我有数据帧，我在代码中将其引用为df，并且我在每个组的多个列上应用聚合函数。我还应用了用户定义的lambda函数f4, f5, f6, f7。有些函数非常相似，比如f4, f6和f7，只是参数值不同。我是否可以从字典d传递这些参数，以便我只需编写一个函数，而不是编写多个函数？
f4 = lambda x: len(x[x>10]) # count the frequency of bearing greater than threshold value
f4.__name__ = 'Frequency'
f5 = lambda x: len(x[x<3.4]) # count the stop points with velocity less than threshold value 3.4
f5.__name__ = 'stop_frequency'
f6 = lambda x: len(x[x>0.2]) # count the points with velocity greater than threshold value 0.2
f6.__name__ = 'frequency'
f7 = lambda x: len(x[x>0.25]) # count the points with accelration greater than threshold value 0.25
f7.__name__ = 'frequency'
d = {'acceleration':['mean', 'median', 'min'],
'velocity':[f5, 'sum' ,'count', 'median', 'min'],
'velocity_rate':f6,
'acc_rate':f7,
'bearing':['sum', f4],
'bearing_rate':'sum',
'Vincenty_distance':'sum'}
df1 = df.groupby(['userid','trip_id','Transportation_Mode','segmentid'], sort=False).agg(d)
#flatenning MultiIndex in columns
df1.columns = df1.columns.map('_'.join)
#MultiIndex in index to columns
df1 = df1.reset_index(level=2, drop=False).reset_index()
我喜欢编写这样的函数
f4(p) = lambda x: len(x[x>p])
f4.__name__ = 'Frequency'
d = {'acceleration':['mean', 'median', 'min'],
'velocity':[f5, 'sum' ,'count', 'median', 'min'],
'velocity_rate':f4(0.2),
'acc_rate':f4(0.25),
'bearing':['sum', f4(10)],
'bearing_rate':'sum',
'Vincenty_distance':'sum'}
数据帧DF的CSV文件在给定的链接上提供，以使数据更加清晰。 https://drive.google.com/open?id=1R_BBL00G_Dlo-6yrovYJp5zEYLwlMPi9
neilaronson可以解决，但不容易解决。
还通过布尔掩码值True的sum简化了求解。
def f4(p):
def ipf(x):
return (x < p).sum()
#your solution
#return len(x[x < p])
ipf.__name__ = 'Frequency'
return ipf
d = {'acceleration':['mean', 'median', 'min'],
'velocity':[f4(3.4), 'sum' ,'count', 'median', 'min'],
'velocity_rate':f4(0.2),
'acc_rate':f4(.25),
'bearing':['sum', f4(10)],
'bearing_rate':'sum',
'Vincenty_distance':'sum'}
df1 = df.groupby(['userid','trip_id','Transportation_Mode','segmentid'], sort=False).agg(d)
#flatenning MultiIndex in columns
df1.columns = df1.columns.map('_'.join)
#MultiIndex in index to columns
df1 = df1.reset_index(level=2, drop=False).reset_index()
编辑：也可以传递大大小小的参数：
def f4(p, op):
def ipf(x):
if op == 'greater':
return (x > p).sum()
elif op == 'less':
return (x < p).sum()
else:
raise ValueError("second argument has to be greater or less only")
ipf.__name__ = 'Frequency'
return ipf
d = {'acceleration':['mean', 'median', 'min'],
'velocity':[f4(3.4, 'less'), 'sum' ,'count', 'median', 'min'],
'velocity_rate':f4(0.2, 'greater'),
'acc_rate':f4(.25, 'greater'),
'bearing':['sum', f4(10, 'greater')],
'bearing_rate':'sum',
'Vincenty_distance':'sum'}
df1 = df.groupby(['userid','trip_id','Transportation_Mode','segmentid'], sort=False).agg(d)
#flatenning MultiIndex in columns
df1.columns = df1.columns.map('_'.join)
#MultiIndex in index to columns
df1 = df1.reset_index(level=2, drop=False).reset_index()
print (df1.head())
userid trip_id segmentid Transportation_Mode acceleration_mean
0 141 1.0 1 walk 0.061083
1 141 2.0 1 walk 0.109148
2 141 3.0 1 walk 0.106771
3 141 4.0 1 walk 0.141180
4 141 5.0 1 walk 1.147157
acceleration_median acceleration_min velocity_Frequency velocity_sum
0 -1.168583e-02 -2.994428 1000.0 1506.679506
1 1.665535e-09 -3.234188 464.0 712.429005
2 -3.055414e-08 -3.131293 996.0 1394.746071
3 9.241707e-09 -3.307262 340.0 513.461259
4 -2.609489e-02 -3.190424 493.0 729.702854
velocity_count velocity_median velocity_min velocity_rate_Frequency
0 1028 1.294657 0.284747 288.0
1 486 1.189650 0.284725 134.0
2 1020 1.241419 0.284733 301.0
3 352 1.326324 0.339590 93.0
4 504 1.247868 0.284740 168.0
acc_rate_Frequency bearing_sum bearing_Frequency bearing_rate_sum
0 169.0 81604.187066 884.0 -371.276356
1 89.0 25559.589869 313.0 -357.869944
2 203.0 -71540.141199 57.0 946.382581
3 78.0 9548.920765 167.0 -943.184805
4 93.0 -24021.555784 67.0 535.333624
Vincenty_distance_sum
0 1506.679506
1 712.429005
2 1395.328768
3 513.461259
4 731.823664
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