来自 pandas 数据框的按名称的正态分布图

本文介绍了来自 pandas 数据框的按名称的正态分布图的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

我有一个如下数据框:

dateTime        Name    DateTime        day seconds zscore
11/1/2016 15:17 james   11/1/2016 15:17 Tue 55020   1.158266091
11/1/2016 13:41 james   11/1/2016 13:41 Tue 49260   -0.836236954
11/1/2016 15:17 james   11/1/2016 15:17 Tue 55020   1.158266091
11/1/2016 15:17 james   11/1/2016 15:17 Tue 55020   1.158266091
11/1/2016 15:17 james   11/1/2016 15:17 Tue 55020   1.158266091
11/1/2016 15:17 james   11/1/2016 15:17 Tue 55020   1.158266091
11/1/2016 15:17 james   11/1/2016 15:17 Tue 55020   1.158266091
11/1/2016 15:17 james   11/1/2016 15:17 Tue 55020   1.158266091
11/1/2016 15:17 james   11/1/2016 15:17 Tue 55020   1.158266091
11/1/2016 15:17 james   11/1/2016 15:17 Tue 55020   1.158266091
11/1/2016 15:17 james   11/1/2016 15:17 Tue 55020   1.158266091
11/1/2016 13:41 james   11/1/2016 13:41 Tue 49260   -0.836236954
11/1/2016 13:41 james   11/1/2016 13:41 Tue 49260   -0.836236954
11/1/2016 13:41 james   11/1/2016 13:41 Tue 49260   -0.836236954
11/1/2016 13:41 james   11/1/2016 13:41 Tue 49260   -0.836236954
11/1/2016 13:41 james   11/1/2016 13:41 Tue 49260   -0.836236954
11/1/2016 13:41 james   11/1/2016 13:41 Tue 49260   -0.836236954
11/1/2016 13:41 james   11/1/2016 13:41 Tue 49260   -0.836236954
11/1/2016 13:42 james   11/1/2016 13:42 Tue 49320   -0.81546088
11/1/2016 13:42 james   11/1/2016 13:42 Tue 49320   -0.81546088
11/1/2016 13:42 james   11/1/2016 13:42 Tue 49320   -0.81546088
11/1/2016 13:42 james   11/1/2016 13:42 Tue 49320   -0.81546088
11/1/2016 13:42 james   11/1/2016 13:42 Tue 49320   -0.81546088
11/1/2016 13:42 james   11/1/2016 13:42 Tue 49320   -0.81546088
11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
11/1/2016 9:08  matt    11/1/2016 9:08  Tue 32880   -0.111873342
11/1/2016 9:48  matt    11/1/2016 9:48  Tue 35280   4.363060322

zscore的计算如下:

zscore is calculated as below:

grp2 = df.groupby(['Name'])['seconds']
df['zscore'] = grp2.transform(lambda x: (x - x.mean()) / x.std(ddof=1))

我想将我的数据绘制成钟形曲线/正态分布图，并将其另存为数据框中每个名称的图片/pdf文件.

I would like to plot my data in a bell curve / normal distribution plot and save this as a picture/pdf file for each Name in my dataframe.

我试图绘制如下的zscores:

I have tried to plot the zscores like below:

df['by_name'].plot(kind='hist', normed=True)
range = np.arange(-7, 7, 0.001)
plt.plot(range, norm.pdf(range,0,1))
plt.show()

如何为数据中的每个名称绘制by_name zscores列?

How would I go about plotting the by_name zscores column for each name in my data?

推荐答案

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()

分割出的地块

split out plots

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()

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