45    100       120     225.3\n",
+      "114        60    108       131     367.6\n",
+      "115        60    108       151     351.7\n",
+      "116        60    116       141     443.0\n",
+      "117        60     97       122     277.4\n",
+      "118        60    105       125       NaN\n",
+      "119        60    103       124     332.7\n",
+      "120        30    112       137     193.9\n",
+      "121        45    100       120     100.7\n",
+      "122        60    119       169     336.7\n",
+      "123        60    107       127     344.9\n",
+      "124        60    111       151     368.5\n",
+      "125        60     98       122     271.0\n",
+      "126        60     97       124     275.3\n",
+      "127        60    109       127     382.0\n",
+      "128        90     99       125     466.4\n",
+      "129        60    114       151     384.0\n",
+      "130        60    104       134     342.5\n",
+      "131        60    107       138     357.5\n",
+      "132        60    103       133     335.0\n",
+      "133        60    106       132     327.5\n",
+      "134        60    103       136     339.0\n",
+      "135        20    136       156     189.0\n",
+      "136        45    117       143     317.7\n",
+      "137        45    115       137     318.0\n",
+      "138        45    113       138     308.0\n",
+      "139        20    141       162     222.4\n",
+      "140        60    108       135     390.0\n",
+      "141        60     97       127       NaN\n",
+      "142        45    100       120     250.4\n",
+      "143        45    122       149     335.4\n",
+      "144        60    136       170     470.2\n",
+      "145        45    106       126     270.8\n",
+      "146        60    107       136     400.0\n",
+      "147        60    112       146     361.9\n",
+      "148        30    103       127     185.0\n",
+      "149        60    110       150     409.4\n",
+      "150        60    106       134     343.0\n",
+      "151        60    109       129     353.2\n",
+      "152        60    109       138     374.0\n",
+      "153        30    150       167     275.8\n",
+      "154        60    105       128     328.0\n",
+      "155        60    111       151     368.5\n",
+      "156        60     97       131     270.4\n",
+      "157        60    100       120     270.4\n",
+      "158        60    114       150     382.8\n",
+      "159        30     80       120     240.9\n",
+      "160        30     85       120     250.4\n",
+      "161        45     90       130     260.4\n",
+      "162        45     95       130     270.0\n",
+      "163        45    100       140     280.9\n",
+      "164        60    105       140     290.8\n",
+      "165        60    110       145     300.0\n",
+      "166        60    115       145     310.2\n",
+      "167        75    120       150     320.4\n",
+      "168        75    125       150     330.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "df=pd.read_csv(\"data.csv\")\n",
+    "for x in df.index:\n",
+    "    if df.loc[x,\"Duration\"]>120:\n",
+    "        df.loc[x,\"Duration\"]=120  #replacing the values as per a condition\n",
+    "print(df.to_string()) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "45dd8530",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Duration  Pulse  Maxpulse  Calories\n",
+      "0          60    110       130     409.1\n",
+      "1          60    117       145     479.0\n",
+      "2          60    103       135     340.0\n",
+      "3          45    109       175     282.4\n",
+      "4          45    117       148     406.0\n",
+      "5          60    102       127     300.0\n",
+      "6          60    110       136     374.0\n",
+      "7          45    104       134     253.3\n",
+      "8          30    109       133     195.1\n",
+      "9          60     98       124     269.0\n",
+      "10         60    103       147     329.3\n",
+      "11         60    100       120     250.7\n",
+      "12         60    106       128     345.3\n",
+      "13         60    104       132     379.3\n",
+      "14         60     98       123     275.0\n",
+      "15         60     98       120     215.2\n",
+      "16         60    100       120     300.0\n",
+      "17         45     90       112       NaN\n",
+      "18         60    103       123     323.0\n",
+      "19         45     97       125     243.0\n",
+      "20         60    108       131     364.2\n",
+      "21         45    100       119     282.0\n",
+      "22         60    130       101     300.0\n",
+      "23         45    105       132     246.0\n",
+      "24         60    102       126     334.5\n",
+      "25         60    100       120     250.0\n",
