大熊猫内存消耗HDF文件分组

Question

我写了下面的脚本，但我有内存消耗，大熊猫被分配RAM的30多G，其中数据文件的总和大约是18G的

import pandas as pd 
import numpy as np 
import matplotlib.pyplot as plt 
import matplotlib 
import time 


mean_wo = pd.DataFrame() 
mean_w = pd.DataFrame() 
std_w = pd.DataFrame() 
std_wo = pd.DataFrame() 

start_time=time.time() #taking current time as starting time 

data_files=['2012.h5','2013.h5','2014.h5','2015.h5', '2016.h5', '2008_2011.h5'] 



for data_file in data_files: 
    print data_file 
    df = pd.read_hdf(data_file) 
    grouped = df.groupby('day') 
    mean_wo_tmp=grouped['Significance_without_muons'].agg([np.mean]) 
    mean_w_tmp=grouped['Significance_with_muons'].agg([np.mean]) 
    std_wo_tmp=grouped['Significance_without_muons'].agg([np.std]) 
    std_w_tmp=grouped['Significance_with_muons'].agg([np.std]) 
    mean_wo = pd.concat([mean_wo, mean_wo_tmp]) 
    mean_w = pd.concat([mean_w, mean_w_tmp]) 
    std_w = pd.concat([std_w,std_w_tmp]) 
    std_wo = pd.concat([std_wo,std_wo_tmp]) 
    print mean_wo.info() 
    print mean_w.info() 
    del df, grouped, mean_wo_tmp, mean_w_tmp, std_w_tmp, std_wo_tmp 

std_wo=std_wo.reset_index() 
std_w=std_w.reset_index() 
mean_wo=mean_wo.reset_index() 
mean_w=mean_w.reset_index() 

#setting the field day as date 
std_wo['day']= pd.to_datetime(std_wo['day'], format='%Y-%m-%d') 
std_w['day']= pd.to_datetime(std_w['day'], format='%Y-%m-%d') 
mean_w['day']= pd.to_datetime(mean_w['day'], format='%Y-%m-%d') 
mean_wo['day']= pd.to_datetime(mean_w['day'], format='%Y-%m-%d') 

问题所以有人有一个想法如何减少内存消耗？

干杯，

Answer 1

我会做这样的事情
解决方案

data_files=['2012.h5', '2013.h5', '2014.h5', '2015.h5', '2016.h5', '2008_2011.h5'] 
cols = ['Significance_without_muons', 'Significance_with_muons'] 

def agg(data_file): 
    return pd.read_hdf(data_file).groupby('day')[cols].agg(['mean', 'std']) 

big_df = pd.concat([agg(fn) for fn in data_files], axis=1, keys=data_files) 

mean_wo_tmp = big_df.xs(('Significance_without_muons', 'mean'), axis=1, level=[1, 2]) 
mean_w_tmp = big_df.xs(('Significance_with_muons', 'mean'), axis=1, level=[1, 2]) 
std_wo_tmp = big_df.xs(('Significance_without_muons', 'std'), axis=1, level=[1, 2]) 
std_w_tmp = big_df.xs(('Significance_with_muons', 'mean'), axis=1, level=[1, 2]) 

del big_df 

---

设置

data_files=['2012.h5', '2013.h5', '2014.h5', '2015.h5', '2016.h5', '2008_2011.h5'] 
cols = ['Significance_without_muons', 'Significance_with_muons'] 

np.random.seed([3,1415]) 
data_df = pd.DataFrame(np.random.rand(1000, 2), columns=cols) 
data_df['day'] = np.random.choice(list('ABCDEFG'), 1000) 

for fn in data_files: 
    data_df.to_hdf(fn, 'day', append=False) 

运行上述溶液
然后

mean_wo_tmp