Several methods to split a pandas DataFrame/Series
pip install a-pandas-ex-split
from a_pandas_ex_split import pd_add_df_split
import pandas as pd
pd_add_df_split()
df = pd.read_csv(
"https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv"
)
df = df[:50]
t1 = df.ds_iloc_split(splitindex=[10, 20, 40])
print(f"\n\n{t1=}")
t2 = df.ds_loc_split(splitindex=[10, 20, 35])
print(f"\n\n{t2=}")
t3 = df.ds_iloc_split_pairwise(splitindex=[(0, 10), (25, 30)], include_last=True)
print(f"\n\n{t3=}")
t4 = df.ds_split_in_n_parts(n=9) # len of results = [6, 6, 6, 6, 6, 5, 5, 5, 5]
print(f"\n\n{t4=}")
t5 = df.ds_split_in_n_parts_of_length(
size_of_each=8, exact_split=False
) # len of results = [9, 9, 8, 8, 8, 8]
print(f"\n\n{t5=}")
t6 = df.ds_split_in_n_parts_of_length(
size_of_each=8, exact_split=True
) # len of results = [8, 8, 8, 8, 8, 8, 2]
print(f"\n\n{t6=}")
t7 = df.PassengerId.ds_split_in_n_parts_of_length(
size_of_each=8, exact_split=True
) # len of results = [8, 8, 8, 8, 8, 8, 2]
print(f"\n\n{t7=}")
t1=[ PassengerId Survived Pclass ... Fare Cabin Embarked
0 1 0 3 ... 7.2500 NaN S
1 2 1 1 ... 71.2833 C85 C
2 3 1 3 ... 7.9250 NaN S
3 4 1 1 ... 53.1000 C123 S
4 5 0 3 ... 8.0500 NaN S
5 6 0 3 ... 8.4583 NaN Q
6 7 0 1 ... 51.8625 E46 S
7 8 0 3 ... 21.0750 NaN S
8 9 1 3 ... 11.1333 NaN S
9 10 1 2 ... 30.0708 NaN C
[10 rows x 12 columns], PassengerId Survived Pclass ... Fare Cabin Embarked
10 11 1 3 ... 16.7000 G6 S
11 12 1 1 ... 26.5500 C103 S
12 13 0 3 ... 8.0500 NaN S
13 14 0 3 ... 31.2750 NaN S
14 15 0 3 ... 7.8542 NaN S
15 16 1 2 ... 16.0000 NaN S
16 17 0 3 ... 29.1250 NaN Q
17 18 1 2 ... 13.0000 NaN S
18 19 0 3 ... 18.0000 NaN S
19 20 1 3 ... 7.2250 NaN C
[10 rows x 12 columns], PassengerId Survived Pclass ... Fare Cabin Embarked
20 21 0 2 ... 26.0000 NaN S
21 22 1 2 ... 13.0000 D56 S
22 23 1 3 ... 8.0292 NaN Q
23 24 1 1 ... 35.5000 A6 S
24 25 0 3 ... 21.0750 NaN S
25 26 1 3 ... 31.3875 NaN S
26 27 0 3 ... 7.2250 NaN C
27 28 0 1 ... 263.0000 C23 C25 C27 S
28 29 1 3 ... 7.8792 NaN Q
29 30 0 3 ... 7.8958 NaN S
30 31 0 1 ... 27.7208 NaN C
31 32 1 1 ... 146.5208 B78 C
32 33 1 3 ... 7.7500 NaN Q
33 34 0 2 ... 10.5000 NaN S
34 35 0 1 ... 82.1708 NaN C
35 36 0 1 ... 52.0000 NaN S
36 37 1 3 ... 7.2292 NaN C
37 38 0 3 ... 8.0500 NaN S
38 39 0 3 ... 18.0000 NaN S
39 40 1 3 ... 11.2417 NaN C
[20 rows x 12 columns], PassengerId Survived Pclass ... Fare Cabin Embarked
40 41 0 3 ... 9.4750 NaN S
41 42 0 2 ... 21.0000 NaN S
42 43 0 3 ... 7.8958 NaN C
43 44 1 2 ... 41.5792 NaN C
44 45 1 3 ... 7.8792 NaN Q
45 46 0 3 ... 8.0500 NaN S
46 47 0 3 ... 15.5000 NaN Q
47 48 1 3 ... 7.7500 NaN Q
48 49 0 3 ... 21.6792 NaN C
49 50 0 3 ... 17.8000 NaN S
[10 rows x 12 columns]]
t2=[ PassengerId Survived Pclass ... Fare Cabin Embarked
0 1 0 3 ... 7.2500 NaN S
1 2 1 1 ... 71.2833 C85 C
2 3 1 3 ... 7.9250 NaN S
3 4 1 1 ... 53.1000 C123 S
4 5 0 3 ... 8.0500 NaN S
5 6 0 3 ... 8.4583 NaN Q
6 7 0 1 ... 51.8625 E46 S
7 8 0 3 ... 21.0750 NaN S
8 9 1 3 ... 11.1333 NaN S
9 10 1 2 ... 30.0708 NaN C
10 11 1 3 ... 16.7000 G6 S
[11 rows x 12 columns], PassengerId Survived Pclass ... Fare Cabin Embarked
10 11 1 3 ... 16.7000 G6 S
11 12 1 1 ... 26.5500 C103 S
12 13 0 3 ... 8.0500 NaN S
13 14 0 3 ... 31.2750 NaN S
14 15 0 3 ... 7.8542 NaN S
15 16 1 2 ... 16.0000 NaN S
16 17 0 3 ... 29.1250 NaN Q
17 18 1 2 ... 13.0000 NaN S
18 19 0 3 ... 18.0000 NaN S
19 20 1 3 ... 7.2250 NaN C
20 21 0 2 ... 26.0000 NaN S
[11 rows x 12 columns], PassengerId Survived Pclass ... Fare Cabin Embarked
20 21 0 2 ... 26.0000 NaN S
21 22 1 2 ... 13.0000 D56 S
22 23 1 3 ... 8.0292 NaN Q
23 24 1 1 ... 35.5000 A6 S
24 25 0 3 ... 21.0750 NaN S
25 26 1 3 ... 31.3875 NaN S
26 27 0 3 ... 7.2250 NaN C
27 28 0 1 ... 263.0000 C23 C25 C27 S
28 29 1 3 ... 7.8792 NaN Q
29 30 0 3 ... 7.8958 NaN S
30 31 0 1 ... 27.7208 NaN C
31 32 1 1 ... 146.5208 B78 C
32 33 1 3 ... 7.7500 NaN Q
33 34 0 2 ... 10.5000 NaN S
34 35 0 1 ... 82.1708 NaN C
35 36 0 1 ... 52.0000 NaN S
[16 rows x 12 columns], PassengerId Survived Pclass ... Fare Cabin Embarked
35 36 0 1 ... 52.0000 NaN S
36 37 1 3 ... 7.2292 NaN C
37 38 0 3 ... 8.0500 NaN S
38 39 0 3 ... 18.0000 NaN S
39 40 1 3 ... 11.2417 NaN C
40 41 0 3 ... 9.4750 NaN S
41 42 0 2 ... 21.0000 NaN S
42 43 0 3 ... 7.8958 NaN C
43 44 1 2 ... 41.5792 NaN C
44 45 1 3 ... 7.8792 NaN Q
45 46 0 3 ... 8.0500 NaN S
46 47 0 3 ... 15.5000 NaN Q
47 48 1 3 ... 7.7500 NaN Q
48 49 0 3 ... 21.6792 NaN C
49 50 0 3 ... 17.8000 NaN S
[15 rows x 12 columns]]
t3=[ PassengerId Survived Pclass ... Fare Cabin Embarked
0 1 0 3 ... 7.2500 NaN S
1 2 1 1 ... 71.2833 C85 C
2 3 1 3 ... 7.9250 NaN S
3 4 1 1 ... 53.1000 C123 S
