charting/2023_03_19_charting.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNIE/JwM5/GcwLKkuysV+E1",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/werowe/HypatiaAcademy/blob/master/charting/2023_03_19_charting.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "4T0CsyT4S3bl"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# charting\n",
        "\n",
        "\n",
        "* we did histogram\n",
        "\n",
        "* we did bar chart\n",
        "\n",
        "* we have figures (matplotlib)\n",
        "\n",
        "* have we done pie chart?\n",
        "\n",
        "* scatter chart\n",
        "\n",
        "* line chart\n",
        "\n",
        "* heat map\n",
        "\n",
        "* area chart\n",
        "\n",
        "* stack bar chart\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "bYwhLaOwS8AN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# cast in computers means to force a number to be a specific type\n",
        "\n",
        "#\n",
        "\n",
        "x = np.linspace(1,300,10).astype(np.int16)\n",
        "y = 2 * x\n",
        "\n",
        "z = 3 * x\n",
        "print(x)\n",
        "\n",
        "print(\"\\n\")\n",
        "\n",
        "[x,y,z]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2KEjfmzjTrD_",
        "outputId": "c181b995-bbd3-4091-9a33-85d6ab9ace88"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[  1  34  67 100 133 167 200 233 266 300]\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([  1,  34,  67, 100, 133, 167, 200, 233, 266, 300], dtype=int16),\n",
              " array([  2,  68, 134, 200, 266, 334, 400, 466, 532, 600], dtype=int16),\n",
              " array([  3, 102, 201, 300, 399, 501, 600, 699, 798, 900], dtype=int16)]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "# Create a scatter plot\n",
        "#sns.scatterplot(x=x, y=y)\n",
        "#plt.plot(x,y)\n",
        "plt.scatter(x,y)\n",
        "#plt.title('Line Chart')\n",
        "#plt.xlabel('X-axis')\n",
        "#plt.ylabel('Y-axis')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "ZrXfYV1EUn0-",
        "outputId": "73689567-87cb-4d3d-f8ee-e71a9589c029"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Create a new figure\n",
        "# this is the plain constructor to make a figure object.  meaning we don't it anything\n",
        "fig = plt.figure()\n",
        "\n",
        "fig, (ax1, ax2) = plt.subplots(2, 1, gridspec_kw={'height_ratios': [2, 1]})\n",
        "\n",
        "\n",
        "# Add an axis (subplot) to the figure\n",
        "#ax1 = fig.add_subplot(1, 1, 1)\n",
        "#ax2 = fig.add_subplot(2, 1, 1)\n",
        "\n",
        "# why can't we do this, because ax1 already exists\n",
        "\n",
        "\n",
        "\n",
        "# Plot some data on the axis\n",
        "ax1.plot(x, y)\n",
        "ax2.plot(y, z)\n",
        "\n",
        "# Customize the plot\n",
        "ax1.set_title('Top Plot')\n",
        "ax1.set_xlabel('X-axis')\n",
        "ax1.set_ylabel('Y-axis')\n",
        "\n",
        "ax2.set_title('Bottom Plot')\n",
        "ax2.set_xlabel('X-axis')\n",
        "ax2.set_ylabel('Y-axis')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "-e2Ts3rIWL9i",
        "outputId": "24d9d5ae-604f-4e62-8fc8-81848183dc2f"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "```\n",
        "class matplotlib.figure.Figure(figsize=None, dpi=None, *, facecolor=None, edgecolor=None, linewidth=0.0, frameon=None, subplotpars=None, tight_layout=None, constrained_layout=None, layout=None, **kwargs)\n",
        "\n",
        "\n",
        "matplotlib.pyplot.figure(num=None, figsize=None, dpi=None, *, facecolor=None, edgecolor=None, frameon=True, FigureClass=<class 'matplotlib.figure.Figure'>, clear=False, **kwargs)\n",
        "\n",
        "```"
      ],
      "metadata": {
        "id": "N5cl5pwFZL1U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Figure:\n",
        "\n",
        "  # a constructor is a function that creates the object\n",
        "  def __init__(self,figsize):\n",
        "    self.figsize = figsize\n",
        "\n",
        "  # member function\n",
        "\n",
        "  def figure(self):\n",
        "\n",
        "    return \"blah blah\"\n",
        "\n",
        "\n",
        "circle = Figure(1)\n",
        "\n",
        "circle.figure()"
      ],
      "metadata": {
        "id": "cBtonUK-Zd06"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Homework:\n",
        "\n",
        "* fix the plot above to put some space between figure 1 and figure 2\n",
        "\n",
        "* how do we add space between two figures\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "extra credit:\n",
        "\n",
        "* what did we not learn?  how add the second line on the chart to plot 2 lines on the same chart"
      ],
      "metadata": {
        "id": "bcNHbQd-bQBI"
      }
    }
  ]
}