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bug/#1226 added doc for polars (#1259)
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* added doc for polars

* re-added flak8

* replace polars representation with actual polars code

* reset minor changes

* align the pandas constructor Python

* return flake8

---------

Co-authored-by: Toan Quach <shiro@192.168.1.8.non-exists.ptr.local>
Co-authored-by: Toan Quach <shiro@Shiros-MacBook-Pro.local>
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@toan-quach
3 people authored Jan 21, 2025
1 parent 74b2a10 commit 759fd13
Showing 1 changed file with 149 additions and 34 deletions.
183 changes: 149 additions & 34 deletions docs/userman/scenario_features/data-integration/data-node-usage.md
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Original file line number Diff line number Diff line change
Expand Up @@ -263,12 +263,11 @@ The following examples represent the results when reading from a CSV data node w
=== "exposed_type = "pandas""

```python
pandas.DataFrame
(
date nb_sales
0 12/24/2018 1550
1 12/25/2018 2315
2 12/26/2018 1832
pandas.DataFrame(
{
"date": ["12/24/2018", "12/25/2018", "12/26/2018"],
"nb_sales": [1550, 2315, 1832]
}
)
```

Expand All @@ -285,6 +284,7 @@ The following examples represent the results when reading from a CSV data node w
```

=== "exposed_type = SaleRow"

```python
[
SaleRow("12/24/2018", 1550),
Expand All @@ -293,6 +293,23 @@ The following examples represent the results when reading from a CSV data node w
]
```

=== "exposed_type = polars"

```python
polars.DataFrame(
{
"date": ["12/24/2018", "12/25/2018", "12/26/2018"],
"nb_sales": [1550, 2315, 1832]
}
)
```

!!! warning "Available in Taipy Enterprise edition"

The Polars exposed type is only available in the Enterprise edition of Taipy.
[Contact us](https://taipy.io/book-a-call){: .tp-btn .tp-btn--accent target='blank' }


When writing data to a CSV data node, the `CSVDataNode.write()^` method can take several datatype as the input:

- list, numpy array
Expand Down Expand Up @@ -409,12 +426,11 @@ The following examples represent the results when reading from an Excel data nod
=== "exposed_type = "pandas""

```python
pandas.DataFrame
(
date nb_sales
0 12/24/2018 1550
1 12/25/2018 2315
2 12/26/2018 1832
pandas.DataFrame(
{
"date": ["12/24/2018", "12/25/2018", "12/26/2018"],
"nb_sales": [1550, 2315, 1832]
}
)
```

Expand All @@ -439,6 +455,22 @@ The following examples represent the results when reading from an Excel data nod
]
```

=== "exposed_type = polars"

```python
polars.DataFrame(
{
"date": ["12/24/2018", "12/25/2018", "12/26/2018"],
"nb_sales": [1550, 2315, 1832]
}
)
```

!!! warning "Available in Taipy Enterprise edition"

The Polars exposed type is only available in the Enterprise edition of Taipy.
[Contact us](https://taipy.io/book-a-call){: .tp-btn .tp-btn--accent target='blank' }

When writing data to an Excel data node, the `ExcelDataNode.write()^` method can take several datatype as the input:

- list, numpy array
Expand Down Expand Up @@ -557,12 +589,12 @@ node with different _exposed_type_:
=== "exposed_type = "pandas""

```python
pandas.DataFrame
(
ID date nb_sales
0 1 12/24/2018 1550
1 2 12/25/2018 2315
2 3 12/26/2018 1832
pandas.DataFrame(
{
"ID": [1, 2, 3],
"date": ["12/24/2018", "12/25/2018", "12/26/2018"],
"nb_sales": [1550, 2315, 1832]
}
)
```

Expand All @@ -587,6 +619,23 @@ node with different _exposed_type_:
]
```

=== "exposed_type = polars"

```python
polars.DataFrame(
{
"ID": [1, 2, 3],
"date": ["12/24/2018", "12/25/2018", "12/26/2018"],
"nb_sales": [1550, 2315, 1832]
}
)
```

!!! warning "Available in Taipy Enterprise edition"

The Polars exposed type is only available in the Enterprise edition of Taipy.
[Contact us](https://taipy.io/book-a-call){: .tp-btn .tp-btn--accent target='blank' }

When writing data to a SQL Table data node, the `SQLTableDataNode.write()^`
method can take several datatype as the input:

Expand Down Expand Up @@ -927,12 +976,11 @@ The following examples represent the results when read from Parquet data node wi
=== "exposed_type = "pandas""