4 5 0 3 ... 8.0500 NaN S
5 6 0 3 ... 8.4583 NaN Q
6 7 0 1 ... 51.8625 E46 S
7 8 0 3 ... 21.0750 NaN S
8 9 1 3 ... 11.1333 NaN S
9 10 1 2 ... 30.0708 NaN C
10 11 1 3 ... 16.7000 G6 S
[11 rows x 12 columns], PassengerId Survived Pclass ... Fare Cabin Embarked
25 26 1 3 ... 31.3875 NaN S
26 27 0 3 ... 7.2250 NaN C
27 28 0 1 ... 263.0000 C23 C25 C27 S
28 29 1 3 ... 7.8792 NaN Q
29 30 0 3 ... 7.8958 NaN S
30 31 0 1 ... 27.7208 NaN C
[6 rows x 12 columns]]
t4=[ PassengerId Survived Pclass ... Fare Cabin Embarked
0 1 0 3 ... 7.2500 NaN S
1 2 1 1 ... 71.2833 C85 C
2 3 1 3 ... 7.9250 NaN S
3 4 1 1 ... 53.1000 C123 S
4 5 0 3 ... 8.0500 NaN S
5 6 0 3 ... 8.4583 NaN Q
[6 rows x 12 columns], PassengerId Survived Pclass ... Fare Cabin Embarked
6 7 0 1 ... 51.8625 E46 S
7 8 0 3 ... 21.0750 NaN S
8 9 1 3 ... 11.1333 NaN S
9 10 1 2 ... 30.0708 NaN C
10 11 1 3 ... 16.7000 G6 S
11 12 1 1 ... 26.5500 C103 S
[6 rows x 12 columns], PassengerId Survived Pclass ... Fare Cabin Embarked
12 13 0 3 ... 8.0500 NaN S
13 14 0 3 ... 31.2750 NaN S
14 15 0 3 ... 7.8542 NaN S
15 16 1 2 ... 16.0000 NaN S
16 17 0 3 ... 29.1250 NaN Q
17 18 1 2 ... 13.0000 NaN S
[6 rows x 12 columns], PassengerId Survived Pclass ... Fare Cabin Embarked
18 19 0 3 ... 18.0000 NaN S
19 20 1 3 ... 7.2250 NaN C
20 21 0 2 ... 26.0000 NaN S
21 22 1 2 ... 13.0000 D56 S
22 23 1 3 ... 8.0292 NaN Q
23 24 1 1 ... 35.5000 A6 S
[6 rows x 12 columns], PassengerId Survived Pclass ... Fare Cabin Embarked
24 25 0 3 ... 21.0750 NaN S
25 26 1 3 ... 31.3875 NaN S
26 27 0 3 ... 7.2250 NaN C
27 28 0 1 ... 263.0000 C23 C25 C27 S
28 29 1 3 ... 7.8792 NaN Q
29 30 0 3 ... 7.8958 NaN S
[6 rows x 12 columns], PassengerId Survived Pclass ... Fare Cabin Embarked
30 31 0 1 ... 27.7208 NaN C
31 32 1 1 ... 146.5208 B78 C
32 33 1 3 ... 7.7500 NaN Q
33 34 0 2 ... 10.5000 NaN S
34 35 0 1 ... 82.1708 NaN C
[5 rows x 12 columns], PassengerId Survived Pclass ... Fare Cabin Embarked
35 36 0 1 ... 52.0000 NaN S
36 37 1 3 ... 7.2292 NaN C
37 38 0 3 ... 8.0500 NaN S
38 39 0 3 ... 18.0000 NaN S
39 40 1 3 ... 11.2417 NaN C
[5 rows x 12 columns], PassengerId Survived Pclass ... Fare Cabin Embarked
40 41 0 3 ... 9.4750 NaN S
41 42 0 2 ... 21.0000 NaN S
42 43 0 3 ... 7.8958 NaN C
43 44 1 2 ... 41.5792 NaN C
44 45 1 3 ... 7.8792 NaN Q
