Cache Pandas Dataframes to Disk
Easily cache Pandas Dataframes to disk using a simple interface.
Sample usage
from cache_df import CacheDF
import pandas as pd
cache = CacheDF(cache_dir='./caches')
# Caching a dataframe
df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
cache.cache(df, 'my_df')
# Checking if a dataframe is cached
df_is_cached = cache.is_cached('my_df')
# Reading a dataframe from cache
try:
df = cache.read('my_df')
df_selective_cols = cache.read('my_df', columns=['a']) # Read only a subset of columns
except FileNotFoundError:
print('Dataframe not cached')
# Deleting a dataframe from cache if it exists
cache.uncache('my_df')
# Clearing all cached dataframes
cache.clear()
Where it can be used
- It can be used when you are using a shared file system across multiple machines such as AWS EFS, GCP Filestore, Azure Files, etc.
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Release files for cache-df 1.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| cache_df-1.1.tar.gz | 2.9 kB | Details |
Release files / cache_df-1.1.tar.gz
| Download URL | cache_df-1.1.tar.gz |
|---|---|
| Size | 2.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
b2925bfa5b4258eac306dc51e9bb77a869b6a64d7935ad3717d0754d96bcf4af
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twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.8.10
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