PyPEF_06_data.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/aleksejalex/PyPEF/blob/main/PyPEF_06_data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BJ38-zM8iNpQ"
      },
      "source": [
        "# PyPEF, lecture 06. Plots (continued). Handling data in Python.\n",
        "\n",
        "Prepared by: Aleksej Gaj ( pythonforstudents24@gmail.com )\n",
        "\n",
        "🔗 Course website: [https://aleksejalex.4fan.cz/pef_python/](https://aleksejalex.4fan.cz/pef_python/)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3bliMUTcb2_5"
      },
      "source": [
        "*Note:* this notebook is partially inspired by [this notebook](https://github.com/janpipek/fbmi-python-course/blob/main/notebooks/130_pandas_intro.ipynb)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZEI2YaSwiPFc"
      },
      "source": [
        "In this tutorial we will\n",
        " - continue to plot figures in Python:\n",
        "    - second (more intuitive) way to use `plt.legend()` (via `label`s)\n",
        "    - subplots ( = how to plot several graphs in one figure)\n",
        "    - two different ways of plotting\n",
        " - learn how to work with data in Python, namely:\n",
        "    - how `pandas` library stores data\n",
        "    - how to import data and change it\n",
        "    - basic functionality of `pandas` library\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RpdlTQwfb2_6"
      },
      "source": [
        "## Solution of the homework from last time - finale\n",
        "**task:** compute a mean (cz. *střední hodnota*) and median of each of functions you plotted above. Copy your solution from above and plot a mean value of each function as a red dot."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "ECtLWuTub2_6"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "ONlNXF2_b2_7",
        "outputId": "d063a1b3-b9aa-4eeb-e51c-ab1c53845b1b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-2-cfdc9e463ebc>:7: RuntimeWarning: invalid value encountered in log\n",
            "  plt.plot(x, np.log(x), label=\"log\")       # note how the legend is constructed here\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 840x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "x = np.arange(-3, 3, 0.01)\n",
        "\n",
        "plt.figure(figsize=(7,4), dpi=120)\n",
        "plt.plot(x, np.sin(x), label=\"sine\")      # note how the legend is constructed here\n",
        "plt.plot(x, np.cos(x), label=\"cosine\")    # note how the legend is constructed here\n",
        "plt.plot(x, np.exp(x), label=\"exp\")       # note how the legend is constructed here\n",
        "plt.plot(x, np.log(x), label=\"log\")       # note how the legend is constructed here\n",
        "plt.xlabel(r\"$x \\in  \\left[ -3, 3  \\right] $\")\n",
        "plt.ylabel(r\"$y \\in \\mathrm{\\mathbb{R}}$ (values of function)\")\n",
        "plt.title(r\"Our plot of goniometric functions\")\n",
        "plt.legend()                              # note the legend\n",
        "plt.grid()\n",
        "plt.savefig(\"my_gonio_functions.png\", dpi=230)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7tbXz8CPb2_9"
      },
      "source": [
        "adding mean and median:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "jbqhsrLYb2_9",
        "outputId": "e816dd81-e0cb-4efe-9be9-5830268167d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "# Define the interval\n",
        "interval = np.linspace(0.001, 2, 100)\n",
        "\n",
        "# Calculate np.log and np.exp for the interval\n",
        "log_values = np.log(interval)\n",
        "exp_values = np.exp(interval)\n",
        "\n",
        "# Calculate mean and median\n",
        "log_mean = np.mean(log_values)\n",
        "log_median = np.median(log_values)\n",
        "exp_mean = np.mean(exp_values)\n",
        "exp_median = np.median(exp_values)\n",
        "\n",
        "# Plot np.log\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.plot(interval, log_values, label='log(x)', color='blue')\n",
        "plt.axhline(y=log_mean, color='blue', linestyle='--', label='Mean of log(x)')\n",
        "plt.axhline(y=log_median, color='red', linestyle='--', label='Median of log(x)')\n",
        "\n",
        "# Plot np.exp\n",
        "plt.plot(interval, exp_values, label='exp(x)', color='green')\n",
        "plt.axhline(y=exp_mean, color='green', linestyle='--', label='Mean of exp(x)')\n",
        "plt.axhline(y=exp_median, color='orange', linestyle='--', label='Median of exp(x)')\n",
        "\n",
        "# Set labels and legend\n",
        "plt.xlabel('x')\n",
        "plt.ylabel('y')\n",
        "plt.title('Plot of log(x) and exp(x)')\n",
        "plt.legend()\n",
        "\n",
        "# Show plot\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P7txxW_ub2_-"
      },
      "source": [
        "🤔 Why mean and median are different?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rBINzQnUb2__"
      },
      "source": [
        "## Plots - part 2\n",
        "\n",
        " - some other type of plots\n",
        " - subplots: several graphs in one figure\n",
        " - two ways of plotting in matplotlib: via pyplot (we did it this way until now), and via axis (considered to be \"more modern\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0dRoLkqJb2__"
      },
      "source": [
        "Recall plot of sine and cosine functions from the last time:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "KbV72dAlb3AA",
        "outputId": "924f33da-1c3a-4f76-f24b-fe000197f6cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 840x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "x = np.arange(-3, 3, 0.01)\n",
        "\n",
        "plt.figure(figsize=(7,4), dpi=120)\n",
        "plt.plot(x, np.sin(x), label=\"sine\")      # note how the legend is constructed here\n",
        "plt.plot(x, np.cos(x), label=\"cosine\")    # note how the legend is constructed here\n",
        "plt.xlabel(r\"$x \\in  \\left[ -3, 3  \\right] $\")\n",
        "plt.ylabel(r\"$y \\in \\mathrm{\\mathbb{R}}$ (values of function)\")\n",
        "plt.title(r\"Our plot of goniometric functions\")\n",
        "plt.legend()                              # note the legend\n",
        "plt.savefig(\"my_gonio_functions.png\", dpi=230)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "D23QK00Fb3AA",
        "outputId": "7538cf7c-0580-44ef-8f0d-43e0f40e8b8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1260x560 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "x = np.arange(-3, 3, 0.01)\n",
        "\n",
        "plt.figure(figsize=(9,4), dpi=140)\n",
        "\n",
        "plt.subplot(1,3,1)\n",
        "plt.plot(x, np.sin(x), label=\"sine\")\n",
        "plt.plot(x, np.cos(x), label=\"cosine\")\n",
        "plt.xlabel(r\"$x \\in  \\left[ -3, 3  \\right] $\")\n",
        "plt.ylabel(r\"$y \\in \\mathrm{\\mathbb{R}}$ (values of function)\")\n",
        "plt.title(r\"usual\")\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "\n",
        "plt.subplot(1,3,2)\n",
        "plt.plot(x, np.sinh(x), label=\"hyperbolic sine\")\n",
        "plt.plot(x, np.cosh(x), label=\"hyperbolic cosine\")\n",
        "plt.xlabel(r\"$x \\in  \\left[ -3, 3  \\right] $\")\n",
        "plt.ylabel(r\"$y \\in \\mathrm{\\mathbb{R}}$ (values of function)\")\n",
        "plt.title(r\"hyperbolic\")\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "\n",
        "plt.subplot(1,3,3)\n",
        "plt.plot(x, np.sin(x) + np.cos(x), label=\"sum\")\n",
        "plt.plot(x, np.sin(x) * np.cos(x), label=\"product\")\n",
        "plt.xlabel(r\"$x \\in  \\left[ -3, 3  \\right] $\")\n",
        "plt.ylabel(r\"$y \\in \\mathrm{\\mathbb{R}}$ (values of function)\")\n",
        "plt.title(r\"sum and product\")\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "\n",
        "plt.suptitle(\"Playing with trigonometry\")\n",
        "plt.tight_layout() # adjust layout to prevent overlapping\n",
        "plt.savefig(\"my_gonio_functions.png\", dpi=230)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "sqQ_7aOcb3AA"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dbzgHd6qb3AA"
      },
      "source": [
        "### Log scale"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "OoaUtv0Rb3AA",
        "outputId": "e749aa01-d674-448b-9b19-f6819600f6b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-6-d94bb1e50b22>:7: RuntimeWarning: invalid value encountered in log\n",
            "  y2 = np.log(x)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1170x1170 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Generate some sample data\n",
        "x = np.linspace(-3,3,100)\n",
        "y = np.exp(x)\n",
        "y2 = np.log(x)\n",
        "\n",
        "# Plot the points with log scale on both axes\n",
        "plt.figure(figsize=(13,13), dpi=90)\n",
        "plt.subplot(2,2,1)\n",
        "plt.scatter(x, y, label=\"exp\")\n",
        "plt.scatter(x, y2, label=\"log\")\n",
        "plt.xscale('linear')\n",
        "plt.yscale('linear')\n",
        "plt.xlabel('X (lin scale)')\n",
        "plt.ylabel('Y (lin scale)')\n",
        "plt.title('')\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "\n",
        "plt.subplot(2,2,2)\n",
        "plt.scatter(x, y, label=\"exp\")\n",
        "plt.scatter(x, y2, label=\"log\")\n",
        "plt.xscale('linear')\n",
        "plt.yscale('log')\n",
        "plt.xlabel('X (lin scale)')\n",
        "plt.ylabel('Y (log scale)')\n",
        "plt.title('')\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "\n",
        "plt.subplot(2,2,3)\n",
        "plt.scatter(x, y, label=\"exp\")\n",
        "plt.scatter(x, y2, label=\"log\")\n",
        "plt.xscale('log')\n",
        "plt.yscale('log')\n",
        "plt.xlabel('X (log scale)')\n",
        "plt.ylabel('Y (log scale)')\n",
        "plt.title('')\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "\n",
        "plt.subplot(2,2,4)\n",
        "plt.scatter(x, y, label=\"exp\")\n",
        "plt.scatter(x, y2, label=\"log\")\n",
        "plt.xscale('log')\n",
        "plt.yscale('linear')\n",
        "plt.xlabel('X (log scale)')\n",
        "plt.ylabel('Y (lin scale)')\n",
        "plt.title('')\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "\n",
        "\n",
        "plt.suptitle(r\"Function $y=e^x$ (different scales)\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kts6dfDMb3AB"
      },
      "source": [
        "⚠️ Note that all points shown **are the same values!** The same `x`,`y` and `y2`.\n",
