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How to use the modin.data_management.functions.ReductionFunction.register function in modin

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github modin-project / modin / modin / backends / pandas / query_compiler.py View on Github external
prod = MapReduceFunction.register(pandas.DataFrame.prod, pandas.DataFrame.prod)
    any = MapReduceFunction.register(pandas.DataFrame.any, pandas.DataFrame.any)
    all = MapReduceFunction.register(pandas.DataFrame.all, pandas.DataFrame.all)
    memory_usage = MapReduceFunction.register(
        pandas.DataFrame.memory_usage,
        lambda x, *args, **kwargs: pandas.DataFrame.sum(x),
        axis=0,
    )

    # END MapReduce operations

    # Reduction operations
    idxmax = ReductionFunction.register(pandas.DataFrame.idxmax)
    idxmin = ReductionFunction.register(pandas.DataFrame.idxmin)
    median = ReductionFunction.register(pandas.DataFrame.median)
    nunique = ReductionFunction.register(pandas.DataFrame.nunique)
    skew = ReductionFunction.register(pandas.DataFrame.skew)
    std = ReductionFunction.register(pandas.DataFrame.std)
    var = ReductionFunction.register(pandas.DataFrame.var)
    sum_min_count = ReductionFunction.register(pandas.DataFrame.sum)
    prod_min_count = ReductionFunction.register(pandas.DataFrame.prod)
    mean = ReductionFunction.register(pandas.DataFrame.mean)
    quantile_for_single_value = ReductionFunction.register(pandas.DataFrame.quantile)

    # END Reduction operations

    # Map partitions operations
    # These operations are operations that apply a function to every partition.
    abs = MapFunction.register(pandas.DataFrame.abs, dtypes="copy")
    applymap = MapFunction.register(pandas.DataFrame.applymap)
    invert = MapFunction.register(pandas.DataFrame.__invert__)
    isin = MapFunction.register(pandas.DataFrame.isin, dtypes=np.bool)
github modin-project / modin / modin / backends / pandas / query_compiler.py View on Github external
all = MapReduceFunction.register(pandas.DataFrame.all, pandas.DataFrame.all)
    memory_usage = MapReduceFunction.register(
        pandas.DataFrame.memory_usage,
        lambda x, *args, **kwargs: pandas.DataFrame.sum(x),
        axis=0,
    )

    # END MapReduce operations

    # Reduction operations
    idxmax = ReductionFunction.register(pandas.DataFrame.idxmax)
    idxmin = ReductionFunction.register(pandas.DataFrame.idxmin)
    median = ReductionFunction.register(pandas.DataFrame.median)
    nunique = ReductionFunction.register(pandas.DataFrame.nunique)
    skew = ReductionFunction.register(pandas.DataFrame.skew)
    std = ReductionFunction.register(pandas.DataFrame.std)
    var = ReductionFunction.register(pandas.DataFrame.var)
    sum_min_count = ReductionFunction.register(pandas.DataFrame.sum)
    prod_min_count = ReductionFunction.register(pandas.DataFrame.prod)
    mean = ReductionFunction.register(pandas.DataFrame.mean)
    quantile_for_single_value = ReductionFunction.register(pandas.DataFrame.quantile)

    # END Reduction operations

    # Map partitions operations
    # These operations are operations that apply a function to every partition.
    abs = MapFunction.register(pandas.DataFrame.abs, dtypes="copy")
    applymap = MapFunction.register(pandas.DataFrame.applymap)
    invert = MapFunction.register(pandas.DataFrame.__invert__)
    isin = MapFunction.register(pandas.DataFrame.isin, dtypes=np.bool)
    isna = MapFunction.register(pandas.DataFrame.isna, dtypes=np.bool)
    negative = MapFunction.register(pandas.DataFrame.__neg__)
github modin-project / modin / modin / backends / pandas / query_compiler.py View on Github external
lambda x, *args, **kwargs: pandas.DataFrame.sum(x),
        axis=0,
    )

