Finds intersections / differences between pandas DataFrames
$pip install a-pandas-ex-set
import numpy as np
import pandas as pd
from a_pandas_ex_set import Setdf
df = pd.read_csv("https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv")
df2=pd.concat([df,df],ignore_index=True)
df2=df2.sample(len(df2))
df3,df4,df5=np.split(df2, 3)
setd=Setdf(df3,df4,df5)
columns=['Cabin', 'Embarked','Sex','Survived']
didis2=setd.get_difference_of_all(columns=columns)
didis3=setd.get_intersection_of_all(columns=columns)
didis4=setd.get_symmetric_difference_and(columns=columns)
print(didis2)
print(didis3)
print(didis4)
{0: PassengerId Survived Pclass ... Fare Cabin Embarked
1631 741 1 1 ... 30.0000 D45 S
1411 521 1 1 ... 93.5000 B73 S
1164 274 0 1 ... 29.7000 C118 C
487 488 0 1 ... 29.7000 B37 C
248 249 1 1 ... 52.5542 D35 S
1615 725 1 1 ... 53.1000 E8 S
318 319 1 1 ... 164.8667 C7 S
337 338 1 1 ... 134.5000 E40 C
1753 863 1 1 ... 25.9292 D17 S
1642 752 1 3 ... 12.4750 E121 S
1536 646 1 1 ... 76.7292 D33 C
449 450 1 1 ... 30.5000 C104 S
1740 850 1 1 ... 89.1042 C92 C
1670 780 1 1 ... 211.3375 B3 S
571 572 1 1 ... 51.4792 C101 S
1680 790 0 1 ... 79.2000 B82 B84 C
1462 572 1 1 ... 51.4792 C101 S
31 32 1 1 ... 146.5208 B78 C
1100 210 1 1 ... 31.0000 A31 C
1340 450 1 1 ... 30.5000 C104 S
209 210 1 1 ... 31.0000 A31 C
943 53 1 1 ... 76.7292 D33 C
751 752 1 3 ... 12.4750 E121 S
558 559 1 1 ... 79.6500 E67 S
671 672 0 1 ... 52.0000 B71 S
724 725 1 1 ... 53.1000 E8 S
520 521 1 1 ... 93.5000 B73 S
849 850 1 1 ... 89.1042 C92 C
867 868 0 1 ... 50.4958 A24 S
1562 672 0 1 ... 52.0000 B71 S
779 780 1 1 ... 211.3375 B3 S
1228 338 1 1 ... 134.5000 E40 C
645 646 1 1 ... 76.7292 D33 C
1687 797 1 1 ... 25.9292 D17 S
862 863 1 1 ... 25.9292 D17 S
922 32 1 1 ... 146.5208 B78 C
1209 319 1 1 ... 164.8667 C7 S
1196 306 1 1 ... 151.5500 C22 C26 S
1758 868 0 1 ... 50.4958 A24 S
273 274 0 1 ... 29.7000 C118 C
1139 249 1 1 ... 52.5542 D35 S
796 797 1 1 ... 25.9292 D17 S
740 741 1 1 ... 30.0000 D45 S
789 790 0 1 ... 79.2000 B82 B84 C
52 53 1 1 ... 76.7292 D33 C
1378 488 0 1 ... 29.7000 B37 C
772 773 0 2 ... 10.5000 E77 S
305 306 1 1 ... 151.5500 C22 C26 S
1449 559 1 1 ... 79.6500 E67 S
1663 773 0 2 ... 10.5000 E77 S
[50 rows x 12 columns], 1: PassengerId Survived Pclass ... Fare Cabin Embarked
21 22 1 2 ... 13.0000 D56 S
583 584 0 1 ... 40.1250 A10 C
445 446 1 1 ... 81.8583 A34 S
245 246 0 1 ... 90.0000 C78 Q
1476 586 1 1 ... 79.6500 E68 S
540 541 1 1 ... 71.0000 B22 S
366 367 1 1 ... 75.2500 D37 C
1136 246 0 1 ... 90.0000 C78 Q
