Code
#python3 -m pip install siuba
Code
import pandas as pdimport numpy as npimport matplotlib.pylab as pltimport seaborn as snsfrom siuba import _, mutate, filter , group_by, summarize,show_queryfrom siuba import * from siuba.data import mtcars,penguins
Code
= mtcars >> select(_.cyl, _.mpg, _.hp)>> head(5 )= penguins>> head(5 )
select
Code
>> select(_.cyl, _.mpg)
0
6
21.0
1
6
21.0
2
4
22.8
3
6
21.4
4
8
18.7
exclude
Code
>> select(~ _.cyl)
0
21.0
110
1
21.0
110
2
22.8
93
3
21.4
110
4
18.7
175
Renaming
Code
>> rename(new_name_mpg = _.mpg)
0
6
21.0
110
1
6
21.0
110
2
4
22.8
93
3
6
21.4
110
4
8
18.7
175
Mutate
Code
>> mutate(gear2 = _.gear+ 1 )
0
21.0
6
160.0
110
3.90
2.620
16.46
0
1
4
4
5
1
21.0
6
160.0
110
3.90
2.875
17.02
0
1
4
4
5
2
22.8
4
108.0
93
3.85
2.320
18.61
1
1
4
1
5
3
21.4
6
258.0
110
3.08
3.215
19.44
1
0
3
1
4
4
18.7
8
360.0
175
3.15
3.440
17.02
0
0
3
2
4
Filter
Code
>> filter (_.gear == 4 )
0
21.0
6
160.0
110
3.90
2.620
16.46
0
1
4
4
1
21.0
6
160.0
110
3.90
2.875
17.02
0
1
4
4
2
22.8
4
108.0
93
3.85
2.320
18.61
1
1
4
1
7
24.4
4
146.7
62
3.69
3.190
20.00
1
0
4
2
8
22.8
4
140.8
95
3.92
3.150
22.90
1
0
4
2
9
19.2
6
167.6
123
3.92
3.440
18.30
1
0
4
4
10
17.8
6
167.6
123
3.92
3.440
18.90
1
0
4
4
17
32.4
4
78.7
66
4.08
2.200
19.47
1
1
4
1
18
30.4
4
75.7
52
4.93
1.615
18.52
1
1
4
2
19
33.9
4
71.1
65
4.22
1.835
19.90
1
1
4
1
25
27.3
4
79.0
66
4.08
1.935
18.90
1
1
4
1
31
21.4
4
121.0
109
4.11
2.780
18.60
1
1
4
2
Filters with OR conditions
Code
>> filter ((_.cyl == 4 ) | (_.gear == 5 ))
2
22.8
4
108.0
93
3.85
2.320
18.61
1
1
4
1
7
24.4
4
146.7
62
3.69
3.190
20.00
1
0
4
2
8
22.8
4
140.8
95
3.92
3.150
22.90
1
0
4
2
17
32.4
4
78.7
66
4.08
2.200
19.47
1
1
4
1
18
30.4
4
75.7
52
4.93
1.615
18.52
1
1
4
2
19
33.9
4
71.1
65
4.22
1.835
19.90
1
1
4
1
20
21.5
4
120.1
97
3.70
2.465
20.01
1
0
3
1
25
27.3
4
79.0
66
4.08
1.935
18.90
1
1
4
1
26
26.0
4
120.3
91
4.43
2.140
16.70
0
1
5
2
27
30.4
4
95.1
113
3.77
1.513
16.90
1
1
5
2
28
15.8
8
351.0
264
4.22
3.170
14.50
0
1
5
4
29
19.7
6
145.0
175
3.62
2.770
15.50
0
1
5
6
30
15.0
8
301.0
335
3.54
3.570
14.60
0
1
5
8
31
21.4
4
121.0
109
4.11
2.780
18.60
1
1
4
2
Dropping NA values
group by
Code
= (mtcars>> group_by(_.cyl)>> summarize(avg_hp = _.hp.mean())
0
4
82.636364
1
6
122.285714
2
8
209.214286
order
Code
>> arrange(_.hp)
2
4
22.8
93
0
6
21.0
110
1
6
21.0
110
3
6
21.4
110
4
8
18.7
175
Sort in descending order
Code
>> arrange(- _.hp)
4
8
18.7
175
0
6
21.0
110
1
6
21.0
110
3
6
21.4
110
2
4
22.8
93
Arrange by multiple variables
Code
>> arrange(_.cyl, - _.mpg)
2
4
22.8
93
3
6
21.4
110
0
6
21.0
110
1
6
21.0
110
4
8
18.7
175
using siuba with database
Code
from sqlalchemy import create_enginefrom siuba.sql import LazyTblfrom siuba import _, group_by, summarize, show_query, collect from siuba.data import mtcars
Code
# copy in to sqlite, using the pandas .to_sql() method = create_engine("sqlite:///:memory:" )"mtcars" , engine, if_exists = "replace" )
Code
# Create a lazy SQL DataFrame = LazyTbl(engine, "mtcars" )
# Source: lazy query
# DB Conn: Engine(sqlite:///:memory:)
# Preview:
0
0
21.0
6
160.0
110
3.90
2.620
16.46
0
1
4
4
1
1
21.0
6
160.0
110
3.90
2.875
17.02
0
1
4
4
2
2
22.8
4
108.0
93
3.85
2.320
18.61
1
1
4
1
3
3
21.4
6
258.0
110
3.08
3.215
19.44
1
0
3
1
4
4
18.7
8
360.0
175
3.15
3.440
17.02
0
0
3
2
# .. may have more rows
Code
# connect with siuba = (tbl_mtcars>> group_by(_.cyl)>> summarize(avg_hp = _.hp.mean())
# Source: lazy query
# DB Conn: Engine(sqlite:///:memory:)
# Preview:
0
4
82.636364
1
6
122.285714
2
8
209.214286
# .. may have more rows
Code
= tbl_query >> show_query()
SELECT mtcars.cyl, avg(mtcars.hp) AS avg_hp
FROM mtcars GROUP BY mtcars.cyl
Code
0
4
82.636364
1
6
122.285714
2
8
209.214286
Reference
https://siuba.org/
## Package