Python -在value_counts上提高速度

Question

我正在争论一些原始数据，并希望计数链中某些度量的实例(列'Stat') (由‘chain_id’列中指出的唯一标识符'c‘命名)，然后将其保存到一个dict中，然后映射到一个新列(下面没有显示)。

不过，我希望：

1. 提高了循环的速度，我必须在34k行上使用10 it/s，从初始的~3。
2. 改进了try/ not语句的结构，注意到每个链在value_counts()输出中并不总是有‘踢’或‘标记’等，所以需要0.

。

我在SOF上搜索了其他方法，但是没有一个现有的答案适合--请忽略for循环的缩进，它将不允许我更正它

import pandas as pd
from tqdm.notebook import tqdm

s = ['Hitout', 'Kick', 'Disposal', 'Centre Clearance', 'Tackle', 'Hitout', 
     'Hitout To Advantage', 'Free Against', 'Contested Possession', 'Free For', 
     'Handball', 'Disposal', 'Effective Disposal', 'Stoppage Clearance', 
     'Uncontested Possession', 'Kick', 'Effective Kick', 'Disposal', 'Effective Disposal', 
     'Mark', 'Uncontested Possession', 'F 50 Mark', 'Mark On Lead', 'Kick', 'Disposal', 
     'Shot At Goal', 'Behind', 'Kick In', 'One Percenter', 'Kick', 'Effective Kick', 
     'Disposal', 'Effective Disposal', 'Rebound 50', 'Spoil', 'One Percenter']

x = ['Hitout', 'RI-1', 'RI-1', 'RI-1', 'RI-1', 'Hitout', 'Hitout', 'RI-7', 'RI-7', 
     'RI-7', 'RI-7', 'RI-7', 'RI-7', 'RI-7', 'RI-7', 'RI-7', 'RI-7', 'RI-7', 'RI-7', 
     'RI-7', 'RI-7', 'RI-7', 'RI-7', 'RI-7', 'RI-7', 'RI-7', 'RI-7', 'CA-27', 'CA-27', 
     'CA-27', 'CA-27', 'CA-27', 'CA-27', 'CA-27', 'CA-27', 'CA-27']

df = pd.DataFrame({'chain_id':x,'Stat':s})
            
for c in tqdm(chains):
    if c == 'Hitout':
        chain_count[c] = 0
        hb_count[c] = 0
        ki_count[c] = 0
        m_count[c] = 0
        goal_count[c] = 0
        behind_count[c] = 0
        cp_count[c] = 0
        up_count[c] = 0
        t_count[c] = 0
        chain_time[c] = 0
    else:
        temp = df[df['chain_id']==c]['Stat'].value_counts()
        try:
            chain_count[c] = temp['Disposal']
        except:
            chain_count[c] = 0
        try:
            ki_count[c] = temp['Kick']
        except:
            ki_count[c] = 0
        try:
            hb_count[c] = temp['Handball']
        except:
            hb_count[c] = 0
        try:
            m_count[c] = temp['Mark']
        except:
            m_count[c] = 0
        try:
            goal_count[c] = temp['Goal']
        except:
            goal_count[c] = 0
        try:
            behind_count[c] = temp['Behind']
        except:
            behind_count[c] = 0 
        try:
            cp_count[c] = temp['Contested Possession']
        except:
            cp_count[c] = 0
        try:
            up_count[c] = temp['Uncontested Possession']
        except:
            up_count[c] = 0
        try:
            t_count[c] = temp['Tackle']
        except:
            t_count[c] = 0
        chain_time[c] = time(c)

df['chain_length'] = df['chain_id'].map(chain_count)
df['chain_hb'] = df['chain_id'].map(hb_count)
df['chain_ki'] = df['chain_id'].map(ki_count)
df['chain_m'] = df['chain_id'].map(m_count)
df['chain_goal'] = df['chain_id'].map(goal_count)
df['chain_behind'] = df['chain_id'].map(behind_count)
df['chain_cp'] = df['chain_id'].map(cp_count)
df['chain_up'] = df['chain_id'].map(up_count)
df['chain_t'] = df['chain_id'].map(t_count)
df['chain_time'] = df['chain_id'].map(chain_time)

编辑:包含一个示例，其输出结果如下

​

​

Answer 1

创建状态列，将处理、手球和其他行为隔离开来。

 df['status']=np.select([df['Stat'].eq('Handball'),df['Stat'].eq('Disposal')],['chain_length','chain_hb'],'Notimportant')

找出每一种行为发生的频率

s=df.join(df.groupby(['chain_id','Stat']).apply(lambda x: pd.get_dummies(x['status'])).fillna(0)).drop(columns=['status','Notimportant'])

使用转换求和和级联总计。

 s[['chain_hb','chain_length']]=s.groupby('chain_id')[['chain_hb','chain_length']].transform('sum')

结局

 chain_id                   Stat      chain_hb  chain_length
0    Hitout                  Hitout       0.0           0.0
1      RI-1                    Kick       1.0           0.0
2      RI-1                Disposal       1.0           0.0
3      RI-1        Centre Clearance       1.0           0.0
4      RI-1                  Tackle       1.0           0.0
5    Hitout                  Hitout       0.0           0.0
6    Hitout     Hitout To Advantage       0.0           0.0
7      RI-7            Free Against       3.0           1.0
8      RI-7    Contested Possession       3.0           1.0
9      RI-7                Free For       3.0           1.0
10     RI-7                Handball       3.0           1.0
11     RI-7                Disposal       3.0           1.0
12     RI-7      Effective Disposal       3.0           1.0
13     RI-7      Stoppage Clearance       3.0           1.0
14     RI-7  Uncontested Possession       3.0           1.0
15     RI-7                    Kick       3.0           1.0
16     RI-7          Effective Kick       3.0           1.0
17     RI-7                Disposal       3.0           1.0
18     RI-7      Effective Disposal       3.0           1.0
19     RI-7                    Mark       3.0           1.0
20     RI-7  Uncontested Possession       3.0           1.0
21     RI-7               F 50 Mark       3.0           1.0
22     RI-7            Mark On Lead       3.0           1.0
23     RI-7                    Kick       3.0           1.0
24     RI-7                Disposal       3.0           1.0
25     RI-7            Shot At Goal       3.0           1.0
26     RI-7                  Behind       3.0           1.0
27    CA-27                 Kick In       1.0           0.0
28    CA-27           One Percenter       1.0           0.0
29    CA-27                    Kick       1.0           0.0
30    CA-27          Effective Kick       1.0           0.0
31    CA-27                Disposal       1.0           0.0
32    CA-27      Effective Disposal       1.0           0.0
33    CA-27              Rebound 50       1.0           0.0
34    CA-27                   Spoil       1.0           0.0
35    CA-27           One Percenter       1.0           0.0