在python中绘制预先聚合的数据 [英] Plotting pre aggregated data in python

问题描述

我有一个预先汇总的元组列表：

I have a list of pre aggregated tuples:

[{'target_y_n': 0, 'value': 0.5, 'count':1000},{'target_y_n': 1, 'value': 1, 'count':10000}, ...]

我如何可视化分布（ https：/ /seaborn.pydata.org/generation/seaborn.distplot.html ），也可以获取频率图，而无需将汇总表示重新扩展为每个值的 k 副本，但仍会尽可能重用现有工具，例如 distplot，countplot ？

How can I visualize the distributions (https://seaborn.pydata.org/generated/seaborn.distplot.html) or get frequency plots without re-expanding the aggregated representation to k copies of each value, but still re-using as much as possible from existing tools like distplot, countplot?

在R http://www.amitsharma.in/post/cumulative-distribution-plots-for-frequency-data-in-r/ 看起来确实很有希望

In R http://www.amitsharma.in/post/cumulative-distribution-plots-for-frequency-data-in-r/ looks really promising

推荐答案

基于R源，这可能是python中的答案

Based on the R source this is a possible answer in python

df = pd.DataFrame([{'target_y_n': 0, 'value': 0.5, 'count':1000}, {'target_y_n': 0, 'value': 0.4, 'count':100},{'target_y_n': 1, 'value': 1, 'count':10000}, {'target_y_n': 1, 'value': 2, 'count':1000}])
df = df.sort_values(['target_y_n', 'value'])
display(df)

df['count_cum'] = df.groupby(['target_y_n'])['count'].cumsum()
display(df)

sns.lineplot(x='value',y='count_cum', drawstyle='steps-pre', data= df, hue='target_y_n')

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