diff --git a/Assignment 1/Assignment1_Pranav/Pranav_Assignment1_QV_Matplotlib.ipynb b/Assignment 1/Assignment1_Pranav/Pranav_Assignment1_QV_Matplotlib.ipynb
new file mode 100644
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+++ b/Assignment 1/Assignment1_Pranav/Pranav_Assignment1_QV_Matplotlib.ipynb	
@@ -0,0 +1,461 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_sfWZU_eST2O"
+      },
+      "source": [
+        "**NOTE: ALL THE COMMANDS FOR PLOTTING A FIGURE SHOULD ALL GO IN THE SAME CELL. SEPARATING THEM OUT INTO MULTIPLE CELLS MAY CAUSE NOTHING TO SHOW UP.**\n",
+        "\n",
+        "# Exercises\n",
+        "\n",
+        "Follow the instructions to recreate the plots using this data:\n",
+        "\n",
+        "## Data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "l0eZ10dLST2X"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "x = np.arange(0,100)\n",
+        "y = x*2\n",
+        "z = x**2"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "veY4-lsGST2c"
+      },
+      "source": [
+        "**Import matplotlib.pyplot as plt**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "tAOmEcNKST2e"
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5j_39gsVST2f"
+      },
+      "source": [
+        "## Exercise 1\n",
+        "\n",
+        "**Follow along with these steps**\n",
+        "* Create a figure object called fig using plt.figure()\n",
+        "* Use add_axes to add an axis to the figure canvas at [0,0,1,1]. Call this new axis ax.\n",
+        "* Plot (x,y) on that axes and set the labels and titles to match the plot below:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "03Ts4r5SST2g",
+        "outputId": "edd2f34e-fdc2-425b-f10a-ef1d12333879",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 582
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig = plt.figure()\n",
+        "ax = fig.add_axes([0,0,1,1])\n",
+        "ax.plot(x,y)\n",
+        "ax.set_xlabel('x')\n",
+        "ax.set_ylabel('y')\n",
+        "ax.set_title('title')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "I8mFrIGwST2i"
+      },
+      "source": [
+        "## Exercise 2\n",
+        "**Create a figure object and put two axes on it, ax1 and ax2. Located at [0,0,1,1] and [0.2,0.5,.2,.2] respectively.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "D4nUfU26ST2k",
+        "outputId": "cc199526-91ec-4bad-ecef-112e1e2c2e5c",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 546
+        },
+        "collapsed": true
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig = plt.figure()\n",
+        "\n",
+        "ax = fig.add_axes([0,0,1,1])\n",
+        "ax = fig.add_axes([0.2,0.5,0.2,0.2])\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SK8p_A2NST2l"
+      },
+      "source": [
+        "**Now plot (x,y) on both axes. And call your figure object to show it.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "m_2gVMryST2m",
+        "outputId": "4dbe1900-ff72-4b48-aaac-fd328d0f76f3",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 540
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig = plt.figure()\n",
+        "\n",
+        "ax = fig.add_axes([0,0,1,1])\n",
+        "ax.plot(x,y, color='red')\n",
+        "ax = fig.add_axes([0.2,0.5,0.2,0.2])\n",
+        "ax.plot(x,y, color='red')\n",
+        "\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hYaQZfbFST2n"
+      },
+      "source": [
+        "## Exercise 3\n",
+        "\n",
+        "**Create the plot below by adding two axes to a figure object at [0,0,1,1] and [0.2,0.5,.4,.4]**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "BuiYx0xbST2o",
+        "outputId": "104b5562-bfd3-4b9d-c354-4570cb3b4a8f",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 546
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig = plt.figure()\n",
+        "ax = fig.add_axes([0,0,1,1])\n",
+        "ax = fig.add_axes([0.2,0.5,0.4,0.4])\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SeRSiZDGST2o"
+      },
+      "source": [
+        "**Now use x,y, and z arrays to recreate the plot below. Notice the xlimits and y limits on the inserted plot:**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "jhy7t5AKST2p",
+        "outputId": "e167e297-b047-408c-da9d-83f2a4f13440",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "x = np.linspace(0, 100, 20)\n",
+        "z = x**2\n",
+        "y = 2*x\n",
+        "fig, ax = plt.subplots(figsize=(8,6))\n",
+        "ax.plot(x, z)\n",
+        "ax.set_xlabel('X')\n",
+        "ax.set_ylabel('Z')\n",
+        "\n",
+        "ax_inset = plt.axes([0.2, 0.5, 0.4, 0.3])  # [left, bottom, width, height]\n",
+        "ax_inset.plot(x, y)\n",
+        "ax_inset.set_xlim(20, 22)\n",
+        "ax_inset.set_ylim(30, 50)\n",
+        "ax_inset.set_xlabel('X')\n",
+        "ax_inset.set_ylabel('Y')\n",
+        "ax_inset.set_title('zoom')\n",
+        "\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TM6XOILJST2q"
+      },
+      "source": [
+        "## Exercise 4\n",
+        "\n",
+        "**Use plt.subplots(nrows=1, ncols=2) to create the plot below.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "id": "osk0Jco2ST2r",
+        "outputId": "6d6f0829-f5c7-416f-a79e-07358c5dd9cd",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 435