```python
pandas.DataFrame
(
date nb_sales
0 12/24/2018 1550
1 12/25/2018 2315
2 12/26/2018 1832
pandas.DataFrame(
{
"date": ["12/24/2018", "12/25/2018", "12/26/2018"],
"nb_sales": [1550, 2315, 1832]
}
)
```

Expand All @@ -957,12 +1005,29 @@ The following examples represent the results when read from Parquet data node wi
]
```

=== "exposed_type = polars"

```python
polars.DataFrame(
{
"date": ["12/24/2018", "12/25/2018", "12/26/2018"],
"nb_sales": [1550, 2315, 1832]
}
)
```

!!! warning "Available in Taipy Enterprise edition"

The Polars exposed type is only available in the Enterprise edition of Taipy.
[Contact us](https://taipy.io/book-a-call){: .tp-btn .tp-btn--accent target='blank' }

When writing data to a Parquet data node, the `ParquetDataNode.write()^` method can take several
datatype as the input depending on the _exposed type_:

- pandas dataframes
- numpy arrays
- any object, which will be passed to the `pd.DataFrame` constructor (e.g., list of dictionaries)
- polars dataframes (Available in Taipy Enterprise edition only)

The following examples will write to the path of the Parquet data node:

Expand Down Expand Up @@ -1556,11 +1621,7 @@ filtered_data = data_node.filter(("nb_sales", 1550, Operator.EQUAL))
=== "exposed_type = "pandas""

```python
pandas.DataFrame
(
date nb_sales
0 12/24/2018 1550
)
pandas.DataFrame({"date": ["12/24/2018"], "nb_sales": [1550]})
```

=== "exposed_type = "numpy""
Expand All @@ -1576,6 +1637,22 @@ filtered_data = data_node.filter(("nb_sales", 1550, Operator.EQUAL))
[SaleRow("12/24/2018", 1550)]
```

=== "exposed_type = polars"

```python
polars.DataFrame(
{
"date": ["12/24/2018"],
"nb_sales": [1550]
}
)
```

!!! warning "Available in Taipy Enterprise edition"

The Polars exposed type is only available in the Enterprise edition of Taipy.
[Contact us](https://taipy.io/book-a-call){: .tp-btn .tp-btn--accent target='blank' }

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. The default join operator is `JoinOperator.AND`.

Expand All @@ -1600,6 +1677,12 @@ filtered_data = data_node.filter(
0 12/24/2018 1550
1 12/26/2018 1832
)
pandas.DataFrame(
{
"date": ["12/24/2018", "12/26/2018"],
"nb_sales": [1550, 1832]
}
)
```

=== "exposed_type = "numpy""
Expand All @@ -1621,6 +1704,22 @@ filtered_data = data_node.filter(
]
```

=== "exposed_type = polars"

```python
polars.DataFrame(
{
"date": ["12/24/2018", "12/26/2018"],
"nb_sales": [1550, 1832]
}
)
```

!!! warning "Available in Taipy Enterprise edition"

The Polars exposed type is only available in the Enterprise edition of Taipy.
[Contact us](https://taipy.io/book-a-call){: .tp-btn .tp-btn--accent target='blank' }

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_:
Expand All @@ -1637,11 +1736,11 @@ filtered_data = data_node.filter(
=== "exposed_type = "pandas""

```python
pandas.DataFrame
(
date nb_sales
0 12/24/2018 1550
1 12/25/2018 2315
pandas.DataFrame(
{
"date": ["12/24/2018", "12/25/2018"],
"nb_sales": [1550, 2315]
}
)
```

Expand All @@ -1664,6 +1763,22 @@ filtered_data = data_node.filter(
]
```

=== "exposed_type = polars"

```python
polars.DataFrame(
{
"date": ["12/24/2018", "12/25/2018"],
"nb_sales": [1550, 2315]
}
)
```

!!! warning "Available in Taipy Enterprise edition"

The Polars exposed type is only available in the Enterprise edition of Taipy.
[Contact us](https://taipy.io/book-a-call){: .tp-btn .tp-btn--accent target='blank' }

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

Expand Down

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