聚合Dask数据框并生成聚合的数据框 [英] Aggregate a Dask dataframe and produce a dataframe of aggregates

问题描述

我有一个Dask数据帧，看起来像这样：

I have a Dask dataframe that looks like this:

url     referrer    session_id ts                  customer
url1    ref1        xxx        2017-09-15 00:00:00 a.com
url2    ref2        yyy        2017-09-15 00:00:00 a.com
url2    ref3        yyy        2017-09-15 00:00:00 a.com
url1    ref1        xxx        2017-09-15 01:00:00 a.com
url2    ref2        yyy        2017-09-15 01:00:00 a.com

我想对url和timestamp上的数据进行分组，汇总列值并生成一个如下所示的数据框：

I want to group the data on url and timestamp, aggregate column values and produce a dataframe that would look like this instead:

customer url    ts                  page_views visitors referrers
a.com    url1   2017-09-15 00:00:00 1          1        [ref1]
a.com    url2   2017-09-15 00:00:00 2          2        [ref2, ref3]

在Spark SQL中，我可以按如下操作：

In Spark SQL, I can do this as follows:

select 
    customer,
    url,
    ts,
    count(*) as page_views,
    count(distinct(session_id)) as visitors,
    collect_list(referrer) as referrers
from df
group by customer, url, ts

我可以用Dask数据帧做任何事情吗？我试过了，但我只能分别计算汇总列，如下所示：

Is there any way I can do it with Dask dataframes? I tried, but I can only calculate the aggregated columns separately, as follows:

# group on timestamp (rounded) and url
grouped = df.groupby(['ts', 'url'])

# calculate page views (count rows in each group)
page_views = grouped.size()

# collect a list of referrer strings per group
referrers = grouped['referrer'].apply(list, meta=('referrers', 'f8'))

# count unique visitors (session ids)
visitors = grouped['session_id'].count()

但是我似乎找不到一种产生所需组合数据框的好方法。

But I can't seem to find a good way to produce a combined dataframe that I need.

推荐答案

以下确实有效：

gb = df.groupby(['customer', 'url', 'ts'])
gb.apply(lambda d: pd.DataFrame({'views': len(d), 
     'visitiors': d.session_id.count(), 
     'referrers': [d.referer.tolist()]})).reset_index()

（假设访问者应为单如上面的sql所示）
您可能希望定义输出的元。

(assuming visitors should be unique as per the sql above) You may wish to define the meta of the output.

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