Athletes (Error Bars)

Dataset: athletes.parquet

Confidence intervals of Olympic athlete heights, in meters. Data are batched into groups of 10 samples per sport. Use the samples slider to see how the intervals update as the sample size increases (as in online aggregation). For each sport, the numbers on the right show the maximum number of athletes in the full dataset.

Code

import pandas as pd
import numpy as np

from inspect_viz import Data, Param, Selection
from inspect_viz.mark import error_bar_x, text
from inspect_viz.plot import plot, legend
from inspect_viz.transform import count
from inspect_viz.input import slider
from inspect_viz.layout import hconcat, vconcat, vspace

# prepare data (create batch column so we can target various numbers of samples)
df = pd.read_parquet("athletes.parquet")
df = df[df['height'].notna()]
df['row_num'] = df.groupby('sport').cumcount() + 1
df['batch'] = 10 * np.ceil(df['row_num'] / 10).astype(int)
df = df.drop('row_num', axis=1)
df = df.reset_index(drop=True)

athletes = Data.from_dataframe(df)

ci = Param(0.95)
query = Selection.single()

vconcat(
    hconcat(
        slider(
            athletes, label="Samples", select="interval", target=query, 
            column="batch", step=10, value=(0,20)
        ),
        slider(
            label="Conf.", target=ci, 
            min=0.5, max=0.999, value=0.95, step=0.001
        )
    ),
    plot(
        error_bar_x(
            athletes, filter_by=query, ci=ci, 
            x="height", y="sport", stroke="sex", stroke_width=1,
            marker="tick", sort={ "y": "-x"}
        ),
        text(
            athletes, text=count(), y="sport", dx=25,
            frame_anchor="right", font_size=8, fill="#999"
        ),
        legend=legend("color", frame_anchor="bottom"),
        x_domain=[1.5,2.1],
        y_domain="fixed",
        y_grid=True,
        y_label=None,
        margin_top=0,
        margin_left=105
    )
)