879 880 1 1 ... 83.1583 C50 C
462 463 0 1 ... 38.5000 E63 S
1431 541 1 1 ... 71.0000 B22 S
275 276 1 1 ... 77.9583 D7 S
871 872 1 1 ... 52.5542 D35 S
1770 880 1 1 ... 83.1583 C50 C
1570 680 1 1 ... 512.3292 B51 B53 B55 C
1189 299 1 1 ... 30.5000 C106 S
912 22 1 2 ... 13.0000 D56 S
1762 872 1 1 ... 52.5542 D35 S
1590 700 0 3 ... 7.6500 F G63 S
1366 476 0 1 ... 52.0000 A14 S
1257 367 1 1 ... 75.2500 D37 C
1268 378 0 1 ... 211.5000 C82 C
700 701 1 1 ... 227.5250 C62 C64 C
1474 584 0 1 ... 40.1250 A10 C
585 586 1 1 ... 79.6500 E68 S
1166 276 1 1 ... 77.9583 D7 S
699 700 0 3 ... 7.6500 F G63 S
679 680 1 1 ... 512.3292 B51 B53 B55 C
630 631 1 1 ... 30.0000 A23 S
1353 463 0 1 ... 38.5000 E63 S
457 458 1 1 ... 51.8625 D21 S
1521 631 1 1 ... 30.0000 A23 S
475 476 0 1 ... 52.0000 A14 S
1336 446 1 1 ... 81.8583 A34 S
298 299 1 1 ... 30.5000 C106 S
1348 458 1 1 ... 51.8625 D21 S
377 378 0 1 ... 211.5000 C82 C
544 545 0 1 ... 106.4250 C86 C
284 285 0 1 ... 26.0000 A19 S
1435 545 0 1 ... 106.4250 C86 C
1591 701 1 1 ... 227.5250 C62 C64 C
1175 285 0 1 ... 26.0000 A19 S
[42 rows x 12 columns], 2: PassengerId Survived Pclass ... Fare Cabin Embarked
872 873 0 1 ... 5.0000 B51 B53 B55 S
712 713 1 1 ... 52.0000 C126 S
618 619 1 2 ... 39.0000 F4 S
1061 171 0 1 ... 33.5000 B19 S
527 528 0 1 ... 221.7792 C95 S
1639 749 0 1 ... 53.1000 D30 S
1509 619 1 2 ... 39.0000 F4 S
1418 528 0 1 ... 221.7792 C95 S
339 340 0 1 ... 35.5000 T S
647 648 1 1 ... 35.5000 A26 C
1538 648 1 1 ... 35.5000 A26 C
1763 873 0 1 ... 5.0000 B51 B53 B55 S
1115 225 1 1 ... 90.0000 C93 S
1603 713 1 1 ... 52.0000 C126 S
54 55 0 1 ... 61.9792 B30 C
1230 340 0 1 ... 35.5000 T S
748 749 0 1 ... 53.1000 D30 S
170 171 0 1 ... 33.5000 B19 S
224 225 1 1 ... 90.0000 C93 S
118 119 0 1 ... 247.5208 B58 B60 C
3 4 1 1 ... 53.1000 C123 S
945 55 0 1 ... 61.9792 B30 C
894 4 1 1 ... 53.1000 C123 S
1009 119 0 1 ... 247.5208 B58 B60 C
[24 rows x 12 columns]}
{0: PassengerId Survived Pclass ... Fare Cabin Embarked
1546 656 0 2 ... 73.5000 NaN S
1217 327 0 3 ... 6.2375 NaN S
664 665 1 3 ... 7.9250 NaN S
754 755 1 2 ... 65.0000 NaN S
727 728 1 3 ... 7.7375 NaN Q
... ... ... ... ... ... ...
1527 637 0 3 ... 7.9250 NaN S
814 815 0 3 ... 8.0500 NaN S
693 694 0 3 ... 7.2250 NaN C
26 27 0 3 ... 7.2250 NaN C
494 495 0 3 ... 8.0500 NaN S
[459 rows x 12 columns], 1: PassengerId Survived Pclass ... Fare Cabin Embarked
1482 592 1 1 ... 78.2667 D20 C
552 553 0 3 ... 7.8292 NaN Q
968 78 0 3 ... 8.0500 NaN S
1205 315 0 2 ... 26.2500 NaN S
1734 844 0 3 ... 6.4375 NaN C
... ... ... ... ... ... ...
