Python dataframe 处理记录

1、分组求和
　　

import pandas as pd
from datetime import datetime, timedelta

data = pd.DataFrame({
    "company": ["A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A"],
    "salary": [10, 12, 13, 36, 12, 10, 12, 13, 36, 12, 36, 12],
    "age": [3, 1, 1, 1, 1, 2, 2, 1, 3, 1, 11, 4],
    "coll_time": ['2022-09-03 20:30:00', '2022-09-03 20:30:10', '2022-09-03 20:30:20', '2022-09-03 20:30:30',
                  '2022-09-03 20:30:40', '2022-09-03 20:30:50', '2022-09-03 20:31:00', '2022-09-03 20:31:00',
                  '2022-09-03 20:31:10', '2022-09-03 20:31:20', '2022-09-03 20:32:10', '2022-09-03 20:32:40']
})


# 时间格式转化
data['coll_time'] = data['coll_time'].astype('datetime64')
print(data)
# 行时间差
data['time_diff'] = data['coll_time'].diff()
data["time_diff_seconds"] = data[["time_diff"]].apply(lambda x: x["time_diff"].total_seconds(), axis=1)
print(data)
# 均匀切片求值
res = []
for i in range(0, int(len(data)) + 1, 6):
    df_slice = data[i:i + 6]
    v = df_slice['salary'].max()/df_slice['time_diff_seconds'].sum()
    res.append(v)

# 取众数
print('众数', data["time_diff_seconds"].mode().pop(0))

# 分组求和
dtd = data.groupby(['salary']).agg({'time_diff_seconds': 'sum', 'salary': 'max'})
# 两列相乘
dtd["mul"] = dtd[["salary", 'time_diff_seconds']].apply(lambda x: x["salary"] * x["time_diff_seconds"], axis=1)
print(dtd)

　　控制台输出

company  salary  age           coll_time
0        A      10    3 2022-09-03 20:30:00
1        A      12    1 2022-09-03 20:30:10
2        A      13    1 2022-09-03 20:30:20
3        A      36    1 2022-09-03 20:30:30
4        A      12    1 2022-09-03 20:30:40
5        A      10    2 2022-09-03 20:30:50
6        A      12    2 2022-09-03 20:31:00
7        A      13    1 2022-09-03 20:31:00
8        A      36    3 2022-09-03 20:31:10
9        A      12    1 2022-09-03 20:31:20
10       A      36   11 2022-09-03 20:32:10
11       A      12    4 2022-09-03 20:32:40
   company  salary  age           coll_time       time_diff  time_diff_seconds
0        A      10    3 2022-09-03 20:30:00             NaT                NaN
1        A      12    1 2022-09-03 20:30:10 0 days 00:00:10               10.0
2        A      13    1 2022-09-03 20:30:20 0 days 00:00:10               10.0
3        A      36    1 2022-09-03 20:30:30 0 days 00:00:10               10.0
4        A      12    1 2022-09-03 20:30:40 0 days 00:00:10               10.0
5        A      10    2 2022-09-03 20:30:50 0 days 00:00:10               10.0
6        A      12    2 2022-09-03 20:31:00 0 days 00:00:10               10.0
7        A      13    1 2022-09-03 20:31:00 0 days 00:00:00                0.0
8        A      36    3 2022-09-03 20:31:10 0 days 00:00:10               10.0
9        A      12    1 2022-09-03 20:31:20 0 days 00:00:10               10.0
10       A      36   11 2022-09-03 20:32:10 0 days 00:00:50               50.0
11       A      12    4 2022-09-03 20:32:40 0 days 00:00:30               30.0
众数 10.0
        time_diff_seconds  salary     mul
salary                                   
10                   10.0      10   100.0
12                   70.0      12   840.0
13                   10.0      13   130.0
36                   70.0      36  2520.0