Penguin Explorer

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1import matplotlib.pyplot as plt
2import pandas as pd
3import seaborn as sns
4
5from shiny import reactive, render
6from shiny.express import input as G
7from shiny.express import module, ui
8
9penguins = sns.load_dataset("penguins").dropna().copy()
10
11SPECIES = sorted(penguins.species.unique().tolist())
12ISLANDS = sorted(penguins.island.unique().tolist())
13SEXES = sorted(penguins.sex.unique().tolist())
14NUMERIC = ["bill_length_mm", "bill_depth_mm", "flipper_length_mm", "body_mass_g"]
15CATEGORICAL = ["species", "island", "sex"]
16
17
18ui.page_opts(title="Penguin Explorer", fillable=False)
19
20with ui.sidebar(title="Global filters"):
21    ui.input_selectize(
22        "species", "Species", SPECIES, selected=SPECIES, multiple=True
23    )
24    ui.input_selectize(
25        "island", "Island", ISLANDS, selected=ISLANDS, multiple=True
26    )
27    ui.input_selectize("sex", "Sex", SEXES, selected=SEXES, multiple=True)
28    ui.input_slider(
29        "bill_length_range",
30        "Bill length range (mm)",
31        float(penguins.bill_length_mm.min()),
32        float(penguins.bill_length_mm.max()),
33        (float(penguins.bill_length_mm.min()), float(penguins.bill_length_mm.max())),
34        step=0.1,
35    )
36
37
38@reactive.calc
39def filtered_data():
40    df = penguins.copy()
41    if G.species():
42        df = df[df.species.isin(G.species())]
43    if G.island():
44        df = df[df.island.isin(G.island())]
45    if G.sex():
46        df = df[df.sex.isin(G.sex())]
47    lo, hi = G.bill_length_range()
48    df = df[(df.bill_length_mm >= lo) & (df.bill_length_mm <= hi)]
49    return df
50
51
52@module
53def scatter_panel(inp, outp, session, title: str = "Scatter plot"):
54    with ui.card():
55        ui.card_header(title)
56        ui.input_selectize("x_var", "X variable", NUMERIC, selected="bill_length_mm")
57        ui.input_selectize("y_var", "Y variable", NUMERIC, selected="bill_depth_mm")
58        ui.input_selectize(
59            "color_by", "Color by", CATEGORICAL, selected="species"
60        )
61        ui.input_slider("panel_point_size", "Point size", 2, 20, 6)
62        ui.input_slider("panel_opacity", "Opacity", 0.1, 1.0, 0.7, step=0.1)
63
64        @render.plot(alt="Scatter plot")
65        def scatter():
66            df = filtered_data()
67            if df.empty:
68                fig, ax = plt.subplots()
69                ax.text(0.5, 0.5, "No data", ha="center")
70                return fig
71
72            fig, ax = plt.subplots()
73            sns.scatterplot(
74                data=df,
75                x=inp.x_var(),
76                y=inp.y_var(),
77                hue=inp.color_by(),
78                ax=ax,
79                s=inp.panel_point_size(),
80                alpha=inp.panel_opacity(),
81            )
82            ax.set_xlabel(inp.x_var().replace("_", " ").title())
83            ax.set_ylabel(inp.y_var().replace("_", " ").title())
84            ax.set_title(title)
85            plt.tight_layout()
86            return fig
87
88        @render.text
89        def scatter_n():
90            return f"Points shown: {len(filtered_data())}"
91
92
93@module
94def distribution_panel(inp, outp, session):
95    with ui.card():
96        ui.card_header("Distribution")
97        ui.input_selectize("var", "Variable", NUMERIC, selected="body_mass_g")
98        ui.input_selectize("group", "Group by", CATEGORICAL, selected="species")
99        ui.input_slider("bins", "Bins", 5, 60, 20)
100
101        @render.plot(alt="Histogram")
102        def hist():
103            df = filtered_data()
104            if df.empty:
105                fig, ax = plt.subplots()
106                ax.text(0.5, 0.5, "No data", ha="center")
107                return fig
108
109            fig, ax = plt.subplots()
110            sns.histplot(
111                data=df,
112                x=inp.var(),
113                hue=inp.group(),
114                bins=inp.bins(),
115                kde=True,
116                ax=ax,
117            )
118            ax.set_xlabel(inp.var().replace("_", " ").title())
119            ax.set_title("Distribution")
120            plt.tight_layout()
121            return fig
122
123
124@module
125def summary_panel(inp, outp, session):
126    with ui.card():
127        ui.card_header("Summary")
128        ui.input_selectize("metric", "Metric", NUMERIC, selected="body_mass_g")
129
130        @render.text
131        def summary():
132            df = filtered_data()
133            if df.empty:
134                return "No rows match the current filters."
135            x = df[inp.metric()]
136            return (
137                f"Rows: {len(df)}\n"
138                f"Mean {inp.metric()}: {x.mean():.2f}\n"
139                f"Median: {x.median():.2f}\n"
140                f"Min: {x.min():.2f}\n"
141                f"Max: {x.max():.2f}\n"
142                f"Std: {x.std():.2f}"
143            )
144
145        @render.table
146        def species_counts():
147            df = filtered_data()
148            if df.empty:
149                return pd.DataFrame({"count": []})
150            return df.groupby("species").size().reset_index(name="count")
151
152
153# ---------------------------------------------------------------------------
154# App body
155# ---------------------------------------------------------------------------
156ui.h2("Penguin data explorer")
157ui.p(
158    "Filter penguins with the sidebar controls; each module (card below) "
159    "updates reactively."
160)
161
162with ui.layout_columns(col_widths=[6, 6]):
163    scatter_panel("scatter", title="Penguin measurements")
164    distribution_panel("dist")
165
166summary_panel("summary")

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