Workshop_Heatmap.ipynb

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        "##Preparing a Dataset for Creating a Heatmap with Seaborn\n",
        "Our first task is to load that data and transform it into the form that Seaborn expects, and is easy for us to work with.\n",
        "\n",
        "We will use the Pandas library for loading and manipulating data:"
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        "import pandas as pd\n",
        "data_url = \"https://bit.ly/3cngqgL\"\n",
        "df = pd.read_csv(data_url,\n",
        "                 parse_dates=['date_utc'],\n",
        "                 dtype={'hour_utc':int,'minute_utc':int,'id':str}\n",
        "                )"
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      "source": [
        "It's always worth using the head method to examine the first few rows of the DataFrame, to get familiar with its shape:"
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        "df.head()"
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              "      <th>id</th>\n",
              "      <th>username</th>\n",
              "      <th>date_utc</th>\n",
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              "                   id         username  ... minute_utc  retweets\n",
              "0  815422340540547073  realDonaldTrump  ...          0     27134\n",
              "1  815930688889352192  realDonaldTrump  ...         40     23930\n",
              "2  815973752785793024  realDonaldTrump  ...         31     14119\n",
              "3  815989154555297792  realDonaldTrump  ...         32      3193\n",
              "4  815990335318982656  realDonaldTrump  ...         37      7337\n",
              "\n",
              "[5 rows x 6 columns]"
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        "Here, we've printed the first 5 elements in the DataFrame. We have the index of each row first, followed by the id of the tweet, the username of the user who tweeted that tweet, as well as time-related information such as the date_utc, hour_utc and minute_utc.\n",
        "\n",
        "Finally, we've got the number of retweets at the end, which can be used to check for interesting relationship between the contents of the tweets and the \"attention\" it got."
      ],
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      "source": [
        "##Transforming the Data Into a Wide-Form DataFrame\n",
        "It is common to find log data like this organized in a long (or tidy) form. This means there is a column for each variable, and each row of the data is a single observation (specific value) of those variables. Here, each tweet is each variable. Each row corresponds to one tweet and contains data about it.\n",
        "\n",
        "But conceptually a heatmap requires that the data be organized in a short (or wide) form. And in fact the Seaborn library requires us to have the data in this form to produce heatmap visualizations."
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        "Here, we've got hours as rows, as unique values, as well as minutes as columns. Each value in the cells is the number of tweet occurrences at that time. With this approach, we've only got 24 rows (24 hours) and 60 columns. If you imagine this spread visually, it essentially is a heatmap, though, with numbers.\n",
        "\n",
        "In our example I want to understand if there are any patterns to how the candidates tweet at different times of the day. One way to do this is to count the tweets created in each the hour of the day and each minute of an hour.\n",
        "\n",
        "Technically, we've got 2880 categories. Each combination of the hour_utc, minute_utc and username is a separate category, and we count the number of tweet occurrences for each of them.\n",
        "\n",
        "This aggregation is straight-forward using Pandas. The hour and the minute of creation are available in the columns hour_utc and minute_utc. We can use the Pandas groupby() function to collect together all the tweets for each combination of username, hour_utc, and minute_utc:"
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        "g = df.groupby(['hour_utc','minute_utc','username'])\n",
        "\n",
        "#This means that only rows that have the same value of hour_utc, minute_utc, username can be considered an occurrence of the same category."
