sankey-diagram-notebook.ipynb

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    {
      "cell_type": "code",
      "source": [
        "import plotly.graph_objects as go\n",
        "import pandas as pd"
      ],
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        "id": "H8Dg2SjSeRad"
      },
      "execution_count": 35,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/titanic.csv')"
      ],
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        "id": "Ib1xgTx1eSeB"
      },
      "execution_count": 36,
      "outputs": []
    },
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      "source": [
        "df.head(3)"
      ],
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          "height": 143
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        "id": "FSOP7lkKeSgW",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
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              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
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              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
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            "text/plain": [
              "   PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0            1         0       3  ...   7.2500   NaN         S\n",
              "1            2         1       1  ...  71.2833   C85         C\n",
              "2            3         1       3  ...   7.9250   NaN         S\n",
              "\n",
              "[3 rows x 12 columns]"
            ]
          },
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        }
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        "df1 = df.groupby(['Pclass', 'Sex'])['Name'].count().reset_index()\n",
        "df1.columns = ['source', 'target', 'value']\n",
        "df1['source'] = df1.source.map({1: 'Pclass1', 2: 'Pclass2', 3: 'Pclass3'})\n",
        "df2 = df.groupby(['Sex', 'Survived'])['Name'].count().reset_index()\n",
        "df2.columns = ['source', 'target', 'value']\n",
        "df2['target'] = df2.target.map({1: 'Survived', 0: 'Died'})\n",
        "links = pd.concat([df1, df2], axis=0)"
      ],
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            "text/plain": [
              "    source    target  value\n",
              "0  Pclass1    female      9\n",
              "1  Pclass1      male     21\n",
              "2  Pclass2    female     12\n",
              "3  Pclass2      male     18\n",
              "4  Pclass3    female     35\n",
              "5  Pclass3      male     61\n",
              "0   female      Died     16\n",
              "1   female  Survived     40\n",
              "2     male      Died     86\n",
              "3     male  Survived     14"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
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    {
      "cell_type": "code",
      "source": [
        "unique_source_target = list(pd.unique(links[['source', 'target']].values.ravel('K')))"
      ],
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      "execution_count": 40,
      "outputs": []
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        "unique_source_target"
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      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Pclass1', 'Pclass2', 'Pclass3', 'female', 'male', 'Died', 'Survived']"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mapping_dict = {k: v for v, k in enumerate(unique_source_target)}"
      ],
      "metadata": {
        "id": "wv05QnOKfJV0"
      },
      "execution_count": 42,
      "outputs": []
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    {
      "cell_type": "code",
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        "mapping_dict"
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      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Died': 5,\n",
              " 'Pclass1': 0,\n",
              " 'Pclass2': 1,\n",
              " 'Pclass3': 2,\n",
              " 'Survived': 6,\n",
              " 'female': 3,\n",
              " 'male': 4}"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
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      "cell_type": "code",
      "source": [
        "links['source'] = links['source'].map(mapping_dict)\n",
        "links['target'] = links['target'].map(mapping_dict)"
      ],
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        "id": "86qgTx2-ejn9"
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      "execution_count": 44,
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      "execution_count": 45,
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              "      <th>value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
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              "  "
            ],
            "text/plain": [
              "   source  target  value\n",
              "0       0       3      9\n",
              "1       0       4     21\n",
              "2       1       3     12\n",
              "3       1       4     18\n",
              "4       2       3     35\n",
              "5       2       4     61\n",
              "0       3       5     16\n",
              "1       3       6     40\n",
              "2       4       5     86\n",
              "3       4       6     14"
            ]
          },
          "metadata": {},
          "execution_count": 45
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      ]
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      "source": [
        "links_dict = links.to_dict(orient='list')"
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      "execution_count": 46,
      "outputs": []
    },
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        "fig = go.Figure(data=[go.Sankey(\n",
        "    node = dict(\n",
        "      pad = 15,\n",
        "      thickness = 20,\n",
        "      line = dict(color = \"black\", width = 0.5),\n",
        "      label = unique_source_target,\n",
        "      color = \"blue\"\n",
        "    ),\n",
        "    link = dict(\n",
        "      source = links_dict[\"source\"],\n",
        "      target = links_dict[\"target\"],\n",
        "      value = links_dict[\"value\"]\n",
        "  ))])"
      ],
      "metadata": {
        "id": "sNEVIqtcgFFl"
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      "execution_count": 47,
      "outputs": []
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      "source": [
        "fig.update_layout(title_text=\"Titanic Survival Sankey Diagram\", font_size=10)\n",
        "fig.show()"
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        "id": "zCX0CFargfWU",
        "outputId": "905f647d-29b2-44dd-9348-6da4e5d953d1"
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      "execution_count": 48,
      "outputs": [
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