        "The difference is just how the points are plotted (or where - in terms of different scales on axes)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7_k52M0yb3AB"
      },
      "source": [
        "### Other types of plots:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X2swCLDwb3AB"
      },
      "source": [
        "#### Histogram (more on histogams later)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "hIRoaVahb3AC",
        "outputId": "4bfb2424-d0e6-402f-c057-ad9de2eb7652",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "data = np.random.randn(1000)  # Generating random data\n",
        "plt.hist(data, bins=30)\n",
        "plt.xlabel('Value')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Histogram')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VmPzJ19sb3AC"
      },
      "source": [
        "#### Pie chart"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "U7iGy9X4b3AC",
        "outputId": "622b4bde-0b96-476f-da3f-c4ec4ca77095",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "sizes = [15, 30, 45, 10]\n",
        "labels = ['A', 'B', 'C', 'D']\n",
        "\n",
        "plt.pie(sizes, labels=labels, autopct='%1.1f%%') # last argument makes the percentages inside figure\n",
        "plt.title('Pie Chart')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mtDAQ3Krb3AC"
      },
      "source": [
        "#### 3D plots...\n",
        " - here we are forced to use slightly different option of plotting (more on this later)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "V0LUzodbb3AD",
        "outputId": "29547427-faa5-40c7-90c7-fe9b3ef6c759",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "import numpy as np\n",
        "\n",
        "fig = plt.figure()\n",
        "ax = fig.add_subplot(111, projection='3d')\n",
        "\n",
        "x = np.random.standard_normal(100)\n",
        "y = np.random.standard_normal(100)\n",
        "z = np.random.standard_normal(100)\n",
        "\n",
        "ax.scatter(x, y, z)\n",
        "ax.set_xlabel('X')\n",
        "ax.set_ylabel('Y')\n",
        "ax.set_zlabel('Z')\n",
        "plt.title('3D Scatter Plot')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "6RVnsrdjb3AD",
        "outputId": "056f5b46-c692-42fe-c848-79aaec7684f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "import numpy as np\n",
        "\n",
        "fig = plt.figure()\n",
        "ax = fig.add_subplot(111, projection='3d')\n",
        "\n",
        "# Create a meshgrid of x and y values\n",
        "x = np.linspace(-5, 5, 100)\n",
        "y = np.linspace(-5, 5, 100)\n",
        "x, y = np.meshgrid(x, y)\n",
        "\n",
        "# Define a function to plot\n",
        "z = x**2 - y**2\n",
        "z = np.sin(np.sqrt(x**2 + y**2))\n",
        "\n",
        "# Plot the function\n",
        "ax.plot_surface(x, y, z, cmap='viridis')\n",
        "\n",
        "ax.set_xlabel('X')\n",
        "ax.set_ylabel('Y')\n",
        "ax.set_zlabel('Z')\n",
        "plt.title('3D Plot of a Saddle')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bolLs6ufb3AD"
      },
      "source": [
        "💡 Note: try to run the last cell locally, in your IDE. Locally, the figureis shown in a separate window, where you can rotate and scale it with you mouse.\n",
        "\n",
        "(This is still very \"manual\" way of plotting. To make cool interactive figure or animation, expect more tinkering with matplotlib *or* another library)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "Ai-337YUb3AD"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FPdGkD_yb3AD"
      },
      "source": [
        "### Two options to plot plots:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7y1jFGepb3AE"
      },
      "source": [
        "#### 1. using `pyplot` - as we did until now\n",
        " - more convenient, simple to use, less customizable  (if it matters)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "NQXsQvJvN_NP",
        "outputId": "4c3859e8-38c4-4215-e314-8f9c46cb42aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "x = [1, 2, 3, 4]\n",
        "y = [1, 4, 2, 3]\n",
        "\n",
        "plt.plot(x, y)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "s_mKoxWHb3AE",
        "outputId": "eb2602d7-f565-4184-eab8-642d2353716c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<module 'matplotlib.pyplot' from '/usr/local/lib/python3.10/dist-packages/matplotlib/pyplot.py'>\n"
          ]
        }
      ],
      "source": [
        "print(plt)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "klHzLhZyb3AE"
      },
      "source": [
        "#### 2. using axis - another option\n",
        " - more explicit, object oriented\n",
        " - much more options to customize your plot elements and its properties\n",
        " - considered to be more modern"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "iir2HmD1b3AF",
        "outputId": "e780639c-7904-48b0-f54c-c9b9458af57c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig, ax = plt.subplots()  # Create a figure containing a single axes.\n",
        "x = [1, 2, 3, 4]\n",
        "y = [1, 4, 2, 3]\n",
        "\n",
        "ax.plot(x, y)  # Plot some data on the axes.\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "qsPDprtCb3AF",
        "outputId": "f1c89139-8fb7-4dab-ec5f-e7718f655e14",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Axes(0.125,0.11;0.775x0.77)\n",
            "<class 'matplotlib.axes._axes.Axes'>\n"
          ]
        }
      ],
      "source": [
        "print(ax)\n",
        "print(type(ax))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "4oty8ucjb3AF"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C7m1gUCGb3AF"
      },
      "source": [
        "## 📊 Data: blessing and curse\n",
        "\n",
        "Nowadays, the importance of data is obvious.\n",
        "\n",
        "When working with data, remember:\n",
        "1) no perfect data exist: any dataset is obtained as the result of some experiment/observation, so there arise problems like:\n",
        "   - **missing data**: not every property of every individual was recorded/obtained (*example:* some people prefer not to say their gender)\n",
        "   - **low quality**: inconsistent values (*How to deal with outliers?*), incorrect formatting, duplicate records,\n",
        "   - **data imbalance:** definitely not all situations occured, some might be underrepresented/overrepresented (*How it comes NN is rasist*)\n",
        "   -\n",
        "2) there are several ways how to analyse the data, and always many ways, how to *interpret* the results:\n",
        "   - two analysts can deduce different conclusions from *the same data*\n",
        "   - context (how? why?) matters, but often even client has no clue how the data were observed and what questions are expected to be answered (why?)\n",
        "   - *analysis of data is more like an art, and interpretation is sometimes close to philosophy*\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "95yO0mnEb3AF"
      },
      "source": [
        "👉 So:\n",
        " - do not analyse data as some kind of routine (unless it is routine): be sure you **understand the phenomena** behind your data and **formulate your task accordingly**\n",
        " - try to **get as much context** (additional information) about your data as possible. It might happen that \"the mistake\" was done already during collecting or preprocessing phase\n",
        " - try to get your hands **on raw data** (in many cases wrong preprocessing kills the valuable infomation in the data)\n",
        "\n",
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p64PdJLcb3AG"
      },
      "source": [
        "🚨🚨🚨 **Warning:** Data analysis and data science are huge areas comprising interdisciplinatry knowledge in probabilisty, statistics, optimisation, etc and programming art and dark magic. There is no chance to cover even brief introduction in this course. However, we will try to learn *how* to use the most popular tools, expecting you will learn *why* to use them in other tasks (or in others subjects).\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AW5XtqKTb3AG"
      },
      "source": [
        "Something to avoid:\n",
        "\n",
        "<img src=\"https://imgs.xkcd.com/comics/flawed_data.png\" alt=\"logo\" width=\"600\">\n",
        "\n",
        "source: [xkcd.com](https://xkcd.com/)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6FDa_2s1b3AG"
      },
      "source": [
        "## Data in Python: pandas\n",
        "<img src=\"https://pandas.pydata.org/pandas-docs/stable/_static/pandas.svg\" alt=\"logo\" width=\"200\">\n",
        "\n",
        " = Python library for data manipulation and analysis\n",
        "\n",
        " - nothing in common with 🐼, name is derived from \"**pan**el **da**ta\" (=econometric term for data sets that include observations over time of the same individuals)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B8lxRpKKb3AG"
      },
      "source": [
        "*Reminder of literature:*\n",
        " - the book written by the creator of Pandas - Wes McKinney\n",
        "\n",
        "<img src=\"https://external-content.duckduckgo.com/iu/?u=https%3A%2F%2Fbookshop.vshcdn.net%2Fo-reilly-media%2Fpython-for-data-analysis-2e_id4950739.jpg&f=1&nofb=1&ipt=fb8aa0538b53aaf9640cd8990c0a88041c295a932722edc875cbded4556b5331&ipo=images\" alt=\"logo\" width=\"150\">"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ic51-Lx3b3AG"
      },
      "source": [
        "Now let's get to technicalities."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "gtJz6fUvb3AG"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GYlQu57Kb3AG"
      },
      "source": [
        "### Basic objects\n",
        "\n",
        " - Series\n",
        " - DataFrame"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EdeFoDUkb3AG"
      },
      "source": [
        "#### Series\n",
        " = basic element of pandas, similar to array in NumPy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "pEDqueU1b3AH",
        "outputId": "da558bb3-6c22-4f13-ff7a-74cd56bdb906",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    101\n",
              "1    102\n",
              "2    103\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "series = pd.Series([101, 102, 103])\n",
        "series"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "YZhU-mLUb3AH",
        "outputId": "748b75c4-cbd2-4ab6-f62e-266330243926",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([101, 102, 103])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "series.values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "uD_rmiWrN_Ni",