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAi4AAAGiCAYAAADA0E3hAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAHVNJREFUeJzt3X9s3XW9+PFX29FTiLTMu7vuh8VlGEQFNt1YLUgIproEM+7+MO6C2ebCj4vuEl2jsjlYRXSdiLgEhosTxD/0bkrAGLcMtbIYZGbJtiZ4GRAYuGlsYdG1c0jL2vf9wy/1262Fne70x3t7PJLzRz98Pj2vAueVZ89pe8pSSikAADJQPtYDAACcLOECAGRDuAAA2RAuAEA2hAsAkA3hAgBkQ7gAANkQLgBANoQLAJAN4QIAZKPocPntb38bCxYsiGnTpkVZWVn87Gc/e9trduzYER/60IeiUCjEe97znnj44YeHMSqQK3sDKJWiw+Xo0aMxa9as2LBhw0md/9JLL8UnPvGJuPrqq6OtrS2+8IUvxI033hiPP/540cMCebI3gFIpO5U3WSwrK4vHHnssFi5cOOQ5t912W2zdujX+8Ic/9B/7z//8zzh8+HBs3759uHcNZMreAE7FhJG+g507d0ZjY+OAY/Pnz48vfOELQ17T3d0d3d3d/R/39fXFX//61/i3f/u3KCsrG6lRgSGklOLIkSMxbdq0KC8f+R+Nszfg9DASu2PEw6W9vT1qa2sHHKutrY2urq74xz/+EWefffYJ17S0tMSdd9450qMBRTp48GC8613vGvH7sTfg9FLK3THi4TIcq1atiqampv6POzs74/zzz4+DBw9GdXX1GE4GZ6aurq6oq6uLc889d6xHGZK9AePPSOyOEQ+XKVOmREdHx4BjHR0dUV1dPeh3TRERhUIhCoXCCcerq6stIBhDo/WSi70Bp5dS7o4Rf7G6oaEhWltbBxz71a9+FQ0NDSN910Cm7A1gKEWHy9///vdoa2uLtra2iPjnry22tbXFgQMHIuKfT9cuWbKk//xbbrkl9u/fH1/+8pfj2WefjQceeCB+8pOfxIoVK0rzFQDjnr0BlEwq0hNPPJEi4oTb0qVLU0opLV26NF111VUnXDN79uxUWVmZZs6cmX7wgx8UdZ+dnZ0pIlJnZ2ex4wIlcKqPQXsDzkwj8Tg8pb/jMlq6urqipqYmOjs7vVYNYyDHx2COM8PpZiQeh96rCADIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAwrXDZs2BAzZsyIqqqqqK+vj127dr3l+evXr4/3vve9cfbZZ0ddXV2sWLEiXn/99WENDOTJ3gBKoehw2bJlSzQ1NUVzc3Ps2bMnZs2aFfPnz49XXnll0PN//OMfx8qVK6O5uTn27dsXDz74YGzZsiW+8pWvnPLwQB7sDaBUig6Xe++9N2666aZYtmxZvP/974+NGzfGOeecEw899NCg5z/11FNxxRVXxPXXXx8zZsyIj3/843Hddde97XdbwOnD3gBKpahw6enpid27d0djY+O/PkF5eTQ2NsbOnTsHvebyyy+P3bt39y+c/fv3x7Zt2+Kaa64Z8n66u7ujq6trwA3Ik70BlNKEYk4+dOhQ9Pb2Rm1t7YDjtbW18eyzzw56zfXXXx+HDh2Kj3zkI5FSimPHjsUtt9zylk/5trS0xJ133lnMaMA4ZW8ApTTiv1W0Y8eOWLt2bTzwwAOxZ8+eePTRR2Pr1q1x1113DXnNqlWrorOzs/928ODBkR4TGEfsDWAoRT3jMmnSpKioqIiOjo4Bxzs6OmLKlCmDXnPHHXfE4sWL48Ybb4yIiEsuuSSOHj0aN998c6xevTrKy09sp0KhEIVCoZjRgHHK3gBKqahnXCorK2POnDnR2traf6yvry9aW1ujoaFh0Gtee+21E5ZMRUVFRESklIqdF8iMvQGUUlHPuERENDU1xdKlS2Pu3Lkxb968WL9+fRw9ejSWLVsWERFLliyJ6dOnR0tLS0RELFiwIO6999744Ac/GPX19fHCCy/EHXfcEQsWLOhfRMDpzd4ASqXocFm0aFG8+uqrsWbNmmhvb4/Zs2fH9u3b+3/w7sCBAwO+U7r99tujrKwsbr/99vjzn/8c//7v/x4LFiyIb3zjG6X7KoBxzd4ASqUsZfC8a1dXV9TU1ERnZ2dUV1eP9ThwxsnxMZjjzHC6GYnHofcqAgCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8MKlw0bNsSMGTOiqqoq6uvrY9euXW95/uHDh2P58uUxderUKBQKceGFF8a2bduGNTCQJ3sDKIUJxV6wZcuWaGpqio0bN0Z9fX2sX78+5s+fH88991xMnjz5hPN7enriYx/7WEyePDkeeeSRmD59evzxj3+M8847rxTzAxmwN4BSKUsppWIuqK+vj8suuyzuv//+iIjo6+uLurq6uPXWW2PlypUnnL9x48b41re+Fc8++2ycddZZwxqyq6srampqorOzM6qrq4f1OYDhO9XHoL0BZ6aReBwW9VJRT09P7N69OxobG//1CcrLo7GxMXbu3DnoNT//+c+joaEhli9fHrW1tXHxxRfH2rVro7e3d8j76e7ujq6urgE3IE/2BlBKRYXLoUOHore3N2prawccr62tjfb29kGv2b9/fzzyyCPR29sb27ZtizvuuCO+/e1vx9e//vUh76elpSVqamr6b3V1dcWMCYwj9gZQSiP+W0V9fX0xefLk+N73vhdz5syJRYsWxerVq2Pjxo1DXrNq1aro7Ozsvx08eHCkxwTGEXsDGEpRP5w7adKkqKioiI6OjgHHOzo6YsqUKYNeM3Xq1DjrrLOioqKi/9j73ve+aG9vj56enqisrDzhmkKhEIVCoZjRgHHK3gBKqahnXCorK2POnDnR2traf6yvry9aW1ujoaFh0GuuuOKKeOGFF6Kvr6//2PPPPx9Tp04ddPkApxd7Ayilol8qampqik2bNsUPf/jD2LdvX3z2s5+No0ePxrJlyyIiYsmSJbFq1ar+8z/72c/GX//61/j85z8fzz//fGzdujXWrl0by5cvL91XAYxr9gZQKkX/HZdFixbFq6++GmvWrIn29vaYPXt2bN++vf8H7w4cOBDl5f/qobq6unj88cdjxYoVcemll8b06dPj85//fNx2222l+yqAcc3eAEql6L/jMhb8PQYYWzk+BnOcGU43Y/53XAAAxpJwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwMK1w2bNgQM2bMiKqqqqivr49du3ad1HWbN2+OsrKyWLhw4XDuFsic3QGcqqLDZcuWLdHU1BTNzc2xZ8+emDVrVsyfPz9eeeWVt7zu5Zdfji9+8Ytx5ZVXDntYIF92B1AKRYfLvffeGzfddFMsW7Ys3v/+98fGjRvjnHPOiYceemjIa3p7e+PTn/503HnnnTFz5sy3vY/u7u7o6uoacAPyNtK7w96AM0NR4dLT0xO7d++OxsbGf32C8vJobGyMnTt3Dnnd1772tZg8eXLccMMNJ3U/LS0tUVNT03+rq6srZkxgnBmN3WFvwJmhqHA5dOhQ9Pb2Rm1t7YDjtbW10d7ePug1Tz75ZDz44IOxadOmk76fVatWRWdnZ//t4MGDxYwJjDOjsTvsDTgzTBjJT37kyJFYvHhxbNq0KSZNmnTS1xUKhSgUCiM4GTCeDWd32BtwZigqXCZNmhQVFRXR0dEx4HhHR0dMmTLlhPNffPHFePnll2PBggX9x/r6+v55xxMmxHPPPRcXXHDBcOYGMmJ3AKVS1EtFlZWVMWfOnGhtbe0/1tfXF62trdHQ0HDC+RdddFE8/fTT0dbW1n+79tpr4+qrr462tjavQcMZwu4ASqXol4qamppi6dKlMXfu3Jg3b16sX78+jh49GsuWLYuIiCVLlsT06dOjpaUlqqqq4uKLLx5w/XnnnRcRccJx4PRmdwClUHS4LFq0KF599dVYs2ZNtLe3x+zZs2P79u39P3R34MCBKC/3B3mBgewOoBTKUkpprId4O11dXVFTUxOdnZ1RXV091uPAGSfHx2COM8PpZiQeh769AQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8MKlw0bNsSMGTOiqqoq6uvrY9euXUOeu2nTprjyyitj4sSJMXHixGhsbHzL84HTl90BnKqiw2XLli3R1NQUzc3NsWfPnpg1a1bMnz8/XnnllUHP37FjR1x33XXxxBNPxM6dO6Ouri4+/vGPx5///OdTHh7Ih90BlEJZSikVc0F9fX1cdtllcf/990dERF9fX9TV1cWtt94aK1eufNvre3t7Y+LEiXH//ffHkiVLBj2nu7s7uru7+z/u6uqKurq66OzsjOrq6mLGBUqgq6srampqTukxONK7w96A8acUu+N4RT3j0tPTE7t3747GxsZ/fYLy8mhsbIydO3ee1Od47bXX4o033oh3vvOdQ57T0tISNTU1/be6urpixgTGmdHYHfYGnBmKCpdDhw5Fb29v1NbWDjheW1sb7e3tJ/U5brvttpg2bdqABXa8VatWRWdnZ//t4MGDxYwJjDOjsTvsDTgzTBjNO1u3bl1s3rw5duzYEVVVVUOeVygUolAojOJkwHh2MrvD3oAzQ1HhMmnSpKioqIiOjo4Bxzs6OmLKlClvee0999wT69ati1//+tdx6aWXFj8pkC27AyiVol4qqqysjDlz5kRra2v/sb6+vmhtbY2GhoYhr7v77rvjrrvuiu3bt8fcuXOHPy2QJbsDKJWiXypqamqKpUuXxty5c2PevHmxfv36OHr0aCxbtiwiIpYsWRLTp0+PlpaWiIj45je/GWvWrIkf//jHMWPGjP7Xs9/xjnfEO97xjhJ+KcB4ZncApVB0uCxatCheffXVWLNmTbS3t8fs2bNj+/bt/T90d+DAgSgv/9cTOd/97nejp6cnPvnJTw74PM3NzfHVr3711KYHsmF3AKVQ9N9xGQsj8XvgwMnL8TGY48xwuhnzv+MCADCWhAsAkA3hAgBkQ7gAANkQLgBANoQLAJAN4QIAZEO4AADZEC4AQDaECwCQDeECAGRDuAAA2RAuAEA2hAsAkA3hAgBkQ7gAANkQLgBANoQLAJAN4QIAZEO4AADZEC4AQDaECwCQDeECAGRDuAAA2RAuAEA2hAsAkA3hAgBkQ7gAANkQLgBANoQLAJAN4QIAZEO4AADZEC4AQDaECwCQDeECAGRDuAAA2RAuAEA2hAsAkA3hAgBkQ7gAANkQLgBANoQLAJAN4QIAZEO4AADZEC4AQDaECwCQDeECAGRDuAAA2RAuAEA2hAsAkA3hAgBkQ7gAANkQLgBANoQLAJAN4QIAZEO4AADZEC4AQDaECwCQDeECAGRDuAAA2RAuAEA2hAsAkA3hAgBkY1jhsmHDhpgxY0ZUVVVFfX197Nq16y3P/+lPfxoXXXRRVFVVxSWXXBLbtm0b1rBA3uwO4FQVHS5btmyJpqamaG5ujj179sSsWbNi/vz58corrwx6/lNPPRXXXXdd3HDDDbF3795YuHBhLFy4MP7whz+c8vBAPuwOoBTKUkqpmAvq6+vjsssui/vvvz8iIvr6+qKuri5uvfXWWLly5QnnL1q0KI4ePRq/+MUv+o99+MMfjtmzZ8fGjRsHvY/u7u7o7u7u/7izszPOP//8OHjwYFRXVxczLlACXV1dUVdXF4cPH46ampphfY6R3h32Bow/pdgdJ0hF6O7uThUVFemxxx4bcHzJkiXp2muvHfSaurq69J3vfGfAsTVr1qRLL710yPtpbm5OEeHm5jbObi+++GIxK2NUd4e94eY2fm/D3R2DmRBFOHToUPT29kZtbe2A47W1tfHss88Oek17e/ug57e3tw95P6tWrYqmpqb+jw8fPhzvfve748CBA6UrthH2ZmXm9N2emUdHjjO/+ezFO9/5zmFdPxq7w94YGznOHJHn3DnOfKq7YzBFhctoKRQKUSgUTjheU1OTzX+sN1VXV5t5FJh5dJSXj99fRLQ3xlaOM0fkOXeOM5dydxT1mSZNmhQVFRXR0dEx4HhHR0dMmTJl0GumTJlS1PnA6cfuAEqlqHCprKyMOXPmRGtra/+xvr6+aG1tjYaGhkGvaWhoGHB+RMSvfvWrIc8HTj92B1Ayxf5QzObNm1OhUEgPP/xweuaZZ9LNN9+czjvvvNTe3p5SSmnx4sVp5cqV/ef/7ne/SxMmTEj33HNP2rdvX2pubk5nnXVWevrpp0/6Pl9//fXU3NycXn/99WLHHTNmHh1mHh2lmHm0d8eZ+u95tOU4c0p5zm3mfyo6XFJK6b777kvnn39+qqysTPPmzUu///3v+//ZVVddlZYuXTrg/J/85CfpwgsvTJWVlekDH/hA2rp16ykNDeTJ7gBOVdF/xwUAYKyM318RAAA4jnABALIhXACAbAgXACAb4yZccny7+2Jm3rRpU1x55ZUxceLEmDhxYjQ2Nr7t1zgSiv33/KbNmzdHWVlZLFy4cGQHHESxMx8+fDiWL18eU6dOjUKhEBdeeOGo//9R7Mzr16+P9773vXH22WdHXV1drFixIl5//fVRmjbit7/9bSxYsCCmTZsWZWVl8bOf/extr9mxY0d86EMfikKhEO95z3vi4YcfHvE5j2dvjA57Y/TktDvGbG+M9a81pfTPv+9QWVmZHnroofS///u/6aabbkrnnXde6ujoGPT83/3ud6mioiLdfffd6Zlnnkm333570X8bZrRnvv7669OGDRvS3r170759+9JnPvOZVFNTk/70pz+N25nf9NJLL6Xp06enK6+8Mv3Hf/zH6Az7/xQ7c3d3d5o7d2665ppr0pNPPpleeumltGPHjtTW1jZuZ/7Rj36UCoVC+tGPfpReeuml9Pjjj6epU6emFStWjNrM27ZtS6tXr06PPvpoiogT3gzxePv370/nnHNOampqSs8880y67777UkVFRdq+ffvoDJzsjfE685vsjZGfe6x3x1jtjXERLvPmzUvLly/v/7i3tzdNmzYttbS0DHr+pz71qfSJT3xiwLH6+vr0X//1XyM65/+v2JmPd+zYsXTuueemH/7whyM14gmGM/OxY8fS5Zdfnr7//e+npUuXjvoCKnbm7373u2nmzJmpp6dntEY8QbEzL1++PH30ox8dcKypqSldccUVIzrnUE5mAX35y19OH/jABwYcW7RoUZo/f/4ITjaQvTE67I3Rk/PuGM29MeYvFfX09MTu3bujsbGx/1h5eXk0NjbGzp07B71m586dA86PiJg/f/6Q55facGY+3muvvRZvvPFGSd8x860Md+avfe1rMXny5LjhhhtGY8wBhjPzz3/+82hoaIjly5dHbW1tXHzxxbF27dro7e0dtzNffvnlsXv37v6nhPfv3x/btm2La665ZlRmHo4cH4M5znw8e+Pt5bg3Is6M3VGqx+CYvzv0aLzdfakNZ+bj3XbbbTFt2rQT/iOOlOHM/OSTT8aDDz4YbW1tozDhiYYz8/79++M3v/lNfPrTn45t27bFCy+8EJ/73OfijTfeiObm5nE58/XXXx+HDh2Kj3zkI5FSimPHjsUtt9wSX/nKV0Z83uEa6jHY1dUV//jHP+Lss88e0fu3N+yNoeS4NyLOjN1Rqr0x5s+4nInWrVsXmzdvjsceeyyqqqrGepxBHTlyJBYvXhybNm2KSZMmjfU4J62vry8mT54c3/ve92LOnDmxaNGiWL16dWzcuHGsRxvSjh07Yu3atfHAAw/Enj174tFHH42tW7fGXXfdNdajMY7YGyMnx70RcebujjF/xiXHt7sfzsxvuueee2LdunXx61//Oi699NKRHHOAYmd+8cUX4+WXX44FCxb0H+vr64uIiAkTJsRzzz0XF1xwwbiaOSJi6tSpcdZZZ0VFRUX/sfe9733R3t4ePT09UVlZOe5mvuOOO2Lx4sVx4403RkTEJZdcEkePHo2bb745Vq9eHeXl4+/7i6Eeg9XV1SP+bEuEvTFa7I3R2RsRZ8buKNXeGPOvKse3ux/OzBERd999d9x1112xffv2mDt37miM2q/YmS+66KJ4+umno62trf927bXXxtVXXx1tbW1RV1c37maOiLjiiivihRde6F+WERHPP/98TJ06dVSWz3Bmfu21105YMG8u0DRO30osx8dgjjNH2BsjPXPE2O+NiDNjd5TsMVjUj/KOkNF+u/uxmHndunWpsrIyPfLII+kvf/lL/+3IkSPjdubjjcVvBxQ784EDB9K5556b/vu//zs999xz6Re/+EWaPHly+vrXvz5uZ25ubk7nnntu+p//+Z+0f//+9Mtf/jJdcMEF6VOf+tSozXzkyJG0d+/etHfv3hQR6d5770179+5Nf/zjH1NKKa1cuTItXry4//w3f63xS1/6Utq3b1/asGHDmPw6tL0x/mY+nr0xcnOP9e4Yq70xLsIlpTzf7r6Ymd/97neniDjh1tzcPG5nPt5YLKCUip/5qaeeSvX19alQKKSZM2emb3zjG+nYsWPjduY33ngjffWrX00XXHBBqqqqSnV1delzn/tc+tvf/jZq8z7xxBOD/v/55pxLly5NV1111QnXzJ49O1VWVqaZM2emH/zgB6M275vsjfE38/HsjeLktDvGam+UpTQOn08CABjEmP+MCwDAyRIuAEA2hAsAkA3hAgBkQ7gAANkQLgBANoQLAJAN4QIAZEO4AADZEC4AQDaECwCQjf8DOu0CiLveMb4AAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "fig, axes = plt.subplots(nrows=1, ncols=2)\n",
+        "\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QWwItNZVST2r"
+      },
+      "source": [
+        "**Now plot (x,y) and (x,z) on the axes. Play around with the linewidth and style**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "id": "ZoEMYyLOST2r",
+        "outputId": "be2cffc8-67a0-4820-fb4c-4d1342342c5b",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 447
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 0 Axes>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig = plt.figure()\n",
+        "plt.subplots(nrows=1, ncols=2)\n",
+        "plt.subplot(1, 2, 1)\n",
+        "plt.plot(x,y, linestyle='dashed', color='blue')\n",
+        "\n",
+        "plt.subplot(1, 2, 2)\n",
+        "plt.plot(x,z, color='red')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hV08IzIvST2s"
+      },
+      "source": [
+        "**See if you can resize the plot by adding the figsize() argument in plt.subplots() are copying and pasting your previous code.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "id": "0pq5vAFZST2s",
+        "outputId": "34728ca6-909d-49f1-a41f-12cbdebba830",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 224
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 0 Axes>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x200 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig = plt.figure()\n",
+        "plt.subplots(nrows=1, ncols=2, figsize=(8, 2))\n",
+        "plt.subplot(1, 2, 1)\n",
+        "plt.plot(x,y, color='blue', linewidth='4')\n",
+        "\n",
+        "plt.subplot(1, 2, 2)\n",
+        "plt.plot(x,z, linestyle='dashed', color='red', linewidth='4')\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-ONbbrWEST2t"
+      },
+      "source": [
+        "# Great Job!"
