# Working with non-tabular files

### **Load a single file**

```python
import redivis
from io import TextIOWrapper
from PIL import Image

# See https://redivis.com/datasets/yz1s-d09009dbb/files for example data
table = redivis.table("demo.example_data_files:yz1s:v1_3.example_file_types:4c10")
text_file = table.file("pandas_core.py")
image_file = table.file("bogota.tiff"")

## Read file contents
str = text_file.read(as_text=True)
bytes = image_file.read()

## Open the file, as if it was on the filesystem
with file.open("rb") as f:
  f.read(100) # read 100 bytes

with file.open() as f:
  f.readline() # read first line
  
# Tools that integrate with fsspec can open Redivis URIs:
pystac.Catalog.from_file("redivis://table_ref/stac/catalog.json")
  
Image.open(table.file("bogota.tiff")) # PIL will automatically call open() on the file
  
## Download the file  
image_file.download("./path") # will be downloaded as ./path/bogota.tiff
text_file.download("./path/renamed.txt") # will be downloaded as ./path/renamed.txt
```

### Load all files in a table

```python
import redivis

table = redivis.table("Demo.example_data_files.example_file_types")

dir = redivis.table("table_ref").to_directory()
file = dir.get("path/to/file.txt") # Will return None if doesn't exist
dir.download("/download/path") # Download all files to the provided path
dir.mount("/download/path")
# dir.mount() behaves the same as dir.download() was called (all files appear on disk)
# However, this doesn't actually download the files until they're needed, 
#    and as such is much faster, particularly when all files may not need to be read.
```


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.redivis.com/api/client-libraries/redivis-python/examples/working-with-non-tabular-files.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
