verticapy.vDataFrame.search¶

Searches for elements that match the input conditions. This method will return a new vDataFrame.

Parameters¶

conditions: SQLExpression, optional: Filters of the search. It can be a list of conditions or an expression.
usecols: SQLColumns, optional: vDataColumns to select from the final vDataFrame relation. If empty, all vDataColumns are selected.
expr: SQLExpression, optional: List of customized expressions in pure SQL. For example: ‘column1 * column2 AS my_name’.
order_by: str / dict / list, optional: List of the vDataColumns used to sort the data, using asc order or a dictionary of all sorting methods. For example, to sort by “column1” ASC and “column2” DESC, write: {"column1": "asc", "column2": "desc"}

Returns¶

vDataFrame: vDataFrame of the search

Examples¶

We import verticapy:

import verticapy as vp

Hint

By assigning an alias to verticapy, we mitigate the risk of code collisions with other libraries. This precaution is necessary because verticapy uses commonly known function names like “average” and “median”, which can potentially lead to naming conflicts. The use of an alias ensures that the functions from verticapy are used as intended without interfering with functions from other libraries.

For this example, we will use the Titanic dataset:

from verticapy.datasets import load_titanic

vdf = load_titanic()

Note

VerticaPy offers a wide range of sample datasets that are ideal for training and testing purposes. You can explore the full list of available datasets in the Datasets, which provides detailed information on each dataset and how to use them effectively. These datasets are invaluable resources for honing your data analysis and machine learning skills within the VerticaPy environment.

We can create a custom search that is looking for the family size and survival of the passengers having adults of more than 50 year old. We can arrange the data in descending order to see who paid the most:

vdf.search(
    conditions = ["age > 50"],
    usecols = ["fare", "survived"],
    expr = ["parch + sibsp + 1 AS family_size"],
    order_by = {"fare": "desc"},
)

Out[4]: 
None      fare    survived  
1     512.3292           1  
2        263.0           0  
3        263.0           1  
4      262.375           0  
5     221.7792           0  
6     221.7792           0  
7     164.8667           0  
8     153.4625           1  
9     146.5208           0  
10    146.5208           1  
11    135.6333           1  
12     113.275           0  
13        93.5           0  
14        93.5           1  
15     83.1583           1  
16     83.1583           1  
17     81.8583           1  
18     81.8583           1  
19        80.0           1  
20       79.65           0  
...        ...         ...  
Rows: 1-20 of 95 | Columns: 2

	123 fare Numeric(12) 98%	123 survived Integer 100%
1	512.3292	1
2	263.0	0
3	263.0	1
4	262.375	0
5	221.7792	0
6	221.7792	0
7	164.8667	0
8	153.4625	1
9	146.5208	0
10	146.5208	1
11	135.6333	1
12	113.275	0
13	93.5	0
14	93.5	1
15	83.1583	1
16	83.1583	1
17	81.8583	1
18	81.8583	1
19	80.0	1
20	79.65	0

Note

Similarly, the same can be done in a Pandas-like way:

vdf.search(
    conditions = vdf["age"] > 50,
    usecols = ["fare", "survived"],
    expr = ["parch + sibsp + 1 AS family_size"],
    order_by = {"fare": "desc"},
)