How to set/get Pandas dataframes into Redis using pyarrow

How to set/get Pandas dataframes into Redis using pyarrow

You can use the pyarrow library along with the redis-py library to set and get Pandas DataFrames into and from Redis. Here's how you can do it:

  1. Install Required Libraries: Make sure you have both pyarrow and redis libraries installed. You can install them using the following commands:

    pip install pyarrow redis

  2. Set Pandas DataFrame to Redis: To set a Pandas DataFrame into Redis, you can use the pyarrow.serialize_pandas function to serialize the DataFrame to a format suitable for storage, and then use the set method from the redis library to store the serialized data.

    import pandas as pd import pyarrow as pa import redis # Create a sample DataFrame data = {'Column1': [1, 2, 3], 'Column2': [4, 5, 6]} df = pd.DataFrame(data) # Serialize the DataFrame using pyarrow serialized_df = pa.serialize_pandas(df).to_buffer().to_pybytes() # Connect to Redis r = redis.Redis(host='localhost', port=6379, db=0) # Set the serialized DataFrame to Redis r.set('my_dataframe', serialized_df)

  3. Get Pandas DataFrame from Redis: To get the Pandas DataFrame back from Redis, you can use the get method from the redis library to retrieve the serialized data and then deserialize it using pyarrow.deserialize_pandas.

    # Get the serialized DataFrame from Redis retrieved_df = r.get('my_dataframe') # Deserialize the DataFrame using pyarrow deserialized_df = pa.deserialize_pandas(retrieved_df) # Convert it back to a Pandas DataFrame df_restored = deserialized_df.to_pandas() print(df_restored)

Keep in mind that this approach serializes the DataFrame to bytes, which can be memory-intensive for large DataFrames. Depending on your use case, you might want to explore other serialization options or consider using database systems that are designed for handling structured data.

Examples

  1. "Store Pandas dataframe in Redis using pyarrow"

    • Description: Discover how to serialize and store a Pandas dataframe into Redis using the pyarrow library.
    • Code:
      import pandas as pd import redis import pyarrow as pa # Create a Pandas dataframe df = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'c']}) # Connect to Redis r = redis.Redis(host='localhost', port=6379, db=0) # Convert dataframe to Arrow table table = pa.Table.from_pandas(df) # Serialize and store Arrow table in Redis r.set('dataframe_key', pa.serialize(table).to_buffer().to_pybytes())

  2. "Serialize Pandas dataframe to Redis with pyarrow"

    • Description: Learn how to serialize a Pandas dataframe into Redis using the pyarrow library for efficient storage and retrieval.
    • Code:
      import pandas as pd import redis import pyarrow as pa # Create a Pandas dataframe df = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'c']}) # Connect to Redis r = redis.Redis(host='localhost', port=6379, db=0) # Serialize dataframe to Arrow table table = pa.Table.from_pandas(df) # Convert to serialized bytes and store in Redis r.set('dataframe_key', pa.serialize(table).to_buffer().to_pybytes())

  3. "Get Pandas dataframe from Redis using pyarrow"

    • Description: Retrieve a Pandas dataframe stored in Redis using the pyarrow library for deserialization.
    • Code:
      import pandas as pd import redis import pyarrow as pa # Connect to Redis r = redis.Redis(host='localhost', port=6379, db=0) # Retrieve serialized data from Redis serialized_data = r.get('dataframe_key') # Deserialize Arrow table from bytes table = pa.deserialize(serialized_data) # Convert Arrow table to Pandas dataframe df = table.to_pandas()

  4. "Load Pandas dataframe from Redis with pyarrow"

    • Description: Load a Pandas dataframe from Redis by deserializing the data using the pyarrow library.
    • Code:
      import pandas as pd import redis import pyarrow as pa # Connect to Redis r = redis.Redis(host='localhost', port=6379, db=0) # Retrieve serialized data from Redis serialized_data = r.get('dataframe_key') # Deserialize Arrow table from bytes table = pa.deserialize(serialized_data) # Convert Arrow table to Pandas dataframe df = table.to_pandas()

  5. "Store Pandas dataframe in Redis with pyarrow serialization"

    • Description: Store a Pandas dataframe in Redis using efficient pyarrow serialization for optimized storage and retrieval.
    • Code:
      import pandas as pd import redis import pyarrow as pa # Create a Pandas dataframe df = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'c']}) # Connect to Redis r = redis.Redis(host='localhost', port=6379, db=0) # Serialize dataframe to Arrow table table = pa.Table.from_pandas(df) # Convert to serialized bytes and store in Redis r.set('dataframe_key', pa.serialize(table).to_buffer().to_pybytes())

  6. "Serialize Pandas dataframe to Redis using pyarrow"

    • Description: Serialize a Pandas dataframe into Redis utilizing the pyarrow library for efficient storage and retrieval.
    • Code:
      import pandas as pd import redis import pyarrow as pa # Create a Pandas dataframe df = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'c']}) # Connect to Redis r = redis.Redis(host='localhost', port=6379, db=0) # Serialize dataframe to Arrow table table = pa.Table.from_pandas(df) # Convert to serialized bytes and store in Redis r.set('dataframe_key', pa.serialize(table).to_buffer().to_pybytes())

  7. "Deserialize Pandas dataframe from Redis using pyarrow"

    • Description: Deserialize a Pandas dataframe stored in Redis back into its original form using the pyarrow library.
    • Code:
      import pandas as pd import redis import pyarrow as pa # Connect to Redis r = redis.Redis(host='localhost', port=6379, db=0) # Retrieve serialized data from Redis serialized_data = r.get('dataframe_key') # Deserialize Arrow table from bytes table = pa.deserialize(serialized_data) # Convert Arrow table to Pandas dataframe df = table.to_pandas()

  8. "Retrieve Pandas dataframe from Redis with pyarrow"

    • Description: Retrieve a Pandas dataframe stored in Redis by deserializing the data using the pyarrow library.
    • Code:
      import pandas as pd import redis import pyarrow as pa # Connect to Redis r = redis.Redis(host='localhost', port=6379, db=0) # Retrieve serialized data from Redis serialized_data = r.get('dataframe_key') # Deserialize Arrow table from bytes table = pa.deserialize(serialized_data) # Convert Arrow table to Pandas dataframe df = table.to_pandas()

  9. "Load Pandas dataframe from Redis using pyarrow"

    • Description: Load a Pandas dataframe from Redis by deserializing the data with the assistance of the pyarrow library.
    • Code:
      import pandas as pd import redis import pyarrow as pa # Connect to Redis r = redis.Redis(host='localhost', port=6379, db=0) # Retrieve serialized data from Redis serialized_data = r.get('dataframe_key') # Deserialize Arrow table from bytes table = pa.deserialize(serialized_data) # Convert Arrow table to Pandas dataframe df = table.to_pandas()

  10. "Serialize and store Pandas dataframe in Redis using pyarrow"

    • Description: Serialize and store a Pandas dataframe in Redis utilizing the pyarrow library for efficient serialization.
    • Code:
      import pandas as pd import redis import pyarrow as pa # Create a Pandas dataframe df = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'c']}) # Connect to Redis r = redis.Redis(host='localhost', port=6379, db=0) # Serialize dataframe to Arrow table table = pa.Table.from_pandas(df) # Convert to serialized bytes and store in Redis r.set('dataframe_key', pa.serialize(table).to_buffer().to_pybytes())

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