How to set/get pandas.DataFrame to/from Redis?

Question

After setting a DataFrame to redis, then getting it back, redis returns a string and I can\'t figure out a way to convert this str to a DataFrame.

How can I do these

Answer 1

I couldn't use msgpack because of Decimal objects in my dataframe. Instead I combined pickle and zlib together like this, assuming a dataframe df and a local instance of Redis:

import pickle
import redis
import zlib

EXPIRATION_SECONDS = 600

r = redis.StrictRedis(host='localhost', port=6379, db=0)

# Set
r.setex("key", EXPIRATION_SECONDS, zlib.compress( pickle.dumps(df)))

# Get
rehydrated_df = pickle.loads(zlib.decompress(r.get("key")))

There isn't anything dataframe specific about this.

Caveats

• the other answer using msgpack is better -- use it if it works for you
• pickling can be dangerous -- your Redis server needs to be secure or you're asking for trouble

Answer 2

set:

redisConn.set("key", df.to_msgpack(compress='zlib'))

get:

pd.read_msgpack(redisConn.get("key"))

Answer 3

import pandas as pd
df = pd.DataFrame([1,2])
redis.setex('df',100,df.to_json())
df = redis.get('df')
df = pd.read_json(df)

Answer 4

For caching a dataframe use this.

import pyarrow as pa

def cache_df(alias,df):

    pool = redis.ConnectionPool(host='host', port='port', db='db')
    cur = redis.Redis(connection_pool=pool)
    context = pa.default_serialization_context()
    df_compressed =  context.serialize(df).to_buffer().to_pybytes()

    res = cur.set(alias,df_compressed)
    if res == True:
        print('df cached')

For fetching the cached dataframe use this.

def get_cached_df(alias):

    pool = redis.ConnectionPool(host='host',port='port', db='db') 
    cur = redis.Redis(connection_pool=pool)
    context = pa.default_serialization_context()
    all_keys = [key.decode("utf-8") for key in cur.keys()]

    if alias in all_keys:   
        result = cur.get(alias)

        dataframe = pd.DataFrame.from_dict(context.deserialize(result))

        return dataframe

    return None