[英]Pandas: Rolling mean using only the last update based on another column
I would like to perform a rolling mean while the mean excludes duplicates found in another column.我想执行滚动平均值,而平均值不包括在另一列中找到的重复项。 Let me provide an example dataframe:让我提供一个示例数据框:
Date Warehose Value
10-01-1998 London 10
13-01-1998 London 13
15-01-1998 New York 37
12-02-1998 London 21
20-02-1998 New York 39
21-02-1998 New York 17
In this example, let's say I like to perform 30-day rolling mean of Value
but taking into account only the last update of the Warehouse location.在此示例中,假设我喜欢执行 30 天滚动Value
,但仅考虑仓库位置的最后一次更新。 The resulting dataframe is expected to be:生成的数据框预计为:
Date Value Rolling_Mean
02-01-1998 10 10
05-01-1998 13 13
15-01-1998 37 20
12-02-1998 21 29
20-02-1998 39 30
21-02-1998 17 19
The data I have is relatively big so as efficient as possible is appreciated.我拥有的数据相对较大,因此尽可能高效。
It's a bit tricky.这有点棘手。 As rolling.apply
works on Series only and you need both "Wharehose" and "Value" to perform the computation, you need to access the complete dataframe using a function (and a "global" variable, which is not super clean IMO):由于rolling.apply
仅适用于 Series 并且您需要“Warehose”和“Value”来执行计算,您需要使用函数(和“全局”变量,这不是超级干净的 IMO)访问完整的数据帧:
df['Date'] = pd.to_datetime(df['Date'], dayfirst=True)
df2 = df.set_index('Date')
def agg(s):
return (df2.loc[s.index]
.drop_duplicates(subset='Warehose', keep='last')
['Value'].mean()
)
df['Rolling_Mean'] = (df.sort_values(by='Date')
.rolling('30d', on='Date')
['Value']
.apply(agg, raw=False)
)
output:输出:
Date Warehose Value Rolling_Mean
0 1998-01-10 London 10 10.0
1 1998-01-13 London 13 13.0
2 1998-01-15 New York 37 25.0
3 1998-02-12 London 21 29.0
4 1998-02-20 New York 39 30.0
5 1998-02-21 New York 17 19.0
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