Exercise_Set_1a.ipynb

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      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/lcipolina/rlvs_rl_fundamentals/blob/main/Exercise_Set_1a.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dY-eYHKi__Ph"
      },
      "source": [
        "# Exercise Set 1a - Python Arrays & DataFrames"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mox-rsA2AMcz"
      },
      "source": [
        "Importing required libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AeBiJyRdAEq-"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BBBi75RGAWHS"
      },
      "source": [
        "Create a Numpy array with 1 column and 100 rows. Fill this with some data of random numbers.\n",
        "\n",
        "You may utilize the random number functions np.random.randn() or np.random.randint() to generate random numbers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dJV1tptqAR9A",
        "outputId": "5433f09e-5efd-4be2-b16e-b2c84b0287a7"
      },
      "source": [
        "rand_int = np.random.randint(1,10,(100,1)) # random numpy array of shape (100,1) with rnd integers between (1,10)\n",
        "print(rand_int)"
      ],
      "execution_count": 24,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "[[1]\n",
            " [4]\n",
            " [3]\n",
            " [9]\n",
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            " [6]\n",
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            " [1]\n",
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            " [7]\n",
            " [3]\n",
            " [8]\n",
            " [3]\n",
            " [2]\n",
            " [6]]\n"
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xVVSzmR8CxTB"
      },
      "source": [
        "Convert numpy array into pandas data series"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "RdV4bjasCSKl",
        "outputId": "3cc30f3a-feac-4127-de45-f5974efddcef"
      },
      "source": [
        "df = pd.DataFrame(rand_int, columns=['Column1'])\n",
        "df"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>99</th>\n",
              "      <td>6</td>\n",
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              "<p>100 rows × 1 columns</p>\n",
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            "text/plain": [
              "    Column1\n",
              "0         1\n",
              "1         4\n",
              "2         3\n",
              "3         9\n",
              "4         2\n",
              "..      ...\n",
              "95        3\n",
              "96        8\n",
              "97        3\n",
              "98        2\n",
              "99        6\n",
              "\n",
              "[100 rows x 1 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wMRFZebvDXS2"
      },
      "source": [
        "Create a Numpy array with 3 columns and 100 rows. \n",
        "Fill this with some data of random numbers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ovbch1X2DVA6",
        "outputId": "fc32855d-c732-4222-b1f2-4743ab5ab668"
      },
      "source": [
        "rand_int = np.random.randint(1,10,(100,3)) # random numpy array of shape (100,3) with rnd integers between (1,10)\n",
        "print(rand_int)"
      ],
      "execution_count": 26,
      "outputs": [
        {
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          "name": "stdout",
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            "[[1 9 4]\n",
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            " [6 6 8]\n",
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            " [6 6 9]\n",
            " [9 5 8]\n",
            " [2 9 6]\n",
            " [6 6 2]\n",
            " [1 1 7]\n",
            " [2 1 4]\n",
            " [7 8 4]\n",
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            " [1 7 9]\n",
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yLxkK9bHDsbi"
      },
      "source": [
        "Convert the above array into a pandas DataFrame. \n",
        "\n",
        "Note that a DataFrame has labels for each column. Choose some appropriate labels, such as X1, X2, X3."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "g3Pu0rcxDJjW",
        "outputId": "f873fd3b-1f3b-44e1-d92b-aea81a26247a"
      },
      "source": [
        "df = pd.DataFrame(rand_int, columns=['X1','X2','X3'])\n",
        "df"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>X1</th>\n",
              "      <th>X2</th>\n",
              "      <th>X3</th>\n",
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              "      <td>9</td>\n",
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              "      <th>...</th>\n",
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              "      <td>9</td>\n",
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              "      <td>8</td>\n",
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              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    X1  X2  X3\n",
              "0    1   9   4\n",
              "1    6   5   9\n",
              "2    2   8   6\n",
              "3    1   5   1\n",
              "4    9   2   7\n",
              "..  ..  ..  ..\n",
              "95   5   3   4\n",
              "96   9   2   8\n",
              "97   2   7   3\n",
              "98   3   3   2\n",
              "99   6   9   6\n",
              "\n",
              "[100 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gu6cacLJECOu"
      },
      "source": [
        "Use the above generated DataFrame to do a pairplot.\n",
        "\n",
        "Utilize the Seaborn function\n",
        "sns.pairplot()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "id": "veRQAjs6D3Lu",
        "outputId": "6175d09d-ba8a-400d-c860-506a0def0ac7"
      },
      "source": [
        "import seaborn as sns\n",
        "g = sns.pairplot(df)\n",
        "\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.show()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 540x540 with 12 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j9wI53UyHceb"
      },
      "source": [
        "\n",
        "### Report\n",
        "\n",
        "* We have generated a matrix of random uncorrelated numbers. \n",
        "\n",
        "* We then plotted the numbers using the \"pairplot()\" function from the seaborn package. \n",
        "\n",
        "\n",
        "  This function generates a pair-wise plot with one variable on the X axis against the other variable plotted on the Y axis.\n",
        "\n",
        "  This is a quick way to study the correlation of both variables. Say they are both similar, we would expect to obtain all the dots aligned over the 45 degree line X=Y.\n",
        "\n",
        "  Here, because we have plotted random numbers, we see that the dots are all over the place, uniformly distributed across the plane.\n",
        "\n",
        "Note that on the diagonal we have a histogram of each of the variables.\n",
        "\n",
        "This is a quick way to get a feel of the separability of the behaviour of the data. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EIZK6_C1Gxq8"
      },
      "source": [
        ""
      ],
      "execution_count": 28,
      "outputs": []
    }
  ]
}