如何让这段代码不消耗那么多 RAM 内存？

Question

我有这两个功能，当我运行它们时，我的内核死得很快。我能做些什么来防止它？它发生在将大约 10 个文件附加到数据帧之后。不幸的是 json 文件太大了（每个大约 150 MB，有几十个）我不知道如何将它们连接在一起。

import os
import pandas as pd
from pandas.io.json import json_normalize
import json

def filtering_nodes(df):
    id_list = df.index.tolist()
    print("Dropping rows without 4 nodes and 3 members...")
    for x in id_list:
        if len(df['Nodes'][x]) != 4 and len(df['Members'][x]) != 3:
            df = df.drop(x)
    print("Converting to csv...")
    df.to_csv("whole_df.csv", sep='\t')
    return df

def merge_JsonFiles(filename):
    result = list()
    cnt = 0
    
    df_all = None
    data_all = None
    
    for f1 in filename:
        print("Appending file: ", f1)
        with open('../../data' + f1, 'r') as infile:
            data_all = json.loads(infile.read())
        if cnt == 0:
            df_all = pd.json_normalize(data_all, record_path =['List2D'], max_level =2 ,sep = "-")
        else:
            df_all = df_all.append(pd.json_normalize(data_all, record_path =['List2D'], max_level =2 ,sep = "-"), ignore_index = True)
        cnt += 1
        
    return df_all

files = os.listdir('../../data')
df_all_test = merge_JsonFiles(files)
df_all_test_drop = filtering_nodes(df_all_test)

编辑： 由于@jlandercy 的回答，我做了这个：

def merging_to_csv():
    for path in pathlib.Path("../../data/loads_data/Dane/hilti/").glob("*.json"):
        # Open source file one by one:
        with path.open() as handler:
            df = pd.json_normalize(json.load(handler), record_path =['List2D'])
        # Identify rows to drop (boolean indexing):
        q = (df["Nodes"] != 4) & (df["Members"] != 3)
        # Inplace drop (no extra copy in RAM):
        df.drop(q, inplace=True)
        # Append data to disk instead of RAM:
        df.to_csv("output.csv", mode="a", header=False)

merging_to_csv()

我遇到了这种类型的错误：

KeyError                                  Traceback (most recent call last)
<ipython-input-55-cf18265ca50e> in <module>
----> 1 merging_to_csv()

<ipython-input-54-698c67461b34> in merging_to_csv()
     51         q = (df["Nodes"] != 4) & (df["Members"] != 3)
     52         # Inplace drop (no extra copy in RAM):
---> 53         df.drop(q, inplace=True)
     54         # Append data to disk instead of RAM:
     55         df.to_csv("output.csv", mode="a", header=False)

/opt/conda/lib/python3.7/site-packages/pandas/util/_decorators.py in wrapper(*args, **kwargs)
    309                     stacklevel=stacklevel,
    310                 )
--> 311             return func(*args, **kwargs)
    312 
    313         return wrapper

/opt/conda/lib/python3.7/site-packages/pandas/core/frame.py in drop(self, labels, axis, index, columns, level, inplace, errors)
   4906             level=level,
   4907             inplace=inplace,
-> 4908             errors=errors,
   4909         )
   4910 

/opt/conda/lib/python3.7/site-packages/pandas/core/generic.py in drop(self, labels, axis, index, columns, level, inplace, errors)
   4148         for axis, labels in axes.items():
   4149             if labels is not None:
-> 4150                 obj = obj._drop_axis(labels, axis, level=level, errors=errors)
   4151 
   4152         if inplace:

/opt/conda/lib/python3.7/site-packages/pandas/core/generic.py in _drop_axis(self, labels, axis, level, errors)
   4183                 new_axis = axis.drop(labels, level=level, errors=errors)
   4184             else:
-> 4185                 new_axis = axis.drop(labels, errors=errors)
   4186             result = self.reindex(**{axis_name: new_axis})
   4187 

/opt/conda/lib/python3.7/site-packages/pandas/core/indexes/base.py in drop(self, labels, errors)
   6016         if mask.any():
   6017             if errors != "ignore":
-> 6018                 raise KeyError(f"{labels[mask]} not found in axis")
   6019             indexer = indexer[~mask]
   6020         return self.delete(indexer)

KeyError: '[ True  True  True  True  True  True  True  True  True  True  True  True\n  True  True  True  True  True  True  True  True  True  True  True  True\n  True  True  True  True  True  True  True  True  True  True  True  True\n  True  True  True  True  True  True  True  True  True  True  True  True\n  True  True  True  True  True  True  True  True  True  True  True  True\n  True  True  True  True  True  True  True  True  True  True  True  True\n  True  True  True  True  True  True  True  True  True  True  True  True\n  True  True  True  True  True  True  True  True  True  True  True  True\n  True] not found in axis'

怎么了？我将在此处上传两个最小的 json 文件： https://drive.google.com/drive/folders/1xlC-kK6NLGr0isdy1Ln2tzGmel45GtPC?usp=sharing

Answer 1

您在原来的方法中遇到了多个问题：

数据帧的多个副本：df = df.drop(...)；
由于 append;
不需要 for 循环来过滤行，请改用布尔索引。

这是根据您提供的数据样本解决问题的基线代码段：

import json
import pathlib
import pandas as pd
    
# Iterate source files:
for path in pathlib.Path(".").glob("result*.json"):
    # Open source file one by one:
    with path.open() as handler:
        # Normalize JSON model:
        df = pd.json_normalize(json.load(handler), record_path =['List2D'], max_level=2, sep="-")
    # Apply len to list fields to identify rows to drop (boolean indexing):
    q = (df["Nodes"].apply(len) != 4) & (df["Members"].apply(len) != 3)
    # Filter and append data to disk instead of RAM:
    df.loc[~q,:].to_csv("output.csv", mode="a", header=False)

它在 RAM 中一个一个地加载文件，然后将过滤后的行附加到磁盘而不是 RAM。这些修复将大大减少 RAM 使用量，并且应该保持为最大 JSON 文件的两倍。

如何让这段代码不消耗那么多 RAM 内存？

How to make this code not to consume so much RAM memory?

python

memory

low-memory

dataframe

pandas