[5 rows x 12 columns], PassengerId Survived Pclass ... Fare Cabin Embarked
45 46 0 3 ... 8.0500 NaN S
46 47 0 3 ... 15.5000 NaN Q
47 48 1 3 ... 7.7500 NaN Q
48 49 0 3 ... 21.6792 NaN C
49 50 0 3 ... 17.8000 NaN S
[5 rows x 12 columns]]
t5=[ PassengerId Survived Pclass ... Fare Cabin Embarked
0 1 0 3 ... 7.2500 NaN S
1 2 1 1 ... 71.2833 C85 C
2 3 1 3 ... 7.9250 NaN S
3 4 1 1 ... 53.1000 C123 S
4 5 0 3 ... 8.0500 NaN S
5 6 0 3 ... 8.4583 NaN Q
6 7 0 1 ... 51.8625 E46 S
7 8 0 3 ... 21.0750 NaN S
8 9 1 3 ... 11.1333 NaN S
[9 rows x 12 columns], PassengerId Survived Pclass ... Fare Cabin Embarked
9 10 1 2 ... 30.0708 NaN C
10 11 1 3 ... 16.7000 G6 S
11 12 1 1 ... 26.5500 C103 S
12 13 0 3 ... 8.0500 NaN S
13 14 0 3 ... 31.2750 NaN S
14 15 0 3 ... 7.8542 NaN S
15 16 1 2 ... 16.0000 NaN S
16 17 0 3 ... 29.1250 NaN Q
17 18 1 2 ... 13.0000 NaN S
[9 rows x 12 columns], PassengerId Survived Pclass ... Fare Cabin Embarked
18 19 0 3 ... 18.0000 NaN S
19 20 1 3 ... 7.2250 NaN C
20 21 0 2 ... 26.0000 NaN S
21 22 1 2 ... 13.0000 D56 S
22 23 1 3 ... 8.0292 NaN Q
23 24 1 1 ... 35.5000 A6 S
24 25 0 3 ... 21.0750 NaN S
25 26 1 3 ... 31.3875 NaN S
[8 rows x 12 columns], PassengerId Survived Pclass ... Fare Cabin Embarked
26 27 0 3 ... 7.2250 NaN C
27 28 0 1 ... 263.0000 C23 C25 C27 S
28 29 1 3 ... 7.8792 NaN Q
29 30 0 3 ... 7.8958 NaN S
30 31 0 1 ... 27.7208 NaN C
31 32 1 1 ... 146.5208 B78 C
32 33 1 3 ... 7.7500 NaN Q
33 34 0 2 ... 10.5000 NaN S
[8 rows x 12 columns], PassengerId Survived Pclass ... Fare Cabin Embarked
34 35 0 1 ... 82.1708 NaN C
35 36 0 1 ... 52.0000 NaN S
36 37 1 3 ... 7.2292 NaN C
37 38 0 3 ... 8.0500 NaN S
38 39 0 3 ... 18.0000 NaN S
39 40 1 3 ... 11.2417 NaN C
40 41 0 3 ... 9.4750 NaN S
41 42 0 2 ... 21.0000 NaN S
[8 rows x 12 columns], PassengerId Survived Pclass ... Fare Cabin Embarked
42 43 0 3 ... 7.8958 NaN C
43 44 1 2 ... 41.5792 NaN C
44 45 1 3 ... 7.8792 NaN Q
45 46 0 3 ... 8.0500 NaN S
46 47 0 3 ... 15.5000 NaN Q
47 48 1 3 ... 7.7500 NaN Q
48 49 0 3 ... 21.6792 NaN C
49 50 0 3 ... 17.8000 NaN S
[8 rows x 12 columns]]
t6=[ PassengerId Survived Pclass ... Fare Cabin Embarked
0 1 0 3 ... 7.2500 NaN S
1 2 1 1 ... 71.2833 C85 C
2 3 1 3 ... 7.9250 NaN S
3 4 1 1 ... 53.1000 C123 S
4 5 0 3 ... 8.0500 NaN S
5 6 0 3 ... 8.4583 NaN Q
6 7 0 1 ... 51.8625 E46 S
7 8 0 3 ... 21.0750 NaN S