    # END MapReduce operations

    # Reduction operations
    idxmax = ReductionFunction.register(pandas.DataFrame.idxmax)
    idxmin = ReductionFunction.register(pandas.DataFrame.idxmin)
    median = ReductionFunction.register(pandas.DataFrame.median)
    nunique = ReductionFunction.register(pandas.DataFrame.nunique)
    skew = ReductionFunction.register(pandas.DataFrame.skew)
    std = ReductionFunction.register(pandas.DataFrame.std)
    var = ReductionFunction.register(pandas.DataFrame.var)
    sum_min_count = ReductionFunction.register(pandas.DataFrame.sum)
    prod_min_count = ReductionFunction.register(pandas.DataFrame.prod)
    mean = ReductionFunction.register(pandas.DataFrame.mean)
    quantile_for_single_value = ReductionFunction.register(pandas.DataFrame.quantile)

    # END Reduction operations

    # Map partitions operations
    # These operations are operations that apply a function to every partition.
    abs = MapFunction.register(pandas.DataFrame.abs, dtypes="copy")
    applymap = MapFunction.register(pandas.DataFrame.applymap)
    invert = MapFunction.register(pandas.DataFrame.__invert__)
    isin = MapFunction.register(pandas.DataFrame.isin, dtypes=np.bool)
    isna = MapFunction.register(pandas.DataFrame.isna, dtypes=np.bool)
    negative = MapFunction.register(pandas.DataFrame.__neg__)
    notna = MapFunction.register(pandas.DataFrame.notna, dtypes=np.bool)
    round = MapFunction.register(pandas.DataFrame.round)
github modin-project / modin / modin / backends / pandas / query_compiler.py View on Github external
sum = MapReduceFunction.register(pandas.DataFrame.sum, pandas.DataFrame.sum)
    prod = MapReduceFunction.register(pandas.DataFrame.prod, pandas.DataFrame.prod)
    any = MapReduceFunction.register(pandas.DataFrame.any, pandas.DataFrame.any)
    all = MapReduceFunction.register(pandas.DataFrame.all, pandas.DataFrame.all)
    memory_usage = MapReduceFunction.register(
        pandas.DataFrame.memory_usage,
        lambda x, *args, **kwargs: pandas.DataFrame.sum(x),
        axis=0,
    )

    # END MapReduce operations

    # Reduction operations
    idxmax = ReductionFunction.register(pandas.DataFrame.idxmax)
    idxmin = ReductionFunction.register(pandas.DataFrame.idxmin)
    median = ReductionFunction.register(pandas.DataFrame.median)
    nunique = ReductionFunction.register(pandas.DataFrame.nunique)
    skew = ReductionFunction.register(pandas.DataFrame.skew)
    std = ReductionFunction.register(pandas.DataFrame.std)
    var = ReductionFunction.register(pandas.DataFrame.var)
    sum_min_count = ReductionFunction.register(pandas.DataFrame.sum)
    prod_min_count = ReductionFunction.register(pandas.DataFrame.prod)
    mean = ReductionFunction.register(pandas.DataFrame.mean)
    quantile_for_single_value = ReductionFunction.register(pandas.DataFrame.quantile)

    # END Reduction operations

    # Map partitions operations
    # These operations are operations that apply a function to every partition.
    abs = MapFunction.register(pandas.DataFrame.abs, dtypes="copy")
    applymap = MapFunction.register(pandas.DataFrame.applymap)
    invert = MapFunction.register(pandas.DataFrame.__invert__)
github modin-project / modin / modin / backends / pandas / query_compiler.py View on Github external
)

    # END MapReduce operations

    # Reduction operations
    idxmax = ReductionFunction.register(pandas.DataFrame.idxmax)
    idxmin = ReductionFunction.register(pandas.DataFrame.idxmin)
    median = ReductionFunction.register(pandas.DataFrame.median)
    nunique = ReductionFunction.register(pandas.DataFrame.nunique)
    skew = ReductionFunction.register(pandas.DataFrame.skew)
    std = ReductionFunction.register(pandas.DataFrame.std)
    var = ReductionFunction.register(pandas.DataFrame.var)
    sum_min_count = ReductionFunction.register(pandas.DataFrame.sum)
    prod_min_count = ReductionFunction.register(pandas.DataFrame.prod)
    mean = ReductionFunction.register(pandas.DataFrame.mean)
    quantile_for_single_value = ReductionFunction.register(pandas.DataFrame.quantile)

