'Can one use comparisons to merge two pandas data-frames?
With the following command:
pandas.merge(df_1, df_2, left_on=['date'], right_on=['from_date'])
I combine two rows from two tables if a value in date-column of the first table is equal to the value in the from_date-column of the second table.
Now I would like to make it slightly more complex. I need to combine a row from the first table with a row from the second table if the value in the date column of the first table is equal or lager than a value of the from_date-column of the second table and smaller than value in the upto_date-column of the second column.
In SQL one would use something like that:
select
*
from
table_1
join
table_2
on
table_1.date >= table_2.from_date
and
table_1.date < table_2.upto_date
Is it possible to do it in pandas.
Solution 1:[1]
pandasql is a pretty useful tool for querying pandas DataFrames using SQLite query syntax.
Resources
- pandasql - PyPI Documentation
- yhat/pandasql - Source on Github
-
pip install -U pandasql
Here's an example similar to the one you describe.
Imports
#!/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())
Fake some data
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
"""
Read datasets to create 2 DataFrames
df1 = pd.read_csv(StringIO(sample_a), parse_dates=['timepoint'])
df2 = pd.read_csv(StringIO(sample_b), parse_dates=['from_date', 'to_date'])
Write a SQL query
Note that this one uses the SQLite BETWEEN operator. You can also swap that out and use something like ON timepoint >= from_date AND timepoint < to_date if you prefer.
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;
"""
Run the query using the helper func
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
Solution 2:[2]
I found a solution, I think. However, I am not sure if it is elegant and optimal:
df_1['A'] = 'A'
df_2['A'] = 'A'
df = pandas.merge(df_1, df_2, on=['A'])
df = df[(df['date'] >= df['from']) & (df['date'] < df['upto'])]
del df['A']
Posted on behalf of the question asker
Solution 3:[3]
conditional_join from pyjanitor could be helpful with non-equi joins :
Using @hernamesbarbara's fake data:
# pip install pyjanitor
import pandas as pd
import janitor
(df1.conditional_join(
df2,
('timepoint', 'from_date', '>='),
('timepoint', 'to_date', '<='))
)
left right
timepoint measure from_date to_date measure
0 2014-01-03 5 2014-01-02 2014-01-06 89
1 2014-01-03 5 2014-01-03 2014-01-07 80
2 2014-01-04 73 2014-01-02 2014-01-06 89
3 2014-01-04 73 2014-01-03 2014-01-07 80
4 2014-01-04 73 2014-01-04 2014-01-05 44
5 2014-01-05 40 2014-01-02 2014-01-06 89
6 2014-01-05 40 2014-01-03 2014-01-07 80
7 2014-01-05 40 2014-01-04 2014-01-05 44
8 2014-01-05 40 2014-01-05 2014-01-12 68
9 2014-01-06 45 2014-01-02 2014-01-06 89
10 2014-01-06 45 2014-01-03 2014-01-07 80
11 2014-01-06 45 2014-01-05 2014-01-12 68
12 2014-01-06 45 2014-01-06 2014-01-11 62
13 2014-01-08 2 2014-01-05 2014-01-12 68
14 2014-01-08 2 2014-01-06 2014-01-11 62
15 2014-01-08 2 2014-01-07 2014-01-14 5
16 2014-01-08 2 2014-01-08 2014-01-09 23
17 2014-01-09 96 2014-01-05 2014-01-12 68
18 2014-01-09 96 2014-01-06 2014-01-11 62
19 2014-01-09 96 2014-01-07 2014-01-14 5
20 2014-01-09 96 2014-01-08 2014-01-09 23
21 2014-01-10 82 2014-01-05 2014-01-12 68
22 2014-01-10 82 2014-01-06 2014-01-11 62
23 2014-01-10 82 2014-01-07 2014-01-14 5
24 2014-01-11 61 2014-01-05 2014-01-12 68
25 2014-01-11 61 2014-01-06 2014-01-11 62
26 2014-01-11 61 2014-01-07 2014-01-14 5
27 2014-01-12 68 2014-01-05 2014-01-12 68
28 2014-01-12 68 2014-01-07 2014-01-14 5
29 2014-01-13 8 2014-01-07 2014-01-14 5
30 2014-01-14 94 2014-01-07 2014-01-14 5
Sources
This article follows the attribution requirements of Stack Overflow and is licensed under CC BY-SA 3.0.
Source: Stack Overflow
| Solution | Source |
|---|---|
| Solution 1 | hernamesbarbara |
| Solution 2 | |
| Solution 3 |