如何在Python中仅包含给定行日期之前/之后的记录，最好在电子表格上使用pandas转换

Question

我有一段摘录，需要我识别某种类型的手术

X

，请参见

Surg Type

列。

我需要保留在一个窗口/时间段内被视为不同行的医疗预约，其中 3 个预约是之前的 (-3、-2、-1) 和 3 个预约是后的 (+1、+2、+3)

我必须将此订单作为附加栏包含在内。

除此之外，我需要排除窗口外的任何预约和任何其他类型的

Surg Type

，在此示例中，任何手术都表示为 Z。

在此示例中，我想要保留 7/9 行/记录和一个附加列

Prior Post

*** 更新示例***

Original Df

| Patient ID | Surg ID | Surg Type | Surg Date  | Medical Appt | Medical Appt Date |
|------------|---------|-----------|------------|--------------|-------------------|
| 1          | 1       | X         | 2022-09-03 | Y            | 2022-01-01        |
| 1          | 1       | X         | 2022-09-03 | Y            | 2022-03-04        |
| 1          | 1       | X         | 2022-09-03 | Y            | 2022-05-04        |
| 1          | 1       | X         | 2022-09-03 | N            | NaT               |
| 1          | 1       | X         | 2022-09-03 | Y            | 2022-11-04        |
| 1          | 1       | X         | 2022-09-03 | Y            | 2022-11-29        |
| 1          | 2       | Z         | 2022-12-01 | N            | NaT               |
| 1          | 1       | X         | 2022-09-03 | Y            | 2023-01-02        |
| 1          | 1       | X         | 2022-09-03 | Y            | 2023-01-13        |



Desired Df

| Patient ID | Surg ID | Surg Type | Surg Date  | Medical Appt | Medical Appt Date | Inclusion   |
|------------|---------|-----------|------------|--------------|-------------------|-------------|
| 1          | 1       | X         | 2022-09-03 | Y            | 2022-01-01        | -3          |
| 1          | 1       | X         | 2022-09-03 | Y            | 2022-03-04        | -2          |
| 1          | 1       | X         | 2022-09-03 | Y            | 2022-05-04        | -1          |
| 1          | 1       | X         | 2022-09-03 | N            | NaT               |             |
| 1          | 1       | X         | 2022-09-03 | Y            | 2022-11-04        | +1          |
| 1          | 1       | X         | 2022-09-03 | Y            | 2022-11-29        | +2          |
| 1          | 2       | Z         | 2022-12-01 | N            | NaT               | Exclude Row |
| 1          | 1       | X         | 2022-09-03 | Y            | 2023-01-02        | +3          |
| 1          | 1       | X         | 2022-09-03 | Y            | 2023-01-13        | Exclude row |

Answer 1

# Remove rows that are not Surgery X or appointment
df = df.loc[df["Surg Type"].eq("X") | df["Medical Appt"].eq("Y")].reset_index(drop=True)
# Fill Prior Post column, assuming only one row is of Surg Type X
surgery_idx = df[df["Surg Type"].eq("X")].index[0]
df["Prior Post"] = df.index - surgery_idx
# Remove rows where Prior Post is outside of limits
df = df.loc[(df["Prior Post"] >= -3) & (df["Prior Post"] <= 3)]