如何在csv文件中添加一列来根据日期统计课程数量

Question

我正在尝试在 colab 的 csv 文件中创建一列，以根据时间戳列的日期来计算类的数量

时间戳/class_id
2021-09-27 06:00:00 / A
2021-09-27 03:00:00 / A
2021-09-27 01:00:00 / A
2021-09-27 08:29:00 / C
2021-05-23 08:08:49 / B
2021-05-23 03:21:49 / B
2021-05-23 01:22:11 / C

预期结果：
计数/时间戳/class_id
1 / 2021-09-27 06:00:00 / A
2 / 2021-09-27 03:00:00 / A
3 / 2021-09-27 01:00:00 / A
1 / 2021-09-27 08:29:00 / C
1 / 2021-05-23 08:08:49 / B
2 / 2021-05-23 03:21:49 / B
1 / 2021-05-23 01:22:11 / C

from google.colab import drive
drive.mount('/content/gdrive')

import pandas as pd

data = pd.read_csv('gdrive/My Drive/Colab_Notebooks/capstoneproject/capstonedata.csv', parse_dates=['visit_date'], index_col='visit_date')
class_id = data['class_id']

count = 0
data['count'] = count
index = -1
for row in data:
  index = index + 1
  if class_id[index] == class_id[index-1]:
    data['count'] = count + 1
  elif count == 0: 
    data['count'] = count + 1
  else:
    data['count'] = 1

data.head()

这是我到目前为止的代码，但这是输出
时间戳/class_id/计数
2021-09-27 06:00:00 / A / 1
2021-09-27 03:00:00 / A / 1
2021-09-27 01:00:00 / A / 1
2021-09-27 08:29:00 / C / 1
2021-05-23 08:08:49 / B / 1
2021-05-23 03:21:49 / B / 1
2021-05-23 01:22:11 / C / 1

Answer 1

from typing import Generator
import datetime as dt
import pandas as pd


def _get_df() -> pd.DataFrame:
    start = dt.datetime(2021, 5, 23, 7, 0)
    df = pd.DataFrame(
        {
            "stamp": [start + dt.timedelta(hours=-i) for i in range(7)],
            "class_id": list("AAACBBC"),
        }
    )
    df["count"] = list(_get_counts(df.class_id))
    return df


def _get_counts(s: pd.Series) -> Generator[int, None, None]:
    prev = None
    count = 1
    for val in s:
        if val == prev:
            count += 1
        else:
            count = 1
        yield count
        prev = val


if __name__ == "__main__":
    print(_get_df())

输出：

                stamp class_id  count
0 2021-05-23 07:00:00        A      1
1 2021-05-23 06:00:00        A      2
2 2021-05-23 05:00:00        A      3
3 2021-05-23 04:00:00        C      1
4 2021-05-23 03:00:00        B      1
5 2021-05-23 02:00:00        B      2
6 2021-05-23 01:00:00        C      1