How to Determine Final State of Sequential Entries in a Pandas DataFrame Using Vectorized Operations?

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How to Determine Final State of Sequential Entries in a Pandas DataFrame Using Vectorized Operations?

This technical blog post will guide you through the process of determining the final state of sequential entries in a Pandas DataFrame using vectorized operations. We'll explore various approaches to achieve this efficiently.

Using the Pandas `apply()` Function

One effective way to handle this task is by utilizing the Pandas `apply()` function. This function allows you to apply a custom function to each row of a DataFrame, making it suitable for processing sequential data.

    # Define a function to check the operation type
    def check_op(operacao):
      if operacao == 'inclusao':
        ...

    # Apply the function to the 'operacao' column
    data['codinccp_dadosrubrica'] = data['operacao'].apply(check_op())
  

This approach enables you to define a custom function that determines the final state based on the operation type and apply it to the entire DataFrame.

Vectorized Solution for Complex Scenarios

For more complex scenarios, where the series of operations is crucial and unique IDs are mutable, a custom vectorized solution can be employed.

    # Sort the DataFrame by processing date
    df = df.sort_values(by=['dh_processamento_rubrica']).reset_index(drop=True)

    # Convert the 'operacao' column to a Categorical dtype for memory optimization
    df["operacao"] = pd.Categorical(df["operacao"])

    # Check for deleted IDs
    deleted = df["operacao"].eq("exclusao")
    deleted |= (df["operacao"].eq("alteracao")) & (~df["inivalid_nova_validade"].isna())

    # Calculate the final status for each unique ID
    final_status = deleted.groupby(df["inivalid_iderubrica"]).last()
    excluded = final_status[final_status].index

    # Handle alterations that change unique IDs
    alter_inivalid_mask = (df["operacao"].eq("alteracao")) & (~df["inivalid_nova_validade"].isna())
    df.loc[alter_inivalid_mask, "inivalid_iderubrica"] = df.loc[alter_inivalid_mask, "inivalid_nova_validade"]
    df.loc[alter_inivalid_mask, "inivalid_nova_validade"] = "NaT"
    df.loc[alter_inivalid_mask, "operacao"] = "inclusao"

    # Get the final state for each unique ID
    res = df[df["operacao"].ne("exclusao")].groupby("inivalid_iderubrica", dropna=False).last().reset_index()

    # Remove those with a final status of excluded
    res = res[~res["inivalid_iderubrica"].isin(excluded)]
  

This comprehensive solution addresses complex scenarios involving mutable unique IDs and provides a robust approach for determining the final state of sequential entries.

Conclusion

In this blog post, we explored different techniques for determining the final state of sequential entries in a Pandas DataFrame using vectorized operations.

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