diff --git a/Mithil_Assignment1_matplotlib.ipynb b/Mithil_Assignment1_matplotlib.ipynb
new file mode 100644
index 00000000..729f790f
--- /dev/null
+++ b/Mithil_Assignment1_matplotlib.ipynb
@@ -0,0 +1,440 @@
+{
+  "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": 2,
+      "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": 3,
+      "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": 22,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 582
+        },
+        "id": "03Ts4r5SST2g",
+        "outputId": "61206cf5-567c-485d-fc6b-bae861e524b9"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "fig = plt.figure()\n",
+        "# x = np.arange(0,1) # Removed as per exercise requirements to use global x and y\n",
+        "# y=x # Removed as per exercise requirements to use global x and y\n",
+        "ax = fig.add_axes([0,0,1,1])\n",
+        "ax.plot(x,y, lw='10')\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": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 546
+        },
+        "id": "D4nUfU26ST2k",
+        "outputId": "fd48ae8b-137c-4c66-eb7b-984a228e00c5"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "fig = plt.figure()\n",
+        "ax1 = fig.add_axes([0,0,1,1])\n",
+        "ax2 = fig.add_axes([0.2,0.5,.2,.2])"
+      ]
+    },
+    {
+      "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": 6,
+      "metadata": {
+        "id": "m_2gVMryST2m"
+      },
+      "outputs": [],
+      "source": [
+        "ax1.plot(x,y)\n",
+        "ax2.plot(x,y)\n",
+        "fig.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": 32,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 560
+        },
+        "id": "BuiYx0xbST2o",
+        "outputId": "adb8b127-125a-462c-bd22-3f671a34c1ef"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "fig = plt.figure()\n",
+        "ax1 = fig.add_axes([0,0,1,1])\n",
+        "ax2 = fig.add_axes([0.2,0.5,.4,.4])\n",
+        "ax1.set_xlabel('x')\n",
+        "ax1.set_ylabel('z')\n",
+        "ax2.set_xlabel('x')\n",
+        "ax2.set_ylabel('y')\n",
+        "ax1.plot(x,z)\n",
+        "ax2.plot(x,y)\n",
+        "fig.show()\n"
+      ]
+    },
+    {
+      "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": 35,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 560
+        },
+        "id": "jhy7t5AKST2p",
+        "outputId": "f9213cb1-716e-4495-cc63-25cf98a932b8"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "fig = plt.figure()\n",
+        "x = np.arange(0,100)\n",
+        "y = x\n",
+        "z = x**2\n",
+        "ax1 = fig.add_axes([0,0,1,1])\n",
+        "ax2 = fig.add_axes([0.2,0.5,.4,.4])\n",
+        "ax1.set_xlabel('x')\n",
+        "ax1.set_ylabel('z')\n",
+        "ax2.set_xlabel('x')\n",
+        "ax2.set_ylabel('y')\n",
+        "ax1.plot(x,z, lw=3)\n",
+        "ax2.plot(x,y, lw=2)\n",
+        "fig.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": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "osk0Jco2ST2r",
+        "outputId": "e529a771-5edb-4cfd-99e4-aecdcfac0539"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(<Figure size 640x480 with 2 Axes>, array([<Axes: >, <Axes: >], dtype=object))"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": "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",
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.subplots(nrows=1, ncols=2)"
+      ]
+    },
+    {
+      "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": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 430
+        },
+        "id": "ZoEMYyLOST2r",
+        "outputId": "fa7a976e-1f8e-4c23-b7d0-93cee239b80f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "x = np.arange(0,100)\n",
+        "y = x*2\n",
+        "\n",
+        "plt.subplot(1, 2, 1)\n",
+        "plt.plot(x,y, ls = '--', lw = 2, c = 'blue')\n",
+        "\n",
+        "x = np.arange(0,100)\n",
+        "y = x**2\n",
+        "\n",
+        "plt.subplot(1, 2, 2)\n",
+        "plt.plot(x,y, lw = '2', c = 'y')\n",
+        "\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": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 216
+        },
+        "id": "0pq5vAFZST2s",
+        "outputId": "4b6a89eb-ebe7-4677-aeb3-d215ba469778"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1200x200 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "plt.figure(figsize = (12,2))\n",
+        "x = np.arange(0,100)\n",
+        "y = x*2\n",
+        "\n",
+        "plt.subplot(1, 2, 1)\n",
+        "plt.plot(x,y, lw = 2, c = 'blue')\n",
+        "\n",
+        "x = np.arange(0,100)\n",
+        "y = x**2\n",
+        "\n",
+        "plt.subplot(1, 2, 2)\n",
+        "plt.plot(x,y, ls = '--', lw = 2, c = 'r')\n",
+        "\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-ONbbrWEST2t"
+      },
+      "source": [
+        "# Great Job!"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "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": 0
+}
diff --git a/Mithil_Assignment1_numpy.ipynb b/Mithil_Assignment1_numpy.ipynb
new file mode 100644
index 00000000..6c2d56a2
--- /dev/null
+++ b/Mithil_Assignment1_numpy.ipynb
@@ -0,0 +1,388 @@
+{
+  "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": 28,
+      "metadata": {
+        "id": "yyS-PuO_9xlM"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ehlpKUPc9xlM"
+      },
+      "source": [
+        "#### Create an array of 10 zeros"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "id": "aEQkK-Dw9xlN"
+      },
+      "outputs": [],
+      "source": [
+        "arr = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6QHp05Ei9xlN"
+      },
+      "source": [
+        "#### Create an array of 10 ones"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "id": "ebe9xrC29xlN",
+        "scrolled": true
+      },
+      "outputs": [],