    # END Reduction operations

    # Map partitions operations
    # These operations are operations that apply a function to every partition.
    abs = MapFunction.register(pandas.DataFrame.abs, dtypes="copy")
    applymap = MapFunction.register(pandas.DataFrame.applymap)
    invert = MapFunction.register(pandas.DataFrame.__invert__)
    isin = MapFunction.register(pandas.DataFrame.isin, dtypes=np.bool)
    isna = MapFunction.register(pandas.DataFrame.isna, dtypes=np.bool)
    negative = MapFunction.register(pandas.DataFrame.__neg__)
    notna = MapFunction.register(pandas.DataFrame.notna, dtypes=np.bool)
    round = MapFunction.register(pandas.DataFrame.round)

    # END Map partitions operations
github modin-project / modin / modin / backends / pandas / query_compiler.py View on Github external
any = MapReduceFunction.register(pandas.DataFrame.any, pandas.DataFrame.any)
    all = MapReduceFunction.register(pandas.DataFrame.all, pandas.DataFrame.all)
    memory_usage = MapReduceFunction.register(
        pandas.DataFrame.memory_usage,
        lambda x, *args, **kwargs: pandas.DataFrame.sum(x),
        axis=0,
    )

    # END MapReduce operations

    # Reduction operations
    idxmax = ReductionFunction.register(pandas.DataFrame.idxmax)
    idxmin = ReductionFunction.register(pandas.DataFrame.idxmin)
    median = ReductionFunction.register(pandas.DataFrame.median)
    nunique = ReductionFunction.register(pandas.DataFrame.nunique)
    skew = ReductionFunction.register(pandas.DataFrame.skew)
    std = ReductionFunction.register(pandas.DataFrame.std)
    var = ReductionFunction.register(pandas.DataFrame.var)
    sum_min_count = ReductionFunction.register(pandas.DataFrame.sum)
    prod_min_count = ReductionFunction.register(pandas.DataFrame.prod)
    mean = ReductionFunction.register(pandas.DataFrame.mean)
    quantile_for_single_value = ReductionFunction.register(pandas.DataFrame.quantile)

    # END Reduction operations

    # Map partitions operations
    # These operations are operations that apply a function to every partition.
    abs = MapFunction.register(pandas.DataFrame.abs, dtypes="copy")
    applymap = MapFunction.register(pandas.DataFrame.applymap)
    invert = MapFunction.register(pandas.DataFrame.__invert__)
    isin = MapFunction.register(pandas.DataFrame.isin, dtypes=np.bool)
    isna = MapFunction.register(pandas.DataFrame.isna, dtypes=np.bool)
github modin-project / modin / modin / backends / pandas / query_compiler.py View on Github external
memory_usage = MapReduceFunction.register(
        pandas.DataFrame.memory_usage,
        lambda x, *args, **kwargs: pandas.DataFrame.sum(x),
        axis=0,
    )

    # END MapReduce operations

    # Reduction operations
    idxmax = ReductionFunction.register(pandas.DataFrame.idxmax)
    idxmin = ReductionFunction.register(pandas.DataFrame.idxmin)
    median = ReductionFunction.register(pandas.DataFrame.median)
    nunique = ReductionFunction.register(pandas.DataFrame.nunique)
    skew = ReductionFunction.register(pandas.DataFrame.skew)
    std = ReductionFunction.register(pandas.DataFrame.std)
    var = ReductionFunction.register(pandas.DataFrame.var)
    sum_min_count = ReductionFunction.register(pandas.DataFrame.sum)
    prod_min_count = ReductionFunction.register(pandas.DataFrame.prod)
    mean = ReductionFunction.register(pandas.DataFrame.mean)
    quantile_for_single_value = ReductionFunction.register(pandas.DataFrame.quantile)