+      ]
+    }
+  ],
+  "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"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/Assignment 1/Assignment1_Pranav/Pranav_Assignment1_QV_Numpy .ipynb b/Assignment 1/Assignment1_Pranav/Pranav_Assignment1_QV_Numpy .ipynb
new file mode 100644
index 00000000..daa1cc4f
--- /dev/null
+++ b/Assignment 1/Assignment1_Pranav/Pranav_Assignment1_QV_Numpy .ipynb	
@@ -0,0 +1 @@
+{"cells":[{"cell_type":"markdown","metadata":{"id":"HfJB9JnS9xlK"},"source":["# NumPy Exercises\n","\n","Now that we've learned about NumPy let's test your knowledge. We'll start off with a few simple tasks, and then you'll be asked some more complicated questions."]},{"cell_type":"markdown","metadata":{"id":"u9POEVg29xlM"},"source":["#### Import NumPy as np"]},{"cell_type":"code","execution_count":4,"metadata":{"id":"yyS-PuO_9xlM","executionInfo":{"status":"ok","timestamp":1765986474253,"user_tz":-330,"elapsed":26,"user":{"displayName":"Pranav Menon","userId":"13680189042040557765"}}},"outputs":[],"source":["import numpy as np"]},{"cell_type":"markdown","metadata":{"id":"ehlpKUPc9xlM"},"source":["#### Create an array of 10 zeros"]},{"cell_type":"code","execution_count":6,"metadata":{"id":"aEQkK-Dw9xlN","executionInfo":{"status":"ok","timestamp":1765978412838,"user_tz":-330,"elapsed":14,"user":{"displayName":"Pranav Menon","userId":"13680189042040557765"}}},"outputs":[],"source":["arr = np.full(10,0)"]},{"cell_type":"markdown","metadata":{"id":"6QHp05Ei9xlN"},"source":["#### Create an array of 10 ones"]},{"cell_type":"code","execution_count":13,"metadata":{"scrolled":true,"id":"ebe9xrC29xlN","executionInfo":{"status":"ok","timestamp":1765979938978,"user_tz":-330,"elapsed":9,"user":{"displayName":"Pranav Menon","userId":"13680189042040557765"}}},"outputs":[],"source":["arr1 = np.full(10,1)"]},{"cell_type":"markdown","metadata":{"id":"ziS-l3xp9xlO"},"source":["#### Create an array of 10 fives"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fFC7bqLM9xlO"},"outputs":[],"source":["arr2 = np.full(10,5)"]},{"cell_type":"markdown","metadata":{"id":"kBugMXlC9xlO"},"source":["#### Create an array of the integers from 10 to 50"]},{"cell_type":"code","execution_count":16,"metadata":{"id":"JsO6qS9R9xlO","executionInfo":{"status":"ok","timestamp":1765980537901,"user_tz":-330,"elapsed":18,"user":{"displayName":"Pranav Menon","userId":"13680189042040557765"}}},"outputs":[],"source":["arr3 = np.arange(10,51)"]},{"cell_type":"markdown","metadata":{"id":"dMw4V2L79xlO"},"source":["#### Create an array of all the even integers from 10 to 50"]},{"cell_type":"code","execution_count":19,"metadata":{"id":"4lK-5SQV9xlO","executionInfo":{"status":"ok","timestamp":1765980628798,"user_tz":-330,"elapsed":21,"user":{"displayName":"Pranav Menon","userId":"13680189042040557765"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"aac29bd9-ce58-4121-b45f-01c80f080279"},"outputs":[{"output_type":"stream","name":"stdout","text":["[10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50]\n"]}],"source":["arr4 = np.arange(10,51,2)\n","print(arr4)"]},{"cell_type":"markdown","metadata":{"id":"g__F0rB39xlP"},"source":["#### Create a 3x3 matrix with values ranging from 0 to 8"]},{"cell_type":"code","execution_count":21,"metadata":{"id":"MmVXCn0K9xlP","outputId":"9f2e5c53-66a7-42ef-9726-abdb481c3853","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765980799172,"user_tz":-330,"elapsed":27,"user":{"displayName":"Pranav Menon","userId":"13680189042040557765"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[[0 1 2]\n"," [3 4 5]\n"," [6 7 8]]\n"]}],"source":["arr5 = np.array([[0,1,2],[3,4,5],[6,7,8]])"]},{"cell_type":"markdown","metadata":{"id":"4YfT0aLo9xlP"},"source":["#### Create a 3x3 identity matrix"]},{"cell_type":"code","execution_count":22,"metadata":{"id":"UHa42UpQ9xlP","outputId":"390e3b9c-7caf-4637-c57a-759f1c59ae3f","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765980874596,"user_tz":-330,"elapsed":36,"user":{"displayName":"Pranav Menon","userId":"13680189042040557765"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[[1 0 0]\n"," [0 1 0]\n"," [0 0 1]]\n"]}],"source":["arr6 = np.array([[1,0,0],[0,1,0],[0,0,1]])"]},{"cell_type":"markdown","metadata":{"id":"bfwDbjhI9xlP"},"source":["#### Use NumPy to generate a random number between 0 and 1"]},{"cell_type":"code","execution_count":26,"metadata":{"id":"Z0OroZxW9xlP","outputId":"3ff148aa-fdeb-4868-d3b4-bfe5c6b14706","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765981034597,"user_tz":-330,"elapsed":22,"user":{"displayName":"Pranav Menon","userId":"13680189042040557765"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["0.9077734232214647\n"]}],"source":["from numpy import random\n","x = random.rand()"]},{"cell_type":"markdown","metadata":{"id":"rLW0Jjzp9xlP"},"source":["#### Use NumPy to generate an array of 25 random numbers sampled from a standard normal distribution"]},{"cell_type":"code","execution_count":27,"metadata":{"id":"szluy14n9xlP","outputId":"6e00b61a-50fe-4997-e121-135c2fb95b93","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765981413000,"user_tz":-330,"elapsed":33,"user":{"displayName":"Pranav Menon","userId":"13680189042040557765"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[-0.39144329 -1.89792753  0.88525041 -0.861758    1.13520986 -1.72737432\n"," -1.68272171  0.91651905  0.97380177 -0.07233815  1.5481985  -1.30207546\n"," -0.92087456 -1.78263218 -0.90728343 -0.02405763  0.23519624  1.48354596\n","  1.16313442 -1.83176626 -0.30579281  1.07813966 -0.7570015  -1.35795288\n","  1.53390608]\n"]}],"source":["arr7=np.random.uniform(-2,2,size=25)"]},{"cell_type":"markdown","metadata":{"id":"_GhI8LYn9xlP"},"source":["#### Create the following matrix:"]},{"cell_type":"code","execution_count":5,"metadata":{"id":"wS1ZBddV9xlP","executionInfo":{"status":"ok","timestamp":1765986492877,"user_tz":-330,"elapsed":15,"user":{"displayName":"Pranav Menon","userId":"13680189042040557765"}}},"outputs":[],"source":["arr8 = np.linspace(0.01,1,100).reshape(10,10)"]},{"cell_type":"markdown","metadata":{"id":"3OqA-QtL9xlQ"},"source":["#### Create an array of 20 linearly spaced points between 0 and 1:"]},{"cell_type":"code","execution_count":8,"metadata":{"id":"FNXTugQ29xlQ","outputId":"16f9c578-6be1-4c8c-dc3d-239fe5cbb1c0","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765986524841,"user_tz":-330,"elapsed":28,"user":{"displayName":"Pranav Menon","userId":"13680189042040557765"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[[0.         0.05263158 0.10526316 0.15789474 0.21052632]\n"," [0.26315789 0.31578947 0.36842105 0.42105263 0.47368421]\n"," [0.52631579 0.57894737 0.63157895 0.68421053 0.73684211]\n"," [0.78947368 0.84210526 0.89473684 0.94736842 1.        ]]\n"]}],"source":["arr9 = np.round(np.linspace(0.00, 1.00, 20), 8).reshape(4, 5)\n"]},{"cell_type":"markdown","metadata":{"id":"elx0EaxE9xlQ"},"source":["## Numpy Indexing and Selection\n","\n","Now you will be given a few matrices, and be asked to replicate the resulting matrix outputs:"]},{"cell_type":"markdown","metadata":{"id":"2Tbm9kVf9xlQ"},"source":[]},{"cell_type":"code","execution_count":10,"metadata":{"id":"Ft8P8e249xlQ","outputId":"2bbe493e-fbdc-49eb-d57c-bbb652bf8cac","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765986836446,"user_tz":-330,"elapsed":30,"user":{"displayName":"Pranav Menon","userId":"13680189042040557765"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[[ 1  2  3  4  5]\n"," [ 6  7  8  9 10]\n"," [11 12 13 14 15]\n"," [16 17 18 19 20]\n"," [21 22 23 24 25]]\n"]}],"source":["arr10 = np.round(np.arange(1,26)).reshape(5,5)\n","print(arr10)"]},{"cell_type":"code","execution_count":16,"metadata":{"id":"WrcFkpGL9xlQ","outputId":"179874b6-d6b4-4937-b1bc-72b0d0daf758","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765987328230,"user_tz":-330,"elapsed":18,"user":{"displayName":"Pranav Menon","userId":"13680189042040557765"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[12, 13, 14, 15],\n","       [17, 18, 19, 20],\n","       [22, 23, 24, 25]])"]},"metadata":{},"execution_count":16}],"source":["arr10[2:5,1:5]"]},{"cell_type":"code","execution_count":18,"metadata":{"id":"rnUSntEa9xlQ","outputId":"5fe22cca-b3a3-4637-db51-df8916f17e5e","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765987385315,"user_tz":-330,"elapsed":21,"user":{"displayName":"Pranav Menon","userId":"13680189042040557765"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["20\n"]}],"source":["print(arr10[3,4])"]},{"cell_type":"code","execution_count":21,"metadata":{"id":"3DMBC_wp9xlQ","outputId":"8a385cdf-0c83-4c8b-c7bb-c11b9e35256a","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765987443541,"user_tz":-330,"elapsed":89,"user":{"displayName":"Pranav Menon","userId":"13680189042040557765"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[ 2],\n","       [ 7],\n","       [12]])"]},"metadata":{},"execution_count":21}],"source":["arr10[0:3,1:2]"]},{"cell_type":"code","execution_count":23,"metadata":{"id":"pGjD4HUK9xlQ","outputId":"9add1558-e44a-4162-c085-5c0a7be925f8","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765987463031,"user_tz":-330,"elapsed":27,"user":{"displayName":"Pranav Menon","userId":"13680189042040557765"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[21, 22, 23, 24, 25]])"]},"metadata":{},"execution_count":23}],"source":["arr10[4:5,0:5]"]},{"cell_type":"code","execution_count":24,"metadata":{"id":"dqsdpxUo9xlR","outputId":"3e32508c-c3c2-4c8c-dcb1-13b24f334084","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765987560541,"user_tz":-330,"elapsed":76,"user":{"displayName":"Pranav Menon","userId":"13680189042040557765"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[16, 17, 18, 19, 20],\n","       [21, 22, 23, 24, 25]])"]},"metadata":{},"execution_count":24}],"source":["arr10[3:5,0:5]"]},{"cell_type":"markdown","metadata":{"collapsed":true,"id":"6vtHuVJU9xlf"},"source":["# Great Job!"]}],"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"},"colab":{"provenance":[{"file_id":"1uvwMRRqMFNlaYoWGT6JhFghskgPy9dzJ","timestamp":1765870475472},{"file_id":"1PacBskAxI7FPP4LIyB_3MysbL8ArUHsV","timestamp":1764745354238},{"file_id":"19ZajGBzqADvFnQ0kzbkad_udXeAh26pj","timestamp":1731497620277}]}},"nbformat":4,"nbformat_minor":0}
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diff --git a/Assignment 1/Assignment1_Pranav/Pranav_Assignment1_QV_Pandas.ipynb b/Assignment 1/Assignment1_Pranav/Pranav_Assignment1_QV_Pandas.ipynb
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+{"cells":[{"cell_type":"markdown","source":["## General Instructions for all Assignments"],"metadata":{"id":"AWDeauE3Bdzs"}},{"cell_type":"markdown","source":["1. Fork the repository and make a copy of this notebook and rename it as your name_Assignment1_Pandas\n","2. Generate a pull request. The pull request message should also be yourname_Assignment1"],"metadata":{"id":"Xaf9uMvXBJEC"}},{"cell_type":"markdown","source":["\n","1. This code notebook is composed of cells. Each cell is either text or Python code.\n","2. To run a cell of code, click the \"play button\" icon to the left of the cell or click on the cell and press \"Shift+Enter\" on your keyboard. This will execute the Python code contained in the cell. Executing a cell that defines a variable is important before executing or authoring a cell that depends on that previously created variable assignment.\n"],"metadata":{"id":"4_6uxHVGB_XT"}},{"cell_type":"markdown","source":[],"metadata":{"id":"b68xNjkKCBIF"}},{"cell_type":"code","execution_count":2,"metadata":{"id":"BiD4gl6_cAYs","executionInfo":{"status":"ok","timestamp":1765987968424,"user_tz":-330,"elapsed":602,"user":{"displayName":"Pranav Menon","userId":"13680189042040557765"}}},"outputs":[],"source":["#import pandas library\n","import pandas as pd"]},{"cell_type":"code","execution_count":9,"metadata":{"id":"JUtm9KricAYu","executionInfo":{"status":"ok","timestamp":1765988383293,"user_tz":-330,"elapsed":15,"user":{"displayName":"Pranav Menon","userId":"13680189042040557765"}}},"outputs":[],"source":["df = pd.read_csv('/content/Iris.csv') #enter path for the Iris csv in the brackets\n","#reads the csv file and stores it in a data frame(define in the pandas library)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qzUnSkfRcAYu"},"outputs":[],"source":["dictionary = {'col':[4,2,3], 'col2':[4,7,6], 'col3':[7,10,9]}"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ACswu4BXcAYu"},"outputs":[],"source":["dictionarydf = pd.DataFrame.from_dict(dictionary)\n","#it creats a new datafram which stores the dictionary made above"]},{"cell_type":"code","source":["df.head()\n","#shows top 5 rows (row indexing starts from 0)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"MvYNz8E2dp3K","executionInfo":{"status":"ok","timestamp":1764745217710,"user_tz":-330,"elapsed":46,"user":{"displayName":"Smira Jaitley","userId":"11414106947643866646"}},"outputId":"366bc39c-7df6-4cc7-ae60-03aedd2571f4"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n","0   1            5.1           3.5            1.4           0.2  Iris-setosa\n","1   2            4.9           3.0            1.4           0.2  Iris-setosa\n","2   3            4.7           3.2            1.3           0.2  Iris-setosa\n","3   4            4.6           3.1            1.5           0.2  Iris-setosa\n","4   5            5.0           3.6            1.4           0.2  Iris-setosa"],"text/html":["\n","  <div id=\"df-5fb77386-d5e3-476d-82ce-c6c6f738c24c\" class=\"colab-df-container\">\n","    <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>Id</th>\n","      <th>SepalLengthCm</th>\n","      <th>SepalWidthCm</th>\n","      <th>PetalLengthCm</th>\n","      <th>PetalWidthCm</th>\n","      <th>Species</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>5.1</td>\n","      <td>3.5</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>4.9</td>\n","      <td>3.0</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>4.7</td>\n","      <td>3.2</td>\n","      <td>1.3</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>4.6</td>\n","      <td>3.1</td>\n","      <td>1.5</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>5.0</td>\n","      <td>3.6</td>\n","   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\"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2588435821108957,\n        \"min\": 3.0,\n        \"max\": 3.6,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3.0,\n          3.6,\n          3.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07071067811865474,\n        \"min\": 1.3,\n        \"max\": 1.5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.4,\n          1.3,\n          1.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.2,\n        \"max\": 0.2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Iris-setosa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":32}]},{"cell_type":"code","execution_count":10,"metadata":{"id":"iAx3B-TacAYu","colab":{"base_uri":"https://localhost:8080/","height":363},"collapsed":true,"executionInfo":{"status":"ok","timestamp":1765988390376,"user_tz":-330,"elapsed":38,"user":{"displayName":"Pranav Menon","userId":"13680189042040557765"}},"outputId":"d19443c1-04ca-4a3d-bf1f-5b6f5a555252"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n","0   1            5.1           3.5            1.4           0.2  Iris-setosa\n","1   2            4.9           3.0            1.4           0.2  Iris-setosa\n","2   3            4.7           3.2            1.3           0.2  Iris-setosa\n","3   4            4.6           3.1            1.5           0.2  Iris-setosa\n","4   5            5.0           3.6            1.4           0.2  Iris-setosa\n","5   6            5.4           3.9            1.7           0.4  Iris-setosa\n","6   7            4.6           3.4            1.4           0.3  Iris-setosa\n","7   8            5.0           3.4            1.5           0.2  Iris-setosa\n","8   9            4.4           2.9            1.4           0.2  Iris-setosa\n","9  10            4.9           3.1            1.5           0.1  Iris-setosa"],"text/html":["\n","  <div id=\"df-15e2b779-cb85-4815-a635-32e178494312\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        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      3.0            5.2           2.3   \n","146  147            6.3           2.5            5.0           1.9   \n","147  148            6.5           3.0            5.2           2.0   \n","148  149            6.2           3.4            5.4           2.3   \n","149  150            5.9           3.0            5.1           1.8   \n","\n","            Species  \n","135  Iris-virginica  \n","136  Iris-virginica  \n","137  Iris-virginica  \n","138  Iris-virginica  \n","139  Iris-virginica  \n","140  Iris-virginica  \n","141  Iris-virginica  \n","142  Iris-virginica  \n","143  Iris-virginica  \n","144  Iris-virginica  \n","145  Iris-virginica  \n","146  Iris-virginica  \n","147  Iris-virginica  \n","148  Iris-virginica  \n","149  Iris-virginica  "],"text/html":["\n","  <div id=\"df-f8aeddba-72e3-4aeb-896f-4f945f9a2912\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","   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<td>3.1</td>\n","      <td>5.5</td>\n","      <td>1.8</td>\n","      <td>Iris-virginica</td>\n","    </tr>\n","    <tr>\n","      <th>138</th>\n","      <td>139</td>\n","      <td>6.0</td>\n","      <td>3.0</td>\n","      <td>4.8</td>\n","      <td>1.8</td>\n","      <td>Iris-virginica</td>\n","    </tr>\n","    <tr>\n","      <th>139</th>\n","      <td>140</td>\n","      <td>6.9</td>\n","      <td>3.1</td>\n","      <td>5.4</td>\n","      <td>2.1</td>\n","      <td>Iris-virginica</td>\n","    </tr>\n","    <tr>\n","      <th>140</th>\n","      <td>141</td>\n","      <td>6.7</td>\n","      <td>3.1</td>\n","      <td>5.6</td>\n","      <td>2.4</td>\n","      <td>Iris-virginica</td>\n","    </tr>\n","    <tr>\n","      <th>141</th>\n","      <td>142</td>\n","      <td>6.9</td>\n","      <td>3.1</td>\n","      <td>5.1</td>\n","      <td>2.3</td>\n","      <td>Iris-virginica</td>\n","    </tr>\n","    <tr>\n","      <th>142</th>\n","      <td>143</td>\n","      <td>5.8</td>\n","      <td>2.7</td>\n","      <td>5.1</td>\n","      <td>1.9</td>\n","      <td>Iris-virginica</td>\n","    </tr>\n","    <tr>\n","      <th>143</th>\n","      <td>144</td>\n","      <td>6.8</td>\n","      <td>3.2</td>\n","      <td>5.9</td>\n","      <td>2.3</td>\n","      <td>Iris-virginica</td>\n","    </tr>\n","    <tr>\n","      <th>144</th>\n","      <td>145</td>\n","      <td>6.7</td>\n","      <td>3.3</td>\n","      <td>5.7</td>\n","      <td>2.5</td>\n","      <td>Iris-virginica</td>\n","    </tr>\n","    <tr>\n","      <th>145</th>\n","      <td>146</td>\n","      <td>6.7</td>\n","      <td>3.0</td>\n","      <td>5.2</td>\n","      <td>2.3</td>\n","      <td>Iris-virginica</td>\n","    </tr>\n","    <tr>\n","      <th>146</th>\n","      <td>147</td>\n","      <td>6.3</td>\n","      <td>2.5</td>\n","      <td>5.0</td>\n","      <td>1.9</td>\n","      <td>Iris-virginica</td>\n","    </tr>\n","    <tr>\n","      <th>147</th>\n","      <td>148</td>\n","      <td>6.5</td>\n","      <td>3.0</td>\n","      <td>5.2</td>\n","      <td>2.0</td>\n","      <td>Iris-virginica</td>\n","    </tr>\n","    <tr>\n","      <th>148</th>\n","      <td>149</td>\n","      <td>6.2</td>\n","      <td>3.4</td>\n","      <td>5.4</td>\n","      <td>2.3</td>\n","      <td>Iris-virginica</td>\n","    </tr>\n","    <tr>\n","      <th>149</th>\n","      <td>150</td>\n","      <td>5.9</td>\n","      <td>3.0</td>\n","      <td>5.1</td>\n","      <td>1.8</td>\n","      <td>Iris-virginica</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f8aeddba-72e3-4aeb-896f-4f945f9a2912')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path 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{\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.23543273227945902,\n        \"min\": 2.5,\n        \"max\": 3.4,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          3.0,\n          3.4,\n          3.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.35697138740881107,\n        \"min\": 4.8,\n        \"max\": 6.1,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          5.2,\n          5.6,\n          5.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.25014281634983754,\n        \"min\": 1.8,\n        \"max\": 2.5,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2.3,\n          2.4,\n          2.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Iris-virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":13}],"source":["#Show the last 15 rows using tail function\n","df.tail(15)"]},{"cell_type":"code","execution_count":14,"metadata":{"id":"QrzERa4qcAYv","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765988475868,"user_tz":-330,"elapsed":21,"user":{"displayName":"Pranav Menon","userId":"13680189042040557765"}},"outputId":"1d24feed-b365-415d-fc6b-e7d53daa4ad7"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Index(['Id', 'SepalLengthCm', 'SepalWidthCm', 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name\n","df.dtypes"]},{"cell_type":"code","execution_count":16,"metadata":{"id":"lT3OEUNDcAYv","colab":{"base_uri":"https://localhost:8080/","height":300},"executionInfo":{"status":"ok","timestamp":1765988653991,"user_tz":-330,"elapsed":70,"user":{"displayName":"Pranav Menon","userId":"13680189042040557765"}},"outputId":"2caed270-50ef-4484-bdca-5c25ce24d696"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n","count  150.000000     150.000000    150.000000     150.000000    150.000000\n","mean    75.500000       5.843333      3.054000       3.758667      1.198667\n","std     43.445368       0.828066      0.433594       1.764420      0.763161\n","min      1.000000       4.300000      2.000000       1.000000      0.100000\n","25%     38.250000       5.100000      2.800000       1.600000      0.300000\n","50%     75.500000       5.800000      3.000000       4.350000      1.300000\n","75%    112.750000       6.400000      3.300000       5.100000      1.800000\n","max    150.000000       7.900000      4.400000       6.900000      2.500000"],"text/html":["\n","  <div id=\"df-5846d2ed-ebb3-4ff8-96bb-2d09c3c9ff03\" class=\"colab-df-container\">\n","    <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>Id</th>\n","      <th>SepalLengthCm</th>\n","      <th>SepalWidthCm</th>\n","      <th>PetalLengthCm</th>\n","      <th>PetalWidthCm</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>150.000000</td>\n","      <td>150.000000</td>\n","      <td>150.000000</td>\n","      <td>150.000000</td>\n","      <td>150.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>75.500000</td>\n","      <td>5.843333</td>\n","      <td>3.054000</td>\n","      <td>3.758667</td>\n","      <td>1.198667</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>43.445368</td>\n","      <td>0.828066</td>\n","      <td>0.433594</td>\n","      <td>1.764420</td>\n","      <td>0.763161</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>1.000000</td>\n","      <td>4.300000</td>\n","      <td>2.000000</td>\n","      <td>1.000000</td>\n","      <td>0.100000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>38.250000</td>\n","      <td>5.100000</td>\n","      <td>2.800000</td>\n","      <td>1.600000</td>\n","      <td>0.300000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>75.500000</td>\n","      <td>5.800000</td>\n","      <td>3.000000</td>\n","      <td>4.350000</td>\n","      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\"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.08647211421483,\n        \"min\": 0.4335943113621737,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.0540000000000003,\n          3.0,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.835227940958106,\n        \"min\": 1.0,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.758666666666666,\n          4.35,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.63663424340991,\n        \"min\": 0.1,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.1986666666666668,\n          1.3,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":16}],"source":["#describe the data through statistics\n","df.describe()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"id":"Z_52ct0BcAYw","executionInfo":{"status":"ok","timestamp":1764745217940,"user_tz":-330,"elapsed":87,"user":{"displayName":"Smira Jaitley","userId":"11414106947643866646"}},"outputId":"048796ec-85e9-4b40-d1a2-609cb068736f"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["     SepalLengthCm  SepalWidthCm\n","0              5.1           3.5\n","1              4.9           3.0\n","2              4.7           3.2\n","3              4.6           3.1\n","4              5.0           3.6\n","..             ...           ...\n","145            6.7           3.0\n","146            6.3           2.5\n","147            6.5           3.0\n","148            6.2           3.4\n","149            5.9           3.0\n","\n","[150 rows x 2 columns]"],"text/html":["\n","  <div id=\"df-43a7cb4b-b995-44f5-a199-a8c490a8ac93\" class=\"colab-df-container\">\n","    <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>SepalLengthCm</th>\n","      <th>SepalWidthCm</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>5.1</td>\n","      <td>3.5</td>\n","    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rows × 2 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-43a7cb4b-b995-44f5-a199-a8c490a8ac93')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: 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\"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":38}],"source":["df[['SepalLengthCm', 'SepalWidthCm']]\n","#From taking multiple columns from the dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JWYXoLl0cAYw","executionInfo":{"status":"ok","timestamp":1764745217997,"user_tz":-330,"elapsed":19,"user":{"displayName":"Smira Jaitley","userId":"11414106947643866646"}},"outputId":"644abfd4-2a4b-45d1-f0d7-828df37a37a1"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([5.1, 4.9, 4.7, 4.6, 5. , 5.4, 4.4, 4.8, 4.3, 5.8, 5.7, 5.2, 5.5,\n","       4.5, 5.3, 7. , 6.4, 6.9, 6.5, 6.3, 6.6, 5.9, 6. , 6.1, 5.6, 6.7,\n","       6.2, 6.8, 7.1, 7.6, 7.3, 7.2, 7.7, 7.4, 7.9])"]},"metadata":{},"execution_count":39}],"source":["df.SepalLengthCm.unique()\n","#gives the unique values of Sepal Length present in that particular column"]},{"cell_type":"code","execution_count":18,"metadata":{"id":"MiOVp1WacAYw","colab":{"base_uri":"https://localhost:8080/","height":272},"collapsed":true,"executionInfo":{"status":"ok","timestamp":1765988703817,"user_tz":-330,"elapsed":19,"user":{"displayName":"Pranav Menon","userId":"13680189042040557765"}},"outputId":"99de78fa-1ae3-4958-f272-82e89f4baa96"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Id                        11\n","SepalLengthCm            5.4\n","SepalWidthCm             3.7\n","PetalLengthCm            1.5\n","PetalWidthCm             0.2\n","Species          Iris-setosa\n","Name: 10, dtype: object"],"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>10</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Id</th>\n","      <td>11</td>\n","    </tr>\n","    <tr>\n","      <th>SepalLengthCm</th>\n","      <td>5.4</td>\n","    </tr>\n","    <tr>\n","      <th>SepalWidthCm</th>\n","      <td>3.7</td>\n","    </tr>\n","    <tr>\n","      <th>PetalLengthCm</th>\n","      <td>1.5</td>\n","    </tr>\n","    <tr>\n","      <th>PetalWidthCm</th>\n","      <td>0.2</td>\n","    </tr>\n","    <tr>\n","      <th>Species</th>\n","      <td>Iris-setosa</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div><br><label><b>dtype:</b> object</label>"]},"metadata":{},"execution_count":18}],"source":["#Get the 11th row from the dataset\n","#Hint: for taking 11th row do we use 11 or 10 index?