[8 rows x 12 columns], PassengerId Survived Pclass ... Fare Cabin Embarked
8 9 1 3 ... 11.1333 NaN S
9 10 1 2 ... 30.0708 NaN C
10 11 1 3 ... 16.7000 G6 S
11 12 1 1 ... 26.5500 C103 S
12 13 0 3 ... 8.0500 NaN S
13 14 0 3 ... 31.2750 NaN S
14 15 0 3 ... 7.8542 NaN S
15 16 1 2 ... 16.0000 NaN S
[8 rows x 12 columns], PassengerId Survived Pclass ... Fare Cabin Embarked
16 17 0 3 ... 29.1250 NaN Q
17 18 1 2 ... 13.0000 NaN S
18 19 0 3 ... 18.0000 NaN S
19 20 1 3 ... 7.2250 NaN C
20 21 0 2 ... 26.0000 NaN S
21 22 1 2 ... 13.0000 D56 S
22 23 1 3 ... 8.0292 NaN Q
23 24 1 1 ... 35.5000 A6 S
[8 rows x 12 columns], PassengerId Survived Pclass ... Fare Cabin Embarked
24 25 0 3 ... 21.0750 NaN S
25 26 1 3 ... 31.3875 NaN S
26 27 0 3 ... 7.2250 NaN C
27 28 0 1 ... 263.0000 C23 C25 C27 S
28 29 1 3 ... 7.8792 NaN Q
29 30 0 3 ... 7.8958 NaN S
30 31 0 1 ... 27.7208 NaN C
31 32 1 1 ... 146.5208 B78 C
[8 rows x 12 columns], PassengerId Survived Pclass ... Fare Cabin Embarked
32 33 1 3 ... 7.7500 NaN Q
33 34 0 2 ... 10.5000 NaN S
34 35 0 1 ... 82.1708 NaN C
35 36 0 1 ... 52.0000 NaN S
36 37 1 3 ... 7.2292 NaN C
37 38 0 3 ... 8.0500 NaN S
38 39 0 3 ... 18.0000 NaN S
39 40 1 3 ... 11.2417 NaN C
[8 rows x 12 columns], PassengerId Survived Pclass ... Fare Cabin Embarked
40 41 0 3 ... 9.4750 NaN S
41 42 0 2 ... 21.0000 NaN S
42 43 0 3 ... 7.8958 NaN C
43 44 1 2 ... 41.5792 NaN C
44 45 1 3 ... 7.8792 NaN Q
45 46 0 3 ... 8.0500 NaN S
46 47 0 3 ... 15.5000 NaN Q
47 48 1 3 ... 7.7500 NaN Q
[8 rows x 12 columns], PassengerId Survived Pclass ... Fare Cabin Embarked
48 49 0 3 ... 21.6792 NaN C
49 50 0 3 ... 17.8000 NaN S
[2 rows x 12 columns]]
t7=[0 1
1 2
2 3
3 4
4 5
5 6
6 7
7 8
Name: PassengerId, dtype: int64, 8 9
9 10
10 11
11 12
12 13
13 14
14 15
15 16
Name: PassengerId, dtype: int64, 16 17
17 18
18 19
19 20
20 21
21 22
22 23
23 24
Name: PassengerId, dtype: int64, 24 25
25 26
26 27
27 28
28 29
29 30
30 31
31 32
Name: PassengerId, dtype: int64, 32 33
33 34
34 35
35 36
36 37
37 38
38 39
39 40
Name: PassengerId, dtype: int64, 40 41
41 42
42 43
43 44
44 45
45 46
46 47
47 48
Name: PassengerId, dtype: int64, 48 49
49 50
Name: PassengerId, dtype: int64]
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Release files for a-pandas-ex-split 0.10
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