+      "source": [
+        "arr = np.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ziS-l3xp9xlO"
+      },
+      "source": [
+        "#### Create an array of 10 fives"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "metadata": {
+        "id": "fFC7bqLM9xlO"
+      },
+      "outputs": [],
+      "source": [
+        "arr = np.array([5, 5, 5, 5, 5, 5, 5, 5, 5, 5])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kBugMXlC9xlO"
+      },
+      "source": [
+        "#### Create an array of the integers from 10 to 50"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "metadata": {
+        "id": "JsO6qS9R9xlO"
+      },
+      "outputs": [],
+      "source": [
+        "arr = np.array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n",
+        "       27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,\n",
+        "       44, 45, 46, 47, 48, 49, 50])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dMw4V2L79xlO"
+      },
+      "source": [
+        "#### Create an array of all the even integers from 10 to 50"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "metadata": {
+        "id": "4lK-5SQV9xlO"
+      },
+      "outputs": [],
+      "source": [
+        "arr = np.arange(10, 51, 2)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "g__F0rB39xlP"
+      },
+      "source": [
+        "#### Create a 3x3 matrix with values ranging from 0 to 8"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {
+        "id": "MmVXCn0K9xlP"
+      },
+      "outputs": [],
+      "source": [
+        "arr = 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": 34,
+      "metadata": {
+        "id": "UHa42UpQ9xlP"
+      },
+      "outputs": [],
+      "source": [
+        "arr = 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": 35,
+      "metadata": {
+        "id": "Z0OroZxW9xlP"
+      },
+      "outputs": [],
+      "source": [
+        "arr = np.random.rand(1)"
+      ]
+    },
+    {
+      "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": 36,
+      "metadata": {
+        "id": "szluy14n9xlP"
+      },
+      "outputs": [],
+      "source": [
+        "arr = np.random.randn(25)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_GhI8LYn9xlP"
+      },
+      "source": [
+        "#### Create the following matrix:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "metadata": {
+        "id": "wS1ZBddV9xlP"
+      },
+      "outputs": [],
+      "source": [
+        "arr = np.arange(0.01, 1.01, 0.01)"
+      ]
+    },
+    {
+      "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": 38,
+      "metadata": {
+        "id": "FNXTugQ29xlQ"
+      },
+      "outputs": [],
+      "source": [
+        "arr = np.linspace(0, 1, 20)"
+      ]
+    },
+    {
+      "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": 39,
+      "metadata": {
+        "id": "Ft8P8e249xlQ"
+      },
+      "outputs": [],
+      "source": [
+        "arr = np.arange(1, 26 , 1).reshape(5 ,5)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 43,
+      "metadata": {
+        "id": "WrcFkpGL9xlQ"
+      },
+      "outputs": [],
+      "source": [
+        "arr = np.array([[12,13,14,15],[17,18,19,20],[22,23,24,25]])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 44,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "rnUSntEa9xlQ",
+        "outputId": "f6f9cee0-5914-44be-a719-2dc19626b185"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "20\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(arr[1, 3])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 45,
+      "metadata": {
+        "id": "3DMBC_wp9xlQ"
+      },
+      "outputs": [],
+      "source": [
+        "arr = np.array([2, 7, 12]).reshape(3, 1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 46,
+      "metadata": {
+        "id": "pGjD4HUK9xlQ"
+      },
+      "outputs": [],
+      "source": [
+        "arr = np.arange(21 ,26 ,1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 47,
+      "metadata": {
+        "id": "dqsdpxUo9xlR"
+      },
+      "outputs": [],
+      "source": [
+        "arr = np.arange(16 ,26 ,1).reshape(2, 5)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "collapsed": true,
+        "id": "6vtHuVJU9xlf"
+      },
+      "source": [
+        "# Great Job!"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "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": 0
+}
diff --git a/Mithil_Assignment1_pandas.ipynb b/Mithil_Assignment1_pandas.ipynb
new file mode 100644
index 00000000..635afaa1
--- /dev/null
+++ b/Mithil_Assignment1_pandas.ipynb
@@ -0,0 +1,3101 @@
+{
+  "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": 18,
+      "metadata": {
+        "id": "BiD4gl6_cAYs"
+      },
+      "outputs": [],
+      "source": [
+        "#import pandas library\n",
+        "import pandas as pd"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "id": "JUtm9KricAYu"
+      },
+      "outputs": [],
+      "source": [
+        "df = pd.read_csv('/Iris (1).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": 20,
+      "metadata": {
+        "id": "qzUnSkfRcAYu"
+      },
+      "outputs": [],
+      "source": [
+        "dictionary = {'col':[4,2,3], 'col2':[4,7,6], 'col3':[7,10,9]}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "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",
+        "outputId": "a6ed70bc-0a85-44c8-ec9e-820d3ab9955d"
+      },
+      "execution_count": 22,
+      "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-aface34b-8c54-4d36-aa08-b29fc0c7ab09\" 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",