+      "26         60     92       118     241.0\n",
+      "27         60    103       132       NaN\n",
+      "28         60    100       132     280.0\n",
+      "29         60    102       129     380.3\n",
+      "30         60     92       115     243.0\n",
+      "31         45     90       112     180.1\n",
+      "32         60    101       124     299.0\n",
+      "33         60     93       113     223.0\n",
+      "34         60    107       136     361.0\n",
+      "35         60    114       140     415.0\n",
+      "36         60    102       127     300.0\n",
+      "37         60    100       120     300.0\n",
+      "38         60    100       120     300.0\n",
+      "39         45    104       129     266.0\n",
+      "40         45     90       112     180.1\n",
+      "41         60     98       126     286.0\n",
+      "42         60    100       122     329.4\n",
+      "43         60    111       138     400.0\n",
+      "44         60    111       131     397.0\n",
+      "45         60     99       119     273.0\n",
+      "46         60    109       153     387.6\n",
+      "47         45    111       136     300.0\n",
+      "48         45    108       129     298.0\n",
+      "49         60    111       139     397.6\n",
+      "50         60    107       136     380.2\n",
+      "51         80    123       146     643.1\n",
+      "52         60    106       130     263.0\n",
+      "53         60    118       151     486.0\n",
+      "54         30    136       175     238.0\n",
+      "55         60    121       146     450.7\n",
+      "56         60    118       121     413.0\n",
+      "57         45    115       144     305.0\n",
+      "58         20    153       172     226.4\n",
+      "59         45    123       152     321.0\n",
+      "63         45    118       141     341.0\n",
+      "64         20    110       130     131.4\n",
+      "68         20    106       136     110.4\n",
+      "71         60    109       153     387.6\n",
+      "72         90    100       127     700.0\n",
+      "74         45    114       146     304.0\n",
+      "75         90     98       125     563.2\n",
+      "76         45    105       134     251.0\n",
+      "77         45    110       141     300.0\n",
+      "78        120    100       130     500.4\n",
+      "80         30    159       182     319.2\n",
+      "81         45    149       169     344.0\n",
+      "82         30    103       139     151.1\n",
+      "83        120    100       130     500.0\n",
+      "84         45    100       120     225.3\n",
+      "85         30    151       170     300.0\n",
+      "86         45    102       136     234.0\n",
+      "87        120    100       157    1000.1\n",
+      "88         45    129       103     242.0\n",
+      "89         20     83       107      50.3\n",
+      "91         45    107       137       NaN\n",
+      "92         30     90       107     105.3\n",
+      "93         15     80       100      50.5\n",
+      "94         20    150       171     127.4\n",
+      "95         20    151       168     229.4\n",
+      "96         30     95       128     128.2\n",
+      "97         25    152       168     244.2\n",
+      "98         30    109       131     188.2\n",
+      "99         90     93       124     604.1\n",
+      "100        20     95       112      77.7\n",
+      "101        90     90       110     500.0\n",
+      "102        90     90       100     500.0\n",
+      "103        90     90       100     500.4\n",
+      "104        30     92       108      92.7\n",
+      "105        30     93       128     124.0\n",
+      "107        30     90       120      86.2\n",
+      "108        90     90       120     500.3\n",
+      "110        60    102       124     325.2\n",
+      "111        45    107       124     275.0\n",
+      "112        15    124       139     124.2\n",
+      "113        45    100       120     225.3\n",
+      "114        60    108       131     367.6\n",
+      "115        60    108       151     351.7\n",
+      "116        60    116       141     443.0\n",
+      "117        60     97       122     277.4\n",
+      "118        60    105       125       NaN\n",
+      "119        60    103       124     332.7\n",
+      "120        30    112       137     193.9\n",
+      "121        45    100       120     100.7\n",
+      "122        60    119       169     336.7\n",
+      "123        60    107       127     344.9\n",
+      "124        60    111       151     368.5\n",
+      "125        60     98       122     271.0\n",
+      "126        60     97       124     275.3\n",
+      "127        60    109       127     382.0\n",
+      "128        90     99       125     466.4\n",
