Question

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），但是在当前的df中没有找到这个。

如您所见，有 2 个“减速”期。我只对停止前最后一个“减速”期开始的数据感兴趣。

如何过滤数据，以便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。

希望能帮到你！

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()

Answer 1

您可以使用 .cumsum() 获得减速期的序列，然后通过 .loc 过滤，其中 Values 等于 Slowing down，并且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

Answer 2

因此，根据我的理解，您需要 'Value' 为 'Slowing down' 的行，后面紧跟 'Stopped'。

*注意：我明白我误解了。您不仅需要最后一行，还需要开始 "Slowing down" 序列的所有先前连续行。我仍会保留此解决方案，但看起来 SeaBean 已满足您的需求。

您可以做的是创建另一列，我将其命名为 'Next_Value'，即向上移动 1 行。然后您可以进行查询/过滤并找到具有 'Value' == 'Slowing down' 和 'Next_Value' == 'Stopped'

的行

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['Next_Value'] = df['Values'].shift(-1)

filtered_df = df.query('Values == "Slowing down" and Next_Value == "Stopped"')

如果您更熟悉此语法而不是 df.query()，请使用以下行：

filtered_df = df[(df['Values'] == "Slowing down") & (df['Next_Value'] == "Slowing down")]

输出：

print(filtered_df)
       Date and Time        Values Next_Value
17  2020-06-07 00:17  Slowing down    Stopped

Answer 3

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["slow_count"] = df.groupby("Values").cumcount()

a = df[(df["slow_count"] == df["slow_count"].max()) & (df["Values"] == "Slowing down" )]

熊猫：回顾过去

3 个答案: