数据框中Pandas中的聚合列(Aggregated Columns in Pandas within a Dataframe)

数据框中Pandas中的聚合列(Aggregated Columns in Pandas within a Dataframe)

我正在创建具有聚合值的列，其中包含来自Pandas Dataframe的数据，使用groupby（）和reset_index（）函数，如下所示：

df=data.groupby(["subscription_id"])["count_boxes"].sum().reset_index(name="amount_boxes") df1=data.groupby(["subscription_id"])["product"].count().reset_index(name="count_product")

想要将所有这些聚合列（“amount_boxes”和“count_product”）与groupby列“subscription_id”组合在一个数据框中。 有没有办法在函数中执行此操作而不是合并数据帧？

I'm creating columns with aggregated values with the data from Pandas Dataframe using groupby() and reset_index() functions like that:

df=data.groupby(["subscription_id"])["count_boxes"].sum().reset_index(name="amount_boxes") df1=data.groupby(["subscription_id"])["product"].count().reset_index(name="count_product")

Want to combine all these aggregated columns ("amount_boxes" and "count_product") in one dataframe with groupby column "subscription_id". Is there any way to do that ithin a function rather than merging the dataframes?

最满意答案

让我们看一下使用.agg和列和聚合函数的字典。

(df.groupby('Subscription_id') .agg({'count_boxes':'sum','product':'count'}) .reset_index() .rename(columns={'count_boxes':'amount_boxes','product':'count_product'}))

样本输出：

Subscription_id amount_boxes count_product 0 1 16 2 1 2 39 6 2 3 47 7

Let's look at using .agg with a dictionary of column and aggregation function.

(df.groupby('Subscription_id') .agg({'count_boxes':'sum','product':'count'}) .reset_index() .rename(columns={'count_boxes':'amount_boxes','product':'count_product'}))

Sample Output:

Subscription_id amount_boxes count_product 0 1 16 2 1 2 39 6 2 3 47 7