pandas-duktape
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---
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title: Spreadsheet Data in Pandas
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sidebar_label: Python + Pandas
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description: Process structured data in Python with Pandas. Seamlessly integrate spreadsheets into your workflow with SheetJS. Analyze complex Excel spreadsheets with confidence.
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pagination_prev: demos/index
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pagination_next: demos/frontend/index
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---
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import current from '/version.js';
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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import CodeBlock from '@theme/CodeBlock';
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Pandas[^1] is a Python software library for data analysis.
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[SheetJS](https://sheetjs.com) is a JavaScript library for reading and writing
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data from spreadsheets.
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This demo uses SheetJS to process data from a spreadsheet and translate to the
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Pandas DataFrame format. We'll explore how to load SheetJS from Python scripts,
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generate DataFrames from workbooks, and write DataFrames back to workbooks.
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The ["Complete Example"](#complete-example) includes a wrapper library that
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simplifies importing and exporting spreadsheets.
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:::info pass
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Pandas includes limited support for reading spreadsheets (`pandas.from_excel`)
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and writing XLSX spreadsheets (`pandas.DataFrame.to_excel`).
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**SheetJS supports common spreadsheet formats that Pandas cannot process.**
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SheetJS operations also offer more flexibility in processing complex worksheets.
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:::
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:::note Tested Environments
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This demo was tested in the following deployments:
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| Architecture | JS Engine | Pandas | Python | Date |
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|:-------------|:----------------|:-------|:-------|:-----------|
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| `darwin-x64` | Duktape `2.7.0` | 2.0.3 | 3.11.7 | 2024-01-29 |
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| `linux-x64` | Duktape `2.7.0` | 1.5.3 | 3.11.3 | 2024-01-29 |
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:::
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## Integration Details
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[`sheetjs.py`](pathname:///pandas/sheetjs.py) is a wrapper script that provides
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helper methods for reading and writing spreadsheets. Installation notes are
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included in the ["Complete Example"](#complete-example) section.
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### JS in Python
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JS code cannot be directly evaluated in Python implementations.
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To run JS code from Python, JavaScript engines[^2] can be embedded in Python
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modules or dynamically loaded using the `ctypes` foreign function library[^3].
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This demo uses `ctypes` with the [Duktape engine](/docs/demos/engines/duktape).
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### Wrapper
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The script exports a class named `SheetJSWrapper`. It is a context manager that
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initializes the Duktape engine and executes SheetJS scripts on entrance. All
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work should be performed in the context:
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```python title="Complete Example"
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#!/usr/bin/env python3
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from sheetjs import SheetJSWrapper
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with SheetJSWrapper() as sheetjs:
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# Parse file
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wb = sheetjs.read_file("pres.numbers")
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print("Loaded file pres.numbers")
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# Get first worksheet name
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first_ws_name = wb.get_sheet_names()[0]
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print(f"Reading from sheet {first_ws_name}")
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# Generate DataFrame from first worksheet
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df = wb.get_df(first_ws_name)
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print(df.info())
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# Export DataFrame to XLSB
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sheetjs.write_df(df, "SheetJSPandas.xlsb", sheet_name="DataFrame")
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```
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### Reading Files
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`sheetjs.read_file` accepts a path to a spreadsheet file. It will parse the file
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and return an object representing the workbook.
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The `get_sheet_names` method of the workbook returns a list of sheet names.
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The `get_df` method of the workbook generates a DataFrame from the workbook. The
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specific sheet can be selected by passing the name.
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For example, the following code reads `pres.numbers` and generates a DataFrame
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from the second worksheet:
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```python title="Generating a DataFrame from the second worksheet"
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with SheetJSWrapper() as sheetjs:
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# Parse file
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wb = sheetjs.read_file(path)
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# Generate DataFrame from second worksheet
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ws_name = wb.get_sheet_names()[1]
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df = wb.get_df(ws_name)
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# Print metadata
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print(df.info())
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```
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Under the hood, `sheetjs.py` performs the following steps:
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```mermaid
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flowchart LR
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file[(workbook\nfile)]
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subgraph SheetJS operations
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bytes(Byte\nstring)
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wb((SheetJS\nWorkbook))
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csv(CSV\nstring)
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end
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subgraph Pandas operations
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stream(CSV\nStream)
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df[(Pandas\nDataFrame)]
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end
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file --> |`open`/`read`\nPython ops| bytes
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bytes --> |`XLSX.read`\nParse Bytes| wb
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wb --> |`sheet_to_csv`\nExtract Data| csv
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csv --> |`StringIO`\nPython ops| stream
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stream --> |`read_csv`\nParse CSV| df
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```
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1) Pure Python operations read the spreadsheet file and generate a byte string.
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2) SheetJS libraries parse the string and generate a clean CSV.
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- The `read` method[^4] parses file bytes into a SheetJS workbook object[^5]
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- After selecting a worksheet, `sheet_to_csv`[^6] generates a CSV string
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3) Python operations convert the CSV string to a stream object.[^7]
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4) The Pandas `read_csv` method[^8] ingests the stream and generate a DataFrame.
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### Writing Files
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`sheetjs.write_df` accepts a DataFrame and a path. It will attempt to export
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the data to a spreadsheet file.
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For example, the following code exports a DataFrame to `SheetJSPandas.xlsb`:
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```python title="Exporting a DataFrame to XLSB"
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with SheetJSWrapper() as sheetjs:
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# Export DataFrame to XLSB
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sheetjs.write_df(df, "SheetJSPandas.xlsb", sheet_name="DataFrame")
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```
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Under the hood, `sheetjs.py` performs the following steps:
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```mermaid
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flowchart LR
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subgraph Pandas operations
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df[(Pandas\nDataFrame)]
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json(JSON\nString)
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end
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subgraph SheetJS operations
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aoo(array of\nobjects)
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wb((SheetJS\nWorkbook))
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u8a(File\nbytes)
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end
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file[(workbook\nfile)]
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df --> |`to_json`\nPandas ops| json
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json --> |`JSON.parse`\nJS Engine| aoo
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aoo --> |`json_to_sheet`\nSheetJS Ops| wb
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wb --> |`XLSX.write`\nUint8Array| u8a
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u8a --> |`open`/`write`\nPython ops| file
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```
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1) The Pandas DataFrame `to_json` method[^9] generates a JSON string.
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2) JS engine operations translate the JSON string to an array of objects.
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3) SheetJS libraries process the data array and generate file bytes.
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- The `json_to_sheet` method[^10] creates a SheetJS sheet object from the data.
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- The `book_new` method[^11] creates a SheetJS workbook that includes the sheet.
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- The `write` method[^12] generates the spreadsheet file bytes.
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4) Pure Python operations write the bytes to file.
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## Complete Example
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This example will extract data from an Apple Numbers spreadsheet and generate a
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DataFrame. The DataFrame will be exported to the binary XLSB spreadsheet format.
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0) Install Pandas:
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```bash
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sudo python3 -m pip install pandas
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```
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:::caution pass
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On Arch Linux-based platforms including the Steam Deck, the install may fail:
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```
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error: externally-managed-environment
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```
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In these situations, Pandas must be installed through the package manager:
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```bash
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sudo pacman -Syu python-pandas
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```
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:::
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1) Build the Duktape shared library:
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```bash
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curl -LO https://duktape.org/duktape-2.7.0.tar.xz
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tar -xJf duktape-2.7.0.tar.xz
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cd duktape-2.7.0
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make -f Makefile.sharedlibrary
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cd ..
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```
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2) Copy the shared library to the current folder. When the demo was last tested,
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the shared library file name differed by platform:
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| OS | name |
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|:-------|:--------------------------|
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| Darwin | `libduktape.207.20700.so` |
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| Linux | `libduktape.so.207.20700` |
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```bash
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cp duktape-*/libduktape.* .
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```
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3) Download the SheetJS Standalone script and move to the project directory:
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<ul>
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<li><a href={`https://cdn.sheetjs.com/xlsx-${current}/package/dist/shim.min.js`}>shim.min.js</a></li>
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<li><a href={`https://cdn.sheetjs.com/xlsx-${current}/package/dist/xlsx.full.min.js`}>xlsx.full.min.js</a></li>
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</ul>
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<CodeBlock language="bash">{`\
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curl -LO https://cdn.sheetjs.com/xlsx-${current}/package/dist/shim.min.js
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curl -LO https://cdn.sheetjs.com/xlsx-${current}/package/dist/xlsx.full.min.js`}
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</CodeBlock>
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4) Download the following test scripts and files:
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- [`pres.numbers` test file](https://sheetjs.com/pres.numbers)
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- [`sheetjs.py` script](pathname:///pandas/sheetjs.py)
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- [`SheetJSPandas.py` script](pathname:///pandas/SheetJSPandas.py)
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```bash
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curl -LO https://sheetjs.com/pres.numbers
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curl -LO https://docs.sheetjs.com/pandas/sheetjs.py
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curl -LO https://docs.sheetjs.com/pandas/SheetJSPandas.py
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```
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5) Edit the `sheetjs.py` script.
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The `lib` variable declares the path to the library:
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```python title="sheetjs.py (edit highlighted line)"
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# highlight-next-line
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lib = "libduktape.207.20700.so"
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```
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<Tabs groupId="triple">
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<TabItem value="darwin-x64" label="MacOS">
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The name of the library is `libduktape.207.20700.so`:
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```python title="sheetjs.py (change highlighted line)"
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# highlight-next-line
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lib = "libduktape.207.20700.so"
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```
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</TabItem>
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<TabItem value="linux-x64" label="Linux">
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The name of the library is `libduktape.so.207.20700`:
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```python title="sheetjs.py (change highlighted line)"
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# highlight-next-line
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lib = "libduktape.so.207.20700"
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```
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</TabItem>
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</Tabs>
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6) Run the script:
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```bash
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python3 SheetJSPandas.py pres.numbers
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```
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If successful, the script will display DataFrame metadata:
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```
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RangeIndex: 5 entries, 0 to 4
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Data columns (total 2 columns):
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# Column Non-Null Count Dtype
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--- ------ -------------- -----
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0 Name 5 non-null object
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1 Index 5 non-null int64
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dtypes: int64(1), object(1)
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```
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It will also export the DataFrame to `SheetJSPandas.xlsb`. The file can be
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inspected with a spreadsheet editor that supports XLSB files.
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[^1]: The official documentation site is <https://pandas.pydata.org/> and the official distribution point is <https://pypi.org/project/pandas/>
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[^2]: See ["Other Languages"](/docs/demos/engines/) for more examples.
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[^3]: See [`ctypes`](https://docs.python.org/3/library/ctypes.html) in the Python documentation.
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[^4]: See [`read` in "Reading Files"](/docs/api/parse-options)
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[^5]: See ["Workbook Object"](/docs/csf/book)
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[^6]: See [`sheet_to_csv` in "Utilities"](/docs/api/utilities/csv#delimiter-separated-output)
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[^7]: See [the examples in "IO tools"](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html) in the Pandas documentation.
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[^8]: See [`pandas.read_csv`](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html) in the Pandas documentation.
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[^9]: See [`pandas.DataFrame.to_json`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_json.html) in the Pandas documentation.
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[^10]: See [`json_to_sheet` in "Utilities"](/docs/api/utilities/array#array-of-objects-input)
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[^11]: See [`book_new` in "Utilities"](/docs/api/utilities/wb)
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[^12]: See [`write` in "Writing Files"](/docs/api/write-options)
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@ -361,11 +361,138 @@ This demo was tested in the following deployments:
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| Architecture | Version | PHP Version | Date |
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|:-------------|:--------|:------------|:-----------|
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| `darwin-x64` | `2.7.0` | `8.3.2` | 2024-01-26 |
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| `linux-x64` | `2.7.0` | `8.2.7` | 2024-01-29 |
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:::
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0) Ensure `php` is installed and available on the system path.
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1) Inspect the `php.ini` configuration file. The location of the file can be
|
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found by running `php --ini`. The following output is from the last macOS test:
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```text pass
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Configuration File (php.ini) Path: /usr/local/etc/php/8.3
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// highlight-next-line
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Loaded Configuration File: /usr/local/etc/php/8.3/php.ini
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Scan for additional .ini files in: /usr/local/etc/php/8.3/conf.d
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Additional .ini files parsed: /usr/local/etc/php/8.3/conf.d/ext-opcache.ini
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```
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The following line should appear in the configuration:
|
||||
|
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```ini title="php.ini (add to end)"
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extension=ffi
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```
|
||||
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If this line is prefixed with a `;`, remove the semicolon. If this line does not
|
||||
appear in the file, add it to the end.
|
||||
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2) Build the Duktape shared library:
|
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|
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```bash
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curl -LO https://duktape.org/duktape-2.7.0.tar.xz
|
||||
tar -xJf duktape-2.7.0.tar.xz
|
||||
cd duktape-2.7.0
|
||||
make -f Makefile.sharedlibrary
|
||||
cd ..
|
||||
```
|
||||
|
||||
3) Copy the shared library to the current folder. When the demo was last tested,
|
||||
the shared library file name differed by platform:
|
||||
|
||||
| OS | name |
|
||||
|:-------|:--------------------------|
|
||||
| Darwin | `libduktape.207.20700.so` |
|
||||
| Linux | `libduktape.so.207.20700` |
|
||||
|
||||
```bash
|
||||
cp duktape-*/libduktape.* .
|
||||
```
|
||||
|
||||
4) Download the SheetJS Standalone script, shim script and test file. Move all
|
||||
three files to the project directory:
|
||||
|
||||
<ul>
|
||||
<li><a href={`https://cdn.sheetjs.com/xlsx-${current}/package/dist/shim.min.js`}>shim.min.js</a></li>
|
||||
<li><a href={`https://cdn.sheetjs.com/xlsx-${current}/package/dist/xlsx.full.min.js`}>xlsx.full.min.js</a></li>
|
||||
<li><a href="https://sheetjs.com/pres.numbers">pres.numbers</a></li>
|
||||
</ul>
|
||||
|
||||
<CodeBlock language="bash">{`\
|
||||
curl -LO https://cdn.sheetjs.com/xlsx-${current}/package/dist/shim.min.js
|
||||
curl -LO https://cdn.sheetjs.com/xlsx-${current}/package/dist/xlsx.full.min.js
|
||||
curl -LO https://sheetjs.com/pres.numbers`}
|
||||
</CodeBlock>
|
||||
|
||||
5) Download [`SheetJSDuk.php`](pathname:///duk/SheetJSDuk.php):
|
||||
|
||||
```bash
|
||||
curl -LO https://docs.sheetjs.com/duk/SheetJSDuk.php
|
||||
```
|
||||
|
||||
6) Edit the `SheetJSDuk.php` script.
|
||||
|
||||
The `$sofile` variable declares the path to the library:
|
||||
|
||||
```php title="SheetJSDuk.php (edit highlighted line)"
|
||||
<?php
|
||||
|
||||
// highlight-next-line
|
||||
$sofile = './libduktape.207.20700.so';
|
||||
```
|
||||
|
||||
<Tabs groupId="triple">
|
||||
<TabItem value="darwin-x64" label="MacOS">
|
||||
|
||||
The name of the library is `libduktape.207.20700.so`:
|
||||
|
||||
```php title="SheetJSDuk.php (change highlighted line)"
|
||||
// highlight-next-line
|
||||
$sofile = './libduktape.207.20700.so';
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="linux-x64" label="Linux">
|
||||
|
||||
The name of the library is `libduktape.so.207.20700`:
|
||||
|
||||
```php title="SheetJSDuk.php (change highlighted line)"
|
||||
// highlight-next-line
|
||||
$sofile = './libduktape.so.207.20700';
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
</Tabs>
|
||||
|
||||
7) Run the script:
|
||||
|
||||
```bash
|
||||
php SheetJSDuk.php pres.numbers
|
||||
```
|
||||
|
||||
If the program succeeded, the CSV contents will be printed to console and the
|
||||
file `sheetjsw.xlsb` will be created. That file can be opened with Excel.
|
||||
|
||||
### Python
|
||||
|
||||
There is no official Python binding to the Duktape library. Instead, this demo
|
||||
uses the raw `ctypes` interface[^2] to the Duktape shared library.
|
||||
|
||||
#### Python Demo
|
||||
|
||||
:::note Tested Deployments
|
||||
|
||||
This demo was tested in the following deployments:
|
||||
|
||||
| Architecture | Version | Python | Date |
|
||||
|:-------------|:--------|:---------|:-----------|
|
||||
| `darwin-x64` | `2.7.0` | `3.11.7` | 2024-01-29 |
|
||||
| `linux-x64` | `2.7.0` | `3.11.3` | 2024-01-29 |
|
||||
|
||||
:::
|
||||
|
||||
0) Ensure `python` is installed and available on the system path.
|
||||
|
||||
1) Build the Duktape shared library:
|
||||
|
||||
```bash
|
||||
|
|
@ -377,10 +504,15 @@ cd ..
|
|||
```
|
||||
|
||||
2) Copy the shared library to the current folder. When the demo was last tested,
|
||||
the file name was `libduktape.207.20700.so`:
|
||||
the shared library file name differed by platform:
|
||||
|
||||
| OS | name |
|
||||
|:-------|:--------------------------|
|
||||
| Darwin | `libduktape.207.20700.so` |
|
||||
| Linux | `libduktape.so.207.20700` |
|
||||
|
||||
```bash
|
||||
cp duktape-*/libduktape.*.so .
|
||||
cp duktape-*/libduktape.* .
|
||||
```
|
||||
|
||||
3) Download the SheetJS Standalone script, shim script and test file. Move all
|
||||
|
|
@ -398,16 +530,50 @@ curl -LO https://cdn.sheetjs.com/xlsx-${current}/package/dist/xlsx.full.min.js
|
|||
curl -LO https://sheetjs.com/pres.numbers`}
|
||||
</CodeBlock>
|
||||
|
||||
4) Download [`SheetJSDuk.php`](pathname:///duk/SheetJSDuk.php):
|
||||
4) Download [`SheetJSDuk.py`](pathname:///duk/SheetJSDuk.py):
|
||||
|
||||
```bash
|
||||
curl -LO https://docs.sheetjs.com/duk/SheetJSDuk.php
|
||||
curl -LO https://docs.sheetjs.com/duk/SheetJSDuk.py
|
||||
```
|
||||
|
||||
5) Run the script:
|
||||
5) Edit the `SheetJSDuk.py` script.
|
||||
|
||||
The `lib` variable declares the path to the library:
|
||||
|
||||
```python title="SheetJSDuk.py (edit highlighted line)"
|
||||
#!/usr/bin/env python3
|
||||
|
||||
# highlight-next-line
|
||||
lib = "libduktape.207.20700.so"
|
||||
```
|
||||
|
||||
<Tabs groupId="triple">
|
||||
<TabItem value="darwin-x64" label="MacOS">
|
||||
|
||||
The name of the library is `libduktape.207.20700.so`:
|
||||
|
||||
```python title="SheetJSDuk.py (change highlighted line)"
|
||||
# highlight-next-line
|
||||
lib = "libduktape.207.20700.so"
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="linux-x64" label="Linux">
|
||||
|
||||
The name of the library is `libduktape.so.207.20700`:
|
||||
|
||||
```python title="SheetJSDuk.py (change highlighted line)"
|
||||
# highlight-next-line
|
||||
lib = "libduktape.so.207.20700"
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
</Tabs>
|
||||
|
||||
6) Run the script:
|
||||
|
||||
```bash
|
||||
php SheetJSDuk.php pres.numbers
|
||||
python3 SheetJSDuk.py pres.numbers
|
||||
```
|
||||
|
||||
If the program succeeded, the CSV contents will be printed to console and the
|
||||
|
|
@ -520,4 +686,5 @@ sudo cpan install File::Slurp
|
|||
|
||||
:::
|
||||
|
||||
[^1]: See [Foreign Function Interface](https://www.php.net/manual/en/book.ffi.php) in the PHP documentation.
|
||||
[^1]: See [Foreign Function Interface](https://www.php.net/manual/en/book.ffi.php) in the PHP documentation.
|
||||
[^2]: See [`ctypes`](https://docs.python.org/3/library/ctypes.html) in the Python documentation.
|
||||
|
|
@ -1,440 +0,0 @@
|
|||
---
|
||||
title: Spreadsheet Data in Pandas
|
||||
sidebar_label: Python (Pandas)
|
||||
description: Process structured data in Python with Pandas. Seamlessly integrate spreadsheets into your workflow with SheetJS. Analyze complex Excel spreadsheets with confidence.
|
||||
pagination_prev: demos/cloud/index
|
||||
pagination_next: demos/bigdata/index
|
||||
---
|
||||
|
||||
import current from '/version.js';
|
||||
import Tabs from '@theme/Tabs';
|
||||
import TabItem from '@theme/TabItem';
|
||||
import CodeBlock from '@theme/CodeBlock';
|
||||
|
||||
Pandas[^1] is a Python software library for data analysis.
|
||||
|
||||
[SheetJS](https://sheetjs.com) is a JavaScript library for reading and writing
|
||||
data from spreadsheets.
|
||||
|
||||
This demo uses SheetJS to process data from a spreadsheet and translate to the
|
||||
Pandas DataFrame format. We'll explore how to load SheetJS from Python scripts,
|
||||
generate DataFrames from workbooks, and write DataFrames back to workbooks.
|
||||
|
||||
:::note
|
||||
|
||||
This demo was tested in the following deployments:
|
||||
|
||||
| Architecture | V8 version | Pandas | Python | Date |
|
||||
|:-------------|:--------------|:-------|:-------|:-----------|
|
||||
| `darwin-x64` | `11.5.150.16` | 2.0.3 | 3.11.4 | 2023-07-29 |
|
||||
|
||||
:::
|
||||
|
||||
:::info pass
|
||||
|
||||
Pandas includes limited support for reading spreadsheets (`pandas.from_excel`)
|
||||
and writing XLSX spreadsheets (`pandas.DataFrame.to_excel`).
|
||||
|
||||
The SheetJS approach supports many common spreadsheet formats that are not
|
||||
supported by the current set of Pandas codecs and offers greater flexibility in
|
||||
processing complex worksheets.
|
||||
|
||||
:::
|
||||
|
||||
## Integration Details
|
||||
|
||||
JS code cannot literally be run in the Python interpreter. To run JS code from
|
||||
Python, JavaScript engines[^2] can be embedded in CPython modules.
|
||||
|
||||
### Loading SheetJS
|
||||
|
||||
This demo uses the `STPyV8` module[^3] to access the V8 JavaScript engine.
|
||||
|
||||
_Initialize V8_
|
||||
|
||||
The engine library provides a convenient context manager `JSContext` for context
|
||||
resource management. Within the context, the `eval` method can evaluate code:
|
||||
|
||||
```py
|
||||
from STPyV8 import JSContext
|
||||
|
||||
# Initialize JS context
|
||||
with JSContext() as ctxt:
|
||||
# Run code
|
||||
res = ctxt.eval("'Sheet' + 'JS'")
|
||||
|
||||
# print result
|
||||
print(res)
|
||||
```
|
||||
|
||||
`STPyV8` handles data interchange for common types. Arrays and JS objects can be
|
||||
translated to Python `list` and `dict` respectively. The following `convert`
|
||||
function is used in the test suite[^4]
|
||||
|
||||
```py
|
||||
# from `tests/test_Wrapper.py` in the STPyV8 library
|
||||
# License: Apache 2.0
|
||||
def convert(obj):
|
||||
if isinstance(obj, JSArray):
|
||||
return [convert(v) for v in obj]
|
||||
if isinstance(obj, JSObject):
|
||||
return dict([[str(k), convert(obj.__getattr__(str(k)))] for k in obj.__dir__()])
|
||||
return obj
|
||||
```
|
||||
|
||||
_Loading the Library_
|
||||
|
||||
The [SheetJS Standalone scripts](/docs/getting-started/installation/standalone)
|
||||
can be parsed and evaluated from the JS engine. Once evaluated, the `XLSX`
|
||||
variable is available as a global.
|
||||
|
||||
Assuming the standalone library is in the same directory as the source file,
|
||||
the script can be evaluated with `eval`:
|
||||
|
||||
```py
|
||||
# Within a JSContext, open `xlsx.full.min.js` and evaluate
|
||||
with open("xlsx.full.min.js") as f:
|
||||
ctxt.eval(f.read())
|
||||
```
|
||||
|
||||
### Reading Files
|
||||
|
||||
The following diagram depicts the spreadsheet salsa:
|
||||
|
||||
```mermaid
|
||||
flowchart LR
|
||||
file[(workbook\nfile)]
|
||||
subgraph SheetJS operations
|
||||
base64(Base64\nstring)
|
||||
wb((SheetJS\nWorkbook))
|
||||
aoo(array of\nobjects)
|
||||
end
|
||||
subgraph Pandas operations
|
||||
lod(list of\nrecords)
|
||||
df[(Pandas\nDataFrame)]
|
||||
end
|
||||
file --> |`open`/`read`\nPython ops| base64
|
||||
base64 --> |`XLSX.read`\nParse Bytes| wb
|
||||
wb --> |`sheet_to_json`\nExtract Data| aoo
|
||||
aoo --> |`convert`\nPython ops|lod
|
||||
lod --> |`from_records`\nPandas ops| df
|
||||
```
|
||||
|
||||
At a high level:
|
||||
|
||||
1) Pure Python operations read the file and generate a Base64 string
|
||||
|
||||
2) SheetJS libraries parse the string and generates JS records
|
||||
|
||||
3) JS engine operations translate the rows to Python `list` of `dicts`
|
||||
|
||||
4) Pandas operations translate the Python data to a DataFrame
|
||||
|
||||
#### Read files
|
||||
|
||||
The safest format for data interchange is Base64-encoded strings:
|
||||
|
||||
```py
|
||||
from base64 import b64encode
|
||||
|
||||
with open(path, mode="rb") as f:
|
||||
file_bytes = f.read()
|
||||
b64 = b64encode(file_bytes)
|
||||
```
|
||||
|
||||
#### Parse bytes
|
||||
|
||||
From JS code, `XLSX.read`[^5] parses the Base64 string
|
||||
|
||||
```py
|
||||
wb = ctxt.eval("(b64 => XLSX.read(b64, {type: 'base64', dense: true}))")(b64)
|
||||
```
|
||||
|
||||
The `wb` object follows the "Common Spreadsheet Format"[^6], an in-memory format
|
||||
for representing workbooks, worksheets, cells, and spreadsheet features.
|
||||
|
||||
#### Get First Worksheet
|
||||
|
||||
As explained in the "Workbook Object"[^7] section:
|
||||
- the `SheetNames` property is a ordered list of the sheet names in the workbook
|
||||
- the `Sheets` property of the workbook object is an object whose keys are sheet
|
||||
names and whose values are sheet objects.
|
||||
|
||||
For use in Python, the `SheetNames` array must be converted to a `list`:
|
||||
|
||||
```py
|
||||
sheet_names = convert(wb.SheetNames)
|
||||
first_sheet_name = sheet_names[0]
|
||||
```
|
||||
|
||||
Since utility functions will process the worksheet object from JavaScript, it is
|
||||
preferable not to convert the object:
|
||||
|
||||
```py
|
||||
first_sheet = wb.Sheets[first_sheet_name] # do not convert
|
||||
```
|
||||
|
||||
#### Generate List of Records
|
||||
|
||||
In JavaScript, the equivalent of the "`list` of `dict`s" or "`list` of records"
|
||||
is "array of objects". They can be created with `XLSX.utils.sheet_to_json`[^8]:
|
||||
|
||||
```py
|
||||
rows = convert(ctxt.eval("(ws => XLSX.utils.sheet_to_json(ws))")(first_sheet))
|
||||
```
|
||||
|
||||
#### Generate Pandas DataFrame
|
||||
|
||||
`rows` is a `list` of `dict` objects. `from_records`[^9] understands this data
|
||||
shape and generates a proper DataFrame:
|
||||
|
||||
```py
|
||||
df = pd.DataFrame.from_records(rows)
|
||||
```
|
||||
|
||||
### Writing Files
|
||||
|
||||
The writing process looks similar to the reading process in reverse:
|
||||
|
||||
```mermaid
|
||||
flowchart LR
|
||||
subgraph Pandas operations
|
||||
df[(Pandas\nDataFrame)]
|
||||
json(JSON\nString)
|
||||
end
|
||||
subgraph SheetJS operations
|
||||
aoo(array of\nobjects)
|
||||
wb((SheetJS\nWorkbook))
|
||||
base64(Base64\nstring)
|
||||
end
|
||||
file[(workbook\nfile)]
|
||||
df --> |`to_json`\nPandas ops| json
|
||||
json --> |`JSON.parse`\nJS Engine| aoo
|
||||
aoo --> |`json_to_sheet`\nSheetJS Ops| wb
|
||||
wb --> |`XLSX.write`\nBase64| base64
|
||||
base64 --> |`open`/`write`\nPython ops| file
|
||||
```
|
||||
|
||||
At a high level:
|
||||
|
||||
1) Pandas operations translate the Python data to JSON string
|
||||
|
||||
2) JS engine operations translate the JSON string to an array of objects
|
||||
|
||||
3) SheetJS libraries parse the array and generate a Base64-encoded workbook
|
||||
|
||||
4) Pure Python operations decode the Base64 string and write the bytes to file.
|
||||
|
||||
#### Generate JSON
|
||||
|
||||
`DataFrame#to_json`[^10] with the option `orient="records"` generates a JSON
|
||||
string that encodes an array of objects:
|
||||
|
||||
```py
|
||||
json = df.to_json(orient="records")
|
||||
```
|
||||
|
||||
#### Generate Worksheet
|
||||
|
||||
In JavaScript, `JSON.parse` will interpret the string as an array of objects.
|
||||
`XLSX.utils.json_to_sheet`[^11] generates a SheetJS worksheet object:
|
||||
|
||||
```py
|
||||
sheet = ctxt.eval("(json => XLSX.utils.json_to_sheet(JSON.parse(json)) )")(json)
|
||||
```
|
||||
|
||||
#### Export Enhancements
|
||||
|
||||
At this point, there are many options for improving the appearance of the sheet.
|
||||
For example, the "Export Tutorial"[^12] shows how to adjust column widths.
|
||||
|
||||
:::tip pass
|
||||
|
||||
[SheetJS Pro](https://sheetjs.com/pro) offers additional styling options such as
|
||||
cell styling and frozen rows.
|
||||
|
||||
"Pro Edit" offers a special approach for inserting data into an existing file.
|
||||
|
||||
:::
|
||||
|
||||
#### Generate Workbook
|
||||
|
||||
`XLSX.utils.book_new`[^13] creates a new workbook and `XLSX.utils.book_append_sheet`[^14]
|
||||
appends a worksheet to the workbook. The new worksheet will be called "Export":
|
||||
|
||||
:::note pass
|
||||
|
||||
The code in the string literal is reproduced below:
|
||||
|
||||
```js
|
||||
(ws, name) => {
|
||||
const wb = XLSX.utils.book_new();
|
||||
XLSX.utils.book_append_sheet(wb, ws, name);
|
||||
return wb;
|
||||
}
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
```py
|
||||
book = ctxt.eval("""((ws, name) => {
|
||||
const wb = XLSX.utils.book_new();
|
||||
XLSX.utils.book_append_sheet(wb, ws, name);
|
||||
return wb;
|
||||
})""")(sheet, "Export")
|
||||
```
|
||||
|
||||
#### Generate File
|
||||
|
||||
`XLSX.write`[^15] with the option `type: "base64"` attempts to create a file and
|
||||
generate a Base64 string:
|
||||
|
||||
```py
|
||||
b64 = ctxt.eval("(wb => XLSX.write(wb, {type:'base64', bookType:'xls'}))")(book)
|
||||
```
|
||||
|
||||
With the Base64 string, standard Python operations can create a file:
|
||||
|
||||
```py
|
||||
from base64 import b64decode
|
||||
|
||||
raw = b64decode(b64)
|
||||
with open("export.xls", mode="wb") as f:
|
||||
f.write(raw)
|
||||
```
|
||||
|
||||
## Complete Demo
|
||||
|
||||
This example will extract data from an Apple Numbers spreadsheet and generate a
|
||||
DataFrame. The DataFrame will be exported to a legacy XLS spreadsheet.