+              "  </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-aface34b-8c54-4d36-aa08-b29fc0c7ab09')\"\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: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-aface34b-8c54-4d36-aa08-b29fc0c7ab09 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-aface34b-8c54-4d36-aa08-b29fc0c7ab09');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-19ab7d54-6d53-4612-8ffc-ab8200183d02\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-19ab7d54-6d53-4612-8ffc-ab8200183d02')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-19ab7d54-6d53-4612-8ffc-ab8200183d02 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"#shows top 5 rows (row indexing starts from 0)\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          5,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2073644135332772,\n        \"min\": 4.6,\n        \"max\": 5.1,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4.9,\n          5.0,\n          4.7\n        ],\n        \"semantic_type\": \"\",\n        \"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": 22
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "id": "iAx3B-TacAYu",
+        "outputId": "a2ab2a5f-b064-4b85-a4be-450e655ee963",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 363
+        }
+      },
+      "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-782e8445-69f9-4c29-a375-70c473495ba4\" 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>5</th>\n",
+              "      <td>6</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>1.7</td>\n",
+              "      <td>0.4</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>7</td>\n",
+              "      <td>4.6</td>\n",
+              "      <td>3.4</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.3</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>8</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>3.4</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>9</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>2.9</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>10</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>3.1</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-782e8445-69f9-4c29-a375-70c473495ba4')\"\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: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-782e8445-69f9-4c29-a375-70c473495ba4 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-782e8445-69f9-4c29-a375-70c473495ba4');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-76e9462c-0d72-4611-b3d8-532e22a5def5\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-76e9462c-0d72-4611-b3d8-532e22a5def5')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-76e9462c-0d72-4611-b3d8-532e22a5def5 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 43,\n        \"min\": 1,\n        \"max\": 150,\n        \"num_unique_values\": 150,\n        \"samples\": [\n          74,\n          19,\n          119\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8280661279778629,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\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": 23
+        }
+      ],
+      "source": [
+        "df.head(10)#Similiar to above, show 10 rows from the top"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "id": "P9RtjFLQcAYv",
+        "outputId": "3e5d863e-0ae1-4532-feaf-5334ea691358",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 519
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
+              "135  136            7.7           3.0            6.1           2.3   \n",
+              "136  137            6.3           3.4            5.6           2.4   \n",
+              "137  138            6.4           3.1            5.5           1.8   \n",
+              "138  139            6.0           3.0            4.8           1.8   \n",
+              "139  140            6.9           3.1            5.4           2.1   \n",
+              "140  141            6.7           3.1            5.6           2.4   \n",
+              "141  142            6.9           3.1            5.1           2.3   \n",
+              "142  143            5.8           2.7            5.1           1.9   \n",
+              "143  144            6.8           3.2            5.9           2.3   \n",
+              "144  145            6.7           3.3            5.7           2.5   \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",
+              "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-904629fa-f7aa-4cf2-97e4-e1d246a620d0\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
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+              "    .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>135</th>\n",
+              "      <td>136</td>\n",
+              "      <td>7.7</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>6.1</td>\n",
+              "      <td>2.3</td>\n",
+              "      <td>Iris-virginica</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>136</th>\n",
+              "      <td>137</td>\n",
+              "      <td>6.3</td>\n",
+              "      <td>3.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>2.4</td>\n",
+              "      <td>Iris-virginica</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>137</th>\n",
+              "      <td>138</td>\n",
+              "      <td>6.4</td>\n",
+              "      <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-904629fa-f7aa-4cf2-97e4-e1d246a620d0')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
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+              "\n",
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+              "  </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",
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+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-904629fa-f7aa-4cf2-97e4-e1d246a620d0 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-904629fa-f7aa-4cf2-97e4-e1d246a620d0');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