        "outputId": "f60704b7-8ec4-46b2-b2e4-ec4e75127fa7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "type(series.values)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zyse0Vr3b3AH"
      },
      "source": [
        "Ah! So `pd.Series` is just a wrapper for `np.ndarray`? 🤔\n",
        "\n",
        "No, there's more: `pd.Series` contains not only the data (as numpy does), but also it assigns some \"label\" to each row. The \"label\" is called index"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "bs3Ck2b4N_Ni",
        "outputId": "f1ddd201-4a0a-4f8f-b12d-d9a183c06c2e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RangeIndex(start=0, stop=3, step=1)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "series.index"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ScHG8Pyb3AI"
      },
      "source": [
        "Index can be used to access some part of `pd.Series`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "cZu9SIExb3AI",
        "outputId": "96731198-ab7c-42eb-a057-389cea7a169a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "102"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "series[1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "yocThDUIb3AI",
        "outputId": "33237017-0f0c-41e9-a5db-07bdab4b0857",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a    2\n",
              "b    4\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "# series from a dictionary\n",
        "series_ab = pd.Series({\"a\": 2, \"b\": 4})\n",
        "series_ab"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M96753qzb3AI"
      },
      "source": [
        "#### DataFrame\n",
        " = something like Excel table 🙂\n",
        "\n",
        " ➕ Python friendly (simple interaction with other libraries) \\\n",
        " ➕ much faster than Excel  \\\n",
        " ➖ no GUI to edit data (at least not as a part of pandas) \\\n",
        " ➖ you don't modify the data, you create a copy with modified values  \\\n",
        " ➖ slow and demanding - whole data need to be loaded into RAM (but still faster than Excel 😜 )\n",
        "\n",
        "Technically - a collection of named columns (collection of `pd.Series`)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a_IN08-8b3AJ"
      },
      "source": [
        "### Main Features of pandas.DataFrame:\n",
        "\n",
        "1. Tabular Data Structure\n",
        "2. Data Handling for Heterogeneous Data Types\n",
        "3. Indexing for Easy Data Access\n",
        "4. Data Manipulation Functions\n",
        "5. Missing Data Handling Methods\n",
        "6. Grouping and Aggregation Capabilities\n",
        "7. Data Cleaning and Preprocessing Tools\n",
        "8. Data Import and Export Functions\n",
        "9. Integration with Data Visualization Libraries\n",
        "\n",
        "### Comparison with Excel:\n",
        "\n",
        "| Feature         | pandas.DataFrame                  | MS Excel                                 |\n",
        "|-----------------|-----------------------------------|---------------------------------------|\n",
        "| Scalability     | Efficient for Larger Datasets     | Limited by Memory                     |\n",
        "| Control         | Programmatic Automation           | Manual Operations                     |\n",
        "| Performance     | Better for Complex Operations     | Slower for Large Datasets             |\n",
        "| Integration     | Seamless with Python Ecosystem    | Standalone Application                |\n",
        "| Customization   | Extensive Flexibility             | Limited to Built-in Functions         |\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Rrkiu4db3AJ"
      },
      "source": [
        "Back to code: ways to create dataframe \"by hand\":"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "cHslboefb3AJ",
        "outputId": "78120788-ad86-436b-8eb2-31776186f59b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   number letter\n",
              "0       1      a\n",
              "1       2      b\n",
              "2       3      c\n",
              "3       4      d"
            ],
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              "        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",
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              "      --bg-color: #E8F0FE;\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "\n",
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              "      const quickchartButtonEl =\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"number\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          4,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"letter\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"b\",\n          \"d\",\n          \"a\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "# specify columns as 'pd.Series':\n",
        "df = pd.DataFrame({\n",
        "    'number': pd.Series([1, 2, 3, 4], dtype = np.int64),\n",
        "    'letter': pd.Series(['a', 'b', 'c', 'd'])\n",
        "})\n",
        "df"
      ]
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    {
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      "execution_count": 23,
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        "outputId": "8e721813-33e3-4987-ab1a-39610441b1fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      name  age   used  price\n",
              "0     Audi    8   True  10000\n",
              "1      BMW   12   True  25000\n",
              "2  Citroen    1  False  11000"
            ],
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              "\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2e48216f-5644-41c2-882c-e79f98c5f925');\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-e642cc40-d221-49e7-bc33-b2ffd570c9c4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e642cc40-d221-49e7-bc33-b2ffd570c9c4')\"\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-e642cc40-d221-49e7-bc33-b2ffd570c9c4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_cars",
              "summary": "{\n  \"name\": \"df_cars\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Audi\",\n          \"BMW\",\n          \"Citroen\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 1,\n        \"max\": 12,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          8,\n          12,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"used\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8386,\n        \"min\": 10000,\n        \"max\": 25000,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          10000,\n          25000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "# create dataframe from `dict`:\n",
        "df_cars = pd.DataFrame(\n",
        "    {\n",
        "        \"name\": ['Audi', 'BMW', 'Citroen'],\n",
        "        \"age\": [8, 12, 1],\n",
        "        \"used\": [True, True, False],\n",
        "        \"price\": [10000, 25000, 11000]\n",
        "    }\n",
        ")\n",
        "\n",
        "df_cars"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4jxSnE4wb3AK"
      },
      "source": [
        "Let's check type of variables in df:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "CmFPf3Kjb3AK",
        "outputId": "0f8584e0-a9bf-4208-e695-47cca4b2c135",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "name     object\n",
              "age       int64\n",
              "used       bool\n",
              "price     int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "df_cars.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "Pbf5bdTtb3AK",
        "outputId": "5d253eb7-8baf-47eb-b7c4-be8657566da9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "df_cars.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c3BA6AWCb3AK"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "tb_FY_fwb3AK"
      },
      "outputs": [],
      "source": [
        "df_cars.set_index('name', inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "SGAFEFtTb3AL",
        "outputId": "93858071-154a-42c6-9757-959e14f3f4bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         age   used  price\n",
              "name                      \n",
              "Audi       8   True  10000\n",
              "BMW       12   True  25000\n",
              "Citroen    1  False  11000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5c3c8fec-5d29-486a-b038-a644c96febb1\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>used</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Audi</th>\n",
              "      <td>8</td>\n",
              "      <td>True</td>\n",
              "      <td>10000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BMW</th>\n",
              "      <td>12</td>\n",
              "      <td>True</td>\n",
              "      <td>25000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Citroen</th>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>11000</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-5c3c8fec-5d29-486a-b038-a644c96febb1')\"\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-5c3c8fec-5d29-486a-b038-a644c96febb1 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-5c3c8fec-5d29-486a-b038-a644c96febb1');\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-8b9be88c-e9e4-4212-835e-0d99c1051cbc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8b9be88c-e9e4-4212-835e-0d99c1051cbc')\"\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",
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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-8b9be88c-e9e4-4212-835e-0d99c1051cbc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_cars",
              "summary": "{\n  \"name\": \"df_cars\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Audi\",\n          \"BMW\",\n          \"Citroen\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 1,\n        \"max\": 12,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          8,\n          12,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"used\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8386,\n        \"min\": 10000,\n        \"max\": 25000,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          10000,\n          25000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "df_cars"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7tmrE3VDb3AL"
      },
      "source": [
        "Different way how to create an index when creating a df:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "XjlwK9Csb3AL",
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          "height": 143
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         name  age   used  price\n",
              "car1     Audi    8   True  10000\n",
              "car2      BMW   12   True  25000\n",
              "car3  Citroen    1  False  11000"
            ],
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              "\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      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",
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              "</div>\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_cars",
              "summary": "{\n  \"name\": \"df_cars\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Audi\",\n          \"BMW\",\n          \"Citroen\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 1,\n        \"max\": 12,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          8,\n          12,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"used\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8386,\n        \"min\": 10000,\n        \"max\": 25000,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          10000,\n          25000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "df_cars = pd.DataFrame(\n",
        "    {\n",
        "        \"name\": ['Audi', 'BMW', 'Citroen'],\n",
        "        \"age\": [8, 12, 1],\n",
        "        \"used\": [True, True, False],\n",
        "        \"price\": [10000, 25000, 11000]\n",
        "    }, index = ['car1', 'car2', 'car3']   # note the index we specify\n",
        ")\n",
        "\n",
        "df_cars"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cVe-dhJGb3AL"
      },
      "source": [
        "Now the \"individuals\" are indexed not via numbers, but via our own indexes."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T_ojlD-9b3AM"
      },
      "source": [
        "### How to access elements?\n",
        "\n",
        "There are two ways how to access them:\n",
        " - using `loc` function for label-based indexing\n",
        " - using `iloc` for order-based indexing\n",
        "\n",
        "❗ Note the square brackets:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "Q-RC_Jmqb3AM",
        "outputId": "276ca5b6-203c-40cd-87a8-976915913270",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "name      Audi\n",
              "age          8\n",
              "used      True\n",
              "price    10000\n",
              "Name: car1, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "df_cars.loc[\"car1\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "sWdujtM_b3AM",
        "outputId": "b88f81c3-6684-4b06-bbae-2275837c4e3e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "name      Audi\n",
              "age          8\n",
              "used      True\n",
              "price    10000\n",
              "Name: car1, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "df_cars.iloc[0]  # first entry is Audi"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GIXt34xeb3AM"
      },
      "source": [
        "How to write out a single column form df?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "F1ZBMyNqb3AM",
        "outputId": "49821777-8829-43c1-8405-e67d868dfb73",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "car1    10000\n",
              "car2    25000\n",
              "car3    11000\n",
              "Name: price, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "df_cars[\"price\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fkwaY5bgb3AM"
      },
      "source": [
        "The column should be `pd.Series`, right?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "sFZAXlTlb3AN",
        "outputId": "3128d37d-a230-4aa3-c07f-4ea9708d5d59",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ],
            "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.series.Series</b><br/>def __init__(data=None, index=None, dtype: Dtype | None=None, name=None, copy: bool | None=None, fastpath: bool=False) -&gt; None</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/pandas/core/series.py</a>One-dimensional ndarray with axis labels (including time series).\n",
              "\n",
              "Labels need not be unique but must be a hashable type. The object\n",
              "supports both integer- and label-based indexing and provides a host of\n",
              "methods for performing operations involving the index. Statistical\n",
              "methods from ndarray have been overridden to automatically exclude\n",
              "missing data (currently represented as NaN).\n",
              "\n",
              "Operations between Series (+, -, /, \\*, \\*\\*) align values based on their\n",
              "associated index values-- they need not be the same length. The result\n",
              "index will be the sorted union of the two indexes.\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "data : array-like, Iterable, dict, or scalar value\n",
              "    Contains data stored in Series. If data is a dict, argument order is\n",
              "    maintained.\n",
              "index : array-like or Index (1d)\n",
              "    Values must be hashable and have the same length as `data`.\n",
              "    Non-unique index values are allowed. Will default to\n",
              "    RangeIndex (0, 1, 2, ..., n) if not provided. If data is dict-like\n",
              "    and index is None, then the keys in the data are used as the index. If the\n",
              "    index is not None, the resulting Series is reindexed with the index values.\n",
              "dtype : str, numpy.dtype, or ExtensionDtype, optional\n",
              "    Data type for the output Series. If not specified, this will be\n",
              "    inferred from `data`.\n",
              "    See the :ref:`user guide &lt;basics.dtypes&gt;` for more usages.\n",
              "name : Hashable, default None\n",
              "    The name to give to the Series.\n",
              "copy : bool, default False\n",
              "    Copy input data. Only affects Series or 1d ndarray input. See examples.\n",
              "\n",
              "Notes\n",
              "-----\n",
              "Please reference the :ref:`User Guide &lt;basics.series&gt;` for more information.\n",
              "\n",
              "Examples\n",
              "--------\n",
              "Constructing Series from a dictionary with an Index specified\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;a&#x27;: 1, &#x27;b&#x27;: 2, &#x27;c&#x27;: 3}\n",
              "&gt;&gt;&gt; ser = pd.Series(data=d, index=[&#x27;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;])\n",
              "&gt;&gt;&gt; ser\n",
              "a   1\n",
              "b   2\n",
              "c   3\n",
              "dtype: int64\n",
              "\n",
              "The keys of the dictionary match with the Index values, hence the Index\n",
              "values have no effect.\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;a&#x27;: 1, &#x27;b&#x27;: 2, &#x27;c&#x27;: 3}\n",
              "&gt;&gt;&gt; ser = pd.Series(data=d, index=[&#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;])\n",
              "&gt;&gt;&gt; ser\n",
              "x   NaN\n",
              "y   NaN\n",
              "z   NaN\n",
              "dtype: float64\n",
              "\n",
              "Note that the Index is first build with the keys from the dictionary.\n",
              "After this the Series is reindexed with the given Index values, hence we\n",
              "get all NaN as a result.\n",
              "\n",
              "Constructing Series from a list with `copy=False`.\n",
              "\n",
              "&gt;&gt;&gt; r = [1, 2]\n",
              "&gt;&gt;&gt; ser = pd.Series(r, copy=False)\n",
              "&gt;&gt;&gt; ser.iloc[0] = 999\n",
              "&gt;&gt;&gt; r\n",
              "[1, 2]\n",
              "&gt;&gt;&gt; ser\n",
              "0    999\n",
              "1      2\n",
              "dtype: int64\n",
              "\n",
              "Due to input data type the Series has a `copy` of\n",
              "the original data even though `copy=False`, so\n",
              "the data is unchanged.\n",
              "\n",
              "Constructing Series from a 1d ndarray with `copy=False`.\n",
              "\n",
              "&gt;&gt;&gt; r = np.array([1, 2])\n",
              "&gt;&gt;&gt; ser = pd.Series(r, copy=False)\n",
              "&gt;&gt;&gt; ser.iloc[0] = 999\n",
              "&gt;&gt;&gt; r\n",
              "array([999,   2])\n",
              "&gt;&gt;&gt; ser\n",
              "0    999\n",
              "1      2\n",
              "dtype: int64\n",
              "\n",
              "Due to input data type the Series has a `view` on\n",
              "the original data, so\n",
              "the data is changed as well.</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, 244);\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": 32
        }
      ],
      "source": [
        "type(df_cars[\"price\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ivPDau6eb3AN"
      },
      "source": [
        "### Importing data to pandas\n",
        "\n",
        " - in pandas I/O is very simple and convenient. You can import (and export) to huge amount of formats:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BvdqmWNqb3AN"
      },
      "source": [
        "| File Format            | Short Comment                                     | Example Command                                   |\n",
        "|------------------------|---------------------------------------------------|---------------------------------------------------|\n",
        "| CSV (Comma-Separated Values) | Common text-based format for tabular data.   | `pd.read_csv('data.csv')`                     |\n",
        "| Excel Spreadsheet     | Popular format for storing data in sheets.         | `pd.read_excel('data.xlsx')`                      |\n",
        "| JSON (JavaScript Object Notation) | Lightweight data interchange format.        | `pd.read_json('data.json')`                     |\n",
        "| SQL Database           | Import data from SQL databases using SQLAlchemy.   | `pd.read_sql('SELECT * FROM table_name', connection)` |\n",
        "| HTML tables            | Extract tables from HTML documents.                | `pd.read_html('page.html')`                      |\n",
        "| HDF5 (hierarchical data format version 5) | Designed for storing large amounts of data. | `pd.read_hdf('data.h5', 'key')`                   |\n",
