Question

我的问题与此one有关。但是，那里的解决方案对我不起作用。

我有一个数据帧df，如下所示。我想以elevation作为权重，取width和counts的加权平均值，按building和day分组。我怎样才能做到这一点？

#Sample data
import pandas as pd
df = pd.DataFrame({
  'building': ['A1', 'A1', 'A1', 'A1'],
  'day': ['2019-07-02', '2019-07-02', '2019-07-03', '2019-07-03'],
  'id': ['alak', 'ldau', 'laud', 'lkdu'],
  'counts': [1, 2, 3, 7],
  'elevation': [5.7, 7.8, 8.7, 6.9],
  'width':[1.2, 2.4, 3.4, 2.7]
})

 df
    building    day      id   counts elevation  width
  0  A1      2019-07-02  alak   1      5.7       1.2
  1  A1      2019-07-02  ldau   2      7.8       2.4
  2  A1      2019-07-03  laud   3      8.7       3.4
  3  A1      2019-07-03  lkdu   7      6.9       2.7

# What I want to get:
    building    day     elevation   width
  0  A1      2019-07-02   7.1        2.0
  1  A1      2019-07-03   7.4        2.9

Answer 1

您可以使用reindex和repeat的技巧

df.reindex(df.index.repeat(df.counts)).drop('counts',1).\
     groupby(['building','day'],as_index=False).mean()
Out[110]: 
  building         day  elevation  width
0       A1  2019-07-02       7.10   2.00
1       A1  2019-07-03       7.44   2.91

Answer 2

我猜想有更好的方法，但是可以做到：

df = pd.DataFrame({
  'building': ['A1', 'A1', 'A1', 'A1'],
  'day': ['2019-07-02', '2019-07-02', '2019-07-03', '2019-07-03'],
  'id': ['alak', 'ldau', 'lauid', 'lkdu'],
  'counts': [1, 2, 3, 7],
  'elevation': [5.7, 7.8, 8.7, 6.9],
  'width':[1.2, 2.4, 3.4, 2.7]
})

df = df.set_index(['building','day'])
sum_count = df.groupby(['building','day']).counts.sum()
df['w_elevation'] = df.elevation*df.counts /sum_count
df['w_width'] = df.width*df.counts / sum_count
df.groupby(['building','day']).sum()

输出：

                     counts  elevation  width  w_elevation  w_width
building day                                                       
A1       2019-07-02       3       13.5    3.6         7.10     2.00
         2019-07-03      10       15.6    6.1         7.44     2.91

Answer 3

您可以执行以下操作：

df_sum= df.copy()
df_sum['elevation']*= df_sum['counts']
df_sum['width']*= df_sum['counts']

df_sum= df_sum.groupby(['building', 'day']).agg(dict(elevation=sum, width=sum, counts=sum))
df_sum['elevation']/= df_sum['counts']
df_sum['width']/= df_sum['counts']
df_sum.reset_index(inplace=True)
df_sum.drop('counts', axis='columns', inplace=True)

结果是：

  building         day  elevation  width
0       A1  2019-07-02       7.10   2.00
1       A1  2019-07-03       7.44   2.91

Answer 4

以防您需要比以前的答案更详细的内容：

result = df

# normalize for the weight
result['elevation'] = result['elevation'] * result['counts']
result['width'] = result['width'] * result['counts']

# let's sum all values per our dimensions
result = result.groupby(['building', 'day']).sum()
# and the get the weighted averages
result['elevation'] = result['elevation'] / result['counts']
result['width'] = result['width'] / result['counts']

# final results
result[['elevation', 'width']]

或通过另一种方式：

result = df

# first of all let's get the sum of counts by building and day
# the indexes will return useful later
result = result.set_index(['building', 'day'])
counts = result['counts'].groupby(['building', 'day']).sum()

# let's normalize the values for elevation and width
result[['elevation', 'width']] = result[['elevation', 'width']].apply(lambda x: x * result['counts'])

# finally calculate the wigthed average
result = result[['elevation', 'width']].groupby(['building', 'day']).sum()
result = result[['elevation', 'width']].apply(lambda x: x / counts)

result

将多个熊猫数据框列的分组平均加权平均值作为数据框返回

4 个答案: