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代碼如下:

def memory_usage_mb(df, *args, **kwargs):"""Dataframe memory usage in MB. """return df.memory_usage(*args, **kwargs).sum() / 1024**2def reduce_memory_usage(df, deep=True, verbose=True, categories=True):# All types that we want to change for "lighter" ones.# int8 and float16 are not include because we cannot reduce# those data types.# float32 is not include because float16 has too low precision.numeric2reduce = ["int16", "int32", "int64", "float64"]start_mem = 0if verbose:start_mem = memory_usage_mb(df, deep=deep)for col, col_type in df.dtypes.iteritems():best_type = Noneif col_type == "object":df[col] = df[col].astype("category")best_type = "category"elif col_type in numeric2reduce:downcast = "integer" if "int" in str(col_type) else "float"df[col] = pd.to_numeric(df[col], downcast=downcast)best_type = df[col].dtype.name# Log the conversion performed.if verbose and best_type is not None and best_type != str(col_type):print(f"Column '{col}' converted from {col_type} to {best_type}")if verbose:end_mem = memory_usage_mb(df, deep=deep)diff_mem = start_mem - end_mempercent_mem = 100 * diff_mem / start_memprint(f"Memory usage decreased from"f" {start_mem:.2f}MB to {end_mem:.2f}MB"f" ({diff_mem:.2f}MB, {percent_mem:.2f}% reduction)")return df

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%%time import multiprocessinglists=[train,test] with multiprocessing.Pool() as pool:train,test = pool.map(reduce_memory_usage, lists)#這里的map就是傳入?yún)?shù)的意思

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