如何根据开始和结束时间将多个列值连接到 Panda 数据框中的单个列中

Question

我是 Python 新手，我正在尝试使用 pandas 创建一个类似于 this 的数据库。

下面是我的 df 的简化版本：

    Timestamp   A   B   C
0   2013-02-01  1   0   0
1   2013-02-02  2   10  18
2   2013-02-03  3   0   19
3   2013-02-04  4   12  20
4   2013-02-05  0   13  21
5   2013-02-06  6   14  22
6   2013-02-07  7   15  23
7   2013-02-08  0   0   0

我做的第一件事是使用以下代码创建一个新的空数据框来存储数据：

# Create frequent pattern source database
df_frequent_pattern = pd.DataFrame(columns = ["Start Time", "End Time", "Active Appliances"])

# Create start_time and end_time series using pd.date_range
df_frequent_pattern["Start Time"] = pd.date_range("2013-02-1", "2013-02-08", freq = "D")
df_frequent_pattern["End Time"] = pd.date_range("2013-02-2", "2013-02-09", freq = "D")

下面的输出：

    Start Time  End Time    Active Appliances
0   2013-02-01  2013-02-02  NaN
1   2013-02-02  2013-02-03  NaN
2   2013-02-03  2013-02-04  NaN
3   2013-02-04  2013-02-05  NaN
4   2013-02-05  2013-02-06  NaN
5   2013-02-06  2013-02-07  NaN
6   2013-02-07  2013-02-08  NaN
7   2013-02-08  2013-02-09  NaN

基于this 和this 堆栈溢出帖子，我编写了以下代码以将设备分配给正确的时间分辨率：

# Add the data to the correct 'active' period based on interval and merge the active appliances in the "active appliances column"
# Row counter for the loop
rows = 8

for row in range(rows):
  # Check if appliance is active during time resoltuion
  if df_frequent_pattern["Start Time"] <= df["Timestamp"] | df["Timestamp" <= df_frequent_pattern["End Time"]:
    # Add all the appliance active during the time resolution to the column as a string value (e.g. "A, B, C")
     df_frequent_pattern["Active Appliances"] = df["A", "B", "C"].apply(lambda row: '_'.join(row.values.astype(str)), axis = 1)

不幸的是，代码不起作用，我收到以下错误

df_frequent_pattern["Active Appliances"] = df["A", "B", "C"].apply(lambda row: '_'.join(row.values.astype(str)), axis = 1)
                                         ^
SyntaxError: invalid syntax

然而，'=' 似乎根据第二个帖子正确放置。关于如何使用我的 df 来获得如上所示的预期结果的任何想法？

应该是这样的：

   Start Time   End Time    Active Appliances
0   2013-02-01  2013-02-02  "A"
1   2013-02-02  2013-02-03  "A,B,C"
2   2013-02-03  2013-02-04  "A,C"
3   2013-02-04  2013-02-05  "A,B,C"
4   2013-02-05  2013-02-06  "A,B,C"
5   2013-02-06  2013-02-07  "A,B,C"
6   2013-02-07  2013-02-08  "A,B,C"
7   2013-02-08  2013-02-09  ""

Answer 1

让我们分几步来完成。

首先，让我们确保您的 Timestamp 是日期时间。

df['Timestamp'] = pd.to_datetime(df['Timestamp'])

然后我们可以根据您的时间戳的最小值和最大值创建一个新的数据帧。

df1 = pd.DataFrame({'start_time' : pd.date_range(df['Timestamp'].min(), df['Timestamp'].max())})

df1['end_time'] = df1['start_time'] + pd.DateOffset(days=1)

 start_time   end_time
0 2013-02-01 2013-02-02
1 2013-02-02 2013-02-03
2 2013-02-03 2013-02-04
3 2013-02-04 2013-02-05
4 2013-02-05 2013-02-06
5 2013-02-06 2013-02-07
6 2013-02-07 2013-02-08
7 2013-02-08 2013-02-09

---

现在我们需要创建一个数据框来合并到您的start_time 列。

让我们过滤掉任何小于 0 的值并创建一个活动设备列表：

df = df.set_index('Timestamp')
# the remaining columns MUST be integers for this to work. 
# or you'll need to subselect them. 
df2 = df.mask(df.le(0)).stack().reset_index(1).groupby(level=0)\
                 .agg(active_appliances=('level_1',list)).reset_index(0)

# change .agg(active_appliances=('level_1',list) > 
# to .agg(active_appliances=('level_1',','.join)
# if you prefer strings.



    Timestamp active_appliances
0 2013-02-01               [A]
1 2013-02-02         [A, B, C]
2 2013-02-03            [A, C]
3 2013-02-04         [A, B, C]
4 2013-02-05            [B, C]
5 2013-02-06         [A, B, C]
6 2013-02-07         [A, B, C]

---

然后我们可以合并：

final = pd.merge(df1,df2,left_on='start_time',right_on='Timestamp',how='left').drop('Timestamp',1)


  start_time   end_time active_appliances
0 2013-02-01 2013-02-02               [A]
1 2013-02-02 2013-02-03         [A, B, C]
2 2013-02-03 2013-02-04            [A, C]
3 2013-02-04 2013-02-05         [A, B, C]
4 2013-02-05 2013-02-06            [B, C]
5 2013-02-06 2013-02-07         [A, B, C]
6 2013-02-07 2013-02-08         [A, B, C]
7 2013-02-08 2013-02-09               NaN