        "| Feather                | Fast, lightweight binary columnar data format.     | `pd.read_feather('data.feather')`                |\n",
        "| Parquet                | Columnar storage format optimized for analytics.   | `pd.read_parquet('data.parquet')`                |\n",
        "| Msgpack                | Efficient binary serialization format.             | `pd.read_msgpack('data.msgpack')`                |\n",
        "| Stata                  | Popular format for social science research data.   | `pd.read_stata('data.dta')`                      |\n",
        "| SAS                    | Statistical Analysis System format.                | `pd.read_sas('data.sas7bdat')`                   |\n",
        "| SPSS                   | Statistical Package for the Social Sciences format.| `pd.read_spss('data.sav')`                       |\n",
        "| Google BigQuery        | Import data from Google BigQuery database tables.  | `pd.read_gbq('SELECT * FROM table_name', project_id)` |\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MdlSyYtsb3AN"
      },
      "source": [
        "Let's try it:\n",
        " - create `.csv` file (for simplicity we will use magic of jupyter notebooks, but locally you can import any local file)\n",
        " - load in pandas\n",
        " - print it out"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gyxeUJL5b3AN"
      },
      "source": [
        "(source: [Kaggle](https://www.kaggle.com/datasets/crawford/80-cereals))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "A5CpZJKrb3AO",
        "outputId": "b260df2c-1323-4f80-a24a-9a2ddaa18a5e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing cereals.csv\n"
          ]
        }
      ],
      "source": [
        "%%writefile cereals.csv\n",
        "name,mfr,type_of,calories,protein,fat,sodium,fiber,carbo,sugars,potass,vitamins,shelf,weight,cups,rating\n",
        "100% Bran,N,C,70,4,1,130,10,5,6,280,25,3,1,0.33,68.402973\n",
        "100% Natural Bran,Q,C,120,3,5,15,2,8,8,135,0,3,1,1,33.983679\n",
        "All-Bran,K,C,70,4,1,260,9,7,5,320,25,3,1,0.33,59.425505\n",
        "All-Bran with Extra Fiber,K,C,50,4,0,140,14,8,0,330,25,3,1,0.5,93.704912\n",
        "Almond Delight,R,C,110,2,2,200,1,14,8,-1,25,3,1,0.75,34.384843\n",
        "Apple Cinnamon Cheerios,G,C,110,2,2,180,1.5,10.5,10,70,25,1,1,0.75,29.509541\n",
        "Apple Jacks,K,C,110,2,0,125,1,11,14,30,25,2,1,1,33.174094\n",
        "Basic 4,G,C,130,3,2,210,2,18,8,100,25,3,1.33,0.75,37.038562\n",
        "Bran Chex,R,C,90,2,1,200,4,15,6,125,25,1,1,0.67,49.120253\n",
        "Bran Flakes,P,C,90,3,0,210,5,13,5,190,25,3,1,0.67,53.313813\n",
        "Cap'n'Crunch,Q,C,120,1,2,220,0,12,12,35,25,2,1,0.75,18.042851\n",
        "Cheerios,G,C,110,6,2,290,2,17,1,105,25,1,1,1.25,50.764999\n",
        "Cinnamon Toast Crunch,G,C,120,1,3,210,0,13,9,45,25,2,1,0.75,19.823573\n",
        "Clusters,G,C,110,3,2,140,2,13,7,105,25,3,1,0.5,40.400208\n",
        "Cocoa Puffs,G,C,110,1,1,180,0,12,13,55,25,2,1,1,22.736446\n",
        "Corn Chex,R,C,110,2,0,280,0,22,3,25,25,1,1,1,41.445019\n",
        "Corn Flakes,K,C,100,2,0,290,1,21,2,35,25,1,1,1,45.863324\n",
        "Corn Pops,K,C,110,1,0,90,1,13,12,20,25,2,1,1,35.782791\n",
        "Count Chocula,G,C,110,1,1,180,0,12,13,65,25,2,1,1,22.396513\n",
        "Cracklin' Oat Bran,K,C,110,3,3,140,4,10,7,160,25,3,1,0.5,40.448772\n",
        "Cream of Wheat (Quick),N,H,100,3,0,80,1,21,0,-1,0,2,1,1,64.533816\n",
        "Crispix,K,C,110,2,0,220,1,21,3,30,25,3,1,1,46.895644\n",
        "Crispy Wheat & Raisins,G,C,100,2,1,140,2,11,10,120,25,3,1,0.75,36.176196\n",
        "Double Chex,R,C,100,2,0,190,1,18,5,80,25,3,1,0.75,44.330856\n",
        "Froot Loops,K,C,110,2,1,125,1,11,13,30,25,2,1,1,32.207582\n",
        "Frosted Flakes,K,C,110,1,0,200,1,14,11,25,25,1,1,0.75,31.435973\n",
        "Frosted Mini-Wheats,K,C,100,3,0,0,3,14,7,100,25,2,1,0.8,58.345141\n",
        "Fruit & Fibre Dates Walnuts and Oats,P,C,120,3,2,160,5,12,10,200,25,3,1.25,0.67,40.917047\n",
        "Fruitful Bran,K,C,120,3,0,240,5,14,12,190,25,3,1.33,0.67,41.015492\n",
        "Fruity Pebbles,P,C,110,1,1,135,0,13,12,25,25,2,1,0.75,28.025765\n",
        "Golden Crisp,P,C,100,2,0,45,0,11,15,40,25,1,1,0.88,35.252444\n",
        "Golden Grahams,G,C,110,1,1,280,0,15,9,45,25,2,1,0.75,23.804043\n",
        "Grape Nuts Flakes,P,C,100,3,1,140,3,15,5,85,25,3,1,0.88,52.076897\n",
        "Grape-Nuts,P,C,110,3,0,170,3,17,3,90,25,3,1,0.25,53.371007\n",
        "Great Grains Pecan,P,C,120,3,3,75,3,13,4,100,25,3,1,0.33,45.811716\n",
        "Honey Graham Ohs,Q,C,120,1,2,220,1,12,11,45,25,2,1,1,21.871292\n",
        "Honey Nut Cheerios,G,C,110,3,1,250,1.5,11.5,10,90,25,1,1,0.75,31.072217\n",
        "Honey-comb,P,C,110,1,0,180,0,14,11,35,25,1,1,1.33,28.742414\n",
        "Just Right Crunchy  Nuggets,K,C,110,2,1,170,1,17,6,60,100,3,1,1,36.523683\n",
        "Just Right Fruit & Nut,K,C,140,3,1,170,2,20,9,95,100,3,1.3,0.75,36.471512\n",
        "Kix,G,C,110,2,1,260,0,21,3,40,25,2,1,1.5,39.241114\n",
        "Life,Q,C,100,4,2,150,2,12,6,95,25,2,1,0.67,45.328074\n",
        "Lucky Charms,G,C,110,2,1,180,0,12,12,55,25,2,1,1,26.734515\n",
        "Maypo,A,H,100,4,1,0,0,16,3,95,25,2,1,1,54.850917\n",
        "Muesli Raisins Dates & Almonds,R,C,150,4,3,95,3,16,11,170,25,3,1,1,37.136863\n",
        "Muesli Raisins Peaches & Pecans,R,C,150,4,3,150,3,16,11,170,25,3,1,1,34.139765\n",
        "Mueslix Crispy Blend,K,C,160,3,2,150,3,17,13,160,25,3,1.5,0.67,30.313351\n",
        "Multi-Grain Cheerios,G,C,100,2,1,220,2,15,6,90,25,1,1,1,40.105965\n",
        "Nut&Honey Crunch,K,C,120,2,1,190,0,15,9,40,25,2,1,0.67,29.924285\n",
        "Nutri-Grain Almond-Raisin,K,C,140,3,2,220,3,21,7,130,25,3,1.33,0.67,40.692320\n",
        "Nutri-grain Wheat,K,C,90,3,0,170,3,18,2,90,25,3,1,1,59.642837\n",
        "Oatmeal Raisin Crisp,G,C,130,3,2,170,1.5,13.5,10,120,25,3,1.25,0.5,30.450843\n",
        "Post Nat. Raisin Bran,P,C,120,3,1,200,6,11,14,260,25,3,1.33,0.67,37.840594\n",
        "Product 19,K,C,100,3,0,320,1,20,3,45,100,3,1,1,41.503540\n",
        "Puffed Rice,Q,C,50,1,0,0,0,13,0,15,0,3,0.5,1,60.756112\n",
        "Puffed Wheat,Q,C,50,2,0,0,1,10,0,50,0,3,0.5,1,63.005645\n",
        "Quaker Oat Squares,Q,C,100,4,1,135,2,14,6,110,25,3,1,0.5,49.511874\n",
        "Quaker Oatmeal,Q,H,100,5,2,0,2.7,-1,-1,110,0,1,1,0.67,50.828392\n",
        "Raisin Bran,K,C,120,3,1,210,5,14,12,240,25,2,1.33,0.75,39.259197\n",
        "Raisin Nut Bran,G,C,100,3,2,140,2.5,10.5,8,140,25,3,1,0.5,39.703400\n",
        "Raisin Squares,K,C,90,2,0,0,2,15,6,110,25,3,1,0.5,55.333142\n",
        "Rice Chex,R,C,110,1,0,240,0,23,2,30,25,1,1,1.13,41.998933\n",
        "Rice Krispies,K,C,110,2,0,290,0,22,3,35,25,1,1,1,40.560159\n",
        "Shredded Wheat,N,C,80,2,0,0,3,16,0,95,0,1,0.83,1,68.235885\n",
        "Shredded Wheat'n'Bran,N,C,90,3,0,0,4,19,0,140,0,1,1,0.67,74.472949\n",
        "Shredded Wheat spoon size,N,C,90,3,0,0,3,20,0,120,0,1,1,0.67,72.801787\n",
        "Smacks,K,C,110,2,1,70,1,9,15,40,25,2,1,0.75,31.230054\n",
        "Special K,K,C,110,6,0,230,1,16,3,55,25,1,1,1,53.131324\n",
        "Strawberry Fruit Wheats,N,C,90,2,0,15,3,15,5,90,25,2,1,1,59.363993\n",
        "Total Corn Flakes,G,C,110,2,1,200,0,21,3,35,100,3,1,1,38.839746\n",
        "Total Raisin Bran,G,C,140,3,1,190,4,15,14,230,100,3,1.5,1,28.592785\n",
        "Total Whole Grain,G,C,100,3,1,200,3,16,3,110,100,3,1,1,46.658844\n",
        "Triples,G,C,110,2,1,250,0,21,3,60,25,3,1,0.75,39.106174\n",
        "Trix,G,C,110,1,1,140,0,13,12,25,25,2,1,1,27.753301\n",
        "Wheat Chex,R,C,100,3,1,230,3,17,3,115,25,1,1,0.67,49.787445\n",
        "Wheaties,G,C,100,3,1,200,3,17,3,110,25,1,1,1,51.592193\n",
        "Wheaties Honey Gold,G,C,110,2,1,200,1,16,8,60,25,1,1,0.75,36.187559"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "vt9VOLuLb3AO",
        "outputId": "06b67455-713c-4b82-9283-109512db1861",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        name mfr type_of  calories  protein  fat  sodium  \\\n",
              "0                  100% Bran   N       C        70        4    1     130   \n",
              "1          100% Natural Bran   Q       C       120        3    5      15   \n",
              "2                   All-Bran   K       C        70        4    1     260   \n",
              "3  All-Bran with Extra Fiber   K       C        50        4    0     140   \n",
              "4             Almond Delight   R       C       110        2    2     200   \n",
              "\n",
              "   fiber  carbo  sugars  potass  vitamins  shelf  weight  cups     rating  \n",
              "0   10.0    5.0       6     280        25      3     1.0  0.33  68.402973  \n",
              "1    2.0    8.0       8     135         0      3     1.0  1.00  33.983679  \n",
              "2    9.0    7.0       5     320        25      3     1.0  0.33  59.425505  \n",
              "3   14.0    8.0       0     330        25      3     1.0  0.50  93.704912  \n",
              "4    1.0   14.0       8      -1        25      3     1.0  0.75  34.384843  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-add1b73a-2de3-4c49-bf40-b5a4d1903671\" 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>name</th>\n",
              "      <th>mfr</th>\n",
              "      <th>type_of</th>\n",
              "      <th>calories</th>\n",
              "      <th>protein</th>\n",
              "      <th>fat</th>\n",
              "      <th>sodium</th>\n",
              "      <th>fiber</th>\n",
              "      <th>carbo</th>\n",
              "      <th>sugars</th>\n",
              "      <th>potass</th>\n",
              "      <th>vitamins</th>\n",
              "      <th>shelf</th>\n",
              "      <th>weight</th>\n",
              "      <th>cups</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100% Bran</td>\n",
              "      <td>N</td>\n",
              "      <td>C</td>\n",
              "      <td>70</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6</td>\n",
              "      <td>280</td>\n",
              "      <td>25</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.33</td>\n",
              "      <td>68.402973</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100% Natural Bran</td>\n",
              "      <td>Q</td>\n",
              "      <td>C</td>\n",
              "      <td>120</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>15</td>\n",
              "      <td>2.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8</td>\n",
              "      <td>135</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>33.983679</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>All-Bran</td>\n",
              "      <td>K</td>\n",
              "      <td>C</td>\n",
              "      <td>70</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>260</td>\n",
              "      <td>9.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>5</td>\n",
              "      <td>320</td>\n",
              "      <td>25</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.33</td>\n",
              "      <td>59.425505</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>All-Bran with Extra Fiber</td>\n",
              "      <td>K</td>\n",
              "      <td>C</td>\n",
              "      <td>50</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>140</td>\n",
              "      <td>14.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0</td>\n",
              "      <td>330</td>\n",
              "      <td>25</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.50</td>\n",
              "      <td>93.704912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Almond Delight</td>\n",
              "      <td>R</td>\n",
              "      <td>C</td>\n",
              "      <td>110</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_cereals",
              "summary": "{\n  \"name\": \"df_cereals\",\n  \"rows\": 77,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 77,\n        \"samples\": [\n          \"Almond Delight\",\n          \"Honey Graham Ohs\",\n          \"Cap'n'Crunch\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mfr\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"N\",\n          \"Q\",\n          \"P\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"type_of\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"H\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"calories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 50,\n        \"max\": 160,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          90,\n          70\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"protein\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          5,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sodium\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 83,\n        \"min\": 0,\n        \"max\": 320,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          220,\n          190\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fiber\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.383363964387222,\n        \"min\": 0.0,\n        \"max\": 14.0,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          2.7,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"carbo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.278956280325908,\n        \"min\": -1.0,\n        \"max\": 23.0,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          5.0,\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sugars\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": -1,\n        \"max\": 15,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          6,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"potass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 71,\n        \"min\": -1,\n        \"max\": 330,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          115,\n          55\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vitamins\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22,\n        \"min\": 0,\n        \"max\": 100,\n        \"num_unique_values\": 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            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "df_cereals = pd.read_csv(\"cereals.csv\", delimiter=',')\n",
        "df_cereals.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2zyh3fR1b3AO"
      },
      "source": [
        "Impractical, right? Luckily pandas can directly read from weblink:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "_F298wwTb3AO",
        "outputId": "540e9193-9632-463d-8fc1-10aaa01c096d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        name mfr type_of  calories  protein  fat  sodium  \\\n",
              "0                  100% Bran   N       C        70        4    1     130   \n",
              "1          100% Natural Bran   Q       C       120        3    5      15   \n",
              "2                   All-Bran   K       C        70        4    1     260   \n",
              "3  All-Bran with Extra Fiber   K       C        50        4    0     140   \n",
              "4             Almond Delight   R       C       110        2    2     200   \n",
              "\n",
              "   fiber  carbo  sugars  potass  vitamins  shelf  weight  cups     rating  \n",
              "0   10.0    5.0       6     280        25      3     1.0  0.33  68.402973  \n",
              "1    2.0    8.0       8     135         0      3     1.0  1.00  33.983679  \n",
              "2    9.0    7.0       5     320        25      3     1.0  0.33  59.425505  \n",
              "3   14.0    8.0       0     330        25      3     1.0  0.50  93.704912  \n",
              "4    1.0   14.0       8      -1        25      3     1.0  0.75  34.384843  "
            ],
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              "      <td>100% Bran</td>\n",
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              "      <td>100% Natural Bran</td>\n",
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              "      <th>2</th>\n",
              "      <td>All-Bran</td>\n",
              "      <td>K</td>\n",
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              "      <td>All-Bran with Extra Fiber</td>\n",
              "      <td>K</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Almond Delight</td>\n",
              "      <td>R</td>\n",
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              "        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",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    border-color: transparent;\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
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              "    }\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-8a495fe5-cd07-4637-a52f-ddc9488596e0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_cereals",
              "summary": "{\n  \"name\": \"df_cereals\",\n  \"rows\": 77,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 77,\n        \"samples\": [\n          \"Almond Delight\",\n          \"Honey Graham Ohs\",\n          \"Cap'n'Crunch\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mfr\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"N\",\n          \"Q\",\n          \"P\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"type_of\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"H\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"calories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 50,\n        \"max\": 160,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          90,\n          70\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"protein\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          5,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sodium\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 83,\n        \"min\": 0,\n        \"max\": 320,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          220,\n          190\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fiber\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.383363964387222,\n        \"min\": 0.0,\n        \"max\": 14.0,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          2.7,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"carbo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.278956280325908,\n        \"min\": -1.0,\n        \"max\": 23.0,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          5.0,\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sugars\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": -1,\n        \"max\": 15,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          6,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"potass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 71,\n        \"min\": -1,\n        \"max\": 330,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          115,\n          55\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vitamins\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22,\n        \"min\": 0,\n        \"max\": 100,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          25,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"shelf\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15047679973689215,\n        \"min\": 0.5,\n        \"max\": 1.5,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1.0,\n          1.33\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cups\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2327161384469139,\n        \"min\": 0.25,\n        \"max\": 1.5,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          1.5,\n          1.33\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.04728874373522,\n        \"min\": 18.042851,\n        \"max\": 93.704912,\n        \"num_unique_values\": 77,\n        \"samples\": [\n          34.384843,\n          21.871292\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "del df_cereals\n",
        "\n",
        "df_cereals = pd.read_csv(\"https://gist.githubusercontent.com/aleksejalex/26a83646c03120af1eaeb117572d895e/raw/2ddc8661d86fbf1b7d09204ff39fdf74ce3723b6/cereals.csv\", delimiter=',')\n",
        "df_cereals.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eS5b1yuab3AP"
      },
      "source": [
        "Now let's have a little fun with those data:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "35YJz-D3b3AP"
      },
      "source": [
        "### Show the data:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "9jB1Iyk2b3AP",
        "outputId": "c7122ec6-a8cb-4203-b633-b111390cf265",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        name mfr type_of  calories  protein  fat  sodium  \\\n",
              "0                  100% Bran   N       C        70        4    1     130   \n",
              "1          100% Natural Bran   Q       C       120        3    5      15   \n",
              "2                   All-Bran   K       C        70        4    1     260   \n",
              "3  All-Bran with Extra Fiber   K       C        50        4    0     140   \n",
