df-diskcache 0.1.0

df-diskcache is a Python library for caching pandas.DataFrame objects to local disk.

df-diskcache

• Summary

• Installation

• Features

• Usage

  • Basic Usage

  • Cache existence check

  • Set TTL for cache entries

  • Delete cache entries

  • Cache lifetime management

• Dependencies

Summary

df-diskcache is a Python library for caching pandas.DataFrame objects to local disk.

Installation

pip install df-diskcache

Features

Supports the following methods:

• get: Get a cache entry (pandas.DataFrame) for the key. Returns None if the key is not found.

• set: Create a cache entry with an optional time-to-live (TTL) for the key-value pair.

• update

• touch: Update the last accessed time of a cache entry to extend the TTL.

• delete

• prune: Delete expired cache entries.

• Dictionary-like operations:

  • __getitem__

  • __setitem__

  • __contains__

  • __delitem__

Usage

Basic Usage

import pandas as pd
from dfdiskcache import DataFrameDiskCache

cache = DataFrameDiskCache()
url = "https://raw.githubusercontent.com/pandas-dev/pandas/v2.1.3/pandas/tests/io/data/csv/iris.csv"

df = cache.get(url)
if df is None:
    print("cache miss")
    df = pd.read_csv(url)
    cache.set(url, df)
else:
    print("cache hit")

print(df)

You can also use operations like a dictionary:

import pandas as pd
from dfdiskcache import DataFrameDiskCache

cache = DataFrameDiskCache()
url = "https://raw.githubusercontent.com/pandas-dev/pandas/v2.1.3/pandas/tests/io/data/csv/iris.csv"

df = cache[url]
if df is None:
    print("cache miss")
    df = pd.read_csv(url)
    cache[url] = df
else:
    print("cache hit")

print(df)

Cache existence check

You can check if a cache entry exists by using the in operator.

import pandas as pd
from dfdiskcache import DataFrameDiskCache

cache = DataFrameDiskCache()
url = "https://raw.githubusercontent.com/pandas-dev/pandas/v2.1.3/pandas/tests/io/data/csv/iris.csv"

if url in cache:
    print("cache exists")
    df = cache[url]
else:
    print("cache does not exist")

Set TTL for cache entries

You can set the default TTL for cache entries by setting the DataFrameDiskCache.DEFAULT_TTL or the ttl parameter of the set method.

import pandas as pd
from dfdiskcache import DataFrameDiskCache

DataFrameDiskCache.DEFAULT_TTL = 10  # you can override the default TTL (default: 3600 seconds)

cache = DataFrameDiskCache()
url = "https://raw.githubusercontent.com/pandas-dev/pandas/v2.1.3/pandas/tests/io/data/csv/iris.csv"

df = cache.get(url)
if df is None:
    df = pd.read_csv(url)
    cache.set(url, df, ttl=60)  # you can set a TTL for the key-value pair

print(df)

Delete cache entries

You can delete a cache entry by using the del operator or the delete method.

import pandas as pd
from dfdiskcache import DataFrameDiskCache

cache = DataFrameDiskCache()

key = "example key"
if key not in cache:
    df = pd.DataFrame([["a", 1], ["b", 2]], columns=["col_a", "col_b"])
    cache.set(key, df)

# delete a cache entry by using del operator
del cache[url]

# delete a cache entry by using delete method
cache.delete(url)

Cache lifetime management

Expired cache entries are automatically deleted when you access a cache entry or invoke the prune method.

You can refresh the last accessed time of a cache entry by using the touch method.

Dependencies

• Python 3.9+

• Python package dependencies (automatically installed)