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      "source": [
        "Now we can count the number of tweets in each group by applying the nunique() function to count the number of unique ids. This method avoids double counting any duplicate tweets that might lurk in the data, if it's not cleaned properly beforehand:"
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        "tweet_cnt = g.id.nunique()\n",
        "tweet_cnt.head()\n",
        "#This gives us a Pandas Series with the counts we need to plot the heatmap:"
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              "hour_utc  minute_utc  username       \n",
              "0         0           JoeBiden           26\n",
              "                      realDonaldTrump     6\n",
              "          1           JoeBiden           16\n",
              "                      realDonaldTrump    11\n",
              "          2           JoeBiden            6\n",
              "Name: id, dtype: int64"
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        "To transform this into the wide-form DataFrame needed by Seaborn we can use the Pandas pivot() function.\n",
        "\n",
        "For this example, it will be easiest to take one user at a time and plot a heatmap for each of them separately. We can put this on a single figure or separate ones.\n",
        "\n",
        "Use the Pandas loc[] accessor to select one users tweet counts and then apply the pivot() function. It uses unique values from the specified index/columns to form axes of the resulting DataFrame. We'll pivot the hours and minutes so that the resulting DataFrame has a wide-spread form:"
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        "jb_tweet_cnt = tweet_cnt.loc[:,:,'JoeBiden'].reset_index().pivot(index='hour_utc', columns='minute_utc', values='id')"
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        "jb_tweet_cnt.iloc[:10,:9]\n",
        "#Then take a peek at a section of the resulting DataFrame:"
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-2753b848-4fcb-4550-9350-8931473511f4 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-2753b848-4fcb-4550-9350-8931473511f4');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "minute_utc     0     1    2    3    4     5    6    7    8\n",
              "hour_utc                                                  \n",
              "0           26.0  16.0  6.0  7.0  4.0  24.0  2.0  2.0  9.0\n",
              "1           24.0   7.0  5.0  6.0  4.0  19.0  1.0  2.0  6.0\n",
              "2            3.0   3.0  3.0  NaN  5.0   1.0  4.0  8.0  NaN\n",
              "3            3.0   3.0  3.0  4.0  5.0   1.0  3.0  5.0  4.0\n",
              "4            1.0   1.0  1.0  2.0  NaN   NaN  1.0  1.0  1.0\n",
              "5            1.0   2.0  NaN  NaN  NaN   1.0  NaN  NaN  NaN\n",
              "6            NaN   NaN  NaN  NaN  NaN   NaN  NaN  NaN  NaN\n",
              "10           7.0   2.0  1.0  NaN  NaN   NaN  NaN  NaN  NaN\n",
              "11           2.0   5.0  NaN  NaN  NaN   NaN  NaN  NaN  NaN\n",
              "12           4.0   NaN  1.0  1.0  1.0   NaN  1.0  NaN  NaN"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Dealing with Missing Values\n",
        "We can see above that our transformed data contains missing values. Wherever there were no tweets for a given minute/hour combination the pivot() function inserts a Not-a-Number (NaN) value into the DataFrame.\n",
        "\n",
        "Furthermore pivot() does not create a row (or column) when there were no tweets at all for a particular hour (or minute).\n",
        "\n",
        "See above where hours 7, 8 and 9 are missing.\n",
        "\n",
        "This will be a common thing to happen when pre-processing data. Data might be missing, could be of odd types or entries (no validation), etc.\n",
        "\n",
        "Seaborn can handle this missing data just fine, it'll just plot without them, skipping over hours 7, 8 and 9. However, our heatmaps will be more consistent and interpretable if we fill in the missing values. In this case we know that missing values are really a count of zero.\n",
        "\n",
        "To fill in the NaNs that have already been inserted, use fillna() like so:"
      ],
      "metadata": {
        "id": "0TZhAx0epiT0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "jb_tweet_cnt.fillna(0, inplace=True)"
      ],
      "metadata": {
        "id": "wRKI9s1Yppac"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "To insert missing rows - make sure all hour and minute combinations appear in the heatmap - we'll reindex() the DataFrame to insert the missing indices and their values:"
      ],
      "metadata": {
        "id": "UNcti7ioptF9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Ensure all hours in table\n",
        "jb_tweet_cnt = jb_tweet_cnt.reindex(range(0,24), axis=0, fill_value=0)\n",
        "# Ensure all minutes in table\n",
        "jb_tweet_cnt = jb_tweet_cnt.reindex(range(0,60), axis=1, fill_value=0).astype(int)"
      ],
      "metadata": {