568 569 0 3 ... 7.2292 NaN C
503 504 0 3 ... 9.5875 NaN S
1544 654 1 3 ... 7.8292 NaN Q
589 590 0 3 ... 8.0500 NaN S
1191 301 1 3 ... 7.7500 NaN Q
[471 rows x 12 columns], 2: PassengerId Survived Pclass ... Fare Cabin Embarked
1596 706 0 2 ... 26.0000 NaN S
792 793 0 3 ... 69.5500 NaN S
481 482 0 2 ... 0.0000 NaN S
508 509 0 3 ... 22.5250 NaN S
149 150 0 2 ... 13.0000 NaN S
... ... ... ... ... ... ...
777 778 1 3 ... 12.4750 NaN S
115 116 0 3 ... 7.9250 NaN S
169 170 0 3 ... 56.4958 NaN S
1162 272 1 3 ... 0.0000 NaN S
963 73 0 2 ... 73.5000 NaN S
[490 rows x 12 columns]}
{0: PassengerId Survived Pclass ... Fare Cabin Embarked
1546 656 0 2 ... 73.5000 NaN S
1217 327 0 3 ... 6.2375 NaN S
664 665 1 3 ... 7.9250 NaN S
754 755 1 2 ... 65.0000 NaN S
727 728 1 3 ... 7.7375 NaN Q
... ... ... ... ... ... ...
814 815 0 3 ... 8.0500 NaN S
1663 773 0 2 ... 10.5000 E77 S
693 694 0 3 ... 7.2250 NaN C
26 27 0 3 ... 7.2250 NaN C
494 495 0 3 ... 8.0500 NaN S
[509 rows x 12 columns], 1: PassengerId Survived Pclass ... Fare Cabin Embarked
1482 592 1 1 ... 78.2667 D20 C
552 553 0 3 ... 7.8292 NaN Q
968 78 0 3 ... 8.0500 NaN S
1205 315 0 2 ... 26.2500 NaN S
1734 844 0 3 ... 6.4375 NaN C
... ... ... ... ... ... ...
568 569 0 3 ... 7.2292 NaN C
503 504 0 3 ... 9.5875 NaN S
1544 654 1 3 ... 7.8292 NaN Q
589 590 0 3 ... 8.0500 NaN S
1191 301 1 3 ... 7.7500 NaN Q
[513 rows x 12 columns], 2: PassengerId Survived Pclass ... Fare Cabin Embarked
872 873 0 1 ... 5.0000 B51 B53 B55 S
712 713 1 1 ... 52.0000 C126 S
1596 706 0 2 ... 26.0000 NaN S
792 793 0 3 ... 69.5500 NaN S
481 482 0 2 ... 0.0000 NaN S
... ... ... ... ... ... ...
115 116 0 3 ... 7.9250 NaN S
1009 119 0 1 ... 247.5208 B58 B60 C
169 170 0 3 ... 56.4958 NaN S
1162 272 1 3 ... 0.0000 NaN S
963 73 0 2 ... 73.5000 NaN S
[514 rows x 12 columns]}
1 maintainer
Metadata
1 maintainer
Release files for a-pandas-ex-set 0.10
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| a_pandas_ex_set-0.10.tar.gz | 7.9 kB | Without provenance | Details for a_pandas_ex_set-0.10.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| a_pandas_ex_set-0.10-py3-none-any.whl | Python 3 | none | any | Details for a_pandas_ex_set-0.10-py3-none-any.whl |
Total release size: 16.6 kB
Release files / a_pandas_ex_set-0.10.tar.gz
| Download URL | a_pandas_ex_set-0.10.tar.gz |
|---|---|
| Size | 7.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
42d70b1fed55b67e9dfc1e7a408a3880a616d8372e5a34d67cc65f8a18479d7d
|
|
BLAKE2b-256 checksum How to use checksums |
047af1cfe80d83f6b2ee78f433a414a1379ea8215fa187854c65656eeb44d5b6
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.1 CPython/3.9.13
|
Release files / a_pandas_ex_set-0.10-py3-none-any.whl
| Download URL | a_pandas_ex_set-0.10-py3-none-any.whl |
|---|---|
| Size | 8.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
d6f2f82616b565dfb54b59159a2dabb70613f70f34bfe49e47a9a60d2476815e
|
|
BLAKE2b-256 checksum How to use checksums |
de2f19e3c670692627a4aa17a9c9aae78c36638ef01e347869cc35aabc99d5cd
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.1 CPython/3.9.13
|
Security for a-pandas-ex-set 0.10
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
