defreduce_memory_usage(df):"""This function takes a pandas DataFrame as input and reduces its memory usage while trying to maintain the precision of each column's values."""# Print the original and new memory usage of the DataFrameprint(f"Original memory usage: {df.memory_usage().sum() ...
具体函数代码如下: import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) def reduce_mem_usage(props): start_mem_usg = props.memory_usage().sum() / 1024**2 print("Memory usage of properties dataframe is :",start_mem_usg," M...
自动识别类型并进行转换 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...
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): start_mem = df.memory_usage().sum() / 1024**2 print('Memory usage of dataframe is {:.2f} MB'.format(start_mem)) for col in df.columns: col_type = df[col].dtype if col_type != object: c_min = df[col].min() ...
首先,确保已经安装了memory_profiler库。可以使用以下命令进行安装: 代码语言:txt 复制 pip install memory_profiler 在需要获取内存使用情况的Python代码文件中,添加装饰器@profile。例如,创建一个名为memory_usage.py的文件,并在其中编写以下代码: 代码语言:txt ...
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 ...
Your Python code reads some data, processes it, and uses too much memory; maybe it even dies due to an out-of-memory error. In order to reduce memory usage, you first need to figure out: Where peak memory usage is, also known as the high-water mark. ...
This improvement helps reduce debugging time and makes Python a more beginner-friendly language. 5. New modules and libraries: Python 3.9 introduces several new modules and updates existing libraries. One notable addition is the `zoneinfo` module, which provides an improved interface for working ...