使用.groupby()的 pandas 时间序列的平均值 [英] Mean of Pandas TimeSeries using .groupby()
问题描述
我从行为实验中获得了一些连续的x/y坐标，我希望在使用熊猫的小组中进行平均.
我在这里使用数据的子集.
data
Out[11]:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 2036 entries, 0 to 1623
Data columns (total 9 columns):
id 2036 non-null values
subject 2036 non-null values
code 2036 non-null values
acc 2036 non-null values
nx 2036 non-null values
ny 2036 non-null values
rx 2036 non-null values
ry 2036 non-null values
reaction_time 2036 non-null values
dtypes: bool(1), int64(3), object(5)
nx和ny包含一系列TimeSeries对象，所有这些对象都具有相同的索引.
data.nx.iloc[0]
Out[16]:
0 0
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
11 0
12 0
13 0
14 0
...
86 1.019901
87 1.010000
88 1.010000
89 1.005921
90 1.000000
91 1.000000
92 1.000000
93 1.000000
94 1.000000
95 1.000000
96 1.000000
97 1.000000
98 1.000000
99 1.000000
100 1.000000
Length: 101, dtype: float64
这些TimeSeries列可以使用data.nx.mean()正常地平均，并表现出预期的效果，但是在尝试对数据进行分组时遇到了麻烦.
grouped = data.groupby(['code', 'acc'])
means = grouped.mean()
print means
id subject reaction_time
code acc
group1 False 1570.866667 47474992.333333 1506.000000
True 1337.076152 46022403.623246 1322.116232
group2 False 1338.180180 48730402.045045 1289.112613
True 1382.631757 42713592.628378 1294.952703
group3 False 1488.587156 43202477.623853 1349.568807
True 1310.415233 47054310.498771 1341.837838
group4 False 1339.682540 52530349.936508 1540.714286
True 1343.261176 44606616.407059 1362.174118
奇怪的是，我可以强迫他们平均化TimeSeries数据，并且可能不得不依靠这种方式进行黑客攻击，就像这样:
for name, group in grouped:
print group.nx.mean()
0 0.000000
1 0.000000
2 0.000000
3 0.000000
4 0.000000
5 0.000667
6 0.000683
7 0.001952
8 0.002000
9 0.002000
{etc, 101 values for 6 groups}
最后，如果我尝试强制GroupBy对象对它们求平均值，则会得到以下结果:
grouped.nx.mean()
---------------------------------------------------------------------------
DataError Traceback (most recent call last)
<ipython-input-25-0b536a966e02> in <module>()
----> 1 grouped.nx.mean()
/usr/local/lib/python2.7/dist-packages/pandas-0.12.0-py2.7-linux-i686.egg/pandas/core/groupby.pyc in mean(self)
357 """
358 try:
--> 359 return self._cython_agg_general('mean')
360 except GroupByError:
361 raise
/usr/local/lib/python2.7/dist-packages/pandas-0.12.0-py2.7-linux-i686.egg/pandas/core/groupby.pyc in _cython_agg_general(self, how, numeric_only)
462
463 if len(output) == 0:
--> 464 raise DataError('No numeric types to aggregate')
465
466 return self._wrap_aggregated_output(output, names)
DataError: No numeric types to aggregate
有人有什么想法吗?
每个条目本身都是一个Series的Series不是惯用的.我认为没有要聚合的数字类型"告诉您熊猫正在尝试获取未定义的系列列表的平均值(而不是它们包含的数字数据的平均值).
您应该整理数据，以便nx和ny包含实际数字.将nx，ny(以及我认为是rx和ry)保存在一个单独的DataFrame中，这可能是最简单的，其中每一列对应一个id.
Hi,
I have some continuous x/y coordinates from a behavioural experiment, that I would like to average within groups using Pandas.
I'm using a subset of the data here.
data
Out[11]:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 2036 entries, 0 to 1623
Data columns (total 9 columns):
id 2036 non-null values
subject 2036 non-null values
code 2036 non-null values
acc 2036 non-null values
nx 2036 non-null values
ny 2036 non-null values
rx 2036 non-null values
ry 2036 non-null values
reaction_time 2036 non-null values
dtypes: bool(1), int64(3), object(5)
nx and ny hold a series of TimeSeries objects, all of which have the same indices.
data.nx.iloc[0]
Out[16]:
0 0
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
11 0
12 0
13 0
14 0
...
86 1.019901
87 1.010000
88 1.010000
89 1.005921
90 1.000000
91 1.000000
92 1.000000
93 1.000000
94 1.000000
95 1.000000
96 1.000000
97 1.000000
98 1.000000
99 1.000000
100 1.000000
Length: 101, dtype: float64
These TimeSeries columns can be average normally, using data.nx.mean(), and behave as expected, but I hit trouble when I try to group the data.
grouped = data.groupby(['code', 'acc'])
means = grouped.mean()
print means
id subject reaction_time
code acc
group1 False 1570.866667 47474992.333333 1506.000000
True 1337.076152 46022403.623246 1322.116232
group2 False 1338.180180 48730402.045045 1289.112613
True 1382.631757 42713592.628378 1294.952703
group3 False 1488.587156 43202477.623853 1349.568807
True 1310.415233 47054310.498771 1341.837838
group4 False 1339.682540 52530349.936508 1540.714286
True 1343.261176 44606616.407059 1362.174118
Strangely, I can force them to average the TimeSeries data, and may have to fall back on hacking this way, like so:
for name, group in grouped:
print group.nx.mean()
0 0.000000
1 0.000000
2 0.000000
3 0.000000
4 0.000000
5 0.000667
6 0.000683
7 0.001952
8 0.002000
9 0.002000
{etc, 101 values for 6 groups}
Finally, if I try to force the GroupBy object to average them, I get the following:
grouped.nx.mean()
---------------------------------------------------------------------------
DataError Traceback (most recent call last)
<ipython-input-25-0b536a966e02> in <module>()
----> 1 grouped.nx.mean()
/usr/local/lib/python2.7/dist-packages/pandas-0.12.0-py2.7-linux-i686.egg/pandas/core/groupby.pyc in mean(self)
357 """
358 try:
--> 359 return self._cython_agg_general('mean')
360 except GroupByError:
361 raise
/usr/local/lib/python2.7/dist-packages/pandas-0.12.0-py2.7-linux-i686.egg/pandas/core/groupby.pyc in _cython_agg_general(self, how, numeric_only)
462
463 if len(output) == 0:
--> 464 raise DataError('No numeric types to aggregate')
465
466 return self._wrap_aggregated_output(output, names)
DataError: No numeric types to aggregate
Has anyone any ideas?
A Series where each entry is itself a Series is not idiomatic. I think "No numeric types to aggregate" is telling you that pandas is trying to take the average of a list of Series (not the average of the numeric data they contain) which is not defined.
You should organize your data so nx and ny contain actual numbers. It might be simplest to keep nx, ny, (and, I think, rx and ry) in a separate DataFrame, where each column corresponds to one id.
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