Description:

apandasexset 0.10

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]}

License

For personal and professional use. You cannot resell or redistribute these repositories in their original state.

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