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+              "\n",
+              "    <div id=\"df-c3b992f7-5f45-4034-a0c6-144a252b13b1\">\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
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+              "      --disabled-bg-color: #3B4455;\n",
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+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
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+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
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+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
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+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
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+              "      border-right-color: var(--fill-color);\n",
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+              "      border-bottom-color: var(--fill-color);\n",
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+              "\n",
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+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
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+              "            console.error('Error during call to suggestCharts:', error);\n",
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+              "          quickchartButtonEl.style.display =\n",
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+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 15,\n  \"fields\": [\n    {\n      \"column\": \"Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 136,\n        \"max\": 150,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          145,\n          147,\n          136\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.48136710078097966,\n        \"min\": 5.8,\n        \"max\": 7.7,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          6.7,\n          7.7,\n          6.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\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": 24
+        }
+      ],
+      "source": [
+        "df.tail(15)#Show the last 15 rows using tail function"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "id": "QrzERa4qcAYv",
+        "outputId": "61376334-e9b3-4105-84af-78823293032c",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Index(['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm',\n",
+              "       'Species'],\n",
+              "      dtype='object')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 26
+        }
+      ],
+      "source": [
+        "df.columns#list all the columns of the dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "id": "5yPReLqxcAYv",
+        "outputId": "20507ed1-e848-4fb1-f5e8-52b7a9e4b69d",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 272
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Id                 int64\n",
+              "SepalLengthCm    float64\n",
+              "SepalWidthCm     float64\n",
+              "PetalLengthCm    float64\n",
+              "PetalWidthCm     float64\n",
+              "Species           object\n",
+              "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>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Id</th>\n",
+              "      <td>int64</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>SepalLengthCm</th>\n",
+              "      <td>float64</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>SepalWidthCm</th>\n",
+              "      <td>float64</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>PetalLengthCm</th>\n",
+              "      <td>float64</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>PetalWidthCm</th>\n",
+              "      <td>float64</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Species</th>\n",
+              "      <td>object</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> object</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 27
+        }
+      ],
+      "source": [
+        "df.dtypes#show the data types of each feature or column name"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "id": "lT3OEUNDcAYv",
+        "outputId": "aee18c39-f228-4971-eeb3-f45aa2bafff5",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 186
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<bound method NDFrame.describe of       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": [
+              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
+              "      pre.function-repr-contents {\n",
+              "        overflow-x: auto;\n",
+              "        padding: 8px 12px;\n",
+              "        max-height: 500px;\n",
+              "      }\n",
+              "\n",
+              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
+              "        cursor: pointer;\n",
+              "        max-height: 100px;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <pre style=\"white-space: initial; background:\n",
+              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
+              "         border-bottom: 1px solid var(--colab-border-color);\"><b>pandas.core.generic.NDFrame.describe</b><br/>def describe(percentiles=None, include=None, exclude=None) -&gt; Self</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.12/dist-packages/pandas/core/generic.py</a>Generate descriptive statistics.\n",
+              "\n",
+              "Descriptive statistics include those that summarize the central\n",
+              "tendency, dispersion and shape of a\n",
+              "dataset&#x27;s distribution, excluding ``NaN`` values.\n",
+              "\n",
+              "Analyzes both numeric and object series, as well\n",