    # END Reduction operations

    # Map partitions operations
    # These operations are operations that apply a function to every partition.
    abs = MapFunction.register(pandas.DataFrame.abs, dtypes="copy")
    applymap = MapFunction.register(pandas.DataFrame.applymap)
    invert = MapFunction.register(pandas.DataFrame.__invert__)
    isin = MapFunction.register(pandas.DataFrame.isin, dtypes=np.bool)
    isna = MapFunction.register(pandas.DataFrame.isna, dtypes=np.bool)
    negative = MapFunction.register(pandas.DataFrame.__neg__)
    notna = MapFunction.register(pandas.DataFrame.notna, dtypes=np.bool)
github modin-project / modin / modin / backends / pandas / query_compiler.py View on Github external
min = MapReduceFunction.register(pandas.DataFrame.min, pandas.DataFrame.min)
    sum = MapReduceFunction.register(pandas.DataFrame.sum, pandas.DataFrame.sum)
    prod = MapReduceFunction.register(pandas.DataFrame.prod, pandas.DataFrame.prod)
    any = MapReduceFunction.register(pandas.DataFrame.any, pandas.DataFrame.any)
    all = MapReduceFunction.register(pandas.DataFrame.all, pandas.DataFrame.all)
    memory_usage = MapReduceFunction.register(
        pandas.DataFrame.memory_usage,
        lambda x, *args, **kwargs: pandas.DataFrame.sum(x),
        axis=0,
    )

    # END MapReduce operations

    # Reduction operations
    idxmax = ReductionFunction.register(pandas.DataFrame.idxmax)
    idxmin = ReductionFunction.register(pandas.DataFrame.idxmin)
    median = ReductionFunction.register(pandas.DataFrame.median)
    nunique = ReductionFunction.register(pandas.DataFrame.nunique)
    skew = ReductionFunction.register(pandas.DataFrame.skew)
    std = ReductionFunction.register(pandas.DataFrame.std)
    var = ReductionFunction.register(pandas.DataFrame.var)
    sum_min_count = ReductionFunction.register(pandas.DataFrame.sum)
    prod_min_count = ReductionFunction.register(pandas.DataFrame.prod)
    mean = ReductionFunction.register(pandas.DataFrame.mean)
    quantile_for_single_value = ReductionFunction.register(pandas.DataFrame.quantile)

    # END Reduction operations

    # Map partitions operations
    # These operations are operations that apply a function to every partition.
    abs = MapFunction.register(pandas.DataFrame.abs, dtypes="copy")
    applymap = MapFunction.register(pandas.DataFrame.applymap)
github modin-project / modin / modin / backends / pandas / query_compiler.py View on Github external
pandas.DataFrame.memory_usage,
        lambda x, *args, **kwargs: pandas.DataFrame.sum(x),
        axis=0,
    )

    # END MapReduce operations

    # Reduction operations
    idxmax = ReductionFunction.register(pandas.DataFrame.idxmax)
    idxmin = ReductionFunction.register(pandas.DataFrame.idxmin)
    median = ReductionFunction.register(pandas.DataFrame.median)
    nunique = ReductionFunction.register(pandas.DataFrame.nunique)
    skew = ReductionFunction.register(pandas.DataFrame.skew)
    std = ReductionFunction.register(pandas.DataFrame.std)
    var = ReductionFunction.register(pandas.DataFrame.var)
    sum_min_count = ReductionFunction.register(pandas.DataFrame.sum)
    prod_min_count = ReductionFunction.register(pandas.DataFrame.prod)
    mean = ReductionFunction.register(pandas.DataFrame.mean)
    quantile_for_single_value = ReductionFunction.register(pandas.DataFrame.quantile)

    # END Reduction operations

    # Map partitions operations
    # These operations are operations that apply a function to every partition.
    abs = MapFunction.register(pandas.DataFrame.abs, dtypes="copy")
    applymap = MapFunction.register(pandas.DataFrame.applymap)
    invert = MapFunction.register(pandas.DataFrame.__invert__)
    isin = MapFunction.register(pandas.DataFrame.isin, dtypes=np.bool)
    isna = MapFunction.register(pandas.DataFrame.isna, dtypes=np.bool)
    negative = MapFunction.register(pandas.DataFrame.__neg__)
    notna = MapFunction.register(pandas.DataFrame.notna, dtypes=np.bool)
    round = MapFunction.register(pandas.DataFrame.round)

modin

Modin: Make your pandas code run faster by changing one line of code.

GitHub
Apache-2.0
Latest version published 22 days ago

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94 / 100
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