累積時間差として余分の列を追加

Question

各コースの時間差の累積値である余分な列を追加するにはどうすればよいですか？たとえば、最初のテーブルには、次のとおりです。

id_A  course  weight    ts_A  value 
id1  cotton  3.5  2017-04-27 01:35:30 150.000000 
id1  cotton  3.5  2017-04-27 01:36:00 416.666667 
id1  cotton  3.5  2017-04-27 01:36:30 700.000000 
id1  cotton  3.5  2017-04-27 01:37:00 950.000000 
id2  cotton blue 5.0  2017-04-27 02:35:30 150.000000 
id2  cotton blue 5.0  2017-04-27 02:36:00 450.000000 
id2  cotton blue 5.0  2017-04-27 02:36:30 520.666667 
id2  cotton blue 5.0  2017-04-27 02:37:00 610.000000 

期待される結果は次のとおりです。

id_A  course  weight    ts_A  value  cum_delta_sec 
id1  cotton  3.5  2017-04-27 01:35:30 150.000000  0 
id1  cotton  3.5  2017-04-27 01:36:00 416.666667  30 
id1  cotton  3.5  2017-04-27 01:36:30 700.000000  60 
id1  cotton  3.5  2017-04-27 01:37:00 950.000000  90 
id2  cotton blue 5.0  2017-04-27 02:35:30 150.000000  0 
id2  cotton blue 5.0  2017-04-27 02:36:00 450.000000  30 
id2  cotton blue 5.0  2017-04-27 02:36:30 520.666667  60 
id2  cotton blue 5.0  2017-04-27 02:37:00 610.000000  90 

Answer 1

することはできチェーンcumsumとdiff方法は：

# convert ts_A to datetime type 
df.ts_A = pd.to_datetime(df.ts_A) 

# convert ts_A to seconds, group by id and then use transform to calculate the cumulative difference 
df['cum_delta_sec'] = df.ts_A.astype(int).div(10**9).groupby(df.id_A).transform(lambda x: x.diff().fillna(0).cumsum()) 
df 

Answer 2

import csv 
import datetime as dt 

with open('path/to/input') as fin, open('path/to/output', 'w') as fout: 
    infile = csv.DictReader(fin, delimiter='\t') 
    outfile = csv.DictWriter(fout, delimiter='\t', fieldnames=infile.fieldnames + ['cum_delta_sec']) 

    cdt = 0 
    last = None 
    for row in infile: 
     if last is None: 
      last = dt.strptime(row['ts_A'], "%Y-%m-%d %H:%M:%S") 
      row['cum_delta_sec'] = 0 
      outfile.writerow(row) 
      continue 

     cdt += (last - dt.strptime(row['ts_A'], "%Y-%m-%d %H:%M:%S")).total_seconds() 
     row['cum_delta_sec'] = cdt 
     outfile.writerow(row) 

Answer 3

使用groupby、transform、および.iloc：

df['ts_A'] = pd.to_datetime(df.ts_A) 
df['cum_delta_sec'] = (df.groupby('id_A')['ts_A'] 
         .transform(lambda x: (x - x.iloc[0]).dt.total_seconds())) 

出力：グループで

id_A  course weight    ts_A  value cum_delta_sec 
0 id1  cotton  3.5 2017-04-27 01:35:30 150.000000    0 
1 id1  cotton  3.5 2017-04-27 01:36:00 416.666667    30 
2 id1  cotton  3.5 2017-04-27 01:36:30 700.000000    60 
3 id1  cotton  3.5 2017-04-27 01:37:00 950.000000    90 
4 id2 cotton blue  5.0 2017-04-27 02:35:30 150.000000    0 
5 id2 cotton blue  5.0 2017-04-27 02:36:00 450.000000    30 
6 id2 cotton blue  5.0 2017-04-27 02:36:30 520.666667    60 
7 id2 cotton blue  5.0 2017-04-27 02:37:00 610.000000    90 

、最初の値から現在の値を減算し、秒に変換するには.dtアクセサを使用します。