How to concatenate multiple pandas.DataFrames without running into MemoryError

Question

I have three DataFrames that I'm trying to concatenate.

concat_df = pd.concat([df1, df2, df3])

This results in a MemoryError. How can I resolve this?

Note that most of the existing similar questions are on MemoryErrors occuring when reading large files. I don't have that problem. I have read my files in into DataFrames. I just can't concatenate that data.

Solution 1

I'm grateful to the community for their answers. However, in my case, I found out that the problem was actually due to the fact that I was using 32 bit Python.

There are memory limits defined for Windows 32 and 64 bit OS. For a 32 bit process, it is only 2 GB. So, even if your RAM has more than 2GB, and even if you're running the 64 bit OS, but you are running a 32 bit process, then that process will be limited to just 2 GB of RAM - in my case that process was Python.

I upgraded to 64 bit Python, and haven't had a memory error since then!

Other relevant questions are: Python 32-bit memory limits on 64bit windows, Should I use Python 32bit or Python 64bit, Why is this numpy array too big to load?

Solution 2

The problem is, like viewed in the others answers, a problem of memory. And a solution is to store data on disk, then to build an unique dataframe.

With such huge data, performance is an issue.

csv solutions are very slow, since conversion in text mode occurs. HDF5 solutions are shorter, more elegant and faster since using binary mode. I propose a third way in binary mode, with pickle, which seems to be even faster, but more technical and needing some more room. And a fourth, by hand.

Here the code:

import numpy as np
import pandas as pd
import os
import pickle

# a DataFrame factory:
dfs=[]
for i in range(10):
    dfs.append(pd.DataFrame(np.empty((10**5,4)),columns=range(4)))
    
# a csv solution
def bycsv(dfs):
    md,hd='w',True
    for df in dfs:
        df.to_csv('df_all.csv',mode=md,header=hd,index=None)
        md,hd='a',False
    #del dfs
    df_all=pd.read_csv('df_all.csv',index_col=None)
    os.remove('df_all.csv') 
    return df_all    

    

Better solutions :

def byHDF(dfs):
    store=pd.HDFStore('df_all.h5')
    for df in dfs:
        store.append('df',df,data_columns=list('0123'))
    #del dfs
    df=store.select('df')
    store.close()
    os.remove('df_all.h5')
    return df

def bypickle(dfs):
    c=[]
    with open('df_all.pkl','ab') as f:
        for df in dfs:
            pickle.dump(df,f)
            c.append(len(df))    
    #del dfs
    with open('df_all.pkl','rb') as f:
        df_all=pickle.load(f)
        offset=len(df_all)
        df_all=df_all.append(pd.DataFrame(np.empty(sum(c[1:])*4).reshape(-1,4)))
        
        for size in c[1:]:
            df=pickle.load(f)
            df_all.iloc[offset:offset+size]=df.values 
            offset+=size
    os.remove('df_all.pkl')
    return df_all
    

For homogeneous dataframes, we can do even better :

def byhand(dfs):
    mtot=0
    with open('df_all.bin','wb') as f:
        for df in dfs:
            m,n =df.shape
            mtot += m
            f.write(df.values.tobytes())
            typ=df.values.dtype                
    #del dfs
    with open('df_all.bin','rb') as f:
        buffer=f.read()
        data=np.frombuffer(buffer,dtype=typ).reshape(mtot,n)
        df_all=pd.DataFrame(data=data,columns=list(range(n))) 
    os.remove('df_all.bin')
    return df_all

And some tests on (little, 32 Mb) data to compare performance. you have to multiply by about 128 for 4 Gb.

In [92]: %time w=bycsv(dfs)
Wall time: 8.06 s

In [93]: %time x=byHDF(dfs)
Wall time: 547 ms

In [94]: %time v=bypickle(dfs)
Wall time: 219 ms

In [95]: %time y=byhand(dfs)
Wall time: 109 ms

A check :

In [195]: (x.values==w.values).all()
Out[195]: True

In [196]: (x.values==v.values).all()
Out[196]: True

In [197]: (x.values==y.values).all()
Out[196]: True


            

Of course all of that must be improved and tuned to fit your problem.

For exemple df3 can be split in chuncks of size 'total_memory_size - df_total_size' to be able to run bypickle.

I can edit it if you give more information on your data structure and size if you want. Beautiful question !

Solution 3

I advice you to put your dataframes into single csv file by concatenation. Then to read your csv file.

Execute that:

# write df1 content in file.csv
df1.to_csv('file.csv', index=False)
# append df2 content to file.csv
df2.to_csv('file.csv', mode='a', columns=False, index=False)
# append df3 content to file.csv
df3.to_csv('file.csv', mode='a', columns=False, index=False)

# free memory
del df1, df2, df3

# read all df1, df2, df3 contents
df = pd.read_csv('file.csv')

If this solution isn't enougth performante, to concat larger files than usually. Do:

df1.to_csv('file.csv', index=False)
df2.to_csv('file1.csv', index=False)
df3.to_csv('file2.csv', index=False)

del df1, df2, df3

Then run bash command:

cat file1.csv >> file.csv
cat file2.csv >> file.csv
cat file3.csv >> file.csv

Or concat csv files in python :

def concat(file1, file2):
    with open(file2, 'r') as filename2:
        data = file2.read()
    with open(file1, 'a') as filename1:
        file.write(data)

concat('file.csv', 'file1.csv')
concat('file.csv', 'file2.csv')
concat('file.csv', 'file3.csv')

After read:

df = pd.read_csv('file.csv')

Solution 4

Kinda taking a guess here, but maybe:

df1 = pd.concat([df1,df2])
del df2
df1 = pd.concat([df1,df3])
del df3

Obviously, you could do that more as a loop but the key is you want to delete df2, df3, etc. as you go. As you are doing it in the question, you never clear out the old dataframes so you are using about twice as much memory as you need to.

More generally, if you are reading and concatentating, I'd do it something like this (if you had 3 CSVs: foo0, foo1, foo2):

concat_df = pd.DataFrame()
for i in range(3):
    temp_df = pd.read_csv('foo'+str(i)+'.csv')
    concat_df = pd.concat( [concat_df, temp_df] )

In other words, as you are reading in files, you only keep the small dataframes in memory temporarily, until you concatenate them into the combined df, concat_df. As you currently do it, you are keeping around all the smaller dataframes, even after concatenating them.

Solution 5

Similar to what @glegoux suggests, also pd.DataFrame.to_csv can write in append mode, so you can do something like:

df1.to_csv(filename)
df2.to_csv(filename, mode='a', columns=False)
df3.to_csv(filename, mode='a', columns=False)

del df1, df2, df3
df_concat = pd.read_csv(filename)

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