Python Pandas合并导致内存溢出

Question

我是Pandas的新手,我正在尝试合并一些数据子集.我给出了一个具体案例,但这个问题很普遍:如何/为什么会发生这种情况,我该如何解决呢？

我加载的数据大约是85兆左右,但我经常看到我的python会话运行接近10 gig的内存使用量然后给出内存错误.

我不知道为什么会发生这种情况,但这让我感到害怕,因为我甚至无法按照我想要的方式开始查看数据.

这就是我所做的:

导入主数据

import requests, zipfile, StringIO
import numpy as np
import pandas as pd 


STAR2013url="http://www3.cde.ca.gov/starresearchfiles/2013/p3/ca2013_all_csv_v3.zip"
STAR2013fileName = 'ca2013_all_csv_v3.txt'

r = requests.get(STAR2013url)
z = zipfile.ZipFile(StringIO.StringIO(r.content))

STAR2013=pd.read_csv(z.open(STAR2013fileName))

导入一些交叉引用表

STARentityList2013url = "http://www3.cde.ca.gov/starresearchfiles/2013/p3/ca2013entities_csv.zip"
STARentityList2013fileName = "ca2013entities_csv.txt"
r = requests.get(STARentityList2013url)
z = zipfile.ZipFile(StringIO.StringIO(r.content))
STARentityList2013=pd.read_csv(z.open(STARentityList2013fileName))

STARlookUpTestID2013url = "http://www3.cde.ca.gov/starresearchfiles/2013/p3/tests.zip"
STARlookUpTestID2013fileName = "Tests.txt"
r = requests.get(STARlookUpTestID2013url)
z = zipfile.ZipFile(StringIO.StringIO(r.content))
STARlookUpTestID2013=pd.read_csv(z.open(STARlookUpTestID2013fileName))

STARlookUpSubgroupID2013url = "http://www3.cde.ca.gov/starresearchfiles/2013/p3/subgroups.zip"
STARlookUpSubgroupID2013fileName = "Subgroups.txt"
r = requests.get(STARlookUpSubgroupID2013url)
z = zipfile.ZipFile(StringIO.StringIO(r.content))
STARlookUpSubgroupID2013=pd.read_csv(z.open(STARlookUpSubgroupID2013fileName))

将列ID重命名为允许合并

STARlookUpSubgroupID2013 = STARlookUpSubgroupID2013.rename(columns={'001':'Subgroup ID'})
STARlookUpSubgroupID2013

成功融合

merged = pd.merge(STAR2013,STARlookUpSubgroupID2013, on='Subgroup ID')

尝试第二次合并.这是内存溢出发生的地方

merged=pd.merge(merged, STARentityList2013, on='School Code')

我在ipython笔记本中做了所有这些,但不要认为这会改变任何东西.

Answer 1

Although this is an old question, I recently came across the same problem.

In my instance, duplicate keys are required in both dataframes, and I needed a method which could tell if a merge will fit into memory ahead of computation, and if not, change the computation method.

The method I came up with is as follows:

Calculate merge size:

def merge_size(left_frame, right_frame, group_by, how='inner'):
    left_groups = left_frame.groupby(group_by).size()
    right_groups = right_frame.groupby(group_by).size()
    left_keys = set(left_groups.index)
    right_keys = set(right_groups.index)
    intersection = right_keys & left_keys
    left_diff = left_keys - intersection
    right_diff = right_keys - intersection

    left_nan = len(left_frame[left_frame[group_by] != left_frame[group_by]])
    right_nan = len(right_frame[right_frame[group_by] != right_frame[group_by]])
    left_nan = 1 if left_nan == 0 and right_nan != 0 else left_nan
    right_nan = 1 if right_nan == 0 and left_nan != 0 else right_nan

    sizes = [(left_groups[group_name] * right_groups[group_name]) for group_name in intersection]
    sizes += [left_nan * right_nan]

    left_size = [left_groups[group_name] for group_name in left_diff]
    right_size = [right_groups[group_name] for group_name in right_diff]
    if how == 'inner':
        return sum(sizes)
    elif how == 'left':
        return sum(sizes + left_size)
    elif how == 'right':
        return sum(sizes + right_size)
    return sum(sizes + left_size + right_size)

Note:

At present with this method, the key can only be a label, not a list. Using a list for group_by currently returns a sum of merge sizes for each label in the list. This will result in a merge size far larger than the actual merge size.

If you are using a list of labels for the group_by, the final row size is:

min([merge_size(df1, df2, label, how) for label in group_by])

Check if this fits in memory

此处定义的函数merge_size返回通过将两个数据帧合并在一起而创建的行数。

通过将其与两个数据帧的列数相乘，然后乘以 np.float[32/64] 的大小，您可以粗略地了解生成的数据帧在内存中的大小。然后可以将其与您的系统进行比较psutil.virtual_memory().available，看看您的系统是否可以计算完整的合并。

def mem_fit(df1, df2, key, how='inner'):
    rows = merge_size(df1, df2, key, how)
    cols = len(df1.columns) + (len(df2.columns) - 1)
    required_memory = (rows * cols) * np.dtype(np.float64).itemsize

    return required_memory <= psutil.virtual_memory().available

该方法已被提议作为本期merge_size的扩展。https://github.com/pandas-dev/pandas/issues/15068。pandas