implement a function for calculating the median absolute deviation using the lazy api from polars in python

To implement a function for calculating the Median Absolute Deviation (MAD) using the Lazy API from Polars in Python, you can follow these steps:

1. Install the Polars library if you haven't already:

main.py
pip install polars
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2. Import the necessary modules:

main.py
import polars as pl
from polars.lazy import *
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3. Create a LazyFrame object from your data:

main.py
df = pl.scan_csv('your_data.csv', infer_schema=True)
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4. Define the function for calculating MAD:

main.py
def mad(column: pl.Series) -> pl.Series:
    median = col.median()
    diff = (col - median).abs()
    return diff.median()
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5. Use the agg method to apply the MAD function on a column of your LazyFrame:

main.py
mad_value = df.select(mad(col("your_column_name"))).collect().first()
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The mad_value will contain the calculated Median Absolute Deviation.

Make sure to replace your_data.csv with the path to your data file and your_column_name with the name of the column you want to compute MAD for.

Note that in Polars, the Lazy API enables you to define computations on the data lazily, and the actual computation is done when you explicitly call the collect method.

Remember to import the necessary modules and replace your_data.csv and your_column_name with the relevant information for your use case.