def reduce_mem_usage(df, verbose=True): numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64'] cateics = ['object'] start_mem = df.memory_usage().sum() / 1024**2 for col in df.columns: col_type = df[col].dtypes num_unique_values = len(df[col].un...
def reduce_mem_usage(df): starttime = time.time() numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64'] start_mem = df.memory_usage().sum() / 1024**2 for col in df.columns: col_type = df[col].dtypes if col_type in numerics: c_min = df[col].min...
def reduce_mem_usage(props): start_mem_usg = props.memory_usage().sum() / 1024**2 print("Memory usage of properties dataframe is :",start_mem_usg," MB") NAlist = [] # Keeps track of columns that have missing values filled in. for col in props.columns: if props[col].dtype !=...
from memory_profiler import profile @profile def my_function(): # 你的代码逻辑 if __name__ == '__main__': my_function() 运行代码并查看内存使用情况。在命令行中执行以下命令: 代码语言:txt 复制 python -m memory_profiler memory_usage.py 运行结果将显示代码每行的内存使用情况,包括内存...
4. Improved error messages: Python 3.9 includes improved error messages, making it easier for developers to understand and troubleshoot issues in their code. Error messages now provide more context and suggestions for potential fixes. This improvement helps reduce debugging time and makes Python a mo...
When using scan_csv() on a BytesIO(), the hope is that it won't make a whole copy of the data if possible, e.g. by doing filtering as it reads. In fact, it currently does make a copy, making memory...
数据集较大，如果计算机读取内存不够用，可以尝试kaggle比赛 # 中的reduce_mem_usage函数，附在文末，...
Two particularly effective patterns for memory efficiency in class design are the Flyweight and Singleton patterns. When correctly implemented in scenarios involving numerous instances of classes or shared data, these patterns can drastically reduce memory consumption. Flyweight design pattern The Flyweight ...
This was added in PEP 412 with the motivation to reduce memory usage, specifically in dictionaries of instances - where keys (instance attributes) tend to be common to all instances. This optimization is entirely seamless for instance dictionaries, but it is disabled if certain assumptions are ...
mrjob - Run MapReduce jobs on Hadoop or Amazon Web Services. PySpark - Apache Spark Python API. Ray - A system for parallel and distributed Python that unifies the machine learning ecosystem. Stream Processing faust - A stream processing library, porting the ideas from Kafka Streams to Python...