| Name | funcnodes-pandas JSON |
| Version |
0.2.8
JSON |
| download |
| home_page | None |
| Summary | pandas nodes for funcnodes |
| upload_time | 2024-10-21 13:52:25 |
| maintainer | None |
| docs_url | None |
| author | Julian Kimmig |
| requires_python | >=3.11 |
| license | MIT |
| keywords |
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| VCS |
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| bugtrack_url |
|
| requirements |
No requirements were recorded.
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|
# funcnodes-pandas
`funcnodes-pandas` is an extension for the [Funcnodes](https://github.com/linkdlab/funcnodes) framework that allows you to manipulate [Pandas](https://pandas.pydata.org/) DataFrames and Series using **FuncNodes**' visual node-based system. It provides a collection of nodes for performing typical operations on Pandas data structures, such as conversions, data manipulations, and calculations.
This library enables **no-code** and **low-code** workflows for **Pandas** by providing drag-and-drop functionality in a visual interface. It also supports Python-based scripting to handle more complex operations.
## Features
- **DataFrame Conversion**:
- Convert DataFrames to dictionaries and vice versa.
- Handle CSV and Excel files easily using DataFrame nodes.
- **Data Manipulation**:
- Add, drop, and manipulate rows and columns of a DataFrame.
- Handle missing data with nodes for `fillna`, `dropna`, `ffill`, and `bfill`.
- Perform merges and joins with intuitive nodes for `merge`, `concatenate`, and `join`.
- **Math & Statistical Operations**:
- Perform descriptive statistics like `mean`, `sum`, `std`, `var`, and `corr`.
- Evaluate custom expressions directly on DataFrames using the `eval` node.
- **Masking & Filtering**:
- Apply masks to filter DataFrame data.
- Use conditions to filter rows and columns dynamically.
- **Grouping & Aggregation**:
- Group data using `groupby` and aggregate it with `sum`, `mean`, `count`, etc.
- Easily convert groups into lists of DataFrames.
- **Series Support**:
- Nodes for converting Series to lists and dictionaries.
- Access individual elements using `iloc` and `loc`.
- Perform string operations on Series.
## Installation
Install the package with:
```bash
pip install funcnodes-pandas
```
Ensure that you have **Pandas** and **FuncNodes** installed.
## Getting Started
Here's an overview of the basic programmatically usage of **funcnodes-pandas**.
1. **Convert a DataFrame to a Dictionary**
```python
import pandas as pd
import funcnodes_pandas as fnpd
df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
node = fnpd.to_dict()
node.inputs['df'].value = df
await node
print(node.outputs['dict'].value)
```
This code converts a DataFrame to a dictionary using the `to_dict` node.
2. **Filling Missing Data**
```python
node = fnpd.fillna()
node.inputs["df"].value = df
node.inputs["value"].value = 0
await node
print(node.outputs["out"].value)
```
The `fillna` node fills missing data in a DataFrame.
3. **Group By Operations**
```python
node = fnpd.group_by()
node.inputs["df"].value = df
node.inputs["by"].value = "A"
await node
print(node.outputs["grouped"].value)
```
This groups data based on column `A` in the DataFrame.
## Testing
The repository contains a suite of tests to ensure that the various functionalities of `funcnodes-pandas` work as expected. The tests are based on **unittest** and **IsolatedAsyncioTestCase**. You can run the tests using:
```bash
python -m unittest discover
```
Test cases for operations such as `groupby`, `add_column`, `dropna`, etc., are included.
## Contribution
Feel free to contribute to this project by submitting pull requests. You can help by adding new nodes, fixing bugs, or enhancing documentation.
## License
This project is licensed under the MIT License.
## Contact
For any questions or issues, please open an issue on the GitHub repository.
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"description": "# funcnodes-pandas\n\n`funcnodes-pandas` is an extension for the [Funcnodes](https://github.com/linkdlab/funcnodes) framework that allows you to manipulate [Pandas](https://pandas.pydata.org/) DataFrames and Series using **FuncNodes**' visual node-based system. It provides a collection of nodes for performing typical operations on Pandas data structures, such as conversions, data manipulations, and calculations.\n\nThis library enables **no-code** and **low-code** workflows for **Pandas** by providing drag-and-drop functionality in a visual interface. It also supports Python-based scripting to handle more complex operations.\n\n## Features\n\n- **DataFrame Conversion**:\n - Convert DataFrames to dictionaries and vice versa.\n - Handle CSV and Excel files easily using DataFrame nodes.\n- **Data Manipulation**:\n\n - Add, drop, and manipulate rows and columns of a DataFrame.\n - Handle missing data with nodes for `fillna`, `dropna`, `ffill`, and `bfill`.\n - Perform merges and joins with intuitive nodes for `merge`, `concatenate`, and `join`.\n\n- **Math & Statistical Operations**:\n\n - Perform descriptive statistics like `mean`, `sum`, `std`, `var`, and `corr`.\n - Evaluate custom expressions directly on DataFrames using the `eval` node.\n\n- **Masking & Filtering**:\n\n - Apply masks to filter DataFrame data.\n - Use conditions to filter rows and columns dynamically.\n\n- **Grouping & Aggregation**:\n\n - Group data using `groupby` and aggregate it with `sum`, `mean`, `count`, etc.\n - Easily convert groups into lists of DataFrames.\n\n- **Series Support**:\n - Nodes for converting Series to lists and dictionaries.\n - Access individual elements using `iloc` and `loc`.\n - Perform string operations on Series.\n\n## Installation\n\nInstall the package with:\n\n```bash\npip install funcnodes-pandas\n```\n\nEnsure that you have **Pandas** and **FuncNodes** installed.\n\n## Getting Started\n\nHere's an overview of the basic programmatically usage of **funcnodes-pandas**.\n\n1. **Convert a DataFrame to a Dictionary**\n\n```python\nimport pandas as pd\nimport funcnodes_pandas as fnpd\n\ndf = pd.DataFrame({\"A\": [1, 2, 3], \"B\": [4, 5, 6]})\nnode = fnpd.to_dict()\nnode.inputs['df'].value = df\nawait node\nprint(node.outputs['dict'].value)\n```\n\nThis code converts a DataFrame to a dictionary using the `to_dict` node.\n\n2. **Filling Missing Data**\n\n```python\nnode = fnpd.fillna()\nnode.inputs[\"df\"].value = df\nnode.inputs[\"value\"].value = 0\nawait node\nprint(node.outputs[\"out\"].value)\n```\n\nThe `fillna` node fills missing data in a DataFrame.\n\n3. **Group By Operations**\n\n```python\nnode = fnpd.group_by()\nnode.inputs[\"df\"].value = df\nnode.inputs[\"by\"].value = \"A\"\nawait node\nprint(node.outputs[\"grouped\"].value)\n```\n\nThis groups data based on column `A` in the DataFrame.\n\n## Testing\n\nThe repository contains a suite of tests to ensure that the various functionalities of `funcnodes-pandas` work as expected. The tests are based on **unittest** and **IsolatedAsyncioTestCase**. You can run the tests using:\n\n```bash\npython -m unittest discover\n```\n\nTest cases for operations such as `groupby`, `add_column`, `dropna`, etc., are included.\n\n## Contribution\n\nFeel free to contribute to this project by submitting pull requests. You can help by adding new nodes, fixing bugs, or enhancing documentation.\n\n## License\n\nThis project is licensed under the MIT License.\n\n## Contact\n\nFor any questions or issues, please open an issue on the GitHub repository.\n\n",
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