用DataFrame.select_dtypes来只选择特定类型列,然后我们优化这种类型,并比较内存使用量。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 df_int=df.select_dtypes(include=['float'])converted_int=df_int.apply(pd.to_numeric,downcast='float')print(df_int.dtypes.iloc[0],df_int.memory_usage(deep=T...
在使用toPandas()方法时,Spark会将整个DataFrame的数据收集到Driver节点上,如果数据量非常大,很容易导致Driver节点的内存溢出。 优化Spark配置: 增加Driver节点的内存分配。可以通过设置spark.driver.memory参数来增加Driver节点的内存。例如: python spark = SparkSession.builder \ .appName("Memory Optimization Example...
Copy-on-Write is a memory optimization technique utilized by Pandas to enhance performance and minimize memory usage while handling large datasets. It makes Pandas more similar to Spark and how lazy operations are performed in Spark. Lazy operations refer to operations that do not execute immediately...
Generate a DataFrame df using the dictionary. Print memory usage before optimization: Use df.info(memory_usage='deep') to display the memory usage of the DataFrame before optimization. Convert data types using astype method: Convert the 'int_col' to 'int16'. Convert the 'float_col' t...
XlsxWriter的constant_memory模式可以用来编写非常大的Excel文件,并且内存使用率非常低。关键是数据需要按行顺序写入,(正如@Stef在上面的注释中指出的那样)Pandas按列顺序写入Excel。所以constant_memory模式对PandasExcelWriter不起作用。 作为一种替代方法,您可以避免使用ExcelWriter,而是将数据从dataframe逐行直接写入XlsxWrit...
Arrow is available as an optimization when converting a PySpark DataFrame to a pandas DataFrame with toPandas() and when creating a PySpark DataFrame from a pandas DataFrame with createDataFrame(pandas_df).To use Arrow for these methods, set the Spark configuration spark.sql.execution.arrow....
DataFrame(df['memory_usage'].tolist(), index=df.index) # now we no longer need the memory_usage column df = df.drop(columns='memory_usage') # melt the DataFrame to make it suitable for grouped bar chart df_melted = df.melt(id_vars='library', var_name='memory_type', value_name...
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In pandas DataFrames, thedtypeis a critical attribute that specifies the data type for each column. Therefore, selecting the appropriatedtypefor each column in a DataFrame is key. On the one hand, we can downcast numerics into types that use fewer bits to save memory. Conversely we can use...
Every Complex DataFrame Manipulation, Explained & Visualized Intuitively- Nov 10, 2020. Most Data Scientists might hail the power of Pandas for data preparation, but many may not be capable of leveraging all that power. Manipulating data frames can quickly become a complex task, so eight of thes...