defreduce_memory_usage(df, verbose=True):numerics= ["int8","int16","int32","int64","float16","float32","float64"]start_mem = df.memory_usage.sum /1024**2forcol in df.columns:col_type = df[col].dtypesifcol_type i
You’re loading a CSV into Pandas, and it’s using too much RAM: your program crashes if you load the whole thing. How do you reduce memory usage without changing any of your processing code? In this article I’ll show you how to reduce the memory your DataFrame uses at the time it...
处理前,df数据框所占用的物理内存是38.1MB; 针对内存占用问题,使用reduce_mem_usage()直接降低物理内存占用; def reduce_mem_usage(df, verbose=True): numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64'] start_mem = df.memory_usage().sum() / 1024**2 for col in ...
AI代码解释 defreduce_memory_usage(df,verbose=True):numerics=["int8","int16","int32","int64","float16","float32","float64"]start_mem=df.memory_usage().sum()/1024**2forcolindf.columns:col_type=df[col].dtypesifcol_typeinnumerics:c_min=df[col].min()c_max=df[col].max()ifstr(...
>>>reduce_memory_usage(tps_october)Mem.usagedecreasedto509.26Mb(76.9%reduction) 1. 2. 我们将数据集从原来的 2.2GB 压缩到 510MB。当我们将df保存到csv文件时,这种内存消耗的减少会丢失因为csv还是以字符串的形式保存的,但是如果使用pickle保存那就没问题了。
Learn how to reduce memory usage in a Pandas DataFrame by converting data types using the astype method. Measure the memory optimization achieved.
def reduce_memory_usage(df, verbose=True):numerics= ["int8", "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 you want to process a large amount data with Pandas, there are various techniques you can use to reduce memory usage without changing your data. But what if that isn’t enough? What if you still need to reduce memory usage? Another technique you can try is lossy compression: drop ...
reduce_memory_usage 以后的情况是这样的。 >>>reduce_memory_usage(tps_october) Mem. usage decreased to509.26Mb (76.9% reduction) 数据集的内存占用从原来的 2.2GB 压缩到 510MB 。不要小看这个压缩量,因为数据分析或者建模的过程中,要做很多数据处理操作,就这导致数据集会被重复使用很多次。如果开始的数据...
## Memory Reducer # :df pandas dataframe to reduce size # type: pd.DataFrame() # :verbose # type: bool defreduce_mem_usage(df, verbose=True): numerics=['int16','int32','int64','float16','float32','float64'] start_mem=df.memory_usage().sum()/1024**2 ...