|
||||
|
||||
### Engine Setup
|
||||
|
||||
0) Follow the official installation instructions[^16].
|
||||
|
||||
<details><summary><b>Instructions for macOS 12</b> (click to show)</summary>
|
||||
|
||||
- Install `boost-python3` package using `brew`:
|
||||
|
||||
```bash
|
||||
brew install boost-python3
|
||||
```
|
||||
|
||||
- Identify python version:
|
||||
|
||||
```bash
|
||||
python3 --version
|
||||
```
|
||||
|
||||
:::note pass
|
||||
|
||||
When the demo was last tested, the version was `3.11.4`
|
||||
|
||||
:::
|
||||
|
||||
- [Download latest release](https://github.com/cloudflare/stpyv8/releases)
|
||||
|
||||
```bash
|
||||
curl -LO https://github.com/cloudflare/stpyv8/releases/download/v11.5.150.16/stpyv8-macos-12-python-3.11.zip
|
||||
```
|
||||
|
||||
- Extract ZIP file and enter folder
|
||||
|
||||
```bash
|
||||
unzip stpyv8-macos-12-python-3.11.zip
|
||||
cd stpyv8-macos-12-3.11
|
||||
```
|
||||
|
||||
- Move `icudtl.dat` to `/Library/Application Support/STPyV8/`:
|
||||
|
||||
```bash
|
||||
sudo mkdir -p /Library/Application\ Support/STPyV8
|
||||
sudo mv icudtl.dat /Library/Application\ Support/STPyV8/
|
||||
```
|
||||
|
||||
- Install wheel:
|
||||
|
||||
```bash
|
||||
sudo python3 -m pip install --upgrade *.whl
|
||||
cd ..
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
### Demo
|
||||
|
||||
1) Download the SheetJS Standalone script and move to the project directory:
|
||||
|
||||
<ul>
|
||||
<li><a href={`https://cdn.sheetjs.com/xlsx-${current}/package/dist/xlsx.full.min.js`}>xlsx.full.min.js</a></li>
|
||||
</ul>
|
||||
|
||||
<CodeBlock language="bash">{`\
|
||||
curl -LO https://cdn.sheetjs.com/xlsx-${current}/package/dist/xlsx.full.min.js`}
|
||||
</CodeBlock>
|
||||
|
||||
2) Install Pandas. On macOS:
|
||||
|
||||
```python
|
||||
sudo python3 -m pip install pandas
|
||||
```
|
||||
|
||||
3) Download the following test scripts and files:
|
||||
|
||||
- [`pres.numbers` test file](https://sheetjs.com/pres.numbers)
|
||||
- [`sheetjs.py` wrapper](pathname:///pandas/sheetjs.py)
|
||||
- [`SheetJSPandas.py` script](pathname:///pandas/SheetJSPandas.py)
|
||||
|
||||
```bash
|
||||
curl -LO https://sheetjs.com/pres.numbers
|
||||
curl -LO https://docs.sheetjs.com/pandas/sheetjs.py
|
||||
curl -LO https://docs.sheetjs.com/pandas/SheetJSPandas.py
|
||||
```
|
||||
|
||||
4) Run the script:
|
||||
|
||||
```bash
|
||||
python3 SheetJSPandas.py pres.numbers
|
||||
```
|
||||
|
||||
If successful, it will display data rows in the file:
|
||||
|
||||
```
|
||||
Reading from sheet Sheet1
|
||||
{'Name': 'Bill Clinton', 'Index': 42}
|
||||
{'Name': 'GeorgeW Bush', 'Index': 43}
|
||||
{'Name': 'Barack Obama', 'Index': 44}
|
||||
{'Name': 'Donald Trump', 'Index': 45}
|
||||
{'Name': 'Joseph Biden', 'Index': 46}
|
||||
```
|
||||
|
||||
If Pandas is installed, the script will display DataFrame metadata:
|
||||
|
||||
```
|
||||
RangeIndex: 5 entries, 0 to 4
|
||||
Data columns (total 2 columns):
|
||||
# Column Non-Null Count Dtype
|
||||
--- ------ -------------- -----
|
||||
0 Name 5 non-null object
|
||||
1 Index 5 non-null int64
|
||||
dtypes: int64(1), object(1)
|
||||
```
|
||||
|
||||
It will also export to `pres.xls`. The file can be read in a spreadsheet editor.
|
||||
|
||||
[^1]: The official documentation site is <https://pandas.pydata.org/> and the official distribution point is <https://pypi.org/project/pandas/>
|
||||
[^2]: See ["Other Languages"](/docs/demos/engines/) for more examples.
|
||||
[^3]: [`STPyV8`](https://github.com/cloudflare/stpyv8) is a fork of the original [`PyV8` project](https://pypi.org/project/PyV8/). It is available under the permissive Apache 2.0 License. Special thanks to Flier Lu and CloudFlare!
|
||||
[^4]: See [`tests/test_Wrapper.py`](https://github.com/cloudflare/stpyv8/blob/410b31abe7a103b408d362cb872ce81604281c48/tests/test_Wrapper.py#L15) in the `STPyV8` code repository.
|
||||
[^5]: See [`read` in "Reading Files"](/docs/api/parse-options)
|
||||
[^6]: See ["SheetJS Data Model"](/docs/csf/)
|
||||
[^7]: See ["Workbook Object"](/docs/csf/book)
|
||||
[^8]: See [`sheet_to_json` in "Utilities"](/docs/api/utilities/array#array-output)
|
||||
[^9]: See [`pandas.DataFrame.from_records`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.from_records.html) in the Pandas documentation.
|
||||
[^10]: See [`pandas.DataFrame.to_json`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_json.html) in the Pandas documentation.
|
||||
[^11]: See [`json_to_sheet` in "Utilities"](/docs/api/utilities/array#array-of-objects-input)
|
||||
[^12]: See ["Clean up Workbook"](/docs/getting-started/examples/export#clean-up-workbook) in "Export Tutorial".
