我是 Python 的新手，我正在尝试创建一个类似于 this 的数据库使用 Pandas 。
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
我做的第一件事是创建一个新的空数据框来存储使用此代码的数据:
# 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")
输出如下:
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来为设备分配正确的时间分辨率:
# 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，根据第二篇文章，“=”似乎是正确放置的。关于如何使用我的 df 获得如上所示的预期结果的任何想法？
它应该是这样的:
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 ""
最佳答案
让我们分几步完成。
首先，让我们确保您的Timestamp 是一个日期时间。
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框。
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以合并到您的 start_time 列。
让我们过滤掉任何小于 0 的值并创建一个事件设备列表:
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
关于python - 如何根据开始和结束时间将多个列值连接到 Pandas 数据框中的单个列，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/66671727/