\n","df.iloc[10]"]},{"cell_type":"code","source":["df.iloc[10,-1]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"id":"TF03jh_MovET","executionInfo":{"status":"ok","timestamp":1764745218042,"user_tz":-330,"elapsed":33,"user":{"displayName":"Smira Jaitley","userId":"11414106947643866646"}},"outputId":"6777bc08-c296-4da3-e55e-8697b827b6ee"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'Iris-setosa'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":41}]},{"cell_type":"code","source":["#Display the second last entry of the 10th row\n","print(df.iloc[9, -2])\n"],"metadata":{"id":"gV-h3rK-g5lZ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765988799862,"user_tz":-330,"elapsed":16,"user":{"displayName":"Pranav Menon","userId":"13680189042040557765"}},"outputId":"ddc0eab7-2b10-430d-e475-59a55f8bccd3"},"execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["0.1\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":394},"id":"KXWCH34ecAYx","executionInfo":{"status":"ok","timestamp":1764745218194,"user_tz":-330,"elapsed":141,"user":{"displayName":"Smira Jaitley","userId":"11414106947643866646"}},"outputId":"15f4faed-a888-4e95-f20d-02ba16939636"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["    Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n","22  23            4.6           3.6            1.0           0.2  Iris-setosa\n","23  24            5.1           3.3            1.7           0.5  Iris-setosa\n","24  25            4.8           3.4            1.9           0.2  Iris-setosa\n","25  26            5.0           3.0            1.6           0.2  Iris-setosa\n","26  27            5.0           3.4            1.6           0.4  Iris-setosa\n","27  28            5.2           3.5            1.5           0.2  Iris-setosa\n","28  29            5.2           3.4            1.4           0.2  Iris-setosa\n","29  30            4.7           3.2            1.6           0.2  Iris-setosa\n","30  31            4.8           3.1            1.6           0.2  Iris-setosa\n","31  32            5.4           3.4            1.5           0.4  Iris-setosa\n","32  33            5.2           4.1            1.5           0.1  Iris-setosa"],"text/html":["\n","  <div id=\"df-aa83c2a6-f2e6-4192-af88-2c4a4734f4f5\" class=\"colab-df-container\">\n","    <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>Id</th>\n","      <th>SepalLengthCm</th>\n","      <th>SepalWidthCm</th>\n","      <th>PetalLengthCm</th>\n","      <th>PetalWidthCm</th>\n","      <th>Species</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>22</th>\n","      <td>23</td>\n","      <td>4.6</td>\n","      <td>3.6</td>\n","      <td>1.0</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>23</th>\n","      <td>24</td>\n","      <td>5.1</td>\n","      <td>3.3</td>\n","      <td>1.7</td>\n","      <td>0.5</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>24</th>\n","      <td>25</td>\n","      <td>4.8</td>\n","      <td>3.4</td>\n","      <td>1.9</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>25</th>\n","      <td>26</td>\n","      <td>5.0</td>\n","   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<td>1.6</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>31</th>\n","      <td>32</td>\n","      <td>5.4</td>\n","      <td>3.4</td>\n","      <td>1.5</td>\n","      <td>0.4</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>32</th>\n","      <td>33</td>\n","      <td>5.2</td>\n","      <td>4.1</td>\n","      <td>1.5</td>\n","      <td>0.1</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aa83c2a6-f2e6-4192-af88-2c4a4734f4f5')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 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Jaitley","userId":"11414106947643866646"}},"outputId":"46c03362-af5f-47a5-968e-7f38f5bcb53e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n","0      1            5.1           3.5            1.4           0.2   \n","1      2            4.9           3.0            1.4           0.2   \n","2      3            4.7           3.2            1.3           0.2   \n","3      4            4.6           3.1            1.5           0.2   \n","4      5            5.0           3.6            1.4           0.2   \n","..   ...            ...           ...            ...           ...   \n","145  146            6.7           3.0            5.2           2.3   \n","146  147            6.3           2.5            5.0           1.9   \n","147  148            6.5           3.0            5.2           2.0   \n","148  149            6.2           3.4            5.4           2.3   \n","149  150            5.9           3.0            5.1           1.8   \n","\n","            Species  \n","0       Iris-setosa  \n","1       Iris-setosa  \n","2       Iris-setosa  \n","3       Iris-setosa  \n","4       Iris-setosa  \n","..              ...  \n","145  Iris-virginica  \n","146  Iris-virginica  \n","147  Iris-virginica  \n","148  Iris-virginica  \n","149  Iris-virginica  \n","\n","[150 rows x 6 columns]"],"text/html":["\n","  <div id=\"df-8885b03f-87d3-4cf2-a529-b3ae2ab2b0c1\" class=\"colab-df-container\">\n","    <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>Id</th>\n","      <th>SepalLengthCm</th>\n","      <th>SepalWidthCm</th>\n","      <th>PetalLengthCm</th>\n","      <th>PetalWidthCm</th>\n","      <th>Species</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>5.1</td>\n","      <td>3.5</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>4.9</td>\n","      <td>3.0</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>4.7</td>\n","      <td>3.2</td>\n","      <td>1.3</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>4.6</td>\n","      <td>3.1</td>\n","      <td>1.5</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>5.0</td>\n","      <td>3.6</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>145</th>\n","      <td>146</td>\n","      <td>6.7</td>\n","      <td>3.0</td>\n","      <td>5.2</td>\n","      <td>2.3</td>\n","      <td>Iris-virginica</td>\n","    </tr>\n","    <tr>\n","      <th>146</th>\n","      <td>147</td>\n","      <td>6.3</td>\n","      <td>2.5</td>\n","      <td>5.0</td>\n","      <td>1.9</td>\n","      <td>Iris-virginica</td>\n","    </tr>\n","    <tr>\n","      <th>147</th>\n","      <td>148</td>\n","      <td>6.5</td>\n","      <td>3.0</td>\n","      <td>5.2</td>\n","      <td>2.0</td>\n","      <td>Iris-virginica</td>\n","    </tr>\n","    <tr>\n","      <th>148</th>\n","      <td>149</td>\n","      <td>6.2</td>\n","      <td>3.4</td>\n","      <td>5.4</td>\n","      <td>2.3</td>\n","      <td>Iris-virginica</td>\n","    </tr>\n","    <tr>\n","      <th>149</th>\n","      <td>150</td>\n","      <td>5.9</td>\n","      <td>3.0</td>\n","      <td>5.1</td>\n","      <td>1.8</td>\n","      <td>Iris-virginica</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>150 rows × 6 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8885b03f-87d3-4cf2-a529-b3ae2ab2b0c1')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 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{\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4335943113621737,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7644204199522617,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7631607417008414,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Iris-setosa\",\n          \"Iris-versicolor\",\n          \"Iris-virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":44}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kZg8z57fcAY2"},"outputs":[],"source":["df.dropna(axis=0, inplace=True)\n","#drops all the rows with missing values"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":272},"id":"FRWcDJA2cAY2","executionInfo":{"status":"ok","timestamp":1764745218284,"user_tz":-330,"elapsed":21,"user":{"displayName":"Smira Jaitley","userId":"11414106947643866646"}},"outputId":"4fbbfdfa-8eff-4e2d-e3a8-5bfc947f3abc"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Id               0\n","SepalLengthCm    0\n","SepalWidthCm     0\n","PetalLengthCm    0\n","PetalWidthCm     0\n","Species          0\n","dtype: int64"],"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>0</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Id</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>SepalLengthCm</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>SepalWidthCm</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>PetalLengthCm</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>PetalWidthCm</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>Species</th>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div><br><label><b>dtype:</b> int64</label>"]},"metadata":{},"execution_count":46}],"source":["df.isnull().sum()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JioW0K_pcAY3"},"outputs":[],"source":["df.to_csv('cleandata_iris.csv')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"asToKeGJcAY4"},"outputs":[],"source":["del df"]}],"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":"3.7.4"},"colab":{"provenance":[{"file_id":"1tVNRnxnutLstrGKY3P0fbmpEVSwXiDnJ","timestamp":1765987686064},{"file_id":"1PJDPRd4uOSxvoSK0o_O22PiQ2-AkDis4","timestamp":1764744257366},{"file_id":"1AjoKNd7ieBCZ4NDPgzWDLDMsHKuYsb5A","timestamp":1731497483327}]}},"nbformat":4,"nbformat_minor":0}
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diff --git a/Assignment 1/Assignment1_Pranav/README.md b/Assignment 1/Assignment1_Pranav/README.md
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+
diff --git a/Assignment 2/Assignment2_Pranav/PranavMenon_Assignment2.ipynb b/Assignment 2/Assignment2_Pranav/PranavMenon_Assignment2.ipynb
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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "PdHWB9klZzpz",
+        "outputId": "1988a414-adb6-46da-ae7c-29b794627567"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: yfinance in /usr/local/lib/python3.12/dist-packages (0.2.66)\n",
+            "Requirement already satisfied: plotly in /usr/local/lib/python3.12/dist-packages (5.24.1)\n",
+            "Requirement already satisfied: pandas>=1.3.0 in /usr/local/lib/python3.12/dist-packages (from yfinance) (2.2.2)\n",
+            "Requirement already satisfied: numpy>=1.16.5 in /usr/local/lib/python3.12/dist-packages (from yfinance) (2.0.2)\n",
+            "Requirement already satisfied: requests>=2.31 in /usr/local/lib/python3.12/dist-packages (from yfinance) (2.32.4)\n",
+            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.12/dist-packages (from yfinance) (0.0.12)\n",
+            "Requirement already satisfied: platformdirs>=2.0.0 in /usr/local/lib/python3.12/dist-packages (from yfinance) (4.5.1)\n",
+            "Requirement already satisfied: pytz>=2022.5 in /usr/local/lib/python3.12/dist-packages (from yfinance) (2025.2)\n",
+            "Requirement already satisfied: frozendict>=2.3.4 in /usr/local/lib/python3.12/dist-packages (from yfinance) (2.4.7)\n",
+            "Requirement already satisfied: peewee>=3.16.2 in /usr/local/lib/python3.12/dist-packages (from yfinance) (3.18.3)\n",
+            "Requirement already satisfied: beautifulsoup4>=4.11.1 in /usr/local/lib/python3.12/dist-packages (from yfinance) (4.13.5)\n",
+            "Requirement already satisfied: curl_cffi>=0.7 in /usr/local/lib/python3.12/dist-packages (from yfinance) (0.13.0)\n",
+            "Requirement already satisfied: protobuf>=3.19.0 in /usr/local/lib/python3.12/dist-packages (from yfinance) (5.29.5)\n",
+            "Requirement already satisfied: websockets>=13.0 in /usr/local/lib/python3.12/dist-packages (from yfinance) (15.0.1)\n",
+            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.12/dist-packages (from plotly) (9.1.2)\n",
+            "Requirement already satisfied: packaging in /usr/local/lib/python3.12/dist-packages (from plotly) (25.0)\n",
+            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.12/dist-packages (from beautifulsoup4>=4.11.1->yfinance) (2.8)\n",
+            "Requirement already satisfied: typing-extensions>=4.0.0 in /usr/local/lib/python3.12/dist-packages (from beautifulsoup4>=4.11.1->yfinance) (4.15.0)\n",
+            "Requirement already satisfied: cffi>=1.12.0 in /usr/local/lib/python3.12/dist-packages (from curl_cffi>=0.7->yfinance) (2.0.0)\n",
+            "Requirement already satisfied: certifi>=2024.2.2 in /usr/local/lib/python3.12/dist-packages (from curl_cffi>=0.7->yfinance) (2025.11.12)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas>=1.3.0->yfinance) (2.9.0.post0)\n",
+            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas>=1.3.0->yfinance) (2025.2)\n",
+            "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests>=2.31->yfinance) (3.4.4)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests>=2.31->yfinance) (3.11)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests>=2.31->yfinance) (2.5.0)\n",
+            "Requirement already satisfied: pycparser in /usr/local/lib/python3.12/dist-packages (from cffi>=1.12.0->curl_cffi>=0.7->yfinance) (2.23)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas>=1.3.0->yfinance) (1.17.0)\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install yfinance plotly"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [],
+      "metadata": {
+        "id": "xRDFDD4eeYLb"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "import yfinance as yf\n",
+        "import plotly.graph_objects as go\n",
+        "ticker_symbol = \"GOOGL\"\n",
+        "ticker = yf.Ticker(ticker_symbol)\n",
+        "\n",
+        "historical_data = ticker.history(period=\"6mo\", interval=\"1d\")\n",
+        "print(f\"Historical Data {historical_data}\")\n",
+        "\n",
+        "# Converting Date from index to a proper column\n",
+        "historical_data = historical_data.reset_index()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "6OSl0ZDBfAkC",
+        "outputId": "bf32ef4c-592d-458c-b761-c07e47aa9162"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Historical Data                                  Open        High         Low       Close  \\\n",
+            "Date                                                                        \n",
+            "2025-06-16 00:00:00-04:00  174.459744  176.666333  174.379866  176.496597   \n",
+            "2025-06-17 00:00:00-04:00  175.428243  177.085679  174.309980  175.677856   \n",
+            "2025-06-18 00:00:00-04:00  175.737768  176.286920  172.932116  173.051941   \n",
+            "2025-06-20 00:00:00-04:00  173.680955  174.070351  165.204096  166.382263   \n",
+            "2025-06-23 00:00:00-04:00  166.012841  167.081178  161.749441  164.934509   \n",
+            "...                               ...         ...         ...         ...   \n",
+            "2025-12-09 00:00:00-05:00  312.369995  317.989990  311.899994  317.079987   \n",
+            "2025-12-10 00:00:00-05:00  315.829987  321.309998  314.679993  320.209991   \n",
+            "2025-12-11 00:00:00-05:00  320.079987  321.119995  308.600006  312.429993   \n",
+            "2025-12-12 00:00:00-05:00  313.700012  314.869995  305.559998  309.290009   \n",
+            "2025-12-15 00:00:00-05:00  311.325012  311.420013  304.880005  308.220001   \n",
+            "\n",
+            "                             Volume  Dividends  Stock Splits  \n",
+            "Date                                                          \n",
+            "2025-06-16 00:00:00-04:00  27389200        0.0           0.0  \n",
+            "2025-06-17 00:00:00-04:00  24973000        0.0           0.0  \n",
+            "2025-06-18 00:00:00-04:00  28707500        0.0           0.0  \n",
+            "2025-06-20 00:00:00-04:00  75659900        0.0           0.0  \n",
+            "2025-06-23 00:00:00-04:00  57671000        0.0           0.0  \n",
+            "...                             ...        ...           ...  \n",
+            "2025-12-09 00:00:00-05:00  30194000        0.0           0.0  \n",
+            "2025-12-10 00:00:00-05:00  33428900        0.0           0.0  \n",
+            "2025-12-11 00:00:00-05:00  42353700        0.0           0.0  \n",
+            "2025-12-12 00:00:00-05:00  35895500        0.0           0.0  \n",
+            "2025-12-15 00:00:00-05:00  29036024        0.0           0.0  \n",
+            "\n",
+            "[127 rows x 7 columns]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "fig = go.Figure(data = go.Candlestick(\n",
+        "                                      x = historical_data['Date'],\n",
+        "                                      open = historical_data['Open'],\n",
+        "                                      high = historical_data['High'],\n",
+        "                                      low = historical_data['Low'],\n",
+        "                                      close = historical_data['Close']\n",
+        "                                      ))\n",
+        "\n",
+        "\n",
+        "fig.update_layout(\n",
+        "                  title='Google Stock Price Analysis (6 Months)',\n",
+        "                  yaxis_title='Stock Price (USD)',\n",
+        "                  xaxis_title='Date')\n",
+        "\n",
+        "fig.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "id": "LZHwoMT0fhmI",
+        "outputId": "f5a33757-f8c9-4eb7-ea82-aebd131f7899"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
+              "<body>\n",
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+        "![PranavMenon_Assignment2.ipynb - Colab - Google Chrome 16-12-2025 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+      ],
+      "metadata": {
+        "id": "fGpMHKYx3KSW"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Candle pattern analysis**\n",
+        "\n",
+        "**Date** : 11th Nov , 2025\n",
+        "\n",
+        "**Name of Pattern** : Bearish Engulfing\n",
+        "\n",
+        "**Outcome** : the price went down immediately after this pattern"
+      ],
+      "metadata": {
+        "id": "BkoEmvicoPix"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [],
+      "metadata": {
+        "id": "qjktrEIt2NLX"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Assignment 2/Assignment2_Pranav/README.md b/Assignment 2/Assignment2_Pranav/README.md
new file mode 100644
index 00000000..8b137891
--- /dev/null
+++ b/Assignment 2/Assignment2_Pranav/README.md	
@@ -0,0 +1 @@
+