+      "129        60    114       151     384.0\n",
+      "130        60    104       134     342.5\n",
+      "131        60    107       138     357.5\n",
+      "132        60    103       133     335.0\n",
+      "133        60    106       132     327.5\n",
+      "134        60    103       136     339.0\n",
+      "135        20    136       156     189.0\n",
+      "136        45    117       143     317.7\n",
+      "137        45    115       137     318.0\n",
+      "138        45    113       138     308.0\n",
+      "139        20    141       162     222.4\n",
+      "140        60    108       135     390.0\n",
+      "141        60     97       127       NaN\n",
+      "142        45    100       120     250.4\n",
+      "143        45    122       149     335.4\n",
+      "144        60    136       170     470.2\n",
+      "145        45    106       126     270.8\n",
+      "146        60    107       136     400.0\n",
+      "147        60    112       146     361.9\n",
+      "148        30    103       127     185.0\n",
+      "149        60    110       150     409.4\n",
+      "150        60    106       134     343.0\n",
+      "151        60    109       129     353.2\n",
+      "152        60    109       138     374.0\n",
+      "153        30    150       167     275.8\n",
+      "154        60    105       128     328.0\n",
+      "155        60    111       151     368.5\n",
+      "156        60     97       131     270.4\n",
+      "157        60    100       120     270.4\n",
+      "158        60    114       150     382.8\n",
+      "159        30     80       120     240.9\n",
+      "160        30     85       120     250.4\n",
+      "161        45     90       130     260.4\n",
+      "162        45     95       130     270.0\n",
+      "163        45    100       140     280.9\n",
+      "164        60    105       140     290.8\n",
+      "165        60    110       145     300.0\n",
+      "166        60    115       145     310.2\n",
+      "167        75    120       150     320.4\n",
+      "168        75    125       150     330.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "df=pd.read_csv(\"Data.csv\")\n",
+    "for x in df.index:\n",
+    "    if df.loc[x,\"Duration\"]>120:\n",
+    "        df.drop(x,inplace=True)   #drops the rows which do not satisfy the mentioned criteria\n",
+    "print(df.to_string())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "fd2c914e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Duration</th>\n",
+       "      <th>Pulse</th>\n",
+       "      <th>Maxpulse</th>\n",
+       "      <th>Calories</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Duration</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.155408</td>\n",
+       "      <td>0.009403</td>\n",
+       "      <td>0.922717</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pulse</th>\n",
+       "      <td>-0.155408</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.786535</td>\n",
+       "      <td>0.025121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Maxpulse</th>\n",
+       "      <td>0.009403</td>\n",
+       "      <td>0.786535</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.203813</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Calories</th>\n",
+       "      <td>0.922717</td>\n",
+       "      <td>0.025121</td>\n",
+       "      <td>0.203813</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Duration     Pulse  Maxpulse  Calories\n",
+       "Duration  1.000000 -0.155408  0.009403  0.922717\n",
+       "Pulse    -0.155408  1.000000  0.786535  0.025121\n",
+       "Maxpulse  0.009403  0.786535  1.000000  0.203813\n",
+       "Calories  0.922717  0.025121  0.203813  1.000000"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#correlation in pandas\n",
+    "df=pd.read_csv(\"Data.csv\")\n",
+    "df.corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0598331e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "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": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/w1/README.md b/w1/README.md
new file mode 100644
index 0000000..6b700ef
--- /dev/null
+++ b/w1/README.md
@@ -0,0 +1,22 @@
+Anaconda is a distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. The distribution includes data-science packages suitable for Windows, Linux, and macOS.
+
+**Anaconda installation in Windows:**
+1. Visit https://www.anaconda.com/products/distribution.
+2. Download the Installer.
+3. Once the download is complete, run the installer.
+5. Click on Next and then click on I agree.
+6. In the following window choose the Just Me option.
+7. Click on Next.
+8. Choose the directory where you want the distribution to install.
+9. Click on Next.
+10. At the Advanced Installation Options screen, I recommend that you do not check Add Anaconda to my PATH   environment variable.
+11. Click on Next and run the installer.
+12. Finish the installation.
+13. Launch Anaconda Navigator.
+
+
+**Installing Dependencies:**
+1. Open Anaconda Navigator.
+2. Launch CMD.exe prompt.
+3. To install Matplotlib, in the cmd window type conda install matplotlib.
+4. Do the same for Numpy and Pandas.
diff --git a/w1/data.csv b/w1/data.csv
new file mode 100644
index 0000000..75e9a2e
--- /dev/null
+++ b/w1/data.csv
@@ -0,0 +1,170 @@
+Duration,Pulse,Maxpulse,Calories
+60,110,130,409.1
+60,117,145,479.0
+60,103,135,340.0
+45,109,175,282.4
+45,117,148,406.0
+60,102,127,300.0
+60,110,136,374.0
+45,104,134,253.3
+30,109,133,195.1
+60,98,124,269.0
+60,103,147,329.3
+60,100,120,250.7
+60,106,128,345.3
+60,104,132,379.3
+60,98,123,275.0
+60,98,120,215.2
+60,100,120,300.0
+45,90,112,
+60,103,123,323.0
+45,97,125,243.0
+60,108,131,364.2
+45,100,119,282.0
+60,130,101,300.0
+45,105,132,246.0
+60,102,126,334.5
+60,100,120,250.0
+60,92,118,241.0
+60,103,132,
+60,100,132,280.0
+60,102,129,380.3
+60,92,115,243.0
+45,90,112,180.1
+60,101,124,299.0
+60,93,113,223.0
+60,107,136,361.0
+60,114,140,415.0
+60,102,127,300.0
+60,100,120,300.0
+60,100,120,300.0
+45,104,129,266.0
+45,90,112,180.1
+60,98,126,286.0
+60,100,122,329.4
+60,111,138,400.0
+60,111,131,397.0
+60,99,119,273.0
+60,109,153,387.6
+45,111,136,300.0
+45,108,129,298.0
+60,111,139,397.6
+60,107,136,380.2
+80,123,146,643.1
+60,106,130,263.0
+60,118,151,486.0
+30,136,175,238.0
+60,121,146,450.7
+60,118,121,413.0
+45,115,144,305.0
+20,153,172,226.4
+45,123,152,321.0
+210,108,160,1376.0
+160,110,137,1034.4
+160,109,135,853.0
+45,118,141,341.0
+20,110,130,131.4
+180,90,130,800.4
+150,105,135,873.4
+150,107,130,816.0
+20,106,136,110.4
+300,108,143,1500.2
+150,97,129,1115.0
+60,109,153,387.6
+90,100,127,700.0
+150,97,127,953.2
+45,114,146,304.0
+90,98,125,563.2
+45,105,134,251.0
+45,110,141,300.0
+120,100,130,500.4
+270,100,131,1729.0
+30,159,182,319.2
+45,149,169,344.0
+30,103,139,151.1
+120,100,130,500.0
+45,100,120,225.3
+30,151,170,300.0
+45,102,136,234.0
+120,100,157,1000.1
+45,129,103,242.0
+20,83,107,50.3
+180,101,127,600.1
+45,107,137,
+30,90,107,105.3
+15,80,100,50.5
+20,150,171,127.4
+20,151,168,229.4
+30,95,128,128.2
+25,152,168,244.2
+30,109,131,188.2
+90,93,124,604.1
+20,95,112,77.7
+90,90,110,500.0
+90,90,100,500.0
+90,90,100,500.4
+30,92,108,92.7
+30,93,128,124.0
+180,90,120,800.3
+30,90,120,86.2
+90,90,120,500.3
+210,137,184,1860.4
+60,102,124,325.2
+45,107,124,275.0
+15,124,139,124.2
+45,100,120,225.3
+60,108,131,367.6
+60,108,151,351.7
+60,116,141,443.0
+60,97,122,277.4
+60,105,125,
+60,103,124,332.7
+30,112,137,193.9
+45,100,120,100.7
+60,119,169,336.7
+60,107,127,344.9
+60,111,151,368.5
+60,98,122,271.0
+60,97,124,275.3
+60,109,127,382.0
+90,99,125,466.4
+60,114,151,384.0
+60,104,134,342.5
+60,107,138,357.5
+60,103,133,335.0
+60,106,132,327.5
+60,103,136,339.0
+20,136,156,189.0
+45,117,143,317.7
+45,115,137,318.0
+45,113,138,308.0
+20,141,162,222.4
+60,108,135,390.0
+60,97,127,
+45,100,120,250.4
+45,122,149,335.4
+60,136,170,470.2
+45,106,126,270.8
+60,107,136,400.0
+60,112,146,361.9
+30,103,127,185.0
+60,110,150,409.4
+60,106,134,343.0
+60,109,129,353.2
+60,109,138,374.0
+30,150,167,275.8
+60,105,128,328.0
+60,111,151,368.5
+60,97,131,270.4
+60,100,120,270.4
+60,114,150,382.8
+30,80,120,240.9
+30,85,120,250.4
+45,90,130,260.4
+45,95,130,270.0
+45,100,140,280.9
+60,105,140,290.8
+60,110,145,300.0
+60,115,145,310.2
+75,120,150,320.4
+75,125,150,330.4
\ No newline at end of file
diff --git a/w1/dirtydata.csv b/w1/dirtydata.csv
new file mode 100644
index 0000000..36e149a
--- /dev/null
+++ b/w1/dirtydata.csv
@@ -0,0 +1,33 @@
+Duration,Date,Pulse,Maxpulse,Calories
+60,'2020/12/01',110,130,409.1
+60,'2020/12/02',117,145,479.0
+60,'2020/12/03',103,135,340.0
+45,'2020/12/04',109,175,282.4
+45,'2020/12/05',117,148,406.0
+60,'2020/12/06',102,127,300.0
+60,'2020/12/07',110,136,374.0
+450,'2020/12/08',104,134,253.3
+30,'2020/12/09',109,133,195.1
+60,'2020/12/10',98,124,269.0
+60,'2020/12/11',103,147,329.3
+60,'2020/12/12',100,120,250.7
+60,'2020/12/12',100,120,250.7
+60,'2020/12/13',106,128,345.3
+60,'2020/12/14',104,132,379.3
+60,'2020/12/15',98,123,275.0
+60,'2020/12/16',98,120,215.2
+60,'2020/12/17',100,120,300.0
+45,'2020/12/18',90,112,
+60,'2020/12/19',103,123,323.0
+45,'2020/12/20',97,125,243.0
+60,'2020/12/21',108,131,364.2
+45,,100,119,282.0
+60,'2020/12/23',130,101,300.0
+45,'2020/12/24',105,132,246.0
+60,'2020/12/25',102,126,334.5
+60,20201226,100,120,250.0
+60,'2020/12/27',92,118,241.0
+60,'2020/12/28',103,132,
+60,'2020/12/29',100,132,280.0
+60,'2020/12/30',102,129,380.3
+60,'2020/12/31',92,115,243.0
\ No newline at end of file