Question

我需要从大约6000万行的数据集中创建一个2000列的数据透视表，大约30-50万行。我已尝试在100,000行的块中进行旋转，这是有效的，但是当我尝试通过执行.append（）后跟.groupby（＆＃39; someKey＆＃39;）来重新组合DataFrame时.sum（），我的所有记忆都被占用，python最终崩溃。

如何使用有限的RAM数量对这么大的数据进行数据透视？

编辑：添加示例代码

以下代码包含各种测试输出，但最后一次打印是我们真正感兴趣的。请注意，如果我们将segMax更改为3而不是4，则代码将产生误报正确的输出。主要问题是如果一个shipmentid条目不在每个总和（wawa）看到的块中，它就不会出现在输出中。

import pandas as pd
import numpy as np
import random
from pandas.io.pytables import *
import os

pd.set_option('io.hdf.default_format','table') 

# create a small dataframe to simulate the real data.
def loadFrame():
    frame = pd.DataFrame()
    frame['shipmentid']=[1,2,3,1,2,3,1,2,3] #evenly distributing shipmentid values for testing purposes
    frame['qty']= np.random.randint(1,5,9) #random quantity is ok for this test
    frame['catid'] = np.random.randint(1,5,9) #random category is ok for this test
    return frame

def pivotSegment(segmentNumber,passedFrame):
    segmentSize = 3 #take 3 rows at a time
    frame = passedFrame[(segmentNumber*segmentSize):(segmentNumber*segmentSize + segmentSize)] #slice the input DF

    # ensure that all chunks are identically formatted after the pivot by appending a dummy DF with all possible category values
    span = pd.DataFrame() 
    span['catid'] = range(1,5+1)
    span['shipmentid']=1
    span['qty']=0

    frame = frame.append(span)

    return frame.pivot_table(['qty'],index=['shipmentid'],columns='catid', \
                             aggfunc='sum',fill_value=0).reset_index()

def createStore():

    store = pd.HDFStore('testdata.h5')
    return store

segMin = 0
segMax = 4

store = createStore()
frame = loadFrame()

print('Printing Frame')
print(frame)
print(frame.info())

for i in range(segMin,segMax):
    segment = pivotSegment(i,frame)
    store.append('data',frame[(i*3):(i*3 + 3)])
    store.append('pivotedData',segment)

print('\nPrinting Store')   
print(store)
print('\nPrinting Store: data') 
print(store['data'])
print('\nPrinting Store: pivotedData') 
print(store['pivotedData'])

print('**************')
print(store['pivotedData'].set_index('shipmentid').groupby('shipmentid',level=0).sum())
print('**************')
print('$$$')
for df in store.select('pivotedData',chunksize=3):
    print(df.set_index('shipmentid').groupby('shipmentid',level=0).sum())

print('$$$')
store['pivotedAndSummed'] = sum((df.set_index('shipmentid').groupby('shipmentid',level=0).sum() for df in store.select('pivotedData',chunksize=3)))
print('\nPrinting Store: pivotedAndSummed') 
print(store['pivotedAndSummed'])

store.close()
os.remove('testdata.h5')
print('closed')

Answer 1

您可以使用HDF5 / pytables进行追加。这使它远离RAM。

使用table format：

store = pd.HDFStore('store.h5')
for ...:
    ...
    chunk  # the chunk of the DataFrame (which you want to append)
    store.append('df', chunk)

现在您可以一次性将其作为DataFrame读取（假设此DataFrame可以适合内存！）：

df = store['df']

您还可以查询，仅获取DataFrame的子部分。

除此之外：您还应该购买更多内存，价格便宜。

编辑：您可以从商店iteratively中分组/总和，因为这样＆＃34; map-reduced＆＃34;在块上：

# note: this doesn't work, see below
sum(df.groupby().sum() for df in store.select('df', chunksize=50000))
# equivalent to (but doesn't read in the entire frame)
store['df'].groupby().sum()

Edit2：如上所述使用sum并不能实际使用pandas 0.16（我认为它在0.15.2中完成），而是可以reduce使用add：

reduce(lambda x, y: x.add(y, fill_value=0),
       (df.groupby().sum() for df in store.select('df', chunksize=50000)))

在python 3中，您必须import reduce from functools。

将它写成以下内容可能更具有pythonic /可读性：

chunks = (df.groupby().sum() for df in store.select('df', chunksize=50000))
res = next(chunks)  # will raise if there are no chunks!
for c in chunks:
    res = res.add(c, fill_value=0)

如果性能不佳/如果有大量新组，那么最好将res作为正确大小的零开始（通过获取唯一的组密钥，例如通过循环遍历块），然后添加到位。

如何在Pandas中的超大型数据框架上创建数据透视表

1 个答案: