pandas 熊猫：过去 n 天的平均值
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Pandas: Average value for the past n days
提问by ahoosh
I have a Pandasdata frame like this:
我有一个这样的Pandas数据框：
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]
})
As you can see below, the data set does not have observations for every day necessarily:
正如您在下面看到的，数据集不一定每天都有观察：
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
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 nanvalue. For example, on 2016-04-06John gets a nanbecause he has no data for 2016-04-05and 2016-04-04. So the result will be something like this:
如果至少有一天可用，我想添加一个新列，该列显示过去 n 天（在本例中 n = 2）每个用户的平均值，否则它将nan有价值。例如，在2016-04-06John 上得到 anan因为他没有2016-04-05和 的数据2016-04-04。所以结果将是这样的：
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
It seems that I should a combination of group_byand customized rolling_meanafter reading several posts in the forum, but I couldn't quite figure out how to do it.
看了论坛的几篇帖子，看来应该是组合group_by和定制rolling_mean了，但是我也不太明白怎么做。
采纳答案by jezrael
I think you can use first convert column Dateto_datetime, then find missing Daysby groupbywith resampleand last applyrolling
我认为您可以先使用 convert column Dateto_datetime，然后Days通过groupbywithresample和 last找到丢失的列applyrolling
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
回答by Alexander
n = 2
# Cast your dates as timestamps.
test['Date'] = pd.to_datetime(test.Date)
# Create a daily index spanning the range of the original index.
idx = pd.date_range(test.Date.min(), test.Date.max(), freq='D')
# Pivot by Dates and Users.
df = test.pivot(index='Date', values='Value', columns='User').reindex(idx)
>>> df.head(3)
User John Mike
2016-04-01 2 1.0
2016-04-02 3 1.0
2016-04-03 NaN 4.5
# Apply a rolling mean on the above dataframe and reset the index.
df2 = (pd.rolling_mean(df.shift(), n, min_periods=1)
.reset_index()
.drop_duplicates())
# For Pandas 0.18.0+
df2 = (df.shift().rolling(window=n, min_periods=1).mean()
.reset_index()
.drop_duplicates())
# Melt the result back into the original form.
df3 = (pd.melt(df2, id_vars='Date', value_name='Value')
.sort_values(['Date', 'User'])
.reset_index(drop=True))
>>> df3.head()
Date User Value
0 2016-04-01 John NaN
1 2016-04-01 Mike NaN
2 2016-04-02 John 2.0
3 2016-04-02 Mike 1.0
4 2016-04-03 John 2.5
# Merge the results back into the original dataframe.
>>> test.merge(df3, on=['Date', 'User'], how='left',
suffixes=['', '_Average_past_{0}_days'.format(n)])
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
Summary
概括
n = 2
test['Date'] = pd.to_datetime(test.Date)
idx = pd.date_range(test.Date.min(), test.Date.max(), freq='D')
df = test.pivot(index='Date', values='Value', columns='User').reindex(idx)
df2 = (pd.rolling_mean(df.shift(), n, min_periods=1)
.reset_index()
.drop_duplicates())
df3 = (pd.melt(df2, id_vars='Date', value_name='Value')
.sort_values(['Date', 'User'])
.reset_index(drop=True))
test.merge(df3, on=['Date', 'User'], how='left',
suffixes=['', '_Average_past_{0}_days'.format(n)])
