Question

我确定这很简单，但是我无法将其包裹住。本质上，我有两个数据帧，一个大的df每六个小时包含一个过程数据，一个较小的df包含一个条件编号，一个开始日期和一个结束日期。我需要用与日期范围相对应的条件编号填充大数据框的条件列，否则，如果日期不在小df中的任何日期范围之间，则将其留空。所以我的两个框架看起来像这样：

Large df
Date            P1  P2
7/1/2019 11:00  102 240
7/1/2019 17:00  102 247
7/1/2019 23:00  100 219
7/2/2019 5:00   107 213
7/2/2019 11:00  100 226
7/2/2019 17:00  104 239
7/2/2019 23:00  110 240
7/3/2019 5:00   110 232
7/3/2019 11:00  102 215
7/3/2019 17:00  103 219
7/3/2019 23:00  107 243
7/4/2019 5:00   107 246
7/4/2019 11:00  103 219
7/4/2019 17:00  105 220
7/4/2019 23:00  107 220
7/5/2019 5:00   107 227
7/5/2019 11:00  108 208
7/5/2019 17:00  110 248
7/5/2019 23:00  107 235


Small df
Condition   Start Time  End Time
A        7/1/2019 11:00 7/2/2019 5:00
B        7/3/2019 5:00  7/3/2019 23:00
C        7/4/2019 23:00 7/5/2019 17:00

我需要结果看起来像这样：

Date            P1  P2  Cond
7/1/2019 11:00  102 240 A
7/1/2019 17:00  102 247 A
7/1/2019 23:00  100 219 A
7/2/2019 5:00   107 213 A
7/2/2019 11:00  100 226 
7/2/2019 17:00  104 239 
7/2/2019 23:00  110 240 
7/3/2019 5:00   110 232 B
7/3/2019 11:00  102 215 B
7/3/2019 17:00  103 219 B
7/3/2019 23:00  107 243 B
7/4/2019 5:00   107 246 
7/4/2019 11:00  103 219 
7/4/2019 17:00  105 220 
7/4/2019 23:00  107 220 C
7/5/2019 5:00   107 227 C
7/5/2019 11:00  108 208 C
7/5/2019 17:00  110 248 C
7/5/2019 23:00  107 235

Answer 1

您需要：

for i, row in sdf.iterrows():
    df.loc[df['Date'].between(row['Start Time'], row['End Time']), 'Cond'] = row['Condition']

输出：

                Date    P1  P2  Cond
0   2019-07-01 11:00:00 102 240 A
1   2019-07-01 17:00:00 102 247 A
2   2019-07-01 23:00:00 100 219 A
3   2019-07-02 05:00:00 107 213 A
4   2019-07-02 11:00:00 100 226 NaN
5   2019-07-02 17:00:00 104 239 NaN
6   2019-07-02 23:00:00 110 240 NaN
7   2019-07-03 05:00:00 110 232 B
8   2019-07-03 11:00:00 102 215 B
9   2019-07-03 17:00:00 103 219 B
10  2019-07-03 23:00:00 107 243 B
11  2019-07-04 05:00:00 107 246 NaN
12  2019-07-04 11:00:00 103 219 NaN
13  2019-07-04 17:00:00 105 220 NaN
14  2019-07-04 23:00:00 107 220 C
15  2019-07-05 05:00:00 107 227 C
16  2019-07-05 11:00:00 108 208 C
17  2019-07-05 17:00:00 110 248 C
18  2019-07-05 23:00:00 107 235 NaN

Answer 2

您可以按以下方式尝试pd.IntervalIndex和map：

inx = pd.IntervalIndex.from_arrays(df2['Start Time'], df2['End Time'], closed='both')
df2.index = inx
df1['cond'] = df1.Date.map(df2.Condition)

Out[423]:
                  Date   P1   P2 cond
0  2019-07-01 11:00:00  102  240    A
1  2019-07-01 17:00:00  102  247    A
2  2019-07-01 23:00:00  100  219    A
3  2019-07-02 05:00:00  107  213    A
4  2019-07-02 11:00:00  100  226  NaN
5  2019-07-02 17:00:00  104  239  NaN
6  2019-07-02 23:00:00  110  240  NaN
7  2019-07-03 05:00:00  110  232    B
8  2019-07-03 11:00:00  102  215    B
9  2019-07-03 17:00:00  103  219    B
10 2019-07-03 23:00:00  107  243    B
11 2019-07-04 05:00:00  107  246  NaN
12 2019-07-04 11:00:00  103  219  NaN
13 2019-07-04 17:00:00  105  220  NaN
14 2019-07-04 23:00:00  107  220    C
15 2019-07-05 05:00:00  107  227    C
16 2019-07-05 11:00:00  108  208    C
17 2019-07-05 17:00:00  110  248    C
18 2019-07-05 23:00:00  107  235  NaN

Answer 3

您可以执行以下操作：

df1 = pd.read_csv(io.StringIO(s1), sep='\s\s+', engine='python',
                                                converters={'Date': pd.to_datetime})

df2 = pd.read_csv(io.StringIO(s2), sep='\s\s+', engine='python',
                converters={'Start Time': pd.to_datetime, 'End Time': pd.to_datetime})


df2 = df2.set_index('Condition').stack().reset_index()
df = pd.merge_asof(df1, df2, left_on='Date', right_on=0, direction='backward')
df.loc[(df['level_1'].eq('End Time')) & (df['Date'] > df[0]), 'Condition'] = ''

print(df.iloc[:, :-2])

                  Date   P1   P2 Condition
0  2019-07-01 11:00:00  102  240         A
1  2019-07-01 17:00:00  102  247         A
2  2019-07-01 23:00:00  100  219         A
3  2019-07-02 05:00:00  107  213         A
4  2019-07-02 11:00:00  100  226          
5  2019-07-02 17:00:00  104  239          
6  2019-07-02 23:00:00  110  240          
7  2019-07-03 05:00:00  110  232         B
8  2019-07-03 11:00:00  102  215         B
9  2019-07-03 17:00:00  103  219         B
10 2019-07-03 23:00:00  107  243         B
11 2019-07-04 05:00:00  107  246          
12 2019-07-04 11:00:00  103  219          
13 2019-07-04 17:00:00  105  220          
14 2019-07-04 23:00:00  107  220         C
15 2019-07-05 05:00:00  107  227         C
16 2019-07-05 11:00:00  108  208         C
17 2019-07-05 17:00:00  110  248         C
18 2019-07-05 23:00:00  107  235

Answer 4

df1.insert(3, "Cond", [None] * len(df1))
for i in range(len(df2)):
    df1.loc[(df1["Date"] >= df2["Start Time"].loc[i]) * (df1["Date"] <= df2["End Time"].loc[i]), "Cond"] = df2["Condition"].loc[i]

如果日期范围介于开始日期和结束日期之间，则将类别追加到列

4 个答案: