vDataFrame[].decode¶
In [ ]:
vDataFrame[].decode(*argv)
Encodes the vcolumn using a user-defined encoding.
Parameters¶
| Name | Type | Optional | Description |
|---|---|---|---|
argv | object | ❌ | Infinite Number of Expressions. The expression generated will look like:
|
In [8]:
from verticapy.datasets import load_titanic
titanic = load_titanic()
display(titanic["sex"])
 | Abc sexVarchar(20) |
| 1 | female |
| 2 | male |
| 3 | female |
| 4 | male |
| 5 | male |
| 6 | male |
| 7 | male |
| 8 | male |
| 9 | male |
| 10 | male |
| 11 | male |
| 12 | male |
| 13 | male |
| 14 | male |
| 15 | male |
| 16 | male |
| 17 | male |
| 18 | male |
| 19 | male |
| 20 | male |
| 21 | male |
| 22 | male |
| 23 | male |
| 24 | male |
| 25 | male |
| 26 | male |
| 27 | male |
| 28 | male |
| 29 | male |
| 30 | male |
| 31 | male |
| 32 | male |
| 33 | male |
| 34 | male |
| 35 | female |
| 36 | male |
| 37 | male |
| 38 | male |
| 39 | male |
| 40 | male |
| 41 | male |
| 42 | male |
| 43 | male |
| 44 | male |
| 45 | male |
| 46 | male |
| 47 | male |
| 48 | male |
| 49 | male |
| 50 | male |
| 51 | male |
| 52 | male |
| 53 | male |
| 54 | male |
| 55 | male |
| 56 | male |
| 57 | female |
| 58 | male |
| 59 | male |
| 60 | male |
| 61 | male |
| 62 | male |
| 63 | male |
| 64 | male |
| 65 | male |
| 66 | male |
| 67 | male |
| 68 | male |
| 69 | male |
| 70 | male |
| 71 | male |
| 72 | male |
| 73 | male |
| 74 | male |
| 75 | male |
| 76 | male |
| 77 | male |
| 78 | male |
| 79 | male |
| 80 | male |
| 81 | male |
| 82 | male |
| 83 | male |
| 84 | male |
| 85 | male |
| 86 | male |
| 87 | male |
| 88 | male |
| 89 | male |
| 90 | male |
| 91 | male |
| 92 | male |
| 93 | male |
| 94 | male |
| 95 | male |
| 96 | male |
| 97 | male |
| 98 | male |
| 99 | male |
| 100 | male |
Rows: 1-100 of 1234 | Column: sex | Type: Varchar(20)
In [9]:
titanic["sex"].decode("female", 1, "male", 0, 2)
titanic["sex"]
Out[9]:
| 123 sexInteger |
| 1 | 1 |
| 2 | 0 |
| 3 | 1 |
| 4 | 0 |
| 5 | 0 |
| 6 | 0 |
| 7 | 0 |
| 8 | 0 |
| 9 | 0 |
| 10 | 0 |
| 11 | 0 |
| 12 | 0 |
| 13 | 0 |
| 14 | 0 |
| 15 | 0 |
| 16 | 0 |
| 17 | 0 |
| 18 | 0 |
| 19 | 0 |
| 20 | 0 |
| 21 | 0 |
| 22 | 0 |
| 23 | 0 |
| 24 | 0 |
| 25 | 0 |
| 26 | 0 |
| 27 | 0 |
| 28 | 0 |
| 29 | 0 |
| 30 | 0 |
| 31 | 0 |
| 32 | 0 |
| 33 | 0 |
| 34 | 0 |
| 35 | 1 |
| 36 | 0 |
| 37 | 0 |
| 38 | 0 |
| 39 | 0 |
| 40 | 0 |
| 41 | 0 |
| 42 | 0 |
| 43 | 0 |
| 44 | 0 |
| 45 | 0 |
| 46 | 0 |
| 47 | 0 |
| 48 | 0 |
| 49 | 0 |
| 50 | 0 |
| 51 | 0 |
| 52 | 0 |
| 53 | 0 |
| 54 | 0 |
| 55 | 0 |
| 56 | 0 |
| 57 | 1 |
| 58 | 0 |
| 59 | 0 |
| 60 | 0 |
| 61 | 0 |
| 62 | 0 |
| 63 | 0 |
| 64 | 0 |
| 65 | 0 |
| 66 | 0 |
| 67 | 0 |
| 68 | 0 |
| 69 | 0 |
| 70 | 0 |
| 71 | 0 |
| 72 | 0 |
| 73 | 0 |
| 74 | 0 |
| 75 | 0 |
| 76 | 0 |
| 77 | 0 |
| 78 | 0 |
| 79 | 0 |
| 80 | 0 |
| 81 | 0 |
| 82 | 0 |
| 83 | 0 |
| 84 | 0 |
| 85 | 0 |
| 86 | 0 |
| 87 | 0 |
| 88 | 0 |
| 89 | 0 |
| 90 | 0 |
| 91 | 0 |
| 92 | 0 |
| 93 | 0 |
| 94 | 0 |
| 95 | 0 |
| 96 | 0 |
| 97 | 0 |
| 98 | 0 |
| 99 | 0 |
| 100 | 0 |
Rows: 1-100 of 1234 | Column: sex | Type: Integer
See Also¶
| vDataFrame[].discretize | Discretizes the vcolumn. |
| vDataFrame[].label_encode | Encodes the vcolumn using the Label Encoding. |
| vDataFrame[].get_dummies | Encodes the vcolumn using the One Hot Encoding. |
| vDataFrame[].mean_encode | Encodes the vcolumn using the Mean Encoding of a response. |