|
||||
[^13]: See [`book_new` in "Utilities"](/docs/api/utilities/wb)
|
||||
[^14]: See [`book_append_sheet` in "Utilities"](/docs/api/utilities/wb)
|
||||
[^15]: See [`write` in "Writing Files"](/docs/api/write-options)
|
||||
[^16]: See ["Installing"](https://github.com/cloudflare/stpyv8#installing) in the `STPyV8` project documentation
|
||||
|
|
@ -146,7 +146,7 @@ const config = {
|
|||
prism: {
|
||||
theme: lightCodeTheme,
|
||||
darkTheme: darkCodeTheme,
|
||||
additionalLanguages: [ "visual-basic", "swift", "java", "csharp", "perl", "ruby", "cpp", "applescript", "liquid", "rust", "dart", "wolfram", "matlab", "stata" ],
|
||||
additionalLanguages: [ "visual-basic", "swift", "java", "php", "csharp", "perl", "ruby", "cpp", "applescript", "liquid", "rust", "dart", "wolfram", "matlab", "stata" ],
|
||||
},
|
||||
liveCodeBlock: {
|
||||
playgroundPosition: 'top'
|
||||
|
|
@ -244,6 +244,7 @@ const config = {
|
|||
/* math */
|
||||
{ from: '/docs/demos/ml', to: '/docs/demos/math/' },
|
||||
{ from: '/docs/demos/bigdata/ml', to: '/docs/demos/math/' },
|
||||
{ from: '/docs/demos/engines/pandas', to: '/docs/demos/math/pandas/' },
|
||||
/* installation */
|
||||
{ from: '/docs/installation/standalone', to: '/docs/getting-started/installation/standalone/' },
|
||||
{ from: '/docs/installation/frameworks', to: '/docs/getting-started/installation/frameworks/' },
|
||||
|
|
|
|||
|
|
@ -1,5 +1,7 @@
|
|||
<?php
|
||||
|
||||
$sofile = './libduktape.207.20700.so';
|
||||
|
||||
$ffi = FFI::cdef('
|
||||
typedef int duk_int_t;
|
||||
typedef unsigned int duk_uint_t;
|
||||
|
|
@ -24,7 +26,7 @@ void duk_destroy_heap(duk_context *ctx);
|
|||
duk_int_t duk_eval_raw(duk_context *ctx, const char *src_buffer, duk_size_t src_length, duk_uint_t flags);
|
||||
duk_context *duk_create_heap(void* alloc_func, void* realloc_func, void* free_func, void *heap_udata, void *fatal_handler);
|
||||
const char *duk_get_string(duk_context *ctx, duk_idx_t idx);
|
||||
', './libduktape.207.20700.so' );
|
||||
', $sofile );
|
||||
|
||||
function duk_create_heap_default() { global $ffi; return $ffi->duk_create_heap(null, null, null, null, null); }
|
||||
function duk_eval_string_noresult($context, $cmd) { global $ffi; return $ffi->duk_eval_raw($context, $cmd, 0, 1 | (1<<3) | (1<<9) | (1<<10) | (1<<8) | (1<<11) ); }
|
||||
|
|
@ -63,7 +65,6 @@ function save_file($context, $path, $var) {
|
|||
fclose($fh);
|
||||
}
|
||||
|
||||
|
||||
function DOIT($cmd) { global $context; return duk_eval_string_noresult($context, $cmd); }
|
||||
|
||||
/* initialize */
|
||||
|
|
|
|||
|
|
@ -0,0 +1,33 @@
|
|||
# usage: perl SheetJSDuk.pl path/to/file
|
||||
use JavaScript::Duktape::XS;
|
||||
use File::Slurp;
|
||||
use MIME::Base64 qw( encode_base64 decode_base64 );
|
||||
|
||||
# Initialize
|
||||
my $js = JavaScript::Duktape::XS->new({ max_memory_bytes => 256 * 1024 * 1024 });
|
||||
$js->eval("var global = (function(){ return this; }).call(null);");
|
||||
|
||||
# Load the ExtendScript build
|
||||
my $src = read_file('xlsx.extendscript.js', { binmode => ':raw' });
|
||||
$src =~ s/^\xEF\xBB\xBF//;
|
||||
my $XLSX = $js->eval($src);
|
||||
|
||||
# Print version number
|
||||
$js->set('log' => sub { print $_[0], "\n"; });
|
||||
$js->eval("log('SheetJS library version ' + XLSX.version);");
|
||||
|
||||
# Parse File
|
||||
my $raw_data = encode_base64(read_file($ARGV[0], { binmode => ':raw' }), "");
|
||||
$js->set("b64", $raw_data);
|
||||
$js->eval(qq{
|
||||
global.wb = XLSX.read(b64, {type: "base64", WTF:1});
|
||||
global.ws = wb.Sheets[wb.SheetNames[0]];
|
||||
void 0;
|
||||
});
|
||||
|
||||
# Print first worksheet CSV
|
||||
$js->eval('log(XLSX.utils.sheet_to_csv(global.ws))');
|
||||
|
||||
# Write XLSB file
|
||||
my $xlsb = $js->eval("XLSX.write(global.wb, {type:'base64', bookType:'xlsb'})");
|
||||
write_file("SheetJSDuk.xlsb", decode_base64($xlsb));
|
||||
|
|
@ -0,0 +1,103 @@
|
|||
#!/usr/bin/env python3
|
||||
|
||||
lib = "libduktape.207.20700.so"
|
||||
|
||||
from ctypes import CDLL, byref, string_at, c_int, c_void_p, c_char_p, c_size_t
|
||||
|
||||
duk = CDLL(lib)
|
||||
|
||||
def str_to_c(s):
|
||||
b = s.encode("utf8")
|
||||
return [c_char_p(b), len(b)]
|
||||
|
||||
def duk_create_heap_default():
|
||||
duk.duk_create_heap.restype = c_void_p
|
||||
return duk.duk_create_heap(None, None, None, None, None)
|
||||
|
||||
def duk_eval_string_noresult(ctx, cmd):
|
||||
[s, l] = str_to_c(cmd)
|
||||
return duk.duk_eval_raw(ctx, s, l, 1 | (1<<3) | (1<<9) | (1<<10) | (1<<8) | (1<<11) )
|
||||
|
||||
def duk_eval_string(ctx, cmd):
|
||||
[s, l] = str_to_c(cmd)
|
||||
return duk.duk_eval_raw(ctx, s, l, 0 | (1<<3) | (1<<9) | (1<<10) | (1<<11) )
|
||||
|
||||
def duk_peval(ctx):
|
||||
return duk.duk_eval_raw(ctx, None, 0, 1 | (1<<3) | (1<<7) | (1<<11) )
|
||||
|
||||
def duk_get_string(ctx, idx):
|
||||
duk.duk_get_string.restype = c_char_p
|
||||
retval = duk.duk_get_string(ctx, idx)
|
||||
return retval.decode("utf8")
|
||||
|
||||
def eval_file(ctx, path):
|
||||
with open(path, "r") as f:
|
||||
code = f.read()
|
||||
[s, l] = str_to_c(code)
|
||||
|
||||
duk.duk_push_lstring(ctx, s, l)
|
||||
retval = duk_peval(ctx)
|
||||
duk.duk_pop(ctx)
|
||||
return retval
|
||||
|
||||
def load_file(ctx, path, var):
|
||||
with open(path, "rb") as f:
|
||||
data = f.read()
|
||||
ptr = c_char_p(data)
|
||||
duk.duk_push_buffer_raw(ctx, 0, 1 | 2)
|
||||
duk.duk_config_buffer(ctx, -1, ptr, len(data))
|
||||
duk.duk_put_global_string(ctx, str_to_c(var)[0])
|
||||
return data
|
||||
|
||||
def save_file(ctx, path, var):
|
||||
duk.duk_get_global_string(ctx, str_to_c(var)[0])
|
||||
sz = c_size_t()
|
||||
duk.duk_get_buffer_data.restype = c_void_p
|
||||
buf = duk.duk_get_buffer_data(ctx, -1, byref(sz))
|
||||
s = string_at(buf, sz.value)
|
||||
with open(path, "wb") as f:
|
||||
f.write(s)
|
||||
|
||||
def process(path):
|
||||
# initialize
|
||||
context = duk_create_heap_default()
|
||||
ctx = c_void_p(context)
|
||||
|
||||
def DOIT(cmd):
|
||||
return duk_eval_string_noresult(ctx, cmd)
|
||||
|
||||
# duktape does not expose a standard "global" by default
|
