使用ggplot和pandas在Python中绘制事件密度 [英] Plotting event density in Python with ggplot and pandas

问题描述

我正在尝试可视化这种形式的数据:

I am trying to visualize data of this form:

  timestamp               senderId
0     735217  106758968942084595234
1     735217  114647222927547413607
2     735217  106758968942084595234
3     735217  106758968942084595234
4     735217  114647222927547413607
5     etc...

如果我不分隔senderId s，则

geom_density可以工作:

geom_density works if I don't separate the senderIds:

df = pd.read_pickle('data.pkl')
df.columns = ['timestamp', 'senderId']
plot = ggplot(aes(x='timestamp'), data=df) + geom_density()
print plot

结果看起来像预期的那样:

The result looks as expected:

但是，如果我要单独显示senderId，请按照以下步骤操作文档，但失败:

However if I want to show the senderIds separately, as is done in the doc, it fails:

> plot = ggplot(aes(x='timestamp', color='senderId'), data=df) + geom_density()
ValueError: `dataset` input should have multiple elements.

尝试使用更大的数据集(约40K个事件):

> plot = ggplot(aes(x='timestamp', color='senderId'), data=df) + geom_density()
numpy.linalg.linalg.LinAlgError: singular matrix

有什么想法吗?关于这些错误，SO上有一些答案，但似乎都没有.

Any idea? There are some answers on SO for those errors but none seems relevant.

这是我想要的图形(来自ggplot的文档):

This is the kind of graph I want (from ggplot's doc):

推荐答案

使用较小的数据集:

> plot = ggplot(aes(x='timestamp', color='senderId'), data=df) + geom_density()
ValueError: `dataset` input should have multiple elements.

这是因为某些senderId仅具有一行.

This was because some senderIds had only one row.

具有更大的数据集:

> plot = ggplot(aes(x='timestamp', color='senderId'), data=df) + geom_density()
numpy.linalg.linalg.LinAlgError: singular matrix

这是因为对于某些senderId，我在完全相同的timestamp处有多行. ggplot不支持此功能.我可以通过使用更精细的时间戳来解决它.

This was because for some senderIds I had multiple rows at the exact same timestamp. This is not supported by ggplot. I could solve it by using finer timestamps.

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