        "id": "TfbnKjBlpttH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we can complete our data preparation by repeating the same steps for the other candidates tweets:"
      ],
      "metadata": {
        "id": "5VTGkfmDp0JV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dt_tweet_cnt = tweet_cnt.loc[:,:,'realDonaldTrump'].reset_index().pivot(index='hour_utc', columns='minute_utc', values='id')\n",
        "dt_tweet_cnt.fillna(0, inplace=True)\n",
        "dt_tweet_cnt = dt_tweet_cnt.reindex(range(0,24), axis=0, fill_value=0)\n",
        "dt_tweet_cnt = dt_tweet_cnt.reindex(range(0,60), axis=1, fill_value=0).astype(int)"
      ],
      "metadata": {
        "id": "gY5wUiGkp1Hr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Creating a Basic Heatmap Using Seaborn\n",
        "Now that we have prepared the data it is easy to plot a heatmap using Seaborn. First make sure you've imported the Seaborn library:"
      ],
      "metadata": {
        "id": "9ojXQJG_p8j6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "yBpG5_UgqAEo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We'll also import Matplotlib's PyPlot module, since Seaborn relies on it as the underlying engine. After plotting plots with adequate Seaborn functions, we'll always call plt.show() to actually show these plots.\n",
        "\n",
        "Now, as usual with Seaborn, plotting data is as simple as passing a prepared DataFrame to the function we'd like to use. Specifically, we'll use the heatmap() function.\n",
        "\n",
        "Let's plot a simple heatmap of Trump's activity on Twitter:"
      ],
      "metadata": {
        "id": "mbZM2XvNqE-O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(dt_tweet_cnt)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "UuH84gXDqF8_",
        "outputId": "c9b29da6-0f88-4514-93ce-2be243f0ff90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "And then Biden's:"
      ],
      "metadata": {
        "id": "Pa8xpmpZqPI7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(jb_tweet_cnt)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "prcRnaiVqP1R",
        "outputId": "f203268a-7cd7-4321-e169-d801832a3431"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The heatmaps produced using Seaborn's default settings are immediately usable. They show the same patterns as seen in the plots at the beginning of the guide, but are a bit more choppy, smaller and the axes labels appear in an odd frequency.\n",
        "\n",
        "That aside, we can see these patterns because Seaborn does a lot of work for us, automatically, just by calling the heatmap() function:\n",
        "\n",
        "It made appropriate choices of color palette and scale\n",
        "It created a legend to relate colors to underlying values\n",
        "It labeled the axes\n",
        "These defaults may be good enough for your purposes and initial examination, as a hobbyist or data scientist. But oftentimes, producing a really effective heatmap requires us to customize the presentation to meet an audience's needs.\n",
        "\n",
        "Let's take a look at how we can customize a Seaborn heatmap to produce the heatmaps seen in the beginning of the guide."
      ],
      "metadata": {
        "id": "4R9mQ0MmqY_1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##How to Customize a Seaborn Heatmap\n",
        "Using Color Effectively\n",
        "The defining characteristic of a heatmap is the use of color to represent the magnitude of an underlying quantity.\n",
        "\n",
        "It is easy to change the colors that Seaborn uses to draw the heatmap by specifying the optional cmap (colormap) parameter. For example, here is how to switch to the 'mako' color palette:"
      ],
      "metadata": {
        "id": "ElO95nILqenN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(dt_tweet_cnt, cmap=\"mako\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "HIJ4HCHgqjpi",
        "outputId": "ed1a7127-3579-452c-8537-b92e7304079b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Seaborn provides many built-in palettes that you can choose from, but you should be careful to choose a good palette for your data and purpose.\n",
        "\n",
        "For heatmaps showing numerical data - like ours - sequential palettes such as the default 'rocket' or 'mako' are good choices. This is because the colors in these palettes have been chosen to be perceptually uniform. This means the difference we perceive between two colors with our eyes is proportional to the difference between the underlying values.\n",
        "\n",
        "The result is that by glancing at the map we can get a immediate feel for the distribution of values in the data.\n",
        "\n",
        "A counter example demonstrates the benefits of a perceptually uniform palette and the pitfalls of poor palette choice. Here is the same heatmap drawn using the tab10 palette:"
      ],
      "metadata": {
        "id": "DRJ8MV4yqqdE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "4FwJI-yDqaL2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(dt_tweet_cnt, cmap=\"tab10\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "3lgXIWUpquv6",
        "outputId": "ef11bd40-b7b5-4c30-ccc4-d6447dcc4a14",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This palette is a poor choice for our example because now we have to work really hard to understand the relationship between different colors. It has largely obscured the patterns that were previously obvious!\n",
        "\n",
        "This is because the **tab10** palette is uses changes in hue to make it easy to distinguish between categories. It may be a good choice if the values of your heatmap were categorical.\n",
        "\n",
        "If you are interested in both the low and high values in your data you might consider using a diverging palette like coolwarm or icefire which is a uniform scheme that highlights both extremes.\n",
        "\n",
        "For more information on selecting color palettes, the Seaborn documentation has some useful guidance : http://seaborn.pydata.org/tutorial/color_palettes.html"
      ],
      "metadata": {
        "id": "06azk1EPUd8B"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Control the Distorting Effect of Outliers\n",
        "Outliers in the data can cause problems when plotting heatmaps. By default Seaborn sets the bounds of the color scale to the minimum and maximum value in the data.\n",
        "\n",
        "This means an extremely large (or small) values in the data can cause details to be obscured. The more extreme the outliers, the farther away we are from a uniform coloring step. We've seen what effect this can have with the different colormaps.\n",
        "\n",
        "One way to handle extreme values without having to remove them from the dataset is to use the optional robust parameter. Setting robust to True causes Seaborn to set the bounds of the color scale at the 2nd and 98th percentile values of the data, rather then the maximum and minimum. This will, in the vast majority of the cases, normalize the color spread into a much more usable state.\n",
        "\n",
        "It is also possible to manually set the bounds of the color scale by setting the values of the parameters vmin and vmax. The can be very useful if you plan on having two heatmaps side by side and want to ensure the same color scale for each:"
      ],
      "metadata": {
        "id": "PQXH1qt8UnHE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(dt_tweet_cnt, cmap=\"mako\")\n",
        "plt.show()\n",
        "\n",
        "sns.heatmap(dt_tweet_cnt, cmap=\"mako\",robust=True)\n",
        "plt.show()\n",
        "\n",
        "sns.heatmap(dt_tweet_cnt, cmap=\"mako\", vmin=0, vmax=40)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 814
        },
        "id": "RuU0DpVlVTqW",
        "outputId": "8839b6b2-90be-44df-8338-bccdc1853dfc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Adding Value Annotations\n",
        "One downside of heatmaps is that making direct comparisons between values is difficult. A bar or line chart is a much easier way to do this.\n",
        "\n",
        "However, it is possible to alleviate this problem by adding annotations to the heatmap to show the underlying values. This is easily done in Seaborn by setting the annot parameter to True, like this:"
      ],
      "metadata": {
        "id": "tJp696S-W2KX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(jb_tweet_cnt.iloc[14:23,25:35], annot=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "JG1e9v67W-3E",
        "outputId": "c5b0e641-82c1-49e7-dd64-c4ef5856f13d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Categorical Heatmaps in Seaborn\n",
        "\n",
        "There are times when it's useful to simplify a heatmap by putting numerical data into categories. For example we could bucket the tweet count data into just three categories 'high', 'medium', and 'low', instead of a numerical range such as 0..40.\n",
        "\n",
        "Unfortunately at the time of writing, Seaborn does not have the built-in ability to produce heatmaps for categorical data like this as it expects numerical input. Here's a code snippet that shows it is possible to \"fake \"it with a little palette and color bar hacking.\n",
        "\n",
        "Although this is one circumstance where you may want to consider the merit of other visualization packages that have such features built-in.\n",
        "\n",
        "We'll use a helping hand from Matplotlib, the underlying engine underneath Seaborn since it has a lot of low-level customization options and we have full access to it. Here, we can \"hack\" the legend on the right to display values we'd like:"
      ],
      "metadata": {
        "id": "Mf4dlJnJXL9J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig,ax = plt.subplots(1,1,figsize=(18,8))\n",
        "my_colors=[(0.2,0.3,0.3),(0.4,0.5,0.4),(0.1,0.7,0),(0.1,0.7,0)]\n",
        "\n",
        "sns.heatmap(dt_tweet_cnt, cmap=my_colors, square=True, linewidth=0.1, linecolor=(0.1,0.2,0.2), ax=ax)\n",
        "\n",
        "colorbar = ax.collections[0].colorbar\n",
