| Name | voiladata JSON |
| Version |
1.0.1
JSON |
| download |
| home_page | None |
| Summary | A versatile Python library to read various file formats into a pandas DataFrame, with robust handling of nested data. |
| upload_time | 2025-07-25 14:46:36 |
| maintainer | None |
| docs_url | None |
| author | Debrup Mukherjee |
| requires_python | >=3.8 |
| license | MIT |
| keywords |
pandas
dataframe
etl
json
yaml
toml
parquet
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# VoilaData
A versatile Python library to read various file formats into a pandas DataFrame, with robust handling of deeply nested data structures.
This package provides a single, convenient class `DataFrameReader` that automatically detects the file type from its extension and uses the best method to load it into a pandas DataFrame. For nested formats like JSON and YAML, it automatically flattens the data into a wide, easy-to-use format.
## Key Features
- **Simple Interface**: A single `read()` method for all supported file types.
- **Wide Format Support**: Handles a large variety of common data file formats.
- **Intelligent Flattening**: Converts deeply nested JSON and YAML into a flat, wide DataFrame.
- **Extensible**: Easily add support for more file types.
## Supported Formats
- `.csv`, `.tsv`
- `.xls`, `.xlsx`
- `.json`, `.ndjson`
- `.yaml`, `.yml`
- `.toml`
- `.parquet`
- `.orc`
- `.feather`
- `.avro`
- `.html`
- `.dta` (Stata)
- `.sav` (SPSS)
## Usage
Using the `DataFrameReader` is straightforward.
```python
from voiladata import DataFrameReader
# 1. Initialize the reader with a file path
reader = DataFrameReader('path/to/your/data.csv')
# 2. Read the file into a pandas DataFrame
df = reader.read()
print(df.head())
```
### Reading Nested JSON
The real power comes when dealing with nested data. Consider this JSON file (`data.json`):
```json
[
{
"id": "user1",
"profile": {
"name": "Alice",
"age": 30
},
"logins": [
{"timestamp": "2024-01-10T10:00:00Z", "ip": "192.168.1.1"},
{"timestamp": "2024-01-11T12:30:00Z", "ip": "192.168.1.2"}
]
}
]
```
`DataFrameReader` will automatically flatten it:
```python
from voiladata import DataFrameReader
# Read the nested JSON
reader = DataFrameReader('data.json')
df = reader.read()
# The resulting DataFrame is wide and flat
print(df)
```
**Output:**
| id | profile_name | profile_age | logins_0_timestamp | logins_0_ip | logins_1_timestamp | logins_1_ip |
|:------|:-------------|:------------|:------------------------|:------------|:------------------------|:------------|
| user1 | Alice | 30 | 2024-01-10T10:00:00Z | 192.168.1.1 | 2024-01-11T12:30:00Z | 192.168.1.2 |
## License
This project is licensed under the MIT [License](LICENSE).````
Raw data
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"description": "# VoilaData\r\nA versatile Python library to read various file formats into a pandas DataFrame, with robust handling of deeply nested data structures.\r\n\r\nThis package provides a single, convenient class `DataFrameReader` that automatically detects the file type from its extension and uses the best method to load it into a pandas DataFrame. For nested formats like JSON and YAML, it automatically flattens the data into a wide, easy-to-use format.\r\n\r\n## Key Features\r\n\r\n- **Simple Interface**: A single `read()` method for all supported file types.\r\n- **Wide Format Support**: Handles a large variety of common data file formats.\r\n- **Intelligent Flattening**: Converts deeply nested JSON and YAML into a flat, wide DataFrame.\r\n- **Extensible**: Easily add support for more file types.\r\n\r\n## Supported Formats\r\n\r\n- `.csv`, `.tsv`\r\n- `.xls`, `.xlsx`\r\n- `.json`, `.ndjson`\r\n- `.yaml`, `.yml`\r\n- `.toml`\r\n- `.parquet`\r\n- `.orc`\r\n- `.feather`\r\n- `.avro`\r\n- `.html`\r\n- `.dta` (Stata)\r\n- `.sav` (SPSS)\r\n\r\n\r\n## Usage\r\n\r\nUsing the `DataFrameReader` is straightforward.\r\n\r\n```python\r\nfrom voiladata import DataFrameReader\r\n\r\n# 1. Initialize the reader with a file path\r\nreader = DataFrameReader('path/to/your/data.csv')\r\n\r\n# 2. Read the file into a pandas DataFrame\r\ndf = reader.read()\r\n\r\nprint(df.head())\r\n```\r\n\r\n### Reading Nested JSON\r\n\r\nThe real power comes when dealing with nested data. Consider this JSON file (`data.json`):\r\n\r\n```json\r\n[\r\n {\r\n \"id\": \"user1\",\r\n \"profile\": {\r\n \"name\": \"Alice\",\r\n \"age\": 30\r\n },\r\n \"logins\": [\r\n {\"timestamp\": \"2024-01-10T10:00:00Z\", \"ip\": \"192.168.1.1\"},\r\n {\"timestamp\": \"2024-01-11T12:30:00Z\", \"ip\": \"192.168.1.2\"}\r\n ]\r\n }\r\n]\r\n```\r\n\r\n`DataFrameReader` will automatically flatten it:\r\n\r\n```python\r\nfrom voiladata import DataFrameReader\r\n\r\n# Read the nested JSON\r\nreader = DataFrameReader('data.json')\r\ndf = reader.read()\r\n\r\n# The resulting DataFrame is wide and flat\r\nprint(df)\r\n```\r\n\r\n**Output:**\r\n\r\n| id | profile_name | profile_age | logins_0_timestamp | logins_0_ip | logins_1_timestamp | logins_1_ip |\r\n|:------|:-------------|:------------|:------------------------|:------------|:------------------------|:------------|\r\n| user1 | Alice | 30 | 2024-01-10T10:00:00Z | 192.168.1.1 | 2024-01-11T12:30:00Z | 192.168.1.2 |\r\n\r\n\r\n## License\r\n\r\nThis project is licensed under the MIT [License](LICENSE).````\r\n",
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