python - 可以使用比较来合并两个 Pandas 数据框吗？

Question

使用以下命令:

pandas.merge(df_1, df_2, left_on=['date'], right_on=['from_date'])

如果 date 中的值，我将两个表中的两行合并- 第一个表的列等于 from_date 中的值- 第二个表的列。

现在我想让它稍微复杂一点。如果 date 中的值，我需要将第一个表中的一行与第二个表中的一行合并第一个表的列等于或大于 from_date 的值- 第二个表的列且小于 upto_date 中的值- 第二列的列。

在 SQL 中，人们会使用类似的东西:

select
    *
from
    table_1
join
    table_2
on
    table_1.date >= table_2.from_date
    and
    table_1.date <  table_2.upto_date

是否可以在 pandas 中实现。

Answer 1

pandasql是一个非常有用的工具，用于使用 SQLite 查询语法查询 pandas DataFrame。

资源

• pandasql - PyPI Documentation
• yhat/pandasql - Source on Github

• Blog post with more examples

  pip install -U pandasql

这是一个类似于您描述的示例。

导入

#!/usr/bin/env python
# -*- coding: utf-8 -*- 
import pandas as pd
from pandas.io.parsers import StringIO
from pandasql import sqldf

# helper func useful for saving keystrokes
# when running multiple queries
def dbGetQuery(q):
    return sqldf(q, globals())

伪造一些数据

sample_a = """timepoint,measure
2014-01-01 00:00:00,78
2014-01-03 00:00:00,5
2014-01-04 00:00:00,73
2014-01-05 00:00:00,40
2014-01-06 00:00:00,45
2014-01-08 00:00:00,2
2014-01-09 00:00:00,96
2014-01-10 00:00:00,82
2014-01-11 00:00:00,61
2014-01-12 00:00:00,68
2014-01-13 00:00:00,8
2014-01-14 00:00:00,94
2014-01-15 00:00:00,16
2014-01-16 00:00:00,31
2014-01-17 00:00:00,10
2014-01-18 00:00:00,34
2014-01-19 00:00:00,27
2014-01-20 00:00:00,75
2014-01-21 00:00:00,49
2014-01-23 00:00:00,28
2014-01-24 00:00:00,91
2014-01-25 00:00:00,88
2014-01-27 00:00:00,98
2014-01-28 00:00:00,39
2014-01-29 00:00:00,90
2014-01-30 00:00:00,63
2014-01-31 00:00:00,77
"""

sample_b = """from_date,to_date,measure
2014-01-02 00:00:00,2014-01-06 00:00:00,89
2014-01-03 00:00:00,2014-01-07 00:00:00,80
2014-01-04 00:00:00,2014-01-05 00:00:00,44
2014-01-05 00:00:00,2014-01-12 00:00:00,68
2014-01-06 00:00:00,2014-01-11 00:00:00,62
2014-01-07 00:00:00,2014-01-14 00:00:00,5
2014-01-08 00:00:00,2014-01-09 00:00:00,23
"""

读取数据集以创建 2 个 DataFrame

df1 = pd.read_csv(StringIO(sample_a), parse_dates=['timepoint'])
df2 = pd.read_csv(StringIO(sample_b), parse_dates=['from_date', 'to_date'])

编写 SQL 查询

请注意，这个使用 SQLite BETWEEN运算符(operator)。你也可以换掉它并使用类似 ON timepoint >= from_date AND timepoint < to_date 的东西如果您愿意。

query = """
SELECT
    DATE(df1.timepoint) AS timepoint
    , DATE(df2.from_date) AS start
    , DATE(df2.to_date) AS end
    , df1.measure AS measure_a
    , df2.measure AS measure_b
FROM
    df1 
INNER JOIN df2
    ON df1.timepoint BETWEEN 
        df2.from_date AND df2.to_date
ORDER BY
    df1.timepoint;
"""

使用辅助函数运行查询

df3 = dbGetQuery(query)

df3
     timepoint       start         end  measure_a  measure_b
0   2014-01-03  2014-01-02  2014-01-06          5         89
1   2014-01-03  2014-01-03  2014-01-07          5         80
2   2014-01-04  2014-01-02  2014-01-06         73         89
3   2014-01-04  2014-01-03  2014-01-07         73         80
4   2014-01-04  2014-01-04  2014-01-05         73         44
5   2014-01-05  2014-01-02  2014-01-06         40         89
6   2014-01-05  2014-01-03  2014-01-07         40         80
7   2014-01-05  2014-01-04  2014-01-05         40         44
8   2014-01-05  2014-01-05  2014-01-12         40         68
9   2014-01-06  2014-01-02  2014-01-06         45         89
10  2014-01-06  2014-01-03  2014-01-07         45         80
11  2014-01-06  2014-01-05  2014-01-12         45         68
12  2014-01-06  2014-01-06  2014-01-11         45         62
13  2014-01-08  2014-01-05  2014-01-12          2         68
14  2014-01-08  2014-01-06  2014-01-11          2         62
15  2014-01-08  2014-01-07  2014-01-14          2          5
16  2014-01-08  2014-01-08  2014-01-09          2         23
17  2014-01-09  2014-01-05  2014-01-12         96         68
18  2014-01-09  2014-01-06  2014-01-11         96         62
19  2014-01-09  2014-01-07  2014-01-14         96          5
20  2014-01-09  2014-01-08  2014-01-09         96         23
21  2014-01-10  2014-01-05  2014-01-12         82         68
22  2014-01-10  2014-01-06  2014-01-11         82         62
23  2014-01-10  2014-01-07  2014-01-14         82          5
24  2014-01-11  2014-01-05  2014-01-12         61         68
25  2014-01-11  2014-01-06  2014-01-11         61         62
26  2014-01-11  2014-01-07  2014-01-14         61          5
27  2014-01-12  2014-01-05  2014-01-12         68         68
28  2014-01-12  2014-01-07  2014-01-14         68          5
29  2014-01-13  2014-01-07  2014-01-14          8          5
30  2014-01-14  2014-01-07  2014-01-14         94          5