slides/data_visualization_playground.ipynb

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  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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    "language_info": {
      "name": "python"
    },
    "colab": {
      "name": "data_visualization_playground.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ContextLab/storytelling-with-data/blob/master/slides/data_visualization_playground.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qYJPc2EczLEC"
      },
      "source": [
        "# Data visualization playground\n",
        "\n",
        "This notebook is based on the Storytelling with Data [demo repository](https://github.com/ContextLab/storytelling-with-data/tree/master/data-stories/demo), and is intended to provide an interactive space for exploring various [data visualization approaches](https://github.com/ContextLab/storytelling-with-data/blob/master/slides/data-viz.md)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "poB_y0KEJcD3",
        "outputId": "7502ce1c-3270-44f0-cc00-b29c574597c0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!pip install davos\n",
        "import davos #this provides the \"smuggle\" keyword-- used as a more robust version of \"import\""
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "i5X225WPGwDo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "631f55bd-68de-459e-c70d-1280cdb5a352"
      },
      "source": [
        "#number crunching\n",
        "smuggle numpy as np\n",
        "smuggle pandas as pd\n",
        "\n",
        "#data import\n",
        "from pydrive.auth smuggle GoogleAuth\n",
        "from pydrive.drive smuggle GoogleDrive\n",
        "from google.colab smuggle auth\n",
        "from oauth2client.client smuggle GoogleCredentials\n",
        "smuggle pandas_datareader as pdr\n",
        "\n",
        "#data visualization\n",
        "smuggle plotly # pip: plotly==4.14.3\n",
        "smuggle plotly.express as px\n",
        "smuggle seaborn as sns\n",
        "smuggle bokeh as bk\n",
        "from matplotlib smuggle pyplot as plt\n",
        "smuggle plotnine as pn\n",
        "smuggle hypertools as hyp\n",
        "smuggle folium as fm\n",
        "from mpl_toolkits.mplot3d smuggle Axes3D\n",
        "\n",
        "#machine learning and stats\n",
        "smuggle scipy as sp\n",
        "smuggle sklearn as sk\n",
        "smuggle tensorflow as tf\n",
        "smuggle statsmodels.api as sm\n",
        "\n",
        "#text analysis\n",
        "smuggle nltk\n",
        "smuggle textblob as tb\n",
        "from flair.embeddings smuggle WordEmbeddings, CharacterEmbeddings, StackedEmbeddings, FlairEmbeddings, BertEmbeddings, ELMoEmbeddings, DocumentPoolEmbeddings\n",
        "from flair.data smuggle Sentence\n",
        "nltk.download('brown')\n",
        "nltk.download('punkt')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
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            "\u001b[?25hCollecting tokenizers!=0.11.3,>=0.10.1\n",
            "  Downloading tokenizers-0.11.4-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.8 MB)\n",
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            "\u001b[?25hRequirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from ftfy->flair) (0.2.5)\n",
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            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers>=4.0.0->flair) (7.1.2)\n",
            "Building wheels for collected packages: gdown, mpld3, overrides, sqlitedict, ftfy, langdetect, wikipedia-api\n",
            "  Building wheel for gdown (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gdown: filename=gdown-3.12.2-py3-none-any.whl size=9692 sha256=1cdc1e23f24cc03ebcda44e76daa7d46e64da7e4acb178a36667d7c1191c8a04\n",
            "  Stored in directory: /root/.cache/pip/wheels/ba/e0/7e/726e872a53f7358b4b96a9975b04e98113b005cd8609a63abc\n",
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            "  Created wheel for overrides: filename=overrides-3.1.0-py3-none-any.whl size=10187 sha256=5acca7c43d848505602f64b852944b0c0b1fa813bc8cd0db0ec17d80df746eca\n",
            "  Stored in directory: /root/.cache/pip/wheels/3a/0d/38/01a9bc6e20dcfaf0a6a7b552d03137558ba1c38aea47644682\n",
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            "  Created wheel for langdetect: filename=langdetect-1.0.9-py3-none-any.whl size=993242 sha256=d06593d3b861b7cf7f3bd4f0783383adc89aca168d46ba8216e8795667f938d7\n",
            "  Stored in directory: /root/.cache/pip/wheels/c5/96/8a/f90c59ed25d75e50a8c10a1b1c2d4c402e4dacfa87f3aff36a\n",
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            "  Created wheel for wikipedia-api: filename=Wikipedia_API-0.5.4-py3-none-any.whl size=13477 sha256=1437aeda4168e27aea3e6463c22992b17c90cf13e6f3b6c9dddddfa33467a2e6\n",
            "  Stored in directory: /root/.cache/pip/wheels/d3/24/56/58ba93cf78be162451144e7a9889603f437976ef1ae7013d04\n",
            "Successfully built gdown mpld3 overrides sqlitedict ftfy langdetect wikipedia-api\n",
