python pandas 中的Groupby:快速方法 [英] Groupby in python pandas: Fast Way

问题描述

我想缩短python pandas中groupby的时间. 我有以下代码:

I want to improve the time of a groupby in python pandas. I have this code:

df["Nbcontrats"] = df.groupby(['Client', 'Month'])['Contrat'].transform(len)

目标是计算客户一个月内有多少份合同，并将此信息添加到新列(Nbcontrats)中.

The objective is to count how many contracts a client has in a month and add this information in a new column (Nbcontrats).

• Client:客户端代码
• Month:数据提取月份
• Contrat:合同编号

• Client: client code
• Month: month of data extraction
• Contrat: contract number

我想缩短时间.下面，我只处理部分真实数据:

I want to improve the time. Below I am only working with a subset of my real data:

%timeit df["Nbcontrats"] = df.groupby(['Client', 'Month'])['Contrat'].transform(len)
1 loops, best of 3: 391 ms per loop

df.shape
Out[309]: (7464, 61)

如何缩短执行时间?

推荐答案

使用DataFrameGroupBy.size方法:

df.set_index(['Client', 'Month'], inplace=True)
df['Nbcontrats'] = df.groupby(level=(0,1)).size()
df.reset_index(inplace=True)

最繁重的工作是将结果分配回源DataFrame的一列.

The most work goes into assigning the result back into a column of the source DataFrame.

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