Pandas:过去 n 天的平均值-Python问题

Pandas: Average value for the past n days(Pandas:过去 n 天的平均值)

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问题描述

我有一个像这样的 Pandas 数据框:

I have a Pandas data frame like this:

test = pd.DataFrame({ 'Date' : ['2016-04-01','2016-04-01','2016-04-02',
                             '2016-04-02','2016-04-03','2016-04-04',
                             '2016-04-05','2016-04-06','2016-04-06'],
                      'User' : ['Mike','John','Mike','John','Mike','Mike',
                             'Mike','Mike','John'],
                      'Value' : [1,2,1,3,4.5,1,2,3,6]
                })

如下所示，数据集不一定每天都有观测值:

As you can see below, the data set does not have observations for every day necessarily:

         Date  User  Value
0  2016-04-01  Mike    1.0
1  2016-04-01  John    2.0
2  2016-04-02  Mike    1.0
3  2016-04-02  John    3.0
4  2016-04-03  Mike    4.5
5  2016-04-04  Mike    1.0
6  2016-04-05  Mike    2.0
7  2016-04-06  Mike    3.0
8  2016-04-06  John    6.0

如果至少有一天可用，我想添加一个新列，显示过去 n 天(在本例中 n = 2)每个用户的平均值，否则它将具有 nan值.例如，在 2016-04-06，John 得到一个 nan，因为他没有 2016-04-05 和 2016 的数据-04-04.所以结果会是这样的:


I'd like to add a new column which shows the average value for each user for the past n days (in this case n = 2) if at least one day is available, else it would have nan value. For example, on 2016-04-06 John gets a nan because he has no data for 2016-04-05 and 2016-04-04. So the result will be something like this:
         Date  User  Value  Value_Average_Past_2_days
0  2016-04-01  Mike    1.0                        NaN
1  2016-04-01  John    2.0                        NaN
2  2016-04-02  Mike    1.0                       1.00
3  2016-04-02  John    3.0                       2.00
4  2016-04-03  Mike    4.5                       1.00
5  2016-04-04  Mike    1.0                       2.75
6  2016-04-05  Mike    2.0                       2.75
7  2016-04-06  Mike    3.0                       1.50
8  2016-04-06  John    6.0                        NaN

看了论坛里的几篇帖子，好像应该把group_by和自定义的rolling_mean结合起来，但是我不太明白怎么做.
It seems that I should a combination of group_by and customized rolling_mean after reading several posts in the forum, but I couldn't quite figure out how to do it.
推荐答案

我想你可以使用先转换列 Date to_datetime，然后通过 Days//pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html" rel="noreferrer">groupby with resample 和最后一个 应用 滚动

I think you can use first convert column Date to_datetime, then find missing Days by groupby with resample and last apply rolling

test['Date'] = pd.to_datetime(test['Date'])

df = test.groupby('User').apply(lambda x: x.set_index('Date').resample('1D').first())
print df
                 User  Value
User Date                   
John 2016-04-01  John    2.0
     2016-04-02  John    3.0
     2016-04-03   NaN    NaN
     2016-04-04   NaN    NaN
     2016-04-05   NaN    NaN
     2016-04-06  John    6.0
Mike 2016-04-01  Mike    1.0
     2016-04-02  Mike    1.0
     2016-04-03  Mike    4.5
     2016-04-04  Mike    1.0
     2016-04-05  Mike    2.0

df1 = df.groupby(level=0)['Value']
        .apply(lambda x: x.shift().rolling(min_periods=1,window=2).mean())
        .reset_index(name='Value_Average_Past_2_days')

print df1
    User       Date  Value_Average_Past_2_days
0   John 2016-04-01                        NaN
1   John 2016-04-02                       2.00
2   John 2016-04-03                       2.50
3   John 2016-04-04                       3.00
4   John 2016-04-05                        NaN
5   John 2016-04-06                        NaN
6   Mike 2016-04-01                        NaN
7   Mike 2016-04-02                       1.00
8   Mike 2016-04-03                       1.00
9   Mike 2016-04-04                       2.75
10  Mike 2016-04-05                       2.75
11  Mike 2016-04-06                       1.50

print pd.merge(test, df1, on=['Date', 'User'], how='left')
        Date  User  Value  Value_Average_Past_2_days
0 2016-04-01  Mike    1.0                        NaN
1 2016-04-01  John    2.0                        NaN
2 2016-04-02  Mike    1.0                       1.00
3 2016-04-02  John    3.0                       2.00
4 2016-04-03  Mike    4.5                       1.00
5 2016-04-04  Mike    1.0                       2.75
6 2016-04-05  Mike    2.0                       2.75
7 2016-04-06  Mike    3.0                       1.50
8 2016-04-06  John    6.0                        NaN

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