        "M=dt_tweet_cnt.max().max()\n",
        "colorbar.set_ticks([1/8*M,3/8*M,6/8*M])\n",
        "colorbar.set_ticklabels(['low','med','high'])\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "vpLXBo5fXh4N",
        "outputId": "090b8558-08e9-400e-92f1-5f52b7de8e8d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preparing Heatmaps for Presentation\n",
        "A couple of last steps to put the finishing touches on your heatmap.\n",
        "\n",
        "Using Seaborn Context to Control Appearance\n",
        "The set_context() function provides a useful way to control some of the elements of the plot without changing its overall style. For example it can be a convenient way to customize font sizes and families.\n",
        "There are several preset contexts available:https://seaborn.pydata.org/generated/seaborn.set_context.html"
      ],
      "metadata": {
        "id": "bXZncpjyX8mQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set_context(\"notebook\", font_scale=1.75, rc={\"lines.linewidth\": 2.5, 'font.family':'Helvetica'})"
      ],
      "metadata": {
        "id": "BO_F53XoYHQd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Using Subplots to Control the Layout of Heatmaps\n",
        "The final step in creating our tweet count heatmap is to put the two plots next to each other in a single figure so it is easy to make comparisons between them.\n",
        "\n",
        "We can use the subplot() feature of matplotlib.pyplot to control the layout of heatmaps in Seaborn. This will give you maximum control over the final graphic and allow for easy export of the image.\n",
        "\n",
        "Creating subplots using Matplotlib is as easy as defining their shape (2 subplots in 1 column in our case):"
      ],
      "metadata": {
        "id": "39H7y2PEYMcq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12,12))\n",
        "sns.heatmap(jb_tweet_cnt, ax=ax1)\n",
        "sns.heatmap(dt_tweet_cnt, ax=ax2)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 744
        },
        "id": "HUP3-0_jYSJ6",
        "outputId": "77138a82-3688-4954-f411-141019c80d86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is essentially it, although, lacks some of the styling we've seen in the beginning. Let's bring together many of the customizations we have seen in the guide to produce our final plot and export it as a .png for sharing:"
      ],
      "metadata": {
        "id": "B9jet1pYYaeX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "fig, ax = plt.subplots(2, 1, figsize=(24,12))\n",
        "\n",
        "for i,d in enumerate([jb_tweet_cnt,dt_tweet_cnt]):\n",
        "\n",
        "    labels = d.applymap(lambda v: str(v) if v == d.values.max() else '')\n",
        "    sns.heatmap(d,\n",
        "                cmap=\"viridis\",  # Choose a squential colormap\n",
        "                #annot=jb_labels, # Label the maximum value\n",
        "                annot_kws={'fontsize':11},  # Reduce size of label to fit\n",
        "                fmt='',          # Interpret labels as strings\n",
        "                square=True,     # Force square cells\n",
        "                vmax=40,         # Ensure same\n",
        "                vmin=0,          # color scale\n",
        "                linewidth=0.01,  # Add gridlines\n",
        "                linecolor=\"#222\",# Adjust gridline color\n",
        "                ax=ax[i],        # Arrange in subplot\n",
        "               )\n",
        "\n",
        "ax[0].set_title('@JoeBiden')\n",
        "ax[1].set_title('@realDonaldTrump')\n",
        "ax[0].set_ylabel('Hour of Day')\n",
        "ax[1].set_ylabel('Hour of Day')\n",
        "ax[0].set_xlabel('')\n",
        "ax[1].set_xlabel('Minute of Hour')\n",
        "plt.tight_layout()\n",
        "plt.savefig('final.png', dpi=120)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        },
        "id": "W5krLeE5YbH2",
        "outputId": "4aaf92bf-167f-4062-9d38-1c0f36aba42c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1728x864 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Conclusion\n",
        "In this guide we looked at heatmaps and how to create them with Python and the Seaborn visualization library.\n",
        "\n",
        "The strength of heatmaps is in the way they use color to get information across, in other words, it makes it easy for anyone to see broad patterns at a glance.\n",
        "\n",
        "We've seen how in order to do this we have to make careful selections of color palette and scale. We've also seen that there are number of options available for customizing a heatmap using Seaborn in order to emphasize particular aspects of the chart. These include annotations, grouping and ordering categorical axes, and layout.\n",
        "\n",
        "As always, editorial judgment on the part of the Data Visualizer is required to choose the most appropriate customizations for the context of the visualization."
      ],
      "metadata": {
        "id": "pWJkl-9KY1Ne"
      }
    }
  ]
}