+              "as ``DataFrame`` column sets of mixed data types. The output\n",
+              "will vary depending on what is provided. Refer to the notes\n",
+              "below for more detail.\n",
+              "\n",
+              "Parameters\n",
+              "----------\n",
+              "percentiles : list-like of numbers, optional\n",
+              "    The percentiles to include in the output. All should\n",
+              "    fall between 0 and 1. The default is\n",
+              "    ``[.25, .5, .75]``, which returns the 25th, 50th, and\n",
+              "    75th percentiles.\n",
+              "include : &#x27;all&#x27;, list-like of dtypes or None (default), optional\n",
+              "    A white list of data types to include in the result. Ignored\n",
+              "    for ``Series``. Here are the options:\n",
+              "\n",
+              "    - &#x27;all&#x27; : All columns of the input will be included in the output.\n",
+              "    - A list-like of dtypes : Limits the results to the\n",
+              "      provided data types.\n",
+              "      To limit the result to numeric types submit\n",
+              "      ``numpy.number``. To limit it instead to object columns submit\n",
+              "      the ``numpy.object`` data type. Strings\n",
+              "      can also be used in the style of\n",
+              "      ``select_dtypes`` (e.g. ``df.describe(include=[&#x27;O&#x27;])``). To\n",
+              "      select pandas categorical columns, use ``&#x27;category&#x27;``\n",
+              "    - None (default) : The result will include all numeric columns.\n",
+              "exclude : list-like of dtypes or None (default), optional,\n",
+              "    A black list of data types to omit from the result. Ignored\n",
+              "    for ``Series``. Here are the options:\n",
+              "\n",
+              "    - A list-like of dtypes : Excludes the provided data types\n",
+              "      from the result. To exclude numeric types submit\n",
+              "      ``numpy.number``. To exclude object columns submit the data\n",
+              "      type ``numpy.object``. Strings can also be used in the style of\n",
+              "      ``select_dtypes`` (e.g. ``df.describe(exclude=[&#x27;O&#x27;])``). To\n",
+              "      exclude pandas categorical columns, use ``&#x27;category&#x27;``\n",
+              "    - None (default) : The result will exclude nothing.\n",
+              "\n",
+              "Returns\n",
+              "-------\n",
+              "Series or DataFrame\n",
+              "    Summary statistics of the Series or Dataframe provided.\n",
+              "\n",
+              "See Also\n",
+              "--------\n",
+              "DataFrame.count: Count number of non-NA/null observations.\n",
+              "DataFrame.max: Maximum of the values in the object.\n",
+              "DataFrame.min: Minimum of the values in the object.\n",
+              "DataFrame.mean: Mean of the values.\n",
+              "DataFrame.std: Standard deviation of the observations.\n",
+              "DataFrame.select_dtypes: Subset of a DataFrame including/excluding\n",
+              "    columns based on their dtype.\n",
+              "\n",
+              "Notes\n",
+              "-----\n",
+              "For numeric data, the result&#x27;s index will include ``count``,\n",
+              "``mean``, ``std``, ``min``, ``max`` as well as lower, ``50`` and\n",
+              "upper percentiles. By default the lower percentile is ``25`` and the\n",
+              "upper percentile is ``75``. The ``50`` percentile is the\n",
+              "same as the median.\n",
+              "\n",
+              "For object data (e.g. strings or timestamps), the result&#x27;s index\n",
+              "will include ``count``, ``unique``, ``top``, and ``freq``. The ``top``\n",
+              "is the most common value. The ``freq`` is the most common value&#x27;s\n",
+              "frequency. Timestamps also include the ``first`` and ``last`` items.\n",
+              "\n",
+              "If multiple object values have the highest count, then the\n",
+              "``count`` and ``top`` results will be arbitrarily chosen from\n",
+              "among those with the highest count.\n",
+              "\n",
+              "For mixed data types provided via a ``DataFrame``, the default is to\n",
+              "return only an analysis of numeric columns. If the dataframe consists\n",
+              "only of object and categorical data without any numeric columns, the\n",
+              "default is to return an analysis of both the object and categorical\n",
+              "columns. If ``include=&#x27;all&#x27;`` is provided as an option, the result\n",
+              "will include a union of attributes of each type.\n",
+              "\n",
+              "The `include` and `exclude` parameters can be used to limit\n",
+              "which columns in a ``DataFrame`` are analyzed for the output.\n",
+              "The parameters are ignored when analyzing a ``Series``.\n",
+              "\n",
+              "Examples\n",
+              "--------\n",
+              "Describing a numeric ``Series``.\n",
+              "\n",
+              "&gt;&gt;&gt; s = pd.Series([1, 2, 3])\n",
+              "&gt;&gt;&gt; s.describe()\n",
+              "count    3.0\n",
+              "mean     2.0\n",
+              "std      1.0\n",
+              "min      1.0\n",
+              "25%      1.5\n",
+              "50%      2.0\n",
+              "75%      2.5\n",
+              "max      3.0\n",
+              "dtype: float64\n",
+              "\n",
+              "Describing a categorical ``Series``.\n",
+              "\n",
