我对Python Pandas相当陌生，我很难将PandasGroupBy与transform结合起来，以我想要的方式行事。我已经无法找到已发布的答案，但我可能错过了一些东西。
我有一个包含大量条目的数据帧，结构如下：
GLT_City = pd.read_csv('GlobalLandTemperaturesByCity.csv', sep=',')
GLT_City.head()
AvgTemp AvgTempUncert City Country Lat Long year month day
0 6.068 1.737 Århus Denmark 57.05N 10.33E 1743 11 01
5 5.788 3.624 Århus Denmark 57.05N 10.33E 1744 04 01
6 10.644 1.283 Århus Denmark 57.05N 10.33E 1744 05 01
7 14.051 1.347 Århus Denmark 57.05N 10.33E 1744 06 01
8 16.082 1.396 Århus Denmark 57.05N 10.33E 1744 07 01
10 12.781 1.454 Århus Denmark 57.05N 10.33E 1744 09 01
11 7.950 1.630 Århus Denmark 57.05N 10.33E 1744 10 01
12 4.639 1.302 Århus Denmark 57.05N 10.33E 1744 11 01
我想计算每个城市每个月的加权平均温度，并使用transform()以最平滑的方式将其作为新列添加到我的原始数据框中，原因更进一步。
首先，我定义一个函数来计算加权平均值：
def wavg(group,data_name,weight_name, sigma=None):
data = group[data_name]
weight = group[weight_name]
#Check whether we have actual weights or measurement uncertainties
if sigma=='sigma':
weight = 1./weight
try:
return (data * weight).sum() / weight.sum()
except ZeroDivisionError:
return data.mean()
然后，我想结合GroupBy和transform()，将此函数应用于我的数据框，并将结果添加为新列，例如：
GLT_City['WeightedMonthlyMean'] = GLT_City.groupby(['City','month']).transform(wavg, 'AvgTemp','AvgTempUncert', sigma='sigma')
现在这会导致复制粘贴下面的非常冗长的错误消息
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5126)()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item (pandas/_libs/hashtable.c:14010)()
TypeError: an integer is required
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
<ipython-input-61-cef679f52b5f> in <module>()
----> 1 GLT_City['WeightedMonthlyMean'] = GLT_City.groupby(['City','month']).transform(wavg,
'AvgTemp','AvgTemp', sigma='sigma')
~/anaconda/envs/python36/lib/python3.6/site-
packages/pandas/core/groupby.py in transform(self, func, *args, **kwargs)
3814 result = getattr(self, func)(*args, **kwargs)
3815 else:
-> 3816 return self._transform_general(func, *args, **kwargs)
3817
3818 # a reduction transform
~/anaconda/envs/python36/lib/python3.6/site-packages/pandas/core/groupby.py in _transform_general(self, func, *args, **kwargs)
3765 # Try slow path and fast path.
3766 try:
-> 3767 path, res = self._choose_path(fast_path, slow_path, group)
3768 except TypeError:
3769 return self._transform_item_by_item(obj, fast_path)
~/anaconda/envs/python36/lib/python3.6/site-packages/pandas/core/groupby.py in _choose_path(self, fast_path, slow_path, group)
3861 def _choose_path(self, fast_path, slow_path, group):
3862 path = slow_path
-> 3863 res = slow_path(group)
3864
3865 # if we make it here, test if we can use the fast path
~/anaconda/envs/python36/lib/python3.6/site-packages/pandas/core/groupby.py in <lambda>(group)
3856 fast_path = lambda group: func(group, *args, **kwargs)
3857 slow_path = lambda group: group.apply(
-> 3858 lambda x: func(x, *args, **kwargs), axis=self.axis)
3859 return fast_path, slow_path
3860
~/anaconda/envs/python36/lib/python3.6/site-packages/pandas/core/frame.py in apply(self, func, axis, broadcast, raw, reduce, args, **kwds)
4260 f, axis,
4261 reduce=reduce,
-> 4262 ignore_failures=ignore_failures)
4263 else:
4264 return self._apply_broadcast(f, axis)
~/anaconda/envs/python36/lib/python3.6/site-packages/pandas/core/frame.py in _apply_standard(self, func, axis, ignore_failures, reduce)
4356 try:
4357 for i, v in enumerate(series_gen):
-> 4358 results[i] = func(v)
4359 keys.append(v.name)
4360 except Exception as e:
~/anaconda/envs/python36/lib/python3.6/site-packages/pandas/core/groupby.py in <lambda>(x)
3856 fast_path = lambda group: func(group, *args, **kwargs)
3857 slow_path = lambda group: group.apply(
-> 3858 lambda x: func(x, *args, **kwargs), axis=self.axis)
3859 return fast_path, slow_path
3860
<ipython-input-58-181ef4bb1f30> in wavg(group, data_name, weight_name, sigma)
10
11 #Extracting data and weights.