||||
DOIT("var global = (function(){ return this; }).call(null);")
|
||||
|
||||
# load library
|
||||
eval_file(ctx, "shim.min.js")
|
||||
eval_file(ctx, "xlsx.full.min.js")
|
||||
|
||||
# get version string
|
||||
duk_eval_string(ctx, "XLSX.version")
|
||||
print("SheetJS Library Version %s" % (duk_get_string(ctx, -1)))
|
||||
duk.duk_pop(ctx)
|
||||
|
||||
# read file
|
||||
# NOTE: data is captured here to avoid GC
|
||||
data = load_file(ctx, path, "buf")
|
||||
print("Loaded file %s" % (path))
|
||||
|
||||
# parse workbook
|
||||
DOIT("wb = XLSX.read(buf.slice(0, buf.length));")
|
||||
DOIT("ws = wb.Sheets[wb.SheetNames[0]]")
|
||||
|
||||
# print CSV
|
||||
duk_eval_string(ctx, "XLSX.utils.sheet_to_csv(ws)")
|
||||
print(duk_get_string(ctx, -1))
|
||||
duk.duk_pop(ctx)
|
||||
|
||||
DOIT("newbuf = (XLSX.write(wb, {type:'buffer', bookType:'xlsb'}));")
|
||||
save_file(ctx, "sheetjsw.xlsb", "newbuf")
|
||||
|
||||
duk.duk_destroy_heap(ctx)
|
||||
return 0
|
||||
|
||||
if("__main__" == __name__):
|
||||
from sys import argv
|
||||
process(argv[1])
|
||||
|
|
@ -1,41 +1,25 @@
|
|||
from sheetjs import SheetJS
|
||||
from sys import argv, exit
|
||||
#!/usr/bin/env python3
|
||||
|
||||
test_pandas = True
|
||||
try:
|
||||
import pandas as pd
|
||||
except:
|
||||
test_pandas = False
|
||||
from sheetjs import SheetJSWrapper
|
||||
|
||||
# Parse file and generate row objects
|
||||
with SheetJS() as sheetjs:
|
||||
# Print library version number
|
||||
print(f"SheetJS Version {sheetjs.version()}")
|
||||
def process(path):
|
||||
with SheetJSWrapper() as sheetjs:
|
||||
|
||||
# Read and parse data from file
|
||||
wb = sheetjs.read_file(argv[1])
|
||||
# Parse file
|
||||
wb = sheetjs.read_file(path)
|
||||
print(f"Loaded file {path}")
|
||||
|
||||
# Get first worksheet name
|
||||
wsname = wb.sheet_names()[0]
|
||||
print(f"Reading from sheet {wsname}")
|
||||
# Get first worksheet name
|
||||
names = wb.get_sheet_names()
|
||||
print(f"Reading from sheet {names[0]}")
|
||||
|
||||
# Get data from first sheet
|
||||
ws = wb.get_sheet(wsname)
|
||||
rows = ws.get_rows()
|
||||
for row in rows: print(row)
|
||||
# Generate DataFrame from first worksheet
|
||||
df = wb.get_df()
|
||||
print(df.info())
|
||||
|
||||
if not test_pandas:
|
||||
print("Pandas could not be loaded, skipping tests")
|
||||
exit()
|
||||
# Export DataFrame to XLSB
|
||||
sheetjs.write_df(df, "SheetJSPandas.xlsb", sheet_name="DataFrame")
|
||||
|
||||
print("\n## Pandas DataFrame\n")
|
||||
|
||||
# generate dataframe
|
||||
df = pd.DataFrame.from_records(rows)
|
||||
print(df.info())
|
||||
|
||||
outf="pres.xls"
|
||||
print(f"\n## Writing to {outf}\n")
|
||||
# write JSON string to XLS worksheet
|
||||
with SheetJS() as sheetjs:
|
||||
sheetjs.book_from_df(df).to_file(outf)
|
||||
if("__main__" == __name__):
|
||||
from sys import argv
|
||||
process(argv[1])
|
||||
|
|
|
|||
|
|
@ -1,136 +1,154 @@
|
|||
from base64 import b64encode, b64decode
|
||||
from contextlib import contextmanager
|
||||
from STPyV8 import JSContext, JSArray, JSObject
|
||||
from functools import wraps
|
||||
from os.path import splitext
|
||||
lib = "libduktape.207.20700.so"
|
||||
|
||||
def to_py(method):
|
||||
# `convert` from STPyV8 tests/test_Wrapper.py
|
||||
def convert(obj):
|
||||
if isinstance(obj, JSArray):
|
||||
return [convert(v) for v in obj]
|
||||
if isinstance(obj, JSObject):
|
||||
return dict([[str(k), convert(obj.__getattr__(str(k)))] for k in obj.__dir__()])
|
||||
return obj
|
||||
from ctypes import CDLL, byref, string_at, c_int, c_void_p, c_char_p, c_size_t
|
||||
from json import loads
|
||||
from io import StringIO
|
||||
from pandas import read_csv
|
||||
|
||||
@wraps(method)
|
||||
def func(self, *args, **kwargs):
|
||||
res = method(self, *args, **kwargs)
|
||||
return convert(res)
|
||||
return func
|
||||
duk = CDLL(lib)
|
||||
|
||||
class SheetJSWorksheet:
|
||||
ws = None
|
||||
ctxt = None
|
||||
def str_to_c(s):
|
||||
b = s.encode("utf8")
|
||||
return [c_char_p(b), len(b)]
|
||||
|
||||
def __init__(self, ctxt, ws):
|
||||
self.ctxt = ctxt
|
||||
self.ws = ws
|
||||
def duk_create_heap_default():
|
||||
duk.duk_create_heap.restype = c_void_p
|
||||
return duk.duk_create_heap(None, None, None, None, None)
|
||||
|
||||
def js(self): return self.ws
|
||||
def duk_eval_string_noresult(ctx, cmd):
|
||||
[s, l] = str_to_c(cmd)
|
||||
return duk.duk_eval_raw(ctx, s, l, 1 | (1<<3) | (1<<9) | (1<<10) | (1<<8) | (1<<11) )
|
||||
|
||||
@to_py
|
||||
def get_rows(self):
|
||||
return self.ctxt.eval("(ws => XLSX.utils.sheet_to_json(ws))")(self.ws)
|
||||
def duk_eval_string(ctx, cmd):
|
||||
[s, l] = str_to_c(cmd)
|
||||
return duk.duk_eval_raw(ctx, s, l, 0 | (1<<3) | (1<<9) | (1<<10) | (1<<11) )
|
||||
|
||||
class SheetJSWorkbook:
|
||||
wb = None
|
||||
ctxt = None
|
||||
def duk_peval(ctx):
|
||||
return duk.duk_eval_raw(ctx, None, 0, 1 | (1<<3) | (1<<7) | (1<<11) )
|
||||
|
||||
def __init__(self, ctxt, wb):
|
||||
self.ctxt = ctxt
|
||||
def duk_get_string(ctx, idx):
|
||||
duk.duk_get_string.restype = c_char_p
|
||||
retval = duk.duk_get_string(ctx, idx)
|
||||
return retval.decode("utf8")
|
||||
|
||||
def eval_file(ctx, path):
|
||||
with open(path, "r") as f:
|
||||
code = f.read()
|
||||
[s, l] = str_to_c(code)
|
||||
|
||||
duk.duk_push_lstring(ctx, s, l)
|
||||
retval = duk_peval(ctx)
|
||||
duk.duk_pop(ctx)
|
||||
return retval
|
||||
|
||||
def load_file(ctx, path, var):
|
||||
with open(path, "rb") as f:
|
||||
data = f.read()
|
||||
ptr = c_char_p(data)
|
||||
duk.duk_push_buffer_raw(ctx, 0, 1 | 2)
|
||||
duk.duk_config_buffer(ctx, -1, ptr, len(data))
|
||||
duk.duk_put_global_string(ctx, str_to_c(var)[0])
|
||||
return data
|
||||
|
||||
def save_file(ctx, path, var):
|
||||
duk.duk_get_global_string(ctx, str_to_c(var)[0])
|
||||
sz = c_size_t()
|
||||
duk.duk_get_buffer_data.restype = c_void_p
|
||||
buf = duk.duk_get_buffer_data(ctx, -1, byref(sz))
|
||||
s = string_at(buf, sz.value)
|
||||
with open(path, "wb") as f:
|
||||
f.write(s)
|
||||
|
||||
def initialize():
|
||||
# initialize
|
||||
context = duk_create_heap_default()