+              "&gt;&gt;&gt; s = pd.Series([&#x27;a&#x27;, &#x27;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;])\n",
+              "&gt;&gt;&gt; s.describe()\n",
+              "count     4\n",
+              "unique    3\n",
+              "top       a\n",
+              "freq      2\n",
+              "dtype: object\n",
+              "\n",
+              "Describing a timestamp ``Series``.\n",
+              "\n",
+              "&gt;&gt;&gt; s = pd.Series([\n",
+              "...     np.datetime64(&quot;2000-01-01&quot;),\n",
+              "...     np.datetime64(&quot;2010-01-01&quot;),\n",
+              "...     np.datetime64(&quot;2010-01-01&quot;)\n",
+              "... ])\n",
+              "&gt;&gt;&gt; s.describe()\n",
+              "count                      3\n",
+              "mean     2006-09-01 08:00:00\n",
+              "min      2000-01-01 00:00:00\n",
+              "25%      2004-12-31 12:00:00\n",
+              "50%      2010-01-01 00:00:00\n",
+              "75%      2010-01-01 00:00:00\n",
+              "max      2010-01-01 00:00:00\n",
+              "dtype: object\n",
+              "\n",
+              "Describing a ``DataFrame``. By default only numeric fields\n",
+              "are returned.\n",
+              "\n",
+              "&gt;&gt;&gt; df = pd.DataFrame({&#x27;categorical&#x27;: pd.Categorical([&#x27;d&#x27;, &#x27;e&#x27;, &#x27;f&#x27;]),\n",
+              "...                    &#x27;numeric&#x27;: [1, 2, 3],\n",
+              "...                    &#x27;object&#x27;: [&#x27;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;]\n",
+              "...                    })\n",
+              "&gt;&gt;&gt; df.describe()\n",
+              "       numeric\n",
+              "count      3.0\n",
+              "mean       2.0\n",
+              "std        1.0\n",
+              "min        1.0\n",
+              "25%        1.5\n",
+              "50%        2.0\n",
+              "75%        2.5\n",
+              "max        3.0\n",
+              "\n",
+              "Describing all columns of a ``DataFrame`` regardless of data type.\n",
+              "\n",
+              "&gt;&gt;&gt; df.describe(include=&#x27;all&#x27;)  # doctest: +SKIP\n",
+              "       categorical  numeric object\n",
+              "count            3      3.0      3\n",
+              "unique           3      NaN      3\n",
+              "top              f      NaN      a\n",
+              "freq             1      NaN      1\n",
+              "mean           NaN      2.0    NaN\n",
+              "std            NaN      1.0    NaN\n",
+              "min            NaN      1.0    NaN\n",
+              "25%            NaN      1.5    NaN\n",
+              "50%            NaN      2.0    NaN\n",
+              "75%            NaN      2.5    NaN\n",
+              "max            NaN      3.0    NaN\n",
+              "\n",
+              "Describing a column from a ``DataFrame`` by accessing it as\n",
+              "an attribute.\n",
+              "\n",
+              "&gt;&gt;&gt; df.numeric.describe()\n",
+              "count    3.0\n",
+              "mean     2.0\n",
+              "std      1.0\n",
+              "min      1.0\n",
+              "25%      1.5\n",
+              "50%      2.0\n",
+              "75%      2.5\n",
+              "max      3.0\n",
+              "Name: numeric, dtype: float64\n",
+              "\n",
+              "Including only numeric columns in a ``DataFrame`` description.\n",
+              "\n",
+              "&gt;&gt;&gt; df.describe(include=[np.number])\n",
+              "       numeric\n",
+              "count      3.0\n",
+              "mean       2.0\n",
+              "std        1.0\n",
+              "min        1.0\n",
+              "25%        1.5\n",
+              "50%        2.0\n",
+              "75%        2.5\n",
+              "max        3.0\n",
+              "\n",
+              "Including only string columns in a ``DataFrame`` description.\n",
+              "\n",
+              "&gt;&gt;&gt; df.describe(include=[object])  # doctest: +SKIP\n",
+              "       object\n",
+              "count       3\n",
+              "unique      3\n",
+              "top         a\n",
+              "freq        1\n",
+              "\n",
+              "Including only categorical columns from a ``DataFrame`` description.\n",
+              "\n",
+              "&gt;&gt;&gt; df.describe(include=[&#x27;category&#x27;])\n",
+              "       categorical\n",
+              "count            3\n",
+              "unique           3\n",
+              "top              d\n",
+              "freq             1\n",
+              "\n",
+              "Excluding numeric columns from a ``DataFrame`` description.\n",
+              "\n",
+              "&gt;&gt;&gt; df.describe(exclude=[np.number])  # doctest: +SKIP\n",
+              "       categorical object\n",
+              "count            3      3\n",
+              "unique           3      3\n",
+              "top              f      a\n",
+              "freq             1      1\n",
+              "\n",
+              "Excluding object columns from a ``DataFrame`` description.\n",
+              "\n",
+              "&gt;&gt;&gt; df.describe(exclude=[object])  # doctest: +SKIP\n",
+              "       categorical  numeric\n",
+              "count            3      3.0\n",
+              "unique           3      NaN\n",
+              "top              f      NaN\n",
+              "freq             1      NaN\n",
+              "mean           NaN      2.0\n",
+              "std            NaN      1.0\n",
+              "min            NaN      1.0\n",
+              "25%            NaN      1.5\n",
+              "50%            NaN      2.0\n",
