如何在Pandas中的超大型数据框架上创建数据透视表

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('someKey').sum()来重新组合DataFrame时,我的所有内存都被占用了和python最终崩溃.

如何利用有限的RAM量对这么大的数据进行调整？

编辑:添加示例代码

下面的代码包括各种测试输出,但最后一个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​​将产生正确输出的误报.主要问题是如果一个shipmentid条目不在每个和(wawa)看的块中,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.

使用表格格式:

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.

旁白:你还应该买更多的RAM,它很便宜.

编辑:您可以迭代地对商店进行分组/求和,因为这会对地块进行"映射缩减":

# 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:使用上述金额实际上并没有在大熊猫0.16工作(我认为这在0.15.2那样),而是可以使用reduce具有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,您必须从functools导入reduce.

也许它更像pythonic /可读写为:

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)

如果性能差/如果存在大量新组,那么最好将res作为正确大小的零(通过获取唯一组密钥,例如通过循环遍历块),然后添加到位.