在GROUPBY集合函数中传递参数

问题描述

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 免费vpn than threshold value 3.4
f5.__name__ = 'stop_frequency'

f6 = lambda x: len(x[x>0.2]) # count the points with velocity 免费vpn greater than threshold value 0.2
f6.__name__ = 'frequency'

f7 = lambda x: len(x[x>0.25]) # count the points with vpn下载 accelration greater 免费vpn下载 than threshold value 0.25
f7.__name__ = 'frequency'

d = {'acceleration':['mean', 'median', 'min'], free vpn vpn下载 免费vpn下载 
 'velocity':[f5, 'sum' ,'count', 'median', 'min'], 
 'velocity_rate':f6,
 'acc_rate':f7,
 'bearing':['sum', f4], 
 'bearing_rate':'sum',   免费vpn   
 'Vincenty_distance':'sum'}

df1 = df.groupby(['userid','trip_id','Transportation_Mode','segmentid'], sort=False).agg(d)

#flatenning MultiIndex in columns
df1.columns 免费vpn = df1.columns.map('_'.join)
#MultiIndex in index to columns
df1 = vpn下载 df1.reset_index(level=2, 免费vpn drop=False).reset_index()

我喜欢编写这样的函数

f4(p) = lambda x: len(x[x>p]) 
f4.__name__ = vpn下载 'Frequency'

d = {'acceleration':['mean', 'median', vpn free 'min'], 
 'velocity':[f5, 'sum' ,'count', 'median', 'min'], 
 'velocity_rate':f4(0.2),
 free vpn 'acc_rate':f4(0.25),
 'bearing':['sum', f4(10)], 
 免费vpn 'bearing_rate':'sum',   免费vpn下载   
 free vpn '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

推荐答案

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):
 免费vpn下载    def ipf(x):
  vpn free       return free vpn (x < p).sum()
        #your solution
 free vpn        #return len(x[x < vpn下载 p])
    ipf.__name__ = 'Frequency'
 vpn下载   免费vpn  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):
     免费vpn    if op == 'greater':
 免费vpn            return (x > p).sum()
        elif op vpn下载 == 'less':
            return (x < p).sum()  
      free vpn   vpn下载 else:
  免费vpn         免费vpn下载   raise ValueError("second argument has to be greater or less only")
    ipf.__name__ = 'Frequency'
    return vpn下载 免费vpn ipf 



d free vpn vpn下载 = 免费vpn {'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, vpn free '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, vpn下载 drop=False).reset_index()

print (df1.head())
   userid  trip_id  segmentid Transportation_Mode  acceleration_mean  
0     141      1.0  免费vpn         vpn下载 1       vpn下载          walk  免费vpn  免费vpn下载         0.061083   
1   vpn下载  免费vpn  141      2.0    免费vpn       1                walk           0.109148   
2     141      3.0          1                walk           0.106771   
3     141   vpn下载    4.0        vpn free   1            vpn free     vpn free walk           0.141180   
4     141      5.0          1                walk        free vpn   vpn下载  免费vpn下载 1.147157   

   acceleration_median  acceleration_min  velocity_Frequency  velocity_sum  
0        -1.168583e-02         -2.994428        vpn free       1000.0   1506.679506   
1   vpn下载       1.665535e-09     免费vpn下载     -3.234188               464.0    712.429005   
2        -3.055414e-08         -3.131293  免费vpn       vpn下载        996.0   1394.746071   
3 vpn free         9.241707e-09         -3.307262               340.0    免费vpn 513.461259   
4        -2.609489e-02       vpn free   -3.190424               493.0    729.702854   

   velocity_count  velocity_median  velocity_min  velocity_rate_Frequency  
0    free vpn         1028         1.294657      0.284747            free vpn         288.0   
1       免费vpn下载   free vpn vpn下载     vpn free 486         1.189650    vpn下载   0.284725                    134.0   
2     免费vpn        免费vpn 1020    vpn下载      1.241419     免费vpn  0.284733 免费vpn  free vpn                   301.0   
3      free vpn vpn free   免费vpn      352         1.326324  vpn下载     免费vpn下载 免费vpn 0.339590          免费vpn下载  vpn free           93.0   
4             504         1.247868   vpn下载    0.284740                    168.0   

   acc_rate_Frequency   bearing_sum  免费vpn bearing_Frequency  bearing_rate_sum  
0    免费vpn下载            169.0  81604.187066    vpn free      vpn下载  free vpn     884.0       -371.276356   
1                89.0  25559.589869    vpn下载      vpn free 免费vpn下载      313.0       -357.869944   
2               203.0 -71540.141199 vpn下载               57.0      免费vpn  免费vpn下载  946.382581   
3    免费vpn下载  vpn下载            78.0   9548.920765              167.0       免费vpn -943.184805   
4    免费vpn          vpn下载    93.0 -24021.555784            vpn下载    67.0        535.333624   

  free vpn  Vincenty_distance_sum  
0 免费vpn下载            1506.679506  
1 vpn free             712.429005  
2     vpn下载      免费vpn下载   1395.328768  
3      免费vpn   vpn下载      513.461259  
4           免费vpn   731.823664  

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