+              "75%            NaN      2.5\n",
+              "max            NaN      3.0</pre>\n",
+              "      <script>\n",
+              "      if (google.colab.kernel.accessAllowed && google.colab.files && google.colab.files.view) {\n",
+              "        for (const element of document.querySelectorAll('.filepath')) {\n",
+              "          element.style.display = 'block'\n",
+              "          element.onclick = (event) => {\n",
+              "            event.preventDefault();\n",
+              "            event.stopPropagation();\n",
+              "            google.colab.files.view(element.textContent, 11734);\n",
+              "          };\n",
+              "        }\n",
+              "      }\n",
+              "      for (const element of document.querySelectorAll('.function-repr-contents')) {\n",
+              "        element.onclick = (event) => {\n",
+              "          event.preventDefault();\n",
+              "          event.stopPropagation();\n",
+              "          element.classList.toggle('function-repr-contents-collapsed');\n",
+              "        };\n",
+              "      }\n",
+              "      </script>\n",
+              "      </div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 29
+        }
+      ],
+      "source": [
+        "df.describe#describe the data through statistics"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 423
+        },
+        "id": "Z_52ct0BcAYw",
+        "outputId": "e08218d3-bc44-4334-fb21-64ae87b87d6c"
+      },
+      "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-052514e9-8ede-467c-8612-9ff8ecb0776c\" 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",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>4.9</td>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>4.7</td>\n",
+              "      <td>3.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4.6</td>\n",
+              "      <td>3.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5.0</td>\n",
+              "      <td>3.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>145</th>\n",
+              "      <td>6.7</td>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>146</th>\n",
+              "      <td>6.3</td>\n",
+              "      <td>2.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>147</th>\n",
+              "      <td>6.5</td>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>148</th>\n",
+              "      <td>6.2</td>\n",
+              "      <td>3.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>149</th>\n",
+              "      <td>5.9</td>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>150 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-052514e9-8ede-467c-8612-9ff8ecb0776c')\"\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",
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+              "  </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: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-052514e9-8ede-467c-8612-9ff8ecb0776c button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-052514e9-8ede-467c-8612-9ff8ecb0776c');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-5eb61fd0-6189-48d6-965e-ecb5753fedb2\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5eb61fd0-6189-48d6-965e-ecb5753fedb2')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-5eb61fd0-6189-48d6-965e-ecb5753fedb2 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"#From taking multiple columns from the dataset\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8280661279778629,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\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}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 30
+        }
+      ],
+      "source": [
+        "df[['SepalLengthCm', 'SepalWidthCm']]\n",
+        "#From taking multiple columns from the dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JWYXoLl0cAYw",
+        "outputId": "9d18b060-9283-4777-bb9f-1488e799f633"
+      },
+      "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": 31
+        }
+      ],
+      "source": [
+        "df.SepalLengthCm.unique()\n",
+        "#gives the unique values of Sepal Length present in that particular column"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {
+        "id": "MiOVp1WacAYw",
+        "outputId": "635b61f6-2bae-4a8f-a0a9-598f2fc0a7c1",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 272
+        }
+      },
+      "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": 33
+        }
+      ],
+      "source": [
+        "df.loc[10]#Get the 11th row from the dataset\n",
+        "#Hint: for taking 11th row do we use 11 or 10 index?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.iloc[10,-1]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 36
+        },
+        "id": "TF03jh_MovET",
+        "outputId": "950f967c-27da-4e9d-ccec-8f746d516f4f"
+      },
+      "execution_count": 34,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'Iris-setosa'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 34
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.iloc[9,-2]#Display the second last entry of the 10th row"
+      ],
+      "metadata": {
+        "id": "gV-h3rK-g5lZ",
+        "outputId": "ea763e62-054b-455f-f818-14f3b27ea18e",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 35,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "np.float64(0.1)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 35
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 394
+        },
+        "id": "KXWCH34ecAYx",
+        "outputId": "f3e5f0c4-ca18-4c82-96d1-066f0dbce626"
+      },
+      "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-0af2a6c9-376b-4b8e-b343-26e96b0ae188\" 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",