              "4             Almond Delight   R       C       110        2    2     200   \n",
              "5    Apple Cinnamon Cheerios   G       C       110        2    2     180   \n",
              "6                Apple Jacks   K       C       110        2    0     125   \n",
              "7                    Basic 4   G       C       130        3    2     210   \n",
              "\n",
              "   fiber  carbo  sugars  potass  vitamins  shelf  weight  cups     rating  \n",
              "0   10.0    5.0       6     280        25      3    1.00  0.33  68.402973  \n",
              "1    2.0    8.0       8     135         0      3    1.00  1.00  33.983679  \n",
              "2    9.0    7.0       5     320        25      3    1.00  0.33  59.425505  \n",
              "3   14.0    8.0       0     330        25      3    1.00  0.50  93.704912  \n",
              "4    1.0   14.0       8      -1        25      3    1.00  0.75  34.384843  \n",
              "5    1.5   10.5      10      70        25      1    1.00  0.75  29.509541  \n",
              "6    1.0   11.0      14      30        25      2    1.00  1.00  33.174094  \n",
              "7    2.0   18.0       8     100        25      3    1.33  0.75  37.038562  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-def782b8-b170-4000-a24f-ea55a0c0d9a2\" 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>name</th>\n",
              "      <th>mfr</th>\n",
              "      <th>type_of</th>\n",
              "      <th>calories</th>\n",
              "      <th>protein</th>\n",
              "      <th>fat</th>\n",
              "      <th>sodium</th>\n",
              "      <th>fiber</th>\n",
              "      <th>carbo</th>\n",
              "      <th>sugars</th>\n",
              "      <th>potass</th>\n",
              "      <th>vitamins</th>\n",
              "      <th>shelf</th>\n",
              "      <th>weight</th>\n",
              "      <th>cups</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100% Bran</td>\n",
              "      <td>N</td>\n",
              "      <td>C</td>\n",
              "      <td>70</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6</td>\n",
              "      <td>280</td>\n",
              "      <td>25</td>\n",
              "      <td>3</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.33</td>\n",
              "      <td>68.402973</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100% Natural Bran</td>\n",
              "      <td>Q</td>\n",
              "      <td>C</td>\n",
              "      <td>120</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>15</td>\n",
              "      <td>2.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8</td>\n",
              "      <td>135</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>33.983679</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>All-Bran</td>\n",
              "      <td>K</td>\n",
              "      <td>C</td>\n",
              "      <td>70</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>260</td>\n",
              "      <td>9.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>5</td>\n",
              "      <td>320</td>\n",
              "      <td>25</td>\n",
              "      <td>3</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.33</td>\n",
              "      <td>59.425505</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>All-Bran with Extra Fiber</td>\n",
              "      <td>K</td>\n",
              "      <td>C</td>\n",
              "      <td>50</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>140</td>\n",
              "      <td>14.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0</td>\n",
              "      <td>330</td>\n",
              "      <td>25</td>\n",
              "      <td>3</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>93.704912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Almond Delight</td>\n",
              "      <td>R</td>\n",
              "      <td>C</td>\n",
              "      <td>110</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>200</td>\n",
              "      <td>1.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>8</td>\n",
              "      <td>-1</td>\n",
              "      <td>25</td>\n",
              "      <td>3</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>34.384843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Apple Cinnamon Cheerios</td>\n",
              "      <td>G</td>\n",
              "      <td>C</td>\n",
              "      <td>110</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>180</td>\n",
              "      <td>1.5</td>\n",
              "      <td>10.5</td>\n",
              "      <td>10</td>\n",
              "      <td>70</td>\n",
              "      <td>25</td>\n",
              "      <td>1</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>29.509541</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Apple Jacks</td>\n",
              "      <td>K</td>\n",
              "      <td>C</td>\n",
              "      <td>110</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>125</td>\n",
              "      <td>1.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>14</td>\n",
              "      <td>30</td>\n",
              "      <td>25</td>\n",
              "      <td>2</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>33.174094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Basic 4</td>\n",
              "      <td>G</td>\n",
              "      <td>C</td>\n",
              "      <td>130</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>210</td>\n",
              "      <td>2.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>8</td>\n",
              "      <td>100</td>\n",
              "      <td>25</td>\n",
              "      <td>3</td>\n",
              "      <td>1.33</td>\n",
              "      <td>0.75</td>\n",
              "      <td>37.038562</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-def782b8-b170-4000-a24f-ea55a0c0d9a2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
              "      cursor: pointer;\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",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "\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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-def782b8-b170-4000-a24f-ea55a0c0d9a2 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-def782b8-b170-4000-a24f-ea55a0c0d9a2');\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",
              "\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",
              "      --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-74b35ff2-a90c-476a-8eca-f64221c6e426 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_cereals",
              "summary": "{\n  \"name\": \"df_cereals\",\n  \"rows\": 77,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 77,\n        \"samples\": [\n          \"Almond Delight\",\n          \"Honey Graham Ohs\",\n          \"Cap'n'Crunch\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mfr\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"N\",\n          \"Q\",\n          \"P\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"type_of\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"H\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"calories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 50,\n        \"max\": 160,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          90,\n          70\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"protein\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          5,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sodium\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 83,\n        \"min\": 0,\n        \"max\": 320,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          220,\n          190\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fiber\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.383363964387222,\n        \"min\": 0.0,\n        \"max\": 14.0,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          2.7,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"carbo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.278956280325908,\n        \"min\": -1.0,\n        \"max\": 23.0,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          5.0,\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sugars\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": -1,\n        \"max\": 15,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          6,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"potass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 71,\n        \"min\": -1,\n        \"max\": 330,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          115,\n          55\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vitamins\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22,\n        \"min\": 0,\n        \"max\": 100,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          25,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"shelf\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15047679973689215,\n        \"min\": 0.5,\n        \"max\": 1.5,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1.0,\n          1.33\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cups\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2327161384469139,\n        \"min\": 0.25,\n        \"max\": 1.5,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          1.5,\n          1.33\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.04728874373522,\n        \"min\": 18.042851,\n        \"max\": 93.704912,\n        \"num_unique_values\": 77,\n        \"samples\": [\n          34.384843,\n          21.871292\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "df_cereals.head(8)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "le-nyPz4b3AP"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rlwkdBE4b3AP"
      },
      "source": [
        "### Check for types and retype if needed"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "_MW9VCqYb3AP",
        "outputId": "cb005461-6b3b-486a-a953-d558fd2d76de",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "name         object\n",
              "mfr          object\n",
              "type_of      object\n",
              "calories      int64\n",
              "protein       int64\n",
              "fat           int64\n",
              "sodium        int64\n",