|
||||
ctx = c_void_p(context)
|
||||
|
||||
# duktape does not expose a standard "global" by default
|
||||
duk_eval_string_noresult(ctx, "var global = (function(){ return this; }).call(null);")
|
||||
|
||||
# load library
|
||||
eval_file(ctx, "shim.min.js")
|
||||
eval_file(ctx, "xlsx.full.min.js")
|
||||
|
||||
# get version string
|
||||
duk_eval_string(ctx, "XLSX.version")
|
||||
print(f"SheetJS Library Version {duk_get_string(ctx, -1)}")
|
||||
duk.duk_pop(ctx)
|
||||
return [context, ctx]
|
||||
|
||||
def parse_file(ctx, path, name):
|
||||
# read file
|
||||
# NOTE: data is captured here to avoid GC
|
||||
data = load_file(ctx, path, "buf")
|
||||
|
||||
# parse workbook
|
||||
duk_eval_string_noresult(ctx, f"{name} = XLSX.read(buf.slice(0, buf.length));")
|
||||
|
||||
def get_sheet_names(ctx, wb):
|
||||
duk_eval_string(ctx, f"JSON.stringify({wb}.SheetNames)")
|
||||
wsnames = duk_get_string(ctx, -1)
|
||||
names = loads(wsnames)
|
||||
duk.duk_pop(ctx)
|
||||
return names
|
||||
|
||||
def get_csv_from_wb(ctx, wb, sheet_name=None):
|
||||
if not sheet_name: sheet_name = f"{wb}.SheetNames[0]"
|
||||
else: sheet_name = f"'{sheet_name}'"
|
||||
duk_eval_string(ctx, f"XLSX.utils.sheet_to_csv({wb}.Sheets[{sheet_name}])")
|
||||
csv = duk_get_string(ctx, -1)
|
||||
duk.duk_pop(ctx)
|
||||
return csv
|
||||
|
||||
def export_df_to_wb(ctx, df, path, sheet_name="Sheet1", book_type=None):
|
||||
json = df.to_json(orient="records")
|
||||
[s, l] = str_to_c(json)
|
||||
duk.duk_push_lstring(ctx, s, l)
|
||||
duk.duk_put_global_string(ctx, str_to_c("json")[0])
|
||||
if not book_type: book_type = path.split(".")[-1]
|
||||
duk_eval_string_noresult(ctx, f"""
|
||||
aoo = JSON.parse(json);
|
||||
newws = XLSX.utils.json_to_sheet(aoo);
|
||||
newwb = XLSX.utils.book_new(newws, '{sheet_name}');
|
||||
newbuf = XLSX.write(newwb, {{type:'buffer', bookType:'{book_type}'}});
|
||||
""")
|
||||
save_file(ctx, path, "newbuf")
|
||||
|
||||
def get_df_from_wb(ctx, wb, sheet_name=None):
|
||||
csv = get_csv_from_wb(ctx, wb, sheet_name)
|
||||
return read_csv(StringIO(csv))
|
||||
|
||||
class SheetJSWorkbook(object):
|
||||
def __init__(self, sheetjs, wb):
|
||||
self.ctx = sheetjs.ctx
|
||||
self.wb = wb
|
||||
|
||||
def js(self): return self.wb
|
||||
def get_sheet_names(self):
|
||||
return get_sheet_names(self.ctx, self.wb)
|
||||
|
||||
@to_py
|
||||
def sheet_names(self):
|
||||
return self.wb.SheetNames
|
||||
|
||||
def get_sheet(self, name):
|
||||
return SheetJSWorksheet(self.ctxt, self.wb.Sheets[name])
|
||||
|
||||
def to_file(self, path, book_type=""):
|
||||
b64ify = self.ctxt.eval("((wb, bT) => XLSX.write(wb, {type:'base64', bookType:bT}))")
|
||||
if not book_type: book_type = splitext(path)[1][1:]
|
||||
b64 = b64ify(self.wb, book_type)
|
||||
raw = b64decode(b64)
|
||||
with open(path, mode="wb") as f:
|
||||
f.write(raw)
|
||||
|
||||
class SheetJSWrapper:
|
||||
ctxt = None
|
||||
def get_df(self, sheet_name=None):
|
||||
if sheet_name is None: sheet_name = self.get_sheet_names()[0]
|
||||
return get_df_from_wb(self.ctx, self.wb, sheet_name)
|
||||
|
||||
class SheetJS(object):
|
||||
def __init__(self, ctx):
|
||||
self.ctxt = ctx
|
||||
with open("xlsx.full.min.js") as f: self.ctxt.eval(f.read())
|
||||
|
||||
def version(self):
|
||||
return self.ctxt.eval("XLSX.version")
|
||||
|
||||
def read_binary(self, data):
|
||||
read = self.ctxt.eval("(b64 => XLSX.read(b64, {type: 'base64', dense: true}))")
|
||||
return SheetJSWorkbook(self.ctxt, read(b64encode(data)))
|
||||
self.ctx = ctx
|
||||
self.wb_names = []
|
||||
|
||||
def read_file(self, path):
|
||||
with open(path, mode="rb") as f:
|
||||
return self.read_binary(f.read())
|
||||
self.wb_names.append(f"wb{len(self.wb_names)}")
|
||||
parse_file(self.ctx, path, self.wb_names[-1])
|
||||
return SheetJSWorkbook(self, self.wb_names[-1])
|
||||
|
||||
def sheet_from_json(self, json):
|
||||
jsonify = self.ctxt.eval("(json => XLSX.utils.json_to_sheet(JSON.parse(json)) )")
|
||||
return SheetJSWorksheet(self.ctxt, jsonify(json))
|
||||
def write_df(self, df, path, sheet_name = None):
|
||||
export_df_to_wb(self.ctx, df, path, sheet_name)
|
||||
|
||||
def book_new(self):
|
||||
booknew = self.ctxt.eval("XLSX.utils.book_new()")
|
||||
return SheetJSWorkbook(self.ctxt, booknew)
|
||||
class SheetJSWrapper(object):
|
||||
def __enter__(self):
|
||||
[context, ctx] = initialize()
|
||||
self.context = context
|
||||
self.ctx = ctx
|
||||
return SheetJS(ctx)
|
||||
|
||||
def book_append_sheet(self, book, sheet, wsname):
|
||||
bas = self.ctxt.eval("((wb, ws, wsname) => XLSX.utils.book_append_sheet(wb, ws, wsname))")
|
||||
bas(book.js(), sheet.js(), wsname)
|
||||
|
||||
def book_from_json(self, json, wsname = "Sheet1"):
|
||||
booknew = self.book_new()
|
||||
sheet = self.sheet_from_json(json)
|
||||
self.book_append_sheet(booknew, sheet, wsname)
|
||||
return booknew
|
||||
|
||||
def book_from_df(self, df):
|
||||
# convert from dataframe to JSON string
|
||||
json = df.to_json(orient="records")
|
||||
return self.book_from_json(json)
|
||||
|
||||
@contextmanager
|
||||
def SheetJS():
|
||||
"""
|
||||
SheetJS Library context manager
|
||||
|
||||
Returns an instance of the SheetJSWrapper class
|
||||
|
||||
Reading data from file to Pandas DataFrame:
|
||||
|
||||
```py
|
||||
with SheetJS() as sheetjs:
|
||||
# read data from file
|
||||
wb = sheetjs.read_file(argv[1])
|
||||
|
||||
# get first worksheet
|
||||
first_ws_name = wb.sheet_names()[0]
|
||||
ws = wb.get_sheet(wsname)
|
||||
|
||||
# get data from first worksheet (list of dicts)
|
||||
rows = ws.get_rows()
|
||||
|
||||
# generate pandas DataFrame
|
||||
df = pd.DataFrame.from_records(rows)
|
||||
```
|
||||
|
||||
Writing data from Pandas DataFrame to file:
|
||||
|
||||
```py
|
||||
with SheetJS() as sheetjs:
|
||||
sheetjs.book_from_df(df).to_file(outf)
|
||||
```
|
||||
|
||||
"""
|
||||
with JSContext() as ctxt:
|
||||
yield SheetJSWrapper(ctxt)
|
||||
def __exit__(self, exc_type, exc_value, traceback):
|
||||
duk.duk_destroy_heap(self.ctx)
|
||||
Loading…
Reference in New Issue