three new review / documentation / target apps

tests/dash/app_dataframe_updating_graph_fe.py

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+from dash.dependencies import Input, Output
+import dash_core_components as dcc
+import dash_html_components as html
+import pandas as pd
+from textwrap import dedent
+
+import dash_table
+from index import app
+
+ID_PREFIX = "app_dataframe_updating_graph"
+IDS = {"table": ID_PREFIX, "container": "{}-container".format(ID_PREFIX)}
+df = pd.read_csv("./datasets/gapminder.csv")
+df = df[df["year"] == 2007]
+
+
+def layout():
+    return html.Div(
+        [
+            html.Div(
+                dash_table.Table(
+                    id=IDS["table"],
+                    columns=[
+                        {"name": i, "id": i, "deletable": True} for i in df.columns
+                    ],
+                    dataframe=df.to_dict("rows"),
+                    editable=True,
+                    filtering=True,
+                    sorting=True,
+                    sorting_type="multi",
+                    row_selectable="multi",
+                    row_deletable=True,
+                    selected_rows=[],
+                    derived_viewport_indices=[],
+                    n_fixed_rows=1,
+                ),
+                style={"height": 300, "overflowY": "scroll"},
+            ),
+            html.Div(id=IDS["container"]),
+            dcc.Markdown(
+                dedent(
+                    """
+            ***
+
+            `Table` includes several features for modifying and transforming the
+            view of the data. These include:
+
+            - Sorting by column (`sorting=True`)
+            - Filtering by column (`filtering=True`)
+            - Editing the cells (`editable=True`)
+            - Deleting rows (`row_deletable=True`)
+            - Deleting columns (`columns[i].deletable=True`)
+            - Selecting rows (`row_selectable='single' | 'multi'`)
+
+            > A quick note on filtering. We have defined our own
+            > syntax for performing filtering operations. Here are some
+            > examples for this particular dataset:
+            > - `lt num(50)` in the `lifeExp` column
+            > - `eq "Canada"` in the `country` column
+
+            By default, these transformations are done clientside.
+            Your Dash callbacks can respond to these modifications
+            by listening to the `dataframe` property as an `Input`.
+
+            Note that if `dataframe` is an `Input` then the entire
+            `dataframe` will be passed over the network: if your dataframe is
+            large, then this will become slow. For large dataframes, you have
+            two options:
+            - Use `dataframe_indicies` instead
+            - Perform the sorting or filtering in Python instead
+
+            Issues with this example:
+            - Row selection callbacks don't work yet: `derived_viewport_indices`
+            isn't getting updated on row selection and `selected_rows` doesn't
+            track the underlying data (e.g. it will always be [1, 3] even after sorting or filtering)
+            """
+                )
+            ),
+        ]
+    )
+
+
+@app.callback(
+    Output(IDS["container"], "children"),
+    [
+        Input(IDS["table"], "derived_virtual_dataframe"),
+        Input(IDS["table"], "selected_rows"),
+    ],
+)
+def update_graph(rows, selected_rows):
+    # When the table is first rendered, `derived_virtual_dataframe`
+    # will be `None`. This is due to an idiosyncracy in Dash
+    # (unsupplied properties are always None and Dash calls the dependent
+    # callbacks when the component is first rendered).
+    # So, if `selected_rows` is `None`, then the component was just rendered
+    # and its value will be the same as the component's dataframe.
+    # Instead of setting `None` in here, you could also set
+    # `derived_virtual_dataframe=df.to_rows('dict')` when you initialize
+    # the component.
+    if rows is None:
+        dff = df
+    else:
+        dff = pd.DataFrame(rows)
+
+    colors = []
+    for i in range(len(dff)):
+        if i in selected_rows:
+            colors.append("#7FDBFF")
+        else:
+            colors.append("#0074D9")
+
+    return html.Div(
+        [
+            dcc.Graph(
+                id=column,
+                figure={
+                    "data": [
+                        {
+                            "x": dff["country"],
+                            # check if column exists - user may have deleted it
+                            # If `column.deletable=False`, then you don't
+                            # need to do this check.
+                            "y": dff[column] if column in dff else [],
+                            "type": "bar",
+                            "marker": {"color": colors},
+                        }
+                    ],
+                    "layout": {
+                        "xaxis": {"automargin": True},
+                        "yaxis": {"automargin": True},
+                        "height": 250,
+                        "margin": {"t": 10, "l": 10, "r": 10},
+                    },
+                },
+            )
+            for column in ["pop", "lifeExp", "gdpPercap"]
+        ]
+    )