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Doc - Update data node filter section #708

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Doc - Update data node filter section #708

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doc: update data node filter section
trgiangdo Nov 6, 2023
b0e6c25
fix: improve examples and fix typos
trgiangdo Nov 6, 2023
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200 changes: 186 additions & 14 deletions docs/manuals/core/entities/data-node-mgt.md
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Expand Up @@ -1163,29 +1163,201 @@ Correspondingly, In memory data node can write any data object that is valid dat
It is also possible to partially read the contents of data nodes, which comes in handy when dealing
with large amounts of data.
This can be achieved by providing an operator, a Tuple of (*field_name*, *value*, *comparison_operator*),
or a list of operators to the `DataNode.filter()^` method:
or a list of operators to the `DataNode.filter()^` method.

```python linenums="1"
data_node.filter(
[("field_name", 14, Operator.EQUAL), ("field_name", 10, Operator.EQUAL)],
JoinOperator.OR
)
Assume that the content of the data node can be represented by the following table.

!!! example "Example data"
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| date | nb_sales |
|------------|----------|
| 12/24/2018 | 1550 |
| 12/25/2018 | 2315 |
| 12/26/2018 | 1832 |

In the following example, the `DataNode.filter()^` method will return all the records from the data node
where the value of the "nb_sales" field is equal to 1500.
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The following examples represent the results when read from a data node with different _exposed_type_:

```python
filtered_data = data_node.filter(("nb_sales", 1550, Operator.EQUAL))
```

!!! example "Filter data where "nb_sales" is equal to 1550"

=== "exposed_type = "pandas""

```python
filtered_data = pandas.DataFrame
(
date nb_sales
0 12/24/2018 1550
)
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```

=== "exposed_type = "modin""

```python
filtered_data = modin.pandas.DataFrame
(
date nb_sales
0 12/24/2018 1550
)
```

=== "exposed_type = "numpy""

```python
filtered_data = numpy.array([
["12/24/2018", "1550"]
])
```

=== "exposed_type = SaleRow"
```python
filtered_data = [SaleRow("12/24/2018", 1550)]
```

If a list of operators is provided, it is necessary to provide a join operator that will be
used to combine the filtered results from the operators.
used to combine the filtered results from the operators. The default join operator is `JoinOperator.AND`.

It is also possible to use pandas style filtering:
In the following example, the `DataNode.filter()^` method will return all the records from the data node
where the value of the "nb_sales" field is greater or equal to 1000 and less than 2000.
The following examples represent the results when read from a data node with different _exposed_type_:

```python linenums="1"
temp_data = data_node["field_name"]
temp_data[(temp_data == 14) | (temp_data == 10)]
```python
filtered_data = data_node.filter(
[("nb_sales", 1000, Operator.GREATER_OR_EQUAL), ("nb_sales", 2000, Operator.LESS_THAN)]
)
```

!!! warning
!!! example "Filter data where "nb_sales" is greater or equal to 1000 and less than 2000"

=== "exposed_type = "pandas""

```python
filtered_data = pandas.DataFrame
(
date nb_sales
0 12/24/2018 1550
1 12/26/2018 1832
)
```

=== "exposed_type = "modin""

```python
filtered_data = modin.pandas.DataFrame
(
date nb_sales
0 12/24/2018 1550
1 12/26/2018 1832
)
```

=== "exposed_type = "numpy""

```python
filtered_data = numpy.array(
[
["12/24/2018", "1550"],
["12/26/2018", "1832"]
]
)
```

=== "exposed_type = SaleRow"
```python
filtered_data = [
SaleRow("12/24/2018", 1550),
SaleRow("12/26/2018", 1832),
]
```

In another example, the `DataNode.filter()^` method will return all the records from the data node
where the value of the "nb_sales" field is equal to 1550 or greater than 2000.
The following examples represent the results when read from a data node with different _exposed_type_:

```python
filtered_data = data_node.filter(
[("nb_sales", 1550, Operator.EQUAL), ("nb_sales", 2000, Operator.GREATER_THAN)],
JoinOperator.OR,
)
```

!!! example "Filter data where "nb_sales" is equal to 1550 or greater than 2000"

=== "exposed_type = "pandas""

```python
filtered_data = pandas.DataFrame
(
date nb_sales
0 12/24/2018 1550
1 12/25/2018 2315
)
```

=== "exposed_type = "modin""

```python
filtered_data = modin.pandas.DataFrame
(
date nb_sales
0 12/24/2018 1550
1 12/25/2018 2315
)
```

=== "exposed_type = "numpy""

```python
filtered_data = numpy.array(
[
["12/24/2018", "1550"],
["12/25/2018", "2315"],
]
)
```

=== "exposed_type = SaleRow"
```python
filtered_data = [
SaleRow("12/24/2018", 1550),
SaleRow("12/25/2018", 2315),
]
```

With Pandas or Modin data frame as the exposed type, it is also possible to use pandas indexing
and filtering style:

```python
sale_data = data_node["nb_sales"]

filtered_data = data_node[(data_node["nb_sales"] == 1500) | (data_node["nb_sales"] > 2000)]
```

Similarly, with numpy array exposed type, it is possible to use numpy style indexing and filtering
style:

```python
sale_data = data_node[:, 1]

filtered_data = data_node[(data_node[:, 1] == 1500) | (data_node[:, 1] > 2000)]
```

!!! warning "Supported data types"

For now, the `DataNode.filter()^` method and the indexing/filtering style are only implemented
for data as:

- a Pandas or Modin data frame,
- a Numpy array,
- a list of objects,
- a list of dictionaries.

Other data types are not supported.

For now, the `DataNode.filter()^` method is only implemented for `CSVDataNode^`, `ExcelDataNode^`,
`SQLTableDataNode^`, `SQLDataNode` with `"pandas"` as the _**exposed_type**_ value.

# Get parent scenarios, sequences and tasks

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