              "fiber       float64\n",
              "carbo       float64\n",
              "sugars        int64\n",
              "potass        int64\n",
              "vitamins      int64\n",
              "shelf         int64\n",
              "weight      float64\n",
              "cups        float64\n",
              "rating      float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "df_cereals.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "gwxeYzcAb3AQ"
      },
      "outputs": [],
      "source": [
        "df_cereals[\"calories\"] = df_cereals[\"calories\"].astype('float')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "dtoaIkOYb3AQ"
      },
      "outputs": [],
      "source": [
        "df_cereals[\"type_of\"] = df_cereals[\"type_of\"].astype(\"category\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "n71U7O3Mb3AQ",
        "outputId": "3e665a75-d0a5-473a-d28e-e858ed7a78a6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "name          object\n",
              "mfr           object\n",
              "type_of     category\n",
              "calories     float64\n",
              "protein        int64\n",
              "fat            int64\n",
              "sodium         int64\n",
              "fiber        float64\n",
              "carbo        float64\n",
              "sugars         int64\n",
              "potass         int64\n",
              "vitamins       int64\n",
              "shelf          int64\n",
              "weight       float64\n",
              "cups         float64\n",
              "rating       float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "df_cereals.dtypes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eaPAf84lb3AQ"
      },
      "source": [
        "### Know your data - basic statistics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "qUDYeiVkb3AQ",
        "outputId": "44c76d14-bedb-47d6-a717-d66820f7ec2e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             name  mfr type_of    calories    protein        fat      sodium  \\\n",
              "count          77   77      77   77.000000  77.000000  77.000000   77.000000   \n",
              "unique         77    7       2         NaN        NaN        NaN         NaN   \n",
              "top     100% Bran    K       C         NaN        NaN        NaN         NaN   \n",
              "freq            1   23      74         NaN        NaN        NaN         NaN   \n",
              "mean          NaN  NaN     NaN  106.883117   2.545455   1.012987  159.675325   \n",
              "std           NaN  NaN     NaN   19.484119   1.094790   1.006473   83.832295   \n",
              "min           NaN  NaN     NaN   50.000000   1.000000   0.000000    0.000000   \n",
              "25%           NaN  NaN     NaN  100.000000   2.000000   0.000000  130.000000   \n",
              "50%           NaN  NaN     NaN  110.000000   3.000000   1.000000  180.000000   \n",
              "75%           NaN  NaN     NaN  110.000000   3.000000   2.000000  210.000000   \n",
              "max           NaN  NaN     NaN  160.000000   6.000000   5.000000  320.000000   \n",
              "\n",
              "            fiber      carbo     sugars      potass    vitamins      shelf  \\\n",
              "count   77.000000  77.000000  77.000000   77.000000   77.000000  77.000000   \n",
              "unique        NaN        NaN        NaN         NaN         NaN        NaN   \n",
              "top           NaN        NaN        NaN         NaN         NaN        NaN   \n",
              "freq          NaN        NaN        NaN         NaN         NaN        NaN   \n",
              "mean     2.151948  14.597403   6.922078   96.077922   28.246753   2.207792   \n",
              "std      2.383364   4.278956   4.444885   71.286813   22.342523   0.832524   \n",
              "min      0.000000  -1.000000  -1.000000   -1.000000    0.000000   1.000000   \n",
              "25%      1.000000  12.000000   3.000000   40.000000   25.000000   1.000000   \n",
              "50%      2.000000  14.000000   7.000000   90.000000   25.000000   2.000000   \n",
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              "summary": "{\n  \"name\": \"df_cereals\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"77\",\n          \"100% Bran\",\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mfr\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          7,\n          \"23\",\n          \"77\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"type_of\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"74\",\n          \"77\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"calories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 42.76880326041628,\n        \"min\": 19.484119056820845,\n        \"max\": 160.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          77.0,\n          106.88311688311688,\n          110.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"protein\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26.328409051716815,\n        \"min\": 1.0,\n        \"max\": 77.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          77.0,\n          2.5454545454545454,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26.76482456152886,\n        \"min\": 0.0,\n        \"max\": 77.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          77.0,\n          1.0129870129870129,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sodium\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 97.03152731397152,\n        \"min\": 0.0,\n        \"max\": 320.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          159.67532467532467,\n          180.0,\n          77.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fiber\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26.351020191481886,\n        \"min\": 0.0,\n        \"max\": 77.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.1519480519480516,\n          2.0,\n          77.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"carbo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24.154985506457294,\n        \"min\": -1.0,\n        \"max\": 77.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          14.597402597402597,\n          14.0,\n          77.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sugars\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25.352504002087382,\n        \"min\": -1.0,\n        \"max\": 77.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6.922077922077922,\n          7.0,\n          77.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"potass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 98.9239180801618,\n        \"min\": -1.0,\n        \"max\": 330.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          96.07792207792208,\n          90.0,\n          77.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vitamins\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33.07142412590913,\n        \"min\": 0.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          77.0,\n          28.246753246753247,\n          100.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"shelf\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26.57915435262897,\n        \"min\": 0.832524100135788,\n        \"max\": 77.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          77.0,\n          2.207792207792208,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26.91445941103625,\n        \"min\": 0.15047679973689215,\n        \"max\": 77.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          77.0,\n          1.0296103896103896,\n          1.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cups\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26.96282870902116,\n        \"min\": 0.2327161384469139,\n        \"max\": 77.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.821038961038961,\n          0.75,\n          77.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27.43614648438216,\n        \"min\": 14.04728874373522,\n        \"max\": 93.704912,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          42.66570498701299,\n          40.400208,\n          77.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "df_cereals.describe(include='all')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pbj8jtj9b3AR"
      },
      "source": [
        "### plot your data:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "qlz4DNccb3AR"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "Vq44Q47Vb3AR",
        "outputId": "974ad211-5ad6-4236-bebc-2aabf512e926",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure()\n",
        "plt.plot(df_cereals[\"calories\"], label=\"calories\")\n",
        "plt.plot(df_cereals[\"protein\"], label=\"proteins\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "y94wmND_b3AR"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IFx3iE_ab3AS"
      },
      "source": [
        "## Optional homework: 2 alternatives:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "BNBkFQ8Nb3AS"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OdrgKdrZb3AS"
      },
      "source": [
        "### Option 1: work with your own data\n",
        "**task:** Create a dataframe, containing some of your subjects. Each subject should have these properties: shortcut, number of credits, boolean value if it's optional or obligatory, and room number where lectures takes place (for example for E455 `room=455`).\n",
        "\n",
        "1) create such dataframe\n",
        "2) compute basic statistics (average amount of credits? do you have more optional subjects than obligatory? how often do you visit odd floors for lectures?) consider writing simple functions for some of questions. Feel free to answer your own questions about your data.\n",
        "3) make basic plots. Consider which variables makes sense to plot and how. Use subplots if it makes sense."
      ]
    },
    {
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      "execution_count": 42,
      "metadata": {
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      "source": []
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    {
      "cell_type": "markdown",
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      "source": [
        "### Option 2: work withcereals data\n",
        "**task:** same as above, but for cereals dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "z3ctUUnJb3AT"
      },
      "outputs": [],
      "source": []
    }
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