python - 熊猫：回顾过去

问题描述

我正在查看车辆的速度，我拥有的唯一数据是速度稳定、减速或停止（参见下面的 df）。还有一个（加速），但在当前的df中找不到这个。

如您所见，有 2 个“减速”时期。我只对从停止前的最后一个“减速”时期开始的数据感兴趣。

如何过滤数据以删除我不感兴趣的前 x 行？由于速度值总是不同的，我不能简单地过滤值。

希望你能帮忙！

import pandas as pd

data = {
  "Date and Time": ["2020-06-07 00:00", "2020-06-07 00:01", "2020-06-07 00:02", "2020-06-07 00:03", "2020-06-07 00:04", "2020-06-07 00:05", "2020-06-07 00:06", "2020-06-07 00:07", "2020-06-07 00:08", "2020-06-07 00:09", "2020-06-07 00:10", "2020-06-07 00:11", "2020-06-07 00:12", "2020-06-07 00:13", "2020-06-07 00:14", "2020-06-07 00:15", "2020-06-07 00:16", "2020-06-07 00:17", "2020-06-07 00:18", "2020-06-07 00:19", "2020-06-07 00:20"],

  "Values": ["Stable","Stable","Stable","Stable", "Slowing down","Slowing down","Slowing down","Stable", "Stable", "Stable", "Slowing down","Slowing down","Slowing down","Slowing down","Slowing down","Slowing down","Slowing down","Slowing down", "Stopped", "Stopped", "Stopped"]
}

df = pd.DataFrame(data)

df.head()

标签： pythonpandas

.cumsum()您可以使用then filter by .locwith Valuesequals获得减速周期的序列，Slowing down新创建的序列是最大值：

df['SlowDownSeq'] = df['Values'].ne(df['Values'].shift()).cumsum()
df_selected = df.loc[df['SlowDownSeq'] ==  df.loc[df['Values'] == 'Slowing down', 'SlowDownSeq'].max()].drop('SlowDownSeq', axis=1)

结果：

print(df_selected)


         Date and Time        Values
10 2020-06-07 00:10:00  Slowing down
11 2020-06-07 00:11:00  Slowing down
12 2020-06-07 00:12:00  Slowing down
13 2020-06-07 00:13:00  Slowing down
14 2020-06-07 00:14:00  Slowing down
15 2020-06-07 00:15:00  Slowing down
16 2020-06-07 00:16:00  Slowing down
17 2020-06-07 00:17:00  Slowing down

python - 熊猫：回顾过去

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