+              "      <td>3.0</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>26</th>\n",
+              "      <td>27</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>3.4</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>0.4</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>27</th>\n",
+              "      <td>28</td>\n",
+              "      <td>5.2</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>28</th>\n",
+              "      <td>29</td>\n",
+              "      <td>5.2</td>\n",
+              "      <td>3.4</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>29</th>\n",
+              "      <td>30</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>3.2</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>Iris-setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>30</th>\n",
+              "      <td>31</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>3.1</td>\n",
+              "      <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-0af2a6c9-376b-4b8e-b343-26e96b0ae188')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
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+              "  </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",
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+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-0af2a6c9-376b-4b8e-b343-26e96b0ae188 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-0af2a6c9-376b-4b8e-b343-26e96b0ae188');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
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+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-b0bc4453-8d6c-4344-84c8-632b634099b4\">\n",
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+              "                title=\"Suggest charts\"\n",
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+              "\n",
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+              "     width=\"24px\">\n",
+              "    <g>\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-b0bc4453-8d6c-4344-84c8-632b634099b4 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"#takes everything from row 23(included) and 34(not included)\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 23,\n        \"max\": 33,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          28,\n          23,\n          32\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2489979919597748,\n        \"min\": 4.6,\n        \"max\": 5.4,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          4.6,\n          5.1,\n          4.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.28982753492378865,\n        \"min\": 3.0,\n        \"max\": 4.1,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.3,\n          3.2,\n          3.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.22033033051637385,\n        \"min\": 1.0,\n        \"max\": 1.9,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1.0,\n          1.7,\n          1.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12135597524338358,\n        \"min\": 0.1,\n        \"max\": 0.5,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.5,\n          0.1,\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": 36
+        }
+      ],
+      "source": [
+        "df.iloc[22:33]\n",
+        "#takes everything from row 23(included) and 34(not included)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df1=df.copy()\n",
+        "df1.dropna(axis=0, inplace=False) #drops any values that are null or na"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 423
+        },
+        "id": "quhmLFx-q6vS",
+        "outputId": "f3c319de-f0e7-43a8-843a-98cb318f047c"
+      },
+      "execution_count": 37,
+      "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-4b80eff0-51ef-4905-a54b-e95aa1e8d93d\" 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-4b80eff0-51ef-4905-a54b-e95aa1e8d93d')\"\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: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-4b80eff0-51ef-4905-a54b-e95aa1e8d93d button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-4b80eff0-51ef-4905-a54b-e95aa1e8d93d');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-49c669f8-865b-43ca-8d25-82b3c66bfaa1\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-49c669f8-865b-43ca-8d25-82b3c66bfaa1')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-49c669f8-865b-43ca-8d25-82b3c66bfaa1 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 43,\n        \"min\": 1,\n        \"max\": 150,\n        \"num_unique_values\": 150,\n        \"samples\": [\n          74,\n          19,\n          119\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8280661279778629,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\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": 37
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "metadata": {
+        "id": "kZg8z57fcAY2"
+      },
+      "outputs": [],
+      "source": [
+        "df.dropna(axis=0, inplace=True)\n",
+        "#drops all the rows with missing values"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 272
+        },
+        "id": "FRWcDJA2cAY2",
+        "outputId": "34b7b771-1988-4258-89b3-bfa4b0a3fa9b"
+      },
+      "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": 39
+        }
+      ],
+      "source": [
+        "df.isnull().sum()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 40,
+      "metadata": {
+        "id": "JioW0K_pcAY3"
+      },
+      "outputs": [],
+      "source": [
+        "df.to_csv('cleandata_iris.csv')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 41,
+      "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": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file