---> 12 data = group[data_name]
13 weight = group[weight_name]
14 #Check whether we have actual weights, or measurement uncertainties
~/anaconda/envs/python36/lib/python3.6/site-packages/pandas/core/series.py in __getitem__(self, key)
599 key = com._apply_if_callable(key, self)
600 try:
--> 601 result = self.index.get_value(self, key)
602
603 if not is_scalar(result):
~/anaconda/envs/python36/lib/python3.6/site-
packages/pandas/core/indexes/base.py in get_value(self, series, key)
2475 try:
2476 return self._engine.get_value(s, k,
-> 2477
tz=getattr(series.dtype, 'tz', None))
2478 except KeyError as e1:
2479 if len(self) > 0 and self.inferred_type in ['integer', 'boolean']:
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_value (pandas/_libs/index.c:4404)()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_value (pandas/_libs/index.c:4087)()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5210)()
KeyError: ('AvgTemp', 'occurred at index AvgTemp')
所以这显然是行不通的，但我不清楚为什么。任何指示/解决方案都将非常受欢迎。
我可以使用apply()方法来获得所需的输出，但由于我对组进行平均，因此我无法真正将其与原始数据帧合并，因为apply()生成的序列将具有不同的大小。
transform函数分别应用于每个组列。 将print语句放入wavg将帮助您查看问题：
def wavg(group,data_name,weight_name, sigma=None):
print(group)
...
df['WeightedMonthlyMean'] = df.groupby(['City','month']).transform(wavg, 'AvgTemp','AvgTempUncert', sigma='sigma')
指纹
1 5.788
Name: AvgTemp, dtype: object
在提高KeyError之前.这表明group只是一个系列，而不是整个(组(数据帧。
因此，请使用apply，然后将result合并回df：
result = df.groupby(['City','month']).apply(wavg, 'AvgTemp','AvgTempUncert', sigma='sigma').reset_index(name='wavg')
result = pd.merge(df, result)
例如
import pandas as pd
df = pd.DataFrame({'AvgTemp': [6.068, 5.787999999999999, 10.644, 14.050999999999998, 16.082, 12.780999999999999, 7.95, 4.638999999999999], 'AvgTempUncert': [1.7369999999999999, 3.6239999999999997, 1.2830000000000001, 1.347, 1.396, 1.454, 1.63, 1.3019999999999998], 'City': ['Århus', 'Århus', 'Århus', 'Århus', 'Århus', 'Århus', 'Århus', 'Århus'], 'Country': ['Denmark', 'Denmark', 'Denmark', 'Denmark', 'Denmark', 'Denmark', 'Denmark', 'Denmark'], 'Lat': ['57.05N', '57.05N', '57.05N', '57.05N', '57.05N', '57.05N', '57.05N', '57.05N'], 'Long': ['10.33E', '10.33E', '10.33E', '10.33E', '10.33E', '10.33E', '10.33E', '10.33E'], 'day': [1, 1, 1, 1, 1, 1, 1, 1], 'month': [11, 4, 5, 6, 7, 9, 10, 11], 'year': [1743, 1744, 1744, 1744, 1744, 1744, 1744, 1744]})
def wavg(group,data_name,weight_name, sigma=None):
data = group[data_name]
weight = group[weight_name]
#Check whether we have actual weights or measurement uncertainties
if sigma=='sigma':
weight = 1./weight
try:
return (data * weight).sum() / weight.sum()
except ZeroDivisionError:
return data.mean()
result = df.groupby(['City','month']).apply(wavg, 'AvgTemp','AvgTempUncert', sigma='sigma').reset_index(name='wavg')
result = pd.merge(df, result)
print(result)
收益 率
AvgTemp AvgTempUncert City Country Lat Long day month year wavg
0 6.068 1.737 Århus Denmark 57.05N 10.33E 1 11 1743 5.251227
1 4.639 1.302 Århus Denmark 57.05N 10.33E 1 11 1744 5.251227
2 5.788 3.624 Århus Denmark 57.05N 10.33E 1 4 1744 5.788000
3 10.644 1.283 Århus Denmark 57.05N 10.33E 1 5 1744 10.644000
4 14.051 1.347 Århus Denmark 57.05N 10.33E 1 6 1744 14.051000
5 16.082 1.396 Århus Denmark 57.05N 10.33E 1 7 1744 16.082000
6 12.781 1.454 Århus Denmark 57.05N 10.33E 1 9 1744 12.781000
7 7.950 1.630 Århus Denmark 57.05N 10.33E 1 10 1744 7.950000
使用apply然后merge将其放入同一DataFrame怎么样？例：
import numpy as np
import pandas as pd
data = pd.DataFrame({'City': np.random.randint(0, 4, 1000), 'Month': np.random.randint(1, 12, 1000), 'T': np.random.randn(1000)})
pd.merge(data, data.groupby(['City', 'Month']).apply(lambda x: x['T']*2).reset_index()[['City', 'Month', 'T']].rename(columns={'T': 'WeightedT'}), left_on=['City', 'Month'], right_on=['City', 'Month'])