np.random.seed([3,1415])
df = pd.DataFrame(dict(
Name='matt joe adam farley'.split() * 100,
Seconds=np.random.randint(4000, 5000, 400)
))
df['Zscore'] = df.groupby('Name').Seconds.apply(lambda x: x.div(x.mean()))
df.groupby('Name').Zscore.plot.kde()
分割地块
g = df.groupby('Name').Zscore
n = g.ngroups
fig, axes = plt.subplots(n // 2, 2, figsize=(6, 6), sharex=True, sharey=True)
for i, (name, group) in enumerate(g):
r, c = i // 2, i % 2
group.plot.kde(title=name, ax=axes[r, c])
fig.tight_layout()
kde + hist
g = df.groupby('Name').Zscore
n = g.ngroups
fig, axes = plt.subplots(n // 2, 2, figsize=(6, 6), sharex=True, sharey=True)
for i, (name, group) in enumerate(g):
r, c = i // 2, i % 2
a1 = axes[r, c]
a2 = a1.twinx()
group.plot.hist(ax=a2, alpha=.3)
group.plot.kde(title=name, ax=a1, c='r')
fig.tight_layout()