            "Installing collected packages: requests, pyyaml, importlib-metadata, tokenizers, sentencepiece, sacremoses, overrides, huggingface-hub, wikipedia-api, transformers, sqlitedict, segtok, mpld3, more-itertools, langdetect, konoha, janome, gdown, ftfy, deprecated, conllu, bpemb, flair\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.23.0\n",
            "    Uninstalling requests-2.23.0:\n",
            "      Successfully uninstalled requests-2.23.0\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "  Attempting uninstall: importlib-metadata\n",
            "    Found existing installation: importlib-metadata 4.10.1\n",
            "    Uninstalling importlib-metadata-4.10.1:\n",
            "      Successfully uninstalled importlib-metadata-4.10.1\n",
            "  Attempting uninstall: more-itertools\n",
            "    Found existing installation: more-itertools 8.12.0\n",
            "    Uninstalling more-itertools-8.12.0:\n",
            "      Successfully uninstalled more-itertools-8.12.0\n",
            "  Attempting uninstall: gdown\n",
            "    Found existing installation: gdown 4.2.1\n",
            "    Uninstalling gdown-4.2.1:\n",
            "      Successfully uninstalled gdown-4.2.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "markdown 3.3.6 requires importlib-metadata>=4.4; python_version < \"3.10\", but you have importlib-metadata 3.10.1 which is incompatible.\n",
            "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.27.1 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Successfully installed bpemb-0.3.3 conllu-4.4.1 deprecated-1.2.13 flair-0.10 ftfy-6.0.3 gdown-3.12.2 huggingface-hub-0.4.0 importlib-metadata-3.10.1 janome-0.4.1 konoha-4.6.5 langdetect-1.0.9 more-itertools-8.8.0 mpld3-0.3 overrides-3.1.0 pyyaml-6.0 requests-2.27.1 sacremoses-0.0.47 segtok-1.5.11 sentencepiece-0.1.95 sqlitedict-1.7.0 tokenizers-0.11.4 transformers-4.16.2 wikipedia-api-0.5.4\n",
            "[nltk_data] Downloading package brown to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/brown.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XHvFYl-aOHpN"
      },
      "source": [
        "# Data\n",
        "\n",
        "As a class, we'll choose one or more datasets from [this list](https://github.com/awesomedata/awesome-public-datasets) to explore."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pMalPJ-XMuVh"
      },
      "source": [
        "# Provide code for downloading or importing the data (ideally into a Pandas DataFrame) here..."
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2hpbjrd8Ofpa"
      },
      "source": [
        "# Analysis\n",
        "\n",
        "We'll use plotly, seaborn, bokeh, matplotlib, plotnine, hypertools, and/or folium to visualize different aspects of our sample dataset(s)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iVmcKfDp1o2C"
      },
      "source": [
        "# Examining raw examples\n",
        "\n",
        "Print out or visualize some of the raw data to get a sense of a few examples."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "02SFytVD183H"
      },
      "source": [
        "# Statistical summary\n",
        "\n",
        "Display some sort of statistical summary of the dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_CqcWQ3E25Vo"
      },
      "source": [
        "# Bar graph\n",
        "\n",
        "Use a bar graph to display some property or trend in the dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u_JnrA923LUg"
      },
      "source": [
        "# Scatterplot\n",
        "\n",
        "Generate a 2D or 3D scatterplot to visualize some property or trend in the dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qui12Zy23T8X"
      },
      "source": [
        "# Heatmap\n",
        "\n",
        "Use a heatmap or 2D histogram to visualize some property of the dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CjUO6ssh3jQP"
      },
      "source": [
        "# Circular data\n",
        "\n",
        "If there are any circular (cyclical, angular) aspects of the dataset, visualize them using a rose plot."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wU6uuA0J3vII"
      },
      "source": [
        "# Clustering analysis\n",
        "\n",
        "Show how different observations or parts of the dataset are grouped."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X7W7G1fk3_gu"
      },
      "source": [
        "# Timeseries data\n",
        "\n",
        "If there is a temporal aspect of the dataset, visualize it (e.g., using a lineplot, animation, or some other type of figure)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g68sAter4MPR"
      },
      "source": [
        "# Network data\n",
        "\n",
        "If there are network properties of the dataset, visualize them in some way."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HUPQ_p6Y4UT4"
      },
      "source": [
        "# Geospatial data\n",
        "\n",
        "If there are locations provided in the dataset, visualize them using some sort of map or geographic plot."
      ]
    }
  ]
}