Problem Explanation

The problem is to find the result of a series of operations on a given DataFrame. The goal is to join the original DataFrame with its shifted version, apply conditional logic based on the overlap between the two DataFrames, and finally select specific columns.

Solution Explanation

There are five different approaches presented in the solution, each with its strengths and weaknesses.

Approach 1: Joining with Left Outer Merge

This approach involves joining the original DataFrame with a new DataFrame that contains the same columns but with the date shifted by three months. The join is performed using an outer merge, which includes all rows from both DataFrames.

result = (
    df.with_columns(pl.col("reported_period").dt.offset_by("-3mo").alias("lookup_date"))
    .join(
        df.select(["id", pl.col("reported_period").alias("lookup_date"), "variable"]),
        on=["id", "lookup_date"],
        how="left",
    )
    .rename({"variable_right": "result"})
    .select(["id", "reported_period", "variable", "result"])
)

Approach 2: Joining with Inner Merge and Renaming

This approach is similar to the first one but involves renaming the column before joining.

df = (
    df.with_columns(
        pl.col("reported_period").dt.offset_by("-3mo").alias("reported_period_1Q")
    )
    .join(
        df.select([
            pl.col("id"),
            pl.col("reported_period_1Q"),
            pl.col("variable").alias("result")
        ]),
        on=["id", "reported_period_1Q"],
        how="left"
    )
    .select(["id", "reported_period", "variable", "result"])
)

Approach 3: Using Map Elements with no Join

This approach involves mapping the variable column for each row, shifting by one row over in the original DataFrame to get the result.

result = (
    df.with_columns(
        pl.col("reported_period").dt.offset_by("-3mo").alias("lookup_date")
    )
    .with_columns(
        pl.struct(["id", "lookup_date"]).map_elements(
            lambda x: df.filter((pl.col("id") == x["id"]) & (pl.col("reported_period") == x["lookup_date"])).select("variable").get_column("variable").first(),
            return_dtype=pl.Int64
        ).alias("result")
    )
    .select(["id", "reported_period", "variable", "result"])
)

Approach 4: Using When Clause with no Join

This approach involves shifting the variable column over in the original DataFrame using a when-then-otherwise clause.

result = (
    df.with_columns(
        pl.col("reported_period").dt.offset_by("-3mo").alias("lookup_date")
    )
    .with_columns(
        pl.when(pl.col("reported_period").shift(1).over("id") == pl.col("lookup_date"))
          .then(pl.col("variable").shift(1).over("id"))
          .otherwise(None)
          .alias("result")
    )
    .select(["id", "reported_period", "variable", "result"])
)

Approach 5: Joining with Inner Merge and Selecting Specific Columns

This approach involves joining the original DataFrame with a new DataFrame that contains the same columns but with the date shifted by three months. The join is performed using an inner merge, which only includes rows where both DataFrames have matching values.

df_shifted = df.select([
    pl.col("id"),
    pl.col("reported_period").alias("reported_period_1Q"),
    pl.col("variable").alias("result")
])

df_result = df.join(
    df_shifted,
    on=["id", "reported_period_1Q"],
    how="left"
).select(["id", "reported_period", "variable", "result"])

Comparison of Approaches

Each approach has its strengths and weaknesses. The best approach depends on the specific requirements of the problem, such as performance, readability, and maintainability.

• Approach 1 is simple to understand but may have performance issues due to the use of an outer merge.
• Approach 2 is similar to Approach 1 but involves renaming columns before joining, which can be confusing for some readers.
• Approach 3 uses map_elements with no join, which can be faster than approaches that involve joins. However, it may not work well for large datasets due to performance issues.
• Approach 4 uses a when-then-otherwise clause with no join, which can be efficient but may not be as easy to read and maintain as other approaches.
• Approach 5 involves joining with an inner merge and selecting specific columns, which is clear and straightforward. However, it may have performance issues due to the use of an inner merge.

Conclusion

The solution provided multiple ways to solve a complex problem by joining a DataFrame with its shifted version and applying conditional logic based on overlap between DataFrames. The best approach depends on specific requirements such as performance, readability, and maintainability.

Last modified on 2024-04-01