Question

我想将一个数据框合并到另一个数据框，其中合并是以特定范围内的日期/时间为条件的。

例如，假设我有以下两个数据框。

import pandas as pd
import datetime

# Create main data frame.
data = pd.DataFrame()
time_seq1 = pd.DataFrame(pd.date_range('1/1/2016', periods=3, freq='H'))
time_seq2 = pd.DataFrame(pd.date_range('1/2/2016', periods=3, freq='H'))
data = data.append(time_seq1, ignore_index=True)
data = data.append(time_seq1, ignore_index=True)
data = data.append(time_seq1, ignore_index=True)
data = data.append(time_seq2, ignore_index=True)
data['myID'] = ['001','001','001','002','002','002','003','003','003','004','004','004']
data.columns = ['Timestamp', 'myID']

# Create second data frame.
data2 = pd.DataFrame()
data2['time'] = [pd.to_datetime('1/1/2016 12:06 AM'), pd.to_datetime('1/1/2016 1:34 AM'), pd.to_datetime('1/2/2016 12:25 AM')]
data2['myID'] = ['002', '003', '004']
data2['specialID'] = ['foo_0', 'foo_1', 'foo_2']

# Show data frames.
data
             Timestamp myID
0  2016-01-01 00:00:00  001
1  2016-01-01 01:00:00  001
2  2016-01-01 02:00:00  001
3  2016-01-01 00:00:00  002
4  2016-01-01 01:00:00  002
5  2016-01-01 02:00:00  002
6  2016-01-01 00:00:00  003
7  2016-01-01 01:00:00  003
8  2016-01-01 02:00:00  003
9  2016-01-02 00:00:00  004
10 2016-01-02 01:00:00  004
11 2016-01-02 02:00:00  004

data2
                 time myID specialID
0 2016-01-01 00:06:00  002     foo_0
1 2016-01-01 01:34:00  003     foo_1
2 2016-01-02 00:25:00  004     foo_2

我想构造以下输出。

# Desired output.
             Timestamp myID special_ID
0  2016-01-01 00:00:00  001        NaN
1  2016-01-01 01:00:00  001        NaN
2  2016-01-01 02:00:00  001        NaN
3  2016-01-01 00:00:00  002        NaN
4  2016-01-01 01:00:00  002      foo_0
5  2016-01-01 02:00:00  002        NaN
6  2016-01-01 00:00:00  003        NaN
7  2016-01-01 01:00:00  003        NaN
8  2016-01-01 02:00:00  003      foo_1
9  2016-01-02 00:00:00  004        NaN
10 2016-01-02 01:00:00  004      foo_2
11 2016-01-02 02:00:00  004        NaN

特别是，我想将special_ID合并到data，以便Timestamp首次出现在time的值之后。例如，foo_0将与2016-01-01 01:00:00对应myID = 002对应的行，因为这是data紧跟2016-01-01 00:06:00后的下一次{{1}在time的行中包含special_ID = foo_0}。

注意，myID = 002不是Timestamp的索引，而data不是time的索引。大多数其他相关帖子似乎依赖于使用datetime对象作为数据框的索引。

Answer 1

你可以使用Pandas 0.19中新增的merge_asof来完成大部分工作。然后，合并let expValue = e2.expressionValue(with: nil, context: nil) // Error和loc以删除辅助匹配：

duplicated

结果输出：

# Data needs to be sorted for merge_asof.
data = data.sort_values(by='Timestamp')

# Perform the merge_asof.
df = pd.merge_asof(data, data2, left_on='Timestamp', right_on='time', by='myID').drop('time', axis=1)

# Make the additional matches null.
df.loc[df['specialID'].duplicated(), 'specialID'] = np.nan

# Get the original ordering.
df = df.set_index(data.index).sort_index()

Answer 2

不是很漂亮，但我觉得它很有效。

data['specialID'] = None
foolist = list(data2['myID'])
for i in data.index:
    if data.myID[i] in foolist:
        if data.Timestamp[i]> list(data2[data2['myID'] == data.myID[i]].time)[0]:
            data['specialID'][i] = list(data2[data2['myID'] == data.myID[i]].specialID)[0]
            foolist.remove(list(data2[data2['myID'] == data.myID[i]].myID)[0])

In [95]: data
Out[95]:
             Timestamp myID specialID
0  2016-01-01 00:00:00  001      None
1  2016-01-01 01:00:00  001      None
2  2016-01-01 02:00:00  001      None
3  2016-01-01 00:00:00  002      None
4  2016-01-01 01:00:00  002     foo_0
5  2016-01-01 02:00:00  002      None
6  2016-01-01 00:00:00  003      None
7  2016-01-01 01:00:00  003      None
8  2016-01-01 02:00:00  003     foo_1
9  2016-01-02 00:00:00  004      None
10 2016-01-02 01:00:00  004     foo_2
11 2016-01-02 02:00:00  004      None

pandas：合并条件的时间范围

2 个答案: