I searched almost all over the internet and somehow none of the approaches seem to work in my case.
I have two large csv files (each with a million+ rows and about 300-400MB in size). They are loading fine into data frames using the read_csv function without having to use the chunksize parameter. I even performed certain minor operations on this data like new column generation, filtering, etc.
However, when I try to merge these two frames, I get a MemoryError. I have even tried to use SQLite to accomplish the merge, but in vain. The operation takes forever.
Mine is a Windows 7 PC with 8GB RAM. The Python version is 2.7
Thank you.
Edit: I tried chunking methods too. When I do this, I don't get MemoryError, but the RAM usage explodes and my system crashes.
When you are merging data using pandas.merge it will use df1 memory, df2 memory and merge_df memory. I believe that it is why you get a memory error. You should export df2 to a csv file and use chunksize option and merge data.
It might be a better way but you can try this. *for large data set you can use chunksize option in pandas.read_csv
df1 = pd.read_csv("yourdata.csv")
df2 = pd.read_csv("yourdata2.csv")
df2_key = df2.Colname2
# creating a empty bucket to save result
df_result = pd.DataFrame(columns=(df1.columns.append(df2.columns)).unique())
df_result.to_csv("df3.csv",index_label=False)
# save data which only appear in df1 # sorry I was doing left join here. no need to run below two line.
# df_result = df1[df1.Colname1.isin(df2.Colname2)!=True]
# df_result.to_csv("df3.csv",index_label=False, mode="a")
# deleting df2 to save memory
del(df2)
def preprocess(x):
df2=pd.merge(df1,x, left_on = "Colname1", right_on = "Colname2")
df2.to_csv("df3.csv",mode="a",header=False,index=False)
reader = pd.read_csv("yourdata2.csv", chunksize=1000) # chunksize depends with you colsize
[preprocess(r) for r in reader]
this will save merged data as df3.